Philippine Institute for Development Studies Surian sa mga Pag-aaral Pangkaunlaran ng Pilipinas Scenarios and Options for Productivity Growth in Philippine Agriculture An Application of the AMPLE Roehlano M. Briones DISCUSSION PAPER SERIES NO. 2010-05 The PIDS Discussion Paper Series constitutes studies that are preliminary and subject to further revisions. They are be- ing circulated in a limited number of cop- ies only for purposes of soliciting com- ments and suggestions for further refine- ments. The studies under the Series are unedited and unreviewed. The views and opinions expressed are those of the author(s) and do not neces- sarily reflect those of the Institute. Not for quotation without permission from the author(s) and the Institute. February 2010 For comments, suggestions or further inquiries please contact: The Research Information Staff, Philippine Institute for Development Studies 5th Floor, NEDA sa Makati Building, 106 Amorsolo Street, Legaspi Village, Makati City, Philippines Tel Nos: (63-2) 8942584 and 8935705; Fax No: (63-2) 8939589; E-mail: [email protected] Or visit our website at http://www.pids.gov.ph ABSTRACT  Sustaining and accelerating agricultural growth remains a development imperative in view of  persistent rural poverty and emerging threats to food security. While growth can be achieved by  expansion of agricultural area and input intensification, growth through improvement in productivity is a  promising option. However, productivity growth appears to be a relatively low priority for policy. Rather,  the agricultural strategy is oriented toward domestic protection to achieve self‐sufficiency and to  support production by generous subsidies. In contrast, an alternative strategy may be one that is  competition‐oriented and productivity‐based, i.e., one that favors integration with the international  economy through trade, as well as making domestic investments targeted at productivity growth.    Scenarios for Philippine agriculture under these policy options are evaluated using a new supply  and demand model (Agricultural Multi‐market Model for Policy Evaluation or AMPLE). Model  simulations suggest that: rapid productivity growth, even when combined with trade liberalization, is  generally favorable for farmers and consumers based on improved outlook on production, exports, and  food consumption. In contrast, trade liberalization alone has a contractionary effect on agriculture; and  production support is a costly instrument for promoting agricultural growth. The model experiments  suggest that a back‐to‐basics strategy for agriculture, incorporating various productivity‐based  instruments such as investments in R&D, extension, rural infrastructure, protection of the resource base  of agriculture, and even human capital formation and institutional reforms, are key to long‐term  agricultural growth.     Keywords: Productivity growth, agriculture, scenario analysis, supply and demand, technological change Scenarios and Options for Productivity Growth in Philippine Agriculture An Application of the Agricultural Multimarket Model for Policy Evaluation (AMPLE) Roehlano M. Briones Senior Research Fellow, Philippine Institute for Development Studies December 20, 2009 1. INTRODUCTION Sustaining and accelerating agricultural growth remains a development imperative. Poverty in the country is concentrated in rural areas, for which agriculture remains a major if not primary source of income. The importance of agriculture has been highlighted recently following concerns about food security after the recent round of commodity price increases in world markets. For agricultural output to grow, the simplest approach would be to increase the flow of resources into agriculture, such as say by the expansion of farming area. Farmland however is scarce and, and many countries (including the Philippines) are already approaching their agricultural land frontier. Area expansion is therefore an unsustainable strategy for agricultural growth. Alternatively, agricultural output can grow by the addition of more inputs per unit of land, a process called intensification. However resource scarcity can also be a limiting factor to growth by intensification; moreover, intensification does not guarantee that farmer incomes increase (as added output revenue is accompanied by added input cost). There is a third route, which is productivity growth. Evidence reviewed in World Bank (2008) show that, at least in Asia, productivity growth has been steady at about 1-2% per year since the 1960s. Investments in science, roads, human capital, and adoption of better policies and institutions, made these productivity gains possible. From 1980 to 2004, world agriculture 1 has been doing well, with agricultural GDP growing faster than that of global population. Much of was driven by productivity growth; hence, for instance, the real prices of grain in world markets fell by 1.8% p.a. over the same period. The country’s agricultural development strategy does affirm the need for productivity improvement, but appears to assign it a less-than-primary status terms of its expenditure programs and market policies. The response to the rice crisis, dubbed FIELDS (Fertilizers, Irrigation, Extension, Loans, Dryers and other postharvest facilities, and Seeds), emphasizes a significant subsidy component, expanding input usage in agriculture, and thereby boosting agricultural output. Policies are explicitly inward looking: growth in output, specifically for rice, aims at achieving self-sufficiency at an early date of 2013, when domestic production would have equaled domestic demand (estimated at about 140 kg/yr per capita). Meanwhile the government continues to protect heavily the major import-competing sectors, such as rice, corn, sugar, and meat. The current policy may be characterized as biased towards input support and price intervention. The tendency to insulate domestic agriculture from the world market is motivated by large price volatilities in the latter, combined with the perception that world markets are heavily distorted by protectionist and subsidy-oriented policies in OECD countries. Possibly, liberalization of domestic markets may follow as a quid pro quo for foreign market access, negotiated in trade agreements. There is however another alternative, which is more open to the price system, but strives as well to address market failures; the outcome of which is demonstrable improvement in productivity growth. This paper provides an assessment of the future of Philippine agriculture under the current and alternative strategies for agricultural growth. Productivity growth immediately impacts supply; however economic outcomes result from the interaction of both supply and demand in agricultural markets. To incorporate these interactions into our analysis, we apply AMPLE (Agricultural Multi-market Model for Policy Evaluation), a new supply demand model of 2 Philippine agriculture. AMPLE draws heavily from previous modeling work on Philippine agriculture, hence we proceed first with a review of related studies (Section 2), before presenting the model in detail (Section 3). Past growth and productivity performance of Philippine agriculture is reviewed in Section 4; future scenarios are presented in Section 5. Section 6 concludes. 2. SURVEY OF AGRICULTURE- RELATED MODELS Overview of models We limit our review of models to those that are based on an equilibrium in supply and demand. Quantities of supply and demand for each given market are represented by functions of price and other variables; equilibrium is represented by the constellation of price and other endogenous variables that equalize the quantity supplied in each market. We include in this review only those models that have been specified numerically, i.e. the functions are assigned numerical parameters and baseline data, which is replicated as a baseline equilibrium. These models are based on static equilibrium, i.e. conditions of demand and supply balance within a single time period. Dynamic equilibrium models allow supply and demand to be determined over multiple periods; applications of these in for Philippine are sparse or non- existent. Static models can be distinguished according to the scope of equilibrium being computed. General equilibrium models attempt to simulate the operations of the entire real economy, i.e. the complete set of goods and factor markets (suitably disaggregated). Partial equilibrium models attempt to simulate only a subset of the real economy (often omitting factor markets altogether). These models in turn divide into multi-market and single-market models; in the former, price variables affect supply and demand of different commodities, while for the latter, the supply and demand of a commodity is affected only by its own price. Due to the obvious limitations of the latter we omit it from the coverage of this survey. 3 Computable general equilibrium models A number of computable general equilibrium (CGE) models have been constructed for the Philippines. In the following we focus only on those that have been applied to agricultural policy analysis. The following discussion draws heavily from Yap (2003) for models up to 2002. Ramon Clarete, Cielito Habito, and Romeo Bautista can be credited as the pioneers of CGE modeling for the Philippines in the 1980s (Bautista, 1988). In the 1990s the most disaggregated CGE model for the country (50 sectors) was the Agricultural Policy Experiments (APEX) model (Clarete and Warr, 1992). The APEX has 16 agricultural sectors. One important feature of the model is that a large number of elasticities for supply, demand, trade were estimated from data. The TARFCOM model (Horridge et al 2001) has now replaced the APEX as the most disaggregated CGE model of the Philippines. Based on the ORANI-G of Australia, the model has 229 industries, 28 of which are under agriculture. Simulations run by Cabalu and Rodriguez (2007) finds that agriculture contracts under all scenarios (actual tariff reductions, target tariffs in agriculture, uniform tariffs, and removal of tariffs). In the 1990s several environmental CGEs were developed, some of which were applied to the assessment of the impact of land degradation in agriculture. Coxhead and Jayasuriya (1994, 1995) specified three goods (manufactures, tree crop, and food) and two regions (lowland, upland). Manufactures are importable, tree crops are exportable, and food is nontradable. Food production in the uplands is erosive. Their simulations showed that trade liberalization in the form of tariff reduction for manufacturing shifts land use in the uplands to tree crop production from food crop production, thus reducing soil erosion. Subsequent studies using the APEX model led to similar results (e.g. Coxhead and Jayasuriya, 2003). CGEs of recent vintage (2000 onwards) have focused on agricultural trade policies. One strand extends analysis of WTO-related reforms to household welfare. The envisaged Doha round, which continues the WTO program of trade liberalization in world agriculture, is 4 evaluated in Cororaton, Cockburn, and Corong (2006). While having the expected positive effect on total household incomes, poverty rises slightly, especially among rural households. Similarly, liberalization of international trade in rice is found to increase poverty as a large subset of the poor are palay farmers (Cororaton and Cockburn, 2006). Another strand applies updated versions of earlier CGE models for agricultural trade policy. Rodriguez and Cabanilla (2006) examine a possible US – Philippine free trade agreement (FTA) and its impact on agriculture; they find that such an agreement would benefit Philippine agriculture. A broader agreement covering Asia and the Pacific (the FTAAP) was also evaluated using the TARFCOM (Rodriguez, 2006). It finds that while an FTAAP would benefit the economy in general, it would have an adverse impact on agriculture. The major advantage of applying a CGE is its comprehensive approach to economic modeling. However, for the limited purpose of agricultural sector analysis, this very comprehensiveness could be a drawback. A CGE modeler may have to rely on extensively on imputation of price (and even income) response to be able to cover all production sectors and factors of production, as well as macro-closure conditions such as the balance of trade. If the majority of economic activity were coursed through agriculture then accounting for these behaviors would make sense. However according to NSDB data, agriculture in 2007 accounts for only 14% of GDP and under 35% of employment. Hence one may trade off the need to make strong assumptions for reduced comprehensiveness, if agriculture-specific policies play a relatively minor role in economywide adjustment. This trade-off is implicit in adopting a multi- market partial equilibrium (as opposed to general equilibrium) approach to agriculture sector modeling, as advocated in this study. Certain techniques popular in the CGE literature may on the other hand can be readily borrowed for partial equilibrium modeling, particularly in the area of trade. A common approach for modeling imports and exports (exemplified in say the TARFCOM) is to distinguish, respectively, domestic demand by source (foreign or local supply) as well as domestic supply by 5 destination (foreign or local market). The substitution/transformation of demand/supply by source/destination is modeled by a constant elasticity function. For the demand side this is the constant elasticity of substitution first suggested by Armington (1969). For the supply side this is the constant elasticity of transformation (Powell and Gruen, 1967). This is more general than the the alternative of treating domestic and foreign sources/supplies as perfect substitutes, effectively confronting domestic producers and consumers with world prices subject to some constant margin attributed to trade barriers. Partial equilibrium models In terms of relevance to Philippine agriculture, agricultural multi-market models are either international (or even global) in scope with a country-level disaggregation explicitly incorporating the Philippines, or else have been specifically built to represent Philippine agriculture with the rest of the world as a foreign sector. Widely used for long term projections of global agriculture and food security is the IFPRI’s International Model for Agricultural Commodities and Trade or IMPACT. Market equilibrium in IMPACT is at international market clearing, i.e. the sum of net trade across countries by commodity is zero; domestic producer prices equal world prices with adjustment term for marketing margin and producer subsidy equivalent On the demand side, total demand is the sum of food, feed, and other uses. Per capita food demand is a constant elasticity function of own-price, cross-prices, income. Feed demand is a constant elasticity function of own-price, other feed prices, and quantity supplied of feed using commodity (adjusted by feed ratio). Finally, other uses is assumed to change at similar rate as rate of change of food and feed demand. On the supply side, IMPACT adopts the widely-used area x yield formulation for modeling crop supply based on constant elasticity. The exogenous yield trend incorporates the supply shifters, primarily those relates to productivity growth, brought about by the following 6 policy levers, among others: research; agricultural extension and farmers schooling; infrastructure; and irrigation. The literature on multi-market modeling of Philippine agriculture is sparse. Rosegrant- Rozelle (1993), cited and applied in Balisacan and David (1995), was an earlier attempt to model Philippine agriculture. This model turns out to hae been an early version of the IMPACT model. The Philippine agricultural model that remains in active use is the Agricultural Policy Simulation Model or APSIM, which documented in APPC (2003). Figure 1 displays a schematic of APSIM. Figure 1: Schematic of the APSIM   FERT PRICE  AREA PLANTED  PRODUCTION  BUFFER STOCK  R&D  YIELD  IRRIGATION  EXTENSION  DOMESTIC  EXPORTS SUPPLY  GDP  Consumption  PRICE POPULATION  Demand  IMPORTS OTHERPRICES Feed Demand TOTAL DEMAND WORLD PRICES LIVESTOCK  Seed and other  COMPONENT  Uses  It has affinities with the IMPACT: consumption and production follow the constant elasticity framework, while the latter is determined by area and yield response functions. The latter are affected by “policy interventions, and other environmental variables such as input price policy, research and development [R&D] expenditures, irrigation investments, agricultural extension, and other policy variables (p. 7).” 7 As with IMPACT, demand components are: consumption, livestock use, and other uses (i.e. processing and seeds). Modeling of international trade allows limited pass-through from world to domestic prices via Armington coefficients; however in applied work, domestic and foreign products are perfect substitutes, hence domestic prices are equal to world prices plus tariffs (with the exception of markets with binding quantitative restrictions or for nontradable goods.) An important capability of APSIM is the calculation of income and welfare changes, comparing alternative and reference scenarios, or runs. These calculations are generated from a detailed household module, which is solved recursively from the multi-market model. 3. STRUCTURE AND SPECIFICATION OF THE AMPLE AMPLE a multi-sector partial equilibrium model, with 18 production sectors covering crops, livestock, poultry, aquatic products. It is capable of generating projections on output, area, consumption, imports, exports, and prices. In common with other supply-demand models, AMPLE is suitable for understanding the evolution of underlying economic fundamentals, rather than in actually predicting market movements. It adopts features from APSM, and other multi- market models. Sets, variables, and equations are listed in the Annex. The model is programmed and solved with the Generalized Algebraic Modeling System (GAMS). Supply block The sectors in the AMMPLE should cover the major agricultural products (Table 1). We distinguish between primary and processed form of output; likewise we distinguish production systems of rice (irrigated and rainfed) as well as freshwater and marine fish (aquaculture and capture), corresponding to degree of culture intensity. The inputs are: Chemical, Fishmeal, Feed, Other intermediate inputs, labor, and other primary inputs. For crops, we need to impose the total agricultural land area as a quasi-fixed factor, hence we model supply in terms of area allocation and yield response function (S1, S2). Both 8