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Agricultural fluctuations and global economic conditions

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Abstract

This research examines the global implications of agricultural production and price fluctuations via Global Bayesian Vector Autoregression (GBVAR) model. We develop a novel Paasche agriculture price index, based on the relative time-varying contribution of four dominant food commodities shifting contributions of major food commodities (rice, maize, soybeans, wheat), pivotal for global sustenance. The analysis reveals that while agricultural production shocks are important, they exert a comparatively lesser impact than agriculture price shocks. Higher agriculture inflation can be destabilizing via lower output and higher aggregate inflation.

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Notes

  1. We convert nominal global GDP (FRED mnemonic NYGDPMKTPCDWLD), which is denominated in U.S. Dollars (FRED mnemonic CPALTT01USA661S), to real GDP by dividing the former with the U.S. CPI.

  2. The CPI data is based on the first principal component of aggregate CPI data for 25 countries (see Sect. 2.1).

  3. The data is publicly available https://www.worldbank.org/en/research/commodity-markets.

  4. Our choice of countries in the sample is based on data availability. The 25 economies include: Australia (”AUS”), Austria (”AUT”), Belgium (”Belgium”), Canada (”CAN”), Switzerland (”CHE”), Germany (”DEU”), Spain (”ESP”), Finland (”FIN”), France (”FRA”),United Kingdom (”GBR”), Greece (”GRC”), India (”IND”), Iceland (”ISL”), Italy (”ITA”), Japan (”JPN”), South Korea (”KOR”), Luxembourg (”LUX”), Netherlands (”NLD”), Norway (”NOR”), New Zealand (”NZL”), Portugal (”PRT”), Sweden (”SWE”), Turkey (”TUR”), United States (”USA”) and South Africa (”ZAF”).

  5. Our results are similar to Ratti and Vespignani (2016), who show that the first principal component captures 89.6% of the variation for prices relating to the G5 countries. Figure 1 shows noticeable fluctuations of the inflation rate attributed to ISL and TUR. We provide an additional scree plot in Fig. 9 in the Appendix, where the first principal component captures 86.3% of the total variance when excluding ISL and TUR.

  6. See https://www.fao.org/3/u8480e/u8480e07.htm.

  7. While the Laspeyres index also holds a place in economic analysis, we opt for the Paasche index due to its adaptability to account for fluctuating quantities across time. Unlike the Laspeyres counterpart, which remains rooted in the initial period’s quantities, the Paasche index factors in changing production patterns over time. This approach ensures the index’s relevancy by mirroring the shifting composition of agricultural output.

  8. Hamilton (2021) uses OECD industrial production index plus six additional countries as opposed to shipping costs. Hamilton (2021) shows that oil inflation is driven by the OECD industrial production and is statistically significant, whereas the influence of the Kilian index from Kilian (2009) is not statistically significant. Furthermore, while the Kilian index identifies 2016 as the worst downturn, Hamilton (2021) used the OECD production index which he posits as a better indicator, which now shows the global financial crisis having a more negative cyclical component. Hamilton (2021) cautions that using shipping costs is problematic for 2016 considering “a big factor in the record low shipping prices in 2016 was overbuilt shipping capacity, not a severe global economic contraction.” Lastly, Hamilton (2021) finds that industrial production has statistically significant coefficients for every commodity except aluminum, whereas the Kilian-Zhou index (see Kilian & Zhou, 2018) does not exhibit any statistical significance.

  9. The World Bank “Pink Sheet” includes the “Cereals” aggregate price index based on the rice, wheat, maize and barley. In the empirical analysis, the Baseline model is based on rice, wheat, maize and soybean grains prices. To make the empirical analysis comparable, we remove soybeans and add barley in the Paasche index.

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  1. Labcorp, Sr. Economist Data Scientist, Artificial Intelligence, Durham, NC, USA

    William Ginn

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  1. William Ginn
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Correspondence to William Ginn.

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Appendices

Appendix

International inflation data: scree plots (Figs. 8 and 9)

Fig. 8
figure 8

Scree Plot: Inflation (based on 25 countries). The top 10 dimensions are shown

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Fig. 9
figure 9

Scree Plot: Inflation (based on 23 countries). The top 10 dimensions are shown. The model excludes Iceland and Turkey

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2.1 Production cycle for grains

The year in which the crop was harvested could be different from year in which the crop was planted. Based on the G20 Agricultural Market Information System (AMIS), we provide a general summary of the production cycle by each commodity at monthly frequency:

  • Wheat: planting in the fall or early spring, depending on the variety and the climate. Winter wheat is typically planted in the fall and harvested in the early summer, while spring wheat is planted in the spring and harvested in late summer. Harvesting: late spring or early summer for winter wheat, and late summer for spring wheat.

  • Corn (Maize): planting in the spring is the common planting time when soil temperatures are suitable, usually between April and June. Harvesting: late summer to early fall, typically around September to October.

  • Rice: planting is usually done in the spring in warmer regions, while in cooler regions, it may be planted in late spring to early summer. Harvesting: late summer to early fall, typically around September to October.

  • Soybean: planting is often done in the spring when soil temperatures are suitable for germination. Planting usually occurs from April to June, depending on the region. Harvesting is usually harvested in the fall, typically from September to November.

We also plot the crop production cycle for grains by month for the largest producers (source: AMIS) in Fig. 10. Overall, these insights show that planting and harvesting may occur over a longer period, which may extend beyond a single year.

Fig. 10
figure 10

Source: G20 AMIS. See https://www.amis-outlook.org/fileadmin/user_upload/amis/docs/Crop_Calendar/AMIS_Crop_Calendar.pdf

Production Cycle: Leading Producers.

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IRFs (Additional models) (Figs. 11, 12, 13 and 14)

Fig. 11
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IRF Responses (Alternative Model I)

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Fig. 12
figure 12

IRF Responses (Alternative Model II)

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Fig. 13
figure 13

Time-series plot of Real Grains Prices (log, indexed 2000 = 100). The World Banks grains price is based on a fixed ratio of barley, wheat, maize and rice. The Paasche index is based on a time-varying index. The contemporaneous correlation is 0.9738

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Fig. 14
figure 14

IRF Responses (Alternative Model III)

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Ginn, W. Agricultural fluctuations and global economic conditions. Rev World Econ (2024). https://doi.org/10.1007/s10290-023-00522-4

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