In this paper we aim to assess the mechanics of the global food price increases experienced in the recent years, most profoundly during the 2007-2008 food price spikes. At this stage, we aim to test, whether there is an empirically assessable relationship between World agricultural commodity prices, World oil prices and Hungarian producer and consumer food prices. After briefly discussing the background of the food price surge, and some studies empirically assessing it, we estimate a Vector Error Correction Model (VECM) with two long-run relationships, modelling vertical and horizontal price relationships (price transmissions). Preliminary results express (as somewhat expected for a small open economy as Hungary) that global developments have direct and significant effects upon price levels in Hungary regardless whether a vertical or horizontal price dimension is used. Further research will focus on determination of the magnitude, speed of occurrence and duration (needed to return to equilibrium) of the abovementioned global shifters express upon domestic agricultural and food price levels

Bakucs, Lajos Zoltán

Fertő, Imre

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Bakucs, Zoltán

EconStor (ZBW Kiel)

World prices and domestic food price spikes

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MŰHELYTANULMÁNYOK      DISCUSSION PAPERS INSTITUTE OF ECONOMICS, RESEARCH CENTRE FOR ECONOMIC AND REGIONAL STUDIES, HUNGARIAN ACADEMY OF SCIENCES BUDAPEST, 2013MT-DP – 2013/4World prices and domestic food price spikesZOLTÁN BAKUCS - IMRE FERTŐ2Discussion papersMT-DP – 2013/4Institute of Economics, Research Centre for Economic and Regional Studies, Hungarian Academy of SciencesKTI/IE Discussion Papers are circulated to promote discussion and provoque comments. Any references to discussion papers should clearly state that the paper is preliminary. Materials published in this series may subject to further publication.World prices and domestic food price spikesAuthors:Zoltán Bakucs senior research fellowInstitute of EconomicsResearch Centre for Economic and Regional StudiesHungarian Academy of SciencesEmail: bakucs.zoltan@krtk.mta.huImre Fertőresearch advisorInstitute of EconomicsResearch Centre for Economic and Regional StudiesHungarian Academy of SciencesEmail: ferto.imre@krtk.mta.huJanuary 2013ISBN 978-615-5243-47-9ISSN 1785 377X3World prices and domestic food price spikesZoltán Bakucs - Imre FertőAbstractIn this paper we aim to assess the mechanics of the global food price increases experienced in the recent years, most profoundly during the 2007-2008 food price spikes. At this stage, we aim to test, whether there is an empirically assessable relationship between World agricultural commodity prices, World oil prices and Hungarian producer and consumer food prices. After briefly discussing the background of the food price surge, and some studies empirically assessing it, we estimate a Vector Error Correction Model (VECM) with two long-run relationships, modelling vertical and horizontal price relationships (price transmissions). Preliminary results express (as somewhat expected for a small open economy as Hungary) that global developments have direct and significant effects upon price levels in Hungary regardless whether a vertical or horizontal price dimension is used. Further research will focus on determination of the magnitude, speed of occurrence and duration (needed to return to equilibrium) of the abovementioned global shifters express upon domestic agricultural and food price levels.Keywords: agricultural prices, crude oil prices, cointegration, vector error correctionJEL classification: Q1Acknowledgements:Zoltán Bakucs is greatfully acknowledges financial support through OTKA research project K101868 ‘Ártranszmisszió a magyar agrár-élelmiszer piacokon. Egy átfogó szemlélet’.4              Világgazdasági terményárrobbanás                      és a hazai élelmiszerárakBakucs Zoltán - Fertő Imre ÖsszefoglalóA tanulmány célja a 2007–2008-ban megfigyelt globális agrár-/élelmiszerár-robbanás nyomán annak az elemzése, hogy vajon létezik-e empirikusan kimutatható kapcsolat a globális agrárárak, a nyersolajárak, valamint a magyar agrár-élelmiszeripar termelői és fogyasztói árai között. Miután röviden bemutatjuk az említett árrobbanás hátterét és hatását, vektorhiba-korrekciós modell segítségével két hosszú távú kointegrációs kapcsolatot definiálunk, amelyekben az első a vertikális, míg a második a horizontális ártranszmissziós kapcsolatot modellezi. Eredményeink, figyelembe véve a magyar gazdaság méretét és nyitottságát, nem meglepő módon azt mutatják, hogy a globális folyamatok közvetlen és szignifikáns hatást gyakorolnak a hazai árak alakulására úgy a horizontális, mint a vertikális dimenzió esetében. További kutatást igényel a globális hatások magyar árakra gyakorolt befolyása mértékének, és főképp egy-egy sokkhatás begyűrűzési sebességének, valamint tartósságának az elemzése.Tárgyszavak: agrárárak, nyersolajár, kointegráció, vektorhiba-korrekciós modellJEL kódok: Q151. INTRODUCTIONAn increasingly rich international literature (e.g. McCalla 2009; Mitchell 2008; Lang 2010 just to name a few) focuses on the World commodity price surge experienced during 2007 –2008. A number of global and local factors are usually listed as responsible for the food price spikes. Some often cited reasons are climate conditions and government policy interventions (e.g. grain export bans in some BRIC countries). On other hand however, global commodity supply and demand shifts (e.g. biofuels1) are found to have significantly influenced price instability. In this paper, we aim to map the way domestic food price inflation connects to global trends, through a set of macroeconomic variables, meant to simultaneously model horizontal and vertical price transmission. Figures 1 and 2 graphically depict the differences between overall price inflation and food price inflation. The much higher volatility of consumer food prices is evident from the graphs.Figure 1. Monthly Hungarian food price and overall consumer price indices-1001020304090 92 94 96 98 00 02 04 06 08 10fpihu - same month previous year =100cpihu - same month previous year =100Source: Own calculations, data from Hungarian central BankColumns 2 and 3 of tables 1 and 2 present the descriptive statistics of the price index series for the full sample. Columns 4 and 5 present the same statistics for the ‘price spike’ period of 2007 and 2008. 1From  5% in 2000, by 2010 40% of United States maize production went for energy production, and between 2000 and 2007 ethanol tripled (Davidson et al. 2012).6Table 1. Comparative descriptive statistics of FPIHU and CPIHU variables (base: same period in previous year)Descriptive statistics1993m1-2012m3 2007m1-2008m12FPIHU CPIHU FPIHU CPIHUMean 11.38052 10.77229 11.60833 7.020833Median 9.100000 7.500000 13.05000 6.950000Maximum 39.00000 31.00000 14.70000 9.000000Minimum -3.100000 2.300000 2.900000 3.500000Std. Dev. 9.219653 7.656490 3.412244 1.418505Obs. 231 231 24 24Source: Own calculationsBy taking a closer look on the descriptive statistics, it is worth noting that the 1.05 FPIHU/CPIHU full sample mean ratio, is increased to 1.65 for the price spike sample. If the volatility of the index series is considered, the standard deviation ratio of FPIHU and CPIHU variables amounts to 1.2 for the full sample and 2.41 in the sub-sample (table 1.). Similar conclusions may be derived by analysing the sample mean and variance ratios in table 2.  Standard price transmission analysis focuses on either horizontal or vertical transmission. In the former, the relationship between geographically distinct prices yet the same market level (e.g. World commodity and national prices), whilst with the latter prices observed in the same geographical region, but different levels (e.g. producer and consumer prices) are the focus of analysis.Table 2. Comparative descriptive statistics of FPIHU and CPIHU variables (base: previous month)Descriptive statistics1992m1 – 2012m3 2007m1 – 2008m12FPIHU CPIHU FPIHU CPIHUMean 0.918930 0.837037 0.658333 0.462500Median 0.800000 0.600000 0.700000 0.450000Maximum 10.80000 6.800000 2.700000 1.200000Minimum -3.700000 -0.400000 -2.000000 -0.300000Std. Dev. 1.790578 0.945513 1.238132 0.477140Obs. 243 243 24 24Source: Own calculations, data from Hungarian Central Bank7Figure 2. Monthly Hungarian food price and overall consumer price indices-4-202468101290 92 94 96 98 00 02 04 06 08 10fpihu - previous month = 100cpihu - previous month =100Source: Own calculations, data from Hungarian Central BankFigure 3. emphasizes the indirect effect of oil prices upon international raw food prices, induced by biofuel demand, linked to oil prices. The correlation coefficient between oil and US maize prices is close to 1, however the higher volatility of maize prices is shown by a standard deviation statistic double than the one measure for oil prices.  Figure 3. World oil and US maize price indices05010015020025030090 92 94 96 98 00 02 04 06 08 10USMPI WOPISource: IMF data8Domestic consumer food price spikes can be just as well induced by domestic (vertical transmission) or international, global (horizontal) processes or even both. In their seminal paper, Davidson et al. (2012) assess the causes of UK food price inflation in the recent years by considering horizontal as well that vertical transmission. Authors found that drivers of UK food inflation are raw food world prices and GBP/USD exchange rate. World crude oil prices indirectly influence domestic food price inflation, whilst a set of macroeconomic variables, such as interest rate, unemployment rate etc., were used as proxies for the evolution of national demand.In this research, we employ a similar set of variables, to assess the influence of domestic and global processes on Hungarian consumer food inflation, using vector error cointegration time series methodology. The rest of this paper is organized as follows: section 2 briefly presents the non-stationary time series methodology employed, followed by the presentation of dataset used for empirical analysis in section 3. Empirical analysis and results are presented in section 4, and finally, section 5 concludes. 2. METHODOLOGY Up to date time series methodology is employed.  Unit root tests2 are used to assess the stationarity properties of time series, followed by cointegration tests and the estimation of a vector error correction model (1), capable to incorporate structural changes within the long-run trends as well as to simultaneously depict the short and long-run dynamics of the system. Â ÂÂ---- ++DG++˜¯˜ˆÁÁËÊ˜¯˜ˆÁÁËÊD11 1 2,,1' pipiqjtitjijititttt uDYEtEYY kgmba(1)where Yt is a vector of 5 non-stationary variables (Hungarian agricultural output and food consumer price indices, World agricultural commodity price and oil price indices, HUF/USD exchange rate), p is the lag number, Et =(E1t E2t…Eqt)’ is a matrix of q dummy variables, where Ej,t = 1 if observation t belongs to the jth period and 0 otherwise, Dj,t-i is an impulse dummy that equals 1 if observation t is the ith observation of the jth period, meant to render the corresponding residuals to zero. Гi and Кj,i are short run matrices, α is the speeds of adjustment parameter matrix, β are the long run cointegration coefficients and μ are the long run drift parameters. The ut residuals are supposed to be independently 2 See Maddala and Kim, 1998 for a detailed discussion of the unit roots in a non-stationary time series econometrics framework.9and identically distributed with zero mean and symmetric and positive definite variance-covariance matrix Ω.3.DATAA dataset of five monthly time series variables between January 1996 and July 2007, consisting of 199 observations each are employed in the empirical analysis. Hungarian agrifood sector prices are represented by the Hungarian agricultural output price index (PPI2005) and Hungarian food consumer price index (FCPI2005), presented in figure 4.Figure 4. Hungarian agricultural output and food consumer price indices (2005=100)1.61.71.81.92.02.12.22.32.496 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12FCPI2005 PPI2005Source: Hungarian Central Statistical AgencyWorld prices used are World agricultural raw materials price index (WAP2005), and World oil price index (crude petroleum), denoted WOPI2055 and depicted on figure 5. And finally, the connection between World and Hungarian markets is made through the USD/Hungarian forint exchange rate, presented on figure 6. 10Figure 5. World agricultural raw material price and World oil price indices index (2005=100)1.21.41.61.82.02.22.42.696 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12WAP2005 WOPI2005Source: IMF DataFigure 6.USD/Hungarian forint exchange rate2.102.152.202.252.302.352.402.452.5096 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12XRUSDSource:  Hungarian Central Bank114. RESULTS AND DISCUSSIONThe logged variables presented in previous section were used in estimation. Standard ADF unit root tests3 were used to assess time series properties of data. Results revealed, as expected, that all 5 series employed contain one unit root each, i.e. they are integrated of order one, I(1). Consequently, cointegration procedure is used to discover whether there are any common long-run trends between series, known as cointegrating vectors (CI).  Johansen trace and maximum Eigen-value tests were applied to data after a careful assessment of various information criteria resulted that 5 lags in first difference were sufficient to result white nose residuals in cointegration test models. Trace and Max-Eigen value results are presented in table 3.Table 3. Johansen Trace and Max-Eigen value cointegration testsNo. of CIvectors TraceStatisticP- value Max-Eigen StatisticP-valueNone 126.6565 0.0000 63.38936 0.0000At most 1 63.26717 0.0061 37.14534 0.0032At most 2 26.12183 0.3351 14.51620 0.4159At most 3 11.60563 0.4848 6.408537 0.7409At most 4 5.197097 0.2625 5.197097 0.2625Note: with respect to deterministic specification of the test, we followed the ‘simple-to-general’ Pantula principle, by starting with the most simple realistic specification, and moving towards more complex ones, should the null of no cointegration not being rejected. Thus the test procedure above includes a single deterministic term, a long-run intercept (but no trend) included in the CI relations.  Both test statistic uniformly reject the null of ‘at most one cointegrating vector’ however cannot reject the ’at most 2 cointegrating vectors’ null hypothesis, thus we may conclude the existence of two stable long-run relationships between Hungarian food consumer price index, agricultural producer (output) price index, World agricultural price index, World oil price index and Hungarian forint/ USD exchange rate. Identifying are required to establish unique CI relationships. Since these restrictions are somewhat arbitrary, and because we wish to simultaneously focus on vertical (from home producer to consumer) and horizontal (from world markets to home producer prices) price relationships, while accounting for exogenous effects, we impose the following binding restrictions (table 4.), and use a likelihood ratio test, to check their appropriateness. Likelihood ratio test reveal, the restrictions cannot be rejected (χ2(1)=0.241, p=0.623), thus we estimate the fully identified VECM presented in table 5. 3 Not presented here, to save space, but available from authors upon request.12Table 4. Restrictions on the βCI/Variable FCPI2005 PPI2005 WAP2005 WOPI2005 XRUSDCI 1 1 * 0 * *CI 2 0 1 * * *Note: * represents no imposed restriction on the βTable 5. The VECM model: long-run cointegration relationship and error correction (t-stats in brackets) Cointegrating Eq: CointEq1 CointEq2FCPI2005(-1) 1.000000 0.000000PPI2005(-1) -0.494624 1.000000[-3.80601]WAP2005(-1) 0.000000 -0.468256[-3.82479]WOPI2005(-1) -0.241025 -0.311281[-4.87622] [-11.8136]XRUSD(-1) 0.000000 -0.340225[-3.93136]C -0.588782 0.208525[-3.08619] [ 0.59096]Error Correction:D(FCPI2005) D(PPI2005)D(WAP2005)D(WOPI2005) D(XRUSD)CointEq1 -0.031593 0.102862 0.031035 -0.089749 -0.006736[-3.16894] [ 2.74647] [ 1.12410] [-1.17354] [-0.22559]CointEq2 -0.026593 -0.221125 -0.050946 0.056249 -0.052063[-2.45273] [-5.42899] [-1.69678] [ 0.67630] [-1.60319]R-squared 0.479608 0.413640 0.276871 0.213525 0.200784Adj. R-squared 0.398101 0.321800 0.163610 0.090343 0.075606F-statistic 5.884248 4.503935 2.444542 1.733404 1.603983Log likelihood 777.7036 522.2621 581.1156 384.4736 565.9804Akaike AIC -7.779312 -5.132249 -5.742131 -3.704390 -5.585289Schwarz SC -7.322873 -4.675811 -5.285692 -3.247951 -5.128851Note: short- run dynamics represented by first difference of variables up to the fifth lags were included in the VECM estimation. Since at these stage of the analysis they are not relevant, were omitted from the table. The first CI vector captures the vertical dimension of the price relationship. Since data are in logs, estimated coefficients may be interpreted as elasticities. As expected, coefficients show inelastic estimates with the right sign (negative in vector, positive in equation specification). Thus the vertical price transmission equation can be interpreted as follows. 131% increase in domestic agricultural producer prices, World oil prices, results 0.49% and 0.24% increase in Hungarian food consumer price index respectively. With respect to horizontal transmission, 1% increase in World agricultural commodity price index, World oil prices index induce a 0.46% and 0.31% increase in Hungarian agricultural producer prices respectively. The World oil prices index is a common explanatory variable in both identified long-run equations. Thus it would be reasonable to expect to have a similar impact upon Hungarian food consumer and Hungarian agricultural producer price indices. Table 6. Restricted long-run cointegrating relationships (t-stats in brackets)Cointegrating Eq: CointEq1 CointEq2FCPI2005(-1) 1.000000 0.000000PPI2005(-1) -0.290698 1.000000[-4.67869]WAP2005(-1) 0.000000 -0.425354[-3.87843]WOPI2005(-1) -0.312785 -0.312785[-12.7161] [-12.7161]XRUSD(-1) 0.000000 -0.303511[-4.07206]C -0.886487 0.038245[-7.72606] [ 0.11978]This hypothesis can be built into the VECM estimation and use a likelihood ration test to assess its appropriateness. The resulting χ2(2)=0.854 (p=0.653) is not significant, concluding the statistical equality of WOPI2005 coefficient in the two long-run equations.  And finally, 1% increase in the USD/HUF exchange rate results in 0.34% increase in domestic agricultural producer prices. Re-estimating the model with the WOPI2005 variable’s coefficient equality restriction induces only some minor magnitude changes in the estimated values of other variables (table 6.). 5. CONCLUSIONSPreliminary results emphasise the major role international raw agricultural commodity and oil prices prices play in the determination of domestic producer and, more importantly consumer prices. Considering that Hungary is a small open economy, where significant agricultural exports are driven by international trends, the effect of exchange rate in the horizontal price transmission is emphasised. In further reserahc Vector error correction models will be very useful to assess the implications (magnitude as well as duration) of an 14unexpected shock to one of the integrated time series variables. Thus impulse response functions able to graphically depict the influence and pass-through of oil, macroeconomic (e.g. income or unemployment) and world raw food prices upon domestic consumer food prices can be employed in further analysis.REFERENCESDavidson, J., Halunga, A.,   Lloyd, T.A., McCorriston, S. and Morgan., C.W. (2012): Explaining UK Food Price Inflation. Working Paper No. 1 Transparency of Food Pricing, Seventh Framework Programme, Grant Agreement No. KBBE-265601-4-TRANSFOP.Lang, T. (2010): Crisis? What Crisis? The Normality of the Current Food Crisis. Journal of Agrarian Change Vol.10 (1), 87–97.Maddala, G.S. and Kim, In-Moo (1998): Unit Roots, Cointegration and Structural Change. Cambridge University Press, Cambridge, UK, 1998. McCalla, A.F. (2009): World Food Prices: Causes and Consequences. Canadian Journal of Agricultural Economics Vol. 57., 23 – 34.Mitchell, D. (2008): A Note on Rising Food Prices. Policy Research Working 4682. The World Bank, Development Prospects Group. July 2008.

World prices and domestic food price spikes

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