A linear panel model with a log-log functional form and two-way fixed effects specification is assumed to estimate the effects of GDP per capita on risk-accounting factors associated with governance performance. To account for potential endogeneity between governance performance and GDP per capita, the time-lagged variable of GDP per capita is used as IV. By controlling country and time effects, within estimates indicate that GDP per capita has a negative and significant effect on the risks associated with governance performance, suggesting that if GDP per capita increases by 1%, the risk account factor will decrease by 0.43%.
𝐿𝑛𝑌𝑖,𝑡 = 𝛼 + 𝛽𝐿𝑛𝐺𝐷𝑃_𝑐𝑎𝑝𝑖𝑡𝑎𝑖,𝑡 + 𝜀𝑖,𝑡, 𝑖 = 1, ⋯ , 𝑁, 𝑡 = 1, ⋯ , 𝑇 𝜀𝑖,𝑡 = 𝜇𝑖 + 𝜆𝑡+ 𝑒𝑖,𝑡, 𝑒𝑖,𝑡 ~ (0, 𝜎𝑒2𝐼)
Tab. C-4. Estimate of the effects of GDP per capita on risks associated with governance performance using a cross-country panel data from 1996 to 2011.
Dependent variable: log lending interest rates
log GDP per capita -0.430***
(0.017) 429788
Observations 2375
R2 0.276
Adjusted R2 0.222
F-statistic 843.1
Note: *** < 0.01, ** < 0.05, and * < 0.1.
For projecting future risk accounting factors, the data of GDP per capita from the SSP database is used. For the reference governance scenario, the GDP per capita trajectory is assumed following SSP2 scenario, while the GDP per capita trajectory is assumed following SSP5 scenario for the strong governance.
92
C.3 Additional figures and tables
Fig. C-1. Cross-validation of net exports of rice w.r.t. AgMIP model projections in reference scenario. Actual modeled growth rates are represented by red dots and lines, whereas dashed yellow dots and lines are projections from AgMIP models for each simulated time step. Shaded areas depict two times standard deviations from the sample mean of AgMIP model projections.
Fig. C-2. Cross-validation of net exports of oil crops w.r.t. AgMIP model projections in reference scenario. Actual modeled growth rates are represented by red dots and lines, whereas dashed yellow dots and lines are projections from AgMIP models for each simulated time step. Shaded areas depict two times standard deviations from the sample mean of AgMIP model projections.
93
Fig. C-3. Net exports of cereals and oil crops for MAgPIE regions in the two trade scenarios (BAS and LIB) for four time-spans (A = 2005- 2025, B= 2030 – 2050, C = 2055 -2075, D = 2080 -2010) when strong governance scenario is assumed. Panel i refers to the net trade pattern of cereals, while panel ii refers to the net trade patterns of oil crops. The height of bars indicates the averaged net exports across five different GCMs, while the error bars refer to two times standard deviations from the sample mean of global net exports and global net imports.
94
Fig. C-4. Net exports of livestock products and sugar crops for MAgPIE regions in the two trade scenarios (BAS and LIB) for four time-spans (A = 2005- 2025, B= 2030 – 2050, C = 2055 -2075, D = 2080 -2010), when reference governance scenario is assumed. Panels i and iii refer to the net trade pattern of livestock products in the reference and strong governance scenarios, respectively. Panels ii and iv refer to the net trade patterns of sugar crops in the reference and strong governance scenarios, respectively. The height of bars indicates the averaged net exports across five different GCMs, while the error bars refer to two times standard deviations from the sample mean of global net exports and global net imports.
95
Fig. C-5. Regional land-use intensity in the different scenarios of trade liberalization and governance performance. Panel A refers to the model result when the reference governance scenario is assumed, while panel B is the model results under the strong governance scenario. For each of the five GCMs used in the analysis, actual simulated land-use intensity index is indicated by dots, while solid lines for each panel refer to the mean with respect to different trade scenarios (BAS and LIB). The shaded areas depict two standard deviations from the sample mean across the GCMs for each trade scenario.
96
Fig. C-6. Regional land-use intensity in different trade scenarios and governance scenarios. The values are averaged across the five GCMs.
Tab. C-5. Changes of global cropland area in the trade scenarios of BAS and LIB when the reference governance is assumed.
Scenarios y2005 y2025 y2050 y2075 y2100
BAS 1494.5 1658.8 1853.2 1948.1 1849.7
LIB 1493.4 1627.6 1800.5 1824.7 1693.4
ΔBAS−LIB 1.1 31.2 52.7 123.4 156.3
Units: million ha.
Tab. C-6. Changes of global cropland area in the trade scenarios of BAS and LIB when the strong governance is assumed.
Scenarios y2005 y2025 y2050 y2075 y2100
BAS 1494.5 1659.1 1863.4 1932.1 1835.2
LIB 1493.4 1627.8 1811.4 1799.5 1683.8
ΔBAS−LIB 1.1 31.4 51.9 132.6 151.4
Units: million ha.
97
Fig. C-7. Regional cropland area in the different scenarios of trade liberalization and governance performance.
Panel A refers to the model result when the reference governance scenario is assumed, while panel B is the model results under the strong governance scenario. For each of the five GCMs used in the analysis, actual simulated cropland area is indicated by dots, while solid lines for each panel refer to the mean. The shaded areas depict two standard deviations from the sample mean across the GCMs.
98
Fig. C-8. Relative change in cropland share due to trade liberalization (LIB - BAS) under different governance scenarios in 2090. The values are averaged across the five GCMs.
99
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