<table><caption>Table 8: Alternative measures of agricultural land expansion and undernourishment</caption><thead><tr><th rowspan="2">Variables</th><th colspan="4">Dependent Variable: Prevalence of undernourishment</th><th colspan="4">S-GMM</th></tr><tr><th colspan="4">D-GMM</th><th colspan="4">S-GMM</th></tr></thead><tbody><tr><td>Undernourishment, (t-1)</td><td>0.896***<br>(0.0762)</td><td>0.914***<br>(0.0780)</td><td>0.903***<br>(0.0794)</td><td>0.905***<br>(0.0774)</td><td>0.837***<br>(0.100)</td><td>0.862***<br>(0.102)</td><td>0.849***<br>(0.105)</td><td>0.853***<br>(0.102)</td></tr><tr><td>Arable land</td><td>-0.0767*<br>(0.0466)</td><td></td><td></td><td></td><td>-0.0939*<br>(0.0483)</td><td></td><td></td><td></td></tr><tr><td>Cereal land</td><td></td><td>-0.0228<br>(0.0468)</td><td></td><td></td><td></td><td>-0.0136*<br>(0.0228)</td><td></td><td></td></tr><tr><td>Forest land</td><td></td><td></td><td>-0.1000*<br>(0.0518)</td><td></td><td></td><td></td><td>-0.112**<br>(0.0551)</td><td></td></tr><tr><td>Permanent cropland</td><td></td><td></td><td></td><td>0.285**<br>(0.143)</td><td></td><td></td><td></td><td>0.247*<br>(0.139)</td></tr><tr><td>Production Index</td><td>-0.00485<br>(0.0129)</td><td>-0.00730<br>(0.00675)</td><td>-0.0110<br>(0.00735)</td><td>-0.0106<br>(0.00706)</td><td>-0.00958<br>(0.0143)</td><td>-0.0119*<br>(0.00722)</td><td>-0.0167*<br>(0.00859)</td><td>-0.0159*<br>(0.00833)</td></tr><tr><td>CO2 emissions (log)</td><td>1.058<br>(0.759)</td><td>1.074*<br>(0.570)</td><td>1.040*<br>(0.601)</td><td>1.063*<br>(0.590)</td><td>1.060<br>(0.761)</td><td>1.083*<br>(0.568)</td><td>1.041*<br>(0.608)</td><td>1.069*<br>(0.593)</td></tr><tr><td>Governance index</td><td>-0.308<br>(0.262)</td><td>-0.289*<br>(0.176)</td><td>-0.297*<br>(0.180)</td><td>-0.305*<br>(0.182)</td><td>-0.398<br>(0.285)</td><td>-0.356*<br>(0.192)</td><td>-0.379*<br>(0.201)</td><td>-0.384*<br>(0.202)</td></tr><tr><td>GDP per capita</td><td>1.670<br>(1.166)</td><td>2.078<br>(1.429)</td><td>2.095<br>(1.393)</td><td>2.203<br>(1.410)</td><td>1.128<br>(1.277)</td><td>1.640<br>(1.552)</td><td>1.686<br>(1.487)</td><td>1.781<br>(1.510)</td></tr><tr><td>Food price vulnerability</td><td>0.106**<br>(0.0428)</td><td>0.107***<br>(0.0275)</td><td>0.110***<br>(0.0289)</td><td>0.113***<br>(0.0285)</td><td>0.104**<br>(0.0482)</td><td>0.105***<br>(0.0306)</td><td>0.108***<br>(0.0322)</td><td>0.110***<br>(0.0316)</td></tr><tr><td>Population growth</td><td>81.75*<br>(46.26)</td><td>79.12***<br>(17.26)</td><td>81.74***<br>(17.28)</td><td>83.38***<br>(15.86)</td><td>93.12*<br>(49.18)</td><td>89.23***<br>(19.56)</td><td>92.31***<br>(19.81)</td><td>93.32***<br>(18.65)</td></tr><tr><td>Rural population growth</td><td>0.123<br>(0.123)</td><td>0.118***<br>(0.0424)</td><td>0.134***<br>(0.0418)</td><td>0.125***<br>(0.0410)</td><td>0.160<br>(0.143)</td><td>0.151***<br>(0.0483)</td><td>0.170***<br>(0.0481)</td><td>0.159***<br>(0.0480)</td></tr><tr><td>Industrial spillovers</td><td>-0.422<br>(4.338)</td><td>-0.365<br>(2.616)</td><td>-0.822<br>(2.563)</td><td>-0.699<br>(2.485)</td><td>-1.430<br>(4.499)</td><td>-1.349<br>(3.104)</td><td>-1.815<br>(3.024)</td><td>-1.585<br>(2.937)</td></tr><tr><td>Services spillovers</td><td>2.596<br>(4.168)</td><td>2.191<br>(2.996)</td><td>2.567<br>(2.941)</td><td>2.407<br>(2.860)</td><td>4.031<br>(4.360)</td><td>3.498<br>(3.600)</td><td>3.873<br>(3.506)</td><td>3.636<br>(3.427)</td></tr><tr><td>Incidence of TB</td><td>-0.00173<br>(0.00509)</td><td>-0.00140<br>(0.00177)</td><td>-0.00170<br>(0.00181)</td><td>-0.00159<br>(0.00177)</td><td>-0.00300<br>(0.00544)</td><td>-0.00251<br>(0.00206)</td><td>-0.00288<br>(0.00215)</td><td>-0.00275<br>(0.00212)</td></tr><tr><td>Prevalence of HIV</td><td>0.232<br>(0.239)</td><td>0.267**<br>(0.133)</td><td>0.292**<br>(0.122)</td><td>0.275**<br>(0.124)</td><td>0.225<br>(0.249)</td><td>0.266**<br>(0.134)</td><td>0.298**<br>(0.122)</td><td>0.278**<br>(0.124)</td></tr><tr><td>Constant</td><td></td><td></td><td></td><td></td><td>-21.88**<br>(9.606)</td><td>-27.44*<br>(14.48)</td><td>-25.08*<br>(14.44)</td><td>-29.12**<br>(13.80)</td></tr><tr><td>Observations</td><td>378</td><td>378</td><td>378</td><td>378</td><td>406</td><td>406</td><td>406</td><td>406</td></tr><tr><td>Number of id</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td></tr><tr><td>Instruments</td><td>18</td><td>18</td><td>18</td><td>18</td><td>19</td><td>19</td><td>19</td><td>19</td></tr><tr><td>AR1</td><td>0.0813</td><td>0.0302</td><td>0.0727</td><td>0.0598</td><td>0.0594</td><td>0.0435</td><td>0.0487</td><td>0.0376</td></tr><tr><td>AR2</td><td>0.594</td><td>0.573</td><td>0.613</td><td>0.601</td><td>0.750</td><td>0.702</td><td>0.758</td><td>0.744</td></tr><tr><td>Sargan OIR</td><td>0.459</td><td>0.349</td><td>0.207</td><td>0.385</td><td>0.616</td><td>0.387</td><td>0.242</td><td>0.459</td></tr><tr><td>Hansen OIR</td><td>0.0458</td><td>0.283</td><td>0.841</td><td>0.839</td><td>0.0823</td><td>0.831</td><td>0.859</td><td>0.797</td></tr></tbody></table>

Robust standard errors in parentheses

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

The coefficient of permanent cropland however, is positive and significant. This is a sign that the expansion of permanent cropland deteriorates food insecurity. This result is justified because most of the crops such as cocoa, coffee and rubber are produced within these lands dedicated to exportation in European countries. The exceptions are countries such as South Africa, Côte d'Ivoire, which processes half of its cocoa production locally. Agricultural expansion for this purpose is of substantial benefit to developed countries which import these crops for food security. This result converges with that of Mechiche-Alami et al (2021), who show that, in general, agricultural expansions for export or cash crops do not benefit local food security or generate conflicts. Moreover, they contribute to the food security of the populations of the host countries on the one hand. On the other hand, to their economic development when they process these products.
### 5.2.4. Robustness check: alternative measures of food security

To assess the robustness of agricultural land expansion effects on food security, the study considers its use dimension and proceeds in two phases.

<table><caption>Table 9: Effects of agricultural land expansion on water and sanitation (overall)</caption><thead><tr><th rowspan="3">Variables</th><th colspan="4">Dependent variable: Sanitation Total</th><th colspan="4">Dependent variable: Water Total</th></tr><tr><th colspan="2">Annual</th><th colspan="2">5 years averaged</th><th colspan="2">Annual</th><th colspan="2">5 years averaged</th></tr><tr><th>D-GMM</th><th>S-GMM</th><th>D-GMM</th><th>S-GMM</th><th>D-GMM</th><th>S-GMM</th><th>D-GMM</th><th>S-GMM</th></tr></thead><tbody><tr><td>Agricultural land</td><td>0.0663***<br>(0.00896)</td><td>0.0655***<br>(0.00954)</td><td>0.032***<br>(0.00513)</td><td>0.0568***<br>(0.0228)</td><td>0.01688*<br>(0.00965)</td><td>0.0089**<br>(0.00420)</td><td>0.0132***<br>(0.00512)</td><td>-0.0396<br>(0.0321)</td></tr><tr><td>Production Index</td><td>-0.00727**<br>(0.00343)</td><td>-0.00693**<br>(0.00348)</td><td>0.0177***<br>(0.00327)</td><td>0.00436<br>(0.00910)</td><td>-0.0846*<br>(0.00488)</td><td>-0.00434**<br>(0.00220)</td><td>-0.0409***<br>(0.00936)</td><td>0.0103**<br>(0.00444)</td></tr><tr><td>CO2 emissions (log)</td><td>-0.0565**<br>(0.0287)</td><td>-0.0478*<br>(0.0289)</td><td>0.889***<br>(0.0854)</td><td>0.345<br>(0.319)</td><td>-0.285***<br>(0.0886)</td><td>-0.154**<br>(0.0707)</td><td>-3.662***<br>(1.254)</td><td>2.701***<br>(0.166)</td></tr><tr><td>Governance index</td><td>0.0913**<br>(0.0421)</td><td>0.0994*<br>(0.0592)</td><td>0.152***<br>(0.0255)</td><td>0.164**<br>(0.0657)</td><td>0.107**<br>(0.0490)</td><td>0.0363***<br>(0.00973)</td><td>-0.612***<br>(0.0356)</td><td>-0.147***<br>(0.0308)</td></tr><tr><td>GDP per capita</td><td>-0.5848*<br>(0.337)</td><td>-0.8162**<br>(0.339)</td><td>-3.095***<br>(0.160)</td><td>0.0566<br>(0.598)</td><td>-0.603*<br>(0.357)</td><td>-0.291<br>(0.192)</td><td>-0.640<br>(1.506)</td><td>-1.426<br>(1.518)</td></tr><tr><td>Food price vulnerability</td><td>-0.0230**<br>(0.0111)</td><td>-0.0233**<br>(0.0116)</td><td>-0.315***<br>(0.0931)</td><td>-0.226**<br>(0.0884)</td><td>-0.00898*<br>(0.00468)</td><td>-0.0147***<br>(0.00343)</td><td>-1.145***<br>(0.247)</td><td>-0.214***<br>(0.0480)</td></tr><tr><td>Population growth</td><td>-34.37<br>(29.51)</td><td>-32.91<br>(29.72)</td><td>127.7***<br>(1.369)</td><td>83.79***<br>(24.15)</td><td>-14.36<br>(9.226)</td><td>-1.421<br>(2.019)</td><td>-155.7*<br>(86.32)</td><td>80.11**<br>(33.98)</td></tr><tr><td>Rural pop growth</td><td>-0.0132<br>(0.0104)</td><td>-0.0179*<br>(0.0109)</td><td>-0.162***<br>(0.0206)</td><td>-0.206***<br>(0.0252)</td><td>0.0187<br>(0.0118)</td><td>-0.0474***<br>(0.0147)</td><td>0.394***<br>(0.152)</td><td>-0.0474<br>(0.109)</td></tr><tr><td>Industrial spillovers</td><td>4.075***<br>(1.207)</td><td>4.149***<br>(1.086)</td><td>-8.887***<br>(1.735)</td><td>-6.052***<br>(0.227)</td><td>2.137***<br>(0.818)</td><td>0.761**<br>(0.302)</td><td>17.73**<br>(7.404)</td><td>-11.92***<br>(0.610)</td></tr><tr><td>Services spillovers</td><td>-2.243**<br>(1.010)</td><td>-2.289**<br>(0.939)</td><td>6.609***<br>(1.293)</td><td>3.283***<br>(0.152)</td><td>2.092**<br>(0.995)</td><td>-0.185<br>(0.162)</td><td>-10.52***<br>(3.548)</td><td>7.714***<br>(0.867)</td></tr><tr><td>Incidence of TB</td><td>-0.00054**<br>(0.000210)</td><td>0.000396*<br>(0.000220)</td><td>0.00166**<br>(0.000722)</td><td>0.00223**<br>(0.000967)</td><td>-0.00040**<br>(0.000200)</td><td>-0.000465*<br>(0.000274)</td><td>-0.00278***<br>(0.000988)</td><td>0.000297<br>(0.00070)</td></tr><tr><td>Prevalence of HIV</td><td>0.0414<br>(0.0326)</td><td>0.0411<br>(0.0315)</td><td>0.178<br>(0.150)</td><td>0.763***<br>(0.116)</td><td>-0.0679<br>(0.0562)</td><td>-0.0109<br>(0.0142)</td><td>-0.274***<br>(0.0795)</td><td>-0.0178<br>(0.302)</td></tr><tr><td>Constant</td><td></td><td>-2.323<br>(3.968)</td><td></td><td>1.200<br>(1.053)</td><td></td><td>-0.599<br>(1.212)</td><td></td><td>1.902<br>(0.110)</td></tr><tr><td>Dependent variable (t-1)</td><td>0.928***<br>(0.00947)</td><td>0.828***<br>(0.00977)</td><td>0.905***<br>(0.0329)</td><td>0.908***<br>(0.0450)</td><td>0.818***<br>(0.0136)</td><td>0.943***<br>(0.0162)</td><td>0.873***<br>(0.107)</td><td>0.826***<br>(0.110)</td></tr><tr><td>Observations</td><td>378</td><td>406</td><td>55</td><td>83</td><td>378</td><td>406</td><td>55</td><td>83</td></tr><tr><td>Number of id</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td></tr><tr><td>Instruments</td><td>15</td><td>16</td><td>15</td><td>17</td><td>14</td><td>15</td><td>14</td><td>16</td></tr><tr><td>AR1</td><td>0.0280</td><td>0.0343</td><td>0.00538</td><td>0.000140</td><td>0.0244</td><td>0.0176</td><td>0.0930</td><td>0.016</td></tr><tr><td>AR2</td><td>2.243</td><td>2.249</td><td>0.548</td><td>0.181</td><td>0.501</td><td>0.351</td><td>1.358</td><td>0.557</td></tr><tr><td>Sargan OIR</td><td>0.0791</td><td>0.5066</td><td>0.583</td><td>0.856</td><td>0.578</td><td>0.2004</td><td>0.175</td><td>0.577</td></tr><tr><td>Hansen OIR</td><td>0.304</td><td>0.302</td><td>0.232</td><td>0.296</td><td>0.1595</td><td>0.2897</td><td>0.590</td><td>0.318</td></tr></tbody></table>

Robust standard errors in parentheses

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

#### 5.2.4.1. Robustness check: total water and total sanitation

Table 9 presents the estimated coefficients for analysis of the effects of agricultural expansion on food security, as captured by improved water sources and sanitation facilities. Estimates are based on both annual and average data. The coefficients obtained show that an expansion of agricultural land is accompanied by an improvement in the utilisation dimension of food security. More specifically, a 1% increase in agricultural land is accompanied by a significant improvement in nutrient intake by the population.
Both crop and livestock production contribute to water pollution (UNEP, 2016), although the type of contamination differs across food production types. In crop production, it is mainly due to unsustainable farming practices like overuse of organic and chemical fertilizers, and over use of pesticides including herbicides. It leads to the leaching of nitrogen, phosphorus and pesticide residues into the soil and runoff into surface waters. Nitrogen and phosphorus contamination of surface waters is also observed in areas with intensive livestock production due to unsustainable management of manure of livestock. These areas also show pathogen contamination (Linderhof et al., 2021). In those cases, more people will access contaminated water, which will degrade population food security.

The direct relationship obtained could be justified by modernisation's slow process. According to Marinoudi et al. (2018) agricultural modernisation process is slow, with a slow adoption rate of chemicals and fertilisers. As a result, the expansion may provide better water access and sanitization facilities. Besides, only permanent crop land expansion appears to threaten water access in Africa.

As a matter of fact, palm oil production in Cameroon, Niger, Tanzania and Zambia causes land to dry out and reduces water access. As a result of the substantial amount of water involved in their production, all nearby water is dried out. However, the model shows that small scale agricultural expansion raises the income of small-holders and improve their capacity to build and to access improved water and sanitation facilities as well.

#### 5.2.4.2. Robustness check: Water and sanitation in urban or rural milieus

Table 10 presents the analysis of the relationship between agricultural expansion and food security, disaggregated by the living environment. It also serves as a robustness and sensitivity analysis.

The estimated coefficients show that the variable of interest is significant for all dependent variables and in all settings. The limited knowledge of our study of the relationship between agricultural land expansion and the use dimension of food security allows us to naively conclude that the latter is sensitive to the former. Agricultural land expansion affects sanitation access negatively in urban areas but positively in rural areas. In contrast, the effect on access to water is positive in urban areas but negative in rural areas. It can be concluded that agricultural expansion in Africa affects the use dimension of food security, and are sensitive to the milieu.
<table><caption>Table 10: Sensitivity analysis</caption><thead><tr><th rowspan="3">Variables</th><th colspan="4">Dependent variable: Sanitation</th><th colspan="4">Dependent variable: Water</th></tr><tr><th colspan="2">Urban</th><th colspan="2">Rural</th><th colspan="2">Urban</th><th colspan="2">Rural</th></tr><tr><th>D-GMM</th><th>S-GMM</th><th>D-GMM</th><th>S-GMM</th><th>D-GMM</th><th>S-GMM</th><th>D-GMM</th><th>S-GMM</th></tr></thead><tbody><tr><td>Agricultural land</td><td>-0.0711**<br>(0.0301)</td><td>-0.116*<br>(0.0688)</td><td>0.0668***<br>(0.0189)</td><td>0.0646***<br>(0.0211)</td><td>0.0137**<br>(0.00565)</td><td>0.01475*<br>(0.00866)</td><td>-0.0246**<br>(0.0109)</td><td>-0.0302**<br>(0.0144)</td></tr><tr><td>Production Index</td><td>-0.0228**<br>(0.0106)</td><td>-0.0231**<br>(0.0106)</td><td>-0.0126*<br>(0.00688)</td><td>-0.0546***<br>(0.0101)</td><td>0.00384<br>(0.00278)</td><td>0.000479<br>(0.00436)</td><td>0.00347<br>(0.00715)</td><td>0.00466<br>(0.00783)</td></tr><tr><td>CO2 emissions (log)</td><td>-0.298<br>(0.457)</td><td>-1.833*<br>(1.054)</td><td>-0.613<br>(0.816)</td><td>-0.508<br>(1.207)</td><td>-0.304<br>(0.246)</td><td>0.0393<br>(0.370)</td><td>-0.442*<br>(0.266)</td><td>-0.441*<br>(0.232)</td></tr><tr><td>Governance index</td><td>0.0410<br>(0.123)</td><td>0.0579<br>(0.0728)</td><td>-0.112<br>(0.0957)</td><td>-0.111<br>(0.0994)</td><td>0.0562**<br>(0.0251)</td><td>0.0761***<br>(0.0267)</td><td>0.0968*<br>(0.0529)</td><td>-0.0762<br>(0.0713)</td></tr><tr><td>GDP per capita</td><td>0.993*<br>(0.640)</td><td>3.718**<br>(1.571)</td><td>0.682***<br>(0.058)</td><td>0.732*<br>(0.399)</td><td>-0.108<br>(0.241)</td><td>-0.203<br>(0.264)</td><td>-1.196**<br>(0.539)</td><td>-1.394*<br>(0.737)</td></tr><tr><td>Food price vulnerability</td><td>-0.0202<br>(0.0194)</td><td>-0.0222*<br>(0.0125)</td><td>-0.00508<br>(0.0132)</td><td>-0.00507<br>(0.0213)</td><td>-0.000833<br>(0.00335)</td><td>0.00252<br>(0.00172)</td><td>-0.0226*<br>(0.0122)</td><td>-0.024***<br>(0.00872)</td></tr><tr><td>Population growth</td><td>-40.10<br>(28.50)</td><td>33.67<br>(44.40)</td><td>12.67<br>(64.55)</td><td>18.19<br>(100.1)</td><td>-15.30<br>(15.83)</td><td>5.742<br>(20.19)</td><td>-29.95**<br>(14.67)</td><td>-32.34**<br>(12.73)</td></tr><tr><td>Rural pop. growth</td><td>-0.0769*<br>(0.0467)</td><td>-0.268*<br>(0.144)</td><td>-0.0864***<br>(0.0327)</td><td>-0.0776*<br>(0.0439)</td><td>-0.00826<br>(0.0109)</td><td>-0.00318<br>(0.0180)</td><td>-0.0966***<br>(0.0288)</td><td>0.0120<br>(0.0248)</td></tr><tr><td>Industrial spillovers</td><td>-2.449<br>(3.020)</td><td>-10.07*<br>(5.891)</td><td>3.608***<br>(1.282)</td><td>3.734**<br>(1.536)</td><td>-0.827<br>(0.597)</td><td>-1.063***<br>(0.377)</td><td>-3.169<br>(2.304)</td><td>-3.396<br>(2.230)</td></tr><tr><td>Services spillovers</td><td>3.248*<br>(2.041)</td><td>9.682*<br>(5.142)</td><td>-2.487*<br>(1.442)</td><td>-2.691<br>(1.953)</td><td>0.991<br>(0.708)</td><td>0.621<br>(0.731)</td><td>2.619<br>(1.970)</td><td>2.847<br>(2.052)</td></tr><tr><td>Incidence of TB</td><td>-0.000272<br>(0.000973)</td><td>-0.00204*<br>(0.00122)</td><td>-0.000537*<br>(0.000291)</td><td>0.000536<br>(0.000361)</td><td>-0.000132<br>(0.000149)</td><td>-0.000110<br>(0.000161)</td><td>-0.000568*<br>(0.000336)</td><td>-0.00067*<br>(0.000384)</td></tr><tr><td>Prevalence of HIV</td><td>-0.0206<br>(0.0893)</td><td>0.292*<br>(0.155)</td><td>-0.0441<br>(0.109)</td><td>-0.0290<br>(0.163)</td><td>-0.0319**<br>(0.0147)</td><td>-0.0193<br>(0.0156)</td><td>-0.179**<br>(0.0832)</td><td>-0.181**<br>(0.0742)</td></tr><tr><td>Constant</td><td></td><td>-5.982<br>(9.859)</td><td></td><td>-0.159<br>(22.56)</td><td></td><td>0.551<br>(4.313)</td><td></td><td>14.34***<br>(5.545)</td></tr><tr><td>Sanit. urban, (t-1)</td><td>0.912***<br>(0.0324)</td><td>0.962***<br>(0.0353)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sanit. rural, (t-1)</td><td></td><td></td><td>0.905***<br>(0.0227)</td><td>0.903***<br>(0.0298)</td><td></td><td></td><td></td><td></td></tr><tr><td>Water urban, (t-1)</td><td></td><td></td><td></td><td></td><td>0.996***<br>(0.00610)</td><td>0.912***<br>(0.0169)</td><td></td><td></td></tr><tr><td>Water rural, (t-1)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.945***<br>(0.0131)</td><td>0.946***<br>(0.0130)</td></tr><tr><td>Observations</td><td>378</td><td>406</td><td>378</td><td>406</td><td>378</td><td>406</td><td>378</td><td>406</td></tr><tr><td>Number of id</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td><td>28</td></tr><tr><td>Instruments</td><td>18</td><td>18</td><td>17</td><td>18</td><td>14</td><td>14</td><td>14</td><td>14</td></tr><tr><td>AR1</td><td>0.073</td><td>0.021</td><td>0.060</td><td>0.080</td><td>0.052</td><td>0.093</td><td>0.024</td><td>0.018</td></tr><tr><td>AR2</td><td>0.533</td><td>0.126</td><td>0.223</td><td>0.426</td><td>0.792</td><td>0.690</td><td>0.238</td><td>0.858</td></tr><tr><td>Sargan OIR</td><td>0.963</td><td>0.747</td><td>0.326</td><td>0.286</td><td>0.672</td><td>0.530</td><td>0.898</td><td>0.388</td></tr><tr><td>Hansen OIR</td><td>0.153</td><td>0.512</td><td>0.696</td><td>0.514</td><td>0.436</td><td>0.226</td><td>0.626</td><td>0.732</td></tr></tbody></table>

Standard errors in parentheses

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

## 6. Conclusion

The social dimension of agricultural sustainability implies that the proportion of food insecure populations should steadily increase. One of the greatest paradoxes of Sub-Saharan Africa is that the region holds more than 2/3 of the world's arable land, meanwhile the number of undernourished people in the region increased by 23.9% between 1994 and 2014 (FAO, 2015). A further expansion of agricultural land is also necessary to mitigate the food shortage and undernourishment effects caused by the Ukraine War.
Numerous studies have shown that various factors can affect food security such as climate variability (Affoh et al., 2022; Kinda and Badolo, 2019), climate change (Tankeu, 2021), epidemics or pandemics (Nchanji and Lutomia, 2021). Non-consensual results about the relationship between agricultural land expansion and food security justify exploration of this link. This essay fills this gap in SSA using macroeconomic data. Specifically, it investigates whether agricultural land expansion can reduce undernourishment.

Due to data availability, the study covers the period 2001–2016 and focuses on 28 countries in Sub-Saharan Africa. The essay uses a simple regression with random effects (RE) to estimate the baseline model. For robustness check, general method of moments in difference (DGMM), then in system (SGMM) techniques are mobilised.

This essay suggests that the expansion of agricultural land reduces undernutrition. This result is consistent with the literature. The reason provided here is based on the assumption of increasing returns. Primary arable land is generally more fertile than secondary land. The use of the former will require more inputs to perform agricultural activity and generate higher yields. Agricultural products prices reduce, and become more affordable to the community. Finally, it improves the access to food and reduces undernutrition, keeping other factors constant.

The secondary, arable land expansion, cereal land expansion and forest land expansion alleviate significantly undernourishment prevalence. However, permanent cropland increases exportations of crops and reduces the access to food. This increases the proportion of undernourished in Africa.

Third results in this section show that vulnerability to food prices, population growth and the shift of labour to the service sector are the main factors that increase the proportion of undernourished people. In contrast, factors such as the production index, the quality of institutions and labour movements in industry reduce the proportion of undernourished people.

To withstand the crisis, it can be recommended in the very short term that African countries identify producers, finance, promote and assist them in the use of new agricultural and arable land for both cereal and crop production.
ESSAY II

EFFECTS OF AGRICULTURAL LAND EXPANSION ON STRUCTURAL CHANGE IN
SUB-SAHARAN AFRICA

This essay has been presented at the Second Young Scholars Conference on Structural Change and Industrial Policy in Africa, organised by the South African Research Chair in Industrial Development (SARCHI-ID) and Young Scholars Initiative. Conference held on between the 21–22 June 2022, at the University of Johannesburg.

And

At the Annual conference on African political economy2, ACAPE2 & Annual Conference on Regional Integration In Africa12, ACRIA12 organised by the African Political Economy Association (APEA), at the University of Nigeria , October 22-23, 2022.
# 1. Introduction

Structural transformation is referred to as the shift of nations from predominantly an agricultural to an industrial or services society (Bloem, 2019). Sub-Saharan Africa is the world's most agrarian continent with output and employment shares in the agricultural sector higher than in any other world region (Barrett et al., 2017; Bates and Block, 2013; Diao et al., 2017).

Despite the abundance of uncultivated land, different outcomes emerge from the debate linking agricultural growth and structural change. Firstly, agricultural land expansion neither supports structural change (Foster and Rosenzweig, 2004; Marinoudi et al., 2019; Pingali, 2007), nor reduces youth involvement in agriculture (Yeboah et al., 2019), but reduces industrial development (Rozaki, 2020). Secondly, extensive growth may increase agricultural productivity and production (Barrett et al., 2017; Caselli et al., 2012; Ngoma et al., 2021) and enhance structural change in Sub-Saharan Africa (African Center for Economic Transformation, 2017; Santangelo, 2018), with the latter constrained by little improvement in production factors (NEPAD, 2013). Thirdly, agricultural growth may have mixed effects on structural change. It supports the local non-agricultural and rural economy, through agricultural expansion, and may also crowd-out non-agricultural activity (Hornbeck and Keskin, 2011). Crop productivity growth leads to a more agrarian economy while labour productivity growth leads to an industry economy (Bustos et al., n.d.; McGowan and Vasilakis, 2019).

Several determinants of structural change include the role of governance (Totouom et al., 2019), education (Edokat and Njong, 2019), natural endowments (Mensah et al., 2016), climate change (Tankeu, 2021), labour productivity (Johnson and Vollrath, 2020), climate regions (Eberhardt and Vollrath, 2018) and conflicts or terrorism (Noubissi and Njangang, 2020).

Recent studies show that agricultural land expansion is associated with long-term economic growth (Barbier, 2020, 2021). Economic growth is inseparable from structural change (Gabardo et al., 2017). However, nothing is said about the effects of agricultural land expansion on structural change in sub-Saharan Africa, nor about the type of structural change it causes. To fill this gap, the study evaluates the effects of agricultural land expansion on the type of structural change in sub-Saharan Africa.

The rest of the essay is organised as follows. Section 2 presents some stylised facts on structural transformation in SSA; Section 3 discusses the literature review; Section 4 exposes the methodology; Section 5 analyses and interprets the results and Section 6 gives the conclusion.
## 2. Stylised facts on the structural change process in sub-Saharan Africa: A Markov Chain Analysis

This section anticipates the methodology presentation and analyses the structural change process of sub-Saharan Africa, using the built structural change variable and Markov chain analysis methodology.

### 2.1. Analysing the structural change process in Sub-Saharan Africa.

The following transition diagram (figure 9) provides probabilities of remaining in a particular state or moving to another state, in the next period. As the diagram shows, the highest probabilities from one year to the next are found within states: 97.8%, 77% and 97.4% for states S0, S1 and S2 respectively.

<figure><img src="image_11.png" /></figure>

Structural Change States: S0 = Underdeveloped, S1 = Developing, S2 = Developed

<caption>Figure 9: Transition diagram of structural change, All countries</caption>

Source: author.

When countries start at the stage of structurally underdeveloped (S0), they have 97.8% chances of remaining there the next year. However, countries move from the structurally underdeveloped stage to the structurally developing stage (S1) with a probability of 2.05%, and to the structurally developed stage (S2) with a probability of 0.13%. This clearly established the idea of a structural change that ‘bypasses’ the industrial sector (Cadot et al., 2015). Countries starting at the stage of structurally developing have about 77% probability of remaining in that stage the next year. They also have greater likelihood (20.67%) of returning to structurally underdeveloped stage (S0) than move to the state of structurally development (S2) with the probability of 2.4%. This implies overall productivity decreases and confirming the idea of dynamic losses as countries go back more frequently to less productive stage S1 to S0 than the reverse (de Vries et al., 2015).

When countries are in structurally developed stage (S2) at the current period, they are almost bound, with a probability of 97.4% to remain there. The only issue is to return back to stage S1, with the
chances of 2.6% in the next period. The lowest probability of the diagram is 0.13%, on the path linking structurally underdeveloped (S0) to structurally developed (S2). It shows that, despite a possibility of reaching the (S2) state, it remains too low.

Finally, it comes out that the greatest probabilities are those of remaining in the same state. These probabilities range from 77% to 98.8% for two consecutive years. Consequently, the type of structural change is close to a time-invariant variable or process (Cameron and Trivedi, 2009).

**2.2. Analysing the structural change process in sea opened countries.**

The following transition graph (figure 10) is a birth/death chain. It is a Markov chain whose state space is a set of consecutive integers. It can only move from (S1) to (S2) – a birth, or from (S2) to (S1) – a death, or stay in place in one step. The graph establishes the probabilities of transitions between different stages of structural change for all countries with sea openings. It emerges that structural changes are almost immutable. So, knowing that the current year state is (S0), there is a low probability – about 3% – of moving to the (S1) state in the next year. However, it is 97% likely that a structurally underdeveloped country (S0) will remain the following year.

<figure><img src="image_12.png" /><figcaption>Figure 10: Transition diagram of structural change, sea cost countries</figcaption></figure>

Source: author.

The same trend can be observed for countries which, in the current year, are in state (S1). In 82% of cases, they remain in the same state the following period, or they either return to the state of structurally underdeveloped (S0) with a probability of 16%, or they progress with a low chance – less than 2% – to the state of structurally developed (S2).

Countries whose state is S2 during the reference year remain 98% of the time in the same state. They have a low probability - 2% - of returning to state (S1), and a zero probability of falling, returning directly to state (S0). Additionally, the phenomenon is reciprocal, since it is impossible for countries exposed to the sea to move directly from structurally underdeveloped to structurally developed. They must therefore necessarily go through the intermediate stage S1, in line with the ‘Bypassed’ hypothesis of the industrial sector as emphasised by Cadot et al. (2015) and Baymul and Sen (2020).
## 2.3. Analysing the structural change process in landlocked countries.

The last transition diagram (figure 11) gives the transition probabilities for countries without an opening to the sea. It is observed that in 99% of cases, structurally underdeveloped countries do not move the following year. There is a very low probability - 0.65% and 0.32% - of progressing to stages S1 and S2 respectively.

<figure><img src="image_13.png" /><figcaption>Figure 11: Transition diagram of structural change, landlocked countries
Source: author.</figcaption></figure>

It is further observed that all countries at step S1 remain at this step or return to step S0 with the same probability - 46.67%. As for countries in the initial state S2, they remain at 96.15% in the same state or pass through state S1 with a probability of 3.85%.

The analysis of structural change states clearly shows that structural change cannot be sustainable in the long run. On average, countries will remain in their initial stages of structural transformation. The results are close to (CUA and OECD, 2018) findings, which claim that expected positive results of structural change are constrained as labour force move to low productivity sectors like services. Furthermore, the section highlights three major facts: First, the temporal invariability of structural change process with probabilities of remaining in the same state, from one year to another is greater than or equal to 77%; Second, structural change states are of the same class, so there is always a way out from one stage to the next; Third, the process of structural change in sub-Saharan Africa is close to a time-invariant process, because the state in which one finds oneself does not depend on the period.
### 3. Literature review linking agricultural land expansion and structural change

This section presents both theoretical and empirical studies linking agricultural land expansion to structural change.

#### 3.1. Agricultural land expansion and structural change: Theoretical Frameworks

The theories linking agricultural land expansion and structural change can be subcategorised into root approach and inefficient approach.

##### 3.1.1. The root link approach to agricultural land expansion for structural change

This approach sheds light on the expected or true links tying agricultural sector with other sectors, especially, industrial sector. Two main theories located in this approach explain the cohesion between agricultural land expansion and structural change, namely, the physiocrat theory and the Hirschman theory.

##### 3.1.1.1. The physiocrat surplus of agriculture as a base of structural change

In his *Essay on the nature of general trade*, composed of three parts on production, money and foreign trade, Cantillon (1755) on production states that ‘*Land is the source or the material from which one draws wealth; the work of man is the form which produces it. Wealth in itself is nothing but the food, the conveniences and the pleasures of life.*’

This informed speculator defends land and labour as a real and objective source of wealth. If Cantillon's agricultural ideas foreshadowed those of the Physiocrats, he amended them with a very modern vision of the 'entrepreneur', reminiscent of that which would be developed by J. B. Say (1861) in the 19th century and then by Schumpeter (1981) in the 20th century. He is the one who invents this term and gives it its current meaning. Although attached to an agricultural vision of growth, he sees a specific and positive role of the entrepreneur compared to that of the ordinary peasant. The latter is often limited to repetitive work, carried out within the restrictive framework of feudal allegiances. Driven by the spirit of risk, the entrepreneur clears new land, transforms agricultural products, making wool and flax clothes pleasant to wear. He invents a world where growth is created because he dares. The entrepreneur is the economic agent who, although he is not completely certain of finding outlets, invests. It is poles apart from the world of the aristocracy, a world that lives in stagnation, a world where everyone abandons themselves to routine reinforced by social immobility.

Precursors of political economy, physiocrats like Xenophon based all economic achievements on agriculture. Physiocrats gave to land not only a leading role, but they also made it the hero of their scheme. The belief in the abundances of Nature, with rent the gift of God to the landowners, put land
in a preferred position in the *Tableau économique*. They thought that agricultural sector was central
to wealth creation and economic growth.

The natural resource that is the earth, is the basis of all virtuous production which is transmitted to
the entire economy. Thus, the product of the agricultural sector is based on working the land. It has
the capacity to maintain a dynamic link between the agricultural sector and the industrial sector.

The leader of this school of thought, Quesnay (1758), believed that wealth comes from primitive goods such as land, people and cattle. Thus, soil fertility, associated with the work and intelligence of men and the work of animals create wealth that is greater than the needs of men. It is this excess product, referred to as overproduct or surplus, that is fundamental wealth. It is essential for both farmers and traders, or even those who operate in the manufacturing sector.

For this school of thought, the agricultural sector creates subsistence goods and the non-agricultural
sector (especially industry) consumes the surplus or surplus created, or transforms it into luxury goods
(Quesnay, 1758). Agriculture therefore plays a fundamental role, as food is provided by subsistence
production. On the other hand, the emergence of the nation, guided by the expansion of industry, is
subordinated by the productivity of the agricultural sector. Agriculture is therefore a prerequisite for
the rise of a nation. The theory thus calls for the aggregation of several links and their synergy, to
achieve good production performance and therefore growth.

The idea that economic growth is based on agriculture, through structural change, is from the capacity
of the earth to provide a harvest greater than the seeds that the accumulation of wealth is born. Thus,
developing an ambition for emergence, subsistence needs can be met through soil fertilisation using
manure or chemical fertilisers, improved seeds, mechanisation or the conversion of land into
agricultural land. Human work, for its part, closely resembles the strengthening of managerial
capacities, for the management of industrial production, by combining the other factors of production
as well as possible. Finally, animal labour, as far as it is concerned, is very close to physical capital,
the good management of which requires organisational capacities of production, both at the level of
the producer and at the level of the King.

To be productive, agriculture must be well managed, both in terms of policy design and in terms of
practical implementation. Quesnay with his precursor theory of both structural change and growth
theory, also believes that agriculture must be sufficiently organised. Each doing the best they can, in
the interests of all, allow the nation to be abundant or in surplus of products. Consequently, every
agent of the nation would be happy, because the net product created determines the development of
the kingdom, but the latter is constrained by that of the farmers. This is probably why he says: ‘*Poor
peasants, poor kingdom: poor kingdom, poor king*’: Quesnay (1758) in Daniel (2010).

This work tries to find a solution to the poverty of the ‘kingdoms’. The poverty of Sub-Saharan Africa seems to have its roots in the poverty of African peasants. This poverty itself is due to the low subsistence and low overproduction of agriculture. Which in turn explains the weak industrial structural transformation, in favour of tertiary structural transformation. Following Quesnay, it is hypothesised that the use of land, a fundamental and sufficiently abundant input in sub-Saharan Africa, is important to increase the agricultural surplus and promote an industrial-type structural transformation.

3.1.1.2. The Hirschman structural change model and agricultural expansion

This study is rooted in Hirschman’s theory of unbalanced growth, because it accounts for the forward
and backward linkages that are associated with agriculture and industry relationship. The Hirschmann
(1957) theory postulates an unbalanced growth model, arising from certain common characteristics
exhibited by developing countries, such as low levels of Gross National Income (GNI) per capita,
slow growth of GNI per capita, large inequality and widespread poverty, low levels of productivity,
high dependence on agriculture, a backward industrial structure, weak consumption and low savings,
high rate of population growth, high dependency ratio, high levels of underemployment and
unemployment, technological backwardness and existence of both the traditional and modern sectors
(dualism).

Traditional sector is the agricultural sector, which bears around 70% of the labour force. The modern
sector is the industrial and service sector employing the remaining 30% of the workforce. In addition,
there exists inadequate infrastructure to harness the available resources, and lack of entrepreneurs and
investors to channel the cash flow through the sectors for a balanced economic growth. To this end,
Hirschman proposed a deliberate unbalanced economic development strategy to maintain the existing
structural imbalance, viz., complementarity, external economies and induced investment.

Hirschman unbalanced growth theory relates this study to Sub-Saharan Africa based on the need for
investment in strategic sectors of the economies instead of all the sectors simultaneously. The
agriculture and industrial sector in sub-Saharan have potential of generating a high level of
productivity. However, since the theory assumes that the sectors would automatically develop
themselves through the linkages effect, the concept of Hirschman’s backward and forward linkage
comes into play. The forward linkage deals with the growth of certain projects, owing to the initial
growth which supplies raw materials. That is, the products produced by this industry are used as
inputs for other industries. A backward linkage is created when a project encourages investment in
facilities that enable the project to succeed. Following this theory, the agricultural productivity will
increase as the industrial sector will have a backward linkage with the agriculture sector by providing
inputs and technologies. While, the agricultural sector will have a forward linkage with the industrial
manufacturing sector by providing raw materials for processing. Therefore, expanding agricultural
land will increase agricultural productivity and allow people to reallocate in the industry which
promotes industrial type of structural change.

**3.1.2. The inefficiency approach of agricultural land expansion for structural change**

The inefficient deals with gathers theories that study the failing role of agricultural land expansion in
economic dynamics. The theories exposed here are comprised of the Marxian theory of class view
and the Ricardian theory.

**3.1.2.1. Agricultural land expansion from the Marxian theory of class view:**
**Landowners and structural change**

In economics, land plays important roles. For Marx, the existing landlords became a unique species
of capitalists of feudal vintage, but obsolete, and therefore soon to be replaced by genuine industrial
capitalists. Again, land was to be cast as the unmitigated villain by Henry George, who saw it as the
instrument that transfers the unearned social increment of economic progress to landlords Importance
of land to productivity.

At the difference of many of his colleagues of the same generation, he did not see land as a fixed
entity since the fertility of land can be affected by human labour (Marx, 1967). He thought that in the
capitalist system there is an inherent tendency to decrease the fertility of land. One reason for this is
the fact that most land at his time was rented by farmers, who tried to increase their return on
investments before expiration of the lease, and the second reasons are the unpredictability of returns
from land and the variability of nature, both of which threaten return on capital investments (Marx,
1967; Marx and Engels, 1942).

So, according to Marx, land is the channel which allows the transfer of surplus produced by the
farmers to the landlords. Expanding agricultural land corresponds to increasing the way by which
undue earns are transmitted to landowners. Therefore, the proportion of output produced by each of
the classes, will remain unchanged no matter the amount of surplus produced by the worker class.
This will encourage farmers to attempt to become landowners, which is at least unrealistic, resulting
in no structural transformation. The frustrations from captured surpluses are a deep root and causes
of farmers' covetousness, strikes and class fight.
He further adds that landlords are parasitic and capitalists exploitative. Consequently, their revenues in the form of rent and interest constitute distinct categories of the surplus created by farmers. Furthermore, he says 'Rent is a product of society and not of the soil,' to justify that even farmers should pretend to that product (Hubacek and van den Bergh, 2006). From this view, any increase will lead either to no structural change or to a bad structural change caused by conflict of classes.

#### 3.1.2.2. The law of diminishing marginal returns in agriculture and the Ricardian doubt of structural change

Ricardo and the older English economists who followed his casting also gave to land a key role but made it the villain of the piece. The concept of the niggardliness of Nature placed landowners in a strategic position in a community with a rapidly growing population.

Ricardo (1817) is against the increase in land because the resulting increase in production is very short-lived. The productive system, as it increases production, becomes more and more inefficient: it is the law of diminishing returns. Each additional acre of land that is cultivated has a lower fertility, each peasant that is torn from his countryside to join a factory is less trained than the workers already in place and therefore less productive. Humanity is subjected to the finiteness of nature in this mechanism of diminishing returns which it tries to overcome thanks to technical progress. But this finitude is ultimately unavoidable. Humanity will reach what the Ricardian economist John Stuart Mill calls the 'steady state'. The steady state is the moment when growth runs out and the population stabilises. The exponential can be bypassed for a while, but there comes a time when everything stops, when the limits are reached.

The Ricardian view of expanding agricultural land area is pessimistic (Daniel, 2010). Its extensive diminishing returns, suggests that many people are supposed to be farmers as lands' production potential decreases with time. This view fits clearly with the contraction of labour force in the agricultural activities, while the other sectors are not flourishing.

Ricardo's implicit conception of structural change has to do with technical progress. His relationship to technical progress is complex. He condemns Luddits and is by nature favourable to progress. But sometimes he was seized with doubt. In his *Principles of Economy and Tax*, entitled 'Machines', he considers that technical progress, by substituting machines for men, makes it possible to bypass diminishing returns. In the relation to work, the economy is therefore threatened by the inability to determine a working time in accordance with productivity: by making workers work too much, there is a risk of overproduction; by not making them work enough, there is a risk of a shortage.
Here is the relationship with structural change. An important aspect is that although agricultural land expansion from technical progress leads to risk of overproduction, it is probable that it creates unemployment. Jobless people will be forced to convert into less prestigious activities and sectors, or to farm on impoverished areas, with smaller productivity, which is considered as a bad structural change.

In addition to technical progress, agricultural intensification and mechanisation, the policies recommended by the African Union (AU) to increase agricultural investments by 10% are slow to take shape in Africa. As part of this thesis, it is postulated that African countries are still far from having saturated their land availability constraints. On this basis it will be hypothesised that the increase in agricultural land generates beneficial effects for the agricultural, industrial and service sectors. The Ricardian pessimistic view maintains that the limits of the earth give humanity limits to its wealth. This view clashes with the researcher's optimistic view, according to which accumulating knowledge can break down the barrier that diminishes marginal returns from land use imposed to generate structural change.

This subsection helped to posit agricultural land expansion in the theoretical debate of structural change. Its possibility to promote structural change may depend on both the stock of arable land and the development level of countries. In a nutshell, there is a possibility that increasing agricultural land causes structural transformation in land abundant sub-Saharan Africa countries.

**3.2. Agricultural expansion and structural change: Empirical literature review**

There is a paucity of literature on agricultural land expansion and structural change. Studies linking agricultural land expansion and structural change, on the one hand, then studies linking agricultural intensification and structural change, on the other hand, were reviewed, followed by agricultural growth and structural transformation, to end up with other aspects of land.

**3.2.1. Agricultural land expansion promoting structural change, growth and development**

Torres-rojo et al. (2020) mobilises power-law models and “System for the Accountability in Fiscal Resources Dispersion” databases of Mexico to show that the density of agricultural lands and government spending to foster agriculture around a locality show a power-law relationship related to the distance from a centroid locality. The result is relevant as it shows that the expansion of the agricultural frontier around a centroid locality has a predictable dynamic, despite the complex set of social, economic, political, cultural, and physical variables which shape this expansion. Similarly, Johnson and Vollrath (2020) use Global Agro-Ecological Zone to estimate aggregate land elasticity.
Findings suggest that land elasticity influences the degree of decreasing returns to labour and capital in agriculture, and thus how sensitive living standards are to shock in productivity and population.

Xiao and Zhao (2018) investigate on the effects of agricultural land on rural-urban migration and the labour market outcomes in the context of China. Using a longitudinal household survey of 15,927 observations, they apply a Regression and Multinomial Logit Regression with the Y variable being the workers' destinations, those staying at home being the base category. They find the increased land leads to a higher propensity for migration and more land pushes people to move further. Authors also show that land affects people's time allocation between farming work, local non-farming work, and non-farming work outside their hometown. All these findings provide new understandings of the role of agricultural land in rural-urban migration in China and other developing countries. Despite the context the findings are consistent with this study's position. It is expected that more land will be a signal for people to move away from the agricultural sector, to work in the industrial sector.

Deininger and Xia (2016) try to assess evidences of spillovers from large farm establishment in Mozambique using plot-level data in Mozambique from 2002–14. Using difference-in-difference strategy, the results outline that farm establishment had short-term spillovers on neighbouring small farmers where spillover increased adoption of new practices and access to inputs. The negative spillover decreased perceived well-being. This study hypothesises that increasing land for agriculture makes the industrial sector more ready to invest in agricultural employment. Nolte and Ostermeier (2017) try to understand what effects the establishment of a large-scale farm has on the rural labour market in Kenya, Ethiopia, Nigeria, Tanzania and Uganda and whether large-scale farming creates or destroys employment. Their analysis is based on 1,346 deals of Land Matrix Global Observatory and the FAO smallholder data. It shows that agricultural expansion from foreign investments massively crowd out smallholder farmers and concludes that these effects tend to be large on the local scale (i.e. in the immediate surroundings of the investment site) but small in relation to total national employment in agriculture.

Yaro et al. (2017) analyses with model description methodology which forms of commercialisation – plantation, contract farming and medium-scale commercial farming – models will reduce impoverishment of smallholders, and transform smallholder agriculture and the wider economy in Ghana. They use data from focus group discussions and show that due to the processing units in the plantation and the outgrown models, they provided more employment but less food insecurity.

Santangelo (2018) studies the influence of large-scale farms captured by foreign direct investments (FDI) in land, in agriculture in both developed and developing countries, on host country structural change. Results from this conditional mix process model suggest positive spillovers or structural
change for developed countries and negative structural change for developing countries. Applying
his mapping methodology Sauer (2018) shows with extensification can promote the creation of
agribusiness enterprises – like in Brazil, but can also cause territorial disputes which discourages and
impacts social harmony.

In northern Sierra Leone, Yengoh and Armah (2015) apply both quantitative and qualitative research methods on 442 households. Parametric and non-parametric mean tests show that because of land acquisition, employment by the land investing company is limited in terms of the number of people it employs relative to the population of communities in which it operates. Also, wages from employment by the company cannot meet the staple food needs of its employees, which pushes population to move to other sectors. Moreover, Nyantakyi-Frimpong and Kerr (2017) try to account for impacts LSLA on food security of 155 households in Ghana. Applying comparison methodologies, they find that land grabbing exacerbates food insecurity, decreases job opportunities, increases social conflicts, sparks migration in host country.

According to Mensah et al. (2016) show using system generalized method of moments on 21 countries in SSA over the period 1970–2012, that increasing land for agriculture will lead to an improvement of industrial, manufacturing and service value-added shares in African countries. However, they fail to see structural transformation as the labour movement, which characterises the most sub-Saharan African countries. What this work intends to do.

Barbier (2020, 2021) shows that agricultural land expansion is linked to economic growth, in 97 low and middle-income countries. He uses in 2020 both quadratic and cubic models estimated with random and fixed effects and find that expanding land has been an engine of growth in developing economies. Similarly, in 2021 he draws the same conclusion using Solow-Swan model over 1990–2018 for 138 low- and middle-income countries (Barbier, 2021). The same relationship is identified as a secondary result, while studying the Nepalese economy from 1970 to 2015 and using an autoregressive distributed lagged model (Ghimire et al., 2021).

**3.2.2. Agricultural intensification**

Foster and Rosenzweig (2004) use a series of household data sets from India covering the period 1971–99 to measure how agricultural development affects the composition of rural activities and incomes. Growth in income from the nonfarm sector in rural India over the last 30 years has been substantial, and the primary source of this growth, is the expansion of rural industry, rather than the expansion of local agricultural productivity. The study used a Laspeyres-weighted (1971) price index for four High-Yield Variety seeds for four crops – corn, rice, sorghum, and wheat, to capture
agricultural change. It deals with intensification rather than extensification of agriculture. The present study, however, seeks to analyse if the agricultural land expansion is a good option for structural change in Africa.

Hornbeck and Keskin (2011) analyse whether windfall agricultural gains in United States countries generate local economic spillovers in both the short and long term. Using econometric model on cross-sectional pooled data, they find that agricultural productivity promotes structural change in the short run. Comparing counties over the Ogallala with similar counties, non-agricultural sectors experienced only short-run relative benefits. There was no long-run relative expansion in Ogallala countries' nonagricultural economic activity. However, the study analyses how agricultural expansion promotes economic spillovers from agriculture, irrespectively to the runs. It expects that expansion of agriculture in SSA, be able to generate benefits to the industrial sector.

McGowan and Vasilakis (2019) study how productivity-enhancing agricultural technology affects structural change. Applying a difference-in-difference estimation strategy, they conclude that technology improves land productivity and increase the concentration of economic activity in the agriculture. They reach the conclusion that technologies which raised crop productivity led to a less urbanised economy as economic activity relocates from manufacturing and services towards agriculture. In other words, agricultural growth, as a result of intensification made the economy be more agrarian. This is true if the technology necessitates increased agricultural labour demand.

In Brazil, Bustos et al. (2016) study the adoption of new technologies on structural transformation. Authors present a simple model where the effect of agricultural productivity on industrial development varies according to the factor bias of technical change. They found that the effects of agricultural productivity on structural change depends on whether crop productivity or agricultural labour productivity increases. The former leads to a more agrarian economy while the latter is favourable to a less agrarian economy. They find that technical change in soy production was strongly labour-saving and led to industrial growth in Brazil.

There is also a strand of literature which argues that agricultural productivity may not always enhance structural transformation. For instance, studies in Brazil and the United States of Bustos et al. (2016) and McGowan and Vasilakis (2019) respectively, show that in an open economy the agricultural productivity and structural change linkages rely on the factor bias of the productivity shock. Clearly, on one hand, improvements in agricultural labour productivity (output per worker) reduce agricultural labour requirements, leading to an expansion of industry as workers relocate to manufacturing. On the other hand, increase in land productivity (output per acre) raises agricultural labour demand as a greater number of workers are required to process the additional output. In equilibrium, this bids up
wages leading to factor reallocation as manufacturing workers transition to employment in agriculture. This makes economy be more agriculture-oriented and less industrialised.

Binswanger-Mkhize and d'Souza (2012) analyse the structural transformation of the Indian economy and its agriculture. They decompose total factor productivity and find that although the agricultural share of gross domestic product (GDP) plunged between the early 1960s and the late 2000s, it remained the main employment sector throughout the period as in Sub-Saharan African countries. The authors conclude that growth of the agricultural sector, and productivity growth in particular, should be accelerated for India to generate sufficient new employment for its burgeoning rural population. In accordance with their study, it is expected that increasing agricultural land can provoke productivity shocks and structural transformation. Thus, still in line with the study, it is hypothesised that increasing productivity will generate more employment in the industrial sector for outflows of agriculture labour force.

Gasques et al. (2012) analyse the TFP growth and structural transformation in Brazil. They capture structural transformation by using the agricultural census between years 1970 and 2006, they find that productivity growth was the major driver of growth in Brazilian agriculture. They estimate a 2.1% per annum TFP growth rate for the whole period, with accelerating TFP growth over time. Moreover, they find more varieties of output in the agricultural production and hold that this diversification can have advantageous effects on employment as farmers allocate production factors to higher valued products. Following this study, one assumes that increasing land may allow producers to increase the varieties of species to be produced and will create new types of employment and may lead to structural transformation.

In his dissertation thesis, McCullough (2017) explores the link between labour productivity and the occupational choice that underlies the structural transformation process. He draws a model of households' decisions to participate in various activities like farming, wage employment, and self-employment – through operation of a household nonfarm enterprise. He finds that participation in farming is not responsive to productivity shocks of any sort. However, wage employment and self-employment participation do respond to wage and self-employment shocks of productivity, respectively. This study is conceptually close to his, only few differences can be made: This uses a macroeconomic analysis, and one thinks that land productivity shock could increase the probability of labour force to locate in the industrial sector, but less in the service sector.

Using a two-sector model economy, and general equilibrium analysis, Chen and Liao B.-L. Chen and Liao (2015) find that in the long run, growth in agricultural productivity increases nonagricultural capital and decreases agricultural capital and, through the complementarity between labour and
capital, it eventually causes a negligible agricultural employment share. Then, authors find that in the model with capital in both sectors, agricultural productivity growth can generate structural change that matches reasonably well with the data; meanwhile non-agricultural productivity growth cannot. They finally conclude that, in richest countries, growth in agricultural productivity is crucial to governing long-term and massive structural change. In a similar way, for developing countries, agricultural productivity provoked by land expansion for crop production purposes can lead to massive reallocation in the non-agricultural sector. In the context of sub-Saharan Africa, the agricultural land expansion may cause inflow of labour force in the industrial sector, more than in the service sector.

Zhang and Diao (2020) use China's case to assess implication of structural change to the evolving role of agriculture. By combining a twofold growth decomposition exercise with Input-Output (IO) and CGE model analyses, the analysis shows that the direct contribution of productivity growth within agricultural sector to the broad economic growth is impressively high in China. Although the contribution remains significant, becomes modest because of rapid falling of agricultural shares in the economy and total employment. Rising labour productivity in agriculture has led to rapid agricultural growth without increasing agricultural employment. This allows agriculture to indirectly contribute to the economywide productivity growth through structural change, confirming the arguments of many development economists who emphasise the impact of agriculture on structural change in the relationship between agriculture and structural change.

Marinoudi et al. (2019) assess the theoretical links of robotics and labour in agriculture in the short and mid-term. Author's claim that standardised tasks executed by machines increase labour productivity and drives out labour from the sector. Meanwhile, non-standardised tasks executed by robotics and artificial intelligence, are expected to have a mitigated effect on labour share in the agricultural sector. Low-skill labour (offering routine tasks) is to be substituted (the substitution effect) while, high-skill labour (cooperation in cognitive tasks) is to be complemented (the complementarity effect).

With her specific features, Africa has been an abandoned case by such studies. This study proposes to fill that gap and in accordance with their results, thinks that agriculture growth can also promote industrial reallocation change in sub-Saharan Africa region.

### 3.2.3. Agricultural growth as determinant of structural change

There is a long tradition in economics of studying the links between agricultural productivity and industrial development. Nurkse (1953), Schultz (1953) and Rostow (1960) argued that agricultural
productivity growth was an essential precondition for the Industrial Revolution. Classical models of structural transformation formalised their ideas by proposing two main mechanisms through which agricultural productivity can speed up industrial growth in closed economies. (i) First, the demand channel: agricultural productivity growth rises income per capita, which generates demand for manufacturing goods if preferences are non-homothetic (Gollin et al., 2002). The higher relative demand for manufactures generates a reallocation of labour away from agriculture. (ii) Second, the supply channel: if productivity growth in agriculture is faster than in manufacturing and these goods are complements in consumption, then the relative demand for agriculture does not grow as fast as productivity and labour reallocates towards manufacturing (Baumol, 1967; Ngai and Pissarides, 2007).

The view that agricultural productivity can generate manufacturing growth was challenged by scholars studying industrialisation experiences in open economies. These scholars argued that high agricultural productivity can retard industrial growth as labour reallocates towards the comparative advantage sector (Field, 1978; Mokyr, 1976; Wright, 1979). Their ideas were formalised by Matsuyama (1992) who showed that the demand and supply channels are not operative in a small open economy that faces a perfectly elastic demand for both goods at world prices. The open economy model presented in this essay differs from Matsuyama in one key dimension. In his model, there is only one input to production thus technical change is, by definition, Hicks-neutral. Bustos et al. (2016) models consider two complementary factors, land and labour in agricultural production. Thus, technical change can be factor-biased. From that setting, a new prediction emerges: when technical change is labour augmenting, an increase in agricultural productivity leads to a reallocation of labour towards the industrial sector even in open economies. This study instead tries to link reallocation of labour across sectors to agricultural land expansion. If the previous studies have analysed how intensification of agriculture can affect structural change in developed countries, less evidence is known about the relationship between agricultural expansion and structural transformation in Africa south of the Sahara.

### 3.2.4. Other aspects of land expansion and structural change, growth and development

This subsection reviews studies and discusses the characteristics of land and land tenure that are important to productivity. It focuses on John Mellor (2017), which addresses many aspects of the links between structural transformation and agricultural land.

#### 3.2.4.1. Land productivity: the traditional versus the modern context

Land productivity is characterised by a high variability around a low yield average, in the traditional context (Mellor, 2017). Moreover, combinations of labour leisure for a low – productivity function of
production, will not increase land productivity. If this is the case, it will cause the number of people
to stick at high levels. However, with modernisation, production possibility curve moves outward,
and combination of labour-leisure yields higher quantities, and production variability drops. Thus,
countries that have engaged agricultural modernisation will see higher productivity. This is what is
expected for Africa. Despite African countries have not yet completely modernise their agriculture,
some efforts are being made. On this process, increasing land areas will allow unused labour to
relocate in other sectors thus creating structural change.

3.2.4.2. Fragmentation of Farms

There is a common problem of farm fragmentation in Africa – that means a division into many small parts, often quite distant from each other (Mellor, 2017). Land is generally transmitted from previous generations to the next generations. Then, fragmentation arises partly from the complexity of generations of inheritance, and occasionally from small purchases. Although fragmentation has the advantage of diversifying risk-cutting across different soils and even rainfall regimes in a traditional agriculture, it reduces chances of either economy of scale or high production. In Africa, 80% of the smallholder have farm size less than 2 ha (Masere and Worth, 2022; Ncoyini et al., 2022). This can explain high number of people still in the agricultural sector.

However, with modernisation, especially the tapping of ground water on a small scale, and to some extent with machinery and for marketing purposes, consolidating land holdings has a major advantage. This thesis assumes that land expansion for agricultural purposes, that means consolidating lands and allowing productive farmers to get land, could help to generate agricultural outflows of labour. This can be done with government intervention that may be necessary to reconcile local differences on whether and how to proceed.

**3.2.4.3. Land registration**

Africa is rooted in his ancestral culture. Corollary, land ownership is commonly legitimised by tradition rather than legal registration and documentation. Feder and Nishio (1998) expose that it is beneficial to development that land markets with land registration have well-working, as modernisation goes on. They even argue that it is strongly desirable. According to Mellor (2017), in a traditional agriculture and in early stages of modernisation, premature registration of land and consequent formal title may simply strengthen those who would like to dispossess weaker members of the community. In a situation of domination by a tribal chief, the less scrupulous may allocate land to themselves and seal the transfer with registration. It may also strengthen corrupt elements in government who could seize land and register it in their own names before the weaker members in the community can respond. The work supposes that in that stage where African countries are still in
the process of land registration, increase of secured agricultural land will help structural transformation to occur.

Given the importance of land as a storehouse of wealth in low- and middle-income countries, it is better to develop open, well-operating, rental market than the market for sales. The most important feature of a well-functioning rental market is security of ownership in the face of land being operated for years by another.

#### 3.2.4.4. Land tenure

The analysis of Deininger et al. (2017) suggests that land markets are more active and have potential to contribute to structural transformation in Africa than commonly assumed. Using micro-level data sets of six countries (Ethiopia, Malawi, Niger, Nigeria, Tanzania and Uganda), authors conclude that, despite the expected low potential of land rental in the countries, great differences in land endowments and productivity generate potential for land markets to equalise endowments and contribute to higher levels of productivity. In addition, authors suggest that land rental markets build more equity by promoting land access to those with limited land endowments, and increase productivity.

This study enlightens the relationship between land and structural change in Africa. Two outcomes are possible. Agricultural land expansion may create structural change by increasing productivity of farmers. But conversely, it may reduce the process of structural change if rental land market performs bad. That means the insecurity of tenure reduces farmers’ devotion to their production activity. While carrying this macroeconomics analysis, it is expected that improvements accomplished in the land tenure domain, can allow land expansion in Africa to cause structural change.

#### 3.2.4.5. Small – Commercial Farms, large scale farms and plantations

African's agriculture is dominated by small commercial farms and great inequalities among them. Mellor (2017) holds that there is little or no evidence of political processes that would reduce that inequality or distribute land to those with little or no land. Moreover, he claims that such efforts would be counter-productive in the context of growth.

In some parts of Africa, vast areas are devoted to large commercial farms and plantations, which are simply large-scale commercial farms producing tropical export commodities. These were mainly the product of colonial systems that were reinforced by the special conditions of tropical export commodities, particularly the need for quality control and conglomeration for export, and the high level of profitability for colonial owners. Although large-scale farms are productive as they have
access to modern technology and inputs (Deininger and Byerlee, 2012) they fail to support local, rural
nonfarm population activities.

We assume that, land expansion and government intervention can generate help to generate more
output. Some governments in Africa imposed that, transformation of part of their agricultural products
takes place within countries, as it is the case in Ivory Coast for cocoa. Such type of policy if
implemented will benefit to local non-agricultural communities.

3.2.4.6. Increasing land area and agricultural productivity with respect to population density

In densely populated countries like Ethiopia, for more than 20 years, increased land accounted for about 40 percent of the rapid agricultural growth rate (Mellor 2017). Clearly, small technological change brings increased returns to land, covering the costs of bringing additional land into cultivation. Concurrently, a change in attitudes and availability of consumer tastes for goods increases the value of the output of labour relative to leisure. Even though land is limited, increased land area will add to the growth for a short period, as shown by India and Ethiopia, perhaps for a decade or somewhat more (Mellor, 2017).

Conversely the situation in low population density areas is very different to that in high population
density areas. Generally, such areas are dry and inhabited sparsely by semi-nomadic families relying
on cattle herding for the bulk of their income. In other cases, their use may be restricted, by disease
as in some areas of West Africa. While having high potential of productivity, these lands tend to
require significant investment in roads and irrigation systems. Then developing it may be highly
profitable. This fits the particular case of East Africa. If so, it should cover all its costs, leaving public
investment for the high-potential, densely populated areas.

The link with the present study is clear. In the case that land expansion for agriculture is followed by
land fragmentation, the economy will be more agrarian and structural change may not happen.
However, if agricultural land expansion conducts to more investments into large-scale farms, it is
expected to cause agricultural productivity and then structural transformation. This point is in line
with the study's position.

Some studies (Edokat and Njong, 2019 and Totouom et al., 2019) identify institutional quality and governance as determinants of structural change. For this reason, the study incorporates the quality of institutions which is the best way of capturing government intervention in social studies. Following these studies, it is theorised that good quality of institutions is more likely to sustain structural
transformation towards industry and terms like institutional quality and governance interchangeably
are used.

Concerning trade openness, dependence on primary commodity exports is associated with lower labour productivity in SSA (Kaba et al., 2022; Kacou et al., 2022). In SSA, however, manufacturing exports positively impact structural change (Kaba et al., 2022).

# 4. Methodology of analysis of the effects of land expansion on structural change

This section of the study presents the methodology to assess the effects of agricultural land expansion on structural change in sub-Saharan Africa. It starts by a general presentation of data and variables, followed by a presentation of our descriptive statistic. Last but not least, we expose the estimation strategy.

## 4.1. Data, sources and variable construction

This subsection provides information on the data and scope of the study. It also provides construction and justification of variables retained in the model, tools used to describe links and stationarity tests.

### 4.1.1. Data and Data sources

The essay seeks to assess the effects of agricultural expansion on the type of structural change process. All data used come from the World Development Indicators (WDI, 2021) except the Governance and Institutional variables coming from the World Governance Indicator (WGI, 2021).

The WDI database provides datasets on structural change for the majority of countries of the scope of interest. It also supplies the interest variable agricultural land, cereal land and permanent crop land and many other variables used in this study including population, per capita Gross domestic product, value added of different sectors. These variables are detailed more in the next subsection.

As mentioned in the first essay, the WGI database has the advantage of containing six variables measuring the quality of political institutions since 1996. These values range from around -2.5 (worst institutional quality) to 2.5 (best institutional quality). The WGI database covers a wider range of countries and territories (200 precisely) than the International Country Risk Guide (ICRG), which only involves 140 countries, and the Polity IV database, which only concerns 167 countries. The Principal Component Analysis (PCA) is used to build a composite measure of institutional quality. This composite index is calculated by applying the PCA to the following six variables: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government's Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.
The sample is constituted of 41 sub-Saharan African countries, of which 27 and 14 are sea opened and landlocked countries respectively. The panel is a strongly balanced. The period of the analysis is from 1990 to 2016 owed to the availability of entries in the model variables. The period and sample are dictated by the availability of relevant data. While some variables are raw, others are constructed according to literature of their respective fields. It is the case for the dependent variable *type of structural change* (TSC) and for the regressor *governance* (Gov).

**4.1.2. Construction of variables**

Both variables of structural change and the governance index are constructed.

**4.1.2.1. Building the variable type of structural change**

Literature provides many ways of measuring the structural change: some authors use shares of value added of economic sectors (Syrquin, 1988), others use in addition, employment shares of each sector and final consumption expenditures (Herrendorf et al., 2014). The main limitation of these structural change indicators is that they do not provide an overall amplitude of the structural change phenomenon as for Norm Absolute Value (NAV) and the Modified Lilien Index (MLI) (Dietrich, 2012). Productivity decomposition methods are also used to capture the structural change effect on growth which they call dynamic structural change (de Vries et al., 2015; Fagerberg, 2000) by the first ones who use a threefold decomposition. On the other hand, there is a twofold decomposition (Diao et al., 2017; McMillan et al., 2014; McMillan and Rodrik, 2011) which does not distinguish between static and dynamic structural change as the change itself is obviously a dynamic process. Another measure of structural change uses simultaneously, the Norm Absolute Value and the twofold decomposition method (Vu, 2017). Baymul and Sen (2020) consider the twofold decomposition, the country's incomes level and the size of employment shares in different sectors to categorise structural change types at the time of their analysis. The present study mobilises a similar methodology to build a comprehensive indicator of the overall structural change for each period of time.

According to Baymul and Sen (2020), countries can fall under the following three main stages of structural transformation: First of all, the set of countries where the proportion of workers in agriculture is higher than any other sector for the most recent period for which the data are qualified as countries *structurally underdeveloped*. Then the set of countries for which the percentage of labour force in services is greater than that in agriculture, though the share of workers in agriculture is still greater than that in manufacturing is referred to as countries *structurally developing* experiencing a services-driven structural change. Finally, countries where the share of workers in manufacturing is greater than that in agriculture. This set of countries is called *structurally developed* countries undergoing manufacturing-driven structural change.
Following Baymul and Sen (2020), we code the dependent variable as follows:

$$TSC = \begin{cases} 0 & \text{Structurally Underdeveloped if } \text{agrsh} > \text{indsh} \text{ and } \text{agrsh} > \text{scesh} \\ 1 & \text{Structurally Developing if } \text{scesh} \ge \text{agrsh} \text{ and } \text{agrsh} > \text{indsh} \\ 2 & \text{Structurally Developed if } \text{indsh} > \text{agrsh} \end{cases}$$

Where `agrsh`, `indsh` and `scesh` represent the proportion of agricultural, industrial and service labour force respectively, in the total employment.

To simplify the following nomenclature can be adopted: Consider that agrarian countries have `TSC = 0`, countries bypassing the secondary sector have a 'services-oriented' structural change and corresponds `TSC = 1`; Finally, countries qualified as structurally developed are following an 'industrial-oriented' structural change (Baymul and Sen, 2020; Hölzl, 2021).

Although this codification of the dependent does not want to establish a superiority between modalities, it presents a threefold advantage. Firstly, one can observe the country shares of different sectors at each point of time and identify the type of structural change. Secondly, it enables one to analyse the dynamics or transitions from one type of structural transformation to the other. It also enables one to appreciate if the structural change process is time invariant or not. Clearly, it means to identify if the states of structural change are likely to last for long or not. Third, this model can identify which type of structural change countries are likely to undergo after being structurally underdeveloped. Moreover, one may know which variable contributes to the 'bypass' of the secondary sector, and which one promotes the industrial oriented of structural change.

#### 4.1.2.2. Building the Governance index

The construction of the Governance index is intentionally omitted in this essay. The institutional variable in this essay is the governance index. Its construction is described in the first essay of this thesis.

### 4.2. Descriptive statistics

#### 4.2.1. Markov chain analysis of the structural change

The study analyses how the structural change variable varies over both time and countries. Variation over time for a given country is called within variation, and variation across countries are called between variation. This distinction is important because estimators differ in their use of within and between variation. In particular, in the FE model the coefficient of a regressor with little within variation will be imprecisely estimated and will not be identified if there is no within variation at all. A time-invariant variable will have a within percent of 100 (Cameron and Trivedi, 2009).
#### 4.2.1.1. Transition probabilities

According to Markov Chain Analysis, the three modalities of response variables are states. Working with cross-sectional and time series data, allows calculation of transition probabilities from one stage to the other, and from one period to the next. It suits with a Markov chain process.

A Markov process or chain consists of a sequence of repeated trials of an experiment whose outcomes have the following two properties (Lipschutz and Lipson, 2011):

* Each outcome belongs to a finite set {$S_1, S_2, ..., S_n$} called the state space of the system; if the outcome on the nth trial is $S_i$, then it is said that the system is in state $S_i$ at time n or at the nth step.

* The outcome of any trial depends, at most, on the outcome of the preceding trial and not on any other previous outcome.

Accordingly, with each pair of states ($S_i, S_j$), there is given probability $p_{ij}$ that $S_j$ occurs immediately after $S_i$ occurs. The probabilities $p_{ij}$ form the following n-square matrix:

$$M = \begin{bmatrix} p_{11} & \cdots & p_{1n} \\ \vdots & p_{ij} & \vdots \\ p_{n1} & \cdots & p_{nn} \end{bmatrix}$$

Where matrix M is a stochastic matrix, and regular in this case, called the transition matrix of the Markov process. For each state $S_i$ there corresponds the ith row [$p_{i1}, p_{i2}, ..., p_{in}$] of the transition matrix M. Moreover, if the system is in state $S_i$, then this row represents the probabilities of all the possible outcomes of the next trial and so it is a probability vector. As applied to this study, the fixed lines represent the probability of being in one stage (Structurally Underdeveloped for example) and moving to any other stage trough sectoral dynamics.

The fundamental hypothesis here is that the future stage ($Y_{t+1}$) depends only on the current stage ($Y_t$). For time-invariant data, the diagonal entries will be 100% and the off-diagonal entries will be 0%. The diagram of transition probabilities is drawn for landlocked countries and for open (sea) countries.

#### 4.2.1.2. Graphics of transition

Thanks to the calculation of transition probabilities one can draw graphs clearly showing the transitions from one step to the other. To provide a detailed analysis, three transition graphs were produced: The first gives the transitions for the overall sample, the second focuses on the countries that are open to the sea, and the third shows the state transitions of landlocked countries.
### 4.2.2. Graphical analysis of variables

The descriptive statistics give the broad features of variables and their stationarity level on the one hand. Then, the graphical disclosure of land expansion paths is used in sub-Saharan Africa, and finally, a link between labour displacements with respect to agricultural land expansion. The latter is done by using scatterplots for within and between variation. After these presentations of preliminary relationships, an econometric estimation of the model is made.

### 4.3. Estimation strategy

The estimation strategy displays the model to be estimated and estimation techniques, the justification and expected relations and the interpretation of the coefficients of the model.

#### 4.3.1. Baseline model and estimation techniques

The baseline statistical model takes the following form

$$Y_{it} = \alpha_0 + \alpha_1 aland_{it} + \beta_2 X_{it} + \varepsilon_{it} \quad (eq7)$$

Where $\alpha_0, \alpha_1$ and $\beta$, are the vectors of parameters to estimate. $\varepsilon$ is the error term following hypothetically a multinomial logistic distribution. $Y_{it}$ is the categorical dependent variable capturing structural change with three categories as described in the construction subsection. The variable aland$_{it}$ is the variable of interest capturing the agricultural land expansion. $X_{it}$ is the set of control variables, including governance, the sea openness status, the population growth, and other variables as justified in subsequent paragraphs.

The statistical model of the study is estimated with a fixed effects estimator. However, to perform this estimation on this type of discrete polytomous dependent variables, no statistical software is yet available (Allison, 2009). Different options remain available for this type of regression with such dependent variables. For example, Pooled Multinomial Logistic (POMLOGIT), Random Effects Multinomial Logistic Regression (REMLOGIT) (Rabe-Hesketh and Skrondal, 2012; Wooldridge, 2010). Both estimation techniques are used.

For the first model, the Pooled Multinomial Logit (POMLOGIT), panel – robust standard errors are used to account for plausible time correlation across waves. The verb ‘pool’ means to combine or merge things. Consequently, econometricians talk about the combined data of all individuals in all periods as a pooled sample. Then the regression equation is a pooled model and if one applies OLS to this pooled model it is called pooled least square, or pooled OLS. As well, if an MNL is applied to the pooled model, it is named Pooled Multinomial Logit (POMLOGIT).
The second model is fitted with gsem Stata command, as described in [SEM] example 41g of the Stata Manual (Pforr, 2014). The Pooled Multinomial Model is estimated, and robustness is checked with the estimates of the multinomial random-effects logit (REMLOGIT). The latent variable for countries means constant within countries and vary across countries. Two latent variables are used to capture countries effect to yield random effects.

The third model is estimated with fixed effects estimators, which is a user-written STATA command. The model offers the possibility to control for unobserved heterogeneity, which makes these models a prime tool for causal analysis. Using such estimators is constrained by many computation problems and convergence problems as well. This occurs, like in the present study's case, when the period is high.

*'... This means that computation time increases with the number of permutations in the dependent variable. In practice, this will rise with $T_i$. The computation time can be very high, even if $T_i$ is large for only a small subset of individuals $i = 1, ..., N$. If computation becomes unwieldy, a random subset of available measurements of all observation units should be analysed.'* (Pforr, 2014).

Following Pforr suggestion, the sample from the 2004–2016 period is analysed, which is randomly determined.

The study's statistical model can be estimated with a fixed-effect estimator. Indeed, for polytomous discrete dependent variables, no statistical software package is available yet (Allison, 2009). Various options remain available for this type of regression with such dependent variables. For instance, the pooled multinomial logistic or probit regression and the multinomial logistic or probit regression with random effects (Rabe-Hesketh and Skrondal, 2012; Wooldridge, 2010). For both models, it must be assumed that any unobserved heterogeneity is independent of the observed covariates, which is not always true (Pforr, 2014). A multinomial logit with fixed effects (FEMLOGIT) helps control for unobserved heterogeneity (Pforr, 2017) and is used as a robustness check.

Two reasons justify that neither the Hausman test nor the Likelihood Ratio (LR) test to choose the best estimators are performed. The first reason is methodological; when the robust option is applied while running our commands, it disables the researcher to perform the Hausman and LR tests as the variances are robust and modified (Stata Manual 14). The second reason is conceptual: this essay shows that agricultural expansion promotes an industrial type of structural transformation. Achieving this aim more important than making estimators compete.
4.3.2. Justification of variables in the model and expected type of structural change

The introduction of a lagged dependent (yᵢ,t-1) variable is intuitive, as the type of structural change of the current period (yᵢ,t) could certainly be explained by the last year structural change type (yᵢ,t-1) in equation (1). Thus, the effects of X, Z and T are estimated controlling for the former status of the unit. However, methods for these kinds of dynamic models, are not readily available for categorical data (Long and Freese, 2001, 2014).

Agricultural land refers to the share of land area that is arable<fnref n="5" />, under permanent crops, and under permanent pastures. Arable land accounts for the cross-country differences in real output of the industrial sector. It is measured as a percentage of total area. Similarly, arable land is a significant factor determining real value added of the sector (Mensah et al., 2016). With an extended Solow-Swan model, Barbier (2020) indicates that land expansion boosts growth, and this effect increases with the relative share of land in income. This essay shows that agricultural expansion is more likely to make a country structurally developed than structural developing.

Agricultural land per capita (arlandcap): The concentration of population (rural or urban) on land is essentially a barometer of economy-wide development (Barbier, 2004b). Where there is abundant marginal land for cultivation, it can absorb rural migrants, extra population, and displaced unskilled labour from elsewhere in the economy. However, the per capita area of agricultural land declined from 0.55 ha in 1961 to 0.23 ha in 2009 (FAOSTAT, 2013), leading to a fall in agricultural output, then may lead countries to shift from an industrial or service economy.

Gross Domestic Product is included in the equation as a log. It is found in the literature that the economic growth increases the real value added of manufacturing (Totouom et al., 2019; Mensah et al., 2016) and the real value added of the service sector, with a weaker effect on the latter sector (Mensah et al., 2016). From the researcher's point of view, economic growth is more likely to lead economies to a tertiary stage than to an industrialised economy stage, as the tertiary sector has similar characteristics to the agricultural sector.

The variable population growth was included in the model to determine how demographic changes affect structural changes. There are evidences that population size increases the value-added share in the industrial sector (Totouom et al., 2019), increases the service sector real product and reduces agricultural value added (Mensah et al., 2016). However, the agricultural sector is still important for

<footnote n="5">⁵ Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow.</footnote>
sub-Saharan countries to an extent that, an increase in the population growth will result to a more agrarian economy (Tankeu, 2021).

The quality of institutions, captured by the variable governance (gov) has a major role in promoting structural change. As developed by many authors (Edokat and Njong, 2019; Totouom et al., 2019), promoting structural transformation requires better institutional qualities. Henceforth, good governance is expected to spur an industrial type of structural transformation.

To determine whether structural change depends on international trade, the variable trade was used in the model. The literature identifies reduction in trade barriers as a factor not only promoting growth, but also leading to structural change (Mallick, 2017; Roy and Roy, 2017). In Africa, it has contributed to a boom in the service sector (Mensah et al., 2016). The variable trade is expected to lead countries at the structurally developing stage.

Being a landlocked country can slow process of structural change due to the high transportation costs. Some scholars argue that the location of countries, and essentially whether they are landlocked or opened to sea determines their ability to grow and transform their production structures (Collier, 2008; Faye et al., 2004; Sachs et al., 2004). The variable *sea coast* (with '1' for *landlocked countries*) is inserted into the model and it is expected that landlocked countries have lower chances of engaging in structural development than sea coast countries.

International trade openness is proxied and included in the model by the variable *trade* as a percentage of GDP. Barbier (2021), holds that economic growth is accelerated by trade. It is expected that *trade* variable favors ‘services – type’ of structural change than underdeveloped type of structural change.

Following Barbier (2021), the interaction variables of population and agricultural value added in real terms (*popgXagva*) and the variables agricultural land expansion and agricultural real value added (*aglXva*) are introduced in the model. The former enables the investigation of the combined role of population growth and agricultural growth on structural change, while the latter explains how both agricultural land expansion and value-added influences the structural change processes.

African countries are often reliant on their natural resources. Thus, just as Mensah et al. (2016), the regressor mineral rent was introduced as a percentage of GDP, as a proxy for mineral (*mineral*) resource endowment. The variable will help analyse the structural effects of natural endowments. The variable is expected to have a positive effect on ‘industrial-type’ of structural change.

Finally, the variable permanent crop land is introduced as a percentage of land area. It helps to assess the importance of permanent cropping on type of structural change in Africa. According to FAO
(2019), permanent cropland is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. It is expected that the variable will increase the probability of countries being structurally developed, as the crops produced are inputss for agribusiness industries and trade.

The expected relationships of variables with the type of structural change are summarised in table 11.

<table><caption>Table 11: Variables and expected type of structural transformation</caption><thead><tr><th></th><th></th><th>Structurally<br>Developing</th><th>Structurally<br>Developed</th></tr></thead><tbody><tr><td>aland</td><td>Agricultural land</td><td></td><td>+</td></tr><tr><td>arlandcap</td><td>Agricultural land per capita</td><td></td><td>+</td></tr><tr><td>Gov</td><td>Governance</td><td></td><td>+</td></tr><tr><td>Trade</td><td>International trade openness</td><td>+</td><td></td></tr><tr><td>minerent</td><td>Mineral rent</td><td></td><td>+</td></tr><tr><td>popgXagva</td><td>Population and agricultural value<br>added</td><td></td><td>+</td></tr><tr><td>aglXva</td><td>Agricultural land and value added</td><td></td><td>+</td></tr><tr><td>lngdpcap</td><td>Gross domestic product</td><td>+</td><td></td></tr><tr><td>Popg</td><td>Population growth</td><td>+</td><td></td></tr><tr><td>Pland</td><td>Permanent crop land</td><td></td><td>+</td></tr><tr><td>1 if<br>landlocked</td><td>Landlocked country</td><td>+/-</td><td>+/-</td></tr></tbody></table>

Source: author.

Coefficients of the variables of a multinomial logit model with the interpretation need some explanations.

### 4.3.3. Interpretation of the parameters of the model

Care is needed in the interpretation of parameters in any nonlinear model. This is particularly true for multinomial models where, for example, there is not necessarily a one-to-one correspondence between coefficient sign and coefficient probability (Cameron and Trivedi, 2009) The negative sign does not mean that the increase of the regressor value will reduce the explained variable. For the MNL model, the comparison is to a base category, which is the alternative normalised to have coefficients equal to zero.

For economists, it is more natural to interpret $\beta_j$ as **semi-elasticity**. Then, taking a calculus approach, a logit model slope parameter of 0.1 is interpreted as meaning that a one-unit increase in the regressor increases the odds ratio by a multiple 0.1. This coincides exactly with the interpretation used in statistics for very small $\beta_j$, since then $exp(\beta_j) - 1 = \beta_j$.
Another useful interpretive device is the probability ratio or relative risk ratio. It shows how many times more likely category j is to be chosen relative to the first category.

$$ \frac{P(y_i = j)}{P(y_i = 1)} = \frac{p_{ij}}{p_{i1}} = \exp(\beta_{1j} + \beta_{2j} x_i), \quad j = 2, 3 \qquad (\text{eq8}) $$

The effect on the probability ratio of changing the value of $x_i$ is given by the derivative

$$ \frac{\partial(p_{ij}/p_{i1})}{\partial x_i} = \beta_{2j}\exp(\beta_{1j} + \beta_{2j} x_i), \quad j = 2, 3 \qquad (\text{eq9}) $$

The value of the exponential function $\exp(\beta_{1j} + \beta_{2j} x_i)$ is always positive. Thus, the sign of $\beta_{2j}$ tells whether a change in $x_i$ will make the jth category more or less likely relative to the first category.

An interesting feature of the probability ratio is that it does not depend on how many alternatives there are in total. There is the implicit assumption in logit models that the probability ratio between any pair of alternatives is independent of irrelevant alternatives (IIA).

Relative risk ratios describe the multiplicative effect of a unit increase in each predictor as the odds of observing a particular category (Hamilton, 2013). Here in this case, ‘structurally developing’ or ‘structurally developed’ responses instead of the base category ‘structurally underdeveloped’.
# 5. Results and Discussions

This section presents the results of the study. They are organised into descriptive statistics, graphical illustrations of our relationships, and econometric baseline and robustness results.

## 5.1. Descriptive statistics

### 5.1.1. Summary details of variables

Table 12 below displays the various summary statistics for the full sample, for the studied period. The percentage of agricultural land in the full sample is 45, 88%, but with great disparities in the sample, as the standard deviation is 20.4. To add up, the lowest value of agricultural land is 3.26% of the land area while the highest is up to 82.67% of the land area. Increasing land area by using agricultural land brings changes to countries and may generate industrial development.

<table><caption>Table 12: Descriptive statistics of the overall sample</caption><thead><tr><th></th><th>aland</th><th>arlandcap</th><th>gdpcap</th><th>popg</th><th>gov</th><th>pland</th><th>trade</th></tr></thead><tbody><tr><th>Mean</th><td>45.879</td><td>0.258</td><td>2054.287</td><td>0.0253</td><td>5.377</td><td>2.951</td><td>67.221</td></tr><tr><th>S.d.</th><td>20.391</td><td>0.184</td><td>3037.133</td><td>0.0114</td><td>1.792</td><td>5.145</td><td>35.098</td></tr><tr><th>Min</th><td>3.260</td><td>0.0015</td><td>164.337</td><td>-0.0677</td><td>1.063</td><td>.00176</td><td>19.68</td></tr><tr><th>Max</th><td>82.671</td><td>1.516</td><td>20532.95</td><td>0.0812</td><td>10</td><td>29.554</td><td>311.35</td></tr><tr><th>Obs</th><td>1101</td><td>1104</td><td>1090</td><td>1061</td><td>738</td><td>1101</td><td>1018</td></tr></tbody></table>

Source: author

More than 4 out of 5 people practice agriculture. On average, arable land per capita is 0.26 Ha. The variable has homogeneous behaviour, as the overall standard deviation is low (0.18). The lowest value of arable land per person is 0.0015 Ha, while the highest value is 1.52 Ha. This is in line with the literature which holds that most Sub-Saharan Africa farmers are smallholders, with farm size lower than 2 Ha (Masere and Worth, 2022; Ncoyini et al., 2022).

Over the study period, average of per capita GDP $2054.29. However, the standard deviation of 3037, implies great disparities in this variable distribution around the mean. This can explain why the minimum value of GDP is $164.34 while the maximum GDP value culminates at $20,532.95.

The arithmetic mean of the variable population growth is 2.5%, which shows that the population growth rate over the period is relatively low. With the low standard deviation of the variable, 0.011, it is assumed that there is not too much heterogeneity for demographic change distribution. Some countries have a negative rate of population growth due to wars<fnref n="6" />. The highest rate of demographic increase is 0.812 recorded in Rwanda, between 1998–99.

<footnote n="6">⁶ Example, the civil war and Tutsi genocide in Rwanda between 1991 and 1994.</footnote>
The institutional or governance index indicates an overall average of 5.38. This means that countries display neither too bad nor too good governance. Which should be interpreted with cautions, as the standard deviation its 1.79. The smallest value of our indicator is 1,063 for countries with low institutional quality (Democratic Republic of Congo), and the highest value is 10 for well-established democracy countries (such as Mauritius and Botswana).

Permanent crop land average is 2.95%, with wide disparities in its distribution. The minimum portion of permanent cropland is 0.002 recorded by Botswana. On the other hand, the highest value of permanent cropland is 29.55% recorded in Comoros in 2004.

The variable trade displays an average of 67.22% but the variable dispersion is average. Indeed, the standard deviation of the variable is approximately 35. The lowest contribution of Trade in a country's GDP is 20% while the highest is close to 311%.

### 5.1.2. Analysing the within and between variation of types of structural change

Table 13 provides more details about both within and between variations of the dependent variable (type of structural change) of the sample of 41 countries. The overall summary shows that 73% of the 807 country-year observations had structurally underdevelopment, almost 20% had structurally developing and only 7% had structurally developed.

<table><caption>Table 13: Summary statistics of the dependent variable.</caption><thead><tr><th rowspan="2">TSC</th><th colspan="2">Overall</th><th colspan="2">Between</th><th>Within</th></tr><tr><th>Freq.</th><th>Percent</th><th>Freq.</th><th>Percent</th><th>Percent</th></tr></thead><tbody><tr><td>Structurally Underdeveloped</td><td>807</td><td>72.90</td><td>36</td><td>87.80</td><td>83.02</td></tr><tr><td>Structurally Developing</td><td>219</td><td>19.78</td><td>41</td><td>100.00</td><td>19.78</td></tr><tr><td>Structurally Developed</td><td>81</td><td>7.32</td><td>6</td><td>14.63</td><td>50.00</td></tr><tr><td>Total</td><td>1107</td><td>100.00</td><td>83</td><td>202.44</td><td>50.00</td></tr></tbody><tfoot><tr><td colspan="6">(n = 41)</td></tr></tfoot></table>

Source: author.

The between summary informs that, of the 41 countries, 87.80% had structurally underdeveloped status at least once, every country had at least once a structural developing status and only 14.63% countries had structurally developed status at least. The between total percentage is 202.44% because all the countries (102.44%) had some of the time, the structural underdevelopment status and some of the time another status between structurally developing or developed and are therefore counted twice.
Looking at within summary, it indicates that 83% of countries structurally underdeveloped never escaped to this status during the period of study (1990–2016). Conversely, 20% of the countries that were at the structurally developing stage have not changed their status. In a similar way, 50% of the countries remained in their category of structurally developed. This means structurally underdeveloped stage is the most stable status, followed by the structurally developed stage. Further, a description of the states of structural change helps to analyse probabilities for a country to move from one step to the other.

**5.2. Agricultural land expansion and labour movements**

Here is documented the graphical relationship between agricultural expansion and movements of labour. This gives an overview of agricultural land expansion, then links agricultural expansion with labour spillovers.

**5.2.1. Agricultural land expansion evolution**

The agricultural land trend is plotted in the general introduction to this thesis (figure 2). It shows agricultural land-use coupled to an expansion movement of the agricultural sector. This means an expansion of agriculture similar to Barbier (2021). However, this agricultural land expansion may generate two opposite outcomes in terms labour movements.

**5.2.2. Sectors value added response to agricultural land expansion**

Structural change process is the reallocation of production factors from low productivity sectors to high productivity sectors (Bloem, 2019). More generally, the process describes movements production factors among sectors, irrespective of their productivity. It is also known that structural change entails that the contribution of agricultural value added in the overall value-added decreases, while the service's sector increases continuously and that of the industrial sector has an inverted-U shape (Herrendorf et al., 2014).

As a result of the expansion of agricultural land, the value-added shares of different sectors were analysed (Figure 12). The graphs give within variations, to explain GDP shares of sectors are associated with agricultural land expansion in countries. To identify possible optimums for value-added shares as agricultural land increases requires a quadratic adjustment for each sector. It appears that each sector has special behaviours illustrated by the three sub-figures.

Firstly, figure 12a shows an inverse relationship between agricultural land expansion and agricultural value-added share. High shares of agricultural sector value added to the total value added are associated with low areas of agricultural land. In contrast low levels of agricultural value added shares are associated with high agricultural land. This feature is in line with the theory of structural change,
which states that agriculture should back the growth of other sectors. Therefore, its own contribution to the overall value added should decrease. Then, the adjustment line is downward sloping, so the proportion of agricultural sector value added reduces as agricultural land expands. However, this feature of structural change is not followed by expected movements of shares of other sectors.

<figure><img src="image_14.png" /><figcaption>Figure 12: Sectoral value-added share and agricultural land
Source: author.</figcaption></figure>

Looking at figure 12b, a ‘U relationship’ between the secondary sector value added share and agricultural land is visible. This illustrates the importance of agriculture land for industrial GDP. Agricultural land expansion first of all, makes the share of the industrial sector to decrease, and then, to rise. Assuming that the percentage of agricultural land is the input and industrial value added is the output, then increasing returns and decreasing returns are observed. An increase in the former causes an increase in industrial value-added share before 46% of agricultural land area. However, diminishing returns operate when agricultural land exceeds 46%. From that level, any increase in agricultural land causes the proportion of the industrial value added to increase and to contribute more to overall value added. This means that diminishing returns of the land factor are overcome by increasing returns when agricultural land reaches and exceeds 46% of the country’s total land area. Thus, expanding agricultural land may be a good way to promote structural change.

Finally, figure 12c displays the inverted-U relationship between agricultural land and services value-added share. The tertiary service value added contributes the greatest percentage to overall value added. The higher the percentage of agricultural land the faster the tertiarisation process goes on but slows down when the percentage of expansion approaches 46%. This is where the law of
decreasing returns operates. After 46%, any marginal increase in agricultural land generates constant
and further, increasing returns. The latter is characterized by the decrease in the value added of the
service sector. Thus, increasing agricultural land to that extent will generate a less important
contribution of the service sector to the overall value added of countries.

To sum up, the study shows that agricultural land increase is associated with different contributions
of sectoral value added to the overall value added. Following the extensification of agriculture, the
agricultural sector's contribution to total value-added decreases steadily at a high rate. However,
converting land area for agriculture use, will likely reduce the value added of industrial output and
then, cause industrial output to increase faster than the increase of overall output. Finally, as
agricultural land increases, countries start by enjoying raising contribution of service sector value
added in the overall GDP, followed by a fall, at the benefit of industrialisation. These stylised facts
emphasise the suspected relationship that agricultural expansion generates an industrial type of
structural change

**5.2.3. Agricultural land expansion and labour spillovers: Overall**

The labour spillovers refer to movements from the agricultural sector to either the industry or to the
services sector. To show the link between agricultural land expansion and the different outward
movements of labour, one proceeded in two steps: (1) linking agricultural land expansion and
spillovers for an overall variation, and (2) linking the same variables for a within variation.

**5.2.3.1. Linking agricultural land and labour spillovers: Overall**

The above figure 13 relates labour movements into different sectors with agricultural land growth,
for all countries and on overall period of time. Despite the fact that the countries are stuck in the
agricultural sector, there is a direct relationship between agricultural expansion and an increase in
industrial share (figure 13a) or service share (figure 13b).

The slope of the linear adjustment between land and movements industry seems to be flatter than that
of land and labour spillovers into service. Authors find that the increase in industrial value added is a
response of land-use expansion (Mensah et al., 2016). These figures illustrate that agriculture also
leads to a service-oriented structural change. However, with respect to its sharper slope, expanding
land seems to have a greater impact on inward industrial labour movements.
<figure><img src="image_15.png" /><figcaption>Figure 13: Agricultural land vs industry and services spillovers, overall
Source: author.</figcaption></figure>

In addition, it appears that landlocked countries have lower and flatter adjustment lines. The lower adjustment fitting reveals that landlocked countries have smaller average spillovers than other countries, in general. On the other hand, the flatter adjustment lines show the smaller responsiveness of labour movements to land expansion for these countries.

5.2.3.2. Linking agricultural land and labour spillovers: within variation

The figure 13 illustrates the link of how movements of workers from agriculture to industry (figure 14a) or to service (figure 14b) are related with growth of agricultural land. This means, for the same country it shows how labour movements react to land expansion over time. One notices a direct relationship between the variables. As agricultural land increases more people are moving to the industrial sector, and the service sector alike.

As the country increases its cultivated area across time, the labour force is attracted in both the industrial and service sectors. A vast majority of spillovers is still negative, showing that sub-Saharan Africa countries remains concentrated in the agricultural sector. Only few countries have positive industrial spillovers (about 2 points), while several tertiary spillovers are above the zero line.
<figure><img src="image_16.png" /><figcaption>Figure 14: Agricultural land vs industrial and services spillovers, within variation
Source: author.</figcaption></figure>

The adjustment line of the service sector is above that of the industrial sector, showing the higher employment share of the service sector, and a shrinking industrial sector. Moreover, the slopes of the lines are almost identical, with that of the service sector slightly steeper than that of the industrial sector. Hence, as agriculture land expands across time, workers' movement into industry is almost equal to workers' movement into the service sector.

#### 5.2.3.3. Linking agricultural land and labour spillovers between variation.

Finally, the researcher analyses the variation across individuals known as between variation, where the main assumption is the homogeneity of the unit or the country. The graphs of figure 15 show similar characteristics and relationships as the previous models. More precisely, the countries are still agrarian, with a steady progress toward industrialisation (figure 15a) and a slightly fast work toward the service sector. Nonetheless, the slopes of the adjustment lines are almost the same. Thus, continuous expansion of land within its availability will result in almost equal responsiveness of both secondary and tertiary sector.

When compared with all countries, landlocked countries' adjustment lines are still below all countries. This indicates that they are more agrarian than other sub-Saharan countries. An important feature is that the curves are of flatter slopes, which means that structural change is slower than in other countries when agriculture expands
<figure><img src="image_17.png" /><figcaption>Figure 15: Agricultural land vs industrial and services spillovers, between
Source: author.</figcaption></figure>

## 5.3. Estimation results

This subsection is subdivided into two: the stationarity tests results and the estimation results

### 5.3.1. Stationarity tests

Here the stationarity results of variables of the models illustrated in table 14 are presented. The Im-Peasaran-Shin (2003) unit root test which is a second-generation test, shows that all the variables are stationary at level. So, there is no need to differentiate them.

<table><caption>Table 14: Unit root test</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">ips</th></tr><tr><th>Decision</th><th>P-values</th></tr></thead><tbody><tr><td>Agricultural land</td><td>I(0)</td><td>0,03</td></tr><tr><td>Agricultural land per capita</td><td>I(0)</td><td>0,02</td></tr><tr><td>GDP per capita (log)</td><td>I(0)</td><td>0,0001</td></tr><tr><td>Population growth</td><td>I(0)</td><td>0,084</td></tr><tr><td>Governance Index</td><td>I(0)</td><td>0,0775</td></tr><tr><td>Trade</td><td>I(0)</td><td>0,001</td></tr><tr><td>Permanent crop land</td><td>I(0)</td><td>0,084</td></tr><tr><td>Mineral rent</td><td>I(0)</td><td>0,084</td></tr></tbody></table>

Source: author.

It is not possible to perform a stationarity test for categorical variables, there is no unit root test nor stationarity test allowing to identify the level of integration of a categorical variable. Additionally, being stationary at level is disqualifying for estimator time.
### 5.3.2. Baseline model results

#### 5.3.2.1. Estimation Results

At first glance, the number of observations varies between 1,034 and 690, for 41 countries included in the sample. Multinomial regressions (POMLOGIT) show acceptable statistical characteristics. The pseudo-R2 statistic varies between 0.41 and 0.58. The LR Chi2 coefficients of overall significance of all regression coefficients are high and statistically significant at the 1% level.

From table 15 a major fact emerges. The variable of interest *agricultural land* is statistically significant in all regressions (1–3). Thus, it withstands different model specifications. In addition, it is noted that the variable land expansion, always and for all models, displays a higher coefficient in the equation of the structurally developed category.

Model POMLOGIT2 indicates that an agricultural land expansion of one percentage point significantly increases the likelihood of a country being structurally developing, rather than structurally underdeveloped, by 8.8%. Similarly, increasing the same agricultural expansion variable by one percentage point increases by 32% the likelihood of a country being structurally developed rather than structurally underdeveloped.

The variable is significant at 1% level. This reflects that agricultural expansion has increased potential to transform structurally underdeveloped economies into structurally developed countries, all other things being equal. Several studies lead to a similar result (Barbier, 2020; 2021) which establishes that agricultural expansion is a source of economic growth according to a Solow-Swan model formulated by Solow (1956). On the other hand, this study diverges from Mensah et al. (2016) establishing that the increase in arable land results in a greater increase in tertiary value added.

The variable per capita arable land is not significant for any category. An increase in land per person by 1 ha reduces by 32% the probability of being a structurally developing country to the benefit of being structurally underdeveloped. Conversely, the same increase of 1 ha per inhabitant raises the probability of being structurally developed instead of being structurally underdeveloped. This means that increasing land ownership, attracts people in the agricultural sector. However, this is insufficient enough to build up an industrial-oriented structural change. This outcome is in line with country specific analyses (Rozaki, 2020; Yeboah et al., 2019) and diverges from Mensah et al. (2016). Possibly, poor poverty constraints force smallholders to practice subsistence farming with low transformation potential. Additionally, the lack of access to capital, infrastructure, and technology further impede the development of the agricultural sector. This means that even if the land is in people's hands, they may not be able to make use of it effectively. Without access to capital and
technology, smallholders are unable to make the investments they need to move beyond subsistence farming and into a more profitable and sustainable agricultural sector.

<table><caption>Table 15: Results of the baseline model</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">POMLOGIT 1</th><th colspan="2">POMLOGIT 2</th><th colspan="2">POMLOGIT 3</th></tr><tr><th>Structurally Developing</th><th>Structurally Developed</th><th>Structurally Developing</th><th>Structurally Developed</th><th>Structurally Developing</th><th>Structurally Developed</th></tr></thead><tbody><tr><td>Agricultural land</td><td>1.050**<br>(0.0232)</td><td>1.180**<br>(0.0907)</td><td>1.088***<br>(0.0292)</td><td>1.319***<br>(0.0741)</td><td>0.948<br>(0.106)</td><td>0.496***<br>(0.0970)</td></tr><tr><td>Ag. land (t-1)</td><td></td><td></td><td></td><td></td><td>1.148<br>(0.133)</td><td>2.702***<br>(0.609)</td></tr><tr><td>Ag. land per cap</td><td>3.314<br>(11.45)</td><td>0.968<br>(4.774)</td><td>0.320<br>(1.589)</td><td>15.09<br>(37.45)</td><td>0.361<br>(1.784)</td><td>32.22<br>(73.37)</td></tr><tr><td>agIXva</td><td>0.999<br>(0.000545)</td><td>0.994**<br>(0.00242)</td><td>0.999<br>(0.000796)</td><td>0.988***<br>(0.00300)</td><td>0.999<br>(0.000793)</td><td>0.987***<br>(0.00371)</td></tr><tr><td>GDP p cap (log)</td><td>3.160**<br>(1.459)</td><td>0.120<br>(0.174)</td><td>9.120***<br>(4.676)</td><td>0.0373***<br>(0.0342)</td><td>9.171***<br>(4.697)</td><td>0.0273***<br>(0.0284)</td></tr><tr><td>Pop. growth</td><td>3.73e-08<br>(1.39e-06)</td><td>0.0001**<br>(0.004)</td><td>0.0001*<br>(0.006)</td><td>0.0001***<br>(1.45e-06)</td><td>0.0001*<br>(0.0015)</td><td>0.00001***<br>(0.004)</td></tr><tr><td>Governance</td><td>1.698***<br>(0.342)</td><td>8.633*<br>(10.28)</td><td>1.761***<br>(0.351)</td><td>19.07***<br>(12.90)</td><td>1.767***<br>(0.356)</td><td>21.64***<br>(15.57)</td></tr><tr><td>Landlocked<br>(1 if yes)</td><td>0.226*<br>(0.184)</td><td>0.0968<br>(0.275)</td><td>0.312<br>(0.262)</td><td>0.203<br>(0.340)</td><td>0.305<br>(0.251)</td><td>0.217<br>(0.320)</td></tr><tr><td>Trade</td><td>1.008<br>(0.0116)</td><td>0.996<br>(0.0315)</td><td>1.005<br>(0.0109)</td><td>1.003<br>(0.0150)</td><td>1.005<br>(0.0109)</td><td>1.005<br>(0.0138)</td></tr><tr><td>popgXagva</td><td></td><td></td><td>9.808**<br>(10.77)</td><td>200,245***<br>(421,223)</td><td>9.770**<br>(10.73)</td><td>346,337***<br>(811,825)</td></tr><tr><td>Perm. cropland</td><td></td><td></td><td>0.772**<br>(0.0820)</td><td>0.989<br>(0.294)</td><td>0.771**<br>(0.0825)</td><td>1.022<br>(0.357)</td></tr><tr><td>Mineral rent</td><td></td><td></td><td>0.986<br>(0.0604)</td><td>1.155<br>(0.444)</td><td>0.986<br>(0.0610)</td><td>1.163<br>(0.460)</td></tr><tr><td>Constant</td><td>6.48e-07***<br>(3.44e-06)</td><td>0.315<br>(2.638)</td><td>2.54e-10***<br>(1.55e-09)</td><td>1.144<br>(9.953)</td><td>2.33e-10***<br>(1.43e-09)</td><td>1.587<br>(12.64)</td></tr><tr><td>Observations</td><td>690</td><td>690</td><td>690</td><td>690</td><td>690</td><td>690</td></tr><tr><td>Pseudo R2</td><td>0.530</td><td>0.530</td><td>0.578</td><td>0.578</td><td>0.581</td><td>0.581</td></tr><tr><td>N</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>557.2***</td><td>557.2***</td><td>1523***</td><td>1523***</td><td>2386***</td><td>2386***</td></tr><tr><td>Wald Test<br>Chi2(2)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ag. land</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Ag. land (t-1)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Robust se eform in parentheses

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

Source: author.

The first reason is that low education levels hinder people's ability to make effective use of the land they have (Martins, 2019). The second reason relates to the drudgery imposed by agricultural activities and the insufficient modernisation of agriculture. This may suggest that land may remain unused. Owning more land will be welcomed by people, but it might not be used for agricultural production. As a result, industrial output is likely to remain low. A proper land policy should accompany those with agriculture experiences, skills and knowledge in the process of acquiring agricultural lands.

No matter what model specification is used, GDP per capita always has a significant effect on structural change. The magnitude, however, depends on the model specification. According to the
preferred model (POMLOGIT 2), an increase of $1 in per capita GDP increases 10 times the likelihood that the country becomes structurally developing rather than structurally underdeveloped. Contrarily, the same rise in GDP per head will reduce by 35% the relative probability for a country of being structurally underdeveloped. This means the actual growth of African countries is mostly beneficial to the service sector, and less to the industrial sector. This result contradicts Mensah et al. (2016) who found that the elasticity of manufacturing value added with respect to the log of income per capita is significantly higher than in the service sector. It can be argued that countries lack incentives to promote industrial sector expansion, scaring away both domestic and foreign investors. One is more likely to encounter many people in the agricultural sector than in the secondary sector. Conversely, the improvement of population living standards can lead them into the service sector. This is less risky and has similar characteristics to the agricultural sector.

Taking into account the population growth, estimated coefficients are always significant in the regressions, but less than one in the preferred model (model 2). The relative risk ratios are close to zero, but highly significant. When population grows the relative risk of being either structurally developing or structurally developed reduces to the merit of being structurally underdeveloped. Population growth increases the probability of a country becoming more agrarian. One justification of such a fact, is that directly or indirectly, 80% of the population in Africa practises agriculture, so a positive demographic shock, increases at a very low rate, but with a high significance, the probability of the workforce being located in the agricultural sector. The result is in line with the finding of Barbier (2020) which shows that when the demographic rate climbs, it contracts the growth expansion. It is distinct from Totouom et al. (2019) asserting that population growth increases the manufactured value added.

The institutional quality variable (Gov), is significant at the 10% threshold in all specifications. The variable coefficient is always greater in structurally developed equations. According to the preferred model (model 3), an improvement in institutional quality increases the relative risk by 76%, resulting in a structurally developing country rather than an underdeveloped one. This result differs from Mensah et al. (2016) whose work finds that the democracy index reduces the service sector share in overall GDP.

Nonetheless, improving the quality of governance (Gov) multiplies by almost 19 the ratio risk of being structurally developed industrial instead of being structurally underdeveloped. Arguments supporting this result include the promotion of a good business climate. Effective governance allows governments to formulate and execute the industrial policies required for their economies to structurally transform; they discourage unproductive rent-seeking and harmful business practices.
Also, it improves the business climate which in turn attracts investors. Another explanation may be
government policy to promote modernization (using modern inputs), or by intervening on the land
rental market, or by promoting land registration. It further may increase their will to invest in the
transformation of their own products, thereby resulting in structural change. This result is close to
both theoretical developments (Edokat and Njong, 2019) and to empirical findings (Totouom et al.,
2019; Martins 2019). The former holds that good institutional quality hinders structural change and
economic growth in sub-Saharan Africa. The latter reveals that institutions are key determinants of
industrial performance growth in Sub-Saharan Africa.

In this essay, we discuss whether a country's landlocked status affects the type of structural change it experiences. The landlocked dummy variable (1 = if landlocked) is introduced in the regressions. The estimations suggest that statistically, it has no significant effect on the type of structural change. Despite this fact, models 2-4 show that the coefficients for the structurally developing equations are greater than those of the structurally developed. The chances of a landlocked country reaching the structurally developing stage instead of underdeveloped stage are 69% lower than those of an opened sea country, all other factors remaining the same. Similarly, landlocked country has 80% fewer chances of attaining the structurally developed stage rather than being at the underdeveloped stage. Although this sounds like a 'curse', it can be explained by the high cost of transportation, which inhibits the development and participation in Global Value Chains. This in turn holds the labour force in the agricultural sector and makes them more ready to transit in the service sector. The results are consistent with those in the literature (Collier, 2007; Sachs 2007). The statistical non-significant result indicates that discrimination against landlocked countries can be overcome, and that the 'curse' can be broken.

International openness measured by *trade* variable has no explanatory power in our models. Despite the fact that trade coefficients are not significant in model 3, one identifies a direct relationship between trade and the probability of countries having a greater labour force in the tertiary sector than in the agricultural sector. When the ratio of trade increases by one percentage point, the countries have 0.5% more chances of being of the structurally developing type than structurally underdeveloped. Similarly, every 1 percentage point increase in international trade value added increases by 0.3% the likelihood of an 'industrial-oriented' type structural change than no structural change. Hence, trade is more likely to lead economies to a 'service-type' of structural change. Plausible justification for this outcome is found in the constitution of the tertiary sector in Africa. Tertiary services are mostly informal activities and retail sales, while the secondary sector comprises more efficient firms. In order to capture the benefits of trade improvements, people will be motivated
to invest in services sectors with fewer barriers. It is also convincing that firms' trade growth favors people's willingness to invest in the industrial sector. This result is different from a similar study in the literature (Thurlow and Wobst, 2005), which shows that trade liberalisation in Zambia had the unintended consequence that low-skilled workers were shed from manufacturing industries and ended up back in agriculture.

The expansion of land under permanent crops reduces by 23% the relative risk of observing service type of structural change than underdeveloped type of structural change. This is significant at the threshold of 1% in models 2 and 3. Here, expanding permanent cropland under cultivation is more likely to make a country more agrarian. This is justified because perennial crops need more care during their lifespan. Agricultural farms will then hire workers to maintain crops, reducing services sector employment. Contrarily, an increase in perennial cropland has had little impact on industrial development. In model 3, it lowers by 2.1% the relative risk of observing industrial-type of structural change than the agrarian-type of structural change. In model 4, it increases by 2.2% the probability of having structurally developed than structurally underdeveloped countries.

The natural endowments proxied by the *mineral rent* variable has the expected relation with the industrial-type of structural change. Indeed, an increase by one percentage point of the rent value added increases by 15% the probability of a country to be of the structurally developed category than the underdeveloped category. Meanwhile, where rent increases by 1 percentage point, it reduces by about 2%. This reduces relative probability of observing a ‘service – type’ of structural change than structural underdevelopment. Despite not being statistically significant, the result approximates that of Mensah et al. (2016). It traduces a better management of rents from natural endowments. In the similar vein, management of the resources by Africans will retain direct capital flight from Africa and stimulate better sustainable development (Njimated and Yakum, 2019).

The interaction variable of agricultural land expansion and agricultural real value added (*Aglxval*) is significant at the 1% threshold in equation of structurally developed. An increase of agricultural land and real value added will reduce by 1.4–2.3% the relative probability of having structural developed countries than structural underdeveloped countries. This can translate the fact that the raw materials are sold without transformation. It could encourage some industries to engage backward vertical integration. Indeed, Africa low mechanisation levels will increase the proportion of workers to support the productivity of the agricultural sector, other things held constant. On the other hand, increase of variables (*AglXva*) will allow unchanged the relative risk of observing tertiary type of structural change than the absence of structural change. The result deviates from the findings of Barbier (2020).
Interaction variable of population and agricultural value added in real terms (*popgXagva*) appears to have significant contributions in the models. A simultaneous population and agricultural growth will multiply by 9.7–9.8 times the relative risk of observing a ‘service-type’ of structural change than the underdeveloped type of structural transformation. The result is significant at the level of 5%. Simultaneous population and agricultural growth have high impact on the probability observing a structurally developed country than a structurally underdeveloped country. Demographic pressure and agricultural expansion increase at the threshold of 1% the relative risk that a country follows more paths of industrial structural change than remaining in the agrarian stage of structural transformation. This result is close to that of Barbier (2020).

While controlling last year's land expansion for agriculture (L. aland), findings suggest similarities with model 2. In spite of the insignificance of agricultural expansion on the structurally developing equation, two important results from the structurally developed equation are derived. The agricultural land expansion in the current year and the year before is statistically significant at the threshold of 1%. It is found that a 1% expansion of land during the current year decreases by 51% the probability of observing structural development than structural underdevelopment. It shows that the expansion of land will increase the probability that people will move from the industrial sector to the agricultural sector, ceteris paribus. The contemporaneous effect of land expansion changes the economy to an agrarian one. However, the effect is cleansed by last year's land growth. The last year's 1% increase in land multiplies by 1.7 the probability for countries to engage in industrial-type of structural transformation instead of being agrarian. This indicates that last year's expansion will provide a greater opportunity for industrial oriented structural change. This result is robust and corresponds to literature findings that suggest agriculture generates local economic spillovers (Fuglie and Rada, 2013). It rather differs from (Hornbeck and Keskin, 2011) who find that agricultural gains generate short-run benefits but not long-term benefits for the non-agricultural sector.

### 5.3.2.2. Margins effects of agricultural land on structural change

Here, the margin effects of agricultural land on the probabilities are analysed to observe each outcome of the structural change variable in Africa. Results are differentiated between opened sea and landlocked countries. Then the combined effects of agricultural land and governance on the probability to observe specific structural change is interpreted.
<figure><img src="image_18.png" /><figcaption>Figure 16: Marginal effects of agricultural land expansion on probability of structurally underdeveloped economy</figcaption></figure>

Source: author.

**a. Marginal effects of agricultural land expansion on the probability of being a structurally underdeveloped economy**

To start up, figure 16 highlights the relationship between the probability of observing an underdeveloped type of structural change and agricultural expansion for countries with a sea coast and landlocked ones. In both categories of countries, two similar phenomena operate at the same time.

The first phenomenon is concerned with the nature of the relationship between the two variables. It shows that there is an inverse and significant relationship between the expansion of agricultural land and the probability of observing an economic structure of the underdeveloped type. The more land increases, the more agricultural sectors release labour for other sectors.

The second phenomenon is concerned with the sensitivity of this relationship. It is noted that the relationship is less sensitive to the increase in agricultural land when it represents less than 45% of the total surface of the country. Promoting the expansion of agricultural land does not always decrease the likelihood of having an agrarian economy. However, above 45%, this probability decreases more noticeably with agricultural land expansion. This result is in line with that of Johnson and Vollrath (2020) which shows that the higher the land elasticity of agricultural production, the greater the propensity of individuals to leave the agricultural sector.
<figure><img src="image_19.png" /><figcaption>Figure 17: Marginal effects of agricultural land expansion on probability of structurally developing economy
Source: author.</figcaption></figure>

There are similarities and differences between depending on the openness of the countries to the sea. Like countries with an opening to the sea, landlocked countries have an inverse relationship between the probability of observing an agrarian economy and agricultural expansion. In addition, this probability decreases timidly before the average of 45% and then accelerates after the average. The difference between landlocked countries and countries open to the sea is that landlocked countries are always more agrarian than countries with a sea coast. Landlocked countries, need an agricultural land expansion of 65% to move from structural underdevelopment. However, an agricultural land expansion of 60%, is associated with the probability to observe an agrarian economy as a non-agrarian economy in sea-opening. This gap is not closing.

b. Marginal effects of agricultural land expansion on the probability of being a structurally developing economy

Then, figure 17 above highlights the relationship between the probability of observing a tertiary-type structural change and agricultural land increase. The two variables appear to be positively correlated at first glance. Between 5% and 45%, one notes that the increase in land is accompanied by a monotonous increase in the probability of observing a tertiary economy, both for countries open to the sea and for landlocked countries. For the latter, however, the effect is slower. This results is close to Cadot et al. (2016).

The second major fact emerging is that 45% and 75% of the ‘tertiarisation process’ seem to be running out of steam. An agricultural land expansion is accompanied by an increase in the probability of observing a developing economic structure, to a lesser extent. Lastly, the third fact observed is that countries open to the sea are more likely to develop a service sector than those without an opening to the sea. They have a lower observation curve and weaker growth than countries with costal openings.
This can be explained by the lower transport costs for non-landlocked countries, as well as the abundance of imported products, which facilitate integration into and being member in the tertiary sector which is mainly made up of informal activities, trade in retail and distribution. This result is in line with Mensah et al. (2016);

### c. Marginal effects of agricultural land expansion on the probability of being a structurally developed economy

Finally, figure 18 establishes the relationship between the probability of observing an economic structure of the developed or industrialised type and the expansion of agricultural land. Two major facts emerge from this modelling.

<figure><img src="image_20.png" /><figcaption>Figure 18: Marginal effects of agricultural land expansion on probability of structurally developed economy
Source: author.</figcaption></figure>

The direct relationship between the probability of observing an industrialised economy and agricultural expansion as well as the difference in the process of structural transformation depends on whether the countries have an opening to the sea. The first fact is in line with the research hypothesis of this essay. Indeed, agricultural expansion is a significant determinant for industrial structural transformation in sub-Saharan Africa. It is observed that the expansion of land-use is accompanied by an increase in the probability of observing an economy with an industrial structure. This process has two sub-phases, as identified in the previous types of structural change.

The first is set for expansion between 5% and 45%. It is characterised by a low sensitivity to the country's probability of having an industrial structure as expansion continues. The second sub-phase is established at 45%. The model predicts a probability of observing an industrial structure that grows at increasing rate, following the expansion of agricultural land. The second fact emerges from the comparison between sea-opened countries and landlocked. Unlike the other predicted probabilities, the probability of observing a developed economic structure is initially zero for the two categories of
countries. Therefore, the countries open to the sea have an advantage, and develop a more industrialised economic structure than landlocked countries. This shows that agricultural expansion generates a faster structural industrialisation process for countries adjacent to the sea than for other countries, all other things being equal.

<figure><img src="image_21.png" /><figcaption>Figure 19: Marginal effects of Governance on types of structural change
Source: author.</figcaption></figure>

**d. Analysing the Marginal effects of Governance on types of Structural Change**

Figure 19, helps to examine the marginal effects of institutional quality on types of structural change. One starts with the structurally underdeveloped type, then followed by the developing, and ended with the structurally developed type.

First, when it comes to agrarian economies (figure 19a) there is an inverse relationship between improving the quality of institutions and the probability of observing a structurally underdeveloped economy. In addition, the relationship is strengthening as agricultural expansion continues. Thus, countries with 40% agricultural land have a lower probability of releasing labour in other sectors, than countries with 60% land expansion. In addition, the likelihood of observing a structurally underdeveloped economy decreases rapidly as institution quality improves. Thus, it is important to combine land-use expansion with improving quality of institutions. Indeed, better land governance integrated into agricultural policies, a land market allocating land efficiently to farmers, would encourage the exodus of labour to other sectors. This result is similar to that of Johnson and Vollrath (2020) which shows that in the event of a positive productivity shock, for regions with high elasticity – land – as shown in the previous subsection – s’ accompanied by a release of the agricultural workforce towards other sectors.
Secondly, Figure 19b explores for each level of agricultural expansion, the marginal effect of improving quality of institutions on the probability of observing a tertiary economy. The figure discloses an inverted-U relationship, for different levels of expansion. Improved governance promotes the tertiaryisation. However, it has a threshold which depends on agricultural expansions. This result is different from that of Mensah et al. (2016) who show in particular that a qualitative leap in governance would have a negative and insignificant effect on the value added of the tertiary sector.

<table><caption>Table 16 : Effects of agricultural land expansion on stages of structural change: Random effects</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">REMLOGIT (1)</th><th colspan="2">REMLOGIT (2)</th><th colspan="2">REMLOGIT (3)</th></tr><tr><th>Structurally Developing</th><th>Structurally Developed</th><th>Structurally Developing</th><th>Structurally Developed</th><th>Structurally Developing</th><th>Structurally Developed</th></tr></thead><tbody><tr><td>Agricultural land</td><td>1.101***<br>(0.0205)</td><td>1.730***<br>(0.300)</td><td>1.232***<br>(0.0444)</td><td>1.753***<br>(0.260)</td><td>1.154***<br>(0.0346)</td><td>2.829***<br>(1.109)</td></tr><tr><td>Perm cropland</td><td>0.846*<br>(0.0768)</td><td>1.164<br>(1.281)</td><td>0.910<br>(0.0979)</td><td>0.405<br>(0.417)</td><td>0.898<br>(0.0818)</td><td>0.209<br>(0.353)</td></tr><tr><td>Ag land per cap</td><td>0.137<br>(0.360)</td><td>7.107e+09**<br>(7.630e+10)</td><td>0.0184<br>(0.0548)</td><td>16.54<br>(1,473)</td><td>0.000703**<br>(0.00206)</td><td>0.709<br>(8.317)</td></tr><tr><td>Ag1Xva</td><td>0.998***<br>(0.000720)</td><td>0.976***<br>(0.00911)</td><td>0.997***<br>(0.000934)</td><td>0.983**<br>(0.00771)</td><td>0.998***<br>(0.000807)</td><td>0.970**<br>(0.0136)</td></tr><tr><td>Lnwdpcap</td><td>6.786***<br>(2.740)</td><td>402.2**<br>(1,002)</td><td>14.40***<br>(7.618)</td><td>0.0453<br>(0.175)</td><td>6.588***<br>(3.128)</td><td>0.00415<br>(0.0241)</td></tr><tr><td>Pop. growth</td><td>0***<br>(0)</td><td>2.255e+44<br>(2.199e+46)</td><td>0<br>(0)</td><td>4.410e+42<br>(6.763e+44)</td><td>0**<br>(0)</td><td>3.978e+103<br>(9.961e+105)</td></tr><tr><td>Governance</td><td>1.692***<br>(0.311)</td><td>65.55***<br>(88.78)</td><td>7.119***<br>(2.313)</td><td>35.44***<br>(526.1)</td><td>3.590***<br>(0.866)</td><td>18.96***<br>(4,948)</td></tr><tr><td>Landlocked<br>(I if yes)</td><td>0.0596***<br>(0.0559)</td><td>7,022**<br>(24,841)</td><td>0.212<br>(0.234)</td><td>109.9*<br>(286.5)</td><td>0.253<br>(0.285)</td><td>0.354<br>(1.408)</td></tr><tr><td>Trade</td><td></td><td></td><td>1.026***<br>(0.00854)</td><td>1.156***<br>(0.0639)</td><td>1.032***<br>(0.00845)</td><td>1.279**<br>(0.139)</td></tr><tr><td>Mineral rent</td><td></td><td></td><td></td><td></td><td>1.030<br>(0.0414)</td><td>0.777<br>(0.197)</td></tr><tr><td>Constant</td><td>1.25e-08***<br>(4.63e-08)</td><td>0***<br>(0)</td><td>0***<br>(0)</td><td>0*<br>(0)</td><td>5.92e-11***<br>(3.07e-10)</td><td>0<br>(0)</td></tr><tr><td>RI2[c_id]</td><td></td><td>1</td><td></td><td>1</td><td></td><td>1</td></tr><tr><td>RI3[c_id]</td><td></td><td>1</td><td></td><td>1</td><td></td><td>1</td></tr><tr><td>var(RI2[c_id])</td><td></td><td>265.6***<br>(371.4)</td><td></td><td>89,116***<br>(287,249)</td><td></td><td>794.9***<br>(1,418)</td></tr><tr><td>var(RI3[c_id])</td><td></td><td>5.884e+31*<br>(2.612e+33)</td><td></td><td>5.300e+07**<br>(4.724e+08)</td><td></td><td>4.770e+75<br>(6.149e+77)</td></tr><tr><td>cov(RI3[c_id],<br>RI2[c_id])</td><td></td><td>1.709<br>(3.821)</td><td></td><td>0.189<br>(0.565)</td><td></td><td>0.972<br>(2.241)</td></tr><tr><td>N</td><td>712</td><td>712</td><td>690</td><td>690</td><td>690</td><td>690</td></tr><tr><td>k</td><td>24</td><td>24</td><td>26</td><td>26</td><td>28</td><td>28</td></tr></tbody></table>

se Eform in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Source: author.

The present result is justified because, with poor quality institutions such as corruption, lack of regulation, political instability, workers are generally forced to invest in temporary and informal activities to protect themselves from the prevailing corrosive institutional climate, so as to make the most of it. When democratic institutions take hold, informal activities ceases and the curve decreases.
In addition, it is noted that the combined effect of improving the quality of institutions and agricultural expansion slows down faster the vicious explosion of the tertiary sector, to the benefit of the secondary sector, than does agricultural expansion alone.

Finally, figure 19c describes the marginal relationship between the probability of observing an industrialised-type of structural transformation and the qualitative strengthening of institutions. One of the most important findings is that the quality of institutions has a positive relationship with industrialisation. Greater political stability and democracy are conducive to the transformation of the economic structure to accommodate more industries. The probability of observing industrial-type structural change increases rapidly. This result is similar to those who show the theoretical link between governance and structural change (Edokat and Njong, 2019), and those who test it empirically (Totouom et al., 2019). In addition to this result, it is noted that for countries with high agricultural land expansion, the effect of improving quality of institutions is greater. Hence, governance is an essential component in generating virtuous structural change, which promotes industrialisation.

### 5.3.3. Robustness Check

The results obtained from the estimation of equation (1) in table 16 show that agricultural land enhances the industrial type of structural change in the region. Hence, the validity of our results is verified by robustness checks. Firstly, alternative specifications were considered to confirm the baseline results displayed in table 11. Moreover, an alternative estimation technique was used, namely the Multinomial Logit with Random effects.

#### 5.3.3.1. Alternative model estimation

##### a. Random effects

Looking at models in columns 1 to 3, it is observed that the significance of the expansion variable is the same. Agricultural expansion promotes industrial type of structural change. In each column, the probability of experiencing an industrial-oriented structural change is greater than having an underdeveloped structure. This risk, in favour of an industrial structure, is greater than the probability of observing a tertiary economic structure compared to an agrarian economy. Agricultural expansion increases the probability of industrialisation of the economy. However, it is more likely to contribute to cause the industrialisation of the economic structure (columns 1–3).

The variable of land under permanent crop land does not withstand the specifications of the model. In columns 3–4 the variable is not significant and it reduces the probability of observing structurally developed countries relative to underdeveloped countries. There is a similar relationship between
being structurally developing and structurally underdeveloped. In the second model, on the other hand, the variable has no significant effect on the probability of observing an industrial type of transformation rather than an underdeveloped economic structure. However, the variable reduces by 15% the risk of observing a tertiary-type structural change, compared to the probability of observing an agrarian economic structure.

The variable arable land per capita does not withstand various model specifications. Its increase reduces in models (1 and 4) and increases in models (2 and 3) the probability of observing a mutation of the economic structure polarised towards the industrial sector that has an intense economy in hand of agriculture work. This can be explained by landowners use of their spaces. Some may use them for agropastoral production, which will inflate the agricultural sector, relative to the industrial sector. On the other hand, others may use the space to construct industrial buildings or invest in real estate. In this case, the industrial sector increases relative to the agricultural sector. However, it is statistically significant.

The proxy variable of economic growth also has inconsistent effects on types of structural change. The improvement in the living conditions of the populations in models 2, 3 and 4 increases the risk of an economic structure more oriented towards the tertiary sector, relative to the agricultural sector. In model 2, the improvement in the well-being of the population is accompanied by an increase in the probability of observing an economy with an industrialised structure than an agrarian economy. This result corroborates those of table 10 which highlight the existence of unstable growth, accompanied by the expansion of the tertiary sector.

The proxy variable for institutional quality is robust to all specifications. Here, improving institutions is more likely to promote industrialisation than tertiarisation for a structurally underdeveloped economy. Similarly, Acemoglu et al. (2018) emphasise that democracy causes economic growth as it promotes better business climate. The model shows that good institutions promote industrialisation than tertiarisation of economic structures. This result is significant at the 1% level.

International openness, measured by the trade variable in the models, plays important role in promoting structural changes. The coefficients of the trade variable in random effects models become significant, unlike the estimates obtained by POMLOGIT. From REMLOGIT models 1–3, it is noted that a one percentage point rise in trade increases by 15–28% the risk of observing a structural change in the industrial type, rather than an agrarian economic structure. This result is consistent with previous findings (Kacou et al., 2022; van Neuss, 2019) Indeed, these authors suggest that trade openness stimulates and enhances the manufacturing productivity. In the same models, it is noted that the increase in international trade product increases by 2 to 3% the risk of observing a structural
change in the type of services, relative to structural underdevelopment. It falls in line with the
resources curse hypothesis and contradicts the first effect just described (Kaba et al., 2022; Monga
and Lin, 2019). Indeed, Monga and Lin (2019) justify that the significance of trade to enhance
productivity of the service sector comes from the inability for African countries to convert their
natural endowments into advantages as suggested by comparative advantage theory.

Attachment to resources, measured by variable mineral rent, has no statistically significant influence on structural change. However, it increases by 3% the chances of a tertiary economic structure occurring, relative to an agrarian economy. On the other hand, the mineral rent variable reduces by 23% the risk of observing a structurally industrialised economy relative to the risks of observing an agrarian economy. This result corroborates those of Kaba et al. (2022) and Kacou et al. (2022). They explain that the resource curse hypothesis also operates when countries' exports are mostly consisted of primary products. The latter is associated with low productivity, sticking thus the structural change process.

Coefficients var (RI2[c_id]) and var (RI3[c_id]) terms are the variances of random effects. In practical terms, these are the variances of idiosyncratic individual effects. The covariance is not significant, so it is not possible to conclude that there is a correlation between the two random intercepts. Moreover, the computed correlation remains at -0.0021, meaning there is a negative relationship between the two outcomes (Pope, 2014).

There has been an attempt to model the effects of mechanisation on types of structural change. The model estimates were inconsistent. The variable number of tractors and tractor density per kilometre are the best measures to capture agricultural modernisation (Daum and Birner, 2020; Huttunen, 2019). To understand its effect on the type of change, more analysis needs to be done, which is beyond the scope of the objective of this work.

**b. Marginal effects**

Figure 20 describes the relationships between the probability of observing one of the stages of structural change, and the expansion in agricultural land-use. The marginal effects obtained here are similar to those obtained in the POMLOGIT model.
<figure><img src="image_22.png" /><figcaption>Figure 20: Margins after GSEM, random effects</figcaption></figure>

Source: author.

It is noted that an increase in the land-use for agriculture is accompanied by a decrease in the probability of observing an agrarian-type economic structure (figure 20 on the left). This decrease happens at an increasing rate. Landlocked countries generally remain more agrarian than opened sea countries.

Then there is a timid increase in the probability of a change in the type of service following agricultural expansion. On average, landlocked countries develop a service economy more often than that of opened sea countries, consecutively to an increase in agricultural land. Finally, the likelihood of seeing a structurally industrialised economy is growing at an increasing rate as agricultural land expands. The model predicts, moreover, that the marginal effects are indifferent as to landlocked status. For this reason, the curves of countries with and without coastal openings are almost mixed up.

### 5.3.3.2. Varying the model specification: Fixed effects

This part introduces the results of the fixed effect model (FEMLOGIT). The period used for the estimation is 2004–2016. It is dictated by the convergence of the model and by the recommendations of the inventor of the technique (Pforr, 2019). The estimates interpreted in this subsection compare the coefficient estimates of the multinational fixed-effects logit model to those of the multinomial random-effects logit model and the stacked multinomial logit model.

#### a. Global analysis of the model

The fixed-effect model that is estimated and interpreted in this subsection has acceptable statistical characteristics. A pseudo-R2 which culminates at 41%, which shows that the variables integrating
the model explain 41% of the variations of the dependent variable (TSC). Furthermore, Wald's global significance test, significant at 1% and amounts to a value Chi2 = 6367 shows that the estimates are robust to the heteroskedasticity that may possibly be encountered (Wooldridge, 2010; Pforr, 2014).

<table><caption>Table 17: Effects of agricultural land expansion on stage of structural change: Fixed-effects</caption><thead><tr><th rowspan="3">Period</th><th colspan="2">POMLOGIT<br>(Model 1)<br>1990-2016</th><th colspan="2">REMLOGIT<br>(Model 2)<br>1990-2016</th><th colspan="2">FEMLOGIT<br>(Model 3)<br>2004-2016</th></tr><tr><th>Structurally<br>Developing</th><th>Structurally<br>Developed</th><th>Structurally<br>Developing</th><th>Structurally<br>Developed</th><th>Structurally<br>Developing</th><th>Structurally<br>Developed</th></tr></thead><tbody><tr><td>Agricultural land</td><td>1.089***<br>(0.0294)</td><td>1.312***<br>(0.0931)</td><td>1.154***<br>(0.0346)</td><td>2.837***<br>(1.130)</td><td>0.664<br>(0.260)</td><td>3.184**<br>(1.732)</td></tr><tr><td>Ag. land per cap</td><td>0.360<br>(1.710)</td><td>8.999<br>(26.26)</td><td>0.000684**<br>(0.00200)</td><td>1.530<br>(17.65)</td><td></td><td></td></tr><tr><td>Perm. cropland</td><td>0.771**<br>(0.0830)</td><td>1.026<br>(0.199)</td><td>0.897<br>(0.0818)</td><td>0.331<br>(0.509)</td><td></td><td></td></tr><tr><td>Governance</td><td>1.766***<br>(0.357)</td><td>14.67**<br>(16.47)</td><td>3.596***<br>(0.870)</td><td>16.86***<br>(4,419)</td><td>29.01**<br>(49.31)</td><td>191.8<br>(803.2)</td></tr><tr><td>Trade</td><td>1.005<br>(0.0110)</td><td>1.001<br>(0.0185)</td><td>1.032***<br>(0.00846)</td><td>1.275**<br>(0.138)</td><td>1.065***<br>(0.0209)</td><td>1.316***<br>(0.0679)</td></tr><tr><td>Mineral rent</td><td>0.986<br>(0.0604)</td><td>1.155<br>(0.444)</td><td>1.030<br>(0.0414)</td><td>0.770<br>(0.197)</td><td>1.180<br>(0.250)</td><td>0.721***<br>(0.0552)</td></tr><tr><td>popgXagva</td><td>9.924**<br>(10.88)</td><td>94,406***<br>(270,770)</td><td></td><td></td><td>2.06e-07***<br>(1.17e-06)</td><td>0.03***<br>(1.17)</td></tr><tr><td>aglXva</td><td>0.999<br>(0.000774)</td><td>0.988***<br>(0.00375)</td><td>0.998***<br>(0.000807)</td><td>0.971**<br>(0.0141)</td><td></td><td></td></tr><tr><td>GDP per cap</td><td>9.303***<br>(4.723)</td><td>0.0677<br>(0.111)</td><td>6.612***<br>(3.143)</td><td>0.00521<br>(0.0321)</td><td></td><td></td></tr><tr><td>Pop. growth</td><td>0*<br>(0)</td><td>0***<br>(0)</td><td>0**<br>(0)</td><td>1.239e+103<br>(3.169e+105)</td><td></td><td></td></tr><tr><td>Landlocked<br>(1 if yes)</td><td>0.306<br>(0.254)</td><td>0.279<br>(0.637)</td><td>0.257<br>(0.288)</td><td>0.551<br>(2.311)</td><td></td><td></td></tr><tr><td>Constant</td><td>1.95e-10***<br>(1.16e-09)</td><td>0.0801<br>(0.641)</td><td>5.76e-11***<br>(2.99e-10)</td><td>0<br>(0)</td><td></td><td></td></tr><tr><td>var(RI2[c_id])</td><td></td><td></td><td>815.5***<br>(1,462)</td><td></td><td></td><td></td></tr><tr><td>var(RI3[c_id])</td><td></td><td></td><td>7.645e+72<br>(9.873e+74)</td><td></td><td></td><td></td></tr><tr><td>cov(RI3[c_id],RI2[c_id])</td><td></td><td></td><td>0.932<br>(2.198)</td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>690</td><td>690</td><td>690</td><td>690</td><td>143</td><td>143</td></tr><tr><td>R squared</td><td>0.577</td><td>0.577</td><td></td><td></td><td>0.431</td><td>0.431</td></tr><tr><td>K</td><td>24</td><td>24</td><td>28</td><td>28</td><td>10</td><td>10</td></tr><tr><td>N</td><td>41</td><td>41</td><td></td><td></td><td></td><td></td></tr><tr><td>chi2</td><td>674.0***</td><td>674.0***</td><td></td><td></td><td>6367***</td><td>6367***</td></tr></tbody></table>

Robust seeform in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Source: author.

## b. Analysis of variables

Table 17 discloses the results of the fixed effect model in the 3rd column. A good observation reveals that in that model, the coefficient of the variable of interest agricultural land expansion increases and multiplies by 2.18 the probability of observing a structural change of the industrial type. Indeed, this result attests to the fact that agricultural expansion increases the chances of an industrial-type structural change, relative to the observation of an underdeveloped economic structure. Robust result to various specifications and estimation techniques. In the 3rd same column, agricultural expansion
reduces by 33% the probability of observing an economic structure of the type of service, relative to an agrarian economic structure.

Despite the insignificance of the industrial-type structural change equation, the institutional quality variable also retains an expected effect on the types of structural change. It significantly increases the chances of an economy wherein employment is more concentrated in the service sector than in the agricultural sector. In addition, improving the quality of institutions also promotes an economic structure with a high concentration of industrial jobs relative to an agricultural concentration.

The foreign trade variable, captured here by *trade*, has the same effect on types of structural change. Instead of observing an economy dominated by agricultural jobs, a 1% increase in international trade will increase the chances of observing a tertiary-type structural change by 6%, while it increases the probability by 32% to observe an industrialised-type of structural change. This result is significant at the 1% level and in line previous studies (Baymul and Sen, 2020; Roy and Roy, 2017).

The fixed effects estimate reveals that the attachment to natural resources rent reduces by 28% the probability of observing an economic structure of the industrial type rather than an economy of the agricultural type. This result is similar to that obtained by Random Effects (REMLOGIT), whose explanation is recorded in the previous subsection.

The combined effect of agricultural value added and population growth is not robust to estimation methods. Using the technique of intra-variance, the simultaneous growth of agricultural value added and the population is accompanied by a reduction in the chances of observing a tertiary economic structure compared to an agrarian structure. It also significantly reduces the chances of observing an industrial economic structure, in favour of an agrarian economic structure. Some possible reasons for this fact are Africans' attachment to agricultural activities with low returns. First, cultural dimension of agriculture makes it a default activity and absorb rural populations when positive population and agricultural value-added shock arise. Populations are flocking more to agriculture or pastoral production activities to face demographic pressure as developed by Malthusian theory, which causes increases jobs in the agricultural sector. Second, poverty and food insecurity are best tackled by agricultural growth (Christiaensen and Demery, 2007). In addition, population growth increases the supply of employment in the agricultural sector. Then, simultaneous and positive shock of agricultural value added and demography will sound as a signal of resilience against poverty and food insecurity, causing a more agrarian economy.
## 6. Conclusion

The economic dimension of agricultural sustainability implies that overall product increases. During recent decades, sub-Saharan African countries have expanded their agricultural land and experienced an atypical structural transformation. The expansion of agricultural land is a source of long-term growth. However, the effects of agricultural expansion on structural change in Sub-Saharan Africa remains largely unknown. In this essay, investigations are made to understand the process of structural transformation and the effects of agricultural land expansion on the type of structural transformation in Africa.

The study covers the 1990–2016 period and focuses on 41 Sub-Saharan African countries. The dependent variable type of structural change (TSC) is constructed with three modalities: Structurally underdeveloped, structurally developing and structurally developed. The results are robust to different model specifications and estimation techniques likewise.

The results suggest that agricultural land expansion has a significant effect on structural transformation in the region. It is specified that it further increases the possibility for countries to have a predominantly industrial structure. The results are similar to those of Barbier (2019; 2020), Hornbeck and Keskin (2015), Eberhardt and Vollrath (2018) but diverge from those of Mensah et al. (2016), Martins (2019) and Johnson and Vollrath (2020). The reasons behind the results are the increase in land causing a more than proportionate increase in yields (Wise, 2020) which does not only release a surplus of production but also, a surplus of labour from the agricultural sector, to be captured by other sectors. Furthermore, the increase in production offers prospects for processing the agricultural surplus. Thus, producers who left the agricultural sector are more attracted to the secondary sector and motivated by the downstream diversification of their former activities. This increases the likelihood of observing a more industrialised economic structure. Hence the research null hypothesis that agricultural land expansion causes industrial-type of structural change in Africa cannot be rejected.

Overall, this essay shows that land expansion for agriculture is beneficial for ‘virtuous’ structural change, especially for the abundant land sub-Saharan. In addition, the quality of institutions is likely to encourage this same type of structural transformation. Moreover, the effects are faster when the two levers are activated jointly. In terms of economic policy recommendations, the following measures can be adopted to bring about an industrial-oriented type of structural transformation.
ESSAY III

EFFECTS OF AGRICULTURAL LAND EXPANSION ON BIODIVERSITY LOSS IN SUB-
SAHARAN AFRICA

This essay has successfully received acceptance letters for two scholarships programmes. Pre-registration for a doctoral scholarship "Eugen Ionescu" at the Doctoral School, Faculty of Horticulture, Plant Protection Laboratory, "Ion Ionescu de la Brad" of IASI. Doctoral supervisor: Prof. Univ. Dr. Mihai TALMACIU

And

Pre-registration to the Coimbra Scholarship Programme, at the University of Cologne, Germany with Prof. Dr. Peter Dannenberg as research supervisor.
# 1. Introduction

Biodiversity conservation requires attention for two reasons. First, it provides a wide range of direct and indirect benefits for humans. Second, human activities contribute to unprecedented rates of biodiversity loss, which threaten the stability and continuity of ecosystems as well as their provision of goods and services to humans (Dasgupta, 2021; Markandya et al., 2008).

Human-induced biodiversity loss is greater now than at any time in human history (Kontoleon et al., 2008). Agricultural expansion causes 80% of forest area loss, which accelerates at a rate of 4 million ha each year (FAO, 2019). The expanding global economy coupled with an increasing human population poses a continuous threat to biodiversity (Sachs et al., 2009). As a matter of fact, an alarming 65% decrease in population sizes of mammals, fish, amphibians, and reptiles has been reported (Almond et al., 2020). Additionally, about half of the world's wildlife mammals and birds, could be on the verge of extinction by 2100 because of anthropogenic or human activities (Pimm and Raven, 2000; Sol, 2019).

Some of the causes of biodiversity loss are global warming and pollution (Dalgupta, 2021), populations and economic growth (Pagani-Núñez et al., 2022; Sol, 2019), agricultural land expansion (Molotoks et al., 2021; Perrings and Halkos, 2015; Tan et al., 2022), intensification (Kehoe et al., 2017), governance or institutional quality (Iritie, 2015; Muchapondwa and Stage, 2015; 't Sas-Rolfes, 2017), community protected areas (Ullah and Kim, 2021), habitat and land fragmentation (Conceição et al., 2022), trade and migration of species (Perrings and Kinzig, 2020).

The negative relationship linking agricultural land expansion and biodiversity poses a debate for two reasons. Some authors claim that agriculture could expand while coexisting peacefully with and supporting biodiversity. This is especially true in Africa, with 60 percent of the world's arable land, where there is an opportunity to do things differently (Almond et al., 2020; Darkoh, 2003). Contrarily, other authors posit that land fragmentation is a common feature of agricultural farmers, and those areas are positively associated with increasing threats to biodiversity (Markuszewska, 2013). Furthermore, the underpricing and underfunding of natural areas identified in sub-Saharan Africa exacerbate biodiversity depletion. The first increases the incentives to expand agricultural land at the expense of terrestrial biodiversity, while the second reduces the incentives to protect and preserve natural habits (Barbier, 2022; Dasgupta, 2021).

This essay aims at assessing the impact of agricultural land expansion on threatened species in sub-Saharan African countries. The rest of the essay is organised as follows: Section 2 reviews the literature; Section 3 exposes the methodology of the study; Section 4 analyses and interprets the results; and finally, Section 5 concludes the essay.
## 2. Stylised Facts on the Biodiversity status in Africa

The presentation of the main trends in biodiversity proceeds in two stages: starting with a comparative description of the evolution of biodiversity conservation in different regions of the world and further focusing on the analysis of how biodiversity status is going in sub-Saharan Africa.

### 2.1. Evolution of the extinction risk of species in the world

To analyse the rate at which biodiversity is changing, many indicators can be used, among which the Red list index<fnref n="7" /> albeit imperfect.

<figure><img src="image_23.png" /><figcaption>Figure 21: Redlist Index</figcaption></figure>

The Red List Index (RLI) defines the conservation status of major species groups and measures trends in the proportion of species expected to remain extinct in the near future without additional conservation action. When the RLI value equals 1.0, all species are classified as ‘Least Concern’, and hence none are expected to go extinct in the near future. However, a value of zero indicates that all species have gone extinct.

From figure 21, it is seen that globally and for all regions of the world, trends in the state of biodiversity have a negative shape. From the figure, two groups can be identified. The first part is comprised of Northern Africa, Europe, and Northern America. These regions had RLI values close to 0.9 in 1993, which declined to around 0.85 in 2021. This depicts a slight decrease, especially in Europe and Northern America, with a reduction of less than one percentage point of evolution. Low pressure on land used for agriculture, mastery of agricultural technology, and low relative abundance

<footnote n="7">⁷ Other measures are presented in the methodology section of the present essay.</footnote>
of species can conjointly explain the flatness of their curves as compared to those of regions in the
second group.

<figure><img src="image_24.png" /><figcaption>Figure 22: Redlist index for groups of species</figcaption></figure>

Source: BirdLife International and IUCN (2021)

The second group is composed of tropical regions. They are known to be very abundant in species, but with a rapid decline in their RLI. In 1993, Sub-Saharan Africa's RLI value was 0.8, while Southern Asia showed 0.77. The new values are 0.7 for the former and about 0.63 for the latter. This means that biodiversity in these regions is more threatened, and species suffer a higher risk of extinction. The reduction of more than one percentage point of the RLI can be explained by at least two factors: the rate at which populations grow and the rate at which natural environment areas are depleted. This causes a reduction in different species.

**2.2. Evolution of groups of species**

Most species that move between categories on the IUCN Red List do so because of improved
knowledge or revised taxonomy. It is not possible to determine any meaningful trends in the status of
biodiversity simply by looking at overall changes in the numbers of threatened species between
updates. For this reason, the statistics for numbers of threatened species in each IUCN Red List since
1996 must be interpreted with extreme care; these figures illustrate increasing assessment efforts to
refine current understanding of the status of biodiversity rather than showing genuine status changes
over time.

Currently, the RLI is available for five taxonomic groups only (those in which all species have been
assessed at least twice): birds, mammals, amphibians, cycads, and warm-water reef-forming coral
(Figure 22). The RLI clearly demonstrates that the status of these major groups is still declining. The
blue line indicates the overall RLI for all the taxa combined. It shows the general level of threat for all groups. Coral species are moving towards increased extinction risk most rapidly as their indicator value declines by almost 2 percentage points, going from 0.98 to 0.8 in about 10 years since 1999.

It can be seen that amphibians are, on average, the most threatened animal group, with a RLI of about 0.6, with little variation between 1980 and 2005. It is also noted that, on average, birds are the least threatened group of species from 1990 to 2020, as their RLI value is constant at around 0.9. They are followed by mammals. The divergences between threats can be correlated to their respective natural habitats. As a matter of fact, human activities such as crop cultivation may hurt both birds and amphibians, as forests may host varieties of the said species (Ahrends et al., 2015). However, amphibians have two natural habitats, which make them twice as exposed as other species.

## 2.3. Some facts on biodiversity in Africa

Tropics are generally less diverse in terms of species, genera, and families of plants. The African mainland has between 40,000 and 60,000 plant species, of which approximately 35,000 are endemic (Mutke and Barthlott, 2005). There is a great deal of diversity and endemism in aquatic life in isolated lakes and large river systems. Primates, insects, and birds are most prevalent in forest regions, while ungulates and large carnivores are most prevalent in savannah regions. Isolated mountain regions are home to many rare endemic species. Other species, such as the African elephant, have a broader habitat use and are widespread across the continent. A quarter (1229 species) of the world's approximately 4700 mammal species are found in Africa, including 960 in sub-Saharan Africa (SSA) and 137 in Madagascar. They contain over 2000 bird species, nearly a fifth of the approximately 10,000 bird species in the world (De Klerk et al., 2004). About 1600 bird species are endemic to SSA (de Klerk et al., 2002; Kinzig and McShane, 2015).

Three reasons justify the choice of Sub-Saharan African countries for the study: First, the severe biodiversity loss, particularly in mammals and birds, coincides with high poverty rates in Sub-Saharan African countries (Sachs et al., 2009); second, over the last twenty years, community-based interventions have been widely used in Sub-Saharan Africa as mechanisms to combine rural development and conservation efforts (Brooks et al., 2004; Lindsey et al., 2007; Martins et al., 2018); third, Sub-Saharan African countries share similar characteristics in terms of biodiversity richness and species diversity (Ullah and Kim, 2021).
**3. Literature review linking agricultural land and biodiversity loss**

This section shows different theoretical explanations and reviews empirical studies on biodiversity loss as a result of agricultural land expansion.

**3.1. Expansion of agricultural land and biodiversity change: Theoretical Background**

Three approaches explain the ways in which agricultural land expansion is linked to biodiversity change in literature: protectionist, extensive marginal, spatial, and temporal approaches.

**3.1.1. Agricultural land expansion and biodiversity change: a protectionist approach**

From the perspective of environmental protection approaches, Pigou and Coase's theories of protection-measures can explain the link between agricultural expansion and biodiversity loss.

**3.1.1.1. Pigouvian externalities from agricultural land expansion**

Following Marshall (1890), Pigou (1920) was interested in the issue of externalities, i.e., the consequences of economic activity that are not accounted for in prices. Externalities can be positive or negative. He understood that economic activity could have an impact on agents who do not participate in the bilateral market relationship. When an industry pollutes, it negatively disrupts the life and activity of its neighbour without having to compensate the neighbour for the damages incurred.

In economics, an externality is said to be negative when an agent is responsible for a social cost greater than the private cost imposed on him for the damage he causes. The role of the state is to levy a tax equivalent to the difference between these two costs (the Pigouvian tax). This is referred to as the "polluter pays" principle. The advantage of this policy is twofold. On the one hand, it reduces negative externalities by sending a clear and understandable signal to the market. On the other hand, it generates tax revenue for the state (Belpaire, 2013).

The economic activity of agricultural production on increasingly large areas can lead to the destruction of natural capital (mammals, birds, amphibians, reptiles, and plants). This wildlife often has cultural values for the indigenous populations of the Americas and especially of sub-Saharan Africa. Destroying these elements of biodiversity leads to the disruption of the beliefs, traditions, rites, and religions of these populations. Worse, it can lead to claims and uprisings. A Pigouvian tax is a means of deterring or redressing crimes against biodiversity and the people who are culturally connected to it.
3.1.1.2. Coase property rights to prevent agricultural land sourcing biodiversity loss

In his theorem, Coase (1960) posits an alternative solution to environmental preservation that dispenses with public intervention to create a market for environmental damage. It states that if property rights can be properly defined, if the sender and the victims of the externality are rigorously identified, and in the absence of transaction costs (i.e., valuation, negotiation, or similar costs), then the affected parties can negotiate of their own free will and thus fix a price for the damage. The damage or environmental impact then becomes an economic good that can be traded on a market. Under these conditions, it is possible to find an optimal allocation of resources in accordance with the individual preferences of the agents without the intervention of the state. All decisions become private, and the problem loses its public nature.

Coase’s theorem seems to be misinterpreted as an apology for the privatisation of natural spaces and a criticism of state intervention. On the contrary, Coase’s argument is to show the importance of transaction costs. The theorem shows that if these negotiated solutions do not exist in the real world, it is because transaction costs are present and determine in a fundamental way the inefficiency of arrangements between parties. For Coase, individuals are constrained by a series of social, legal, or institutional norms that do not always fit the neoclassical model.

It is interesting that, in Coase's view, substantial state intervention and fresh institutional arrangements to enhance coordination between interdependent agents can justify the existence of transaction costs. In this sense, Coase is much more interventionist than Pigou. In fact, Coase (1960) believed that everything in the business world was random, so it was necessary to consider the function of institutions in the economy. Roughly speaking, in the case where economic activity is perfectly influenced by the legal framework, Coase (1992) recognised that the usefulness of government intervention would be limited to the clear definition of property rights, considering an efficient economic system without externality (without transaction costs).

According to Coase’s reasoning, the expansion of agricultural land can be liberal but regulated by a functioning market with state intervention. The definition of property rights could help to responsibly increase agricultural land at the expense of natural habitats. This would make it possible to simultaneously take into account the problems of static coordination and regulation and dynamic coordination, centred on exhaustible natural resources, and intergenerational equity.

To conclude, if protection measures are not well defined or respected, agricultural land expansion and other human economic activities are expected to damage the environment. This is the case for
sub-Saharan African countries; hence, we may expect a negative relationship between the two variables.

**3.1.2. Agricultural land expansion and biodiversity change: an extensive margin approach**

Increasingly, the extensive growth of farmland areas poses threats to many animals and nature as a whole.

**3.1.2.1. The Ricardian theory of diminishing returns and biodiversity loss**

The law of diminishing returns, developed by David Ricardo (1817), is a concept that has been applied to agriculture. It states that agricultural returns fall as land is cultivated to meet the needs of an ever-increasing population. This law states the principle that the marginal productivity of land decreases, all else being equal.

Drawing on Malthus, Ricardo compares land to a series of machines for producing wheat and raw materials. Each machine is imperishable and indestructible. Nature is inexhaustible, but the fertility of the land decreases as the population and its food needs increase. Agricultural yields, therefore, decrease as the population increases; the best land being used first, according to Ricardo, becomes less and less efficient. On this note, he states: 'There is no rent at the outset when the pioneers arrive in a fertile region where the land is abundant and only a small portion is necessary to feed the population, or, moreover, that only a small portion can be cultivated with the capital available to the population.' Indeed, no one is willing to pay for an abundant good that is unappropriated and, therefore, available to everyone who wants to use it. With each increase in population, which forces a country to exploit inferior land in order to increase food production, the rent on fertile land will increase.

In the long term, land is a variable factor, and due to decreasing yields from primary land, new space will be needed for agricultural production. As a result, there will be an increase in pollution and the expropriation of wildlife from its living environment. This leads to biodiversity loss and the depletion of natural resources.

In a similar vein, J. S. Mill (1848) held that agricultural land expansion damages natural beauty. Here, natural beauty is understood as the environment in all its diversity. Irrespective of the time span, it is expected from Mill's point of view that the expansion of agricultural land will deplete natural resources, bringing pollution and biodiversity threats.
#### 3.1.2.2. Kuznets Environmental Curve: Agricultural growth and biodiversity loss

Neoclassical economic theorists argue that environmental quality is a luxury good and, therefore, only affluent societies are willing to heavily invest in environmental protection. The environmental Kuznets curve (EKC) is the statistical hypothesis that describes this expected relationship between affluence and environmental quality (Kuznets, 1955).

The existence of technical and natural capital was assumed. The exploitation of the natural capital, which is land, to produce economic goods leads to the exhaustion of natural resources, increasing their price. The reduction in natural capital should be compensated by the increase in available quantities of technical capital. The latter must increase at the same rate as the rent from exploited and limited resources.

The model assumes the absence of technical progress and that adequate investments can enhance sustainable development as long as capital is available. Grossman and Krueger (1995) hold this upbeat view of development. They show that environmental degradation and GDP have a quadratic relationship.

The EKC hypothesis suggests that environmental problems follow an inverted U-shaped curve relative to affluence (typically measured as GDP per capita), where environmental problems escalate in the early stages of development, but eventually a tipping point is reached, after which further economic growth leads to improvements in environmental quality.

According to the environmental Kuznets Curve (EKC) theory, there is a close relationship between agricultural land and biodiversity depletion. Agricultural crops or livestock are produced using both technical and natural capital. The natural capital here is the agricultural land, forests, or wetlands about to be converted for agriculture. In the absence of technical progress, producing greater agricultural output requires more land conversion, as land is natural. Consequently, many species lose their natural habitat and may face extinction. In the absence of technical progress, production increases so as to satisfy the economic agent's needs. No more land expansion is needed, and this reduces, in relative terms, the extinction rate of species. Here, it is the extensive growth of agricultural output that has caused the biodiversity loss.

#### 3.1.2.3. The environmental sociology ecological modernisation theory

This theory parallels the EKC argument. The main difference is that it is not focused on a single economic development argument. The environmental sociology ecological modernization theory (EMT) suggests that, although nations may alter the environment in the early stages of modernisation, the later stages of modernisation are accompanied by the emergence of 'ecological rationality', where
environmental concerns diffuse throughout society, leading to the restructuring of major political, economic, and social institutions along ecologically sustainable lines (Clausen, 2008). From the EMT perspective, instead of economic development, it is rather the institutional changes that accompany modernisation, such as the development of scientific organisations and the ongoing "rationalisation" of bureaucracies, that lead to environmental reform and protection.

Although the scale of economic production is one indicator of the modernisation of institutions, some scholars suggest that urbanisation is particularly pertinent as an indicator of the institutional restructuring that is integral to the modernisation process (Ehrhardt-Martinez, 1998; Ehrhardt-Martinez et al., 2002). This leads to the hypothesis that environmental impacts may follow an EKC related to urbanisation.

Ehrhardt-Martinez et al. (2002) suggest that such a relationship is likely to exist for direct impacts on the land, such as deforestation (and, by implication, species endangerment as well), because urbanisation concentrates people in cities, reducing population density in natural habitats and the number of people who are dependent for their livelihoods on direct extraction of natural resources.

According to Bren d'Amour et al. (2017), urban expansion generally hurts croplands. They claim that sub-Saharan African countries experience the greatest percentage loss of croplands, while Asian countries experience the highest absolute loss of croplands due to urbanisation. Consequently, the 'ecological rationality' of people would emerge more as the loss of croplands would call upon the conversion and dedication of areas for agriculture at the expense of formerly secured species.

From this approach, obvious biodiversity loss comes from agriculture, with some turning points.

However, the work omits to consider the possible existence of such relationships because they go beyond its objectives.

### 3.1.3. Spatial economics approaches

From a spatial perspective, either land rent or displacement effects account for the ways in which agricultural land expansion increases threats to biodiversity.

#### 3.1.3.1. The land rent theory of natural habitat destruction

Among natural habitats, forests have a richer biodiversity. Von Thunen (1826) developed a land value framework for natural habitat destruction, focusing on the case of forests. The main idea of this spatial economic theory of land is that a piece of land should be allocated to the use that would generate the highest potential rent (Chomitz and Griffiths, 1996; Von Amsberg, 1994). In summary, by assuming profit maximisation motivation, competition among land uses will be determined by which land use yields the highest land rent or value, as illustrated in figure 23 in the next page:
<figure><img src="image_25.png" /><figcaption>Figure 23: Model for land rent for natural habitats: Case of deforestation
Source: simplified from von Amsberg (1994)</figcaption></figure>

The key to explaining changes in land uses and land cover is changes in land rent for different uses (Angelsen, 2007; Hyde et al., 1996). Particularly, forest as another natural habitat could be conserved when land use for forest can generate the maximum value compared to other possible land uses. On the other side, forest or prime land preservation could be encouraged on a given land if its land use for being reforested or conserved can compete with other land uses. In the case of competition between agriculture and forest land uses, as described in figure 23, forest land use will start to take place in the location (dotted line) where the rent for forest land is higher than that of agriculture. The use of forest land for agricultural purposes could increase the expected value of that use.

#### 3.1.3.2. Displacement Effects of agricultural land and natural habitats losses

This development is found in Slingenberg et al. (2009). Direct land use changes are, for all the challenges they present, broadly understood according to their effects on the environmental impacts of biofuel production. However, indirect land use change, also known as displacement effects, is perhaps the most daunting, complex, and consequential aspect of increasing biomass production globally.

In figure 24, Y is the new demand from the biofuel sector from existing plantations. X refers to the expansion of existing plantations as a result of displacement effects. Displacement effects may occur when the production of energy crops displaces economic activities in other areas, causing environmental degradation as a result of the new activity (in this case, biomass production). These effects can range from local to larger and more complex global consequences.
<figure><img src="image_26.png" /><figcaption>Figure 24: Example of land displacement mechanism causing indirect deforestation
Source: Slingenberg et al. (2008).</figcaption></figure>

If, for instance, a soy plantation was previously used primarily for food purposes and converted the same production to energy crops, then that same demand for food from soy would have to be met elsewhere in the long run. In the short run, prices of foods derived from soy would rise. The conversion of soy plantations from food to energy will spark the supply of soy plantations for food elsewhere. Consequently, higher pressure for expanding land use for agricultural production will increase, heightening the risk of environmental degradation.

This approach shows that agricultural land expansion will cause biodiversity loss because of the benefit of rents or indirect deforestation.

**3.1.4. Agricultural land expansion and biodiversity loss debate: Temporality approach**

According to the timelessness of natural resources, the contradicting theories analysing the relationship between agricultural land expansion and biodiversity loss are exposed: the atemporal theory of natural resources and the temporal theory of natural resources.

**3.1.4.1. The theory of the atemporality of natural resources and agricultural expansion**

A priori, natural resources are infinite and free. They belong by right to all individuals in a nation and are perceived as a public good whose stock is not limited. The most explicit text on this subject is by Frédéric Bastiat, on coal: *'Newcastle coal is lavished gratuitously on all men; it is lavished gratuitously on them like water from the stream, on the sole condition that they fetch it or return the trouble to those who take it for us. When we buy coal, it is not the coal that we pay for, but the work that had to be done to extract and transport it'* (René, 1996).

For Bastiat, the true measure of value is the service rendered. Coal, like all other natural resources, has no value because it is unlimited in quantity. Their value is the same as the value of the digging,