ARE USDA REPORTS STILL NEWS TO CROP MARKETS IN THE BIG DATA ERA? Berna Karali, University of Georgia. Olga Isengildina-Massa, Virginia Tech University

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1 ARE USDA REPORTS STILL NEWS TO CROP MARKETS IN THE BIG DATA ERA? by Berna Karali, University of Georgia Olga Isengildina-Massa, Virginia Tech University Scott H. Irwin, University of Illinois Michael K. Adjemian, USDA, ERS Robert Johansson, USDA, OCE Funding support from the Office of the Chief Economist of the U.S. Department of Agriculture under Cooperative Agreement No is gratefully acknowledged. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and not necessarily reflect the view of the U.S. Department of Agriculture.

2 Are USDA Reports Still News to Crop Markets in the Big Data Era? Abstract This study investigates whether major USDA reports still provide important news to crop markets in the big data era. The news component of each report, or market surprise, is measured as a difference between the USDA estimate and its private expectation in corn, soybeans, and wheat markets. Changes in the relevance of USDA information are assessed by examining changes in the magnitude of market surprises and shifts in the futures price reaction to these surprises, which isolates the impact of each report. Our findings show that USDA reports continue to provide news to the markets in the big data era. In fact, the impact of most reports has increased over the last decade, suggesting a growing importance of these reports. The impact of Crop Production reports, while still substantial, has slightly decreased possibly reflecting increased competition from the private sector in generating production forecasts. Key words: announcement effects, big data, crop futures, market surprise, price reaction, USDA reports JEL codes: C32, D80, D84, G14, Q11, Q13

3 Are USDA Reports Still News to Crop Markets in the Big Data Era? Introduction The United States Department of Agriculture (USDA) has long published various reports on crop acreage, production, and stocks that serve as benchmarks in the market. More formally, these reports have fulfilled the three main roles of public information: facilitation of efficiently functioning markets, reduction of information asymmetries, and informing policy and program formation, operation and evaluation processes (C-FARE 2013). However, the traditional role of USDA crop reports has come under scrutiny in an era where access to information and technology has dramatically evolved, with the surge in communications technology, computing power, storage, and remote sensing commonly referred to as big data (Sonka 2014). For instance, several new companies, such as Climate Corp. (founded in 2006, acquired by Monsanto in 2013) and Descartes Labs (founded in 2014) have entered the crop production forecasting space. The growth of big data in agriculture can also be tracked by the amount of venture capital funding of the sector, with the first wave of agritech funds beginning in 2006 and growing to 13 core firms in 2016 that have raised a total of $1 billion (Burwood-Taylor 2016). The new big data firms tend to rely on information from precision agriculture sensors and satellites rather than surveys for providing crop production forecasts. For example, Climate Corp. s estimates are based mostly on field data from sensors mounted on agricultural equipment, combined with data from Planet Labs (founded in 2010) and other satellites as well as detailed weather forecasts and information about soil types. The main differences between big data forecasts and USDA reports can be summarized as follows: 1) big data forecasts rely on large non-random samples of data while USDA forecasts are based on smaller random samples; 1

4 2) big data forecasts may be available almost in real-time while USDA forecasts are usually delayed as time is needed to collect and process survey data; 3) the quality of big data forecasts is not yet known while USDA forecasts have a long track record; 4) big data forecasts are private and available to subscribers only while USDA forecasts are public and freely available to all market participants. Given the growing presence of big data production forecasts in agriculture and continuously shrinking federal budgets a major controversy is brewing regarding whether USDA should continue its crop forecasting program, or alternatively, how its programs can be revised to incorporate big data information streams (NASEM 2017). The value of USDA crop reports has typically been measured based on price impacts in agricultural commodity markets (e.g., Colling and Irwin 1990; Fortenbery and Sumner 1993; Baur and Orazem 1994; Isengildina-Massa et al. 2008; McKenzie 2008; Adjemian 2012; Lehecka, Wang, and Garcia 2014; Dorfman and Karali 2015). 1 This literature provides ample evidence that USDA crop reports have moved markets for decades. However, less is known about changes in impact over time. Some evidence of diminished market impact starting in the mid-1980s has been reported (Fortenbery and Sumner 1993), but other evidence indicates no change or even an increasing impact after the mid-1980s (Garcia et al. 1997; Isengildina-Massa et al. 2008). Two recent studies estimate market impacts during the big data era (Ying, Chen, and Dorfman 2017; Isengildina-Massa et al. 2017). These studies suggest that USDA crop reports are still informative and influential in grain futures markets, with a generally increasing trend in price impact, but there are some reports whose impact appears to be declining. For 1 A few studies provide empirical estimates of the direct welfare benefits of public crop forecasts (Hayami and Peterson 1972; Freebairn, 1976; Bradford and Kelejian 1978; Abbott, Boussios, and Lowenberg-DeBoer 2016; Gouel 2017). In these studies, a theoretical supply/demand structure for a market is proposed, parameter estimates are obtained, and then social welfare is estimated under different information or expectation assumptions. The estimation results suggest the social welfare value of USDA forecasts has substantially exceeded the cost. 2

5 example, Isengildina-Massa et al. (2017) showed that the impact of USDA Crop Production reports decreased in recent years, consistent with the effect of competition from private production forecasts enhanced by big data capabilities. This discussion indicates that the available evidence on the changing market impact of USDA crop reports is limited. In addition, previous studies have important limitations. First, most studies focus on the price impact of only one type of USDA report in one or two markets. This prevents comparison of market impacts across major USDA acreage, production, and stock reports and across markets, which may be important because different reports and markets could be differentially impacted by big data. Second, price impact is most frequently measured in the literature using a dummy variable (or its equivalent) on report release days, which makes it impossible to separate the impact of reports released on the same day. This clustering is a nontrivial problem. For example, almost all Crop Production Annual Summary and Prospective Plantings reports since 1985 were released on the same day as quarterly Grain Stocks reports. Therefore, the dummy variables used to measure price impact pick up the combined impact of all reports released on the same day. An alternative approach that avoids these limitations is to measure the market news or surprise component of the reports as the difference between USDA and private analysts forecasts, and then estimate price impact based on the news component. This allows the price impact of a given USDA report to be uniquely identified. The purpose of our study is to investigate whether USDA crop reports are still news to markets in the big data era using the most comprehensive set of market expectations in the literature to date. The comprehensive data allows us to disentangle the news impact of each report within a properly identified system of all major reports that avoids problems related to report clustering. The data set includes 3

6 expectations for all major USDA crop reports including Prospective Plantings, Acreage, Winter Wheat Seedings, Crop Production, Crop Production Annual Summary, and Grain Stocks, for corn, soybeans, winter wheat, and spring wheat. The sample period for the expectations data is long, covering the 1984/85 through 2016/17 marketing years. Our comprehensive approach allows us to compare changes in the impacts of reports that face competition from the new big data-based forecast providers to the ones that do not. Based on the discussion above, our study hypothesizes that the influence of big data has been increasing since If big data in private forecasts is replacing USDA information, both market surprise and price reaction would decrease in magnitude. We test these hypotheses by assessing the equality in absolute surprises between pre- and post-2006 sub-periods and evaluating the differences in price reactions between pre- and post-2006 in a generalized autoregressive conditional heteroskedasticity (GARCH) framework. Our GARCH modelling framework is novel, as the individual price impacts of acreage, production, and stock reports are estimated simultaneously, due to the availability of expectations data, and the release of reports is also allowed to impact volatility. Our findings demonstrate that the value of USDA reports has not been replaced by private information sources during the big data era. The first set of results shows no systematic decline in market surprises of important USDA crop reports. Thus, the USDA forecasts generally carry new information beyond private information sources, even in more recent years. The second set of results demonstrates that price movements continue to occur in response to market surprises, even for those USDA reports that are most likely to be affected by big data. 4

7 Conceptual Framework Previous theoretical studies of the value of public information focus on how this information may improve economic welfare. Stein (1992a, 1992b) developed commodity market models based on the rational expectations theory that explicitly incorporates learning behavior and costly information. This framework suggests two plausible ways that public information may increase the speed of convergence to a rational expectations equilibrium resulting in welfare gain (reduction in net social loss). First, public information programs may increase the number of producers that employ more sophisticated learning methods (i.e., Bayesian instead of OLS). In other words, the programs educate producers regarding the structure and parameters of the underlying economic model and prospective economic conditions. This is consistent with the long-held view that a vital aspect of public situation and outlook programs is economic education (e.g., Benedict 1953; Kunze 1990). Since big data are likely to lead to increasingly complex models of production and consumption forces and their market interactions, this educational role of public information may become more relevant leading to complementarity between big data and the value of public information. Second, a government agency may be able to collect information more inexpensively than private firms. The agency may be able to achieve economies of size that a single firm cannot achieve, or the agency may have lower marginal costs of sampling. For example, if producers believe a government agency collects and disseminates information objectively, then producers may be willing to freely deliver information. A private firm seeking the same information for private gain may have to pay a substantial premium to producers in order to obtain the information. However, big data may provide a significant advantage to private companies with respect to information collection. Precision agriculture provides a good example 5

8 of such advantageous access to big data by private firms but not public agencies. Satellite data are also extensively used by private forecasting companies (like Descartes Labs) and much less by USDA. Therefore, big data are likely to reduce the marginal cost of information collection for some private firms which may lead to a substitution relationship between big data and public information. Furthermore, Stein (1992a, 1992b) argues that the speed of convergence to a rational expectations equilibrium is inversely related to market heterogeneity (uncertainty). The impact of market heterogeneity is discussed in several studies devoted to the news versus noise hypothesis (Mankiw, Runkle, and Shapiro 1984; Mankiw and Shapiro 1986; Aruoba 2008). These studies show that unclear data quality may make it more difficult to reach equilibrium resulting in higher social loss. Some studies (e.g., Lahiri and Sheng 2008) claim that big data will result in higher costs of information processing due to more complex models of market equilibrium and unclear data quality. In the environment of greater informational uncertainty and higher costs of information processing, the value of reliable USDA information may increase. While the previous arguments focused on direct welfare impacts of public information, there are two important externalities discussed by Hirshleifer and Riley (1992) that should be noted. The first is the public good effect that tends to cause an underinvestment in information-producing activities, and the second externality is the speculative effect that leads to an overinvestment. The public good effect is prevalent if information is non-excludable and because of these shared effects, a firm is not able to fully appropriate the returns to the information. USDA forecasts are considered public goods as everyone has free access to this information. Under these conditions, due to underinvestment, public information would have a 6

9 higher social value. However, similar private forecasts can be made excludable through restricted access, and therefore may likely be affected by the speculative effect. The speculative effect may result in overinvestment in information-creation by private companies that may make USDA information redundant. On the other hand, the speculative effect may create incentives for information asymmetries as private firms strive to create information advantages for themselves. In this environment, the role the USDA is playing in reducing information asymmetry will increase. Overall, this discussion demonstrates that in some cases the emergence of big data may enhance the role of public information (complementary relationship), while under other conditions public information may become less valuable or even redundant. This study provides empirical evidence on which effect dominates by examining changes in the impact of public information over time. If the emergence of big data is making USDA information redundant, it should result in a smaller market surprise and a reduction in price reaction to USDA reports post On the other hand, increases in market surprise and price reaction would point to a complementary relationship between big data and public information enhancing the value of USDA reports in the post-2006 environment. Data This study examines the informational value of USDA s key crop reports that are prepared and issued by their National Agricultural Statistics Service (NASS) agency over 1984/85 through 2016/17 marketing years. NASS is the main branch of USDA responsible for collection and dissemination of surveys related to agricultural operations to provide estimates of production, supply, and prices. The annual forecasting cycle for crops starts with Winter Wheat Seedings reports in early January followed by Prospective Plantings reports in March. Winter Wheat 7

10 Seedings reports contain seeded area estimates based primarily on surveys conducted during the first two weeks of December. 2 Annual Prospective Plantings reports contain survey-based estimates of producers planting intentions as of March 1 st and are typically released at the end of March. Good and Irwin (2011) provide a thorough review of the survey procedures used by the USDA. Additional expected supply information is disclosed in Acreage reports. These reports provide updated survey information on planted and/or harvested acreages for those commodities included in Prospective Plantings reports and are released at the end of every June. Planted acreage is finalized in the Crop Production Annual Summary report released in January, and represents the final USDA estimate for both Prospective Plantings and Acreage. Crop Production reports include information from Acreage reports and other sources, and contain forecasts of marketing year yield and production for major crops consistent with their growing cycles. These reports are typically released between the 9 th and 15 th of each month. 3 For corn and soybeans, the production forecasts are typically released from August through November. Production forecasts for wheat are released from May through August for winter wheat, and from July through August for spring wheat. All forecasts are finalized in January. Thus, production forecasts released before January represent an update of the previous forecast describing a marketing year total production, which is released in January in the Crop Production Annual Summary report. For more information on the preparation of Crop Production reports, see Irwin, Sanders, and Good (2014). Grain Stocks reports track available supply throughout the marketing year, which is a function of annual production and the pace of use, and are issued by NASS quarterly (January, 2 The title of the report was Winter Wheat and Rye Seedings from 1964 to 1999, changed to Winter Wheat Seedings from 2000 to 2016 and to Winter Wheat and Canola Seedings in This annual report is simultaneously released with the Crop Production Annual Summary report during our sample period. 3 Starting in 1985, Crop Production and WASDE reports were released simultaneously. 8

11 March, June, and September). These reports describe stocks of multiple crops, including corn, soybeans, and wheat, as well as the number and capacity of on- and off-farm storage facilities, and are used by the market as important indicators of the pace of usage within the marketing year relative to projections. Industry analysts estimates, which are usually released a few days before the USDA reports, have been traditionally used as a proxy for market expectations of government reports (e.g., Colling and Irwin 1990; Grunewald, McNulty, and Biere 1993; Garcia et al. 1997; Egelkraut et al. 2003). We follow the same approach and construct private expectations series illustrated in table 1 using the following sources: Winter Wheat Seedings, Prospective Plantings, and Acreage report expectations for corn, soybeans, and wheat are obtained from Knight Ridder/Dow Jones through 2015; 2016 expectations are from Thomson Reuters. Private analysts estimates for corn and soybean Crop Production reports use an average of forecasts by Conrad Leslie and Informa Economics (formerly Sparks Companies, Inc.) during ; the average between the Informa Economics estimate and the average analyst estimate reported by the Dow Jones Newswire survey for ; the average of the Dow Jones survey for ; and the average of the Bloomberg survey during Private expectations for wheat Crop Production reports are based on the average analysts forecasts reported by Knight- Ridder/Dow Jones Newswire. We use daily price data of new crop futures contracts. Corn, soybeans, and soft red winter wheat futures contracts are traded at the Chicago Board of Trade (CBOT), and hard red spring wheat contracts are traded at the Minneapolis Grain Exchange (MGEX). Table 2 lists the specific contract maturities used in each calendar month for these new crop futures price series. 4 See Good and Irwin (2006) for further details on the pre-release analysts forecasts for corn and soybeans. 9

12 Specifically, the primary new crop futures contracts are December for corn, November for soybeans, July for winter wheat, and September for spring wheat. 5 Descriptive Statistics Market surprise reflects the additional, new information contained in the USDA reports beyond private analysts expectation. Our study defines market surprise as the percentage difference between the USDA estimate,,, and the average private analysts estimate,,, on day for report as follows: (1) 100 ln, ln,. Descriptive statistics shown in table 3 demonstrate a general lack of statistically significant bias in analysts expectations of USDA estimates for corn. The only average surprise that is marginally larger than zero is associated with September Crop Production forecasts, suggesting under-estimation of USDA forecasts by private analysts. Market surprises for soybean reports are also generally unbiased with two significant exceptions associated with Grain Stocks reports: private analysts tend to over-estimate USDA s January soybean stocks forecasts and underestimate September stocks forecasts. Little evidence of bias is found in spring wheat report expectations: private analysts appear to over-estimate Prospective Plantings estimates, but no bias is found for other reports. Winter wheat expectations exhibit the most extensive evidence of bias, particularly for market surprise in Winter Wheat Seedings (shown as Acreage in table 3), late season Crop Production (July and August) reports as well as Grain Stocks reports. Analysts tend to over-estimate USDA s Winter Wheat Seedings forecasts, underestimate Crop Production 5 The analyses of hard red winter (HRW) wheat futures contracts traded at the Kansas City Board of Trade (KCBT) are available from the authors upon request. 10

13 estimates in June and August reports, underestimate all wheat Grain Stocks in January and June reports, and overestimate Grain Stocks forecasts in September reports. The magnitude of overestimation for Winter Wheat Seedings, 3.5%, is especially large. The magnitudes of market surprises across commodities for various reports are shown in figure 1. Among Prospective Plantings reports, the largest smallest surprises are found in spring wheat and the smallest in corn. Average absolute surprises in Acreage report for spring wheat and in Winter Wheat Seedings report for winter wheat are more than twice as large as in corn and soybeans. Among Crop Production reports, the largest surprise tends to be associated with the first report (August for corn and soybeans, May for winter wheat, and July for spring wheat). While not dramatically larger, it is interesting to note that Crop Production surprises in wheat tend to be larger than in corn or soybeans. Crop Production Annual Summary surprises in corn and soybeans tend to be slightly larger than the last Crop Production report surprise and comparable in the average magnitude of around 1%. Furthermore, for corn and soybeans, the largest market surprises are associated with Grain Stocks surprises, particularly with September surprises that are several orders of magnitude greater than the surprises for any other report for these commodities. For wheat Grain Stocks reports, the largest surprises are associated with June reports and are similar in size to Winter Wheat Seedings surprises. Overall, the magnitude of surprises for the Grain Stocks report generally are the larger than the other reports, which is interesting considering this is one of the least studied USDA reports in the literature. Correlations in market surprises across commodities are presented in table 4 and demonstrate that surprises are mostly independent between corn and wheat (except for Acreage and January Grain Stocks) as well as between soybean and wheat reports (except for September Grain Stocks) but not across corn and soybean reports. These correlations likely highlight the 11

14 fact that corn and soybeans are close substitutes commonly grown in the same production areas. Negative correlations in surprises for Prospective Plantings and Acreage reports reflect the fact that corn and soybeans are competing for the same acreage and an over-statement of acreage in one commodity will be associated with an under-statement of the other. Positive correlation in surprises in August, October, November and the Annual Summary Crop Production reports is likely caused by the fact that corn and soybeans are affected by the same growing conditions that affect yields and production. Correlations are also examined among surprises for other reports released around the same time, such as March Grain Stocks and Prospective Plantings, June Grain Stocks and Acreage, and January Grain Stocks and Crop Production Annual Summary. In most cases, these surprises are not correlated with two exceptions: correlation between spring wheat and winter wheat July Crop Production surprises, and correlation between March corn Grain Stocks and soybean Prospective Plantings. Figure 2 shows daily close-to-close returns for new crop futures contracts characterized by consistent volatility with some apparent spikes. These graphs also demonstrate the non-linear dynamics in futures returns, which make traditional ordinary least squares (OLS) regressions unsuitable for their analysis (e.g., Yang and Brorsen 1993). Therefore, GARCH models are used in our price reaction analysis to account for time-varying volatility and dynamic patterns in the distribution of daily futures returns. Analysis of Changes in Market Surprise One of the implications of the impact of big data on USDA information would be a change in the size of market surprise, with smaller surprise indicating better informed private markets (a substitute relationship between public information and big data) and larger surprise suggesting a 12

15 greater role of private forecasts (complementary relationship). We use 2006 as a breakpoint for this analysis due to a sharp growth of private investment in agricultural technology that started that year, as discussed in the introduction. Mean differences in absolute market surprises between pre-2006 and post-2006 sub-periods are evaluated using a t-test with Welch adjustment 6 and the results are shown in table 5. Furthermore, to better demonstrate the impact of big data, we compare our findings across two groups of reports based on their likelihood of being affected by big data. We argue that the Acreage, Winter Wheat Seedings, Crop Production, and Crop Production Annual Summary reports are most affected by big data because they contain information most directly observable by sensors and satellite imagery. In contrast, Prospective Plantings and Grain Stocks reports comprise the group of reports that are less likely to be affected as they contain information that is not observable using big data tools. Figures 3 and 4 show differences in the size of market surprises between the two sub-periods for the reports most likely to be affected by big data and the reports less likely to be affected, respectively. Our findings shown in both table 5 and figure 3 demonstrate that market surprises associated with Acreage and Winter Wheat Seedings reports remain stable over time. The mean differences are statistically insignificant, with slight increases observed in corn, soybeans, and winter wheat and a small decrease observed in spring wheat. Changes in Crop Production Annual Summary surprises also do not show a consistent pattern with an insignificant increase observed in corn and an insignificant decline detected in soybeans. Crop Production report surprises also appear relatively stable over time with one exception. The absolute surprise for soybeans in November Crop Production report increased from 0.65% over to 1.17% during In all other cases, the differences are not statistically significant with slight 6 Welch s (1947) adjustment of the t-test relaxes the assumption of equal variances across samples and can be used to compare means of sub-periods with unequal variances. 13

16 decreases detected for the overall (pooled report months) results in corn and winter wheat and slight increases in soybeans and spring wheat. Table 5 and figure 4 show that Prospective Plantings reports remain stable over time, with statistically insignificant slight decreases observed in corn and insignificant slight increases detected in soybeans and spring wheat. The biggest changes in market surprises are observed for Grain Stocks reports. The average absolute surprise associated with pooled Grain Stocks reports in corn increased from 2.08% over to 3.92% during , with the largest increase detected for September Grain Stocks that grew from 2.61% to 8.50% between these subperiods. A similar pattern is detected in soybeans with pooled Grain Stocks surprise rising, although insignificantly, from 3.97% to 5.53%. The mean differences in Grain Stocks surprises for wheat are not statistically significant between the pre-2006 and post 2006 sub-periods, with slight increases detected in pooled Grain Stocks surprises. In order to test the sensitivity of our results to the choice of 2006 as the big data breakpoint, we conducted tests on the surprise data separated on either side of 2006 by both one year (2005 and 2007) and by two years (2004 and 2008). While the results for winter wheat stay the same, there are a few exceptions for the other crops. Specifically, the changes in corn surprises in June Grain Stocks reports become statistically significant at the 10% level with breakpoints after 2006; the changes in spring wheat surprises in August Crop Production is significant with breakpoints 2003 and 2004 and in pooled Grain Stocks with breakpoint Overall, the results convey the same qualitative conclusions; i.e., generally stable absolute market surprises over time. 14

17 Analysis of Changes in Price Reaction Following Colling and Irwin (1990) and Garcia et al. (1997), we use price reaction tests based on the efficient market hypothesis which asserts that market prices reflect all publicly available information and instantly adjust to incorporate new information entering the market (Fama 1970). Accordingly, prices will respond only to the unanticipated news component of the new information. The main premise of these price reaction tests is that the USDA reports have value for the market if futures prices change in response to the unanticipated information contained in the reports, whereas they have no value if futures prices do not change. To investigate possible changes in the price reactions over time and to allow for timevarying volatility observed in futures prices, we estimate the following GARCH system for each commodity separately: (2),, exp, where 100 ln ln is the percentage change in futures contract s settlement price from day 1 to day, representing close-to-close returns, is the regression error term, and is a standard normal random variable. The variable is defined before as the percentage difference between the USDA s, and private analysts estimates, for report on day, and takes its corresponding value on the exact release day for reports released before or during trading hours or on the following trading day for reports released after trading hours, and the 15

18 value of zero on non-report days. 7 Specifically, the release times of the considered USDA reports during our study period are 3:00pm EST (January 1984-April 1994), 8:30am EST (May 1994-December 2012), and 12:00pm EST (January January 2017). While close-to-close returns may not reflect the full price reaction to USDA news (Isengildina, Irwin, and Good 2006), their use is required in this study due to changes in both trading times and the report release times during our sample period. The lagged values of the dependent variable in the conditional mean equation are included to account for serial correlation in the daily futures price changes. 8 The dummy variable, taking the value of one on the release days of any of the USDA reports considered and zero otherwise, is included in the conditional variance equation to allow futures price volatility to increase on announcement days as shown in the literature (e.g., Sumner and Mueller 1989; Isengildina, Irwin, and Good 2006; Isengildina-Massa et al. 2008; Adjemian 2012; Karali 2012). Following Adjemian (2012), equation (2) is also estimated by incorporating interaction terms between the surprise variables,, and an indicator variable for low beginning stocks to use ratio calculated from the figures in WASDE reports to control for inventory effects, with the premise that market reaction would be larger during low-inventory regimes. However, the estimation results revealed a potential issue of multicollinearity due to correlation between those interaction terms and surprise variables. Therefore, price reaction to market surprises is not conditioned on low-inventory regimes in the models presented. Further, equation (2) incorporates market surprises in Crop Production and Grain Stocks in different report months 7 Any bias in surprises detected by the earlier tests should be absorbed in the intercept of the mean equation of the GARCH model. 8 The length of autoregressive lags,, is set to five for all commodities in all estimations. Their parameter estimates,, are not included in the tables for brevity, but available from the authors upon request. 16

19 separately rather than pooled as the joint equality of surprise coefficients across report months is rejected in most cases. Estimation results for futures price reaction regressions are presented in tables 6-9. Each table shows two sets of results: regression (I) includes the entire sample period; regression (II) separates pre-2006 and post-2006 surprise data into separate variables, which allows us to estimate two sets of coefficients and conduct a Wald test to assess their equality. The discussion of the results focuses on differences in these coefficients. For corn, all surprise coefficients in table 6 are statistically significant with one exception and inversely related to futures price changes as expected. Since these reports convey information on supply, positive (negative) surprises indicate higher (lower) than expected supply, and result in a decrease (increase) in prices. Further, the report-day dummy variable in the variance equation is statistically significant and positive in both sets of results, consistent with the earlier studies that showed volatility spikes around announcement days. Comparing the magnitudes of the surprise coefficients across sub-periods shows an increasing market response to all reports but Crop Production. Thus, the impact of Prospective Plantings reports intensified from 0.53 percentage point decrease in response to a 1% positive surprise during to 0.87 percentage point reaction over Similarly, the impact of Acreage reports increased in magnitude from to percentage points after A statistically significant increase in market reaction is observed for each quarterly Grain Stocks reports across the two sub-periods. The impact of Crop Production Annual Summary and November Crop Production reports also increased, but these changes are not statistically significant. Decreases in the price reaction to August, September, and October Crop Production reports are not significant as well. 17

20 Our findings for soybeans shown in table 7 are more mixed with statistically significant decreases in price reaction to October and November Crop Production reports that declined in magnitude from to percentage points and from to percentage points after 2006, respectively. Soybean market reaction to Crop Production Annual Summary reports exploded from to percentage points after The decrease in soybean market reaction to Acreage reports and the increase in reaction to Prospective Plantings reports are not statistically significant. On the other hand, while changes in the soybean market reaction to Grain Stocks reports are significant in all cases, the directions are mixed, with an increase in reaction observed for March and June reports, a decrease in reaction detected for September reports, and a switch from negative to an unexpected positive response observed for January reports. The results for winter wheat shown in table 8 stand in contrast to previous sets of results because of the general lack of statistical significance of surprise coefficients. It appears that the winter wheat futures market only reacted to January, March, and September Grain Stocks reports and this reaction decreased for January and September reports, and increased for March reports, although these changes are not statistically significant. Among Crop Production reports, only June surprises lead to a significant price reaction of percentage points after The Crop Production results are puzzling in light of the large relative magnitude of surprises for winter wheat reports discussed earlier. This may reflect less news in the computed Crop Production surprises or significant measurement error in the expectations for these reports. Our findings for spring wheat shown in table 9 are based on a smaller set of data (starting in 1994, rather than 1984) but demonstrate a statistically significant change in market reaction to Prospective Plantings reports that increased in magnitude from to percentage points 18

21 after On the other hand, market reaction to August Crop Production reports decreased in magnitude from to percentage points post The decrease in market reaction to Acreage report is not statistically significant according to the Wald test. January and September Grain Stocks surprises result in significant price reactions in the spring wheat futures market during both periods. While market reaction to January Grain Stocks reports is statistically unchanged across sub-periods, reaction to September reports decreased in magnitude from to percentage points after Figures 5 and 6 summarize the price reaction findings for the two groups of USDA reports based on their likelihood of being affected by the big data. Figure 5 shows that the results are mixed among the reports that are more likely to be affected by big data (Acreage, Winter Wheat Seedings, Crop Production, and Crop Production Annual Summary). While the only significant change in market reaction to Acreage reports across two sub-periods is observed in corn where it was increasing, additional evidence from the other markets points to a slightly decreasing reaction. Our findings for Crop Production Annual Summary reports are more consistent with a significant increase in price reaction in the soybean market mimicked by an insignificant increase in corn. The impact of Crop Production reports shown in figure 5 is decreasing across crop markets with two exceptions: November reports in corn and September reports in soybeans (however, these increases are not statistically significant). Even though there seems to be positive price reactions in the winter wheat futures market to surprises in July and August Crop Production and in Winter Wheat Seedings post-2006, these estimates are not statistically significant. Our findings for the reports that are less likely to be affected by big data (Prospective Plantings and Grain Stocks) shown in figure 6 are more consistent. Market reaction to 19

22 Prospective Plantings reports is significantly larger post-2006 in corn and spring wheat markets. At the same time, most of the evidence points to an increase in the news impact of Grain Stocks reports, particularly those released in March and June. This set of results helps provide a context for the findings shown in figure 5. We found an increase in the impact of USDA reports that are less likely to be affected by big data, as well as Crop Production Annual Summary reports. Acreage reports appear on the border with increased impact found in corn markets, but some weakening (though not statistically significant) observed in soybean and wheat markets. However, the impact of Crop Production reports, while still strong and statistically significant appears to be decreasing. As with the surprise tests in the previous section, we test the sensitivity of price reaction results to the choice of 2006 to represent breakpoint in the big data evolution. We estimated the price reaction models with surprise data separated on either side of 2006 by both one year (2005 and 2007) and by two years (2004 and 2008). While most results stay similar, the decreases in price reactions to August Crop Production surprises for corn and soybeans and to March Grain Stocks surprises for winter and spring wheat become significant with breakpoints after In general, though, the results convey the same qualitative conclusions: the lack of a decrease in price reactions during the more recent time periods. Summary and Conclusions USDA acreage, production, and stock reports have served as the benchmark in crop markets for almost a century. However, access to information and technology has dramatically evolved in recent years, with the surge in satellite imagery, remote sensing data, and computing power commonly referred to as big data. A number of new firms have arisen in the last decade that use big data tools to provide competing crop production forecasts. The growing presence of 20

23 these big data firms and continuously shrinking federal budgets has spawned a major controversy with respect to USDA s crop forecasting program. Some argue that these public programs can be downsized or eliminated because big data sources offer faster turnaround and higher-spatialresolution data on crop distribution, maturation status, and yield predictions than the USDA. The implication is that USDA crop reports no longer provide important news to the markets. This study investigates whether USDA reports still provide important news to crop markets in the big data era using the most comprehensive set of market expectations in the literature to date. This allows the news impact of each report to be estimated within a properly identified system of all major reports that avoids problems related to report clustering. The data set includes expectations for all major USDA crop reports (Prospective Plantings, Acreage, Winter Wheat Seedings, Crop Production, Crop Production Annual Summary, and Grain Stocks) for corn, soybeans, winter wheat, and spring wheat over the 1984/85 through 2016/17 marketing years. One of the implications of the impact of big data is a change in the news component of USDA crop reports, with a smaller news component indicating better informed private markets. We measure news based on the surprise component of each USDA report, which is simply the difference between the USDA information and the average of private analysts expectations. We test for differences in the absolute size of surprises pre- and post-2006 and generally find no significant changes for all reports except Grain Stocks, where the magnitude of surprises was substantially larger in the later period. The stable or increasing size of market surprises suggests that big data has not reduced the news component of important USDA crop reports. A second implication of the impact of big data is a decline in the size of price reactions to the news in USDA crop reports. Our price reaction tests show that exactly the opposite tends to 21

24 be happening. That is, the price impact of the news component of most USDA reports has tended to increase since 2006 rather than decrease. We do find that the price reaction to the news in USDA Crop Production reports, while still strong and statistically significant, has declined slightly since This finding is consistent with conclusions of Tack et al. (2017) who examined whether non-random samples (such as ones that may be available from the precision agriculture technology) may be successfully used for predicting national corn yield. They concluded that large volumes of data can, to some degree, overcome forecasting bias caused by non-representative samples. This can be achieved through benchmarking of nonrepresentative data to objective estimates, such as those issued by USDA, which allows calculating correction factors to remove bias if it is consistent over time. In sum, this suggests that private big data companies may be able to generate competitive crop production forecasts using non-random data. The slight decrease in price reaction to Crop Production reports stands in contrast to the increasing reaction to the news in Prospective Plantings, Acreage, Crop Production Annual Summary, and Grain Stocks reports. In this context, it is important to keep in mind that Crop Production reports are based on the information collected from Agricultural Yield (between approximately 10,000 and 23,000 samples) and Objective Yield (between approximately 960 and 1,920 samples) surveys. On the other hand, the other reports reflect information obtained in the quarterly Crops Acreage, Production, and Stocks (APS) survey that includes over 80,000 samples. Thus, it appears that while the big data sources are becoming more competitive with Agricultural Yield and Objective Yield data, they are still not able to match the precision of the larger quarterly APS survey. 22

25 Overall, the notion that USDA information is being replaced by big data and thus is becoming redundant is not supported by the findings of this study. Even though there appears to be an impact of big data on Crop Production reports, it is relatively small and does not display a consistent pattern across commodities and report months. Thus, while the USDA s performance in collecting and disseminating high quality information can be improved by modernizing technology and more efficiently utilizing big data tools, the USDA reports based on NASS surveys continue to provide valuable information to commodity markets in the big data era. 23

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