In this section of the paper, we perform a placebo test. We also investigate channels other than information through which congregational mergers might be driving fertilizer adoption, and provide evidence against these other possible explanations.It is still possible that our results are being driven by something other than a congregational merger driven information effect. Here, we explore two other possible explanations for our results. The first is the presence of agricultural extension. Agricultural extension, formally introduced in the United States by the Smith-Lever Act of 1914, plays a major role in information dissemination in agriculture. There is a large literature on the effect of agricultural extension, both in the United States and elsewhere, on agricultural productivity and technology adoption ; Huffman ; Birkhaeuser et al. ; Dercon et al.. Despite the importance of extension, we argue that it is in fact congregational mergers and not extension services that generate the results we find in this paper: because of the fixed effects strategy, in order for agricultural extension to be driving these results, we would need to see agricultural extension services changing differently over time in treatment counties than in control counties, having removed the state time trend, only over the 1959 to 1964 time period. This is potentially plausible, but seems unlikely, especially because extension funding and the number of extension agents allowed is governed by state laws, which do not change often. For example, the Minnesota statutes outlining extension were first passed in 1923, updated in 1953, and were not revised again until 1969. The law allows for “the formation of one county corporation in each county in [Minnesota]” to act as an extension agency, stackable flower pots with in most cases one extension agent and a specified budget, based on the number of townships in the county.

While county extension offices documented their activities for mandatory state reports, these reports were inconsistent across different counties and years. Also, many of the variables measured were endogenous, such as the number of phone calls received or the number of attendees at extension events. As a result, it is impossible to credibly measure the intensity and efficacy of extension efforts over our sample period. We argue in this paper that congregational mergers impact fertilizer use through information. Another plausible explanation would be that the mergers also facilitated increased access to capital. In order to provide evidence against this possibility, we estimate Equation again, this time with the number of farms with each of a variety of capital-intensive technologies as outcome variables. Table 2.8 shows the impact congregational mergers have on the number of farms with cars, trucks, tractors, bailers, and freezers. As expected, we find no statistically or economically significant effect of congregational mergers on capital-intensive inputs: the standard errors are quite wide, and the effect sizes small: the coefficient on cars, for example, is only a 0.01 percent increase relative to the control group mean, and the standard error is almost one hundred times the size of the coefficient. This suggests that congregational mergers did not substantially increase access to capital, and provides additional evidence that information is the main channel through which congregational mergers impacted technology adoption. Finally, one might worry that by only using TALC congregational mergers in our analysis, we are understating the true treatment effect. We argue above that the TALC mergers are exogenous, and, due to the heavily Lutheran populations in these regions, the mergers where we would expect to see an effect. Indeed, the congregations that are merging in these data have, on average, 492 baptized members, so seeing an additional 35 farms begin to use fertilizer is an entirely reasonable effect size. There is another major Lutheran church branch, the Lutheran Church – Missouri Synod , that was not directly involved in the TALC merger, but whose mergers could be attributed to increased discussion about merger surrounding TALC.

We collected data from Concordia Historical Institute, the LCMS seminary, on congregational mergers between LCMS churches during the sample period. There is only one merger that occurs in a non-metropolitan county during this time period, and the inclusion of said merger does not produce a statistically distinguishable result from using only the TALC mergers. Ultimately, given the range of tests that we perform, we have confidence that our results are robust and that we are correctly attributing them to the information effect of congregational mergers.Since the early 2000s, US ethanol production has exploded in response to federal policies incentivizing the production of renewable fuels. In 2005, Congress passed the Energy Policy Act introducing a Renewable Fuel Standard mandating that 2.78% of gasoline sold in the US be from renewable sources. In 2007, Congress passed the Energy Independence and Security Act setting annual renewable fuel mandates for US production with an ultimate goal of 36 billion gallons by 2022. Of these 36 billion gallons, 15 billion are to be conventional bio-fuels – corn-based ethanol in particular. The US ethanol industry has clearly responded to the Renewable Fuel Standards established in the EPAct and EISA. Between 2002 and 2014, US ethanol production has increased from just over 2 billion gallons per year to over 14 billion gallons per year . In order to produce such quantities of ethanol, the number of corn ethanol refineries in the US has increased from 62 in 2002 to 204 in 2014 . The striking increase in US corn ethanol production has raised several important questions about its unintended consequences. One strand of research has explored how increased demand for ethanol has affected land use in the US corn belt as aggregate demand for corn increases . Another strand of research has been more concerned about the environmental externalities of changing agricultural patterns, particularly focused on nitrate runoff and water pollution . In this chapter, I explore both the land use change effects and environmental effects of expanding ethanol production.

In particular, I study the geospatial effect of ethanol re- fineries’ placement on nearby land use change and use my results to estimate environmental consequences. I am specifically interested in how the location of ethanol refineries spatially affects agricultural land, and I do not attempt to identify the full general equilibrium effect of the 14 billion gallon US corn ethanol industry. Put another way, I study how the distribution of ethanol refineries differentially affects different agricultural areas net of the ethanol industry’s aggregate effect on corn prices. I find that within a population of almost 114 million acres of agricultural land in Illinois, Indiana, Iowa, and Nebraska, nearly 300,000 more acres of corn were grown in 2014 than in 2002 due merely to ethanol refinery location effects. This represents approximately 21,000 tons of nitrogen applied as fertilizer. Almost all the 300,000 acres of increased corn acreage exist within 30 miles of an ethanol refinery, suggesting that these refineries have strong local effects on land use change and nitrogen use. There is clear economic intuition for why ethanol refineries would differentially affect nearby and faraway agricultural land. When a corn-fed ethanol refinery is built, it represents a new terminal market for corn. Since refineries operate continuously, they have an inelastic demand for this input. And since transportation costs are significant for grains, one would expect an ethanol refinery to source its corn from the nearest producers. Thus, by reducing transportation costs for nearby producers , ethanol refineries essentially subsidize corn production for nearby farmers. On the margin, this subsidy incentivizes farmers to grow more corn – or grow corn more often – than they otherwise would. As corn production increases, so will nitrogen fertilizer use. Corn requires higher levels of nitrogen fertilizer than other Corn Belt crops, tower garden and particularly high levels of fertilizer when grown successively corn-after-corn. Thus, economic intuition suggests ethanol refineries would have a localized effect increasing corn production and nitrogen fertilizer use. Consequently, these refineries would also have an effect on localized nitrate runoff due to the increased nitrogen fertilizer use. Researchers have previously addressed different components of the ethanol industry’s effects on land use change and nitrate runoff. One line of research has explored whether the hypothesized local corn subsidy provided by nearby ethanol refineries actually exists. In a frequently cited paper, McNew and Griffith find that corn prices at an ethanol refinery are 12.5¢ higher than average, that the effect is slightly stronger for “upstream” refineries than for “downstream” refineries, and that price effects can be detected up to 68 miles from a refinery. However, Katchova and O’Brien both fail to find such a subsidy. Gallagher et al. highlight that locally-owned and non-locally-owned refineries have different effects on corn prices: the authors find that corn prices are increased by proximity to a non-locally-owned refinery, but not by proximity to a locally-owned refinery. Finally, Lewis finds different results in different states: ethanol refineries in Michigan and Kansas affect local corn prices, but refineries in Iowa and Indiana do not.

Fatal and Thurman use county-level data to estimate the corn acreage effect of ethanol re- fineries. They find that a typical ethanol refinery increases corn acreage in its home county by over 500 acres and has effects that can persist for up to 300 miles. Miao also uses county-level data and finds a significant effect of ethanol refineries on corn acreage, as well as a differential effect between locally-owned and non-locally-owned refineries. Turnquist et al. , in contrast to more recent studies, fail to find any significant agricultural land conversion in areas near Wisconsin ethanol refineries. Finally, Feng and Babcock explore the full general equilibrium effect of increased ethanol production and find an unambiguous increase in corn acreage. Several researchers have focused on how ethanol production affects water quality and nitrate runoff. Donner and Kucharik highlight how the aggregate impact of the EISA will likely make achieving nitrate level goals in the Mississippi impossible. Thomas et al. use hydrologic models to estimate the water quality impacts of corn production caused by increased demand due to biofuel mandates. They find significant negative results. While it is likely true that “refineries cause corn,” it is also likely true that “corn causes refineries.” Ethanol refineries are not located at random, and several researchers have explored the topic of ethanol refinery placement. A series of papers have shown, unsurprisingly, that ethanol refineries are more likely to locate near areas with large corn production, near transportation infrastructure, and not near existing ethanol refineries . This finding is important because it highlights that ethanol refinery placement cannot be treated as truly random in econometric analyses without accounting for the underlying drivers of this placement. In my analysis, I argue that field-level fixed effects appropriately account for the major determinants of refinery placement. In particular, I study how distance-to-nearest-refinery affects the probability of a field being planted to corn. Whenever a new refinery is built, its presence differentially affects fields close to it relative to fields slightly farther away.However, due to the spatial characteristics of soil quality and topology, “more-treated” and “less-treated” fields are qualitatively comparable. My project improves upon previous work by leveraging new sources of field-level land use data and exploiting a finer-scaled panel of observations than previous authors. I exploit both the Cropland Data Layer and Common Land Unit to create annual observations of field-level land use. These agricultural micro-data allow for much more nuanced econometric estimation than in previous studies. Other authors have exploited similar micro-data in agricultural research to great effect . I also highlight the locality effect of ethanol refineries rather than the general equilibrium effect, focusing on small-scale heterogeneous effects that have not been well identified in previous work. The remainder of this paper is divided into a theoretical framework , a summary of my data, an overview of my econometric methods, a discussion of my results, and a conclusion.The net increase in corn acreage of 298,718 that I find is only 0.26% of the 113,978,323 acres in my population, but it is 0.76% of all corn acreage in my population in 2014. This is a significant number given that it can be attributed to only the distance-to-nearest-refinery effect. In other words, the effect of new ethanol refineries since 2002 on lowering transportation costs can explain almost 300,000 acres of the corn grown in a subset of the fields across Illinois, Indiana, Iowa, and Nebraska. Figure 3.11 highlights that the entirety of this acreage effect is captured by fields less than 30 miles from the nearest ethanol refinery.