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Weather Fluctuations, Expectation Formation, and Short-Run Behavioral Responses to Climate Change

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Abstract

One premise adopted in most previous studies is that weather fluctuations affect economic outcomes contemporaneously. Yet under certain circumstances, the impact of weather fluctuations in the current year can be carried over into the future. Using agricultural production as an example, we empirically investigate how past weather fluctuations affect economic decision-making by shifting agents’ subjective expectations over future climate. We find that agricultural producers do not form expectations on future climate using only long-run normals, and instead engage in a combination of heuristics, including the availability heuristic and the reinforcement strategy. Adopting these learning mechanisms causes farmers to significantly over-react to more recent fluctuations in weather and water availability when making ex ante acreage and crop allocation decisions.

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Notes

  1. According to the World Meteorological Organization (WMO), normals are defined as “period averages computed for a uniform and relatively long period comprising at least three consecutive 10-year periods.” Other organizations and agencies use the same standard, for example, USGS, National Weather Service, and USDA-NRCS, to produce their weather and surface water flow normals.

  2. The assumption of a non-informative prior is not needed for our empirical analysis because any individual-specific prior will be subsumed by the inclusion of individual level fixed-effects.

  3. Only Water District #11, the governing body of the Bear River, has similar temporal coverage regarding water calls in Idaho. WD11 is not an ideal study site because the river is collectively managed by Idaho, Utah, and Wyoming, which leads to complex water right structures.

  4. Ji and Cobourn (2018) documents that most irrigation districts are formed between 1890 and 1920, with the purpose of securing fundings for large irrigation projects. Thus boundaries of water providers are less likely to be correlated with production conditions a century later.

  5. In rare cases, farms may choose to acquire their own water rights apart from getting water from water providers. We exclude those farmers in our analysis.

  6. Figure 5 illustrates the reaches in WD1.

  7. For example, on May 20th, 2014, reaches on most of the main stem of the Snake River, as well as reaches on upstream tributaries of the Grey River, Salt River, Henry’s Fork, and Falls River, share a cutoff priority date of December 14th, 1891. Reaches on the Teton River, Willow Creek, as well as the lower main stem of the Snake River have a cutoff priority date that differs from the main stem. Table 6 shows the output from the water accounting model on May 20th, 2014.

  8. Geographical boundaries are obtained from the water rights place of use (WRPOU) database from IDWR. Diversion point, diversion volume, and priority date are from Olenichak (2015).

  9. According to the definition from USDA-Farm Service Agency, a CLU is an individual contiguous farming parcel, which is the smallest unit of land that has four characteristics: (1) a permanent, contiguous boundary; (2) common land cover and land management; (3) a common owner, and/or (4) a common producer association.

  10. We define this using the criteria that over 90% of the total land mass of the farm is covered by that provider.

  11. Personal communication with Brian Olmstead, General Manager of the Twin Falls Canal Company, 6/16/2016.

  12. This is calculated based on the number of pixels in the Cropland Data Layer in each land use within each farm. There are 60 other categories of crops reported in the CDL. The majority of land in other crops is in dry beans (1.1% on average across years). All other crops occupy less than 1% of the land base in agriculture (e.g., oats, canola, peas, onions, rye, mint, hops, grapes, tree crops).

  13. We construct GDD by accumulating degree days using the following equation:

    $$\begin{aligned} GDD_t= & {} {\left\{ \begin{array}{ll} b_2 - b_1 &{} \text{if} \quad tmean \ge b_2 \\ tmean - b_1 &{} \text{if} \quad b_2> tmean > b_1 \\ 0 &{} \text{if} \quad tmean \le b_1 \end{array}\right. } \nonumber \\ GDD= & {} \sum _{t}GDD_t \end{aligned}$$
    (8)

    where \(b_1=8\) and \(b_2=32\) is the lower and upper threshold of GDD, and tmean is the daily mean temperature.

  14. We calculate EDD by accumulating daily degree days‘ using a cutoff temperature of 32 \(^\circ\)C, i.e.,

    $$\begin{aligned} EDD_t= & {} {\left\{ \begin{array}{ll} tmax - b_2 &{} \text{if} \quad tmax > b_2 \\ 0 &{} \text{if} \quad tmax \le b_2 \end{array}\right. } \nonumber \\ EDD= & {} \sum _{t}EDD_t \end{aligned}$$
    (9)

    where \(b_2=32\) is the threshold for EDD, and tmax is the daily maximum temperature.

  15. While the non-separability between endowments can potentially bias estimates when modeling farm profit (Fezzi and Bateman 2015; Hendricks 2018), in a land allocation model the impact of separability is negligible (Ji et al. 2018).

  16. Water portfolios were established from the late 1800s through the mid-1900s and rarely include new rights with priority dates after the year 2000. Even if new rights are added to the portfolio, these newer water rights have close to zero probability of receiving water during the growing season. This means that the fundamentals of water portfolios remain mostly unchanged during our study period.

  17. A back-of-the-envelope calculation suggests that a one-standard-deviation increase in GDD corresponds to about 0.7 \(^\circ\)C of warming, assuming that warming is evenly spread out over the growing season.

  18. A back-of-the-envelope calculation suggests that one-standard-deviation increase in EDD corresponds to about 1.1 \(^\circ\)C of warming, assuming that warming is evenly spread over a 30-day period where daily temperatures exceed the EDD threshold.

  19. For example, long-run climate forecasts from IPCC and short-run weather patterns drawn from El Niño-southern oscillation (ENSO) forecasts only have time-series variations at the regional scale, and will be assimilated in the time fixed effects.

  20. We acquire reservoir level at the beginning of the growing season (Mar.31) from the monthly water outlook report published by NRCS. Reservoir levels are then rank-standardized based on 30-year historical observations. We do not use the NRCS forecasts on surface water flows because (1) empirically, the forecast does not predict the actual surface water flows well (\(R^2 < 0.1\)); and (2) the prediction is mainly based on El Niño-southern oscillation (ENSO) forecasts and winter precipitation, both of which are controlled in our model.

  21. We compound our aggregation methods with three components: TND versus Streak denotes whether we aggregate the total number or the longest streak of curtailment days. Season versus Summer denotes whether the aggregation is over the entire season or only in summer months. 100% versus 50% denotes the qualification for a curtailment day using all, or half, of the water in a portfolio is curtailed.

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Acknowledgements

The authors gratefully acknowledge support from the NASA Land Cover/Land Use Change (LCLUC) Program (Award NNX14AH15G), the Virginia Tech Institute for Critical Technology and Applied Science, and the National Science Foundation’s Dynamics of Coupled Natural and Human Systems (CNH) program (Award 1517823). All errors are our own.

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  1. Brandeis University, 415 South St MS 021, Waltham, MA, 02453, USA

    Xinde Ji

  2. Virginia Tech, 310 West Campus Dr, Blacksburg, VA, 24061, USA

    Kelly M. Cobourn

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Correspondence to Xinde Ji.

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Appendix: Additional Figures and Tables

Appendix: Additional Figures and Tables

See Fig. 5 and Tables 6, 7, 8, 9 and 10.

Fig. 5
figure 5

Schematic illustration of relative positions of Snake River and tributary reaches in the Upper Snake River Basin (from Olenichak 2015)

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Table 6 Sample water accouting record in WD1
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Table 7 Parameter estimates with ex ante information
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Table 8 Im-Pesaran-Shin test for unit roots in panel data
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Table 9 Parameter estimates with different aggregation methods for water curtailment variables
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Table 10 Parameter estimates with extended lags
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Ji, X., Cobourn, K.M. Weather Fluctuations, Expectation Formation, and Short-Run Behavioral Responses to Climate Change. Environ Resource Econ 78, 77–119 (2021). https://doi.org/10.1007/s10640-020-00525-x

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