Southeast Asia is one of the world’s major exporters of agricultural products. Agriculture is the major source of livelihoods for millions of people in the region. However, the sector faces challenges from unsustainable agricultural practices and increasing climate change-induced natural disasters. A transition to sustainable agriculture is urgently needed in order to curb soil degradation, deforestation and biodiversity loss. This chapter presents the findings from a simulation exercise that looks at the effects on employment and income of a transition from conventional to organic rice farming in Southeast Asia.
Towards Greener and More Inclusive Societies in Southeast Asia
Development Centre Studies
5. The green transition in agriculture: Labour implications of a conversion to organic rice
Abstract
Agriculture plays a critical role in the economic development and rural livelihoods of people living in Southeast Asia. Members of the Association of Southeast Asian Nations (ASEAN) are some of the top exporters of rice, maize, soybeans, cassava and sugar, as well as palm oil, coconut and rubber, which accounted for 8.3% of the world’s agricultural products traded in 2021 (Teng and Oliveros, 2016[1]; FAO, 2023[2]). The growth in rural economies and agricultural labour productivity, together with outmigration from rural areas, have alleviated extreme and moderate poverty substantially since the late 1980s (FAO, 2020[3]; IFAD, 2019[4]). Similarly, the region successfully reduced the prevalence of undernourishment from 20.6% in 2000 to 7.3% in 2020, lower than the world average of 9.9% (FAO/UNICEF, 2021[5]).
Agriculture and the agri-food system as a whole hold great potential for green growth in the region (ADB, 2022[6]). Agriculture is closely linked to natural resources such as land, water and biodiversity. Implementing sustainable agricultural practices could therefore reduce environmental impacts while fostering technological innovation and development. The structural transformation of the agri-food system could also create new jobs across agri-food value chains and closely related sectors. Agri-food value chains include various sectors and sub-sectors ranging from manufacturing, importing and distributing immediate agricultural inputs to processing, storing, packaging and transporting the final outputs to markets (Greenville and Kawasaki, 2018[7]; CARE, 2021[8]). The Asian Development Bank (ADB) estimates that green growth in the agriculture and food system could create 6.5 million jobs by 2030, including 2 million jobs in the organic food and beverage sectors in Southeast Asia, provided there is an annual capital expenditure of USD 6.9 billion (United States dollars) until 2030 (ADB, 2022[6]). However, a green transition would be accompanied by a restructuring of the agriculture and food-related sectors and would necessarily lead to job redistribution, with some sectors experiencing job losses.
Understanding the green transition in agriculture and its potential effects on employment is essential in order to ensure that policies and interventions are designed to mitigate any adverse effects on rural livelihoods and vulnerable workers while maximising the benefits of sustainable agriculture. This chapter reviews the trends in socio-demographic and environmental conditions that call for a green transition in agriculture in Southeast Asia and the current policy frameworks for transitioning to sustainable agriculture at the regional and national levels. It then presents the findings of a simulation exercise that looks at the potential effects of a conversion from conventional to organic farming for rice in Indonesia, the Philippines and Thailand, as well as a proxy estimation for the whole region. The exercise aims to demonstrate the effects of such a transition on employment and income.
Southeast Asian agriculture: The case for a green transition
Despite its declining share of gross domestic product (GDP), agriculture still holds an important place in Southeast Asian economies and societies. In 2022, value added from the agricultural sector (including forestry and fishing) was approximately USD 323 billion, representing about 10% of the region’s GDP (World Bank, n.d.[9]). Agriculture contributes on average to about 11% of GDP in ASEAN countries, with the share reaching 14% in Lao People’s Democratic Republic (hereafter “Lao PDR”) and 20% in Myanmar (Figure 5.1). The sector remains an important source of livelihoods for millions of Southeast Asians, accounting for about 96 million workers, or 27% of the total workforce in 2020 (FAO, 2023[10]), despite this share decreasing from about 40% in 2000.
Figure 5.1. Agriculture holds an important place in the economies and societies of Southeast Asia
Note: Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs as of 2022. Employment is the share of employment in agriculture, forestry and fishing out of total employment, estimated by International Labour Organization (ILO) models in 2021. The value for ASEAN is the mean value across the ten ASEAN countries.
Source: World Development Indicators.
Agriculture is particularly vital for the livelihoods and food security of smallholder farmers and the rural poor (USAID, 2022[11]). In Southeast Asia, there are about 100 million smallholder farmers with less than 2 hectares of farmland who produce roughly 30% of most food commodities in the region (Teng and Oliveros, 2016[1]; WWF, 2021[12]; Lowder, Sánchez and Bertini, 2021[13]). Most of these smallholder farmers are subsistence farmers who operate low-technology farming practices (ADB, 2022[6]). Southeast Asian smallholders depend more heavily on farm income than on any other source of earnings (68% in Asia, compared with 60% of farmers in Latin America or 58% of farmers in Africa) (Charlton, Rutledge and Taylor, 2021[14]).
Growing populations, rising incomes and rapid urbanisation have changed Southeast Asians’ dietary preferences. In order to respond to changing food demand, the agricultural sector has diversified and intensified outputs. Total food consumption and non-cereal crop consumption, particularly animal products and fruits and vegetables, increased significantly in the region between 1990 and 2013 (IFAD, 2019[4]). Southeast Asia will continue to see an upward trend in domestic demand for food as population, income and urbanisation growth persists in the medium term. Southeast Asians are forecast to continue to spend more on food and lead market growth at an annual rate of 4.7% between 2019 and 2030 (PwC, Rabobank and Temasek, 2021[15]). By 2030, food and beverages will be the largest spending category for consumption in ASEAN countries, representing 30% on average and up to 40% for the Philippines and Viet Nam (World Economic Forum/Bain & Company, 2020[16]). By 2031, average calorie availability in South and Southeast Asia is projected to increase by almost 200 kilocalories (kcal) per person per day to average about 2 850 kcal, just 6.5% below the world average. The increase will be mainly driven by consumption of dairy products, meat and vegetable oils (OECD/FAO, 2022[17]).
Demographic and lifestyle changes have also led to a transformation of agri-food value chains, with a growing focus on improving productivity, efficiency and sustainability. Agriculture in Southeast Asia has achieved stable and sustained productivity growth, recording the highest agricultural value added per worker compared with other regions in the world (OECD/FAO, 2017[18]; IFAD, 2019[4]). This improvement in agricultural productivity has been a key driver of agricultural output growth, accounting for over 60% of output growth between 2001 and 2013, compared with 13% in the 1980s (OECD/FAO, 2017[18]). However, this output growth has been significantly influenced by increased input use (including land, labour capital – animals and machinery, fertiliser and feed use), which are due to a combination of increased intensification of activities and area expansion, particularly linked to palm oil production (ibid.).
Behind Southeast Asia’s growing agri-food industry, however, is the degradation of its ecosystem. While agricultural production is heavily reliant on biodiversity, natural cycles and ecological processes, paradoxically, current agricultural practices accelerate freshwater depletion, soil degradation and air pollution in Southeast Asia (ADB, 2022[6]; ILO, 2018[19]). The increased need for arable land for livestock and higher-value crops has led to the massive conversion of primary forests for agriculture (IFAD, 2019[4]). Indonesia and Malaysia, where this land conversion has been particularly pronounced, are reported to have lost vast areas of biodiversity-rich tropical forests and observe an increase in carbon emissions from agriculture (OECD/FAO, 2017[18]). Palm oil production has been the leading cause of deforestation, with foreign demand being the main driver. Half of the production in Indonesia was exported and three quarters of the production in Malaysia was exported in 2021 (FAOSTAT, 2022[20]). The case of palm oil raises concerns as it is increasingly used for biofuels and other non-food demand. In 2018, more than half (53%) of all palm oil imported to the EU was used to make biodiesel for cars and 12% to generate electricity and heating (Muzii, 2019[21]).
The intensification of fertiliser inputs, which surpassed growth in labour inputs since the 1990s, has also worsened the quality of soil, water and air (IFAD, 2019[4]; OECD, 2017[22]). The use of heavy machinery releases harmful gases and contributes to soil compaction, erosion, and eventually air pollution and loss of habitat for wildlife. At the same time, agriculture produces more greenhouse gas (GHG) emissions than any other sector in Southeast Asia, including rice farming, a major source of methane gas emissions (Aryal, 2022[23]). The Agriculture, Forestry and Other Land Use sector contributes to 23% of global greenhouse gas (GHG) emissions (IPCC, 2007[24]). In Southeast Asia, the agri-food makes up 50% of all emissions (PwC et al., 2023[25]). Such environmental degradation increases agricultural sectors’ vulnerability to natural disasters and climate change and intensifies sectoral competition with domestic and industrial users of land and freshwater (OECD, 2017[22]; IFAD, 2019[4]).
Social challenges related to the agricultural sector also continue to linger. One such challenge is the difficulty for smallholders and small agribusinesses to integrate into global agri-food value chains. In Southeast Asia, there has been a shift towards large-scale, commercial agriculture, with the emergence of large agribusinesses that are involved in the entire value chain, from production to processing and marketing. This has led to greater consolidation and integration of the agri-food sector, with larger enterprises acquiring smaller ones and expanding their operations across the value chain (Meemken et al., 2021[26]; Marques Vieira et al., 2013[27]). This approach to value chain development, often supported by governments, has excluded small-scale producers, low-income producers and the extensive network of informal traders and small businesses in the region (OECD, 2021[28]). Another challenge is persistent rural poverty. Across Southeast Asian countries, between 40% and 95% of the extremely poor live in rural areas (IFAD, 2019[4]). The increase in agricultural productivity based on capital and non-labour investment did not necessarily translate into better earnings for smallholders and the rural population.
An alternative approach is increasingly needed in Southeast Asian agriculture. The region offers several enabling conditions for boosting sustainable agriculture and developing organic markets. First, there is a growing demand for organic consumption in the region. A World Economic Forum survey shows that 80% of the region’s consumers appreciate sustainability and an eco-friendly lifestyle and strongly prefer fresh and healthy food (World Economic Forum/Bain & Company, 2020[16]). Second, sustainable agri-food systems could offer lucrative business opportunities for investors. Agri-food technology investments in the region quadrupled to USD 423 million between 2014 and 2019, and the region’s continued transition towards sustainable agriculture offers USD 1.6 trillion in investment opportunities (PwC, Rabobank and Temasek, 2021[15]). Finally, Southeast Asian governments are increasingly cognisant of the fact that more sustainable agricultural practices are needed. Countries are now integrating the sustainability concept into their agricultural development plans and putting forward policy measures to promote sustainable agricultural practices, including establishing standards and certification for organic products (Indonesia), promoting a Participatory Guarantee System (PGS) (the Philippines and Viet Nam) and providing training and raising awareness on organic farming practices (the Philippines) (FiBL/IFOAM, 2022[29]).
Southeast Asia’s policy frameworks for sustainable agriculture
Policies and institutional frameworks facilitate the green transition in agriculture and pave the way for potential green jobs in the future. Southeast Asia is committed to promoting the development of a green and sustainable agri-food sector, albeit with varying degrees of enforcement across countries. At the regional level, pursuing ecologically sound agriculture has become one of ASEAN’s core principles to ensure food security and rural development in order to combat hunger and poverty in the medium to long term, as shown in the ASEAN Integrated Food Security (AIFS) Framework and Strategic Plan of Action on Food Security in the ASEAN Region (SPA-FS) 2021-2025 (ASEAN, 2020[30]). The political direction towards the sustainable development of agriculture was reaffirmed by ASEAN’s ambitious long-term vision of transitioning into a low-carbon economy, which was announced in 2021 (ASEAN, 2021[31]). Specifically, the Strategic Plan of ASEAN Cooperation in Food, Agriculture and Forestry, (2016-2025) defines the vision and goals for the food, agriculture and forestry sector. The aims of this ten-year plan include ensuring equitable, sustainable and inclusive growth in the agri-food and forestry sectors and increasing resilience to climate change, natural disasters and other shocks (ASEAN, 2017[32]). Other policy documents such as on the ASEAN Guidelines for Sustainable Agriculture provide additional resources to ensure sustainable development of the food, agriculture and forestry sectors (ASEAN, 2022[33]). Recognising the substantial challenges posed by highly hazardous pesticides to human health and the environment, the ASEAN Ministers on Agriculture and Forestry (AMAF) issued a Statement to reduce the use of harmful agrochemicals for food safety, public health, occupational safety, and environmental protection on the occasion of the 45th AMAF Meeting in Kuala Lumpur, on 4 October 2023.
ASEAN has established two major voluntary quality standards for safe food product aimed at supporting sustainable agriculture. One is the ASEAN GAP: good agricultural practices for production of fresh fruit and vegetables in the ASEAN region. It is a voluntary standard, adopted in 2006, for handling fruit and vegetables throughout the producing, harvesting and post-harvesting processes. Although the ASEAN GAP was originally designed to promote food safety, it contains quality guidelines that encourage minimal use of chemical fertilisers and soil additives in order to reduce the risk of environmental harm during food production and environment management (ASEAN/AusAID, 2021[34]). The ASEAN GAP has been the agreed benchmark for national good agricultural practices (GAPs) in the Southeast Asia region. Subsequently, in 2014, ASEAN approved the aquaculture equivalent of this standard, Good Aquaculture Practices (GAqP). Another guiding document is the ASEAN Standard for Organic Agriculture (ASOA). The ASOA, which was adopted in 2014, sets the standards for organic food products at every stage of the supply chain from production to labelling.
ASEAN has produced various policy frameworks promoting sustainable agriculture, mainly in the form of voluntary guidelines to expand sustainable and organic farming in the region. The documents clearly identify those responsible for the strategies, but the performance review of these activities is less obvious. Although these policy documents are intended to support ASEAN Member States to introduce binding laws and directives (GrowAsia, 2022[35]), it is difficult to assess how much these measures actually lead to regulatory changes in the countries. Furthermore, their effects on the actual adoption of sustainable farming remain to be further evaluated.
At the national level, each of the ASEAN countries has created legislative or regulatory frameworks with varying degrees of enforcement and support in order to encourage the growth of sustainable agriculture. Indonesia, the Philippines, Thailand and Viet Nam are key agricultural economies in the region that had been encouraging fertiliser and nitrogen overuse through subsidies since the 1970s (ADB, 2021[36]). However, more recently, Lao PDR, the Philippines and Viet Nam have put in place stand-alone frameworks that include targeted policies, laws and initiatives to support the transition to sustainable agriculture. Other countries in the Southeast Asian region have developed legislative frameworks to promote sustainable agriculture as part of larger policy documents, such as national agriculture or economic development plans. Given that these frameworks are included among other development objectives, the level of material support given to sustainable agriculture varies widely across countries. An overview of these policy frameworks and instruments is provided in Table 5.1.
Table 5.1. Policy frameworks for promoting sustainable agriculture in Southeast Asia vary at the national levels
Note: Legislative frameworks include laws, policies, initiatives and statements focused on creating an enabling environment for, and facilitating, sustainable agriculture. Mainstreaming mechanisms refer to the implementation and monitoring structures aiming to bring about the targeted effects of the legislative frameworks (e.g. public expenditure review, strategic assessment, councils for sustainable development). Policy instruments for sustainable agriculture may include certification of sustainable agri-food production and trade, subsidies for sustainable farming, financial support for sustainable farmers and landowners, sustainable agriculture investment frameworks and incentives, inclusive green social enterprises, sustainable public procurement, and green innovation. The three dimensions are based on the Organisation for Economic Co‑operation and Development (OECD) policy framework for greening growth in developing countries (OECD, 2012[37]). Green cell shading indicates that there is targeted support (bodies or documents dedicated to developing sustainable agriculture), yellow cell shading indicates that there is auxiliary support (the existence of sustainable agriculture policies as part of a larger plan or strategy) and pink cell shading indicates that there is little or no policy support for sustainable agriculture.
Source: Authors’ own elaboration
Relative to the steady progress in legislative support, the mainstreaming mechanisms for implementing the regulations and rules created for sustainable agriculture remain underdeveloped. Four out of ten ASEAN countries have developed some form of overseeing committee for sustainable agriculture with varying degrees of intervention. Indonesia, the Philippines, and Thailand have established oversight boards with the aim of promoting and regulating organic agriculture in these countries, whereas Cambodia has created a consortium to monitor sustainable agriculture more broadly. In the remaining ASEAN countries, the monitoring of sustainable agriculture either falls under the scope of larger bodies or is non-existent. However, even when countries appoint an organisation responsible for overseeing policy implementation, they often leave the precise monitoring and evaluation processes and mechanisms remain obscure.
Most ASEAN Member States have policy instruments to support the transition to sustainable or organic farming. The most common policy tools include nationwide quality assurance standards, direct and indirect subsidies for farmers converting to organic farming, or other fixed-term one-off farm development programmes. Thailand has provided financial incentives and subsidies to encourage organic farming and has developed a peer-to-peer certification system (ADB, 2017[38]). For example, farmers who participate in organic rice cultivation are eligible for a government subsidy of 2 000‑4 000 Thai baht (USD 58‑115) per rai (1 600 square metres) for a period of three years (National News Bureau of Thailand, 2023[39]). Many countries have developed a certification body for organic agriculture, which often includes programmes that reduce the financial burden of certification for farmers. Although Singapore does not provide direct support for sustainable agriculture, it employs several satellite support measures through programmes such as “30 by 30” and the ACT Fund, which encourages sustainable urban agriculture projects in a bid to boost food security.
Overall, sustainable agriculture frameworks in Southeast Asia require improvement, despite varying levels of commitment across different countries. Legislative frameworks for sustainable agriculture should have more prominence in high-level documents, while mainstreaming mechanisms and policy instruments need concrete programmes as well as monitoring and evaluation systems in order to measure achievements. Governments in Southeast Asia need to invest more in the relevant policy measures put in place and enhance regulatory frameworks for sustainable agriculture.
The potential of organic agriculture in the green transition
An arguably more sustainable alternative to conventional agriculture is organic farming, which offers a strict framework for agricultural practices based on principles of natural resource protection and sustainability (FiBL/IFOAM, 2022[29]; ADB, 2021[36]). A shift towards organic agriculture has been associated with a 20% reduction in GHG emissions through abstention from using fertiliser (Scialabba and Müller-Lindenlauf, 2010[40]). In some cases, organic farming has been associated with increased carbon sequestration by 40‑72% (ibid.). Organic farming methods avoid the use of non-natural inputs (such as artificial fertilisers, synthetic chemicals and genetically modified organisms) during the farming process (ILO, 2018[19]). In 2020, organic agricultural land in Asia accounted for 8% of global organic agricultural land, an increase by almost 14 times since 2001 (FiBL/IFOAM, 2022[29]). Despite this promising trend, organic farming represents a small percentage of overall agriculture in Southeast Asia (Table 5.2). There is, however, large potential for conversion, with Indonesia, the Philippines, Thailand and Viet Nam ranked in the top ten countries with the largest organic agricultural land area in Asia in 2020 (FiBL/IFOAM, 2022[29]).
Table 5.2. Organic agricultural land area in Southeast Asia in 2020
Note: There are large variations between Research Institute of Organic Agriculture (FiBL) data and National Statistics Office of the Philippines data, which estimated the amount of organic agricultural land at 350 000 hectares in 2016.
Source: FiBL/IFOAM (2022[29]).
Local demand for organic food in Southeast Asia has been steadily increasing, despite the majority of organic products produced in Southeast Asia being exported to other regions. The COVID‑19 pandemic dealt a heavy blow to the agri-food trade, but the organic food market has rebounded strongly due to heightened awareness of health and nutrition (ADB, 2021[36]). In 2018, organic retail sales in Thailand were valued at EUR 12 million and at EUR 18 million in Viet Nam (FiBL/IFOAM, 2020[41]). Southeast Asian food markets forecast a 4.7 annual growth rate between 2019 and 2030, totalling USD 500 billion in consumer-driven spending on food (PwC, Rabobank and Temasek, 2021[15]). A market survey by PwC shows that Southeast Asian consumers value freshness and health-based products over price factors when making a purchasing decision (ibid.), which are characteristics associated to organic food products. Other significant markets for the organic food industry are in Japan, India, South Korea and Chinese Taipei. Organic farming provides one avenue the region can explore further in order to recover from pandemic-induced setbacks and contribute to food security.
New production methods and input changes in organic agriculture could increase employment, particularly in the upstream activities of the agriculture value chain. Higher labour intensity for organic practices than for conventional practices provides more on-farm jobs and wage work (Prihtanti et al., 2014[42]; ILO, 2018[19]). The ILO estimates that a transition to sustainable farming (measured by conservation and organic farming) would moderately increase labour demand in livestock farming, as well as waste management, construction, renewable energy and services (ILO, 2018[19]). On the other hand, labour demand in primary cereal production would significantly decrease, with a minor decrease in the mining and manufacturing of fossil fuels, and nuclear sectors as well (ILO, 2018[19]). Farmers and wage workers engaged in organic agriculture could also have greater opportunities for longer-term employment and a secure income, as organic crop diversification and rotation need more farm labour all year round or for extended periods (Finley et al., 2018[43]; Feliciano, 2019[44]).
Organic farming requires different types of inputs (e.g. fertilisers and pesticides) and techniques (e.g. crop diversification and cross-crop cultivation) which are less harmful for the environment than those used in conventional farming. Expanding organic agriculture will, by definition, reduce the demand for genetically modified, synthetic, or mineral inputs and increase the demand for organic equivalents, together with waste management and renewable energy (ILO, 2018[19]; Hijbeek et al., 2019[45]). Studies confirm that organic farming systems are consistently favourable for soil carbon levels, soil quality, plant diversity and biodiversity, and energy efficiency (Reganold and Wachter, 2016[46]). However, organic production methods tend to have 20% lower yields on average than conventional agriculture (Meemken and Qaim, 2018[47]), which would require organic farming to use more land for comparable output volumes. There are exceptions with certain organic farming techniques (e.g. crop diversification, intercropping) that perform better and achieve higher margins for a longer period than conventional sole cropping (Bedoussac et al., 2015[48]). The above studies relate mostly to developed countries, as data from developing countries are scarce.
When compared with conventional farming, organic farming could, in theory, improve incomes of farmers and wage workers. Price premiums for organic products, which are on average 50% above conventional products (Meemken and Qaim, 2018[47]), are the main mechanism for these economic returns, together with reduced production cost, diversified crops and income sources, and increased resilience to input price volatility on the supply side (Reganold and Wachter, 2016[46]; Seufert and Ramankutty, 2017[49]). Several studies show that organic premiums at the farmer level in developing countries can range from 6% to 44% (Setboonsarng, Leung and Cai, 2006[50]; Doanh, Thuong and Heo, 2018[51]; Beuchelt and Zeller, 2011[52]; Ibanez and Blackman, 2016[53]). However, the price premium at the retail level is often not necessarily reflected in what farmers actually receive; this is due to numerous actors along the value chain capturing some of the benefits (Minten et al., 2018[54]), with payment received by organic farmers in some cases not being any higher than that in conventional markets (Meemken and Qaim, 2018[47]). Price premiums captured will depend on multiple external factors, such as the efficiency of the organic value chain, the distance between major urban areas and rural organic areas, accessibility of the certified markets, and transport and retail infrastructure (Finley et al., 2018[43]; Meemken and Qaim, 2018[47]).
Despite optimistic employment and income prospects, the effects of a conversion to organic farming on job quality or the bargaining power of small-scale producers are less clear. How organic certification schemes affect power hierarchies, working conditions, and the redistribution of certificate-oriented benefits for wage labourers (particularly those employed in small farms) is relatively unclear (Meemken et al., 2021[26]). Whether the conversion to organic farming boosts decent work opportunities for farmers is also debatable, and there is little evidence from which to draw conclusions (Orsini, Padel and Lampkin, 2018[55]). Moreover, sustainable certification has generated more power and revenues for large companies in food manufacturing, retail and transportation, influenced by the consolidation of the market for organic produce (Meemken et al., 2021[26]; Marques Vieira et al., 2013[27]). Farmers do not always receive a share of wider retail profit margin due to their weaker bargaining power in the distribution structure. More research is needed in order to determine how the adoption of organic farming affects full and productive employment, rights at work, social protection, and the promotion of social dialogue among farm workers.
The social outcomes of organic agriculture are consistently positive. The adoption of organic agriculture encourages women’s engagement in paid employment by opening up new employment opportunities along the organic value chain, as well as increasing women’s access to information and technical training (German et al., 2020[56]; Setboonsarng and Gregorio, 2017[57]). It also helps to promote local and indigenous agricultural knowledge and preserves cultural heritage (Jouzi et al., 2017[58]). Organic agriculture offers social benefits indirectly through the services provided by farmers’ organisations. Transitioning to organic farming promotes certified farmers’ organisations at local and regional levels and gives small-scale farmers a chance to define new responsibilities and rules for the management of resources (UNCTAD/UNEP, 2008[59]). Farmers’ organisations provide financial and technical support, training and education for agricultural productivity and invest in community development and social services (e.g. education, healthcare) (Lin et al., 2022[60]; Jouzi et al., 2017[58]; Qiao et al., 2016[61]). The services for capacity building and organic technology are particularly important for integrating small and medium-sized farms, as well as women and young rural workers, into the agri-food value chain (ADB, 2022[6]). Farmers’ organisations can also help farmers improve the profitability of their organic products by offering bargaining power, collective marketing and access to credits and markets (Meemken and Qaim, 2018[47]). Sustainable and diversified farming systems could eventually lead to the enhanced resilience of rural communities to food insecurity, climate change and other external shocks.
Understanding the labour implications of a green transition in agriculture is essential in order to inform Southeast Asian policy makers about how to make the transition more inclusive for all. The employment risks associated with the transition are particularly important for the livelihoods of farmers and other actors in the agri-food value chain. Among sustainable farming methods and practices, organic agriculture is relatively well defined and regulated, which is advantageous for measurement and analytical purposes. Nonetheless, at the same time, organic agriculture is a contentious farming method in terms of its environmental and nutritional merits, and uptake remains very limited (Paarlberg, 2021[62]). An analysis of the conversion to organic farming and labour market changes can help to identify the most vulnerable sectors and workers during the agricultural green transition and to anticipate the policies and programmes that will support their inclusion. By considering the interconnections between organic agriculture and other policy objectives, policy makers can develop coherent and effective policies that promote more sustainable and resilient food systems.
Understanding the impact of a green transition in agriculture: A simulation using organic rice farming
This section illustrates the effects of a conversion from conventional to organic farming practices on output, employment and income. Organic agriculture provides a number of environmental benefits, such as enhanced soil fertility, increased biodiversity and reduced pollution (IFOAM, 2021[63]). Given these advantages, several countries in Southeast Asia have enacted policies to encourage farmers to transition to organic farming. The Philippines and Thailand in particular have set targets for the expansion of organic agriculture which we used to create scenarios for this analysis. Organic agriculture is also clearly defined by international standards and certifications, which confirms that definitions of “organic” are harmonised between the literature and surveys (ILO, 2018[64]). At the regional level, ASOA provides standards for organic practices across countries in the region as part of the Standards in the Southeast Asian Food Trade (SAFT) (ASEAN, 2017[65]).
This section presents the results of a simulation exercise done for three scenarios of rice farmland conversion from conventional to organic farming practice. The model looks at reaching a 3%, 5% and 7% share of organic rice farmland, reflecting the national targets set by the Philippines and Thailand as closely as possible. The Philippines’ overall goal was to reach 5% of organic agriculture land area by 2020 (Philippines Bureau of Agriculture and Fisheries Standards, 2018[66]). In its National Organic Agriculture Development Strategy (2017-2021), Thailand also provides a target to specifically increase organic rice farming to 400 000 hectares of land area by 2021, which represents 2.5% of total arable land area (FAOSTAT, 2022[20]; Thailand Government Public Relations Department, 2017[67]).
The simulation exercise is based on input-output table (IOT) modelling. IOTs describe the sale and purchase relationships between producers and consumers within an economy, using data from a broad range of sources. IOTs are an optimal choice for evaluating cascading effects of the growth or change in demand in one sector by identifying the impact on sectors closely linked to agriculture. Alternatives like computable general equilibrium (CGE) models would allow simulating changes in the prices of outputs and inputs of the different sectors, thus affecting the new equilibrium. Nonetheless, the choice of IOT modelling is appropriate for simulating small shocks like done here. IOTs are readily available for most countries and are transparent in their assumptions. The challenge is that most national IOTs do not include a predefined sector for organic agriculture, and therefore this was estimated and constructed within pre-existing tables (see Annex 4.A for details on the methodology used).
Due to data limitations on organic crops, rice was selected for this exercise, as it is the single most critical crop in Southeast Asia, responsible for 50% of the calorie intake of the region’s population. Rice is also the highest emitter of methane, the second most important greenhouse gas contributor to climate change following carbon dioxide. Rice is responsible for 10% of global methane emissions (FAO, 2020[68]). In Southeast Asia, rice cultivation accounts for as much as 25-33% of the region’s methane emissions (Umali-Deininger, 2022[69]). Rice is cultivated in more land areas than any other crops in Cambodia, Lao PDR, Myanmar, Thailand and Viet Nam (IRRI, 2020[70]).
The simulation using rice provides useful insights for Southeast Asia’s labour force engaged in agriculture and the sectors that are directly and indirectly linked to agriculture. Data for three countries (Indonesia, the Philippines and Thailand) were available for the simulation. An average for Southeast Asia is derived using the rice sector estimates in Thailand’s IOTs as a proxy for Cambodia, Lao PDR and Viet Nam’s rice industries, which do not exist in their national IOTs.
Data on the organic farmland area come from the Research Institute of Organic Agriculture (FiBL) and are the result of a global survey on organic farming (FiBL/IFOAM, 2021[71]). The organic indicators begin in 2000 and are broken down by crops, the organic area covered (in hectares) and the share of the organic farmland area compared with the total farmland are. Due to the volatility in agricultural production unrelated to organic production (e.g. rainfall, crop cycling, etc.), as well as gaps in data collection, organic farmland area is estimated as the average of the last five to ten years between 2010 and 2020, due to high data volatility (Table 5.3). This evens out production and provides a more stable estimate of organic production, particularly given some notable variations in FiBL estimates and what has been published in national reports.
There is limited literature exploring the cascading effects of a shift from conventional to organic agriculture in developing countries. Much of the literature focuses on the effects on farm costs and profitability in developed countries or requires intensive data collection through the design of specific projects or surveys (Reddy et al., 2022[72]; Seufert, 2012[73]; Meemken and Qaim, 2018[47]; Reganold and Wachter, 2016[46]). Furthermore, the literature often does not look explicitly at the economic impacts of the switch to organic agriculture on the food system. There are examples where life cycle assessment is applied to organic agriculture, but the focus tends to be on the practice’s environmental benefits rather than on economic outcomes (van der Werf, Knudsen and Cederberg, 2020[74]; Meier et al., 2017[75]). This simulation using rice provides insights into the economic effects of a shift towards organic agriculture and the possible changes it brings to the food system, including food processing industries and food-related services, as well as chemical industries that provide inputs to the sector.
A conversion to organic rice farming practice has positive effects on output, employment and income
A simulated increase in the farmland area dedicated to organic rice farming generally yields more employment and income than an equivalent increase in farmland area dedicated to conventional agriculture. “Employment” is expressed in the number of full-time equivalent (FTE) jobs and includes direct and indirect jobs created. “Income” is expressed in the monthly earnings of workers in the sector in USD.
The expansion scenarios simulated are the targets of a 3%, 5% and 7% share of organic farmland area out of total rice farmland area. The expansion refers to a conversion from conventional rice farms to organic rice farms, no additional land use. The results are compared with the same hectare increase in conventional rice farmland area. For example, if an organic conversion to reach 5% land share is equivalent to 300 000 hectares, the comparison is done for the same nominal increase in conventional rice farmland. The effects of this expansion on employment and income are described first for the rice sector, then for the agricultural sector (including hunting and forestry) and, finally, for sectors closely linked to the agri-food system.
An increase in organic rice farmland results in more jobs and income than an equivalent increase in conventional rice farmland
An expansion of organic rice farmland to reach a 5% share of total rice farmland area (medium scenario) results in an increase in employment of 27 FTE jobs in the organic rice sector, on average for countries in Southeast Asia. This can be considered as the direct effect of the expansion. With an equivalent expansion of conventional rice farmland area, only 5 FTE jobs are created in the conventional rice farming sector (Table 5.4).
The gains in income from an expansion in organic rice farmland are also greater for organic rice workers than for conventional rice workers. Organic rice farmers/workers can earn USD 7.52 more per month from a medium expansion scenario. The model assumes that the output value increase is captured by farmers and workers in the organic rice sector. In countries where farmers’ monthly income ranges from USD 20 to USD 400, this increase can represent a significant contribution. On the other hand, an equivalent expansion of conventional rice farmland area increases rice farmers’/workers’ income by only USD 0.42 per month on average for countries in the region (Table 5.4).
Table 5.4. Effects of rice land expansion on output, employment and income in the organic rice sector compared with the conventional rice sector
Note: Southeast Asia values include the average of six countries: Cambodia, Indonesia, Lao PDR, the Philippines, Thailand and Viet Nam. The rice sector estimated in Thailand’s IOT is used as a proxy for Cambodia, Lao PDR and Viet Nam due to missing data in respective national IOTs. Employment does not include part-time and seasonal employment. Reading: In Indonesia, an increase in organic rice farmland to reach 5% share of total rice farmland creates 117 additional FTE jobs in the organic rice sector. An equivalent size expansion of conventional rice farmland creates 22 jobs in the conventional rice sector.
Source: Authors’ own calculations based on OECD IOTs.
The effect on employment in the overall rice sector is greater for an expansion in organic rice farmland than for an expansion in conventional rice farmland. In the medium scenario, the rice sector in Southeast Asia would create an average of 126 more FTE jobs, compared with an average of only 6 more FTE jobs created under an equivalent conventional rice farmland expansion (Table 5.5). In Indonesia, the medium scenario increase in organic rice farmland would create 543 FTE jobs in the rice sector compared to 22 FTE jobs for the equivalent conventional expansion. This can be explained by the fact that organic farming practice requires higher labour inputs than conventional farming, but also includes indirect employment effects in the overall rice sector. The simulation does not include part-time and seasonal employment, which is likely to experience similar positive gains and is very relevant for the agricultural sector.
The income gains in a medium scenario are also greater with organic rice farmland expansion than with conventional rice farmland expansion. Rice farmers/workers in Southeast Asia, on average, can earn USD 8.11 more per month from an expansion in organic rice farmland. On the other hand, a conventional rice farmland expansion of the same magnitude increases income by only USD 0.21 per month, on average, for countries in the region (Table 5.5).
Table 5.5. Effects of organic and conventional rice farmland expansion on employment and income in the rice sector
Note: Southeast Asia values include the average of six countries: Cambodia, Indonesia, Lao PDR, the Philippines, Thailand and Viet Nam. The rice sector estimated in Thailand’s IOT is used as a proxy for Cambodia, Lao PDR and Viet Nam due to missing data in respective national IOTs. Employment does not include part-time and seasonal employment.
Source: Authors’ own calculations based on OECD IOTs.
Expansion of organic rice farmland area creates winning and losing sectors
Observing the changes at the sectoral level provides a dynamic picture of how output, employment and income are affected in different sectors through direct and indirect input-output linkages. Not surprisingly, the agricultural sector (including hunting and forestry) sees the largest return from the expansion of organic rice farmland; it is followed by several closely linked sectors in the agri-food system such as food products, beverages and tobacco, and wholesale and retail trade.
The medium scenario expansion creates an additional USD 2.2 million in agricultural output, on average, for countries in the region. The equivalent area expansion of conventional rice farmland results in an overall agricultural output increase of USD 1.5 million, or 68% of the organic rice farmland expansion scenario. Agricultural output value increases by USD 7.22 million in Indonesia, by USD 1.60 million in the Philippines, and by USD 1.42 million in Thailand. Organic rice farmland expansion yields higher outputs than for an equivalent expansion in conventional rice farmland, for most countries, except Thailand (Table 5.6).
Employment gains in the agricultural sector in the medium scenario is 43% higher for an expansion in organic rice farmland scenario compared with the equivalent increase in conventional rice farmland. Employment in the agricultural sector increases by an average of 0.023 FTE jobs in the 5% organic rice farmland expansion scenario, compared with 0.013 FTE jobs for the equivalent size expansion in conventional rice farmland (Table 5.6). Regarding income, workers in the agricultural sector do not see significant gains, with an average increase of USD 0.017 in monthly income for workers across countries in Southeast Asia (Table 5.6), but still 30% higher than for conventional rice farmland increase. The small nominal increases, in both employment and income, likely relates to the small contribution that rice farming makes to overall employment and wages compared with other sectors in the economy, as well as the small percentage of land increase simulated.
Southeast Asian workers in the agricultural sector (including hunting and forestry) are older, are less educated, are paid less, and are more likely to live in rural areas than workers in non-agricultural sectors. Farmers are more likely to be men than women. With regard to their occupation, 71% of agricultural workers are skilled agricultural and trade workers, while the rest are elementary workers. These jobs require a medium to low level of skills. Most farmers (80%) are self-employed, and more than 80% are working informally with little access to social protection programmes. A significant shift towards greener production practice – using organic or other more sustainable methods – implies that farmers and workers in the agricultural sector will adopt these practices. Given the profiles of agricultural workers, technical training and financial support will be needed in order to overcome the initial barriers created by the transition.
Table 5.6. Top three winning sectors from a medium scenario expansion in organic rice farmland area, compared with equivalent conventional rice farmland expansion
Note: Conventional rice farmland expansion refers to the medium scenario of increasing organic rice land area to reach 5% of total rice land. Conventional farmland expansion refers to the equivalent land size expansion of conventional rice farm. Output results are the sum of direct and indirect effects of the change in final demand and indicate additional monthly income in USD in the medium scenario. Income results aggregate 15 sectors according to ILO designations, rather than the 40‑sector aggregation of output and employment. Employment does not include part-time and seasonal employment.
Source: Authors’ own calculations based on OECD IOTs.
The chemicals and chemical products sector consistently experiences the highest losses from a transition to organic farming across all dimensions. With the medium scenario, the chemicals and chemical products sector is estimated to lose USD 1.17 million in output. The sector’s loss in output ranges from USD 0.4 million (the Philippines) to USD 2.5 million (Indonesia) relative to 2018 levels. Both organic and conventional rice farming conversions put a very slight downward pressure on employment in other sectors, between -0.006 FTE jobs (the Philippines) and -0.033 FTE jobs (Indonesia). No sector experiences a loss in income with the increase in organic rice farming (Table 5.7).
Table 5.7. Top three losing sectors from an expansion in organic rice farmland area
Note: The simple (output) multiplier is the sum of direct and indirect effects of the change in final demand and indicates additional monthly income in USD with a 5% increase in land used for organic rice farming. Income sectors for Southeast Asia and Thailand are aggregated into 15 sectors according to ILO designations, rather than the 40‑sector aggregation of output and employment. Employment does not include part-time and seasonal employment.
Source: Authors’ own calculations based on OECD IOTs.
The concentration of losses in the chemicals and chemical products sector can be attributed to the prohibition of synthetic fertilisers and pesticides in organic farming. Losses in the chemicals and chemical products sector will be felt most acutely in the fertiliser-producing countries of Southeast Asia. The Philippines and Thailand will likely feel a smaller impact than estimated because they import the majority of the fertiliser used in their agricultural production. Between 2018 and 2021 the average fertiliser consumption was 17.5 times the production for Thailand and 6.8 times for the Philippines (World Bank, 2023[76]). Indonesia consumes more domestically produced fertiliser compared with other countries in the region, with the ratio of fertiliser consumption to fertiliser production standing at 1.39 for the same period average (ibid.). This explains the losses in the chemicals and chemical products sector being larger in Indonesia than in the other Southeast Asian countries examined. Finally, the energy production and petroleum sectors see the second largest decrease in both output and employment in the region from a transition to organic farming, likely related to reduced reliance on heavy machinery in agricultural production in this scenario.
Although not overly large, a conversion to organic rice farming practice would affect jobs and incomes for workers in the chemicals and chemical products sector. Relative to other sectors, Southeast Asian workers in this sector are younger, are more educated, are paid more and are more likely to live in urban areas. Workers in chemical manufacturing sectors are more likely to have intermediate or basic education and tend to be men. While plant/machine operators and assemblers (28%) and basic occupations (23%) account for just over one-half of the workforce, managers, professionals and technicians represent 21% of total employment. One-half of jobs demand a medium level of skills, while 21% of jobs require a high level of skills. With regard to workers’ status, employees are prevalent in the manufacture of chemicals and chemical products. Likewise, a little less than one-half of the workforce has access to social security (49%), paid leave (54%) and sick leave (44%), although access to parental leave is relatively limited, at 24%.
An increase in organic farmland combined with additional final demand spending can help offset sectoral losses
When an increase in organic farmland is combined with an increase in final demand spending for organic rice, sectoral losses can be minimised with relatively small increases in spending. The medium scenario of a 5% expansion of organic farmland area is estimated alongside an increased in final demand spending for organic rice of USD 100 000. As a reference, Indonesia currently spends USD 16.2 million on all organic food products (Organic Trade Association, n.d.[77]). Demand value is kept relatively small in order to avoid violating key assumptions for IOTs. Table 5.8 shows the effects of the additional spending in conjunction with organic farmland area expansion.
An increase in demand for organic rice combined with an expansion of organic rice farmland area leads to a more robust and positive impact on output and employment. On average in the region, there is an additional USD 3.05 million increase in output under the medium scenario combined with a USD 100 000 increase in final demand spending. At its highest, the output increases by a factor of nine in Indonesia and more than triples in the Philippines. When compared with the medium scenario, the boost in employment from an additional USD 100 000 in final demand spending increases the number of FTE jobs by 0.042 for the region as a whole, by 0.009 in the Philippines and by 0.223 in Indonesia.
Table 5.8. Changes in output, employment and income from medium scenario expansion of organic rice farmland combined with final demand spending increase
Note: Southeast Asia regional estimates are the average of six countries: Cambodia, Indonesia, Lao PDR, the Philippines, Thailand and Viet Nam. The Cambodia, Lao PDR and Viet Nam rice sectors are estimated using Thailand as a proxy. Employment does not include part-time and seasonal employment.
Source: Authors’ own calculations based on OECD IOTs.
When these changes are examined at the sectoral level, with the increase in demand, even the chemicals and chemical products sector is expected to experience considerably less employment losses than the scenarios without increase in demand. Indonesia sees a decrease in the number of FTE jobs from 0.033 FTE jobs loss in the medium scenario to 0.002 FTE jobs gain in the USD 100 000 spending combined scenario, almost completely compensating for the downward pressure that an expansion of organic agriculture could have on the sector. This highlights the distributional effects of additional consumer spending on organic rice, which boosts overall economic benefits and slows down job losses caused by the conversion to organic rice farming.
The estimated boost from an increase in consumption demand for organic rice, rather than from an expansion in organic rice farmland area alone, suggests positive linkages from increased spending on organic rice. The organic rice market in Southeast Asia has room for further development. In support of this, studies have highlighted the growing demand for organic agriculture in the region. One analysis conducted in the Greater Mekong Subregion highlighted that as incomes in the region rise, so does the demand for more premium foods such as organic products (ADB, 2017[38]). A similar report designates Viet Nam as a potential growth market for organic goods, citing the growing middle class, consumer concerns about health and the limited domestic supply of organic products as driving factors for a recent increase in the import of organic products in the country (USDA, 2021[78]). This demand forecast provides an incentive for policy makers to support the growth of domestic organic markets to meet growing consumer demand.
Policy implications to make the green agricultural transition more inclusive
In the organic farmland area expansion scenarios, Southeast Asia would likely experience increases in output, employment and income in the agricultural sector. Comparing an equivalent increase in conventional agriculture farmland area yields lower output (in value), employment creation and income gains. The simulation, using organic rice farming practice to illustrate a green transition in agriculture, shows that a more sustainable agricultural model has the potential to achieve the triple objectives of better livelihoods of farmers, economic gains and environmental sustainability.
Facilitate the transition by ensuring social protection and job reallocations for workers in losing sectors
Employment losses in upstream sectors like chemical manufacturing and energy production are relatively small and manageable. Workers in the affected sectors tend to be younger, more educated and paid more compared with the national average, and are more likely to live in urban areas. Facilitating their labour mobility through a combination of reskilling/upskilling and social protection will likely be cost-effective. The simulation also indicates that an increase in organic rice agriculture combined with increased final demand spending could help to balance out the losses in negatively affected sectors. To achieve a smooth transition to greener agriculture, the transition plans would also need to include policies that promote consumer awareness about the benefits of organic food and other sustainable production methods.
As a green transition could create jobs and increase income in the agricultural sector, making this sector attractive in order to recruit new talent should be a priority. Countries could launch campaigns highlighting the employment benefits and opportunities in sustainable agriculture, such as organic farming, in order to attract new recruits among the youth population. At the same time, governments should collaborate with educational institutions, research centres and industry stakeholders to develop vocational training programmes focused on sustainable agricultural practices and technologies. This could include courses in organic and conservation farming techniques, precision farming, integrated pest management, and soil science, which would allow to reduce the yield gap between organic and conventional production techniques. Marketing and business development skills focused on organic products and food processing would support entrepreneurs and SMEs engaged in organic or value-added food products. OECD countries provide good examples of preparing young people for “green jobs” in agriculture. The Netherlands’ Green Pact helps to strengthen the connection between green education and industry in order to attract students and young professionals to the agricultural sector (Box 5.1).
Box 5.1. Improving the connection between green education and the job market: The Dutch Green Pact
The Green Pact (Groenpact) is a national initiative aimed at improving green education in agriculture, horticulture, food, and nature and the living environment through the renewal of the Dutch knowledge and innovation system and professional practices. It is a co‑operative initiative between the government (specifically the Ministry of Agriculture, Nature and Food Quality (Ministerie van Landbouw, Natuur en Voedselkwaliteit or LNV)), education institutions and business communities that was started in 2016. The Green Pact seeks to broaden knowledge and innovation by creating stronger connections between education and business; to enhance the internationalisation of education; to promote lifelong learning through closer relationships with other fields such as healthcare, engineering, and information and communication technology (ICT); and to invest in the enrolment of students in Dutch green education programmes and related institutions.
The Green Pact implements acceleration programmes, practice-oriented research through clusters, knowledge-sharing activities, practical co-financing arrangements, and subsidies. The acceleration programmes focus on promoting progress in four specific subjects:
1. education-labour market alignment
2. knowledge sharing and application for organisations in education, research, civil society and policy development
3. internationalisation of the Dutch green knowledge and education system
4. digitalisation and technology in green education of all levels.
In addition, the Green Pact Monitor will soon be introduced in order to identify skills gaps and develop a new lifelong learning strategy. Research is being conducted to forecast the skills that will be required in the green sectors.
The Green Pact promotes younger generations’ interests in agriculture and food-related careers in various ways. One way is by partnering with youth organisations in agriculture, food and climate to generate fresh ideas and knowledge from students and young professionals. Another way is through the Green Pact Impact Prize, an annual competition for students studying green impacts or launching start-ups in agri-food, horticulture, nature and the living environment. Eligible students in secondary or higher education can participate in the competition to win EUR 2 500 (euro).
The Green Pact is funded annually by LNV with a budget of around EUR 4.2 million and by participating organisations with cash and in-kind contributions (in line with their areas of interest). Between 2022 and 2023, the number of participating organisations increased from about 80 to more than 90. The participants operate in various sectors, including education/knowledge, biodiversity, the food chain, water management and area development across the public and private sectors.
Source: Groenpact (2023[79]); OECD (2023[80]); van Leeuwen (2022[81]).
Supporting existing farmers to convert to sustainable agricultural practices is also important. Converting conventionally farmed land to organically farmed land can take up to three years, during which time farmers can expect both yield and income losses. Subsidies and access to low-cost financing schemes during this transition phase will be necessary in order to protect the livelihoods of farmers. Examples include specialised financial products and services tailored to the needs of smallholders, including micro-loans, crop insurance and weather-based index insurance. The Philippines, through its Organic Agriculture Act of 2010, provides various tax incentives to organic farmers and Thailand subsidises rice farmers during the first three years of conversion to organic rice (Box 5.2). At the same time, providing technical assistance and training to rural financial institutions (e.g. farmers’ co‑operatives and community-based organisations) could enhance their ability to support smallholder farmers in the green transition. These supply-side interventions include support for technology upgrades, inclusion of digital inventions, delivery of resources and irrigation system improvements (World Bank, 2022[82]). Furthermore, improving market access can help small-scale producers to integrate into the sustainable agri-food value chain. Reliable market access is crucial for promoting sustainable (and organic) agriculture, ensuring reasonable prices for sustainable food products and decent livelihoods for farmers (Ume, 2023[83]; Guarín et al., 2022[84]).
Box 5.2. Incentive schemes for organic farmers in the Philippines and Thailand
In the Philippines, the Organic Agriculture Act of 2010 or Republic Act No. 10068 is the landmark legislation aimed at the promotion of organic agriculture. It promotes ecologically-sound, socially acceptable, economically viable and technically feasible production of food and other fibers. This Act declares that the State will promote the practice of organic agriculture in order to enrich the fertility of the soil, increase farm productivity, reduce pollution and destruction of the environment and prevent the depletion of natural resources. Furthermore, the Act aims to protect the health of farmers, consumers, and the general public, and save on imported farm inputs.
Recognising the central role of the farmers, indigenous people and other stakeholders at the grassroot level, the Act makes provisions for various support mechanisms for organic farmers. This includes, among others, tax incentives:
a) Exemption from the payment of duties on the importation of agricultural equipment, machinery and implements;
b) Identification by local government units of local taxes that may be offered as incentives to organic input production and utilisation;
c) Provision of preferential rates and special window to organic input producers and users by the Land Bank of the Philippines;
d) Subsidies for certification fees and other support services to facilitate organic certification;
e) Zero-rated value-added tax on transactions involving the sale/purchase of bio-organic products, whether organic inputs or organic produce; and
f) Income tax holiday and exemption for seven years, starting from the date of registration of organic food and organic input producers on all income taxes levied by the National Government.
In Thailand, the government launched in 2017 a programme to promote organic rice farming in view of increasing production to meet global and domestic demands for organic rice. The programme aimed to reduce the area of standard rice cultivation by 1m rai (160 000 ha) within five years. Farmers who sign up for the scheme could receive financial support to assist them in buying organic seeds, reducing their dependence on pesticides, and building barriers to prevent contamination from neighbouring farmland. Subsidies started at THB 2 000 (Thai Baht) (USD 56.30) for every rai (0.16 ha) of land cultivated the first year to THB 3 000 (USD 84.50) per rai for the second year and THB 4 000 (USD 112.70) for the third year.
Source: Republic of the Philippines (2009[85]); Oxford Business Group (2017[86]).
For the losing sectors, such as the chemical manufacturing and energy production, countries could implement policies and programmes aimed at reskilling or upskilling the affected workforce in order to facilitate labour mobility. Training programmes need to reflect the changing demands of the chemical and energy sectors. Strengthening extension services to provide continuous support and training to the affected workers would improve their employability. Such training initiatives can be jointly developed and delivered through public-private partnerships, leveraging the expertise and resources of various stakeholders. Governments could consider establishing various tools for training, including platforms for continuous learning and professional development through online courses, workshops, and mentoring programmes.
Creating enabling conditions could also make the green transition in agriculture more inclusive. Boosting market demand for sustainable food products has the potential to promote greener agriculture. Enhancing quality control systems is one way to reassure consumers about the origin and production methods used for organic food products. Countries are therefore encouraged to establish reliable certification and labelling systems to inform customers of the sustainable agricultural practices used. Another way to increase the demand for organic food products is to strengthen supply chain linkages between farmers, processors, distributors and retailers. A more efficient flow of food products could improve produce quality, avoid losses and improve farmers’ access to markets like supermarkets. Collaborating with retailers and food service providers to promote and highlight sustainable food choices would help raise awareness and further stimulate demand. Furthermore, Southeast Asia could foster international trade agreements that prioritise sustainable agriculture. This could involve advocating for certification standards and simplifying trade procedures for small-scale producers, enabling them to access international markets and export sustainably produced food products. For example, the United States Department of Agriculture (USDA) provides funding opportunities to strengthen the market for organic foods and to support local farmers seeking organic certification (Box 5.3).
Box 5.3. Enabling the growth of organic markets and supporting organic farmers in the United States: The Organic Market Development Grant programme
In 2023, the USDA announced several funding opportunities for existing and transitioning organic farmers to help lower the costs and risks involved in their organic transition. This funding is part of the Organic Transition Initiative, launched in 2022, which includes various programmes ranging from conservation assistance to improved crop insurance options. Despite the remarkable growth in organic market demand, several challenges discourage farmers from transitioning to organic certification: risks associated with limited organic processing, storage, and handling capacity; insufficient markets for rotational crops; uncertainty about market access; and a shortage of certain organic ingredients. In addition, certain organic products grown in the United States (US) – particularly organic livestock and processed products – heavily depend on imported feed grains and ingredients, making their supply susceptible to external shocks in the critical organic supply regions outside the US.
The Organic Market Development Grant (OMDG) programme offers competitive grants of up to USD 75 million for businesses producing or handling organic foods, non-profit organisations, and governments financing projects designed to promote markets for domestically grown organic products. The OMDG aims to boost domestic organic product consumption by expanding existing markets and developing new markets and related facilities while encouraging their use. Potential projects may include providing funding to develop new organic products or investing in organic infrastructure to improve market access for producers. The OMDG prioritises smaller, emerging, underserved, and veteran producers as well as underserved communities.
The Organic Certification Cost Share Program (OCCSP) covers producers’ and handlers’ costs of obtaining or renewing organic certification. The OCCSP provides up to 75% of certification costs and up to USD 750 for crops, wild crops, livestock, processing/handling and state organic programme fees (California only) during the programme year (1 January 2022 to 30 September 2023).
Source: USDA (2023[87]).
When developing sustainable or organic food value chains, Southeast Asian countries should prioritise investments in agricultural infrastructure in order to strengthen the inclusiveness of value chains. Improving irrigation systems, enhancing storage and processing facilities, and upgrading transportation networks in rural areas can increase the overall efficiency of agri-food value chains, benefitting all actors engaged in the agri-food system. For individual farmers, enhanced infrastructure is fundamental to increasing productivity, reducing post-harvest losses and accessing larger markets. Moreover, such an infrastructure upgrade can enable and sustain rapid growth in sustainable agriculture and its local markets. To ensure inclusive infrastructure investment, governments should involve farmers’ organisations and rural communities in order to give voice to smallholders, particularly resource-poor and women farmers (Guarín et al., 2022[84]). Governments should ensure efficient and sustainable infrastructure development in agriculture through collaboration with development partners, private sector stakeholders and local communities (World Bank, 2022[82]). Adequate funding and technical assistance should be allocated to address the specific needs and challenges of different regions and farming systems within each country.
Lastly, improving social protection and labour standards is important for creating decent work in emerging sectors and addressing livelihood challenges in declining sectors. Southeast Asia could perform a review and update of existing labour laws and regulations in order to ensure alignment with international labour standards. This includes providing fair wages, safe working conditions and access to social protection for agricultural workers. Countries could encourage the establishment of agricultural workers’ associations or unions to empower (mostly informal) workers and protect their rights. Providing training and support to enhance their capacity for negotiation and collective bargaining could further strengthen agricultural workers’ position in the agri-food value chain. In the meantime, the development of targeted social protection programmes (such as crop insurance, health insurance, and pension schemes) is essential in order to safeguard the livelihoods and well-being of smallholder farmers and agricultural workers. Unemployment benefits and temporary income assistance are necessary to provide social support for workers in the chemical manufacturing and energy production sectors who may undergo a job seeking and re-employment process. Engaging with the private sector to promote responsible and inclusive business practices is also crucial. This includes encouraging fair treatment of workers, respect for human rights and adherence to environmental sustainability principles.
Develop mainstreaming mechanisms and policy measures to implement strategies for sustainable agriculture
Southeast Asian countries need to put more political effort into creating enabling conditions to expand sustainable farming. The region’s current policy frameworks for sustainable agriculture are highly concentrated on high-level legislation or development plans. In contrast, concrete policy actions to implement the laws are less visible, except in a few countries. As part of the implementation strategies, the region has set quality standards for agricultural products at the regional and national levels. However, these are guidelines, which, in the absence of other actions, may have minimal impacts on the adoption of sustainable agriculture. Tools such as certification schemes also need to be carefully measured in terms of their impact on smallholders’ participation. Certification may promote non-random selection, favouring specific locations, organisations, farmers and workers who already participate in the existing marketplace rather than improving or changing their circumstances on average (Meemken et al., 2021[26]). Compliance with the guidelines and standards is currently entirely up to value chain actors, thus raising more questions than answers.
Governments are encouraged to establish mainstreaming mechanisms that involve integrating sustainable practices and principles into agricultural policies, programmes and practices. Countries that specify the organisations responsible for the implementation of national strategies for sustainable agriculture need to be more transparent about how those organisations will facilitate the transition to sustainable agriculture and with what policy tools, and how they will measure, monitor and evaluate the policy outcomes. Regular expenditure review or strategic assessment of sustainability indicators could help to measure the effectiveness of relevant policies and programmes. Meanwhile, countries without designated institutional mechanisms should consider leveraging an existing institution or creating a new one to facilitate cross-sectoral co‑ordination, information sharing and joint planning for sustainable agriculture. For some countries, developing a stand-alone regulatory framework for sustainable agriculture should be prioritised in order to accelerate the green transition process. This would ensure that sustainable agriculture becomes a shared priority across relevant institutions and fosters the coherent implementation of sustainable practices.
To ensure successful implementation, Southeast Asian countries should tailor these recommendations to their specific contexts and regularly monitor, evaluate and provide feedback on their implementation. Adjustments should be made as needed in order to address the unique challenges and opportunities of each country. By implementing these policies, Southeast Asian countries can promote and enhance access to sustainable agriculture, and provide their workforces with the skills necessary to foster inclusive and resilient agriculture systems in the region.
Conclusion
Promoting sustainable agriculture has become a central agenda for Southeast Asia to achieve long-term environmental, economic and social well-being. By adopting sustainable farming practices, such as organic farming, Southeast Asian countries could mitigate the negative impacts of agriculture on the environment, conserve natural resources and improve food security. While the existing regional policy frameworks have room for improvement, some countries have been pioneers in encouraging sustainable farming practices through their own legislation and policy mechanisms. Sustainable agriculture is known to enhance the livelihoods of rural communities by bringing direct and indirect positive socio-economic benefits. However, during the transition period, the risks engendered by structural change could spill over to other sectors linked to agriculture.
In Southeast Asia, the green transition in agriculture, measured by an expansion of organic rice farmland area, is estimated to generate gains in rice outputs without negatively affecting employment and income in the agricultural sector and related sectors downstream. In contrast, output losses will likely be concentrated in the chemical manufacturing and energy production sectors linked to the rice value chain. The estimated effects, while generally minor, are much more pronounced than with the conventional farming scenario and vary from country to country. The socio-demographic and job profiles of the farmers and workers affected most by the conversion to organic farming practice also differ by industry. Southeast Asian countries should establish a broad, comprehensive skills strategy to support workforce recruitment in the emerging sectors as well as smooth labour reallocation in the declining sectors. Governments should also invest in creating enabling macro conditions for the inclusive expansion of sustainable farming and its value chains. Establishing implementation mechanisms using various potential policy instruments overseen by a responsible governing body should be the priority for policy makers. By doing so, Southeast Asia could pave the way for a sustainable and prosperous future for rural development and the entire economy.
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Annex 5.A. Methodology of the organic rice farmland expansion simulation
An input-output analysis was used to evaluate the labour and macroeconomic effects of a shift towards organic agricultural practice in Southeast Asia. Input-output tables (IOTs) describe the relationships between sectors of an economy by depicting sales and purchases from industry to industry (Miller and Blair, 2009[88]). IOTs take the form of a matrix whose values constitute the intermediate demand of each sector, with the rows representing the destination of the outputs from that sector and the columns representing the inputs to that sector (Annex Figure 5.A.1, Panel A). IOTs also include a final demand portion which quantifies the final use of the output based on whether the output was purchased by consumers or government bodies, stored in inventories, etc. IOTs are often complemented by a “value added” portion that consists of additional outputs of sectors, such as employment or wages. By “shocking” a final demand column or expanding a sector, the cascading effects of a change in the economy can be evaluated in terms of changes in output, employment and wages for each sector present in the table.
IOTs are frequently applied in contexts related to food systems and agriculture because of their flexible framework and ability to provide granular insights into changes across sectors (Jablonski et al., 2022[89]). IOTs can provide a granular perspective on which industries will be most affected by changes or a shock in an economy. This is because IOTs are less demanding from a data and computational perspective compared with common alternatives such as computable general equilibrium (CGE) models, and therefore it is much easier to estimate the effects on many sectors. IOTs do have limitations in that neither prices nor technology adjust to reflect changes in demand as would typically occur in a CGE model. This requires smaller shocks to be applied during IOT analysis in order to avoid infringement of assumptions of fixed relationships between sectors. This also circumvents concerns over the lack of supply constraints that also apply to IOTs, as the simulation does not foresee a large increase in production.
An organic rice sector is estimated in the OECD IOTs borrowing from the rice sectors in three Southeast Asian national IOTs. The OECD produces IOTs every three years for 66 countries (including most ASEAN countries and 45 industries). The OECD IOTs were selected due to their standardisation in accounting methodology and sectoral classification, which makes the results directly comparable across the region (Annex Figure 5.A.1, Panel A). The sectors in the national IOTs, which are 80‑sector models, are mapped to the OECD IOT sectors, leaving 40 industries in the final augmented OECD IOTs. The latest version, covering the 2018 calendar year, is used as a framework to develop an IOT augmented with an estimated organic rice sector (Annex Figure 5.A.1, Panel B). The analysis covers three countries with full tables (Indonesia, the Philippines and Thailand), where the technical coefficients for the rice sector were taken directly from each country’s own national IOT. Three additional countries (Cambodia, Lao PDR and Viet Nam) were estimated as “hybrid” tables, borrowing technical coefficients from Thailand to create the rice sector.
The organic rice sector is estimated in several steps using data from a number of national and international sources. The technical coefficients of the rice sectors borrowed from national IOTs are weighted by sectors based on intensity of use in organic rice production. Weights were constructed using agricultural surveys comparing input costs for conventional and organic rice farming in Asian and Southeast Asian countries. Each cost component was mapped to sectors present in the OECD IOTs and the organic cost was divided by the conventional cost for each sector to create the weight. The average weight for each sector was calculated across 15 surveys (Annex Table 5.A.3) and was then applied to the corresponding technical coefficient in the OECD IOTs. The weighted technical coefficients for the organic rice sector were then converted to intermediate demand values using an estimate for organic rice production.
Annex Figure 5.A.1. IOT with organic rice sector
Note: Sectors have been truncated for brevity. Organic rice, shaded in blue, is the new sector estimated.
Source: Authors’ own elaboration.
Construction of output, employment and income matrices
Employment. We use the labour force surveys (LFS) and country reports on agricultural employment to construct employment data in selected Southeast Asian countries Annex Table 5.A.1. The number of employees by sector (ISIC rev. 4, 2-digits) and the average wage by sector (ISIC rev. 4, 2-digits) for 2018 is extracted from raw LFS or from ILOTSAT for each of the treated countries (ILOSTAT, 2023[90]). The sectors are aggregated to match the grouping of the final 40-sectors in the OECD IOTs. While the Philippines, Cambodia and Indonesia include ISIC rev. 4, 4-digit designations in their LFS that provide estimates for some of the agricultural product employment, additional figures for employment by agricultural crop are pulled from agricultural censuses or other similar literature.
Income. A measure for income is constructed similarly to employment. Income data for the 40-sectors is only available for Indonesia and Thailand, whereas a more aggregated version of the OECD IOTs with 15-sectors is created to estimate broad sectoral impacts for the other countries. The Indonesia income data comes from estimates constructed using the LFS from 2016. Thailand’s income data is sourced from the country’s national IOTs. However, due to limited sectoral data availability the OECD IOTs must be even further aggregated to have estimates for each country. This relates to a total of 21 sectors.
Output. We gather output data from both the national IOTs and production data from national sources or the FAO. When the value for output is borrowed from the national IOTs we use the exchange rate to USD for that country and for the year the IOTs were published. This converts it to a currency consistent with the OECD IOTs values. Where there are no national IOTs to draw upon, data is gathered with preference to agricultural census reports published by the country’s statistical authority. That failing, the data is sourced from the FAOSTAT database on agricultural production.
Organic rice output is estimated using rice output values for 2018 taken from either national sources or the FAO database. The rice output value is discounted according to an output coefficient developed from the agricultural surveys and the land estimates of organic rice production taken from Research Institute of Organic Agriculture (FiBL). The same methodology is applied to estimate the number of employees for organic rice across countries, but using ILO and national estimates for number of employees in rice. The organic rice sector is then inserted into the IOTs and subtracting the total amount from the agriculture, hunting and forestry sector, assuming its entire production was already captured in that sector.
Organic land area. To evaluate expansion of organic agriculture and come up with organic output and employment estimates as the sector expands, organic land area data is needed. This data comes from Research Institute of Organic Agricultural (FiBL) and is the result of a global survey on organic farming (FiBL/IFOAM, 2021[71]). The organic indicators begin in 2000 and are broken down by crops, covering organic area (hectares) and the share of organic land area compared to the total farmland. Due to the volatility in agricultural production unrelated to organic production (e.g. rainfall, crop cycling, etc.) as well as gaps in data collection, we estimate organic land area as the average of the last ten years, as available. This evens out production and provides a more stable estimate of organic production, particularly given some notable variations in FiBL estimates and what has been published in national reports. This data is collected for each of the countries we are treating and for each of the agricultural products, which are also selected based on their availability in this dataset.
Agricultural surveys. Finally, organic coefficients, which are used for weighting the technical coefficients pulled from national IOTs to more accurately represent organic production of that product, are developed from a number of agricultural surveys and academic papers. These coefficients are constructed using data on the difference in cost structure between organic and conventional agriculture production provided by this literature. These agriculture surveys are collected with preference to Southeast Asian and Asian countries and focus on a single agricultural product at a time. However, due to data limitations, supplementation from surveys conducted outside of the region is also considered for some of the agricultural products. Rice in particular includes estimates from Bangladesh and India. At least ten surveys must be collected for a set of coefficients to be considered viable, although a higher number is preferred.
Annex Table 5.A.1 summarises the baseline data used for the simulation.
Annex Table 5.A.1. Baseline of rice and organic rice output, employment and income, 2018
Note: Data on organic rice are estimations made using various surveys and literature and are not official data. They serve as starting points for the simulations. Data for employment are from 2018 unless otherwise stated.
Source: EMPLOYMENT: Philippines and Cambodia: ILOSTAT (2023[90]); Indonesia: Sujianto et al. (2022[91]), Toharisman and Triantarti (2014[92]); Thailand: Kusanthia (2010[93]), ILO (2021[94]); Viet Nam: Vietnam Trade Promotion Agency (2008[95]); Lao PDR: Lao PDR Ministry of Agriculture and Forestry (2020[96]). INCOME: Indonesia: ILOSTAT (2023[90]); Viet Nam: ERI (2023[97]). OUTPUT: Philippines: Philippine Statistics Authority (2023[90]); Indonesia: Statistics Indonesia (2021[98]); Thailand: Thailand Office of the National Economic and Social Development Council (2023[99]); Cambodia, Viet Nam and Lao PDR: FAOSTAT (2022[20]).
Simulation methodology
1. Extraction of agricultural product sectors
As a first step, we break organic agriculture down into the production of several organic agricultural products. We do this because of the lack of data on the cost structure of organic agriculture as a whole in the region. Instead, agricultural surveys at the crop and livestock level are much more readily available, allowing us to construct coefficients without the need for supplementary surveys. We can then apply relevant coefficients to the production functions of each crop, which we built up to estimate organic agriculture as a whole. However, it does not capture all of organic production, given that we select only crops that are particularly relevant to each country’s production and that agricultural surveys do not exist for all crops that are produced organically in the country. Breaking down organic agriculture into specific agricultural products also opens up the possibility for pursuing complementary frameworks such as life cycle analysis should we want a more micro-perspective.
The OECD IOTs have a single sector that captures all of agricultural production in their national industry-by-industry tables: Agriculture, hunting and forestry. We assume that all of agricultural production, both conventional and organic, is captured in this sector. To parse out our targeted agricultural product from this sector, we borrow from either the country’s national IOT or from a similar neighbouring country. When available, we use the national IOT from the same year as the OECD, 2018, or the latest year available. As an example, we focus on the rice sector in the Philippine Statistics Authority’s (PSA) IOT.
Each sector in the PSA IOT is mapped to the 40 sectors available in the OECD IOTs using ISIC rev. 4 2‑digit designations. We next calculate a vector of technical coefficients for both inputs into and outputs of the rice sector. This is done with the following equation,
where represents the rice sector, represents one of the 40 other sectors in the OECD IOTs, represents the technical coefficient at the conjunction of and rice, is the intermediate or final demand value for from rice, and is the total output of rice. This is repeated for the 40 sectors, creating two vectors: one for origin of rice production, what are the necessary inputs for production, and the other for destination of rice production, which industries use rice production as an input.
2. Addition of organic agricultural product sectors
Our next step requires that there is already a non-zero amount of organic agricultural production in the country. National reports and FiBL data make it clear that each of the six countries have some organic production already underway for each of the agricultural products we are treating. We also assume that there is a notable difference in the cost structures between organic and conventional agricultural production. These differences are related to the (non-)use of chemical pesticides and fertilisers, farming techniques, tillage practices, types of seeds used and a focus on biodiversity (IFOAM, 2021[63]). Furthermore, we require that these differences are strictly controlled through national and international certification standards. Given these assumptions, there should be a notably large impact on factors that can be captured by looking at variations in cost structures between conventional and organic farms such as inputs, labour and yield. We thus undertake a process similar to how IOTs are originally constructed and map purchases of organic farmers to sectors using financial statements and agricultural surveys. However, since we already have robust technical coefficients borrowed from national IOTs, this process can be far less data-intensive while still providing credible estimates.
To account for cost variations between conventional and organic for each of the agricultural products, we construct coefficients that will essentially function as weights for the technical coefficients. We create these coefficients using the literature available on cost structure variations between conventional and organic agriculture. This literature consists of agricultural surveys, farmers financial statements, and other cost structure data. We extract the total cost for a number of inputs for the agricultural products, which include expenses such as seeds, fertiliser and labour, but also cover yield, output and destination categories occasionally. Each of these expenses are mapped to the inputs of the corresponding sector in the OECD IOTs (Annex Table 5.A.2). When aggregated, the sum across the OECD sectors is calculated and the total cost for that sector for both conventional and organic farming.
Annex Table 5.A.2. Sectoral mapping of farmers expenses from agricultural surveys
Source: Authors’ own elaboration.
Certain challenges arise from the lack of standardization in the literature as many of the surveys do not include exact definitions for their expenses. For example, the definition of inorganic and organic fertiliser can vary in its specification, with definitions ranging from animal manure to organic fertiliser processed in plants similar to those of non-organic fertiliser. When available, we verify that definitions match as much as possible across surveys through reviewing the questions posed during the survey. Otherwise, we rely on a large number of surveys to smooth any misalignments in designations. Other issues arise from categorization of broad costs, such as potash, which passes through several sectors to reach its final destination, starting with the chemical sector and ending with retailers. Additional research is conducted here to evaluate which sector would be considered the largest contributor to the expense’s production.
3. Calculating ratio between organic and conventional rice costs
We next calculate the ratio between organic and conventional total cost to create a coefficient. Drawing inspiration from Garett-Pelitter (2017[100]), we assume this coefficient represents the relative difference in input or output between the two farming practices and will use it as a weight for each of the agricultural product’s technical coefficients. Given that we aggregate from a number of different sources, we account for differences in currencies by calculating the ratio from each source individually. The final coefficients are the mean of each source’s ratios by sector. The coefficients for rice are the average of 15 cost surveys for rice in the Southeast Asia and neighbouring countries (Annex Table 5.A.3).
Annex Table 5.A.3. Rice coefficients, comparing organic and conventional agriculture
Note: These coefficients are a summary of 15 cost surveys for the production of rice.
Source: Authors’ own calculations.
The coefficients for rice align with what would be expected when estimating the difference in conventional and organic farming practices. They also follow along generalised organic coefficients that have been calculated for similar methodologies (ILO, 2018[64]). The labour coefficient of 1.58 indicates that organic farming is more labour-intensive than its conventional counterpart. This aligns with the consensus in organic farming data and literature that more labour is required when employing organic farming practices (Orsini, Padel and Lampkin, 2018[55]). This is attributed to the need for additional land to maintain yields and infrequent use of large machinery in tillage and weeding. The income coefficient’s value of 1.54 also reflects expectations, given the price premium afforded to organic products has led to higher profitability from organic farms (Crowder and Reganold, 2015[101]). Finally, the much lower incidence of Chemical and chemical products in organic farming, here a ratio of 0.36, reflects the fact that industrial pesticides, herbicides and fertiliser are prohibited on organic farms, which are captured in this sector.
Contrary to Garrett-Peltier (2017[100]), we do not need to normalise our coefficients to fit between 0 and 1 to keep output estimate constant in the IOTs. This is because our methodology focuses on a single sector with our new organic sector being calculated relative to the Agriculture, hunting and forestry sector. This means that we assume that the growth of one is at the expense of the other, so that as organic rice expands, Agriculture, hunting and forestry will shrink. This follows the assumption that most farms convert from conventional to organic farming, so they would take away from the conventional farming values included in Agriculture, hunting and forestry rather than expand the sector through formation of new farms. This allows us to subtract the new organic values from the conventional sector, keeping total sectoral output values fixed and only changing production relationships through weighting.
For each of the sectors that have an organic agriculture coefficient, we multiply the technical coefficient for that sector by the corresponding coefficient. This creates a new vector with technical coefficients for both origin and destinations representative of organic production of that product. Next, to create intermediate demand vectors that can be inserted into the OECD IOTs, we must estimate the organic agricultural product’s current level of production. Since actual output data does not exist for most organic crops, we estimate a proxy using land area, output, and relative yield of organic and conventional agriculture.
The land area values represent the possible productivity of the land for a given agriculture product based on its current output. This relative productivity value is weighted by a yield coefficient to account further for the variance in yield between organic and conventional production for the crop, borrowed from agriculture surveys. The equation resembles the following,
where is the organic output intensity coefficient, is the total output for that agricultural product, is the organic land area in hectares and is the total land area in hectares for the crop. The output values are similar to what is provided by limited literature (Annex Table 5.A.1).
With organic output estimated, we are able to calculate the sectoral intermediate demand. These values are inserted into the OECD IOTs as a new organic sector for the agricultural product through subtracting from the original Agriculture, hunting and forestry sector. This is done for both origin and destination values in the OECD IOTs, creating a 41x41 matrix.
4. Estimation of organic agricultural employment
In order to provide an estimate on how a shift towards organic can impact employment, we must construct an employment requirements (ER) matrix. To build the ER matrix, it is necessary to have data on the number of people employed for each of the 41 sectors represented in the organic augmented IOT. For the majority of the sectors, we can use data provided by national estimates or LFS. However, there is no reliable employment data available for organic rice farming in the region. As a result, we must estimate organic employment similarly to how it was done for organic output.
We assume that the number of employments for organic employment is a function of the land area and the labour needed at its current capacity, thus given the organic land area we can estimate organic employment for that crop. The number of employees needed to cultivate the organic land area is then weighted by the labour coefficient for the organic agricultural product. The weighting should account somewhat for the differences in labour needs between conventional and organic agriculture. The equation resembles the following,
where is the organic labour requirement coefficient, is the total number of individuals employed in that agricultural product, is the organic land area in hectares and is the total land area in hectares for the crop. The organic employment estimates track estimates from unsubstantiated sources such as press releases, news articles and unpublished papers in the country.
Using these estimate we calculate the ER table. This requires calculating the employment-to-output ratio by dividing the vector of number of employees for the 41 sectors by the output of the 41 sectors. The result is transformed into a diagonal matrix, which is post-multiplied by the Leontief inverse matrix. The equation resembles the following,
where represents the employment-to-output ratio and represents the Leontief inverse matrix.
5. Construction of scenarios
Scenarios were created to evaluate different policies for encouraging sustainable agriculture in Southeast Asia using our new organic sector. IOTs provide several pathways to evaluate policy scenarios, primarily through estimating how a change in demand in one sector has cascading effects on its inter-related sectors. One possibility is through simulating a policy aimed at boosting in consumer spending in the sector, which can take the form of price subsidies or vouchers. This would increase final demand for households equivalent to the size of policy support, which when estimated in the IOT will produce a multiplier for the policy’s impact across output, employment and income. It will also highlight which sectors would see the most gains from the policy and those which may exhibit losses because of it. It is also possible to model a policy that takes on the form of direct purchases of organic rice in the public sector. This would create an increase in the government final demand column, producing the same results as the private sector.
Beyond these commonly used levers in IOTs, we are also able to evaluate how an expansion of the organic sector itself affects output, employment and income. Given that estimates for output and employment are based on the size of the organic land area, we can easily re-estimate new values for these indicators based on growth in the organic industry. This is particularly interesting as it makes it possible to evaluate the targets set out in the agriculture strategies in some of the Southeast Asian countries. For example, the Philippines goal was to reach 5% of organic agriculture land area by 2020, a goal which it has almost achieved by 2018 (Philippines Bureau of Agriculture and Fisheries Standards, 2018[66]). Thailand also provides a target in their National Plan for Organic Farming to increase organic rice farming in particular to 400 000 ha of land area by 2021, which represents 2.5% of total arable land area in Thailand (FAOSTAT, 2022[20]; Thailand Government Public Relations Department, 2017[67]). We extrapolate these goals to the other countries being estimated, evaluating how an expansion of similar size would look within their economy.
The scenarios are applied in stages according to their magnitude, which we assign as low, medium and high relating to the relative expansion of organic agriculture as well as the size of government intervention (Annex Table 5.A.4).
Annex Table 5.A.4. Scenarios for increased organic land area and increased final demand
Given that overall land area dedicated to organic agriculture is smaller in the other four countries treated, with about 0.2% on average, we take 5% expansion of organic agriculture area as the medium scenario, with plus or minus 2% for the low (3%) and high (7%) growth scenarios. We then add in various increases in government final demand. Given that direct government spending is relatively low in Agriculture, hunting and forestry, we must employ a larger nominal increase in government final demand than that of household final demand for there to be any effect.
To evaluate the scenarios, we compare the total value added (employment) for the baseline scenario, with organic rice at its estimated level of current production and employment, to the total value added (employment) with the scenarios applied. This resembles the following for the output,
where represents the final demand or organic area at the baseline (0) and with the scenario (1), represents the change in output and represents the Leontief inverse matrix. This quantifies how a change in organic rice impacts the other sectors in the IOT.
Caveats
IOTs have two main drawbacks. The first is that IOTs are static in the year they are estimated. When there is a change in demand for a good, such that is assumed in our scenarios, prices will adjust relative to the supply. Unlike CGE models, IOTs include no dynamic process to capture price changes. This can be a problem because this price change would likely have some impact on the choices industries make, such as substituting relatively cheaper goods from another industry, which would alter the intersectoral relationships in the tables. To combat this, we impose relatively small changes in demand that would only have a minimal impact on the production choices of firms. This assumption works doubly to mitigate any issues related to the lack of supply constraints that come from IOTs. Similarly, the second drawback of IOTs is the fact that the tables assume a linear and constant relationship across all sectors of the table. As a result, they do not account for any changes in technology that could occur given an increase in demand. This underscores the fact that IOTs are not an appropriate tool for forecasting changes, but instead highlight how an increase in demand or expansion of a sector work to influence an economy in the observation year. As such, we assume that the output and employment impacts we observe have occurred in short-term, within one to two years from the year the IOT was developed, before the sectors have time to adapt.