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Research Article
Open Access Peer-reviewed

How to Improve World Food Supply Stability under Future Uncertainty: Potential Role of WTO Regulation on Export Restrictions in Rice

Tetsuji Tanaka , Baris Karapinar
Journal of Food Security. 2019, 7(4), 129-150. DOI: 10.12691/jfs-7-4-5
Received June 14, 2019; Revised July 20, 2019; Accepted August 27, 2019

Abstract

In recent years, international grain markets have been exposed to considerable price volatility which was partly caused by supply shocks driven by extreme climate events affecting major grain exporters. In addition, a number of exporting countries resorted to distortive trade measures in the form of export restrictions which have led to additional shortages, undermining the reliability of the world trading system. Recent climate studies suggest that climate change-induced extreme events are likely to increase yield fluctuations. As trade volumes are also projected to increase, export restrictions constitute a systemic threat to the security of the global food supply. However, WTO rules and regulations on export restrictions are lenient, offering ample ‘policy space’ to member countries. In this context, this paper explores the potential welfare implications of productivity shocks and consequent export restrictions imposed on rice. We use a world trade stochastic computable general equilibrium (CGE) model with the Monte Carlo method, taking into account risk factors in the form of a wide range of productivity shocks to world rice supplies. Our findings suggest that welfare losses that are likely to be caused by increased yield variability, due to climate change or other factors, are expected to grow substantially if countries react to productivity shocks by imposing export restrictions. Losses incurred by rice importing countries in Asia and Africa are expected to be particularly high. The paper links these results to potential WTO reform initiatives aiming at improving world food supply stability under future uncertainty.

1. Introduction

International grain markets have been exposed to considerable price volatility in recent years. The underlying cyclical and structural causes and the effectiveness of policy measures that aim to stabilize prices have been at the top of the research and policy agenda 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14. Continued price fluctuations were partly caused by supply volatility driven by extreme climate events which have affected major grain exporters such as Ukraine, Australia, Russia, Thailand and the United States (US) 15. In addition, a number of exporting countries resorted to distortive trade measures in the form of export restrictions which have led to additional shortages, while also undermining the reliability of the world trading system 16, 17, 18, 19, 20.

By imposing export restrictions, countries intend to insulate their markets from external price fluctuations, but doing so often proves to be counterproductive 9, 19, 21. As has been observed in recent years, while exporting countries followed each other’s lead in reacting to price hikes by imposing restrictive measures, importing countries simultaneously reduced their applied tariffs 19. As a result, the insulating effect of export restrictions was offset by increased international prices and higher volatility 20. Hence there seems to be a collective action problem resulting in substantial distortion of commodity markets.

However, international trade rules and regulations, defined by the World Trade Organization (WTO), where such collective action problems could be prevented, are lenient about export restrictions 11, 16, 18, 19, 22, 23. WTO law offers ample ‘policy space’ for its members to institute export quotas if applied ‘temporarily’ to prevent or relieve a ‘critical shortage’ of ‘essential’ commodities like foodstuffs. However, it does not define the trigger mechanisms (i.e. what constitutes reaching the stage of ‘critical shortage’) or the legal boundaries of the legitimate scope and duration of such measures 18. Moreover, WTO law is also almost silent on export duties, leaving this area, which is a growing source of trade distortion, unregulated or under-regulated. It allows Members to impose export duties on any commodity at any time 18, 22, 23.{1}

This is particularly problematic in light of the potential impacts of climate change, which are likely to aggravate price hikes and volatility. Recent model simulations suggest that by 2050, climate change might result in additional price increases ranging from 30–37% for rice and 52–55% for maize, to 94-111% for wheat 24, 25, 26. The frequency and intensity of extreme events, which will damage the world’s food supply chains, are expected to increase too 25, 27. Migration of production to areas of the world which suffer higher yield variability might also lead to a surge in productivity volatility 28, 29, 30, 31, 32. In turn, countries may react to productivity shocks by instituting export restrictions 33.

At the same time, as a result of climate change, developing regions are predicted to increase their imports of grains substantially. For example, based on the CSIRO climate model (see footnote 3), South Asia, which exported around 15 million metric tons (mt) of cereals in 2000, is projected to import up to 54 million mt by 2050; the Middle East, North Africa and sub-Saharan Africa, which are already net importers of cereals, are expected to increase the volumes of cereals they import by around 30% 24. As countries rely more on trade under the impact of climate change, export restrictions constitute a major systemic threat to the reliability of the world trading system 34, 35, 36, 37, 38.

In this context, this article explores the potential welfare implications of productivity shocks and consequent export restrictions imposed on rice.{2} We use a computable general equilibrium (CGE) model with a Monte Carlo simulation. Our method provides a comprehensive framework to analyse international rice markets under uncertainty. We take into account risk factors in the form of a wide range of productivity shocks to world rice supplies.

We simulate export restrictions on rice imposed by rice exporters in reaction to domestic productivity shocks. We explore how these shocks and consequent policy measures might affect domestic and international prices and trade flows in the rice sector. We also explore the implications of various alternatives for potential WTO reform aiming at maintaining sufficient domestic policy autonomy for most Members of the WTO while limiting the global welfare losses caused by export restrictions. Seeking such ‘optimality’, we explore the implications of (i) clearly defined trigger mechanisms and (ii) targeted differentiation of regulatory disciplines for export restrictions, whereby major exporters (defined by market share) react to the same productivity shocks by imposing lower levels of restrictions than minor exporters. By using a CGE model, we can depict the welfare elasticity of various disciplines that are differentiated at the supplier level.

This article is structured as follows. We first explain our world trade CGE model and simulation scenarios. Then, we present simulation results. In the last section we draw conclusions and discuss how our findings could inform the policy debate on the role of the WTO in improving global food security.

2. Structure of the Stochastic CGE Model

The world-scale stochastic computable general equilibrium (CGE) model by Tanaka and Hosoe 39 (also 13, 37, which is constructed based on the single-country CGE model by Devarajan et al. 40 is employed in the present research with the 2007 global social accounting matrices (SAM) composed of the Global Trade Analysis Project (GTAP) database version 8. The regional aggregation is made for rice producing, exporting, and importing countries. Each region has 12 sectors, and five factors of production (Table 1).

Each sector is represented by a perfectly competitive profit-maximizing firm with a Leontief production function for gross output and with a constant elasticity of substitution (CES) production function for value-added components (Figure 1). We quoted the elasticity of substitution for factors of production from the GTAP database, assuming 0.25 for agricultural sectors (paddy rice, wheat, other grains and other agriculture).{3} Assuming relatively short-term and uncertain situations under which farming sectors cannot fully respond to unexpected positive or negative productivity shocks, only unskilled labor is mobile across sectors, but not internationally. Other factors (skilled labor, capital, farmland, and natural resources) are immobile between sectors and between regions. The primary factors are fully employed.

Sectoral gross outputs are split into domestic outputs and composite exports using a constant elasticity of transformation (CET) function. The domestic goods and composite imports are aggregated into composite goods using a CES function as assumed by Armington 41. The composite imports consist of imports from various regions, and the composite exports are decomposed into exports to various regions. For these CES/CET functions we use the elasticity of substitution as suggested in the GTAP database. Only in the rice stock scenarios are rice reserves released in the domestic markets.

The elasticity of substitution represents the similarity of goods differentiated by the origin and destination of trade. For example, the elasticity of substitution between the domestic goods and the composite imports is assumed to be 5.05 for paddy rice and 2.60 for processed rice.{4} Although we do not explicitly control for the different types of rice grains in the model, the nested CES structure approximately reflects the preferences of countries. Share parameters in the CES functions are calibrated to reproduce the actual trade flows of rice. Exchange rates are flexibly adjusted so that the current account balance remains constant in US dollar terms in all regions. The saving-driven investment is adopted as a model closure.

Composite goods are used for consumption by the representative household, as well as for government, investment, and intermediate input. Food commodities are aggregated to make food composite, which contributes to utility with non-food items (Figure 2). This structure describes substitution among foods in household consumption with a CES function, which gives flexibility to our assumptions about the price elasticity of demand of food. Following Tanaka and Hosoe 39, we assume that its elasticity of substitution is 0.1. If the commodity is non-food, it directly influences utility.

3. Simulation Scenarios

We conduct comparative static analyses considering the following scenario factors: (i) fluctuations of productivity in the paddy rice sector; (ii) non-differentiated export duties and quotas imposed by rice exporting countries; (iii) differentiated export duties and quotas imposed by rice exporting countries; (iv) partial abolition of trade barriers by rice importing countries.

  • Table 2. Simulation scenarios(Notes: Diff.=Differentiated, Non-diff=Non-differentiated, SD = standard deviation, DQ = Differentiated quantitative restrictions, NQ = Non-differentiated quantitative restrictions, Dduty = Differentiated duty, Nduty = Non-differentiated duty, _15% = Price trigger of 15%, _25% = Price trigger of 25%, _N: New reference equilibrium, 2xSD = standard deviation twice as observed. Example: DQ25-50_15% = Differentiated quantitative restrictions applied at 25% for major exporter, 50% by minor exporters, both responding to the price trigger of 15%.)

We set up 45 scenarios to determine the extent to which global welfare is affected by those scenario factors identified in Table 2.

Scenario factor 1: Productivity shocks

We assume that productivity shocks occur randomly to the total factor productivity parameter of the gross output production function in paddy rice sector, following the independent identically distributed normal distribution for region {5} We measure the productivity of paddy rice sector as production per acre of harvested area, and estimate the standard deviations of the productivity of these 14 regions with time series data for 21 years (1990–2010) provided by the FAOSTAT, removing effect of time trend on productivity of each region by simple OLS regression (Figure 3). We simulate 1000 Monte Carlo draws for each scenario. Among our 1000 draws, Uruguay shows the largest standard deviation of productivity, followed by Pakistan and the Philippines.

We then conduct simulations with the standard deviations for paddy rice productivity shocks that are twice as large as the observed productivity standard deviations, in order to account for future uncertainty, which might lead to increased productivity fluctuations due to climate change and other factors. The literature on the potential impacts of climate change on future rice productivity projects significant impacts that may cause substantial productivity volatility due to temperature and water stress and extreme climate events such as droughts and flooding 25. In this context, we consider scenarios based on a larger standard deviation than recently observed in order to take into account the potential implications of future uncertainty in general.

Scenario factor 2: Non-differentiated export quotas and duties

We explore the implications of two price triggers combined with various levels of export quotas and duties. As it would be arbitrary to test for a particular trigger or a restriction level, our objective is to illustrate the potential direction and the extent of the welfare implications of alternative triggers and a range of export restrictions. We use two price triggers, namely the Trigger 15% and the Trigger 25%, which would allow a country to institute export restrictions when the domestic price of processed rice exceeds the reference price by 15% and 25%, respectively.

Then we test for the implications of export quotas and export duties applied at identical levels (e.g. non-differentiated) by all exporting countries where the price of rice has reached the trigger percentage. Accordingly, we consider two types of restrictions with two application levels each, namely quantitative restrictions applied at 50% and 95%, and export duties applied at 50% and 100%, respectively. {6}

Scenario factor 3: Differentiated export duties and quotas

We also analyse the implications of ‘differentiated’ restrictions, where major exporters (defined by market share) react to the same price triggers by imposing lower levels of restrictions than minor exporters (or importers).{7} In order to identify the difference between differentiated and non-differentiated restrictions, we first test for quantitative restrictions imposed at 25% by major exporters and at 50% for minor exporters, and secondly at 50% for major exporters and at 95% for minor exporters, respectively. As for export duties, we first take the scenario whereby export duties are imposed at 25% by major exporters and at 50% by minor exporters and, secondly, at 50% by major exporters and at 100% by minor exporters, respectively. In total, four export quota and four export duty scenarios are tested (Table 3).

Scenario factor 4: New reference equilibrium with lower trade barriers

In designing scenarios for the analysis of export restrictions, we also reconsidered the reference equilibrium, which describes the status quo. If we simulate export restrictions based simply on the reference equilibrium that is characterized by relatively ‘thin’ rice markets, {8} it is obvious that the impact of export restrictions will be relatively small. In reality, the damage from export restrictions is serious when importing countries lower their tariff barriers (as they have done in recent years) and start substituting imported rice for domestic rice under a freer rice-trade regime. This new situation may imply a reduction in the domestic production capacity of the importing countries as a result of the reallocation of factors (particularly capital) away from the rice sector in the medium and long run. We simulated this situation by assuming a partial (50%) rice trade liberalization with inter-sectoral mobility of all the factors and defining a new reference equilibrium.

4. Simulation Results

We simulate random productivity shocks and various policies and quantify the welfare implications of various forms of export restrictions and exporters’ stocks. The simulation results are summarized in Table 4.

4.1. Productivity Shocks

We assume that productivity shocks are generated randomly to the total factor productivity parameter of the gross output production function in paddy rice sector, following the independent identically distributed normal distribution. Testing for the original (observed) standard deviation of productivity, we find that productivity shocks lead to a decrease in the mean global welfare by around US$ 195 million (min −5238, max 4287). Importing countries in Asia (including, China, India, the Philippines and rest of Asia) account for significant losses, amounting to US$ 131 million. However, for other major regions, the amount of productivity-shock-induced losses is not substantial. This implies that world rice supplies are likely to be relatively stable under the impact of supply shocks (other things being equal), given the level of productivity variability observed over the past 20 years.

However if the world’s yield variability increases in the future due to climate change or other factors such as the migration of production to areas of the world which suffer higher yield variability, importing countries are likely to incur substantial welfare losses. When we test for a scenario of standard deviation of productivity being twice as big as that observed, mean global welfare losses triple to approximately US$ 751 million (min -11808, max 7754). Asian importers sustain the biggest losses, amounting to US$ 542 million. Mean welfare losses in China and India approach US$ 166 million and US$ 144 million, respectively. The results show that potential increases in yield variability could result in substantial reductions in global and regional welfare.

4.2. Impacts on Prices

We explore the implications of productivity shocks on both world and domestic prices of rice. This exercise is important to identify the implications of using price triggers for imposing export restrictions (see below). Testing for the original (observed) standard deviation of productivity, we find that productivity shocks lead to only a marginal increase in world prices, 0.4% on average, with a likely maximum of 8.9%. In the case of the scenario of standard deviation of productivity being twice as big as that observed, a productivity shock would lead world prices to go up by 1.9% on average, with a maximum potential increase of 26.2%.{9}

Certain countries are likely to be exposed to higher domestic price volatility and hikes. For example, India, Pakistan and the US are likely to face higher price increases and volatility above world averages. In India, productivity shocks lead domestic prices to go up by 9.42 % on average, with a maximum potential increase of 268.27% (based on the scenario of double standard deviation). In Pakistan, the same scenario produces average price increase of 9.23 %, with a maximum likely spike of 150.43%. In the US, average price increase as a result of increased product shocks is 6.35%, with a maximum of 89.41%.

Therefore, we find that productive shocks alone (with the observed and doubled standard deviations) do not lead to substantial price hikes on the world market on average. However, some major consumer and producer countries are exposed to higher productivity-shock-induced price hikes and volatility than other countries.

4.3. Impact of Export Duties and Quotas

We turn now to the scenario combining price shocks and export restrictions and we test for the impacts of export duties and quotas. For this analysis, we identify two price triggers, namely 15%, and 25%. These triggers would allow for a country to institute export restrictions when the domestic price of rice exceeds the corresponding trigger price. We first test for the implications of export quotas and export duties applied at identical levels (e.g. non-differentiated) by all exporting countries which have reached the price trigger. Accordingly, we consider quantitative restrictions of 50% and 95%, and export duties of 50% and 100%, respectively.

4.4. Price Trigger 15%

Testing for the original (observed) standard deviation of productivity, we observe, as expected from the analysis of price impacts, only a few cases where the triggers are reached. Even the lowest price trigger, namely 15%, is reached fewer than 10 times out of 1000 Monte Carlo draws. Since the price impact based on observed variation of productivity is low, the scenario whereby countries would resort to export restrictions if domestic prices were to go up by 15% does not produce significant results. This implies that if patterns of productivity variability remain similar to what has been observed in the past two decades, productivity shocks alone are unlikely to produce domestic price increases which would trigger export restrictions out of concerns about scarcity of domestic supplies.{10}

However, when we account for future uncertainty through the scenario of double standard deviation of productivity, price impacts are large enough for some countries to trigger export restrictions; this allows us to explore their welfare implications. For the price trigger 15%, on average in 60 cases out of 1000 draws, prices reach the trigger (cross country average). Among the major exporting countries, India, Pakistan and Thailand experience the highest number of cases where the price trigger is reached by 136, 94, and 83 times (out of 1000 draws) respectively.

Then we test for the implications of export duties and quotas. As expected, while importing countries largely lose out, exporting countries benefit from export restrictions. Applied at 50% (Level 1) export duties lead to additional decreases in the mean welfare of importing countries (including India) from US$ −813.7 million (mean value of productivity shocks) to US$ −952.43 million, implying a 17% welfare loss in addition to the losses resulting from productivity shocks. Applied at 100% (Level 2) export duties will further decrease the mean welfare to US$ −1074.42 million, resulting in an additional welfare loss of 13%. Africa’s additional losses are particularly high compared to importing country averages. In cases where exporters trigger 50% and 100% duties, Africa suffers additional losses of welfare of 78% and 141% respectively.

Export quotas have stronger impacts on global and domestic welfare. Applied at 50%, export quotas (triggered by a 15% price rise) increase the additional welfare losses (in importing countries and India) from US$ −813.7 million (mean value of productivity shocks) to US$ −958.33 million. An export quota at 95%, which is approaching an export ban, causes substantial welfare damage to importing countries which would incur average additional welfare losses 62% higher than they would incur under productivity shocks alone. Similarly, Africa’s additional losses are particularly high compared to importing country averages. In the cases of exporters triggering 50% and 95% quotas, the continent would suffer additional losses of welfare of 91% and 251% respectively.

Exporters’ gains from instituting export restrictions vary depending on the level and type of the measure. Cumulatively exporting countries – including Thailand, the US, Pakistan, Italy, Uruguay and Vietnam – gain around US$ 219.91 million out of export duties imposed at 50%. Their gain increases to US$ 253.61 million if the duty is increased to 100%. This means that welfare losses incurred by importing countries are much higher than the potential gains that exporters could realize. {11} As such export duties result in higher gains for exporting countries, but these gains lead to disproportionately high welfare losses in importing countries. In fact, for every additional US$ 1 million that exporting countries gain out of export duties, importing countries lose more than US$ 4 million.

If quotas are used instead of duties, exporting countries’ gains are lower while importing countries’ losses are higher. While quotas applied at 50% generate around US$ 208.4 million for exporting countries (which is lower than gains under 50% duty), further increases in quotas reduce gains significantly, to US$ 196.62 million. Africa and rest of Asia (rest of Asia) are the two main regions that would suffer from the severe effects of quantitative export restrictions. In the extreme case of a quantitative restriction of 95% (based on a 15% price trigger) the mean welfare of the two regions drops by around US$ -286.82 million and US$ -419.72 million, respectively. This would have significant implications for food security, as poverty and malnutrition rates are particularly high in these two regions.

4.5. Price Trigger 25%

Using the price trigger 25%, we observe only 18 cases (out of 1000 draws) on average (cross country average) where prices reach the trigger. Among exporting countries, India, Uruguay and Pakistan experience the highest number of triggered cases: 57, 31, and 30, respectively. As expected, when the trigger is higher, the number of cases above the trigger is smaller, thereby reducing the welfare impacts of export restrictions. Applied at 50%, export duties lead to additional decreases in the mean welfare of importing countries (including India) from US$−813.7 million (mean value of productivity shock) to US$−855.66 million. Applied at 100%, export duties will further reduce the mean welfare to US$ −889.42 million, implying a 9% welfare loss in addition to productivity shocks. Similar to the scenario of the trigger 15%, Africa’s additional losses are particularly high in comparison to importing country averages. In the cases of exporters triggering 100% duties, the continent would suffer additional losses of welfare of 36%, which is much lower than 141% in the case of the price trigger of 15%, yet still significant.

At this trigger level, export quotas, even when applied at prohibitive rates, do not cause substantial welfare losses. Applied at 95%, importing countries incur average additional welfare losses that are 20% higher than these countries would incur under productivity shocks alone. As such the amount of welfare losses associated with export restriction is highly sensitive to price triggers. The welfare losses that the restrictions cause diminish significantly with marginal increases in the trigger (Table 4).

4.6. Impact of Differentiated Export Quotas

We also test for the implications of ‘differentiated’ restrictions, where major exporters (defined by market share) react to the same price triggers by imposing lower levels of restrictions than minor exporters. As a significant number of cases is being reached, we use the 15% trigger to illustrate the implications of differentiated application of export restrictions.

As indicated above, if all countries apply the same duty rate of 50%, reaching the price trigger would lead to additional welfare losses for importing countries of US$ - 138.73 million. By reducing the export duties imposed by major exporters, five countries in total, to 25% while keeping them at 50% for the rest of the world, the additional welfare losses could be reduced by 38%. Similarly, for the higher rate scenario, by reducing export duties imposed by major exporters to 50% while keeping them at 100% for the rest of the world, the additional welfare losses could be reduced by 35%. In both scenarios, Africa’s additional welfare losses are reduced by around 32-40%.

As for export quotas, by reducing export quotas imposed by major exporters to 25% while keeping them at 50% for the rest of the world, the additional welfare losses could be eliminated by 68%. For the higher rate scenario, reducing export quotas imposed by major exporters to 50% while keeping them at 95% for the rest of the world could reduce the additional welfare losses by 59%. As such, application of differentiated export restrictions offers significant welfare gains (or reduced welfare losses) (as compared to non-differentiated export restrictions).

5. Regulatory Efficiency and Optimal Reform of WTO Rules

There have been reform efforts at the WTO to bring in some form of regulation of export restrictions imposed on food commodities.{12} Net importing countries such as Jordan, Japan and Switzerland have submitted various reform proposals, involving ‘tariffication’ of export quotas and binding of export duties. Reform proposals also included provisions prohibiting, among others, export restrictions imposed on food aid supplied by the World Food Programme (WFP) to cover its emergency food relief operations. However, all reform efforts have so far failed to make it to formal negotiations. They have faced strong opposition mainly from developing countries{13} that wish to maintain their autonomy, known as ‘policy space,’ to impose export restrictions to respond to domestic and external supply shocks.

There is, however, an emerging consensus, at least in the scholarly literature, that this area of ‘regulatory deficiency’ should be brought under discipline through future negotiations at the WTO 9, 12, 18, 22, 23. In light of the results of this study, we propose an alternative which would aim at maintaining plenty of domestic policy autonomy for most WTO Members while limiting the global welfare losses caused by export restrictions. Such ‘optimality’ could be based on the following features 22.

Objective criteria on triggers: Similar to the negotiations on the Special Safeguard Mechanism (SSM), objective criteria concerning triggers and scope of export restrictions need to be incorporated into the new disciplines that could be negotiated at the WTO 43, 44. {14} In particular, price-based triggers could be used for this purpose. When the domestic price of a food commodity exceeds a certain level, the member country could have the option to restrict the exports of that particular commodity. This would constitute a justified basis for a country to institute a trade-distortive measure. It would also improve the predictability of the policy. As indicated above, price triggers above 25% are not highly sensitive to productivity (only) shocks. As price triggers go below 25%, the likelihood that they are reached increases in light of the observed trends of productivity volatility.

Tariffication of all quotas: Our results suggest that export duties are less distortive than quotas, a finding which is supported by the literature 16, 21. Higher quota levels damage even the exporting countries. In particular, the welfare losses that importing countries, especially those in Africa, face increase dramatically if export quotas applied above the 50% level. Hence our results support the most recent WTO reform efforts of Japan and Switzerland proposing tariffication of all export quotas. This will also bring potential benefits in relation to the negotiation, monitoring and enforcement of future regulation.

Differentiated bound rates for export duties: The maximum level of duties should be negotiated and be based on objective criteria. Market share offers an effective objective criterion for determining the maximum amount of duty that a Member is allowed to charge. Major exporters with significant market share in world export markets would be subject to a lower ceiling than non-major exporters or importers. As our study shows, such a differentiated approach would limit the adverse welfare implications of export restrictions imposed by larger exporters while allowing small exporters (and non-exporters) more policy space in this field – as the impact of their export restrictions on global welfare is smaller than that of those with higher market shares. Similarly, it would also reduce price volatility in commodity groups that are traded at a low intensity and hence are more exposed to the impacts of export restrictions imposed by major suppliers.

Such new disciplines could help avoid the collective action problem mentioned above while maintaining substantial policy flexibility for the vast majority of WTO Members. The latter would also improve the political feasibility of reform efforts through multilateral negotiations.

6. Conclusions

Under future uncertainty, productivity shocks in agriculture might increase due to climate change and other factors, which may prompt countries to impose export restrictions. We have used a world trade stochastic computable general equilibrium (CGE) model with the Monte Carlo method in order to explore the potential welfare implications of productivity shocks and consequent export restrictions imposed on rice. Our results show that, under the impact of productivity shocks alone, world rice supplies are expected to stay relatively stable based on the level of volatility (of productivity) observed in the past 20 years. However, when the volatility of productivity is doubled in our scenario to account for future uncertainty, mean global welfare losses triple. This implies that potential increases in yield volatility in the future, due possibly to climate change and other factors such as migration of production to areas of high volatility, will lead to substantial welfare losses.

We then tested for the implications of export duties and quotas that countries impose in reaction to productivity shocks. While export duties result in net welfare gains for exporting countries, they lead to disproportionately high welfare losses in importing countries. In fact, for every additional US$ 1 million that exporting countries gain, importing countries lose more than US$ 4 million. If quotas are used instead of duties, exporting countries’ gains are lower while importing countries’ losses are even higher than those that result from duties. In all scenarios, the losses in importing countries in Asia and Africa are higher than importing country averages, an important finding which underlines that regions where poverty and malnutrition are prevalent are highly exposed to market distortions caused by export restrictions.

We have also tested for the implications of ‘differentiated’ export restrictions, where major exporters react to the same productivity shocks by imposing lower levels of restrictions than minor exporters. Our results suggest that halving the export duties imposed by the major exporters, five countries in total, while maintaining them for the rest of the world, could reduce the related welfare losses by up to 60%. This is a case for differentiated regulation through the WTO, which could be based on tariffication of all export restrictions, followed by the negotiation of ceilings on duties. We argue that major exporters (defined by market share) should be subject to a lower ceiling than non-major exporters or importers. Such a differentiated approach offers a substantial degree of regulatory efficiency in achieving significant welfare gains (or mitigating losses) by bringing in additional regulation only in a small number of countries while leaving a large policy space for the vast majority of member countries.

Implementing the ideas on stricter regulation of export restrictions and of exporter stocks is not politically feasible in the current phase of WTO negotiations. However, one might envisage a more rationalized regulation of export restrictions and emergency stocks through clearly defined triggers, legal boundaries and enforcement mechanisms. A future trading system where importing countries’ obligations to reduce import barriers are balanced with major exporting countries’ obligations to provide reliable supplies is essential for global food supply security under the impact of future uncertainty exacerbated by climate change.

Notes

1. However, there are exceptions. Some newly acceded members of the WTO, namely Mongolia, Ukraine, China, and the Russian Federation have specific accession commitments (known as “WTO-plus” commitments) to phase out export duties or to limit them to a designated number of tariff lines with a bound rate. See Karapinar 16.

2. Rice has been selected since it is the most important staple food crop for the world’s population (FAO) and it allows for the examination of export restrictions – as rice markets have recently been exposed to substantial price fluctuations, and distortive trade policy interventions.

3. See Appendix for more information.

4. As is often assumed, these elasticities are doubled and used for the elasticity of substitution/transformation in the composite imports/exports aggregation functions. Sensitivity analysis is conducted with 50% larger and smaller elasticity values for paddy and processed rice sectors. The results indicate that our findings are qualitatively robust, as shown in the appendix.

5. See Annex for detail on the assumptions of Monte Carlo draws.

6. Because 100% of quantitative restrictions can cause solution problems, it is approximately set at 95%.

7. In this study, large exporters are defined by the top five exporting countries (FAOSTAT).

8. As rice in many countries is mainly produced and consumed domestically, its international trade is thin. Only a small fraction of production is exported and imported internationally 35.

9. World average domestic prices are calculated with weights of consumption quantity of regions.

10. The price trigger of 25% is reached only in two cases (out of 1000) in India. No other country or region reached the 25% price trigger in any of the 1000 draws. Since the marginal welfare implications of these scenarios are insignificant, we do not consider the analysis of price triggers for 25% and beyond.

11. Exporting countries’ gains are lower if quotas are used instead of duties. While quotas applied at 50% generate around US$ 258.96 million (which is lower than gains under 50% duty), further increases in quotas reduce gains significantly.

12. For proposals on export restrictions, see WTO Secretariat 42, ‘Export Restrictions and Taxes’, <www.wto.org/english/tratop_e/agric_e/negs_bkgrnd09_taxes_e.htm>, 5 Jan 2013. 42

13. See WTO Secretariat (2008), ‘Unofficial Guide to the 10 July 2008 ‘‘Revised Draft Modalities’’’, 18 Jul. 2008,<www.wto.org/english/tratop_E/agric_e/ag_modals_july08_e.htm>, 5 Jan 2013. 43

14. The Special Safeguard Mechanism (SSM) allows countries to go beyond their bound tariffs to apply additional duties to remedy the sudden influx of imports. Based on WTO negotiations on the subject, it could have a price-based trigger which uses a reference price (i.e. three-year moving average of import prices) and when the import price of a particular food commodity that is subject to the SSM falls below 85% of the reference price, an SSM qualifying member country is allowed to impose an additional import tariff to remove the 85% of the shortfall 43, 44. Similarly, the volume-based trigger could be used when the volume of imports in a year exceeds a reference volume (i.e. three-year moving average of import volumes). Depending on how far the reference volumes are exceeded, additional import duties of up to 50% of the binding could be (gradually) imposed 33, 44.

15. About the inter-regional shipping sector, see Hertel 45.

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[5]  Headey, D. 2011. “Rethinking the Global Food Crisis: The Role of Trade Shocks.” Food Policy 36: 136-146.
In article      View Article
 
[6]  Headey, D. 2013. “The Impact of the Global Food Crisis on Self-Assessed Food Security.” World Bank Economic Review 27(1): 1-27.
In article      View Article
 
[7]  Ivanic, M., Martin, W. and Zaman H. 2012. “Estimating the Short-Run Poverty Impacts of the 2010-11 Surge in Food Prices.” World Development 40(11): 2302-2317.
In article      View Article
 
[8]  Nazlioglu, S., C. Erdem, and U. Soytas,. 2013. “Volatility spillover between oil and agricultural commodity markets.” Energy Economics 36: 658-665.
In article      View Article
 
[9]  Anderson, K., 2013. Agricultural price distortions: trends and volatility, past, and prospective. Agric. Econ. 44.
In article      View Article
 
[10]  Anderson, K. and S. Nelgen. 2012. “Trade barrier volatility and agricultural price stabilization.” World Development 40(1): 36-48.
In article      View Article
 
[11]  Beckman, J., Beckman, J., Usda, E.R.S., 2018. THE IMPACTS OF EXPORT TAXES ON AGRICULTURAL TRADE.
In article      View Article
 
[12]  Ouët, A.N.B., Strades, C.A.E., Aborde, D.A.L., 2014. D IFFERENTIAL E XPORT T AXES ALONG THE O ILSEEDS V ALUE C HAIN: A P ARTIAL E QUILIBRIUM A NALYSIS 96, 924-938.
In article      View Article
 
[13]  Rutten, M., Shutes, L., Meijerink, G., 2013. Sit down at the ball game: How trade barriers make the world less food secure. Food Policy 38, 1-10.
In article      View Article
 
[14]  Tanaka, T., 2018. Agricultural Self-sufficiency and Market Stability: A Revenue-neutral Approach to Wheat Sector in Egypt. J. Food Secur. 6, 31-41.
In article      View Article
 
[15]  Erhan Unal, G., Karapinar, B., Tanaka, T., 2018. Welfare-at-Risk and Extreme Dependency of Regional Wheat Yields: Implications of a Stochastic CGE Model. J. Agric. Econ. 69, 18-34.
In article      View Article
 
[16]  Siddhartha, M., and T. Josling. 2009. “Agricultural Export Restrictions: Welfare Implications and Trade Disciplines.” IPC Position Paper, Agricultural and Rural Development Policy Series, International Food & Agricultural Trade Policy Council.
In article      
 
[17]  Bouet, Antoine, and Debucquet, David L. 2010. “Economics of Export Taxation in a Context of Food Crisis. A Theoretical and CGE Approach Contribution.” IFPRI Discussion Paper 00994: 18.
In article      
 
[18]  Karapinar, B. 2012. “Defining the legal boundaries of export restrictions: A Case Law Analysis.” Journal of International Economic Law 15(2): 443-479.
In article      View Article
 
[19]  Philip C. Abbott, Export Restrictions as Stabilization Responses to Food Crisis, American Journal of Agricultural Economics, Volume 94, Issue 2, January 2012, Pages 428-434.
In article      View Article
 
[20]  Will, M. and and A. Kym. 2012. “Export Restrictions and Price Insulation During Commodity Price Booms.” American Journal of Agricultural Economics 94(2):422-427.
In article      View Article
 
[21]  Götz, L., Glauben, T. and Brümmer, B. 2013. “Wheat export restrictions and domestic market effects in Russia and Ukraine during the food crisis.” Food Policy 38: 214-226.
In article      View Article
 
[22]  Karapinar, B. 2011a. “Export Restrictions and the WTO Law: How to Reform the ‘Regulatory Deficiency’.” Journal of World Trade 45(6): 1139-1155.
In article      
 
[23]  Karapinar, B. 2011b. “China’s Export Restriction Policies: Complying with ‘WTO plus’ or Undermining Multilateralism.” World Trade Review 10(3): 389-408.
In article      View Article
 
[24]  Nelson, G., A. Palazzo, C. Ringler, T. Sulser, and M. Batka. 2009. “The Role of International Trade in Climate Change Adaptation.” ICTSD–IPC Platform on Climate Change, Agriculture and Trade, Issue Paper No.4, ICTSD and International Food & Agricultural Trade Policy Council, Washington DC, USA.
In article      View Article
 
[25]  IPCC, 2014. Summary for policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
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[26]  Dasgupta, P., Morton, J.F., Dodman, D., Karapinar, B., Meza, F., Rivera-Ferre, M.G., Toure Sarr, A., Vincent, K.E., 2014. Rural Areas, in: Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.W. (Ed.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 613-657.
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[27]  IPCC, 2014. Summary for policymakers, in: Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.W. (eds.. (Ed.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1-32.
In article      
 
[28]  Reimer, J. J. and M. Li. 2009. “Yield Variability and Agricultural Trade.” Agricultural and Resource Economics Review 38: 258-70.
In article      View Article
 
[29]  He, L., Asseng, S., Zhao, G., Wu, D., Yang, X., Zhuang, W., Jin, N., Yu, Q., 2015. Impacts of recent climate warming, cultivar changes, and crop management on winter wheat phenology across the Loess Plateau of China. Agric. For. Meteorol. 200, 135-143.
In article      View Article
 
[30]  Ray, D.K., Ramankutty, N., Mueller, N.D., West, P.C., Foley, J.A., 2012. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1-7.
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[31]  Wiebe, K., Lotze-Campen, H., Sands, R., Tabeau, A., Van Der Mensbrugghe, D., Biewald, A., Bodirsky, B., Islam, S., Kavallari, A., Mason-D’Croz, D., Müller, C., Popp, A., Robertson, R., Robinson, S., Van Meijl, H., Willenbockel, D., 2015. Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios. Environ. Res. Lett. 10.
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[32]  Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D.B., Huang, Y., Huang, M., Yao, Y., Bassu, S., Ciais, P., Durand, J.-L., Elliott, J., Ewert, F., Janssens, I.A., Li, T., Lin, E., Liu, Q., Martre, P., Müller, C., Peng, S., Peñuelas, J., Ruane, A.C., Wallach, D., Wang, T., Wu, D., Liu, Z., Zhu, Y., Zhu, Z., Asseng, S., 2017. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci. 201701762.
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[33]  Ahmed, S. A., Diffenbaugh N.S., Hertel, T. W. and Martin W. J. 2012. “Agriculture and Trade Opportunities for Tanzania: Past Volatility and Future Climate Change.” Review of Development Economics 16(3): 429-447.
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[37]  Erhan Unal, G., Karapinar, B., Tanaka, T., 2018. Welfare-at-Risk and Extreme Dependency of Regional Wheat Yields: Implications of a Stochastic CGE Model. J. Agric. Econ. 69, 18-34.
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Appendix: Sensitivity Analyses

Robustness examinations are conducted in this section with changes in the Armington elasticity, and the elasticity of factor substitution (Tables A.2-A.5). The alternative assumptions of the parameters are indicated in Table A.1.

Sensitivity analysis: Armington elasticity

Elasticity of substitution for the Armington aggregation and elasticity of transformation for gross output are obtained from the GTAP database (Table A.1). These elasticities are doubled for the elasticities for import variety aggregation and for export variety production. We carried out sensitivity analyses of our simulation results with respect to the Armington elasticity of substitution for the paddy and processed rice sectors. We alternatively assumed 50% larger and smaller for both of the sectors.

Export quota scenarios give relatively larger welfare losses than export tax scenarios, and the scenarios with non-differentiated 95% quota show the lowest economic welfare among the other export restriction scenarios in cases of larger and smaller Armington elasticities. When the standard deviations of productivity shocks are doubled, welfare losses become about triple although in some cases where restrictions are stricter such as NQ95, welfare variations responded more sensitively. Also, the relative relationship of welfare effects in each set of the eight restriction scenarios is well maintained with the changes in the elasticities.

Sensitivity analysis: value added aggregation

While the elasticities of substitution among primary factors are assumed to be 0.25 for agricultural sectors in the analysis, we alternatively assume ±50% of the original values in the robustness tests. The outcomes demonstrate that larger volatility of productivity expand EV losses about as three times big in spite of the fact that 95% export quotas are likely to affect them a little more negatively. In the same as the tests for Armington aggregation, export quota is more distortive than export tax, and 95% export quota marks the lowest value in welfare compared with those in other restriction scenarios of each setting. Moreover, the order of welfare impacts between the scenarios concerning export restrictions is supported.

Annex: Detailed specification of the model and Monte Carlo simulations

The full description of our world trade computable general equilibrium model is shown in Section B.1. This section is followed by Section B.2, where we examine distributions of productivity and the Monte Carlo draws.

Model Structure

-Symbol

Sets

: commodities/sectors (other than the food composite)

: food commodities/sectors

: non-food commodities/sectors

: non-food commodities plus the food composite

: regions

: factors (capital, skilled labor, unskilled labor, farmland, natural resources)

Endogenous variables

: household consumption

: food composite

: government consumption

: investment uses

: intermediate uses of the i-th good by the j-th sector

: factor uses

: value added

: gross output

: Armington composite good

: composite imports

: domestic goods

: composite exports

: inter-regional transportation from the r-th region to the s-th region

: exports of inter-regional shipping service by the r-th region

: composite inter-regional shipping service

: household savings

: government savings

: direct taxes

: production taxes

: import tariffs

: export taxes

: factor input taxes

: price of food composite

: price of Armington composite goods

: price of factors

: price of value added

: price of gross output

: price of composite imports

: price of domestic goods

: price of composite exports

: price of goods shipped from the r-th region to the s-th region

: inter-regional shipping service price in US dollars

: exchange rates to convert the r-th region’s currency into the s-th region’s currency

: release of emergency rice stocks

Exogenous variables and parameters

: current account deficits in US dollars

: factor endowment initially employed in the j-th sector

: productivity;

: standard deviation of productivity in the paddy rice sector

: capacity of emergency rice stocks

: initial amount of gross output

: direct tax rates

: production tax rates

: import tariff rates on inbound shipping from the s-th region

: export tax rates on outbound shipping to the s-th region

: inter-regional shipping service requirement per unit transportation of the i-th good from the r-th region to the s-th region

: factor input tax rates

-Household

(Utility function: )

Demand functions for consumption

Food composite aggregation function

(Note that .)

Savings function

-Value added producing firm

Factor demand function

(Note that )

Value added production function

-Gross output producing firm

(Production function: )

Demand function for intermediates

Demand function for value added

Unit price function

-Government

Demand function for government consumption

Direct tax revenue

Production tax revenue

Import tariff revenue

Export tax revenue

Factor input tax revenue

Government savings function

-Investment

Demand function for commodities for investment uses

-Armington composite good producing firm

Composite good production function

(Note that )

Composite import demand function

Domestic good demand function

-Import variety aggregation firm

Composite import production function

Import demand function

-Gross output transforming firm

i) For (paddy rice):

CET transformation function

(Note that ).

Composite export supply function

Domestic good supply function

For simulation with Scenario S: Release of emergency stocks

ii) For (transportation):

iii) For :

-Export variety producing firm

Composite export transformation function

Export supply function

-Inter-regional shipping sector{15}

Inter-regional shipping service production function

Input demand function for international shipping service provided by the r-th country

-Market-clearing conditions

Commodity market

Capital markets

Labor market

Foreign exchange rate arbitrage condition

Inter-regional shipping service market

Monte Carlo draws and productivity distribution

We assume independent and identically distributed normal distribution in our Monte Carlo simulations to generate random productivity shocks of paddy rice sector. Questions about the assumptions would be the normality of the distributions and the correlation between regions. Figures B.1 and B.2 show the distributions of the residuals of OLS regressions of rice productivity in the world and India over the period 1961-2011, which seem to follow the normal distribution.

In our analyses, we do not consider spatial correlations of rice productivity shocks between regions. Table B.1 indicates the correlations of the OLS residuals of paddy rice productivity over 1990-2010, which are generally quite low, meaning that no systematic spatial correlation is found in terms of the distance or the adjacency among regions.

  • Table B.1. Correlation of the OLS residuals of productivity changes between regions(Source: Authors’ calculation from the FAOSTAT. Notes: ROAS, ROE, ROAF, and ROW stand for Rest of Asia, Rest of Europe, Rest of Africa, and Rest of the World, respectively.)

Published with license by Science and Education Publishing, Copyright © 2019 Tetsuji Tanaka and Baris Karapinar

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Cite this article:

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Tetsuji Tanaka, Baris Karapinar. How to Improve World Food Supply Stability under Future Uncertainty: Potential Role of WTO Regulation on Export Restrictions in Rice. Journal of Food Security. Vol. 7, No. 4, 2019, pp 129-150. http://pubs.sciepub.com/jfs/7/4/5
MLA Style
Tanaka, Tetsuji, and Baris Karapinar. "How to Improve World Food Supply Stability under Future Uncertainty: Potential Role of WTO Regulation on Export Restrictions in Rice." Journal of Food Security 7.4 (2019): 129-150.
APA Style
Tanaka, T. , & Karapinar, B. (2019). How to Improve World Food Supply Stability under Future Uncertainty: Potential Role of WTO Regulation on Export Restrictions in Rice. Journal of Food Security, 7(4), 129-150.
Chicago Style
Tanaka, Tetsuji, and Baris Karapinar. "How to Improve World Food Supply Stability under Future Uncertainty: Potential Role of WTO Regulation on Export Restrictions in Rice." Journal of Food Security 7, no. 4 (2019): 129-150.
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  • Table 2. Simulation scenarios(Notes: Diff.=Differentiated, Non-diff=Non-differentiated, SD = standard deviation, DQ = Differentiated quantitative restrictions, NQ = Non-differentiated quantitative restrictions, Dduty = Differentiated duty, Nduty = Non-differentiated duty, _15% = Price trigger of 15%, _25% = Price trigger of 25%, _N: New reference equilibrium, 2xSD = standard deviation twice as observed. Example: DQ25-50_15% = Differentiated quantitative restrictions applied at 25% for major exporter, 50% by minor exporters, both responding to the price trigger of 15%.)
  • Table B.1. Correlation of the OLS residuals of productivity changes between regions(Source: Authors’ calculation from the FAOSTAT. Notes: ROAS, ROE, ROAF, and ROW stand for Rest of Asia, Rest of Europe, Rest of Africa, and Rest of the World, respectively.)
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[4]  Schüttel, C., U. Kleinwechter, R. Ihle, H. Grethe. 2011. “Domestic policy responses to the food price crisis: The case of Bolivia.” Journal of Agriculture and Rural Development in the Tropics and Subtropics 112(2): 125-139.
In article      
 
[5]  Headey, D. 2011. “Rethinking the Global Food Crisis: The Role of Trade Shocks.” Food Policy 36: 136-146.
In article      View Article
 
[6]  Headey, D. 2013. “The Impact of the Global Food Crisis on Self-Assessed Food Security.” World Bank Economic Review 27(1): 1-27.
In article      View Article
 
[7]  Ivanic, M., Martin, W. and Zaman H. 2012. “Estimating the Short-Run Poverty Impacts of the 2010-11 Surge in Food Prices.” World Development 40(11): 2302-2317.
In article      View Article
 
[8]  Nazlioglu, S., C. Erdem, and U. Soytas,. 2013. “Volatility spillover between oil and agricultural commodity markets.” Energy Economics 36: 658-665.
In article      View Article
 
[9]  Anderson, K., 2013. Agricultural price distortions: trends and volatility, past, and prospective. Agric. Econ. 44.
In article      View Article
 
[10]  Anderson, K. and S. Nelgen. 2012. “Trade barrier volatility and agricultural price stabilization.” World Development 40(1): 36-48.
In article      View Article
 
[11]  Beckman, J., Beckman, J., Usda, E.R.S., 2018. THE IMPACTS OF EXPORT TAXES ON AGRICULTURAL TRADE.
In article      View Article
 
[12]  Ouët, A.N.B., Strades, C.A.E., Aborde, D.A.L., 2014. D IFFERENTIAL E XPORT T AXES ALONG THE O ILSEEDS V ALUE C HAIN: A P ARTIAL E QUILIBRIUM A NALYSIS 96, 924-938.
In article      View Article
 
[13]  Rutten, M., Shutes, L., Meijerink, G., 2013. Sit down at the ball game: How trade barriers make the world less food secure. Food Policy 38, 1-10.
In article      View Article
 
[14]  Tanaka, T., 2018. Agricultural Self-sufficiency and Market Stability: A Revenue-neutral Approach to Wheat Sector in Egypt. J. Food Secur. 6, 31-41.
In article      View Article
 
[15]  Erhan Unal, G., Karapinar, B., Tanaka, T., 2018. Welfare-at-Risk and Extreme Dependency of Regional Wheat Yields: Implications of a Stochastic CGE Model. J. Agric. Econ. 69, 18-34.
In article      View Article
 
[16]  Siddhartha, M., and T. Josling. 2009. “Agricultural Export Restrictions: Welfare Implications and Trade Disciplines.” IPC Position Paper, Agricultural and Rural Development Policy Series, International Food & Agricultural Trade Policy Council.
In article      
 
[17]  Bouet, Antoine, and Debucquet, David L. 2010. “Economics of Export Taxation in a Context of Food Crisis. A Theoretical and CGE Approach Contribution.” IFPRI Discussion Paper 00994: 18.
In article      
 
[18]  Karapinar, B. 2012. “Defining the legal boundaries of export restrictions: A Case Law Analysis.” Journal of International Economic Law 15(2): 443-479.
In article      View Article
 
[19]  Philip C. Abbott, Export Restrictions as Stabilization Responses to Food Crisis, American Journal of Agricultural Economics, Volume 94, Issue 2, January 2012, Pages 428-434.
In article      View Article
 
[20]  Will, M. and and A. Kym. 2012. “Export Restrictions and Price Insulation During Commodity Price Booms.” American Journal of Agricultural Economics 94(2):422-427.
In article      View Article
 
[21]  Götz, L., Glauben, T. and Brümmer, B. 2013. “Wheat export restrictions and domestic market effects in Russia and Ukraine during the food crisis.” Food Policy 38: 214-226.
In article      View Article
 
[22]  Karapinar, B. 2011a. “Export Restrictions and the WTO Law: How to Reform the ‘Regulatory Deficiency’.” Journal of World Trade 45(6): 1139-1155.
In article      
 
[23]  Karapinar, B. 2011b. “China’s Export Restriction Policies: Complying with ‘WTO plus’ or Undermining Multilateralism.” World Trade Review 10(3): 389-408.
In article      View Article
 
[24]  Nelson, G., A. Palazzo, C. Ringler, T. Sulser, and M. Batka. 2009. “The Role of International Trade in Climate Change Adaptation.” ICTSD–IPC Platform on Climate Change, Agriculture and Trade, Issue Paper No.4, ICTSD and International Food & Agricultural Trade Policy Council, Washington DC, USA.
In article      View Article
 
[25]  IPCC, 2014. Summary for policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
In article      
 
[26]  Dasgupta, P., Morton, J.F., Dodman, D., Karapinar, B., Meza, F., Rivera-Ferre, M.G., Toure Sarr, A., Vincent, K.E., 2014. Rural Areas, in: Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.W. (Ed.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 613-657.
In article      
 
[27]  IPCC, 2014. Summary for policymakers, in: Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.W. (eds.. (Ed.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1-32.
In article      
 
[28]  Reimer, J. J. and M. Li. 2009. “Yield Variability and Agricultural Trade.” Agricultural and Resource Economics Review 38: 258-70.
In article      View Article
 
[29]  He, L., Asseng, S., Zhao, G., Wu, D., Yang, X., Zhuang, W., Jin, N., Yu, Q., 2015. Impacts of recent climate warming, cultivar changes, and crop management on winter wheat phenology across the Loess Plateau of China. Agric. For. Meteorol. 200, 135-143.
In article      View Article
 
[30]  Ray, D.K., Ramankutty, N., Mueller, N.D., West, P.C., Foley, J.A., 2012. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1-7.
In article      View Article  PubMed
 
[31]  Wiebe, K., Lotze-Campen, H., Sands, R., Tabeau, A., Van Der Mensbrugghe, D., Biewald, A., Bodirsky, B., Islam, S., Kavallari, A., Mason-D’Croz, D., Müller, C., Popp, A., Robertson, R., Robinson, S., Van Meijl, H., Willenbockel, D., 2015. Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios. Environ. Res. Lett. 10.
In article      View Article
 
[32]  Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D.B., Huang, Y., Huang, M., Yao, Y., Bassu, S., Ciais, P., Durand, J.-L., Elliott, J., Ewert, F., Janssens, I.A., Li, T., Lin, E., Liu, Q., Martre, P., Müller, C., Peng, S., Peñuelas, J., Ruane, A.C., Wallach, D., Wang, T., Wu, D., Liu, Z., Zhu, Y., Zhu, Z., Asseng, S., 2017. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl. Acad. Sci. 201701762.
In article      View Article  PubMed  PubMed
 
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