Part 1 - Summary The paper written by Cornaggia regards a very important topic in today’s corporate finance which is risk management. In the previous literature economists were trying to find out whether risk management affects firm value and the evidence was ambiguous. Some studies showed that companies which were using derivative instruments as a form of risk management had higher value than other companies. On the other hand there were studies which found just a small effect of hedging on a firm's value. Based on these it was hard to decide what was the real relation between productivity which leads to higher value of the firm and the way how the firm manages risk. Cornaggia’s ambition was to investigate this relation and answer the question of whether risk management might play an important role in productivity of the firm. He was also interested in the different impact on the performance based on the indemnity criterion of the policy. Here he compared individual-performance-based with group-performance-based policies. For studying the effects of risk management, data from the US agricultural industry were used for a variety of reasons including accessibility of the data, the size of the agriculture industry and details captured in the data. In the data set there are more than 175 000 county-year-crop observations between years 1989 and 2008. Main method which was employed to answer the research question was tripledifference around 14 different events. New crop insurance was introduced by the Risk Management Agency of the US. Counties were classified by the access to the insurance policy to treatment and control groups. In the same time the counties were also divided by the change in insurance takeup over the period of time (1989-2008) to high-takeup counties (for above median changes) and low-takeup-counties (for below median changes). These steps divided the observations to four categories: ● treated crops in high-takeup counties ● treated crops in low-takeup counties ● control crops in high-takeup counties ● control crops in low-takeup counties Main finding of the paper is that risk management is associated with higher productivity which leads to higher value of the firm. Results also show that positive correlation between insurance-takeup and productivity is stronger for group-performancebased policies than individual-performance-based policies. Part 2 - Interpretation The article by J. Cornaggia, through an analysis of the U.S. agricultural industry, contributes highly to the literature on the value of firms managing risk. The analysis's findings suggest that risk management could benefit owners who, in contrast to welldiversified shareholders of public corporations, are subject to large amounts of unsystematic risk. Since the majority of farms in the United States are small enterprises run by individuals, families, or partnerships this interpretation could be generalized to small firms. The paper uses insurance-based risk management measures. Insurance contracts are only useful for hedging; they cannot be used for speculation, in contrast to derivative instruments, which make up the risk management measures in the majority of studies that have been conducted. Consequently, the conclusions drawn from this work are especially insightful since choosing to buy insurance signifies choosing to manage risk exclusively. The primary discovery of the paper is the correlation between risk management and increased productivity, indicating that risk management may impact firm value through this intermediary channel. Regression results contained in table 8 indicate that most of the introduced risk management policies had positive effects on productivity. Productivity gains are greatest following the implementation of a new policy among county crops that purchase it, as compared to crops that are not eligible for it. However there were some exceptions in the outcomes of this regression. For instance, the regression highlighting the introduction of Income Protection policies to barley crops shows that yields were lower for barley producers who bought more Income Protection policies than for those who bought average amounts of these policies. In a similar manner, the regression analysis showcasing the introduction of Revenue Assurance policies to wheat crops, shows that wheat producers who acquired more policies yielded less than those who acquired average amounts of policies. These outcomes could be the result of a moral hazard effect. According to Table 8, increases in productivity associated with purchasing crop insurance range in size from 1.5% (when Group Risk Plan policies were introduced to peanut crops in 1999) to 3.7% (when Revenue Assurance policies were introduced to cotton and rice crops in 2003) of one standard deviation of yield. In Table 9 the author translated the coefficients from the triple-difference regressions tests into revenue terms. Using this process, a variety of potential financial impacts of acquiring crop insurance can be derived. Using rice as an example, the author calculated that a one standard deviation increase in the purchase of insurance is associated with an increase in county level revenue ranging from $197,424 to$208,705. According to these calculations, purchasing crop insurance for specific county-crop-years has a major economic impact. The author examines both total revenue and average revenue per acre generated by county-crops in 2008 with below-and above-median Total liability per farm. The overall effects are substantial: adding up all sample crops the author estimates that county-crops with below-median Total liability per farm generate revenues of $10.3 billion. In contrast, county-crops with above-median Total liability per farm generate revenues of $81.9 billion. Although not as striking, average revenues per acre for seven of the nine sample crops are also higher among county-crops with above-median total liability per farm. Overall, these results provide clear evidence that the economic effects associated with purchasing crop insurance are significant. Author of the paper also examined how risk management interacts with measures of access to finance in regressions explaining productivity. Regardless of the measure, productivity is high when producers manage risk, and particularly so in the presence of good access to finance. These findings imply that risk management raises productivity because it makes it possible for producers to obtain funding for investments that boost productivity. That is, risk management and access to finance appear to be complements. This outcome is in line with research by Haushalter(2000), who finds that oil and gas producers manage risk to reduce financial contracting costs and work by Campello et al.(2011), who find that hedgers make larger investments because they receive favorable financing terms. A substantial problem discussed in the paper is the variation in productivity across policy types depending on indemnity criterion. Results of multivariate triple-difference regressions show that there is a greater positive correlation between group-performancebased policies, whom indemnities payments are related to county-average performance, and productivity, than individual-performance-based policies, whom indemnities payments are related to individual producer’s performance, and productivity. This relationship may reflect the moral hazard effect inherent in insurance decisions. Producers can safeguard themselves against losses resulting from high-risk activities that raise the probability and severity of crop losses by procuring crop insurance. For instance, moral hazard incentives cause insured agricultural producers to use fewer chemical inputs, according to research by Goodwin and Smith (1996). Conversely, individual farmers should have difficulty manipulating crop insurance policies that have indemnities based on the county average performance of comparable crops. By effectively reducing the effects of moral hazard, these policies may be able to prevent issues that could erode the positive relationship between risk management and productivity. Part 3 - Critical reflection a) The relationship between risk management and productivity is crucial to the agriculture industry. This is because agriculture can be subject to numerous factors, which are unpredictable. These factors include weather conditions, market conditions, pests, etc. Understanding the relationship between insurance and crop yield (which is the dependent variable in the study), can have a huge impact on the industry. Risk hedging can make farmers more certain about their financial future, making them able to make more long-term investments. b) The paper is well defined because J. Cornaggia used data from the US. government and National Agricultural Statistics Service of the USDA. This data is detailed and comprehensive, which gave the study a robust setting. In addition to this, the risk management strategy examined in the paper is crop insurance, giving the reader a clearer understanding of the effects, than if it was used a more complicated/speculative method of risk hedging. c) Lastly, the paper is interesting because of the fact that the findings can be applied to other industries. The fact that insurance affects productivity seems like a statement that can be said about any industry, because it makes people take more risks. The paper clearly states what it aims to test to the very last detail. In addition to the introduction, there are two chapters called Dependent variables and Risk management measures with the goal of leaving the reader with as little confusion as possible. The hypothesis is narrow and original and aims to test a problem which is solvable with the available data. It is also in alignment with the methods used. The methodology, which is quantitative, is the suitable approach to this hypothesis. This is because the data consists of over 175,000 observations. The use of difference-in-differences (DiD) is intended to isolate the casual effect of the independent variable. The use of difference-in-difference-indifferences (DDD) makes it possible to capture the effects between time, treatment group and control group. Validity: The findings of the study are likely due to the investigated variables. Throughout the paper, many measures were taken to prevent interference by other related factors. An example of an effect that has been controlled for is farm size (because crop yields could correlate with farm size). This study can also be generalized and used in other settings than agriculture, like discussed earlier. Regarding reliability, it is uncertain if the study could be replicated for the fact that most farmers likely have insurance today. The underlying statistical assumption of this paper is that there is a correlation between production output and risk management in the agriculture industry. Given this assumption, it seems appropriate that they ran a regression analysis with crop yield as a measure of productivity, looking at the introductions of different insurance types. The author's use of tables provides clarity for the reader. The tables are set up in a textbook manner, which makes it easy to decipher the results from the analysis. Like mentioned earlier regarding validity, there were several control variables. These include farm size, education, population density and meteorological conditions. In conclusion, the tests of the hypotheses are correct, clear, and complete. The findings of the study have been through several robustness-measures, though some unreported (but mentioned). The paper does not have a robustness analysis. Robustness has been evaluated through the paper. The lack of a robustness analysis is a weak-point in this paper. The paper does not contain recommendations for future research or practice, which is a downside in the paper. It does however contain valuable information for future research and practice. One could conduct a similar study on another industry to see the effects of risk management on production output, and the information provided in this paper can be used by companies to understand the effects of risk management.
