Parallel Session IIIa: Do Land Rushes Really Improve Food Security in Sub-Saharan Africa?

1. Senior Economist, The World Bank
Do Land Rushes Really Improve Food
Security in Sub-Saharan Africa?
Dr. Yogo Urbain Thierry

2. #2023 AGRODEP CONFERENCE
END HUNGER IS ONE OF THE 17 SDGS AT HIGH RISK NOT TO BE ACHIEVED
• SDG2 End Hunger, Achieve Food Security and Improved Nutrition
and Promote Sustainable Agriculture
Target 2.1. By 2030, end hunger and ensure access by all people, in
particular the poor and people in vulnerable situations, including
infants, to safe, nutritious and sufficient food all year round

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PROGRESS TOWARD THE ACHIEVEMENT OF SDG2 ARE DISAPPOINTING
0
5
10
15
20
25
World Africa Sub-Saharan
Africa
Latin America and
the Caribbean
Asia
Percent
of
the
population
2010 2019 2021

4. #2023 AGRODEP CONFERENCE
DRIVERS OF FOOD SECURITY: SELECTED ANSWERS FROM THE
LITTERATURE
 Conflicts and institutional quality (Dabalen and Paul, 2014; Rossignoli and Balestri,
2018)
 Climatic shocks/change (Kinda and Badolo, 2019)
 Economic factors: Weak growth, poverty, inflation, commodity price shocks,
agriculture and food system challenges, ..etc (Mabrouk and Mekni 2018; Nsiah and
Fayissa 2019)

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SEARCHING BEYOND THE USUAL SUSPECT
0
5
10
15
20
25
30
0
0.5
1
1.5
2
2.5
3
Cumulated
land
purchased
per
10000
ha
of
arable
lands Land rush and food security in selected African countries
prevalence of undernourished (% of the population) Land rush

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IS LAND RUSH A DRIVER OF FOOD SECURITY?
• Land rush as an opportunity: An opportunity investment in large-scale land in food
insecure countries is seen as an opportunity to boost agricultural productivity through
technology transfer and investment in rural and agricultural infrastructure.
• Land rush as a constraint: these investments are portrayed as an attempt by rich net
food importing countries to secure their food supply by ‘rushing’ land at the expense of
vulnerable people in already food-insecure countries.

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THE OUTLINE OF THIS PRESENTATION
• Selected literature review
• Definition and Data on land rush
• The Empirical strategy
• Results
• Conclusion and recommendations

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SELECTED LITERATURE REVIEW

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LAND RUSH MAY BE A WIN-WIN DEAL
• Land rush can foster agricultural productivity and food
availability in the hosting countries through investment in
agricultural infrastructure and technology (Deininger and
Byerlee, 2011).
• Land rush can increase food accessibility as increased food
productivity can help creating farm and off-farm jobs, rising
household incomes and their ability to buy foods (Hallam, 2009;
Zoomer, 2010)

10. #2023 AGRODEP CONFERENCE
LAND RUSH MAY REDUCE FOOD SECURITY
• Land acquisition in low income countries is often driven by the need to
guarantee food security in the investors’ countries, and the increasing
demand for biofuel (Cotula et al., 2009; Praskova, 2012).
• land deals will displace small farmers, forcing them off their land to make
room for large scale farms producing foods for other
countries………….lost of main source of income and ….increase
vulnerability to food price shocks (Daniel, 2011; Praskova, 2012 ).

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DEFINITION AND DATA ON
LAND RUSH

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WHAT IS LAND RUSH
• Definition: All recorded transactions that entail a transfer to external investor of rights
to use, control or own land through the sale, lease or concession of 200 hectares.
• Source: Land Matrix (https://landmatrix.org)
• Coverage: 32 Sub-Saharan Africa countrie, over 2000-2018.

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VISUALIZING DATA
55%
3%
14% 12%
16%
0%
10%
20%
30%
40%
50%
60%
Eastern
Europe
Oceania Latin
America
and
Carribean
Asia Africa
Land rush by region
15%
56%
9%
20%
0%
10%
20%
30%
40%
50%
60%
Land for biofuel Land for food Land for non-
food
Land for other
use
Land rush by destination of use

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THE EMPIRICAL STRATEGY

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THE ECONOMETRIC MODEL
𝐹𝑆𝑖𝑡 = 𝛽0 + 𝛽1𝑅𝑈𝑆𝐻𝑖𝑡 + 𝛽2 𝑋𝑖𝑡 + 𝛼𝑖 + µ𝑡 + 𝜀𝑖𝑡 (1)
• 𝐹𝑆𝑖𝑡 is the food security variable and proxied by food production measured in millions
of tons and the proportion of undernourished people in percentage of the total
population.
• 𝑅𝑈𝑆𝐻𝑖𝑡 is the land rush variable which refers to the cumulated percentage of the total
land purchased (1/10000 of arable land) or leased by country 𝑖 at time 𝑡,
• Control variables: GDP per capita, financial development, trade openness, foreign aid
(ODA), remittances, fiscal balance, food price index), demographic factors (population
growth), climatic factors (rainfall), and institution quality (internal conflict, investment
profile, corruption and rule of law).

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IDENTIFICATION STRATEGY
• 𝐈𝐧𝐬𝐭𝐫𝐮𝐦𝐞𝐧𝐭𝐚𝐥 𝐯𝐚𝐫𝐢𝐚𝐛𝐥𝐞 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡: use of arable land per capita and
GDP per capita in the investor’s country of origin as instruments
(Arezki, Deininger and Selod, 2015; Collier and Venables, 2012;
Praskova, 2012 )
• Rationale: A high level of GDP per capita can increase the demand for
food, combined with au low level of arable land per capita, this will lead
to greater pressure on limited land resources.
• Robustness check using System GMM:

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RESULTS

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BASELINE FINDINGS: FIXED EFFECT ESTIMATES
Table 2: Effect of Land Rush on Food Security (Fixed effects)
Domestic Cereal Production Undernourished people
(1) (2) (3) (4) (5) (6) (7) (8)
Land Rush -0.0194 -0.0288** -0.0307** -0.0434** 0.2288*** 0.2287*** 0.3126*** 0.3045***
(0.0145) (0.0135) (0.0143) (0.0169) (0.0684) (0.0477) (0.0485) (0.0586)
Country fixed
effect Yes Yes Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes
Hausman test 55.59 41.95 34.52 45.70 28.11 18.06 47.32 276.38
Prob>chi2 0.0000 0.0001 0.0109 0.0022 0.0000 0.1137 0.0001 0.0000
Observations 577 458 458 449 440 356 356 349
Robust standard errors in parentheses. Standard errors in parentheses, *** p<0.01, **p<0.05, *** p<0.10.

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IV ESTIMATES: EXTERNAL INSTRUMENT
Table 4: Effect of Land Rush on Food Security (Instrumental variables estimates)
(1) (2)
Domestic Cereal Production Undernourished people
Land Rush -0.2963*** 0.2951***
(0.0625) (0.0799)
Country fixed effect Yes Yes
Year fixed effects Yes Yes
First stage
Arable Land (Invest) -169.03*** -169.03***
(17.476) (17.476)
GDP (Invest) 0.00017*** 0.00017***
(0.00004) (0.00004)
F-stat for weak ident. 120.68 120.68
Hansen test, p value 0.2147 0.2326
Observations 348 348
Robust standard errors in parentheses, *** p<0.01, **p<0.05, *** p<0.10.

20. #2023 AGRODEP CONFERENCE
(1) (2) (3) (4) (5) (6)
Domestic Cereal
Production
Domestic Cereal
Production
Domestic Cereal
Production
Undernourished people Undernourished people Undernourished people
Lag of the dependent
variable 0.65366*** 0.48180*** 0.12048*** 0.87179*** 0.88689*** 0.86856***
(0.12225) (0.07171) (0.03982) (0.02929) (0.09602) (0.02363)
Land_Rush -0.05955* -0.04723** -0.21164*** 0.10474** 0.11719** 0.10487**
(0.03214) (0.02265) (0.03817) (0.04805) (0.05223) (0.04990)
Observations 428 428 428 332 332 332
Number of countries 28 28 28 22 22 22
Number of intruments 20 31 26 20 33 22
AR(2) 0.36069 0.35995 0.53306 0.43189 0.72929 0.39302
Hansen test 0.67241 0.48263 0.39927 0.78763 0.97971 0.79257
IV ESTIMATES: SYSTEM GMM
Table 5: Effect of Land Rush on Food Security (System GMM
estimates)

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DOES LAND USE MATTERS (I)
Table 6: Effect of Land Rush on Domestic Production (Instrumental variables estimates)
Domestic Cereal Production
(1) (2) (3) (4)
Land_Food -0.3947***
(0.0907)
Land_Biofuel -27.2110
(29.7767)
Land_Mix -4.1296***
(0.9242)
Land_Other -1.7399***
(0.4082)
Country fixed effect Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes
First stage
Arable Land (Invest) -128.62*** -2.0041 -11.602*** -26.800***
(17.402) (2.3379) (1.6560) (4.1949)
GDP (Invest) 0.00011*** -8.03e-07 0.00001** 0.00004***
(0.00003) (0..000011) (6.33e-06) (0.000012)
F-stat for weak ident. 88.572 0.261 38.805 51.194
Hansen test, p value 29.94 0.53 26.74 23.27
Observations 348 348 348 348

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DOES LAND USE MATTERS (II)
Table 7: Effect of Land Rush on Undernourished people (Instrumental variables estimates)
Undernourished people
(1) (2) (3) (4)
Land_Food 0.3885***
(0.1101)
Land_Biofuel 22.4525
(25.0531)
Land_Mix 4.2486***
(1.2790)
Land_Other 1.8207***
(0.5189)
Country fixed effect Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes
First stage
Arable Land (Invest) -128.62*** -2.0041 -11.602*** -26.800***
(17.402) (2.3379) (1.6560) (4.1949)
GDP (Invest) 0.00011*** -8.03e-07 0.00001** 0.00004***
(0.00003) (0..000011) (6.33e-06) (0.000012)
F-stat for weak ident. 88.572 0.261 38.805 51.194
Hansen test, p value 0.2141 0.5256 0.3542 0.3973
Observations 348 348 348 348

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CONCLUSIONS

24. #2023 AGRODEP CONFERENCE
KEY FINDINGS
• Effect of a land rush on food security in 32 Sub- Saharan African countries over the
period 2000–2018.
• Land rush has not only had an adverse effect on cereal production but has also
increased malnutrition.
• Contrary to land acquisition for biofuel, land acquisition for food crops, mixed
production of biofuel and food crops and land for other uses contributes to food
insecurity in Sub-Saharan Africa through a decrease in cereal production and increased
malnutrition.

25. #2023 AGRODEP CONFERENCE
POLICY RECOMMENDATIONS
• Reform land acquisition agreements in order to promote investment and production of
crops for domestic consumption.
• Design appropriate incentives to encourage transfers of agricultural technology and
skills between international land investors and local farmers to boost long-term
agricultural productivity.
• Taxation of exported cash crops and biofuel produced from grabbed lands.

26. THANK YOU