International Journal of Commerce and Finance, Vol. 9, Issue 1, 2023,88-100 * * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 88 Investigating the Effect of Energy Price on Food Price Inflation in Three Asian Countries Ebi Omodejesu David Uyi* Ahmet Oğuz Demir** Submitted: 04.02.2023 Accepted: 01.06.2023 Published: 06.07.2023 Abstract The rising of energy prices resulting from the turmoil in the Middle East may be responsible for the recent food price inflation in the world, which may occur through transmission mechanism. This study investigates the effect of energy prices on food price inflation in three Asian countries, namely, China, Philippine, and Vietnam using monthly data from 2002:M01 to 2020:M12. Employing the Panel Vector Autoregressive (PVAR) model with Impulse Response Functions (IRFs), the results provided that shocks in energy prices and economic growth have a positive and significant effect on food price inflation while shocks in exchange rate and agricultural production have a negative but insignificant effect on food prices inflation. The PVAR causality results revealed that economic growth is a predictor of food price inflation, energy prices, exchange rate, and agricultural production. Also, a causality runs from economic growth and exchange rate to energy prices and again, from economic growth to exchange rate and agricultural production. This implies that a feedback effect is found between economic growth and exchange rate as well as economic growth and agricultural production. Therefore, the study recommended the need to stabilize energy prices through effect energy policies in Asian countries. Keywords: Food prices inflation, Energy prices, Food insecurity, Economic growth JEL classification: O15, M51 1. Introduction Due to the importance of energy and food commodities in our daily activities in an economy, it is considered as a valuable natural resource which enhance economic sustainability for human mailto:odemir@ticaret.edu.tr International Journal of Commerce and Finance Ebi Omodejesu David Uyi Oğuz Demir * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 89 development. Energy particularly crude oil, electricity, biofuel, ethanol, biomass, uranium, water and coal stand as the road map for economic transformation, promote factor of production in the area of agriculture, tourism, industry (infrastructure), and households. Energy market and food security encompasses a resource that provide services for human capital sustainability, zero hunger in the world and growth to economy through efficiency (FAO, 2010). Let us recall from the crude oil embargo in 1970s that happened between Iran, Israel, and OPEC nations, which made some of the Arab crude oil producing nations to put a banned on the supply of crude oil to U.S., Netherlands and Portugal, and due to the restriction of crude oil export to U.S. and other developed nations, it has resulted to a global challenges that create shock and irregularities in the prices of energy market (crude oil and electricity) and agricultural sector (food commodities), (Taghizadeh-Hesary et al. (2013). Some studies have identified that sudden increase in the prices of crude oil and electricity may enhance to recession, food insecurity and economic transformation, yet still causes starvation or poverty through the rise in the prices of food commodities like maize, grain, wheat, rice, groundnut oil which may result to a decline in the aggregate demand and aggregate supply function of an economy. Considering the study outcome of Hamilton (1983), it is wrap up that all economic depression such as high unemployment rate, GDP contraction, poverty, food insecurity, hunger in some part of the developed nations and developing countries, like U.S. has occurred due to increase in prices of energy market (crude oil, electricity) and agricultural sector (food commodities). It is considered that crude oil price shocks possess a high significant reaction on agricultural products prices, which may enhance sudden increase in the prices of food system, and sometime result to sustainability, and food insecurity in an economy, using European economic as an illustration (Cunado and Gracia (2013). Current studies identified that the prices of WTI crude oil and Brent crude oil have a stimulus on China economies and may enhanced to inflation significantly on the prices of staple foods using Panel VAR model. In a current study, two economies were examined to conduct the sequel of crude oil prices movement with agricultural commodities prices over time at macro- economy level on two parameters, such as GDP growth rate and food prices index (FCPI), and the result show a lessen stimulus on the GDP of a developing economy (the People of China), while the economic growth rate show a calmness on the developed economies (U.S. and Japan) (Taghizadeh-Hesary and Yoshino (2015). International Journal of Commerce and Finance Ebi Omodejesu David Uyi Oğuz Demir * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 90 The indicator to hyperinflation of food commodities prices in the two economies, namely as developed nations and vulnerable nations can either be traced through natural disaster, error in population growth, climate change and inflation in energy market prices, like sudden hike in crude oil prices, electricity prices which may also be influence by agricultural products - maize, sugarcane, starch and due to the role they contributed to both ethanol oil for tourism services and food crops for consumable foods, like rice, corn, wheat, grains. If the prices of purchasing energy market is high, it may result to a sudden increase in the price of agricultural market, because maize is a staple food commodity that is use for both energy market (biofuel, ethanol) and food market (maize for cereal and boil corn). Purpose and the Aim of the Study The purpose of this research is to examine the effects of energy prices on food price inflation by controlling for economic growth, exchange rate, and agricultural production in three selected Asian countries (China, Philippine, and Vietnam) over the period 2002:M01 to 2020:M12. The choice of the countries is based on the data availability. Therefore, this study contributes to the existing literature in several ways: First, the study investigates the effect of energy prices on food price inflation in Asian countries, which is the largest energy producer and consumer in the world. Second, the study uses the panel VAR method, which can address the empirical issue of simultaneous bias by using impulse response functions to examine how other variables with the panel VAR system respond to the shocks in impulse variable over time. Finally, the study uses Panel VAR Granger causality to examine the causal relationship between the variables, which can be used by policy makers to formulate appropriate policies to mitigate the energy price effect of food price inflation in the Asian region. The remaining part of this study is structured as follows: Section 2 categorically reviews the related literature. Section 3 describes the data and methodology employed in this study. Section 4 presents and analyses the empirical results as well as discussing these results. Section 5 provides concluding remarks with policy recommendations. 2. Literature Review The interdependence of natural resources (crude oil, biofuel, ethanol) with agricultural products (maize, soybean, sugar cane) have played an essential role in our daily consumption, which result to a great necessity for every economy to focus their priority on it for sustainability. International Journal of Commerce and Finance Ebi Omodejesu David Uyi Oğuz Demir * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 91 Multiple scholars according to their finding and result, identified that sudden increase in the prices of natural resources (crude oil, biofuel) was among the major element that contributed a shocks occurrence in the agricultural sector in an economy (Abbot, Tyner, Hurt 2008), (Baffes 2007, Balcombe and Rapsomanikis 2008), (Chang and Su 2010), (Yang et al. 2008) (Mitchell 2008), (Rosegrant et al. 2008). In comparison, a number of scholars has identified by their experiment and results, no straight correlation between crude oil prices and agricultural commodities prices (Zang et al. 2010), and sudden increase in the prices of crude oil does not support increase to food commodities prices (maize or sugarcane). It was investigated using elasticity of demand and supply theory, that the correlation of cotton, wheat, gold, copper, petroleum oil, cocoa, and lumber prices result to zero, Rotemberg and Pindyck (1990). 2.1 Starvation Versus Food Security Starvation is a demerit factor to good health. Some scholars and FAO report 2010s have identified a plan to resurface or recover the global economy from starvation, poverty, food insecurity, terrorism, and environmental crisis like climate change, flooding. Besides, it is accounted by FAO report that global food reserve is higher than the number of human being population in the world, yet poverty, starvation, and hyperinflation in the staple foods still high in the blink of the developing nations and developed nations (Gustavsson et. al., 2011). The objective of Food and Agriculture Organization (FAO, 2022) with support from IFAD, UNICEF, WFP is to enhance proper administration of food security and to end starvation in the world before 2030. Due to the emergence of covid-19 in 2020 and other economy factor from global Government institutions, it results to low level for FAO to achieve food security, and end starvation in the world, which increase the number of people with starvation or hunger in the world from 7.9% to 8.93% in 2019 to 2020 and rise at a lower point in 2021 to 9.8%. 2.2 The Volatility between Energy Prices and Agricultural Food Prices According to this thesis, the volatility correlation between crude oil prices and staple food prices in some of the regions in Asia. Currently from 2015 to 2014, it was recorded that some parts of developing countries, like Asia reduce inflation from 2.2% to 3.0%. During the process of fall in inflation in the Asia region, it results to economic depression, and caused cost inflation on the demand side. In the supply side it caused decline to global crude oil market and food cost, and result to inflation. In 2015, Brent crude oil prices drop to average which result to $52 per barrel in 2015 from 99$ per barrel in 2014. Besides, agricultural products experienced a fall in International Journal of Commerce and Finance Ebi Omodejesu David Uyi Oğuz Demir * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 92 total prices to 13%, which result to credit crunch economies and food costs 15.4% as an outcome from supply side effect due to volatility in energy prices. The supply side effect has contributed to a fall in prices, in connection to demand side assistance. 3. Data and Methodology 3.1 Data For the purpose of this study, the study uses data based on five (5) variables, namely; food customer price inflation, energy price, GDP, exchange rate, agriculture crop production from 2002:M01 to 2020:M12. The Asian countries selected are China, Philippines, and Vietnam based on data availability. The food price inflation is a new index computed by the World Bank, which is obtained from the website of the World Bank. All the remaining variables are obtained from the World Development Indicators. Therefore, Table 1 presents the variables used and their measurements, including their sources. Table 1: Variable Measurement and Source Variable Measurement Source Food Price Inflation FCPI Food price index World Bank Energy Price Energy price index World Development Indicators GDP Gross Domestic Product (Constant 2002 in USD) World Development Indicators Exchange Rate Official exchange rate in a domestic currency measured in current USD World Development Indicators Agriculture Crop Production The crop production index (2004-2006=100) shows an index of all crops for each year relative to the base period 2004-2006 excluding fodder crops. World Development Indicators Source: compilation from Author’s International Journal of Commerce and Finance Ebi Omodejesu David Uyi Oğuz Demir * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 93 3.2 Panel VAR Model Specification In this study we identify the model function to examine the relationship between energy prices and food price inflation in Asian countries by incorporating other control variables such as exchange rate and agricultural production as follows: FCPI= f (EPR, GDP, EXCH, AGPR) Equation (1) EPR= f (FCPI, GDP, EXCH, AGPR) Equation (2) GDP = f (FCPI, EPR, EXCH, AGPR) Equation (3) EXCH = f (FCPI, EPR, GDP, AGPR) Equation (4) AGPR = f (FCPI, EPR, GDP, EXCH) Equation (5) FCPI = Food inflation EPR = Energy price GDP = Real output growth EXCH = Exchange rate through interest rate AGPR = Crops production The short form to write Panel VAR model in a mathematical language is  , , , i t i i t i tY L Y    ɑ Equation (6) Where i(i=1, N) represent the country; t (t = 1, T) represent time; Y represent the endogenous stationarity variable; Γ(L) denotes the matrix polynomial sign in the lag operator L; ɑi represent the vector of country-fixed effects Ɛi,t is a vector of error terms. 4. Data Analysis and Discussion 4.1 Descriptive Statistics Table 2 provides the descriptive statistics of all the variables used in this study in their natural logarithm form. The mean of GDP is the largest with a value of 8.051975. The second variable with the largest mean is exchange rate while the Food CPI is the smallest with a value of 4.240954. The maximum value among the variables is that of exchange rate, which is 10.05280 while agricultural production has the smallest maximum value of 4.666802. For the minimum, exchange rate has the smallest minimum number while GDP has the largest minimum number. Furthermore, the exchange rate seems to be the only most volatile series among all variables employed. While food CPI and EPR variables are negatively skewed, the remaining variables International Journal of Commerce and Finance Ebi Omodejesu David Uyi Oğuz Demir * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 94 have a positive skewness, and they are all close to zero. Also, the Kurtosis of the variables employed are all positive with evidence of excess kurtosis for the case of EPR while exchange rate and agricultural production have values that are less than normal value. Therefore, the Jarque-Bera statistic for all variables are large, rejecting the null hypothesis of normal distribution of series. Table 2: Descriptive Statistics LNFCPI LNEPR LNGDP LNEXH LNAGPR Mean 4.240954 4.410863 8.051975 5.226132 4.491251 Median 4.357990 4.525586 7.938361 3.869783 4.544174 Maximum 4.900076 4.750136 9.247053 10.05280 4.666802 Minimum 3.153590 3.440418 7.142127 1.813824 4.171825 Std. Dev. 0.425671 0.316230 0.538635 3.367905 0.143347 Skewness -0.803850 -1.477955 0.722375 0.537713 -0.622759 Kurtosis 2.821911 4.499819 2.660630 1.506253 2.105807 Jarque-Bera 74.56788 313.1255 62.77045 96.55284 67.00062 Probability 0.000000 0.000000 0.000000 0.000000 0.000000 Sum 2900.813 3017.030 5507.551 3574.674 3072.015 Sum Sq. Dev. 123.7570 68.30095 198.1570 7747.122 14.03455 Observations 684 684 684 684 684 4.2 Results of Unit Root Tests In other to check the stationarity property of the series employed in this study, we apply a series of panel unit root tests such as Levin-Lin-Chu unit root test, Im-Pesaran-Shin W-stat unit root test, ADF – Fisher Chi-square unit root test, and the PP – Fisher Chi-square unit root test. The results as shown in Table 2. The results suggest that all the series are not stationary in their levels; however, after taking their first differences, it is observed that the variables are all stationary. These results imply that the variables are all integrated of order one, I (1). This means that the analysis of this study will be based on the first difference variables. International Journal of Commerce and Finance Ebi Omodejesu David Uyi Oğuz Demir * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 95 Table 3: Panel VAR Unit Root Result At Level At First Difference Variable Unit Root Tests Statistic P-value Statistic P-value lnFCPI Levin-Lin-Chu -2.00585 0.0224 -5.19898** 0.0000 Im-Pesaran-Shin W- stat 0.72068 0.7644 -8.73083*** 0.0000 ADF - Fisher Chi- square 2.52764 0.8654 88.7963*** 0.0000 PP - Fisher Chi-square 2.53470 0.8646 194.372*** 0.0000 Levin-Lin-Chu 1.28462 0.9005 -7.58856*** 0.0000i InEPR Im-Pesaran-Shin W- stat 2.71061 0.9966 -8.51288*** 0.0000 ADF - Fisher Chi- square 0.51571 0.9976 76.5367*** 0.0000 PP - Fisher Chi-square 0.18606 0.9999 186.313*** 0.0000 InGDP Levin-Lin-Chu 3.83171 0.9999 -12.3573*** 0.0000 Im-Pesaran-Shin W- stat 3.52280 0.9998 -14.0986*** 0.0000 ADF - Fisher Chi- square 0.82051 0.9915 172.511*** 0.0000 PP - Fisher Chi-square 0.10449 1.0000 117.170*** 0.0000 lnEXH Levin-Lin-Chu -1.61042 0.0537 -0.81877*** 0.2065 Im-Pesaran-Shin W- stat -0.25702 0.3986 -1.32956*** 0.0918 ADF - Fisher Chi- square 5.15714 0.5238 9.53446*** 0.1457 PP - Fisher Chi-square 2.55519 0.8622 152.050*** 0.0000 lnAGPR Levin-Lin-Chu 0.42947 0.6662 -4.14430*** 0.0000 International Journal of Commerce and Finance Ebi Omodejesu David Uyi Oğuz Demir * İstanbul Ticaret University, Turkey, eomodejesu.daviduyi@istanbulticaret.edu.tr ** İstanbul Ticaret University, Turkey, odemir@ticaret.edu.tr 96 Note: and denote 1% and 4% is the significance levels. The result is compiled by the researcher from the EVIEW 12 Im-Pesaran-Shin W- stat 1.44357 0.9256 -4.17529*** 0.0000 ADF - Fisher Chi- square 1.99208 0.9204 28.8862*** 0.0001 PP - Fisher Chi-square 0.80143 0.9920 226.701*** 0.0000 International Journal of Commerce and Finance, Vol. 9, Issue 1, 2023, 69-88 * * İstanbul Ticaret University, Turkey, taysabd2@gmail.com ** İstanbul Ticaret University, Turkey, oiyigun@ticaret.edu.tr 89 4.3 Impulse-response functions (IRFs) results Due to the inaccurate estimation outputs of the Panel VAR, Sim (1980) suggested that the impulse-response function (IRFs) have to be used.1 This is because, IRFs provide adequate estimates of panel VAR model for policy analysis. Therefore, Figure 1 provides the responses of all the variables to a shock in food price inflation in the selected countries of Asia. As can be seen, the response of lnFCPI to own shock is positive and statistically significant across the horizon. This means that a 1% standard deviation shock in food price inflation causes food price inflation to rise in the countries studied. The study also finds evidence of a positive response of energy prices to a shock in food prices, while a shock in food price causes a negative and significant response in economic growth. In addition, the response of exchange rate is positive and significant. This indicates that as food prices is increasing exchange rate is rising as well. Note that a rise in exchange rate implies depreciation of domestic currency. Finally, the response of agricultural production to a shock in food prices is positive and insignificant. This crosses to the negative region after the second horizon. Figure 1: Shock to food price inflation 1 For brevity, we did not report the results of the Panel VAR estimates since it is inaccurate and unreliable .0 0 6 .0 0 8 .0 1 .0 1 2 .0 1 4 0 2 4 6 8 step Response: lnfcpi .0 0 1 .0 0 1 5 .0 0 2 .0 0 2 5 .0 0 3 .0 0 3 5 0 2 4 6 8 step Response: lnepr -. 0 0 2 -. 0 0 1 5 -. 0 0 1 -. 0 0 0 5 0 0 2 4 6 8 step Response: lngdp 0 .0 0 1 .0 0 2 .0 0 3 0 2 4 6 8 step Response: lnexh -. 0 0 0 6- .0 0 0 4- .0 0 0 2 0 .0 0 0 2 .0 0 0 4 0 2 4 6 8 step Response: lnagpr 95% low er and upper bounds reported; percentile ci Impulse: lnfcpi International Journal of Commerce and Finance Tays Abderrahim Öykü İyigün 90 Figure 2 reports the responses of food prices, economic growth, exchange rate, agricultural productions, and own shock to the shock in energy prices. The result show that food prices is positively and significant related to a shock in energy prices. The effects of own shock and economic growth as well as agricultural production are positive and statistically significant. This implies that a shock in energy prices causes food prices, economic growth, and agricultural production to rise significantly. However, the effect of shock in energy price to exchange rate is negative and significant. This implies that as energy prices change, agricultural production would reduce significantly. Figure 2: Shock to energy prices Figure 3 presents the graph of the impulse-response functions of the variables in the panel VAR model. As we can see, the response of food prices to a shock in GDP is positive and significant. Equally, the response of GDP to own shock is also positive and significant. However, the effects of energy prices, exchange rate, and agricultural production are not significant with the effect of agricultural production having a negative sign. 0 .0 0 1 .0 0 2 .0 0 3 .0 0 4 0 2 4 6 8 step Response: lnfcpi .0 0 5 .0 0 5 5 .0 0 6 .0 0 6 5 .0 0 7 .0 0 7 5 0 2 4 6 8 step Response: lnepr 0 .0 0 0 5 .0 0 1 .0 0 1 5 .0 0 2 0 2 4 6 8 step Response: lngdp -. 0 0 1 5 -. 0 0 1 -. 0 0 0 5 0 0 2 4 6 8 step Response: lnexh 0 .0 0 0 5 .0 0 1 .0 0 1 5 0 2 4 6 8 step Response: lnagpr 95% low er and upper bounds reported; percentile ci Impulse: lnepr International Journal of Commerce and Finance Tays Abderrahim Öykü İyigün 91 Figure 3: Shock to GDP Figure 4 presents the impulse-response graph of the variables in the panel VAR system. Particularly, the Figure show how a shock to exchange rate causes food prices, energy prices, economic growth, exchange rate itself, and agricultural production to change over time. The effect of food prices is negative and statistically insignificant. Also, energy prices respond a small manner to the shock in exchange rate, which is insignificant. The response of economic growth captured by GDP is also negative and statistically insignificant. The response of agricultural production is negative but only significant after the 4th horizon. Finally, the effect of own shock is positive and highly significant. This implies that exchange rate changes are determined mostly by own shock. 0 .0 0 0 2 .0 0 0 4 .0 0 0 6 .0 0 0 8 .0 0 1 0 2 4 6 8 step Response: lnfcpi -. 0 0 0 2 0 .0 0 0 2 .0 0 0 4 .0 0 0 6 0 2 4 6 8 step Response: lnepr .0 0 2 5 .0 0 3 .0 0 3 5 .0 0 4 0 2 4 6 8 step Response: lngdp -. 0 0 1 -. 0 0 0 5 0 .0 0 0 5 .0 0 1 0 2 4 6 8 step Response: lnexh -. 0 0 0 8- .0 0 0 6- .0 0 0 4- .0 0 0 2 0 .0 0 0 2 0 2 4 6 8 step Response: lnagpr 95% low er and upper bounds reported; percentile ci Impulse: lngdp International Journal of Commerce and Finance Tays Abderrahim Öykü İyigün 92 Figure 4: Shock to exchange rate Figure 5 reports the results of the effect of a change in agricultural production on food prices, energy prices, economic growth, exchange rate, and agricultural production itself. We observe that the effect of a shock in agricultural production causes food price inflation and energy prices to decrease but this decrease is not statistically significant over the study period. The response of economic growth is positive and equally not significant. For exchange rate, the effect is not noticeable and also insignificant. The effect of own shock is positive and statistically significant. This implies that agricultural production responds wholly to own shock than any other shock in the system. -. 0 0 1 5 -. 0 0 1 -. 0 0 0 5 0 .0 0 0 5 0 2 4 6 8 step Response: lnfcpi -. 0 0 0 2 0 .0 0 0 2 .0 0 0 4 .0 0 0 6 .0 0 0 8 0 2 4 6 8 step Response: lnepr -. 0 0 0 4- .0 0 0 3- .0 0 0 2- .0 0 0 1 0 .0 0 0 1 0 2 4 6 8 step Response: lngdp .0 0 3 .0 0 3 5 .0 0 4 .0 0 4 5 .0 0 5 0 2 4 6 8 step Response: lnexh -. 0 0 1 -. 0 0 0 5 0 .0 0 0 5 0 2 4 6 8 step Response: lnagpr 95% low er and upper bounds reported; percentile ci Impulse: lnexh International Journal of Commerce and Finance Tays Abderrahim Öykü İyigün 93 Figure 5: Shock to agricultural production 4.4 Results of Panel Granger Causality Test Table 4 presents the results of the panel Granger causality test. The results show that there is no Granger causality running between food price inflation and energy price, economic growth, exchange rate, and agricultural production. In other words, energy prices, economic growth, exchange rate, and agricultural production are not a good predictor of food price inflation in the selected countries of Vietnam, China and Philippine. The results further show that food price, economic growth, exchange rate are predictors of energy prices. This implies that there is only a causality running from food prices and economic growth to energy prices in the selected countries. However, overall, the causal relationship is also significant. Furthermore, the results reveal that food price inflation, economic growth, exchange rate, and agricultural production are a good predictor of economic growth. In other words, there is a causal relationship running from all the variables controlled for in the model to economic growth in the selected countries. Table 4: Results of Panel VAR (1) Granger Causality Test -. 0 0 2 -. 0 0 1 0 .0 0 1 .0 0 2 0 2 4 6 8 step Response: lnfcpi -. 0 0 1 -. 0 0 0 5 0 .0 0 0 5 0 2 4 6 8 step Response: lnepr -. 0 0 0 4- .0 0 0 2 0 .0 0 0 2 .0 0 0 4 .0 0 0 6 0 2 4 6 8 step Response: lngdp -. 0 0 0 6- .0 0 0 4- .0 0 0 2 0 .0 0 0 2 .0 0 0 4 0 2 4 6 8 step Response: lnexh .0 0 1 5 .0 0 2 .0 0 2 5 .0 0 3 .0 0 3 5 0 2 4 6 8 step Response: lnagpr 95% low er and upper bounds reported; percentile ci Impulse: lnagpr International Journal of Commerce and Finance Tays Abderrahim Öykü İyigün 94 Equation Excluded Test Statistic dF p-value LnFCPI. LnEPR 0.820 1 0.365 LnGDP 0.251 1 0.616 LnEXH 0.021 1 0.804 LnAGPR 1.432 1 0.231 ALL 6.798 4 0.147 LnEPR LnFCPI 1.623 1 0.203 LnGDP 3.231* 1 0.072 LnEXH 7.419** 1 0.006 LnAGPR 0.014 1 0.747 ALL 13.046** 4 0.011 LnGDP LnFCPI 12.458*** 1 0.000 LnEPR 6.405** 1 0.011 LnEXH 24.522*** 1 0.000 LnAGPR 13.542*** 1 0.000 ALL 57.350*** 4 0.000 LnEXH LnFCPI 0.042 1 0.838 LnEPR 0.000 1 1.000 LnGDP 3.555* 1 0.059 LnAGPR 1.211 1 0.271 ALL 64.660*** 4 0.000 LnAGPR LnFCPI 1.338 1 0.247 LnEPR 0.846 1 0.358 LnGDP 3.077* 1 0.079 LnEXH 1.686 1 0.194 ALL 9.730* 4 0.045 Note: ***, ** and * signify rejection of the null hypothesis at 1%, 5%, and 10 % level of significance International Journal of Commerce and Finance Tays Abderrahim Öykü İyigün 95 4.5 Conclusion and Policy Recommendations This study primarily aims to examine the effects of energy prices on food price inflation in three economies of Asian countries, namely: Vietnam, China, and Philippines over the period 2002:M1 to 2020:M12. The study further incorporates the effects of other variables such as economic growth, exchange rates, and agricultural production on food price inflation. The results suggested that energy prices and economic growth increase food price inflation while exchange rate and agricultural production reduce food price inflation but such reduction is not statistically significant. On the basis of panel Granger causality test, our result revealed that economic growth is a good predictor of energy prices, exchange rate, and agricultural production. Also, the study found that exchange rate causes energy prices, food price inflation and energy prices cause economic growth, while exchange rate and agricultural production predict economic growth. Based on the findings of this study, the following policy recommendations are carefully and adroitly made to address food price inflation in the selected countries: (i) There is the need to boost agricultural production in these countries to stabilize food prices and hence reduce inflation. This can be achieved by providing incentives such as tax rate reduction in agricultural sector for the famers to subsidize the agricultural outputs. (ii) Government and policy makers should be encouraged to promote stable energy prices, particularly energy products that are used in the cultivation of food commodities. For example, the price of energy like crude oil, electricity should be regulated to achieve a low and affordable price by the consumers. This will help to boost agricultural production, which will enhance exports of the countries. 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