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The Law of One Food Price

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Abstract

Are food prices more or less equalised across countries? In view of various barriers to trade (both naturally occurring and of a man-made nature) and currency gyrations, the answer would seem to be an unambiguous “No”, but we show this question is worthy of further investigation. In order for the law of one price (LOP) to hold, domestic prices must respond one-for-one to changes in world prices and exchange rates, but this is usually prevented by variations in mark-ups and/or trade barriers. We use data on producer prices from the Food and Agriculture Organization to test for the LOP. The results give surprising support to the LOP: Market wedges appear to be insufficiently important to prevent food prices equalising over the longer term. The law of one food price seems to hold, at least as a first approximation.

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Data Availability

The datasets generated and analysed during the current study are available from the authors on reasonable request.

Notes

  1. Relatedly, Frankel et al. (2012) document a rapid downward trend in the degree of exchange-rate pass-through and speed of adjustment (towards PPP) across both developed and developing countries since the 1990s. Interestingly, Rabe and Waddle (2020) estimate that the half-life of deviations from PPP fell from 3 to 2 years over the last five decades, and suggest that the PPP puzzle may be slightly abating over time due to the increasingly tradable nature of the composition of the consumer price index. This estimate of the half-life is also in broad agreement with that of Kunkler and MacDonald (2015), among others.

  2. Two other possible explanations for the lower food prices should be mentioned. First, because of their superior endowment of agricultural land and favourable climate, rich countries may simply have a comparative advantage in producing food at lower prices. A second explanation is Engel’s law: Higher income is likely to lead to growth in the consumption of most goods, but because of the Engel effect, food consumption grows slower than average. If on the supply side all sectors (food and non-food) expand at approximately the same rate, at constant relative prices there will be an excess supply of food. The combined effects lead to lower relative food prices in rich countries. A possible complication to this explanation is the food price elasticity may differ across countries. Because low-income countries devote a larger share of spending to food, food demand is likely to be more price elastic in these countries. The reverse is true in rich countries, with a smaller food share and a lower price elasticity is likely. As the production of food expands, the lower price elasticity means the incipient excess supply leads to a larger fall in the relative price. This effect would reinforce the above argument of cheap food in rich countries.

  3. The data source, the International Comparison Program (World Bank 2013, unpublished), does not reveal the actual brand name of the whiskey.

  4. The Economist’s approach of adjusting Big Mac prices for differences in country income can also be interpreted as a type of adjustment for differing nontraded components (The Economist (ongoing)). The income-adjusted prices are closer to parity.

  5. Many of these studies also establish that slow PPP convergence is driven primarily by nominal exchange-rate movements, a finding at odds with the conventional emphasis on slow price adjustments (as in the nominal rigidity literature, for example). In the short run, nominal-exchange-rate movements are seemingly disconnected from price movements, leading to the observation that exchange rates appear to disrupt PPP convergence. This idea is reinforced by the market for currencies being much larger than that for commodities: Total global merchandise and service trade value was US$25 trillion in 2018 (WTO 2019)—a mere 1% of total foreign-exchange trade in the same year (BIS 2019). Although the contrast between trade and financial flows is suggestive, the magnitude of the difference needs qualification. There are substantial measurement errors in both types of flows due to (i) double counting of trade volumes (as both exports from the source country and imports into the destination) and (ii) multi-counting of financial flows as a result of the notorious “churn” associated with high daily turnover in foreign-exchange markets.

  6. Additionally, there is some evidence that in the 1980s, the price response was asymmetric: Japanese exporters and importers exploited the temporary decrease in the value of the dollar to extract quota rents by further increasing yen prices, giving rise to a low pass-through coefficient. But when the dollar appreciates, the pass-through coefficient is not significantly different from 1. This is possibly because exporters do not have much incentive for changing prices in the presence of an inelastic destination demand and when the risk of loss of market share is small. From Japanese importers’ point of view, monopolistic power allows them to absorb favourable exchange-rate changes into margins. This type of incomplete and asymmetric pass-through behaviour is typical of food and agriculture markets, as in the recent cases of Japanese meat (Miljkovic and Zhuang 2011), US beer (Hellerstein 2008) and US cocoa bean (Luckstead 2018), for example. Forbes et al. (2018) find that exchange rate pass-through is low in response to domestic demand shocks and high in response to domestic monetary policy shocks. Another reason for a less-than-unitary pass-through is the tendency of exchange rates to “overshoot” economic fundamentals in the short-run (see, e.g., Bjørnland 2009; Dornbusch 1980; Frankel 2008; and Hatzenbuehler 2016).

  7. Other sources of deviations include the costs of distribution, trade policy interventions, insulating price policies, price stickiness and international oil price shocks (Jabara and Schwartz 1987).

  8. These are prices “received by farmers…as collected at the point of initial sale (prices paid at the farm-gate)” (FAO, online).

  9. For details, see Appendix A2.

  10. From the last two columns of panel C, 99 percent of the truncated observations \(\left(=\frac{\mathrm{96,769}}{\mathrm{97,274}}\right)\) are in the range \([ - 3, \, 3]\) while only 32 percent \(\left(\frac{\mathrm{30,752}}{\mathrm{97,274}}\right)\) are in the range \([ - 0.3, \, 0.3]\).

  11. In Appendix A4, we show that these observations persist when compressing the sample using country and commodity averages.

  12. Even when the individual test statistic of each cross-section has a non-standard distribution, under cross-sectional independence, the pooled test statistic (Z) converges to the standard Gaussian distribution and inference can be made on the basis of well-defined critical values obtained from simulations (Choi 2001). A more detailed discussion of this approach is provided in Appendix A5.

  13. The speed of adjustment may depend on the magnitude of the deviations, giving rise to a “no-arbitrage band” of the type investigated by Vo (2018).

  14. See, among others, Bahmani-Oskooee and Wu (2018), Breitung and Candelon (2005), Pesaran (2007), and Westerlund and Blomquist (2013).

  15. As wheat exports are a small fraction of Australia’s GDP, can they plausibly affect the value of the currency? Prominent Australian exports are agricultural commodities, minerals and energy. Some important mineral and energy exports tend to operate under long-term pricing arrangements with limited flexibility (such as iron ore and LNG), while agricultural commodities usually have more flexible transaction-level prices. According to ABARES, wheat represents more than 20% of Australia’s agricultural exports (https://daff.ent.sirsidynix.net.au/client/en_AU/xls (https://live.com). Flexible wheat prices can be taken as indicative of the state of the world market for agricultural commodities in general. As the Australian dollar is widely regarded as a “commodity currency”, it can be seen how variations in wheat prices might affect the exchange rate.

  16. As there is neither trending nor drifting behaviour, we do not include deterministic terms. The lag length is determined to be 4 by the Akaike information criterion. For an examination of generalisations of the VAR model, see Appendix A7.

  17. Appendix A7 provides details on the construction of the GIRFs.

  18. An example of such a sustained disequilibrium of exchange rates is the “dollar effect”, which refers to the persistent misalignment of some currencies against the USD.

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Acknowledgements

This paper benefited from helpful suggestions and comments made by two anonymous referees, the Editor of this journal, Yihui Lan, James Lothian, Ranjan Ray, Jeffrey Sheen and participants at the 1st Australasian Commodity Markets Conference, the 64th Annual Conference of the Australasian Agricultural and Resource Economics Society, the 5th Vietnam’s Conference on Business, Economics and Resources, and various internal seminars at the University of Western Australia (UWA). Long Hai Vo gratefully acknowledges financial support from an Australian Government Research Training Program Scholarship. For provision of unpublished data, the authors thank the International Comparison Program administrators at the World Bank. All errors remain with the authors.

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  1. Jiawei Si: Deceased

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  1. Economics Department, Business School, The University of Western Australia, Crawley, WA, 6009, Australia

    Kenneth W. Clements, Jiawei Si & Hai Long Vo

  2. 35 Stirling Highway, Crawley, Perth, WA, 6009, Australia

    Kenneth W. Clements & Hai Long Vo

  3. Faculty of Finance, Banking and Business Administration, Quy Nhon University, Quy Nhon, Vietnam

    Hai Long Vo

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Clements, K.W., Si, J. & Vo, H.L. The Law of One Food Price. Open Econ Rev 34, 195–216 (2023). https://doi.org/10.1007/s11079-022-09671-9

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