## Investing in Climate Adaptation under Trade and Financing Constraints: Balanced Strategies for Food Security ## Source details **Canonical URL:** [Investing in Climate Adaptation under Trade and Financing Constraints: Balanced Strategies for Food Security](https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024184.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2024/english/wpiea2024184.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2024/english/wpiea2024184.pdf.json) --- ### Introduction — Motivation - Agriculture is one of the most vulnerable sectors to climate change and in many emerging and developing countries (EMDEs) is a prominent sector for economic growth and provides supports to livelihoods. - Agriculture still employs about 30 percent of low-middle-income countries’ labor forces (World Bank Data, accessed in July 2024). - In many lower-income countries, a considerable portion of agricultural output is devoted to subsistence consumption; subsistence consumption provides on average 58 percent of rural households’ calorie consumption in Africa (Sibhatu and Qaim, 2017). - Empirical evidence on climate-driven productivity loss: - Over the past 60 years, climate change was responsible for 20 percent of agricultural productivity loss globally, with the average loss reaching almost 30 percent in EMDEs (Ortiz-Bobea et al., 2021). - Under a high-emission scenario, agricultural productivity in EMDEs could decline by an additional 16 percent from baseline levels, by the 2080s (Cline, 2007). - Financing constraints and vulnerability: - More than half of low-income developing countries are currently in or at high risk of debt distress, and about one fifth of emerging markets have sovereign bonds trading at distressed level (IMF, 2023). - Trade and adaptation: - Trade in agricultural goods buffers domestic production shocks; higher trade barriers and export restrictions can undermine this adaptive role. - Principle of additionality: adaptation should not encompass primary development requirements; targeted adaptation capital aims to specifically reduce damages from climate change (Aligishiev et al., 2022). - Balanced, cost-efficient public-resource allocation between development and adaptation is essential, especially in lower-income countries with prevalent subsistence farming. ### Model overview — purpose, structure, and key questions - Purpose: provide a parsimonious, country-tailorable model to study cost-efficient adaptation strategies and identify investment levels ensuring food security under climate change. - Three key policy questions the model addresses: 1. What is a cost-efficient mix of climate adaptation and standard development investments to ensure food security? 2. What factors influence investment needs for agricultural adaptation? 3. What role does trade play in agricultural adaptation? - Core model structure and assumptions: - Two-sector economy (agriculture A, non-agriculture N) and two regions (Home Country HC and Rest-of-the-World ROW). - Armington differentiation of goods (imports/exports of differentiated goods). - Agricultural productivity declines with climate change; government invests in development capital and adaptation capital. - Resource-constrained government chooses investment mix to ensure food security. ### Model — detailed structure and equations (high-level) - International trade: - Two-country, two-sector setting with Armington differentiation; iceberg trade costs τ_{i,j,k} (τ_{i,j,k} > 1). - Households: - Population-weighted CRRA inter-temporal welfare: W = Σ_{t=0}^T β^t { L_{i,t} (U_{i,t})^{1−α} −1 }/(1−α). - Period utility (Stone-Geary): U_{i,t} = (c_{i,A,t} − a̅)^ω c_{i,N,t}^{1−ω}; subsistence a̅ defines minimum food consumption. - Budget: c_{i,A,t} P_{i,A,t} + c_{i,N,t} P_{i,N,t} = (1 − R_{i,w}) ( w_{i,A,t} * L_{i,A,t}/L_{i,t} + w_{i,N,t} * L_{i,N,t}/L_{i,t} ) + g_t. - Agricultural firms (HC): - Public capital composite: K_{1,t} + κ K_{2,t}, with 0 ≤ κ < 1 (1 − κ is productivity gap between adaptation and development capitals). - Climate damage: Ω_t = a * H_t^2 / (1 + b * K_{2,t}). - Production: Y_{1,1,A,t} + Y_{1,2,A,t} = (1 − Ω_t) * B_{1,A,t} * (K_{1,t} + κ K_{2,t})^ι X_{1,t}^γ L_{1,A,t}^{1−γ}. - Profit: Φ_{1,A,t} = p_{1,1,A,t} Y_{1,1,A,t} + p_{1,2,A,t} (Y_{1,2,A,t}/τ_{1,2,A}) − L_{1,A,t} w_{1,A,t} − X_{1,t} P_{1,N,t}. - ROW agricultural production (no explicit public investment or climate impacts in ROW): - Y_{2,2,A,t} + Y_{2,1,A,t} = B_{2,A,t} * X_{2,t}^γ L_{2,A,t}^{1−γ}. - Non-agricultural firms: - Production: Y_{i,i,N,t} + Y_{i,j,N,t} = B_{i,N,t} L_{i,N,t}. - Profit: Φ_{i,N,t} = p_{i,i,N,t} Y_{i,i,N,t} + p_{i,j,N,t} (Y_{i,j,N,t}/τ_{i,j,N}) − L_{i,N,t} w_{i,N,t}. - Non-agricultural output allocated to consumption, intermediate inputs X_{i,t}, and public investments I_{1,t}, I_{2,t}. - Government (HC): - Budget constraint: L_{1,t} g_t + I_{1,t} + I_{2,t} = R_{1,w} ( L_{1,A,t} w_{1,A,t} + L_{1,N,t} w_{1,N,t} ). - Capital accumulation: - K_{1,t+1} = I_{1,t} + (1 − δ_1) K_{1,t}. - K_{2,t+1} = I_{2,t} + (1 − δ_2) K_{2,t}. - Market clearing and labor distortions: - Agricultural aggregation (CES): ( Y_{i,i,A,t}^{(σ_A−1)/σ_A} + Y_{i,j,A,t}^{(σ_A−1)/σ_A} )^{σ_A/(σ_A−1)} = L_{i,t} c_{i,A,t}. - Non-agricultural aggregation: ( Y_{i,i,N,t}^{(σ_N−1)/σ_N} + Y_{i,j,N,t}^{(σ_N−1)/σ_N} )^{σ_N/(σ_N−1)} = L_{i,t} c_{i,N,t} + X_{i,t} + I_{1,t} + I_{2,t}. - Labor market clearing: L_{i,A,t} + L_{i,N,t} = L_{i,t}. - Labor mobility distortion θ_i (≤ 1): w_{i,A,t} = θ_i w_{i,N,t}. ### Calibration and country applications - Candidate country cases: Ghana, Egypt, Brazil — selected to reflect different development levels, food-security statuses, and climate vulnerabilities. - Country empirical highlights: - Ghana (2022): agriculture ≈ 20 percent of GDP and 40 percent of employment; agricultural production accounts for over 40 percent of export earnings; sector dominated by rain-fed smallholder farms. - Egypt: agriculture ≈ 10 percent of GDP and employs about 25 percent of labor force; heavy irrigation reliance on Nile River; one of world's largest wheat importers. - Brazil: traditional agriculture ≈ 7 percent of GDP; agribusiness ≈ almost 25 percent of GDP and about 50 percent of total exports; leading exporter of soybeans, coffee, sugar. - Common calibration parameters: - ω = 0.01; ι = 0.122; κ = 0.1; γ = 0.5; σ_A = 4.06; σ_N = 4.63; β = 0.985; α = 1.45. - Financing constraints set to be two percent of GDP for all three economies. - Climate damage calibration using Cline (2007) 3.3 degree warming (without adaptation): - Agricultural productivity loss: Ghana 19.8 percent, Egypt 30.9 percent, Brazil 28.7 percent. - Adaptation efficiency calibration (Agrawala, 2010): - Adaptation investment rate of 0.01 percent reduces climate-induced damage by 30 percent in Egypt and Ghana. - Investment rate of 0.005 percent reduces damage by 37 percent in Brazil. - Stylized calibration comparisons: - Ghana additional adaptation investment needs reach 0.24 percent of GDP by 2030 (model projection without financing constraint); comparable CCDR bottom-up estimate ≈ 0.3 percent of GDP annually by 2030. - Brazil broad agricultural adaptation investment needs (unconstrained) reach 0.31 percent of GDP by 2030 (model); Brazil CCDR assessment estimates 0.22 percent of GDP for additional land-use measures. ### Principal findings — cost-efficiency, trade, and constraints - Balanced investment strategy vs development-only under financing constraints: - With financing constraints, reallocating part of finance from development to adaptation improves cost-efficiency: agricultural and non-agricultural outputs return closer to no-climate-change baselines even though total investment is unchanged. - Underinvesting in adaptation requires a higher level of total investment to maintain food production and food security. - Excessive adaptation investment is wasteful because it diverts funds from development. - Quantitative cost benchmark: - The model costs adaptation investments at around 0.15 percent of GDP in 2030 for all three countries under two percent of total constraint for agriculture. - Financing constraint relaxation (Ghana example): - Relaxing financing constraints (extra concessional finance/donor support) allows higher investments in both development and adaptation. - Channeling funds into adaptation remains more cost-efficient than development-only investment. - Channeling extra funds into adaptation could reduce the total investment to GDP ratio by up to one percentage point compared to a development-only strategy. - One percent of GDP would be fourfold the annual optimal adaptation investment in 2030 without financing constraint, and sevenfold if constraint is present. - Role of trade: - Trade openness lowers required adaptation investment by enabling import diversification and buffering domestic shocks. - Under higher trade costs (trade fragmentation): - Food importers require larger domestic agricultural production, reducing non-agricultural output and consumption and increasing adaptation investment needs. - Food exporters face discouraged exports, output declines in both sectors, and slightly higher adaptation investment needs because trade’s buffering role weakens. - Global shocks that limit trade raise adaptation investment needs across countries and can push consumption below subsistence in extreme cases. - Structural and efficiency factors: - Higher agricultural TFP reduces both adaptation and development investment needs, primarily lowering development capital needs. - Higher adaptation efficiency lowers required adaptation investment and raises optimal development investment slightly. - Labor market distortions (θ_i ≤ 1) that impede labor mobility increase adaptation investment needs and offset trade benefits; their impact is larger when trade costs are higher. ### Comparative statics and sensitivity (selected results) - No financing constraints: higher expected climate damages → higher total investment concentrated in adaptation; development investment broadly stable. - Lower trade costs: smaller adaptation investment required even with higher expected damages because trade offsets consumption losses. - Higher agricultural productivity: reduces the need for both adaptation and development investment; when expecting higher climate damages, adaptation needs increase proportionally but higher productivity cushions the effect. - Higher adaptation efficiency: reduces adaptation investment needs; benefits magnify under trade fragmentation. - Labor-market distortions: greater distortions increase adaptation investment needs; reducing distortions lowers adaptation needs and amplifies trade benefits. ### Policy implications and recommendations - Use the model as a framework for cost-benefit analysis of agricultural adaptation investment to identify balanced strategies accounting for financing and trade constraints. - Investment strategy: - Targeted adaptation investments are essential to directly protect production from climate shocks but must be balanced with broader development investment needs under financing constraints. - Avoid underinvestment in adaptation (which requires substantially higher total investment) and avoid excessive adaptation that diverts essential development resources. - Trade policy: - Promote trade openness to complement adaptation by sharing production benefits globally and buffering domestic shocks, thereby reducing required adaptation investment. - Consider trade-offs: domestic agricultural support and exposure to global shocks can offset trade benefits. - Structural reforms (no additional spending required): - Strengthen institutions, government effectiveness, and climate public investment management. - Adapt green public financial management practices to make budget processes environment- and climate-sensitive. - Improve land use planning and regulation to increase adaptation investment efficiency. - Financing: - Mobilize concessional finance for climate adaptation, especially for vulnerable and less developed countries with high debt levels. - Co-benefits with mitigation: - Some adaptation investments (water management, land protection, resilient crops) can restore/expand carbon sinks, improve production efficiency, lower losses, and contribute to mitigation targets. ### Research gaps and suggested model extensions - Distributional analysis: model does not distinguish income groups — needs expansion to capture distributional impacts of climate damage, development investments, adaptation measures, and trade. - Fiscal sector: fiscal representation is highly stylized — richer fiscal modeling could improve analysis of fiscal responses to shocks and adaptation investments. - Trade disaggregation: subdividing ROW into specific regions/countries would aid calibration and understanding of trade–adaptation interplay. - Integrated assessment extension: expanding the model into a full integrated assessment model would allow investigation of co-benefits of agricultural adaptation for emission reductions. *Source: IMF Working Paper No. WP/2024/184 — sections "1. Introduction", "3. Model", and "4.3 Investing in Cost-Efficient Adaptation to Ensure Food Security" (excerpt).* ### 1. Introduction ........................................................................................................ ### 1. Introduction ### Motivation - Agriculture is one of the most vulnerable sectors to climate change and in many emerging and developing countries (EMDEs) is a prominent sector for economic growth and provides supports to livelihoods. - Agriculture still employs about 30 percent of low-middle-income countries’ labor forces (World Bank Data, accessed in July 2024). - In many lower-income countries, a considerable portion of agricultural output is devoted to subsistence consumption; subsistence consumption provides on average 58 percent of rural households’ calorie consumption in Africa (Sibhatu and Qaim, 2017). - Empirical evidence suggests anthropogenic climate change contributed to the loss of agricultural productivity: - Globally, over the past 60 years, climate change was responsible for 20 percent of agricultural productivity loss, with the average loss reaching almost 30 percent in EMDEs (Figure 1; Ortiz-Bobea et al., 2021). - Projections indicate that, under a high-emission scenario, agricultural productivity in EMDEs could decline by an additional 16 percent from baseline levels, by the 2080s (Cline, 2007). - Investment in agriculture is crucial for reducing damages caused by weather and climate-related shocks and for maintaining food security; investments in rural public goods (for example, expansion of water access and technology development) are key drivers of agricultural productivity growth worldwide (Goyal and Nash, 2017). - Intensified climate shocks require additional spending for adaptation (examples: climate-proof technologies, climate-resilient roads, irrigation systems in new areas). - Financing constraints—due to high debt levels, increasing borrowing costs, constrained access to international capital markets, and lower revenue mobilization capacities—severely hinder a country’s ability to invest in climate adaptation and sustainable development. - More than half of low-income developing countries are currently in or at high risk of debt distress, and about one fifth of emerging markets have sovereign bonds trading at distressed level (IMF, 2023). - Low-income countries with higher public debt often underinvest in agriculture and are highly vulnerable to climate change, requiring substantial adaptation investment. - Development and adaptation investments are closely interconnected: - As countries develop, adaptive capacities improve (stronger institutions, social services, infrastructure, technologies). - Successful adaptation helps shield development progress from climate-related disruptions. - Principle of additionality: adaptation should not encompass primary development requirements; targeted adaptation capital aims to specifically reduce damages from climate change and is considered additional to standard development needs (Aligishiev et al., 2022). - Trade plays a vital adaptive role: - Trade in agricultural goods acts as a buffer against domestic production shocks, contributing to adequate food supply and reducing adaptation investment needs. - Higher trade barriers and export restrictions can undermine the adaptive role of trade; trade openness can expose domestic farmers to global market fluctuations and trade policy changes. - Reliance on imports may negatively impact domestic farmers’ income and employment; exports can increase income. - Balanced, cost-efficient strategies are needed to allocate scarce public resources between development and adaptation, particularly in lower-income countries where subsistence farming is prevalent and vulnerabilities are higher. ### Overview of the Model - Purpose: Present a parsimonious, country-tailorable model to study balanced adaptation strategies and identify cost-efficient levels of investment for agricultural adaptation to ensure food security. - The model is designed to analyze three key questions: 1. What is a cost-efficient mix of climate adaptation and standard development investments to ensure food security for a country grappling with negative effects of climate change? 2. What factors influence the investment needs for agricultural adaptation? 3. What role does trade play in agricultural adaptation? - Key model structure and assumptions: - Two-sector economy: agricultural sector and non-agricultural sector. - Households have preferences defined over distinct final agricultural and non-agricultural goods. - Home country (HC) and rest of the world (ROW) trade both goods, differentiated by region of origin (Armington assumption). - Agricultural productivity declines as climate conditions change. - Government invests in two types of capital in agriculture: - Development capital (examples: machinery, infrastructure, knowledge exchange) enhances production. - Adaptation capital (examples: irrigation, resilient crops) mitigates climate change damages. - A resource-constrained government must balance adaptation and development investments to ensure food security. - Principal findings demonstrated in model applications to Ghana, Egypt, and Brazil: - With financing constraints, a balanced investment strategy ensures cost-efficiency of public investment in offsetting climate change damages: - Underinvesting in adaptation requires a higher level of total investment to maintain food production and food security. - Excessive adaptation investment is wasteful because it diverts funds from development. - Trade openness reduces adaptation investment needs, especially for food importers; for food exporters, higher trade costs mainly affect global food supply, and trade fragmentation increases food insecurity risks and adaptation investment needs. - Higher agriculture productivity, higher adaptation efficiency, and fewer labor market distortions reduce adaptation investment needs by improving productive allocation of capital; structural reforms are especially beneficial under trade fragmentation or larger climate shocks. ### Literature Basis and Model Positioning - The model builds on four literature strands: 1. Balancing development and adaptation investments (Agrawala et al., 2010; Millner and Dietz, 2014): optimal investment trajectories in neoclassical growth settings; typically do not include trade. 2. Costing investments to offset food consumption loss from climate change (Nelson et al., 2009; Narain et al., 2011; Sulser et al., 2021): bottom-up, partial equilibrium assessments coupling crop and hydrology models; estimate adaptation needs to achieve desired damage reduction but do not pursue investment optimality. 3. IMF Dynamic General Equilibrium models on macro-fiscal implications of adaptation investments (e.g., Marto et al., 2018): tailored to small open economies, include trade, financing constraints, and differentiate adaptation from development capital; some assume adaptation capital promotes development more effectively, which may not reflect the principle of additionality. 4. Trade and geographic shifts of agricultural comparative advantage under climate change (Costinot et al., 2016; Gouel and Labordeal., 2021; Nath, 2022): examine welfare implications and highlight international trade benefits in mitigating climate costs; adaptation strategies are not commonly incorporated. - Table 1 (Model Comparison) summarizes distinguishing features across representative models (Millner and Dietz, Narain et al., Marto et al., Nath), noting differences in whether models address optimality of investments, international trade, ensuring food security, financing constraints, and additionality. - The paper’s model is a neoclassical growth model with an explicit Ramsey problem to guide governments on efficient public finance allocation for adaptation and food security under climate change; it focuses on structural dynamics, financing and trade constraints, and the interaction among trade, agricultural production, and adaptation. *Source: IMF Working Paper — "1. Introduction" (excerpt).* ### 3. Model ### 3. Model ### International trade - Two-country (Home Country, HC = 1; Rest-of-the-World, ROW = 2) and two-sector (agriculture A, non-agriculture N) model. - Armington differentiation: goods produced in each sector by each economy are differentiated and both economies simultaneously import and export agricultural and non-agricultural goods. - Final goods notation: Y_{i,j,k,t} (sector k ∈ {A, N}, shipped from country i ∈ {1,2} to country j ∈ {1,2} at time t). - Iceberg trade costs: τ_{i,j,k} (τ_{i,j,k} > 1). One unit reaching overseas market requires τ_{i,j,k} units produced and shipped. - Iceberg costs capture multiple risk factors including one-off shocks, cyclical or persistent trends such as global economic fragmentation and climate change. ### Households - Representative household in country i of size L_{i,t} at time t. - Inter-temporal welfare: population-weighted CRRA over finite horizon T: - W = Σ_{t=0}^T β^t { L_{i,t} (U_{i,t})^{1−α} −1 }/(1−α). (Equation (1)) - Parameters: β is the discount factor; 1/α is elasticity of intertemporal substitution. - Period utility (Stone-Geary form) over per-capita non-agricultural consumption c_{i,N,t} and agricultural consumption c_{i,A,t}, with subsistence consumption a̅: - U_{i,t} = (c_{i,A,t} − a̅)^ω c_{i,N,t}^{1−ω}. (Equation (2)) - ω represents long-term Cobb-Douglas weight of agricultural consumption. - Budget constraint: - c_{i,A,t} P_{i,A,t} + c_{i,N,t} P_{i,N,t} = (1 − R_{i,w}) ( w_{i,A,t} * L_{i,A,t}/L_{i,t} + w_{i,N,t} * L_{i,N,t}/L_{i,t} ) + g_t. (Equation (3)) - Households earn sectoral wages w_{i,A,t}, w_{i,N,t}; provide labor L_{i,A,t}, L_{i,N,t}; pay income tax at rate R_{i,w}. - HC households receive per capita government transfer g_t. - Subsistence a̅ defines minimum food consumption and implies food security requirement does not depend on food prices or income in the model’s strict interpretation. ### Agricultural firms - Perfect competition in agricultural sectors in both countries. - Sales: domestic Y_{i,i,A,t} sold at p_{i,i,A,t}; exports Y_{i,j,A,t} sold at p_{i,j,A,t} subject to trade cost τ_{i,j,A}. - Labor inputs used by agricultural firms; in HC, firms also use non-labor inputs X_{1,t} and public capital (composite of standard development capital K_{1,t} and adaptation capital K_{2,t}). - Public capital composite: K_{1,t} + κ K_{2,t}, with 0 ≤ κ < 1; 1 − κ is productivity gap between adaptation and development capitals. - Climate damage: - Productivity of agricultural firm in HC B_{1,A,t} declines by fraction Ω_t due to climate change. - Damage function: Ω_t = a * H_t^2 / (1 + b * K_{2,t}). (Equation (4)) - Agricultural production in HC: - Y_{1,1,A,t} + Y_{1,2,A,t} = (1 − Ω_t) * B_{1,A,t} * (K_{1,t} + κ K_{2,t})^ι X_{1,t}^γ L_{1,A,t}^{1−γ}. (Equation (5)) - X_{i,t} are intermediate inputs from non-agriculture; γ is share of output allocated to private intermediate inputs. - Agricultural firm profit maximization (HC): - Φ_{1,A,t} = p_{1,1,A,t} Y_{1,1,A,t} + p_{1,2,A,t} (Y_{1,2,A,t}/τ_{1,2,A}) − L_{1,A,t} w_{1,A,t} − X_{1,t} P_{1,N,t}. (Equation (6)) - ROW agricultural production not subject to explicit climate impacts or public investments: - Y_{2,2,A,t} + Y_{2,1,A,t} = B_{2,A,t} * X_{2,t}^γ L_{2,A,t}^{1−γ}. (Equation (8)) - ROW profit: Φ_{2,A,t} = p_{2,2,A,t} Y_{2,2,A,t} + p_{2,1,A,t} (Y_{2,1,A,t}/τ_{2,1,A}) − L_{2,A,t} w_{2,A,t} − X_{2,t} P_{2,N,t}. (Equation (7)) - Climate impacts on trade are reflected implicitly via iceberg costs τ_{i,j,k} (greater climate impacts → higher trade costs). ### Non-agricultural firms - Perfect competition; simpler production using only labor. - Sales: domestic Y_{i,i,N,t} at p_{i,i,N,t}; exports Y_{i,j,N,t} at p_{i,j,N,t}. - Profit maximization: - Φ_{i,N,t} = p_{i,i,N,t} Y_{i,i,N,t} + p_{i,j,N,t} (Y_{i,j,N,t}/τ_{i,j,N}) − L_{i,N,t} w_{i,N,t}. (Equation (9)) - Production: - Y_{i,i,N,t} + Y_{i,j,N,t} = B_{i,N,t} L_{i,N,t}. (Equation (10)) - B_{i,N,t} is labor productivity in non-agriculture. - Non-agricultural outputs used for household consumption c_{i,N,t}, agricultural intermediate inputs X_{i,t}, and public investments I_{1,t}, I_{2,t} in HC. ### Government in HC - Benevolent government maximizes household welfare by choosing cost-efficient investment mix. - Government budget constraint: - L_{1,t} g_t + I_{1,t} + I_{2,t} = R_{1,w} ( L_{1,A,t} w_{1,A,t} + L_{1,N,t} w_{1,N,t} ). (Equation (11)) - Depreciation dynamics: - K_{1,t+1} = I_{1,t} + (1 − δ_1) K_{1,t}. (Equation (12)) - K_{2,t+1} = I_{2,t} + (1 − δ_2) K_{2,t}. (Equation (13)) - δ_1 and δ_2 are depreciation rates for development and adaptation capital, respectively; no strong guidance on relative magnitudes. ### Market-clearing conditions and labor distortion - Agricultural final goods aggregation (CES): - ( Y_{i,i,A,t}^{(σ_A−1)/σ_A} + Y_{i,j,A,t}^{(σ_A−1)/σ_A} )^{σ_A/(σ_A−1)} = L_{i,t} c_{i,A,t}. (Equation (14)) - σ_A is elasticity of substitution between agricultural products by origin. - Non-agricultural aggregation and uses: - ( Y_{i,i,N,t}^{(σ_N−1)/σ_N} + Y_{i,j,N,t}^{(σ_N−1)/σ_N} )^{σ_N/(σ_N−1)} = L_{i,t} c_{i,N,t} + X_{i,t} + I_{1,t} + I_{2,t}. (Equation (15)) - σ_N is elasticity of substitution between non-agricultural products by origin. - Labor market clearing: - L_{i,A,t} + L_{i,N,t} = L_{i,t}. (Equation (16)) - Labor mobility distortion θ_i (≤ 1) reflecting barriers to labor mobility (regulatory, legal, skill mismatches): - w_{i,A,t} = θ_i w_{i,N,t}. (Equation (17)) ### Model application to country cases - Candidate country applications: Ghana, Egypt, Brazil. Differences: development levels, food security, vulnerability to agricultural climate impacts. - Empirical context highlights: - Ghana (2022): agricultural sector accounts for 20 percent of GDP and 40 percent of employment; agricultural production accounts for over 40 percent of export earnings; sector dominated by rain-fed smallholder farms; projected crop yield declines and contraction of cocoa-suitable areas. - Egypt: agricultural sector ≈ 10 percent of GDP and employs about 25 percent of labor force; heavy irrigation reliance on Nile River; vulnerable to water availability shocks and sea level rise; one of world's largest wheat importers. - Brazil: traditional agriculture ≈ 7 percent of GDP; agribusiness ≈ almost 25 percent of GDP and about 50 percent of total exports; leading exporter of soybeans, coffee, sugar; projected production decreases under RCP8.5 (examples: soybean up to about 40 percent, corn up to about 30 percent, sugar cane up to about 10 percent in 2050 per Zilli et al., 2020). ### Model calibration (key elements and parameter choices) - Common parameters across country calibrations: - ω = 0.01 (long-run Cobb-Douglas weight of agricultural consumption). - ι = 0.122 (output elasticity for public capital in agricultural production). - κ = 0.1 (discount factor in total public capital composite). - γ = 0.5 (output share to non-agricultural intermediate inputs). - Armington elasticities: σ_A = 4.06, σ_N = 4.63 (Tombe, 2015). - Discount factor β = 0.985; CRRA parameter α = 1.45 (Table 2). - Country-specific calibrations/assumptions: - Non-agricultural labor productivities B_{1,N,t} for HC adopted from World Bank value-added per worker (country specific). - Labor ratios between HC and ROW based on 2015 data (start of simulation). - Labor market distortion θ set equal to ratio between value-added per worker of agriculture, forestry, and fishing sector and that of industry in base year. - Financing constraints set to be two percent of GDP for all three economies. - Climate damage calibration: - Uses country-specific central projections from Cline (2007); parameter a in damage function calibrated accordingly. - Cline (2007) results for 3.3 degree warming (without adaptation): agricultural productivity loss by 19.8 percent (Ghana), 30.9 percent (Egypt), and 28.7 percent (Brazil). - Adaptation efficiency parameter b calibrated based on Agrawala (2010): adaptation investment rate of 0.01 percent reduces climate-induced damage by 30 percent in Egypt and Ghana; investment rate of 0.005 percent reduces damage by 37 percent in Brazil. - Parameters jointly calibrated to match targets: labor share in agriculture; agricultural output share of GDP; agricultural imports share of total; agricultural exports share of total; export-to-GDP ratio; food expenditure share of total; imported goods share of total consumption. - Climate change impacts in ROW are not parameterized explicitly but incorporated into trade costs τ_{i,j,k}. - Stylized calibration comparisons: - Model projection (without financing constraint) finds Ghana’s additional adaptation investment needs reach 0.24 percent of GDP by 2030; comparable CCDR bottom-up estimate ≈ 0.3 percent of GDP annually by 2030. - Model projection for Brazil’s broad agricultural adaptation investment needs (unconstrained) reach 0.31 percent of GDP by 2030; Brazil CCDR assessment estimates 0.22 percent of GDP for additional land-use measures. *Italic source: IMF Working Paper — Section 3 ("Model") from wpiea2024184* ### 4.3 Investing in Cost- Efficient Adaptation to Ensure Food Security ### 4.3 Investing in Cost- Efficient Adaptation to Ensure Food Security ### Adaptation and Development Investments - Objective: Find a cost-efficient mix of adaptation and development investments under financing constraints by contrasting: - Development-only investment strategy (policy option 1): investing only in standard development capital. - Balanced investment strategy (policy option 2): investing in both development and adaptation capitals. - Counterfactual baseline: “no climate change” scenario — only policy option 1 is relevant. - Key model outcomes (percentage deviations from respective “no climate change” baselines): - Under development-only strategy (policy option 1) with financing constraints: - Countries cannot fund more development investments to counteract agricultural damages (dashed orange lines in Figures 7-9). - Agricultural outputs decline, total outputs decline, and agricultural net imports increase (references to Figures 7–9). - Under balanced strategy (policy option 2) with financing constraints: - Reallocating part of finance from standard development to adaptation (solid blue lines in Figures 7–9) allows agricultural and non-agricultural outputs to return closer to no-climate-change baselines. - Total investments remain unchanged due to binding constraints, yet investing in adaptation raises overall cost-efficiency of public investments. - Quantitative cost benchmark from the model: - The model costs adaptation investments at around 0.15 percent of GDP in 2030 for all three countries under two percent of total constraint for agriculture. - The optimality result implies excessive adaptation investment would be wasteful as it diverts resources needed for development. - Country-specific illustrative estimates cited: - Ghana CCDR: between 2022 and 2030, USD 2.7 billion needed for climate-smart agriculture and expansion of irrigation, equivalent to about 0.3 percent of GDP in the same period. - Brazil CCDR: R$124.8 billion needed in total between 2022 and 2030 for pasture recovery, plantation, forestry, and natural forest restoration, equivalent to about 0.2 percent of GDP in the same period. - Robustness to productivity gap parameter κ: - A higher κ increases the optimal level of investment in adaptation, reduces development investment needs, and improves overall cost-efficiency compared to the “development-only” strategy. ### Financing Constraint Relaxation: Example from Ghana - If financing constraints are relaxed (e.g., extra concessional finance/donor support): - Ghana could amplify investments in both development and adaptation. - Channeling funds into adaptation remains more cost-efficient than development-only investment. - By investing in adaptation, the total investment to GDP ratio could reduce by up to one percentage point, compared to a development-only strategy (Figure 10c). - Contextual magnitudes: - One percent of GDP would be fourfold the annual optimal adaptation investment in 2030 without financing constraint, and sevenfold if constraint is present. ### Adaptation Investment and Trade - Trade openness lowers required adaptation investment by enabling diversification to cope with domestic climate shocks and by allowing food imports/exports to alleviate production shocks. - Effects of trade costs: - Under more constrained trade (higher trade costs), countries reliant on food imports require larger domestic agricultural production, which reduces non-agricultural output and consumption (food and non-food), and raises adaptation investment needs. - Egypt (model simulations): - Increasing trade costs by 1.5 times (orange lines in Figure 11) lowers imports, is compensated by expanding agriculture output at the expense of non-agriculture production (Figure 11a and 11b). - Higher trade cost prompts slightly more investment in adaptation even with binding financing constraint (Figure 11c). - Contribution from imports to food consumption is significantly smaller when trade cost is higher (Figure 11d). - Brazil (food exporter): - Higher trade costs discourage exports (Figure 12d), leading to output decline in both agricultural and non-agricultural sectors (Figure 12a and 12b). - Higher trade costs also discourage imports, reducing trade’s buffering role; adaptation investment needs slightly increase despite binding financing constraint (Figure 12c). - Global shocks scenario: - If climate change impacts are global, trade becomes limited as countries prioritize domestic food needs, weakening trade’s adaptive role and necessitating larger adaptation investments across countries. - This is especially critical where consumption could drop below subsistence levels and relying on imports becomes less feasible. - Comparative static exercises (Egypt case) show optimal policy pathways ensure households’ minimal food consumption needs are met in the most cost-efficient manner. ### Comparative Statics and Sensitivity Results - Climate Shocks and Trade: - With no financing constraints (allowing investment to adjust freely), higher expected climate damages require higher total investment to offset losses, with the increase concentrated in adaptation investment while development investment stays broadly the same (reference to Figure 13). - If trade is less constrained (lower trade costs), countries require smaller adaptation investment even when expected damages are higher because trade effectively offsets food consumption losses (Figure 14). - Agriculture Productivity: - Higher agricultural TFP reduces both adaptation and development investment needs. - The effect primarily reduces development capital needs (Figure 15). - When expecting higher climate damages, required adaptation investment increases proportionally; however, higher agricultural productivity can allow production to withstand higher damage without requiring higher adaptation investment (Figure 16). - Adaptation Efficiency: - Higher adaptation efficiency lowers the required adaptation investment to offset climate impacts (Figure 17). - Optimal development investment level increases slightly because adaptation capital better protects productivity. - Benefits of more efficient adaptation increase proportionally as trade becomes more constrained (Figure 18). - Labor Market Distortions: - Labor market distortions capture obstacles to labor mobility from agriculture to non-agriculture, lack of skills, and direct government support/subsidies to agriculture. - Such distortions cause capital and labor misallocation and lower overall consumption. - Larger agricultural support (reflected in distortions) increases adaptation investment needs and offsets benefits of trade (especially for food importers). - The impact of distortions on adaptation investment is larger when trade costs are higher (Figures 19 and 20). - Reducing labor market distortions (e.g., regulatory reforms, upskilling) reduces adaptation investment needs and amplifies benefits from trade openness. ### Key Policy Implications and Recommendations - General: - Use the model as a framework for cost-benefit analysis of agricultural adaptation investment to identify balanced strategies accounting for financing and trade constraints. - Investment strategy: - Targeted adaptation investments are essential to directly protect production from climate shocks but must be balanced with broader development investment needs, particularly under financing constraints. - Inadequate adaptation requires compensating with much higher standard development investment; excessive adaptation diverts funds from essential development. - Trade policy: - Promote trade openness; international trade can complement adaptation by sharing production benefits globally and buffering domestic shocks for food importers, thereby reducing adaptation investment needs. - Consider trade-offs: domestic agricultural support and exposure to global shocks may offset trade benefits. - Structural reforms (no additional spending required): - Strengthen institutional framework, government effectiveness, and climate public investment management. - Adapt green public financial management (PFM) practices to make budget processes environment- and climate-sensitive. - Improve land use planning and regulation to increase adaptation investment efficiency. - Financing: - Mobilize concessional finance for climate adaptation, especially for vulnerable and less developed countries facing high debt levels. - Co-benefits with mitigation: - Some adaptation investments (water management, land protection, resilient crops) can restore/expand carbon sinks, improve production efficiency, lower losses, and contribute to mitigation targets. - Research gaps and future work: - Model does not distinguish income groups — needs expansion to capture distributional impacts of climate damage, development investments, adaptation measures, and trade. - Fiscal sector representation is highly stylized — enhancing it could improve analysis of fiscal responses to shocks and adaptation investments. - Subdividing the ROW (rest of world) into specific regions/countries would aid calibration and understanding of trade–adaptation interplay. - Expanding the model into a full integrated assessment model would allow investigation of co-benefits of agricultural adaptation for emission reductions. *Source: IMF working paper chapter "4.3 Investing in Cost- Efficient Adaptation to Ensure Food Security" (wpiea2024184).* ### 8. Costinot, Arnaud, Dave Donaldson, and Cory Smith. "Evolving comparative advantage and the impact ### Investing in Climate Adaptation under Trade and Financing Constraints: Balanced Strategies for Food Security ### Document identification - Working Paper No. WP/2024/184 ### Selected bibliographic entries cited in this content unit (entries 8–36) - 8. Costinot, Arnaud, Dave Donaldson, and Cory Smith. "Evolving comparative advantage and the impact of climate change in agricultural markets: Evidence from 1.7 million fields around the world." Journal of Political Economy 124, no. 1 (2016): 205-248. - 9. Chen, Chen, Ian Noble, Jessica Hellmann, Joyce Coffee, Martin Murillo, and Nitesh Chawla. “University of Notre Dame global adaptation index: Technical Report”. University of Notre Dame. Notre Dame, Indiana. 2015. - 10. Echevarria, Cristina. “Changes in Sectoral Composition Associated with Economic Growth”. International Economic Review. Vol.38 (2) (1997): 431-452 - 11. Gouel, Christophe, and David Laborde. “The crucial role of international trade in adaptation to climate change”. No. w25221. National Bureau of Economic Research. 2018. - 12. Gaupp, Franziska, Jim Hall, Stefan Hochrainer-Stigler, and Simon Dadson. "Changing risks of simultaneous global breadbasket failure." Nature Climate Change 10, no. 1 (2020): 54-57. - 13. Goyal, Aparajita, and John Nash. “Reaping richer returns: Public spending priorities for African agriculture productivity growth.” World Bank Publications. 2017. - 14. Gouel, Christophe, and David Laborde. “The Crucial Role of Domestic and International Market-Mediated Adaptation to Climate Change.” Journal of Environmental Economics and Management 106 (2021): 102408 - 15. Herrendorf, Berthold, Richard Rogerson, and Akos Valentinyi. "Growth and structural transformation." Handbook of Economic Growth 2 (2014): 855-941. - 16. Herrendorf, Berthold, Richard Rogerson, and Akos Valentinyi. "Two perspectives on preferences and structural transformation." American Economic Review 103, no. 7 (2013): 2752-2789. - 17. IMF Blog “Global Debt Is Returning to its Rising Trend” 2023 https://www.imf.org/en/Blogs/Articles/2023/09/13/global-debt-is-returning-to-its-rising-trend - 18. Kongsamut, Piyabha, Sergio Rebelo, and Danyang Xie. “Beyond Balanced Growth”. The Review of Economic Studies. Vol. 68, No. 4 (2001): 869-882 - 19. Marto, Ricardo, Chris Papageorgiou, and Vladimir Klyuev. "Building resilience to natural disasters: An application to small developing states." Journal of Development Economics 135 (2018): 574-586. - 20. Millner, Antony, and Simon Dietz. "Adaptation to climate change and economic growth in developing countries." Environment and Development Economics 20, no. 3 (2015): 380-406. - 21. Narain, Urvashi, Sergio Margulis, and Timothy Essam "Estimating costs of adaptation to climate change." Climate Policy 11(3) (2011): 1001-1019. - 22. Nath, Ishan. "Climate Change, the Food Problem, and the Challenge of Adaptation through Sectoral Reallocation." Conference papers 333404, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project. 2022. - 23. Nelson, Gerald C., Mark W. Rosegrant, Jawoo Koo, Richard Robertson, Timothy Sulser, Tingju Zhu, Claudia Ringler et al. “Climate change: Impact on agriculture and costs of adaptation.” Vol. 21. International Food Policy Res Institute. 2009. - 24. Ortiz-Bobea, Ariel, Toby R. Ault, Carlos M. Carrillo, Robert G. Chambers and David B. Lobell “Anthropogenic climate change has slowed global agricultural productivity growth.” Nature Climate Change. Vol 11 (2021): 306-312. - 25. Sibhatu, Kibrom T. and Matin Qaim. "Rural food security, subsistence agriculture, and seasonality." PloS one 12.10 (2017): e0186406. - 26. Simonovska, Ina, and Michael E. Waugh. "The elasticity of trade: Estimates and evidence." Journal of international Economics 92, no. 1 (2014): 34-50. - 27. Sulser, Timothy B., Keith Wiebe, Shahnila Dunston, Nicola Cenacchi, Alejandro Nin-Pratt, Daniel Mason-D’croz, Richard Robertson, Dirk Willenbockel, And Mark W. Rosegrant. “Climate Change and Hunger: Estimating Costs of Adaptation in the Agrifood System.” International Food Policy Research Institute, Food Policy Report (2021) - 28. The Economist Group “Global Food Security Index 2019” https://impact.economist.com/sustainability/project/food-security-index - 29. The Sustainable Development Report, 2022 https://www.sustainabledevelopment.report/reports/sustainable-development-report-2022/ - 30. Tombe, Trevor. "The missing food problem: Trade, agriculture, and international productivity differences." American Economic Journal: Macroeconomics 7, no. 3 (2015): 226-258. - 31. United Nations Environment Programme “Adaptation Gap Report 2023: Underfinanced. Underprepared. Inadequate investment and planning on climate adaptation leaves world exposed”. Nairobi, Kenya, 2023. - 32. USAID Climate Change Risk Profile: Ghana, 2017 https://www.climatelinks.org/sites/default/files/asset/document/2017_USAID_Climate%20Change%20Risk%20Profile%20-%20Ghana.pdf - 33. World Bank Group Country Climate and Development Report for Ghana, 2022 https://openknowledge.worldbank.org/server/api/core/bitstreams/9c9764c1-076d-5dcc-8339-6e4f0de2b610/content - 34. World Bank Group Country Climate and Development Report for Brazil, 2023 https://openknowledge.worldbank.org/server/api/core/bitstreams/fd36997e-3890-456b-b6f0-d0cee5fc191e/content - 35. Uy, Timothy, Kei-Mu Yi, and Jing Zhang. "Structural change in an open economy." Journal of Monetary Economics 60, no. 6 (2013): 667-682. - 36. Zilli, Marcia, Marluce Scarabello, Aline C. Soterroni, Hugo Valin, Aline Mosnier, David Leclere, Petr Havlik, Florian Kraxner, Mauricio Antonio Lopes, and Fernando M. Ramos. "The Impact of Climate Change on Brazil's Agriculture." Science of the Total Environment 740 (2020): 139384. ### Thematic scope indicated by citations - Climate change impacts on agriculture and agricultural productivity (entries 8, 12, 24, 36). - Costs and methods of adaptation to climate change, including estimating adaptation costs and investment needs (entries 9, 21, 27, 31). - Role of trade and market-mediated adaptation in responding to climate shocks and food security (entries 11, 14, 30). - Structural transformation, sectoral composition, and implications for growth and adaptation (entries 10, 15, 16, 18, 35). - Food security measurement and indices, and country-level climate and development reporting (entries 28, 29, 32–34). - Policy and public spending priorities for agricultural productivity and resilience (entries 13, 19, 22, 23, 25). *Source: Investing in Climate Adaptation under Trade and Financing Constraints: Balanced Strategies for Food Security — Working Paper No. WP/2024/184* --- _Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024184.pdf_