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Philippines economic outlook 2022

Companies doing business in the Philippines are assessing the implications of COVID-19 on the country’s economy. They are likely to find that three shifts introduced during the pandemic will persist into the future: economic activity will be digitally enabled but also hyperlocal; the wealth gap is widening, and new consumer segments have emerged; and the pandemic is likely to result in a greener and more sustainable economy.

Meanwhile, the consensus view shows the Philippines economy recovering by the fourth quarter of 2022 under a muted scenario, even taking the Omicron wave into account (Exhibit 1).

The economic outlook varies by industry; companies in the consumer and retail sector are likely to see a muted recovery through 2022 (Exhibit 2), but consumer demand for essentials remains strong, while some discretionary spending is likely to rebound in line with other countries in the region. The consumer behaviors learned during the pandemic—digital migration, value hunting, and the homebody economy—may stick.

The travel and hospitality sectors are poised to surpass 2019 growth in 2022, although headwinds could stall tourism recovery until 2024. In the interim, companies can take targeted actions to reinvent themselves and grow out of the pandemic. In financial services, the banking sector could take up to five years to recover from its 2020 drop in return on equity (Exhibit 3). Among Filipino consumers, active use of digital banking and e-wallet services has increased significantly.

The healthcare sector is expected to grow through 2022, while pharmaceutical manufacturing is likely to remain steady. Certain consumer behaviors—digital-care adoption, focus on preventive care and wellness, and interest in value for the money—are likely to stick after the pandemic.

Likewise, the energy and power sector is expected to expand through 2022. Finally, the outlooks for IT business process outsourcing (BPO) and remittances from overseas Filipino workers, a resilient lifeline for the Philippine economy, remain strong (Exhibit 4).

Jon Canto is an associate partner in McKinsey’s Manila office, where Kristine Romano is a partner.

The authors wish to thank Johann Co, Ryan Delos Reyes, Justine Eligio, Jazmin Jabines, Miguel Morales, Danice Parel, Patrick Roasa, and Carlos Syquia for their contributions to this article.

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Article Contents

1. introduction, 2. background, 3. conceptual model and empirical framework, 5. results and discussion, 6. conclusions and policy implications, acknowledgements, conflict of interest, data availability statement.

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Estimating food demand and the impact of market shocks on food expenditures: The case for the Philippines and missing price data

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Harold Glenn Valera, Joaquin Mayorga, Valerien O Pede, Ashok K Mishra, Estimating food demand and the impact of market shocks on food expenditures: The case for the Philippines and missing price data, Q Open , Volume 2, Issue 2, 2022, qoac030, https://doi.org/10.1093/qopen/qoac030

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This study uses the Quadratic Almost Ideal Demand System to estimate food demand among Filipino households. Our study uses the recently released 2018 Family Income and Expenditure Survey and the Stone-Lewbel price index in the absence of price data on food groups. Results show that demand for rice with respect to prices and expenditures is relatively inelastic compared with that for other food groups. The income elasticity for rice is inelastic (0.26), slightly higher than the income elasticity for sugar. Demand for rice is generally less elastic for higher-income Filipinos and families residing in urban areas than for their counterparts. The findings reveal that, in the short term, a 15 per cent decrease in income or a 20 per cent increase in rice prices induces families to spend more of their income on rice at the expense of other cereals, meat, fish, and other food groups. Income and rice price shocks have differential impacts on low-income and high-income Filipino families. Policymakers may be able to moderate the food price impacts of market shocks through targeted interventions and programs that improve the accessibility to and availability of quality agri-fishery products.

The rice sector plays a significant role in Philippine agriculture and the economy. As of 2018, about 10 million farmers and family members—representing 22 per cent of the rural population—depended on growing rice for their livelihood. Recent data show that annual rice production fell to 114.69 kg per capita ( PSA, 2020 ). The per capita output was slightly lower (2.7 per cent) than the record set in 2018. Rice is the staple food for 109.04 million Filipinos ( PSA, 2021 ), who consume an average of about 110 kg of rice per capita per year ( PSA, 2018 ). Rice accounts for more than a third of the average calorie intake of Filipinos. In addition, rice is a major food expense, accounting for 13.1 per cent of total household spending and a third of total food consumption. Thus, rice in the Philippines is a highly political crop and a sensitive issue for policymakers regarding food prices and security.

The 2018 Family Income and Expenditure Survey (FIES) revealed that Filipinos’ average annual income increased by about 17 per cent, from 268,000 pesos in 2015 to 313,000 pesos in 2018. Average family income also increased in all deciles. On the other hand, the average family expenditures during the same period increased by about 11 per cent, from 216,000 pesos to 239,000 pesos. In 2018, 42.6 per cent of the average Filipino family's spending was on food, an increase of 0.8 percentage points from 2015 (41.8 per cent). Of the above proportion, 33.6 per cent was spent on food consumed at home and only 9.0 per cent was spent on food outside the home. Among the food items consumed at home, bread, and cereals had the highest share of food expenditures (11.0 per cent), followed by meat (5.7 per cent) and fish and seafood (5.0 per cent) ( PSA, 2020 ). A 0.7 per cent share of expenditures was for oils and fat. Unlike the income pattern observed in deciles, for families in the bottom 30 per cent income group, 58.2 per cent of their total expenditures went for food compared with 39.5 per cent for families in the upper 70 per cent income group.

Price and income shocks affect families in various income groups differently regarding food expenditures (or food consumption). For instance, for the early 2000s, Ivanic and Martin (2008) noted that price shocks in low-income countries negatively affected poverty rates. The authors stated that rice prices increased by 25 per cent, leading to higher poverty rates in rice-dependent countries. Other studies have also investigated the causes of higher food prices and their impact on household welfare ( Dewbre et al., 2008 ; Coxhead et al., 2012 ; Minot and Dewina, 2015 ). In addition, Valera, Balié, and Magrini (2022) recently noted that rice price shocks have a higher inflationary effect than fuel prices and remittance earnings. Thus, the food security of millions of Filipinos is affected by inflation and the rise in commodity prices. Populations across developing and emerging economies also experience income shocks. These income shocks can arise from natural disasters, for example, flooding, droughts, hurricanes, and typhoons ( Samphantharak, 2014 ; Alano and Lee, 2016 ). The authors found that droughts and typhoons decrease national income in the short and long term—about a 2.3 per cent decrease in gross domestic product (GDP). Additionally, in their study, Tanaka, Ibrahim, and Lagrine (2021) found that although large-scale natural disasters hurt real GDP, the effect of the shock persists for a more extended period in the Philippines than in China, India, and Thailand. The Organization of Economic Co-operation and Development (OECD) reports used income and price shocks to estimate household food insecurity ( OECD, 2015 , AAAAAA 2017 ). A related measure that captures the components of food security is the self-sufficiency ratio (SSR), which is the share of production compared with utilization ( Clapp, 2017 ). The ratio indicates how much a commodity's supply is from domestic production. The higher the SSR, the greater the self-sufficiency. 1 Interestingly, in 2019, the SSR for rice dropped to 79.8 per cent from 86.7 per cent in 2018, implying a rice shortage and thus more imports from world markets ( PSA, 2020 ). In other words, the Philippines imported about 20.2 per cent of its domestic rice supply.

Given rice's budgetary and nutritional importance in the well-being of Filipinos, a further understanding of rice demand behavior would provide valuable information regarding food security, income stabilization, and trade policies. Changes in income or relative prices culminate in shifting purchasing patterns, and changes in these factors can lead to a healthier or more malnourished rural population. Thus, information on food demand behavior is crucial in analyzing the effects of different policies and, in turn, in providing recommendations for planning, designing, and implementing government programs that will help improve Filipinos’ food supply and nutritional status.

Our study examines the influence of income, relative prices, and relevant socioeconomic factors on food purchasing behavior, in total and by primary food categories, among Filipino households. The study uses the Quadratic Almost Ideal Demand System (QUAIDS) and recently collected 2018 FIES that collected detailed information on expenditure patterns among Filipino families. In 2019, the Philippines shifted to a liberalized rice trading regime with the Rice Tariffication Law (RTL). Thus, the findings from our study provide a better understanding of the potential effects of future price and income shocks on rice demand. Second, the study offers complementary information to enrich the Philippine Rice Industry Roadmap 2030 (a guide toward achieving rice security—increasing yields, reducing costs, enhancing resiliency, and ensuring safety and nutrition 2 ) in estimating the country's rice demand in rural and urban locations and income of consumer types and improving the quality of policy recommendations in food security and nutritional programs. Finally, another crucial contribution of our study is in providing policymakers with an up-to-date analysis to quantify the effects of various market shocks on consumer food expenditures. 3 Although considerable literature explores food demand estimation for the Philippines, we offer a first study that considers a two-stage budgeting process in food demand instead of treating demand for food commodities in a one-step budgeting process. The household determines the share of income devoted to food in the first step. Based on the outcome of this first stage, the second stage determines how to allocate food expenditures across the different food categories.

The article is structured as follows. The following section discusses the literature on food demand estimation for the Philippines. The third section describes the conceptual framework and empirical methodology. The fourth section describes the data and the fifth section presents the results. The final section concludes and elaborates on policy implications.

Several studies have investigated food demand in the Philippines. In the late 1980s, Quisumbing et al., (1988) used 1978 and 1982 household surveys collected by the Food and Nutrition Research Institute. The study was the first to estimate the demand elasticities of food and non-food items. The study reported disaggregate demand parameters for food subgroups that accounted for location and occupation when assessing food consumption. In the early 1990s, Bouis (1990) estimated the food demand elasticity for urban and rural Filipinos. The author used 1978 and 1982 household surveys to find that meats have higher own-price and income elasticities. In contrast, Bouis (1990) found that maize had a negative income elasticity for rural and urban families. In addition, the author predicted changes in the consumption levels of food items 4 and overall calorie intakes. The author concluded that lower real wages and rising cereal prices 5 would increase malnutrition.

Two years later, Bouis, Haddad, and Kennedy (1992) compared calorie-income elasticities for Kenya and the Philippines. The authors estimated calorie intake and calorie availability for both countries. For the Philippines, our focus in this study is that the authors found that calorie intake and availability are higher for more affluent families for most food items but not for maize. The authors argue that wealthy families buy extra food for guests and workers. Using household survey data from 1985,1988, and 1991, the FIES, and the Almost Ideal Demand System (AIDS), Balisacan (1994) studied food demand by Filipinos. The authors found that most food items (maize, rice, other cereals, dairy and meat, fruits and vegetables, and other foods) were income-inelastic (about 0.1) and did not change with income levels. Balisacan (1994) concluded that although food price responses vary by income group and household location, the variation was not as large as reported in the media.

In the early 21st century, Mutuc, Pan, and Rejesus (2007) , using 2000 FIES data and the QUAIDS, estimated expenditure elasticities for 11 vegetable types 6 in the Philippines. The authors found significant expenditure elasticities between urban and rural residents. However, the authors did not find significant differences in own-price and cross-price elasticities between urban and rural residents. In a recent study, Fuji (2016) compared food demand in the urban populations of the Philippines and China. Using six rounds of FIES data (1988,1991, 1994, 2000, 2003, and 2006), the author found that, from 1998 to 2006, Filipinos’ diet essentially became more westernized. Additionally, urban Filipinos’ demand for meat, vegetables, and fruits was similar to that of the Chinese urban population. Using the 2008–2009 Survey of Food Demand for Agricultural Commodities and Linear Approximate Almost Ideal Demand System (LA/AIDS), Sombilla, Lantican, and Quilloy (2011) estimated rice demand for Filipinos. The authors noted that rice demand was inelastic to total food expenditure, income, and own-price, especially for rural poor Filipinos.

Finally, Dizon and Wang (2019) used 2015 FIES data in estimating own-price and cross-price food demand elasticities to simulate the impact of the rice tariffication policy, which has abandoned quantitative restrictions on rice imports since the promulgation of the RTL in February 2019. The authors highlighted that the corresponding expected decline in rice prices following the rice tariffication policy would increase rice consumption and that of other food groups, with potential for increased diet diversity. In a recent study, Balié, Minot, and Valera (2021) , using the IRRI Global Rice Model, simulated the RTL on the domestic price of rice. The authors found that the RTL decreased consumer and producer rice prices, thus affecting the production and consumption of rice. Rice farmers who were net sellers were negatively affected, although overall the RTL reduced poverty.

The QUAIDS is an extension of the now-famous AIDS proposed initially by Deaton and Muellbauer (1980) . QUAIDS is quadratic in expenditures, more flexible than the AIDS, and allows demand curves to be non-linear in the logarithm of expenditures, thus exhibiting non-linear Engel curves. 7 Specifically, QUAIDS allows a good to be both a luxury item and a necessity good at the two ends of the income distribution ( Banks, Blundell, and Lewbel, 1997 ). Several studies have used the QUAIDS modeling approach (initially proposed by Banks, Blundell, and Lewbel, 1997 ) to estimate broad food demand in developed and developing countries. Studies in developing countries of interest to us in this study are Hoang (2018) for households in Vietnam; Khanal, Mishra, and Keithly (2016) for rural households in southern India; Boysen (2012) for Uganda; Meenakshi and Ray (1999) for Indian families; and Obayelu et al., (2009) and Fashogbon and Oni (2013) for Nigerian households. Other studies include Ecker and Qaim (2011) , who study food and nutrient demand in Malawi. Two studies ( Gould and Villarreal, 2006 ; Zheng and Henneberry, 2010 ) investigated food demand in urban China. Studies in South and Southeast Asia include, for example, Garcia et al., (2005) , Tey et al., (2008) , and Pangaribowo and Tsegai (2011) , who estimated fish demand in the Philippines, rice demand in Malaysia, and food demand in Indonesia, respectively.

Interestingly, a series of studies estimated food demand projections using QUAIDS for Ethiopia ( Tafere et al., 2011 ), Bangladesh ( Ganesh-Kumar et al., 2012b ), and India ( Ganesh-Kumar et al., 2012a ). Food demand studies using QUAIDS also include Vietnam ( Hoang, 2018 ), India ( Khanal, Mishra, and Keithly, 2016 ), and China ( Fashogbon and Oni, 2013 ). The above studies use Ray's (1983) and Poi's (2012) approach to include differences in demographic factors across households when analyzing food and non-food expenditures in a complete demand system. Recent studies using the QUAIDS model are Law, Fraser, and Piracha (2020) and Hussein, Law, and Fraser (2021) . For instance, Law, Fraser, and Piracha (2020) used the QUAIDS model to estimate the combined demand elasticities for cereals to assess changes in the food preferences of Indian households. Hussein. Law, and Fraser (2021) used the World Bank's 2018 Somalia High Frequency household survey data to show the effects of income shocks (civil war in Somalia) on food consumption elasticities (expenditure, own- and cross-price elasticities for animal products).

The above studies, in general, support the superiority of the QUAIDS model compared with the AIDS model when estimating food expenditures, by category, in a complete demand system. 8

Recall that the QUAIDS model accounts for differences in socioeconomic conditions across households by augmenting demographic and household-specific variables (e.g. household size) using the method proposed by Ray (1983) and Poi (2012) . Therefore, our study employs the QUAIDS method for estimating food demand among the Filipino population. We assume weak separability in the household's two-stage budgeting process ( Boysen, 2012 ). In the first stage, the family decides the percentage of the total budget allocated to food. In the second stage, the household allocates the food budget among different food categories. 9 Note that elasticities contingent on exogenous total group expenditure in the demand system may be inappropriate when assuming a two-stage allocation process. Our study overcomes the limitation of single-stage and conditional elasticities by computing appropriate unconditional elasticities. The unconditional elasticities from the demand model are derived following Edgerton (1993 ; 1997 ) and Carpentier and Guyomard (2001) .

3.1. First-stage: expenditure share of food

3.2. second-stage demand system.

Our study uses the 2018 FIES. 15 The FIES is a nationwide survey of households in the Philippines. The first FIES was conducted in 1957. The Philippine Statistics Authority (PSA) gathers family income and expenditure data. The 2018 FIES was the first to use and interview a sample of 170,917 households, which was deemed sufficient to provide reliable estimates of income and expenditure at the national, regional, provincial, and highly urbanized cities (HUC) levels. The 2018 FIES used the 2013 Master Sample sampling design. A total of 2,695 data items were included in the 2018 FIES questionnaire. 16 The sample households covered in the survey were interviewed in July 2018.

The survey reports total expenditures on food and non-food items. Unfortunately, the 2018 FIES data released by the PSA lack quantity and unit prices for the goods the families consumed.

Total expenditures are the sum of all consumption expenditures. Data cleaning and missing information resulted in 147,717 families for analysis in our study. Appendix  Table A2 shows the average socioeconomic and demographic attributes of the families in the 2018 FIES. The average age of the household head (HH) was 48, 84 per cent reported being married, and 86 per cent were employed. The average family size was 4.6 persons per household and 10 per cent of the sampled households lived in poverty. All food items consumed by families are aggregated into nine categories. These categories are (1) RICE (well-milled, regular, National Food Authority, and other); (2) OTHER CRLS (maize and other cereals—maize, flour, cereal preparation, bread, pasta, and other bakery products); (3) MEAT (beef, chicken, goat, pork, preserved meats); (4) FISH (fresh, dried/smoked, preserved, and seafood); (5) FRUIT (fresh, dried, nuts, preserved, and others); (6) VEGE (vegetables, tubers, preserved, and products of tubers); (7) SUGAR (centrifugal, muscovado, refined brown sugar, and others); (8) DRINKS (soft drinks, mineral, fruit juice, concentrates, and other non-alcoholic beverages); and (9) MISC (milk and others).

We divided the sample into three income terciles (low, middle, and high) and two regional categories (rural and urban). The latter two categories are based on the location of the surveyed households.  Table 1 shows the average budget shares and annual income (expenditures) per capita of each selected food group for the sample, income terciles, and urban and rural families.  Table 1 reveals that low-income households spent more than 55 per cent of their total income buying food. The average family spent nearly 42 per cent of its total income purchasing food and food items.

Food expenditure share ( per cent) by income levels and regions, Philippines, 2018.

Source : 2018 FIES. Philippines Statistical Authority. https://psa.gov.ph/tags/family-income-and-expenditure-survey

Exchange rate USD 1 = PHP 52.41 (Philippine pesos). Food groups: RICE = rice (well-milled, regular, National Food Authority (NFA), and other); OTHER CRLS = maize and other cereals (maize, flour, cereal preparation, bread, pasta, and other bakery products); MEAT = beef, chicken, goat, pork, and preserved; FISH = fresh, dried/smoked, preserved, and seafood; FRUIT = fresh, dried, nuts, preserved, and others; VEGE = vegetables, tubers, preserved, and products of tubers; SUGAR = centrifugal, muscovado, refined brown sugar, and others; DRINKS = soft drinks, mineral, fruit juice, concentrates, and other non-alcoholic beverages; MISC = milk and others.

Table 2 presents the estimates of uncompensated price elasticity, expenditure, and income elasticity.  Table 2 shows that nine food items’ estimates of own-price elasticity (the percentage change in the quantity of food items demanded due to a percentage change in price) are negative and statistically significant at the 1 per cent level of significance. On the one hand,  Table 2 also shows that demand for other cereals, meat, fish, fruit, vegetables, and miscellaneous food groups is elastic. On the other hand, demand for rice, sugar, and drinks food groups is inelastic ( Table 2 ). Cross-price elasticities are consistent and in both directions. Our finding is consistent with Hoang (2018) in her study of Vietnamese food demand.  Table 2 shows that rice, the main food item for Filipinos, complements four other food groups, but rice is a substitute for other cereals, fish, and miscellaneous food groups (milk and others). Similarly, the meat group complements seven other food groups but not other cereals and miscellaneous food. The miscellaneous food group is a substitute for all other food groups. Finally, the fruit food group is substitutable with the other cereals, fish, vegetables, and miscellaneous food groups.

Uncompensated price, expenditure, and income elasticities, Philippines, 2018.

Source: Authors’ computation using FIES 2018 https://psa.gov.ph/tags/family-income-and-expenditure-survey . Numbers in parentheses denote standard errors.

Notes: ***, **, * denote significance at 1 per cent, 5 per cent, and 10 per cent levels, respectively.

Demand for rice is near unitary elastic (−0.93) to change in rice prices compared with that for other food groups, with own-price elasticity ranging from −1.67 (drinks) to −1.00 (meats) and to −1.67 (miscellaneous food group). Demand is less elastic for the sugar and drinks food groups, with own-price elasticity of −0.71 for sugar and −0.70 for drinks. Our estimate is consistent with Quisumbing (1986) , who found an elastic price elasticity of demand for rice in the Philippines. Specifically, our estimates are lower, in absolute terms, than those of Quisumbing (1986) , who discovered an own-price elasticity of rice demand from −1.44 to −1.00, depending on the income group. However, our estimate is closer to that of Vu (2009) , who, using the Vietnam Household Living Standard Survey (VHLSS), found an own-price elasticity of demand for rice of −0.8 for Vietnamese households. Our estimates are also closer to the own-price elasticity estimates (−0.6) obtained by Gibson and Kim (2013) , who analyzed 2010 VHLSS data.

However, our own-price elasticity of demand for rice estimate is about two times larger, in absolute terms, than that obtained by Hoang (2018) using 2010 VHLSS data (−0.47). It should be noted that Hoang's rice food group included white rice, sticky rice, rice noodles, and bun . However, several reasons could explain the higher own-price elasticity. First, the higher own-price elasticity for rice could be due to our use of prices at the provincial level. Second, our study's rice group comprises several rice types, including well-milled, regular, National Food Authority, and others. At the provincial level, the price of rice is not differentiated by the type of rice. Third, the elastic response of rice to its own price appears to reflect a slight variation in rice prices. A plausible argument for elastic rice demand could be the westernization of the Filipino diet ( Fuji, 2016 ). Fuji (2016) notes that, from 1988 to 2006, Filipinos increased their food budget share for dairy, eggs, and meat. The author notes a decline in the expenditure share of cereals, including rice, during the same period.

Column 11 of  Table 2 reports the expenditure elasticity of demand for all nine food groups. The expenditure elasticity of demand for rice is 0.67, slightly higher than the expenditure elasticity of demand for the sugar food group (0.56). Our expenditure elasticity estimate for rice is nearly twice as large as the estimates obtained by Vu (2009) and Hoang (2018) . In contrast, the expenditure elasticity 17 of demand for other food groups is significantly larger, ranging from 0.76 to 1.25. Finally, the last column of  Table 2 reports the income elasticity of each food group. Estimates show that the income elasticity of each food group decreases by 50 per cent or more compared to expenditure elasticity, suggesting that each food group is a necessary good for changes in consumer income. The income elasticity of the rice group is 0.26 ( Table 2 , last column), suggesting that a 1 per cent increase in household income (expenditures) increases rice demand by 0.26 per cent. The results suggest an inelastic demand for rice with respect to changes in Filipino families’ income. Our estimate is lower in absolute terms than the estimates obtained by Abad et al., (2010) and Lantican, Sombilla, and Quilloy (2013) . The miscellaneous food group (0.47) and meat food group (about 0.45) have the highest and second-highest income elasticity, followed by drinks (0.37) and other cereals (about 0.35). Interestingly, the sugar food group's income elasticity is the lowest (0.21).

5.1. Income and location disaggregation

Estimates of expenditure and uncompensated price elasticities by income terciles for urban families are provided in Appendix  Table A3 and for rural families in Appendix  Table A4 .  Table 3 presents the estimates for three income groups (low, middle, and high) and urban and rural subsamples. The left panel of  Table 3 shows the expenditure elasticities and the right panel presents the uncompensated own-price elasticities of each food group. As expected,  Table 3 shows that expenditure elasticities are all positive and own-price elasticities are negative for each food group. All estimates are significant at the 1 per cent level of significance. Table 3’s left panel shows that demand for food items, especially rice, tends to be more elastic with respect to expenditures for lower-income and rural households. For instance, the expenditure elasticity of demand for the rice food group is higher (0.78) for low-income families and lower (0.66) for high-income families. Similarly, the expenditure elasticity of demand for the rice food group is 0.65 for rural families and 0.68 for urban families. However, the expenditure elasticity of demand for the other cereals food group is higher (0.96) for high-income families and lower (0.89) for low-income families. The expenditure elasticity of demand for the other cereals food group is 0.98 for rural families and 0.92 for urban families.

Expenditure and uncompensated own-price elasticities for income and regional subsamples, Philippines, 2018.

On the one hand, estimates in  Table 3 (left panel) show that, regardless of income strata and location of families (rural and urban), meat, fish, and miscellaneous food groups appear to be luxury goods (elasticity > 1). On the other hand, estimates in  Table 3 (left panel) reveal that, regardless of income strata and location of families (rural and urban), vegetables and sugar appear to be normal goods (elasticity < 1). Our finding is consistent with Hoang's (2018) and Vu's (2009) results for Vietnamese households. Lastly, drinks are a luxury good for rural households. Interestingly, estimates from our study show that fruits are normal goods for lower- and middle-income Filipino families and luxury goods for high-income urban and rural families. Demand elasticities with respect to prices reveal a pattern that is consistent with expenditure elasticities. For example, the own-price elasticity of rice group demand is decreasing, in absolute terms, with increasing household income. The elasticity of demand is −0.98 for low-income households compared with −0.91 for high-income households. Our estimates follow a similar pattern and are lower in magnitude, in absolute terms, than those of Quisumbing (1986) , who found that the own-price elasticity of demand for rice was −1.45 for lower-income households and about −1.00 for higher-income households. 18 Similarly, the own-price elasticity of rice group demand is higher (−0.93) for rural households than for urban families (−0.91). Our result is consistent with other studies in the literature. For instance, Hoang (2018) , Vu (2009) , and Canh (2008) found the own-price elasticity of rice demand in urban areas to be less elastic than in rural areas.

5.2. Impact of income and price shocks on budget shares

In our study, we model the impacts of two hypothetical scenarios: a 15 per cent decrease in income and a 20 per cent rise in rice prices in the budget share that Filipino families devote to the various food groups. Specifically, we use Hoang's (2018) procedure to estimate the impacts of income and price shocks on budget shares. T able 5 shows the income and price shock results using 2018 FIES data as the baseline. For reasons of space and brevity, we present only the impact on budget shares and quantities and discuss only low-income and high-income households.

Table 4 shows that a 15 per cent reduction in income increases the budget share for rice by 0.1 percentage points for the entire sample and is compensated for by a decrease in the budget share of other meat (−0.1 percentage points) and miscellaneous (−0.1 percentage points) food groups. Interestingly, the impact of a 15 per cent reduction in income on budget shares differs by income group. For low-income families, a 15 per cent reduction in income decreases the budget share for rice by 1.8 percentage points, for other cereals by −0.1 percentage points, for vegetables by −0.1 percentage points, and for sugar by −0.1 percentage points, and is compensated for by an increase in the budget share of meat by 1.0 percentage points, fish by 0.1 percentage points, drinks by 0.2 percentage points, and miscellaneous by 0.7 percentage points. For high-income families, a 15 per cent decrease in income increases the budget share of rice by 2.8 percentage points, thus increasing rice expenditure and purchased quantity by 6.1 per cent. An income decrease also induces a smaller increase in budget share (0.1 to 0.2 percentage points) for non-rice cereals, fish, and sugar. These budget-share increases are offset by decreases in the budget share of meat (−1.2 percentage points), fruit (−0.3 percentage points), drinks (−0.3 percentage points), and miscellaneous (−1.4 percentage points). Our findings for the entire sample and high-income households are qualitatively consistent with those of Hoang (2018) . However, the percentage in the budget share differs because Hoang considered only a 10 per cent reduction in income compared with a 15 per cent reduction in our study of Filipino families.

Impacts of income and price shocks on budget share and per capita quantity, 2018, Philippines.

Source : Authors’ computation using FIES 2018 https://psa.gov.ph/tags/family-income-and-expenditure-survey .

The last panel of  Table 4 shows the impact of a 20 per cent increase in rice prices. The results reveal that a 20 per cent rise in rice prices increases budget shares for rice by 0.1 percentage points and for the miscellaneous food group by 0.9 percentage points for the entire sample. The increase in budget share for rice and the miscellaneous food group is compensated for by a decrease in the meat (−0.4 percentage points), fish (−0.2 percentage points), fruit (−0.1 percentage points), vegetables (−0.2 percentage points), and drinks (−0.1 percentage points) food groups. We also observe that increased rice prices have a differential impact on budget shares by analyzing family income groups. On the one hand, for low-income families,  Table 4 shows that a 20 per cent increase in rice prices decreases the budget share allocated to rice by 1.7 percentage points, vegetables by 0.3 percentage points, and other cereals by 0.2 percentage points. However, a 20 per cent increase in rice prices increases the budget share of the miscellaneous food group by 1.5 percentage points and the drinks food group by 0.1 percentage points. On the other hand, for high-income families, a 20 per cent increase in rice prices increases the budget share of rice by 2.8 percentage points, thus increasing the quantity by 17 per cent. The same price increase will decrease budget shares for meat by 1.6 percentage points, fish by 0.3 percentage points, fruit by 0.5 percentage points, and drinks by 0.3 percentage points. Our estimates for the entire sample and high-income households are consistent, albeit of a different magnitude, with those of Hoang's (2018) study, which considered a 30 per cent increase in rice prices in Vietnam.

Table 4 shows that urban and rural families allocate more of their budgets to rice and reduce expenses on other food items when income shocks occur. For urban families, when income decreases by 15 per cent, the budget share for rice increases by 0.1 percentage points and is primarily compensated for by a decrease in the budget share for miscellaneous food items. In response to decreased income (a 15 per cent reduction), low-income urban Filipino families diminished their budget share for rice by 2.6 percentage points, other cereals by 0.1 percentage points, fish and vegetables by 0.3 percentage points, and sugar by 0.1 percentage points. On the other hand, low-income urban families increased the budget share for meat (1.4 percentage points) and drinks (0.3 percentage points). In response to decreased income, high-income urban families allocated more of their budgets to rice (increasing quantity by 12.7 per cent), other cereals, fish, vegetables, and sugar food items. Perhaps high-income Filipinos have higher saving rates and use savings to buy more food items. For low-income rural families, a 15 per cent reduction in income decreases the budget share for other cereals by 0.1 percentage points. This increases the expenditure share of miscellaneous food items by 0.4 percentage points ( Table 4 ). In response to decreased income, high-income rural families behave similarly to their urban counterparts. Specifically, high-income rural households allocate more of their budgets to rice (by 4.5 percentage points, a 12.8 per cent increase in quantity) and vegetables and sugar (by 0.2 percentage points), and increase the budget share to other cereals by 0.1 percentage points.

In the case of a 20 per cent increase in rice prices, the response is quite different for urban and rural Filipino families. Low-income urban and rural families allocate less of their budgets to rice (a 2.5 percentage points reduction for low-income urban families versus a 1.4 percentage points reduction for low-income rural families). Similarly, low-income urban and rural families allocate less of their budgets to other cereals, both by 0.2 percentage points. Low-income urban families also reduce their budget shares for fish (0.5 percentage points), vegetables (0.2 percentage points), and sugar food items (0.1 percentage points). In response to a 20 per cent increase in rice prices, low-income urban families increase their budget shares for miscellaneous food items (2.3 percentage points), meat (1.1 percentage points), and drinks and fruit (by 0.2 percentage points each). In contrast, high-income urban and rural families allocate more money to rice (1.8 percentage points more for urban families versus 4.4 percentage points more for rural families) and assign less money to meat (1.2 percentage points for urban families and 4.4 percentage points for rural families). High-income rural families also decrease budget shares for fish, fruit, vegetables, drinks, and miscellaneous food items. On the other hand, high-income urban families decrease budget shares for fruit (0.4 percentage points) and drinks (0.3 percentage points).

In sum, our study suggests that either a 15 per cent decrease in income or a 20 per cent increase in rice prices leads, on average, to an increased share of spending on rice at the expense of decreased spending shares on other goods. An increase in rice prices decreases spending on meat, fish, and fruit and increases spending on miscellaneous food items (maize, bread, flour, milk, and others). In contrast, a decrease in income diminishes spending on miscellaneous food items. Finally, the effects of income and price shocks are heterogeneous across the income spectrum (low and high income) and location (urban and rural areas).

Our study estimated food demand in the Philippines and assessed how income and price shocks affect food purchasing behavior. Unlike most studies that evaluated food demand in a one-step budgeting process, we first examined the household's share of income spent on food. We then studied the allocation of food expenditures across the different food categories. Applying Lewbel's Stone- Lewbel (1989) method to address the absence of price data from the 2018 FIES, the evidence points to a relatively inelastic response of rice demand to prices and expenditures compared to that of other food groups. In addition, we found that income elasticity for rice was inelastic and that demand for rice was less elastic for higher-income urban households than for rural households. In the short term, a market shock such as a 15 per cent drop in income or a 20 per cent rise in rice prices leads families to spend more on rice, which is a less expensive main food staple, and to spend less on relatively more expensive food items such as meat, fish, and other food groups. The evidence points to a differentiated impact of income and rice price shocks on low-income and high-income households.

The findings from our study lead us to several policy recommendations. First, this research has shown that a decrease in income and an increase in rice prices can potentially worsen food insecurity in the most vulnerable and poorest segments of the Filipino population. This implies that the resilience of the poorest consumers and the most vulnerable households must be addressed by providing adequate safety nets. As Valera et al., (2020) pointed out, low-income families would be protected by those safety net measures when, and even before, the income shock threatens their food security. Safety net measures might include expanding existing cash transfer programs or developing new programs. Policymakers, however, would have to ensure that the safety nets are well targeted to the poor and have significant fiscal resources backed by the government.

Second, the Philippine Rice Industry Roadmap (PRIR) aims to fill a major gap in estimating the country's rice demand by different consumer types under a liberalized trading regime for 2021–2035. Thus, the elasticity estimates generated from our study would be helpful for simulation and further analysis of various programs under the PRIR, particularly programs that ensure access to nutritious food. If policymakers adopt this policy lesson, it will further allow them to quantify the welfare effects of the nutritional programs under the PRIR. This, in turn, will improve the quality of advice in the planning, designing, and implementing of government programs and policies.

Third, results from our study show that food demand behavior tends to be different for urban and rural households. Therefore, public policy should focus on designing and implementing a more targeted policy approach tailored to rural and urban areas. Policy efforts in this direction include programs that improve accessibility to and availability of quality agri-fishery products such as rice, fish, poultry, livestock products, fruits and vegetables, and other essential commodities at affordable prices in urban areas.

While highlighting the importance of public policy, our article still has many unanswered questions. Methodologically, demand estimation by different rice classes is essential but is missing. Considering this explicitly, the model can go beyond characterizing specific rice market segments to support modern breeding programs, product profiling, market intelligence, and research and policy implications. It is also essential to do a follow-up study when the next FIES becomes available. In this context, it would be good to know more about the income and price shocks imposed by the COVID-19 pandemic and how the pandemic affects the food purchasing behavior of different households.

An SSR of less than 100 per cent indicates inadequate food production. An SSR of 100 per cent suggests that the sector's food production capacity meets the population's needs. An SSR of greater than 100 per cent indicates that domestic production more than meets domestic requirements.

The Philippine Rice Industry Roadmap 2030 was created by the Department of Agriculture, Government of the Philippines. See, https://www.philrice.gov.ph/wp-content/uploads/2018/09/The-Philippine-Rice-Industry-Roadmap-2030.pdf .

Balié, Minot, and Valera (2021) show an analysis of the potential welfare effects of rice tariffication on different types of households, but they used only 2015 FIES data.

Food items included corns, rice, other cereals, fish, meats, fruits/vegetables, all others.

Note that the real per capita Gross National Product (GNP) declined by 20 per cent for four years in a row immedicately after the Philippines suspended payments on foreign debt.

Includes cabbage, water spinach, horseradish tree leaves, Chinese white cabbage, bitter gourd, eggplant, okra, tomato, hyacinth bean, mung beans, string beans, and others.

For studies discussing the advantages of rank three demand systems such as QUAIDS over other rank two demand systems, see, Decoster and Vermeulen (1998) and Cranfield et al., (2003) .

We conducted a quadratic specification test, which suggested favoring a QUAIDS model.

Additionally, the plot of food group shares over household expenditure and a formal test for quadratic specification in demand system analysis suggest the superiority of the QUAIDS model over AIDS in our estimation.

One can derive this by substituting ordinary intercept term |$\ {\alpha }_F$|⁠ , such that |${\alpha }_F = \alpha _F^{\prime} + \mathop \sum \limits_{d\epsilon D} {\delta }_d{Z}_d$|⁠ .

Most variation in SL prices is derived from household heterogeneity and not from CPIs.

The Lewbel (1989) definition of SL prices uses good-level price indices. The maximum level of disaggregation of CPIs in our data contains category-level price indices. Thus, we use category-level price indices rather than good-level price indices to compute the SL prices.

First-stage results can be obtained from the authors.

Note that the shares of budget allocated to food and non-food items add up to 1. The expenditure elasticity of non-food can be calculated as |${\varphi }_{NF} = \frac{{1 - {\varphi }_F*{S}_F}}{{1 - {S}_F}}$|⁠ .

https://psa.gov.ph/tags/family-income-and-expenditure-survey

The questionnaire consisted of seven parts: Part I ‒ Identification and Other Information; Part II ‒ Expenditures and Other Disbursements; Part III ‒ Housing Characteristics; Part IV—Income and Other Receipts; Part V ‒ Entrepreneurial Activities; Part VI ‒ Social Protection; and Part VII ‒ Evaluation of the Household Respondent by the Interviewer.

Derived by multiplying the expenditure elasticity by the sample mean income elasticity of food expenditures.

Quisumbing (1986) divided the sample into four quartiles.

We would like to thank all Funders who supported this research through their contributions to the CGIAR Trust Fund. In particular, funding from the CGIAR Initiative on Market Intelligence, the CGIAR Initiative on Foresight, and the Bill & Melinda Gates Foundation, Seattle, WA, USA [Grant no. OPP1194925] is greatly acknowledged. We also gratefully acknowledge the Philippine Statistics Authority for providing the 2018 Family Income and Expenditure Survey dataset. We further would like to thank the Editor and two anonymous reviewers for their constructive comments which greatly improved the paper.

The authors declare no conflict of interest.

Publicly available data can be obtained from https://psa.gov.ph/content/highlights-preliminary-results-2021-family-income-and-expenditure-survey-fies-visit-1 Price indices can be downloaded from https://psa.gov.ph/price-indices/cpi-ir/downloads .

Estimated Stone-Lewbel (SL) price indices, Philippines, 2018.

Notes: 1 Includes well-milled rice, regular rice, National Food Authority (NFA) rice, and other rice. 2 Includes maize and other cereals (maize, flour, cereal preparation, bread, pasta, and other bakery products). 3 Includes beef, chicken, goat, pork, and preserved. 4 Includes fish that is fresh, dried/smoked, preserved, and seafood. 5 Includes fruits that are fresh, dried, nuts, preserved, and others. 6 Includes vegetables, tubers, preserved, and products of tubers. 7 Includes centrifugal sugar, muscovado, refined brown sugar, and others. 8 Includes soft drinks, mineral, fruit juice, concentrates, and other non-alcoholic beverages. 9 Includes milk and others.

Numbers in parentheses are standard errors.

Socioeconomic attributes of families in 2018 FIES, Philippines.

Source: FIES 2018, Philippine Statistics Authority https://psa.gov.ph/tags/family-income-and-expenditure-survey .

Notes: Numbers in parentheses are standard errors. Region 1 = Ilocos; Region 2 = Cagayan; Region 3 = Central Luzon; Region 4 = CALABARZON and MIMAROPA Region; Region 5 = Bicol; Region 6 = Western Visayas; Region 7 = Central Visayas.

Notes: Numbers in parentheses are standard errors. Region 8 = Eastern Visayas; Region 9 = Western Mindanao; Region 10 = Northern Mindanao; Region 11 = Southern Mindanao; Region 12 = Southern Mindanao or SOCCSKSARGEN; Region 13 = National Capital Region (NCR); Region 14 = Cordillera Administrative Region (CAR); Region 15 = Autonomous Region in Muslim Mindanao (ARMM); Region 16 = Caraga; Region 17 = Bangsamoro Autonomous Region in Muslim Mindanao (BARMM).

Expenditure and price elasticities by income strata, urban subsample, Philippines, 2018.

Expenditure and price elasticities by income strata, rural subsample, Philippines, 2018.

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Projection of the Effects of the COVID-19 Pandemic on the Welfare of Remittance-Dependent Households in the Philippines

• Original Paper
• Open access
• Published: 25 September 2020
• Volume 5 , pages 97–110, ( 2021 )

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• Enerelt Murakami 1 ,
• Satoshi Shimizutani 1 &
• Eiji Yamada 1  

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The Coronavirus Disease 2019 (COVID-19) is inevitably affecting remittance-dependent countries through economic downturns in the destination countries, and restrictions on travel and sending remittances to their home country. We explore the potential impacts of the COVID-19 pandemic on the welfare of remittance-dependent households using a dataset collected in the Philippines prior to the outbreak. First, we confirm that remittances are associated with welfare of households, particularly for those whose head is male or lower educated. Then, we use the revision of the 2020 GDP projections before and after the COVID-19 crisis to gauge potential impacts on households caused by the pandemic. We find that remittance inflow will decrease by 14–20% and household spending per capita will decline by 1–2% (food expenditure per capita by 2–3%) in one year as a result of the pandemic.

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Introduction

The Coronavirus disease 19 (COVID-19) is a devastating pandemic with global effects and is undoubtedly one of the largest macro-level shocks to the world economy, as evidenced by the already ominous economic indicators. While the adverse effects on the economy are revealing at the macro-level, the impact of the pandemic is likely to be heterogenous across countries and individuals. Moreover, the adverse effects may not be confined to the domestic markets but may be transmitted internationally, particularly in the case of developing countries.

This paper explores the potential impacts of the COVID-19 pandemic on the welfare of households in a remittance-dependent country, which is likely to be severely exposed to external shocks. The pandemic is expected to substantially reduce the amount of remittances that migrants from developing countries can send home. The World Bank estimates that global remittances will decline sharply by about 20% in 2020, the sharpest in recent history, and that remittances to low and middle-income countries are projected to fall by 19.7%. Footnote 1 Many migrants may lose their jobs or be forced to accept lower wages due to lockdowns or oil price crashes in their destination countries (IOM, 2020 ); they may not be able to send remittances due to stringent movement restrictions and exclusion of money transfer service providers from the list of “essential services” (World Bank, 2020b ). Furthermore, many intended migrants who had been preparing for their departure in the near future will be forced to change their livelihood plans for the coming years. In 2019, 80% of the world’s total remittances flowed to low-and-middle-income countries (World Bank, 2020c ); therefore, the negative impacts of the COVID-19 outbreak may be more serious in developing countries whose citizens heavily depend on remittances from migrant family members.

The Philippines is a sensible case to study for several reasons. First, the country is one of the largest source countries for migrants in the world and is one of the most remittance-dependent, ranked fourth in terms of remittance inflow (Yang, 2011 ). The proportion of remittances relative to the country’s GDP was close to 10% (World Bank, 2020a , b , c and d ). Moreover, some of the countries that host Filipino migrants are the most seriously affected by lockdowns and oil price crashes. The number of overseas Filipino workers was estimated at 2.2 million in 2016 with the top destinations being Saudi Arabia, the United Arab Emirates, Kuwait, Qatar, Hong Kong, and Singapore, which combined accounts for two-thirds of total destinations (Philippine Statistics Authority, 2017 ). The diversity of destinations implies that the impact of COVID-19 may be heterogenous even among Filipino migrants. The Philippine Government has reacted by providing cash relief to overseas migrant workers and their families who are suffering hardship. Footnote 2

In this paper, we use a household-level dataset which was collected in in the Philippines before the COVID-19 outbreak. We first pin down the empirical relationship between remittance income and welfare of households by two-stage least squares (2SLS) instrumenting remittance income by a macroeconomic variable exogenous to households. We then project the potential impact of the COVID-19 shock in destination countries on the welfare of remittance-dependent households by utilizing the revision of the 2020 GDP forecasts by the International Monetary Fund (IMF) and the World Bank, which were made before and after the outbreak of the COVID-19 pandemic. Taking the difference between the predicted outcomes of with- and no-COVID projections provides us with the potential shocks on the remittances and other economic welfare outcomes of remittance-receiving households. Our projections show that remittance inflow will decrease by 14–20% and household spending per capita will decline by 1–2% in one year, as a result of the pandemic. Furthermore, the negative impact can substantially vary across different type of households.

This paper proceeds as follows: Section 2 describes the dataset used in this study. Section 3 examines the effect of macroeconomic shocks on household living standards prior to the COVID-19 outbreak. Section 4 performs several projections to gauge the impact of the pandemic on household welfare. Section 5 concludes.

Data Description

This study utilizes the data from “Survey on Remittances and Household Finances in the Philippines,” conducted by the Japan International Cooperation Agency (JICA) in two rural municipalities in the country: Dingras, Ilocos Norte located in the Northern Luzon Island and Bansalan, Davao del Sur located in the southern island of Mindanao (Fig.  1 ). Footnote 3 The survey is constituted of two rounds of data collection. The first-round survey was conducted between August and September 2016. The sample size was 834. The second-round survey was implemented between June and August 2017. The sample size was 668. Footnote 4 The target sample size at the first-round was 200 overseas migrant households and 200 non-overseas migrant households in each municipality, which were randomly selected in each area. A migrant household is defined as a household which had at least one member who permanently resides or used to reside in this household but is now currently working or living overseas. Given that the stock of overseas Filipino was about ten million in 2013 (Commission on Filipinos Overseas, 2013 ), migrant households were oversampled. A total of 2429 overseas migrant households and 5172 non-overseas migrant households were listed in Dingras while a total of 563 overseas migrant households and 19,797 non-overseas migrant households were listed in Bansalan. Next, stratified random sampling was carried out for each municipality. The barangays within each municipality served as strata and the sample households were randomly selected within each barangay. Footnote 5 The sample of 200 overseas migrant households was proportionately distributed among the barangays. Once the number of overseas migrant households was allocated among the barangays, an equal number of non-overseas migrant households was selected within each barangay.

Location maps of two municipalities. Source: Generated by the authors based on GDAL’s administrative boundary shapefiles

Table 1 reports the summary statistics of the variables. Footnote 6 In order to investigate the impact of the crisis on remittance-dependent households, we limit our sample to only those households that reported receiving remittances in at least one survey rounds. Columns (1)–(3) report the summary statistics for those remittance-receiving households and Columns (4)–(6) show those using all the households in our sample. Remittance-receiving households spend more both in terms of food and non-food items. They also make more savings and less loan payment on average. They earn less from agricultural and nonagricultural work and domestic sources. “ ECON ” is a weighted average of destination and home countries per capita GDP, and is explained in more detail in the next section. On average, heads of the households are 54 years old, and households are made up of 5 or more members, which includes overseas migrants. The education level of household heads is diverse. More than one-third completed only elementary school or have a high school degree; a quarter graduated college or higher education. The most common occupation among household heads is agriculture. Both the variables of education and those of occupation are binary (using non-educated or non-working heads of households as the reference). Footnote 7

Empirical Analysis

We aim to measure the impact of the macroeconomic conditions in the destination countries on the outcomes relating to household living standards through remittances. There is a concern about the endogeneity issue since household welfare outcomes are likely to be affected by remittances and vice versa. It is well known that addressing endogeneity is one of the most crucial elements of estimation relating to remittances and the effects (McKenzie et al. ( 2010 )). This is an important issue for our estimation by pooling observations rather than using panel fixed effects to remove latent characteristics of the sample households. In the context of the Philippines, remittances are often motivated to finance non-food consumption in the Philippines, which makes the OLS estimate on non-food consumption biased (less problematic for food consumption). This may be the case for flow of assets too. Moreover, remittances are substitute for domestic income but a third factor like endowment may make the estimate obscure since high endowment migrants holds higher ability to earn domestically.

Thus, we employ a two-stage least squares (2SLS) estimation using an index of the macroeconomic performance of the destination countries as an instrumental variable. Footnote 8 We construct the “economic performance ( ECON )” variable by taking the weighted average per capita GDP of the country of residence of each household member, including overseas migrants. More specifically, the “ ECON ” variable is constructed as:

Here, \( \mathcal{K}(i) \) refers to the set of countries where the members of household i live, g kt is the log GDP per capita in country k in t (2016 or 2017), and n kit is the number of household i ’s adult member who live in country k . Footnote 9

We assume that GDP per capita is exogenous to the amount of remittances in each household. Our assumption means that ECON picks up supply-side shocks on migrants’ remittances, which reflects labor market conditions that they are exposed to in the destination countries. We acknowledge the possibility that our instrumental variable can also be correlated with demand-side shocks that would cause biases of the coefficients. Specifically, it might be the case that household’s latent characteristics and the choice of destination are closely associated; high endowment migrants are also likely to choose a high-income destination country, which could result in overestimation of the coefficient on the remittances. We also notice that it might be hard to establish exclusion restriction here since changes in economic performance outside the Philippines will have direct effect on household welfare in the country not through remittances but trade and financial channels affecting wage and employment prospects.

In the estimation, we use a level specification by pooling the observations at the first and second rounds, rather than a fixed effect model to remove unobserved heterogeneity. The main reason is to utilize a larger variation in the amount of remittances, the main variable, to obtain stable estimation results. Since the survey interval is short (less than one year), we see little change in the amount of remittances during the survey period. Instead of utilizing a variation between two periods in the same households, we pooled the data at both baseline and endline. The advantage is we can obtain a larger variation between households while the disadvantage is to not able to use a fixed effect model but the cost is abbreviated to some extent if we use a valid instrument. Footnote 10

In the first stage, we regress the amount of remittances on the logarithm of the “ ECON ” variable and other covariates.

where i indexes households, and t refers to the survey round with 0 indicating 2016 and 1 indicating 2017. REMITTANCE it is calculated as the monthly average either over the past 12 months for the first-round or for the period since the first-round visit in the case of the second round. Footnote 11 \( \mathbbm{X} \) is a vector of household characteristics that were reported in Table 1 . We also include barangay fixed effect ( barangay i ) and survey round fixed effect ( λ t ). Lastly, ϵ it is a well-behaved error term. This specification exploits cross-country variations of GDP per capita to explain variations in the amount of remittance across households, rather than exploiting within-household variations of remittances between the two survey rounds.

Column (1) of Table 2 shows the results of the first stage regression. We performed a weak IV test and confirmed that F-test statistic for weak IV is 137.48 with p value of 0.00. The coefficient on “ ECON ” is positive and significant and indicates that a 1% increase in “ ECON ” leads to a 1.67% increase in income from remittances per capita; this implies that a significant economic recession in the destination countries will lead to a substantial drop in remittances.

Next, we use the estimated dependent variable of remittances at the second stage regression.

The dependent variables Y it are a logarithm of (1) average monthly household expenditure per capita, (2) average monthly household food expenditure per capita, (3) average monthly household non-food expenditure, (4) average monthly new savings deposits per capita, (5) average monthly loan repayments per capita, (6) agricultural income, (7) non-agricultural income and (8) average monthly household incomes from domestic sources. Footnote 12 The main explanatory variable \( {\overline{REMITTANCE}}_{it} \) is the log average monthly overseas remittance income per capita, which is projected by the first stage estimates.

Columns (2)–(9) of Table 2 convey the second stage results. We will focus on the coefficient on the logarithm of remittance income per capita, the main explanatory variable. The coefficient on the remittance income is positive and significant for household spending per capita and the size is 0.084 (Column (2)), showing that a 1% increase in remittance income is associated with a 0.08% increase in per capita household spending. When we split household expenditure into food and non-food spending, the coefficient is significant and larger for the former (Columns (3) and (4)), showing that a 1% increase in remittance income is associated with a 0.14% increase in per capita food spending. The coefficient is positive for new savings and negative for loan repayments, but it is not significant (Columns (5) and (6)). While the coefficient on agricultural income is not significant, it is negative and significant for non-agricultural income (Columns (7) and (8)). Income from domestic sources is negatively and significantly associated with income from remittances (Column (9)). Both coefficients on non-agricultural income and domestic source income are minus 0.22 and 0.23, showing that one fifth of a change in remittance income is abbreviated by those income under the market situation in 2016 and 2017.

Table 3 reports the estimation result by splitting the sample by type of head of household. We run the regression by subgroups to address heterogenous effect of remittances on welfare of households by sex, age, and educational attainment of the head of household. First, we see that the coefficients on total, food and non-food spending are positive and significant for male headed households while the coefficient is positive and significant, and the size is larger on food expenditure for female headed households. A larger remittance income is negatively and significantly associated with non-agricultural income and domestic sourced income for male-headed households and with agricultural income for female-headed households. Second, if we divided the sample by whether the head of household’s age is greater than 52 years old, the median of the head’s age in our sample, the coefficient on spending is only significant for food expenditure by households whose head is older and remittance income is negatively associated with agricultural income and domestic income for those households. Third, when we divide the sample by the head of household’s educational attainment, the coefficients on household spending are positive and significant for households whose head completed less than secondary school.

In summary, the estimation results confirm that a decline in remittances discourages household spending per capita and is partly abbreviated by non-agricultural income and domestic income.

Projections

To quantify the scale of the economic shocks caused by the COVID-19 pandemic on the relevant countries, we use the per capita GDP predictions available for each country in 2020 from growth forecasts by the International Monetary Fund (IMF) ‘s “World Economic Outlook” published in October 2019 (IMF 2019 ) and June 2020 (IMF 2020 ), and the World Bank (WB)‘s “Global Economic Prospects” published in January (World Bank 2020a ) and June 2020 (World Bank 2020d ). Footnote 13 The IMF’s outlook from October 2019 and the WB’s outlook from January 2020 can be seen as a “no-COVID” forecast, which helps us to construct the hypothetical “ ECON ” variable, where a COVID-19 pandemic had not taken place. Conversely, the revised IMF’s outlook from June 2020 and the WB’s outlook from June 2020 can be used to construct the “with-COVID” economic scenarios that will affect remittances from migrant workers. The “with-COVID” projections contain two cases in the “World Economic Outlook” and three cases in the “Global Economic Prospects”. Details of the scenarios are given in Table 4 . We implicitly assume that the change in the prediction of GDP for 2020 in the two different timings is entirely attributed to the pandemic.

We compute the predicted values by plugging the hypothetical ECON variables, constructed using each of the GDP per capita forecasts for remittance-receiving households, into our 2SLS estimates that shows statistical significance in Table 2 . We then compare the mean predicted values of with-COVID scenarios with that of no-COVID scenario in each growth outlook for the various outcome variables in each projection scenario. We do not consider the compensating effect of domestic income on the decline of remittances because the Philippine economy is also seriously affected by the pandemic.

Table 4 shows the potential impacts of the COVID-19 as percentage changes in the predicted remittances, expenditure and income under the with-COVID scenarios against the no-COVID scenario as per each growth outlook. The negative impact of the pandemic on remittances is serious, with a decline of as high as 14–20%, which is comparable with the World Bank’s forecast for decline in remittances in the East Asia and Pacific region in 2020. Moreover, our estimate is close the recently published ADB projection (Kikkawa et al. 2020 ) showing that the remittance to the Philippines will decline by 20.2%. The adverse effects are more pronounced under the “with-COVID scenario two” by the World Bank, while “with-COVID scenario one” by the IMF and “with-COVID scenario one” by the World Bank are closer in magnitude. The household spending per capita would decline by 1–2% in each scenario. Of the total spending, food expenditure has the highest drop by 2–3%. Thus, our predictions show that remittance inflow will decrease by 14–20% and household spending per capita will decline by 1–2% (food spending by 2–3%) in the space of one year during the COVID-19 pandemic. Reminding our subsample analysis in the previous section, households with male or low educated head will further decrease per capital expenditure while female headed households will see more substantial drop in food consumption due to the decline in remittances income.

Those projections must be understood in conjunction with several reservations. First, we use household data from heavily remittance-dependent regions that do not necessarily conform to the average in the Philippines. Second, our projection captures a short-run (during the year 2020) effect of the pandemic on household welfare but the negative impact would be more serious over a longer term. Third, we summarized all aspects of the virus outbreak into a change in per capita GDP. We may need to take a more nuanced approach using data on international restrictions on travels and remittance transactions. Fourth, we boldly sum up complex processes within a serial decision-making process carried out by households in relation to migration and remittances into the “amount of remittance”. Disentangling the effect of the pandemic over the migration process is an important agenda for future research. Fifth and lastly, we implicitly assume that an increase in remittances will have the same magnitude on household-level outcomes as will decreases in remittances associated with the pandemic. The symmetry assumption that the sensitivity of household-level outcomes remains the same during the pandemic should be examined by the actual post-pandemic data.

Using a household-level dataset in heavy migrant-dependent regions before the outbreak in the Philippines and the 2020 GDP projections made by the IMF and the WB, we evaluated the potential impact of the COVID-19 pandemic. Our projection shows that remittance inflow will decrease by 14–20% and household spending per capita will decline by 1–2% (food expenditure per capita by 2–3%) in one year as a result of the pandemic.

The pandemic is still ongoing. Future research should use the actual data in migrant-sending countries after the COVID-19 outbreak to quantify the adverse effects on household living standards. While it is not easy to conduct a survey during the pandemic, this line of research will be very informative for future policy responses.

https://www.worldbank.org/en/news/press-release/2020/04/22/world-bank-predicts-sharpest-decline-of-remittances-in-recent-history . The decline is projected to be 13% in the East Asia and Pacific region.

https://www.owwa.gov.ph/index.php/news/regional/85-1-600-active-owwa-members-in-davao-del-sur-receive-cash-relief-assistance-from-owwa-xi .

These municipalities were selected in order to oversample households with overseas migrants and provided the necessary collaboration with us to implement the survey and information for listing.

While we chose only two municipalities in the survey due to resource limitation, our study area covers regions with different characteristics in terms of dependency on migration and remittances. 32.3% of the households in Dingras has at least one migrant while only 2.8% in Bansalan (10.6% of the total samples of two municipalities). According to 2018 National Migration Survey (PSA and UPPI 2019 ), 8.9% of the households in the Ilocos Region (where Dingras belongs) have at least one OFW (Overseas Filipino Workers) in the past 12 months and 5.7% in the Davao region where Bansalan is located (6.4% nationwide). Although our sampling design does not generate nationally representative dataset, respondents in our sample are comparable to the 2018 National Migration Survey (NMS) of the Philippines (PSA and UPPI 2019 ). We compare the distributions of age and educational attainments between two surveys, and find that our sample individuals are slightly older and proportion of college attendees or graduates is higher. The detail is reported in Appendix Table 1.

The barangay is the smallest political unit and a subdivision of a city or municipality.

Per capita expenditure is systematically larger and the ages of the heads of household is higher for the attrition households at the first-round.

While the job of a seamen makes up a large part of the migrant job market, our sample does not contain many of these migrants.

In order to gauge the direction of the bias stemming from OLS estimates, we checked to compare the regression outcomes with non-instrumented as well as instrumented remittance values. When we compare those results, we see that the coefficient loses significance in nonfood consumption and the coefficient gains absolute size and significance in non-agricultural and total income from domestic sources. The OLS results are available upon request.

We share the spirit with Ratha and Shaw ( 2007 ) that used weighted value of destination GDP in cross-country estimating remittances inflow.

In addition, we use the sub-sample of households with migrants only. We believe that the most fundamental selection-bias in the decision of whether or not to migrate is well-addressed by this sub-sample strategy.

The qualitative results are not changed if the average over the past 12 months is used for the second round.

The denominator of all “per capita” variables from the household survey is the number of household members excluding migrating members.

The initial projection by the IMF after the pandemic was released in April 2020 and updated in June 2020.

Commission on Filipinos Overseas, Department of Foreign Affairs, and Philippine Overseas Employment Administration. (2013). Stock Estimate of Overseas Filipinos. Retrieved from https://cfo.gov.ph/yearly-stock-estimation-of-overseas-filipinos

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Acknowledgements

This study was conducted as part of the project “Study on Remittances and Household Finances in the Philippines and Tajikistan” carried out by JICA Ogata Sadako Research Institute for Peace and Development. A previous version was featured in Covid Economics: Vetted and Real-Time Papers . We would like to thank Alvin P. Ang, Jeremaiah M. Opiniano, and Akira Murata for their leadership and technical contribution during the data collection in the Philippines. We also thank Yasuyuki Sawada, Hiroyuki Yamada, Aiko Kikkawa Takenaka, Akio Hosono, Etsuko Masuko, Hiromichi Muraoka, Megumi Muto, Ryosuke Nakata, and Shimpei Taguchi for their constructive comments and Pragya Gupta for her excellent research assistance. The views expressed in this paper are our own and do not represent the official positions of either the JICA Ogata Sadako Research Institute for Peace and Development or JICA.

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Murakami, E., Shimizutani, S. & Yamada, E. Projection of the Effects of the COVID-19 Pandemic on the Welfare of Remittance-Dependent Households in the Philippines. EconDisCliCha 5 , 97–110 (2021). https://doi.org/10.1007/s41885-020-00078-9

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Received : 20 July 2020

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DOI : https://doi.org/10.1007/s41885-020-00078-9

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This study evaluated the financial literacy and income management practices of rice farmers in the Philippines. The study employed descriptive quantitative research method with researcher-made questionnaire as the primary tool. Data were collected from purposively chosen rice farmers in San Pablo City. Statistical treatments such as mean and Pearson r correlation were utilized. Results showed that respondents’ age, educational attainment, and monthly income have significant differences on their financial literacy as to attitude whereas gender, educational attainment, and monthly income differ significantly with financial literacy as to behavior. Age, gender, and monthly income differ significantly in income distribution as to family needs whereas age, gender, and educational attainment have significant differences in income distribution as to debt repayment. Financial literacy based on attitude and behavior; family needs have an inverse correlation. Debt repayment has a positive correlation to attitude and behaviors. Farmers spend less than what they earn and seldom save money. Older male farmers have high level of income distribution on basic family needs, paying debts, and financial matters as to attitude. Farmers who are low-income earners prioritize family needs and have positive attitude and behavior toward paying their debts. It was further revealed that there is a positive correlation between financial literacy as to attitude and behavior and income distribution as to family needs and debt repayment. It is recommended to provide knowledge, confidence, and skills for these farmers to manage money effectively and to make financial decisions that promote financial sufficiency, stability, and well- being of their family.

Keywords: farmers, financial, financial literacy, income distribution, literacy, alternative program

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Cite this article:

Sanglay, P.D., Apat, E.C., Sumague, J.A. & Tec, E.T. (2021). Financial Literacy and Income Distribution of Rice Farmers. International Journal of Accounting, Finance and Education. Volume 2 Issue 3. DOI: https://doi.org/10.53378/348732

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Oil Market Report - March 2024

About this report

The IEA Oil Market Report (OMR) is one of the world's most authoritative and timely sources of data, forecasts and analysis on the global oil market – including detailed statistics and commentary on oil supply, demand, inventories, prices and refining activity, as well as oil trade for IEA and selected non-IEA countries.

• Global oil demand is forecast to rise by a higher-than-expected 1.7 mb/d in 1Q24 on an improved outlook for the United States and increased bunkering. While 2024 growth has been revised up by 110 kb/d from last month’s Report, the pace of expansion is on track to slow from 2.3 mb/d in 2023 to 1.3 mb/d, as demand growth returns to its historical trend while efficiency gains and EVs reduce use.
• World oil production is projected to fall by 870 kb/d in 1Q24 vs 4Q23 due to heavy weather-related shut-ins and new curbs from the OPEC+ bloc. From the second quarter, non-OPEC+ is set to dominate gains after some OPEC+ members announced they would extend extra voluntary cuts to support market stability. Global supply for 2024 is forecast to increase 800 kb/d to 102.9 mb/d, including a downward adjustment to OPEC+ output.
• Refinery crude runs are forecast to rise from a February-low of 81.4 mb/d to a summer peak of 85.6 mb/d in August. For the year as a whole, throughputs are projected to increase by 1.2 mb/d to average 83.5 mb/d, driven by the Middle East, Africa and Asia. Refining margins improved through mid-February before receding, with the US Midcontinent and Gulf Coast as well as Europe leading the gains.
• Global observed oil inventories surged by 47.1 mb in February. Offshore stocks dominated gains as seaborne exports reached an all-time high and shipping disruptions through the Red Sea tied up significant volumes of oil on water while onshore inventories declined. Global stocks plunged by 48.1 mb in January, with OECD industry stocks at a 16-month low.
• ICE Brent futures rose by $2/bbl during February as ongoing Houthi shipping attacks in the Red Sea kept a firm bid under crude prices. With oil tankers taking the longer route around Africa more oil was kept on water, further tightening the Atlantic Basin market and sending crude’s forward price structure deeper into backwardation. At the time of writing, Brent was trading at $83/bbl.

Oil on water

Benchmark crude oil prices were range bound in early March, as the market had already priced in the announced extension of OPEC+ voluntary production cuts through 2Q24. North Sea Dated rose by $2.13/bbl to $84.66/bbl during February as continued tanker attacks in the Red Sea lengthened supply routes and global on-land oil inventories fell for a seventh consecutive month to reach their lowest level since at least 2016.

Global onshore oil stocks fell a further 38 mb last month, taking the draw down since July to 180 mb, according to preliminary data. Over the same period, oil on water surged. Trade dislocations from the rerouting of Russian barrels and more recently due to unrest in the Middle East, have boosted oil on water by 115 mb. In February alone, oil on water surged by 85 mb as repeated tanker attacks in the Red Sea diverted more cargoes around the Cape of Good Hope. At nearly 1.9 billion barrels as of end-February, oil on water hit its second highest level since the height of the Covid-19 pandemic.

Trade flow disruptions also boosted bunker fuel use. Longer shipping routes and faster vessel speeds saw Singapore bunkering reach all-time highs. That, along with surging US ethane demand for its petrochemical sector underpins a slight upward revision to our global oil demand expectations for this year by 110 kb/d compared with last month’s Report. World oil demand growth is now forecast at 1.3 mb/d in 2024, down sharply from last year’s 2.3 mb/d expansion.

The slowdown in growth, already apparent in recent data, means that oil consumption reverts towards its historical trend after several years of volatility from the post-pandemic rebound. A weaker economic outlook further tempers oil use, as do efficiency improvements and surging electric vehicle sales. Growth will continue to be heavily skewed towards non-OECD countries, even as China’s dominance gradually fades. The latter’s oil demand growth slows from 1.7 mb/d in 2023 to 620 kb/d in 2024, or from roughly three-quarters to half of the global total, under the gathering weight of a challenging economic environment and slower expansion in its petrochemical sector.

As in 2023, non-OPEC+ oil supply growth will eclipse the oil demand expansion by some margin. Led by the United States, non-OPEC+ production is forecast to rise by 1.6 mb/d in 2024 compared to 2.4 mb/d last year when global oil output climbed by 2 mb/d to 102 mb/d. Substantial gains will also come from Guyana, Brazil and Canada, all forecast to pump at record-highs this year. Together, the non-OPEC+ Americas quartet is set to add 1.3 mb/d of new oil production in 2024.

Iran, which last year ranked as the world’s second largest source of supply growth after the United States, is expected to increase production by a further 280 kb/d this year. Output policy for the remainder of the OPEC+ bloc will be revisited when ministers meet in Vienna on 1 June to review market conditions. In this Report, we are now holding OPEC+ voluntary cuts in place through 2024 – unwinding them only when such a move is confirmed by the producer alliance (see OPEC+ cuts extended). On that basis, our balance for the year shifts from a surplus to a slight deficit, but oil tanks may get some relief as the massive volumes of oil on water reach their final destination.

1. Includes extra voluntary curbs where announced. 2. Capacity levels can be reached within 90 days and sustained for an extended period. 3. Excludes shut in Iranian, Russian crude. 4. Angola left OPEC effective 1 Jan 2024. 5. Iran, Libya, Venezuela exempt from cuts. 6. Mexico excluded from OPEC+ compliance. 7. Bahrain, Brunei, Malaysia, Sudan and South Sudan.

Definitions of key terms used in the OMR, access the OMR Glossary here.

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IEA (2024), Oil Market Report - March 2024 , IEA, Paris https://www.iea.org/reports/oil-market-report-march-2024

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