Reviving the Philippine Economy under a Responsible New Normal

After the reclassification of areas under enhanced community quarantine (ECQ) to general community quarantine (GCQ), the urgent task for the Philippine government is to provide an exit plan to revive the Philippine economy. Given the significant economic damage resulting from the shutdown of roughly 75 percent of the country’s total production in the National Capital Region (NCR) and in the CALABARZON and Central Luzon areas, a gradual reopening of the economy will be necessary to prevent further economic damage that could not only be difficult to repair, but also long to overcome. Indeed, based on recent directives from the government, a substantial number of industries and services have thus been allowed to operate in both the ECQ and GCQ areas. However, as the Philippine government begins to calibrate the opening of sectors, there remain concerns as to how this process will affect jobs and livelihoods now and beyond. In this context, an economic recovery plan that talks about short-term, a transition, and full recovery phases— encompassing a revision of the current Philippine Development Plan without losing sight of the long-term goals envisioned in Ambisyon Natin 2040— is still needed. Indeed, a key component of AmBisyon 2040 has been of building resiliency over the long-term, which includes resiliency in health and economic shocks apart from natural disasters. At the same time, this recovery plan should also be accompanied by structural reforms to enhance its implementation. The Department of Finance has crafted a four-pillar socio-economic strategy aimed at: (a) supporting the more vulnerable sectors of society; (b) increasing medical resources to contain the virus and offer safety to front-liners; (c) keeping the economy afloat through financial emergency initiatives; and (d) creating jobs and sustaining the economy. Yet while enumerating the costs of these plans, the said strategy lacked details on how the country could achieve some of the goals without the availability of widespread testing and adequate health facilities. Loan guarantees, cash transfers, and other forms of subsidies can revive disrupted supply chains but cannot restore productivity in the middle of a persisting health crisis, while the uncertainty of a possible outbreak can keep workers from supplying goods and services. It is crucial to have these programs and institutions in place since a number of cities, regions and provinces have started to reopen. A modified community quarantine without the necessary health system investments, protection measures, and economic recovery plan risks amounting to an unregulated herd immunity strategy. Opting for herd immunity allows governments to blame the failure of the health and economic system on the virus, rather than on bad governance. Under current GCQ protocols, the burden on containing the virus is mostly transferred to the public. Unless the government provides mass testing, the problem of information is aggravated, probably raising the transmission risks. Moreover, unregulated herd immunity will be differentially felt by the poor. As healthy workers may recover their earnings from the modified quarantine, the poor, who have limited access to the health services and are thus more susceptible to the virus, are unlikely to benefit from this system. In effect, this will only exacerbate the inequality that prevails in the country. Moving towards a responsible new normal requires a strategy that addresses both people’s wellbeing and the socio-economic weaknesses exposed by COVID-19. Thus, the strategy should have the following elements

Assessing the Value of SCFs on Farm-level Corn Production through Simulation Modeling

Rainfall variability greatly influences corn production. Thus, an accurate forecast is potentially of value to the farmers because it could help them decide whether to grow their corn now or to delay it for the next cropping opportunity. A decision tree analysis was applied in estimating the value of seasonal climate forecast (SCF) information for corn farmers in Isabela. The study aims to estimate the value of SCF to agricultural decision makers under climate uncertainty. Historical climatic data of Isabela from 1951 to 2006 from PAGASA and crop management practices of farmers were used in the Decision Support System for Agrotechnology Transfer (DSSAT) to test the potential impact of climate change on corn. The approach is developed for a more accurate SCF and to be able to simulate corn yields for wet and dry seasons under different climatic conditions -- El Niño (poor year), La Niña (good year) and Neutral (neutral year) conditions. In order for the forecast to have value, the “with forecast” scenario should lead to better decision making for farmers to eventually get increase production over the “without forecast” scenario. While SCF may potentially affect a number of decisions including crop management practices, fertilizer inputs, and variety selection, the focus of the study was on the effect of climate on corn production. Improving SCF will enhance rainfed corn farmers’ decisionmaking capacity to minimize losses brought about by variable climate conditions.decision tree analysis, seasonal climate forecast (SCF), climate uncertainty, Decision Support System for Agrotechnology Transfer (DSSAT)

Risk-efficient Planting Schedules for Corn in Matalom, Leyte, Philippines

The study was conducted to identify risk-efficient cropping schedules for corn farmers in Matalom, Leyte, Philippines using stochastic dominance analysis of simulated yields, given the El Nino Southern Oscillation (ENSO) forecasts during the cropping period. Actual weather data, with missing observations estimated using a weather generating software, were used in constructing weather data sets. These data, together with crop parameters and soil characteristics in the study site, were used as inputs to generate probability distributions of yields during different planting schedules. The simulated yield distributions were classified according to the ENSO phases prevailing during the cropping period. Stochastic dominance analysis was applied on the yield distributions to determine the first-degree stochastic dominance (FSD) set and the second-degree stochastic dominance (SSD) set. Finally, stochastic dominance with respect to a function (SDWRF) was applied on the SSD set to identify risk-efficient schedules at different levels of risk aversion. Risk-efficient schedules were identified for each cropping season and under each ENSO phase. It was found out that some June-July schedules (during the first season) and some December schedules (during the second season) are more risk-efficient than traditional schedules.Philippines, El Niño Southern Oscillation (ENSO), irrigation, corn farmers, cropping schedule, stochastic dominance analysis

El Nino Southern Oscillation in the Philippines: Impacts, Forecasts, and Risk Management

The climate of the Philippines is highly influenced by the El Nino Southern Oscillation (ENSO). El Nino is associated with an increased chance of drier conditions and La Nina is associated with an increased chance of wetter conditions. Changes in rainfall are associated with changes to tropical cyclone activity in the western equatorial Pacific, the strength of the monsoon, and changes in the onset and/or termination of monsoon rains. ENSO is a naturally occurring phenomenon and has both negative and positive impacts on the various sectors of the society and environment, but experience would show that there are more adverse impacts than beneficial ones. These adverse impacts may be mitigated through using seasonal climate forecasts. This paper looks at the effects of ENSO on droughts, flood, and tropical cyclones in the Philippines before discussing the challenge of using knowledge about the effects of ENSO for decisionmaking and risk management.Philippines, El Niño, El Niño Southern Oscillation (ENSO), tropical cyclone, seasonal climate forecast (SCF), risk management, cropping schedule, stochastic dominance analysis, floods, drought

Assessing the Value of Seasonal Climate Forecasts on Farm-level Corn Production through Simulation Modeling

Rainfall variability greatly influences corn production. Thus, a skillful forecast is potentially of value to the farmers because it could help them make a number of crop management decisions including crop choice, variety selection, timing of planting, and input usage. The study aims to develop an approach in estimating the economic value of seasonal climate forecast (SCF) to corn farmers under climate uncertainty. It focuses specifically on corn farmers in Isabela using the historical climate data of Tuguegarao from 1951 to 2006. The CERES-Maize model of the Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate corn production and management practices of farmers to estimate the potential corn yields for wet and dry seasons under different El Nino Southern Oscillation (ENSO) conditions. The RAINMAN International software was used to generate SCF for El Nino, La Nina, and Neutral conditions. A decision tree analysis was applied in estimating the value of SCF information for corn farmers.Philippines, El Niño Southern Oscillation (ENSO), seasonal climate forecast (SCF), corn productivity, Isabela corn industry, climate information and corn farming

Incorporating Regional Rice Production Models in Rice Importation Simulation Model: a Stochastic Programming Approach

In the Philippines, importation has remained as one of the most feasible options for the government to meet the growing demand for rice. It is thus imperative for the government to develop a strategy that would ensure adequate supply and minimum importation costs. One of the critical factors in import decisionmaking is rice production. The Inter-Agency Committee on Rice and Corn (IACRC), where the National Food Authority (NFA) and Bureau of Agricultural Statistics (BAS) are members, decides on importation when there is an impending production shortfall in the coming season. However, because Philippine agriculture is vulnerable to extreme climate events and climate change is believed to further intensify the effects of seasonal climate variability, rice production forecast is becoming more uncertain. Inaccurate production forecasts could lead to incorrect volume and ill-timing of rice imports, which in turn could result in either a waste of resources for the government or a burden to consumers. Contraction of rice imports in the early 1990s, ill-timing of imports in 1995, and overimportation in 1998 illustrate how inaccurate forecasts of volume and timing of rice importation, especially during El Niño and La Niña years, could result in substantial economic costs such as higher rice prices due to rice shortages, higher storage costs, among others. This paper evaluates the significance of SCF information, among other things, in rice policy decisions of the government, particularly on importation. It presents an alternative method of forecasting the level of rice production through regional rice production models. The rice production models systematically incorporate SCF and could be used in support of the current practice of forecasting rice production based on planting intentions. The paper also demonstrates how SCF, together with these production estimates, could be incorporated in the rice import decisions of the government through the Rice Importation Simulation (RIS) model, which was developed using a Discrete Stochastic Programming (DSP) modelling approach. The RIS model, which recommends a set of optimal rice import strategies, could serve as guide for the government in its rice import decisions in the face of seasonal climate variability and could be used in estimating the potential value of SCF.Bureau of Agricultural Statistics (BAS), El Niño, La Niña, rice, seasonal climate forecast (SCF), National Food Authority (NFA), importation, production models, Discrete Stochastic Programming (DSP), Inter-Agency Committee on Rice and Corn (IACRC)

Seasonal march patterns of the summer rainy season in the Philippines and their long-term variability since the late twentieth century

Abstract This study investigates the seasonal march patterns of rainfall in the Philippines from 1951 to 2012 and their long-term variability. In order to clarify the dominant patterns in the seasonal march of rainfall, an empirical orthogonal function (EOF) analysis was applied to pentad rainfall data of 30 stations. For the first EOF mode (EOF1), we obtained a pattern related to the summer rainy season. We then applied cluster analysis to the time coefficients of EOF1 in each year to classify the seasonal patterns of the summer rainy season. As a result, the patterns were classified into six clusters. We found a long-term change in the pattern appearances with three anomalous patterns frequently observed since the 1990s: (1) a pattern that has an indistinct dry season and a prolonged peak rainfall, (2) a pattern that has a distinct dry season and an earlier withdrawal of the summer rainy season, resulting in a shortened rainy season, and (3) a pattern with a distinct dry season as well as delayed onset and withdrawal of the summer rainy season. This study also shows the relations between these three patterns and the lower atmospheric circulation at the 850 hPa level around the Philippines. Consequently, large positive and negative anomalies in geopotential height were observed around the Philippines for the distinct and indistinct dry seasons, respectively. The duration and condition of the dry season were greatly affected by the strength and location of the subtropical high especially for February–March. It is also noteworthy that the timing of the onset (withdrawal) of the summer rainy season is clearly related to that of the onset of the westerly (northerly) wind in the zonal (meridional) component around the Philippines. Further, the duration and amount of peak rainfall were directly influenced by the strength of the westerly winds in the zonal component. These three anomalous patterns tended to appear in the years when the warm or cold event of the El Niño–Southern Oscillation (ENSO) occurred. This study suggests that the long-term variability in the seasonal march of rainfall is considerably influenced by the variability in ENSO

Incorporating Regional Rice Production Models in a Simulation Model of Rice Importation: a Discrete Stochastic Programming Approach

In the Philippines, importation has remained as one of the most feasible options for the government to meet the growing demand for rice. It is thus imperative for the government to develop a strategy that would ensure adequate supply and minimum importation costs. One of the critical factors in import decisionmaking is rice production. The Inter-Agency Committee on Rice and Corn (IACRC), of which the National Food Authority (NFA) and the Bureau of Agricultural Statistics (BAS) are members, decides on importation when there is an impending production shortfall in the coming season. However, because Philippine agriculture is vulnerable to extreme climate events and climate change is expected to further intensify climate variability, rice production forecast is becoming more uncertain. Inaccurate production forecasts could lead to incorrect volume and ill-timing of rice imports, which in turn, could result in either a waste of resources for the government or a burden to consumers. Contraction of rice imports in the early 1990s and over-importation in 1998 illustrate how inaccurate forecasts of the volume and timing of rice importation, especially during El Nino and La Nina years, could result in substantial economic costs. This paper evaluates the significance of seasonal climate forecast (SCF) in rice policy decisions of the government, particularly on importation. It presents an alternative method of forecasting the level of rice production through regional rice production models. The rice production models systematically incorporate SCF and could be used in support of the current practice of forecasting rice production based on planting intentions. The paper also demonstrates how SCF, together with these production estimates, could be incorporated in the rice import decisions of the government through the Rice Importation Simulation (RIS) model, which was developed using a Discrete Stochastic Programming (DSP) modeling approach. The RIS model, which recommends a set of optimal rice import strategies, could serve as guide for the government in its rice import decisions in the face of seasonal climate variability and could be used in estimating the potential value of SCF.Philippines, El Niño, La Niña, rice, seasonal climate forecast (SCF), importation, production models, Discrete Stochastic Programming (DSP)

Additional file 1: of Seasonal march patterns of the summer rainy season in the Philippines and their long-term variability since the late twentieth century

Figure S11. Composite anomalies of geopotential height at the 850 hPa level averaged for the 7th–18th pentads (the end of January to the end of March) between C1 and each cluster. (a) Shows a composite map averaged for the 7th–18th pentads of the years classified in C1. (b)–(f) Show composite anomalies between C1 and each cluster. (EPS 4054 kb

Developing a Philippine climate-ocean typology as input to national vulnerability assessments

Remotely-sensed information was utilized to naturally divide the archipelagic waters of the Philippines into distinct clusters of historical air-sea climate exposures. For data, we made use of satellite-derived Sea surface temperature (SST), and sea surface height (SSH), wind data (W), and precipitation (P). Results show that the Philippines naturally divide into 11 exposure clusters. Within each cluster the trends and anomalies of SST, anomalies and future scenarios of precipitation, and trends of sea surface height (SSH) were further calculated. Results were then compared amongst the clusters and against global statistics to gain insight on the behavior in each of the clusters. Analysis shows that the entire Philippines suffer twice to 3-times the magnitude of the global sea level rise. The northwestern (cluster II) and the tip of the northeastern (cluster X) coastal and marine areas of the Philippines are most prone to extreme temperature and precipitation hazards. In comparison the south Sulu Sea (cluster XI) and Sulawesi (cluster VI) are the sites with the lowest magnitude of air-sea hazards. So far, these hazard typologies has serve as input to the Philippine I-C-SEA-Change tool built to guide non-specialists in local and adaptive capacity assessments