Pastors, trained in centralized seminary programs, were a scarce and expensive resource, occasionally serving multiple congregations at once. Pastors in these roles frequently pressured their congregations to consolidate resources and merge into a single entity ; Grace Lutheran Church.Importantly, these congregational mergers occurred for reasons that were unrelated to agricultural fertilizer use. Indeed, we show later in Table 2.1 that population trends are uncorrelated with congregational mergers.We combine our data on merging congregations with data from the United States Department of Agriculture ’s Census of Agriculture. In the 1950s and 1960s, the Census was designed to have full coverage of every farm in the United States. Censuses were taken every five years, and data gathering for these Censuses took place in the fall. Enumerators visited every dwelling, and administered the Census to any household engaged in agriculture. After collection, the Census underwent a multi-stage quality control process. The final dataset is available at the county level. Wherever possible, we use a digitized version of the dataset made available by the University of Michigan’s Inter-university Consortium for Political and Social Research. Several variables were unavailable in the digitized data; we hand-coded these from PDFs made available from the USDA’s own archive.We use the 1954, 1959, and 1964 waves of the Census. Using earlier waves is impossible: the two earlier Censuses, taken in 1950 and 1944, did not include county-level information on fertilizer use.
We are also unable to use later waves of data: after the 1964 wave,greenhouse bench top data was only collected for farms selling over $2,500 worth of goods per year, and there is no way to reconcile the two sampling frames. We use the 1954 Census to test for differential trends among counties with and without congregational mergers. We perform our main analysis using the 1959 and 1964 Censues. We combine the Census of Agriculture data from these years with our congregational data to create a balanced panel of 197 counties. The Census of Agriculture contains data on our main outcomes of interest: the number of farms using fertilizer, acres fertilized, tons of commercial fertilizer used, and corn acres fertilized, tons of dry and liquid fertilizer used on corn. It also contains information on the use of agricultural lime, a complement to nitrogen fertilizer. The Census also includes data on other agricultural practices, such as strip cropping and irrigation; other types of land use, such as orchards; and capital-intensive farm durables, including vehicles. Table 2.1 presents summary statistics from the 1954 Census for counties that did and did not experience congregational mergers between 1959 and 1964. Treatment and control counties are statistically indistinguishable on most observable characteristics prior to the mergers.The major exception is in harvested acreage: treatment counties harvested approximately 81,650 more acres than control counties in 1954. This difference is statistically significant at the one percent level. Treatment counties also harvested 14,200 more acres of corn relative to the control group, a difference which is statistically significant at the ten percent level.
Overall, these summary statistics reveal that treatment and control counties are relatively similar to one another prior to the congregational mergers that took place between 1959 and 1964. These statistics support the notion that mergers were not driven by the agricultural sector or other potentially endogenous factors.In our empirical context, this suggests that mergers should lead to increased adoption of commercial fertilizer. We test for adoption along the extensive margin by estimating Equation 2.1 with the number of farms using fertilizer as the outcome of interest. We also test for effects of mergers on the number of acres fertilized and tons of fertilizer applied, which capture both intensive and extensive margin effects.Though not directly predicted by our model, we expect the use of agricultural lime to increase with congregational mergers as well. Nitrogen, the primary component of commercial fertilizer, adds acidity to soil, which can impede crop growth. Agricultural lime helps to reverse this process, making it a natural complement to fertilizer use. We test for these effects by by estimating Equation 2.1 using the number of farms using lime, the number of acres limed, and tons of lime used as outcomes. We also test for the effects of congregational mergers on fertilizer use on corn, which benefits greatly from the use of fertilizer, and is one of the region’s major commercial crops.We expect congregational mergers to increase total fertilizer use on corn. The Census of Agriculture distinguishes between dry and liquid fertilizer used on corn. We expect to find stronger effects on dry fertilizer, since the major technological advances of the time period occurred in dry, rather than liquid, fertilizers ; Young and Hargett. Proposition 3 implies that congregational mergers will not affect adoption of technologies that all farmers are already informed about.
In the Upper Midwest in the 1950s, we can test this theory using three well-established technologies: strip cropping, irrigation, and orchards. Strip cropping, in which farmers alternate crop types in tight rows to prevent soil erosion, is an established practice in the United States. It was introduced to Minnesota in the early 1930s, and was in widespread use in the region by 1940. Irrigation was another well-known technology: the most common irrigation system in use in this area was the center pivot system, which had spread to farmers by the late 1950s ; Granger and Kelly. Finally, using land for orchards, vineyards, groves, and nut trees was a well-established practice in the Midwest by the 1950s ; Burrows ; Smith . Our model predicts that, as farmers were informed about them prior to our study period, strip cropping, irrigation, and orchard lands should not respond to congregational mergers. We believe that this assumption is reasonable: as discussed in Section 2.2, exogenous factors, including national-level church branch mergers and building fires, caused the congregational mergers we study. While we fundamentally cannot empirically test our identifying assumption, we provide evidence in support of it in two ways. First, in Table 2.2, we estimate Equation 2.1 using data from 1954 and 1959, for four of our main outcomes of interest: the number of farms using fertilizer, acres fertilized, the number of farms using lime, and acres limed. Our “post” period, 1959, is before our congregational mergers, so we should expect to find no statistically significant effects of mergers on our outcomes of interest. In all cases, we fail to reject the null hypothesis that counties with mergers are trending similarly to counties without mergers, prior to treatment. Figure 2.5 demonstrates this graphically, showing that, from 1959 to 1959, counties that experienced mergers were on a similar path to counties that did not. It was only after 1959, when our mergers occurred, that the groups of counties began to diverge. We begin by testing Proposition 2. We first estimate the effects of congregational mergers on fertilizer use on the extensive margin, using the number of farms using fertilizer as the dependent variable. We estimate five specifications, each with a different set of controls. Table 2.3 reports the results. Column is the most parsimonious specification, including only the interaction term of interest , a 1964 dummy, and a “merger county” dummy. In column , we replace the “merger county” dummy with county fixed effects. Column adds four weather controls: temperature, precipitation, heating degree days, and cooling degree days.In order to control for time-varying unobservables, we also include state-by-year fixed effects in column . This is our preferred specification. In column ,cannabis dry rack we include both state-by-year fixed effects and weather controls, though given our small sample, we expect this specification to be under powered. The results in Table 2.3 are consistent with Proposition 2: as expected, counties that experienced congregational mergers see higher rates of fertilizer adoption than those that did not. These effects are economically meaningful, and appear to be relatively consistent across specifications.Using our preferred specification, displayed in Column , we find that congregational mergers caused 40.07 additional farms per county to begin using fertilizer, a large increase of 7.3 percent over the mean in the control group. Our results are statistically significant at the 10 percent level, which, given our relatively small sample size, is encouraging. We present the results from our main specification graphically in Figure 2.6.
The dashed grey line is the kernel density of the change in the number of farms using fertilizer between 1959 and 1964 for counties that did not experience congregational mergers. The solid blue line is the same change for counties that did experience mergers. The changes in the control distribution are centered around zero. In contrast, the treated distribution lies markedly to the right of the control distribution. This shift appears to be present throughout the distribution. In order to ensure that these effects are not spurious, and instead result from congregational mergers, we implement a randomization inference procedure. We randomly reassign exactly 37 counties to treatment 10,000 times. For each run, we estimate every specification in Table 2.3, and store the estimated βˆ. We display the results of this procedure in Figure 2.7. The gray histograms show coefficients from these 10,000 random draws, and the blue lines denote the treatment effect using the real assignment vector. In each case, the real effect lies in the far right portion of the distribution – and in our preferred specification, lies above the 96th percentile – which suggests that our results are not an artifact of random chance. We next estimate Equation 2.1, our preferred specification, using acres fertilized and tonnage of fertilizer applied as dependent variables. We also test for effects on the number of farms using agricultural lime, acres limed, and the tons of lime applied. Since lime is a complement to fertilizer, we expect to find positive effects of congregational mergers on lime use. Table 2.4 presents these results. Table 2.4 shows that, as expected, congregational mergers increased acres fertilized. Counties with mergers fertilized, on average, 8,370.5 acres more than counties without mergers, a 13.6 percent increase over the control group mean, and statistically significant at the 5 percent level. We do not find a corresponding increase in the tonnage of fertilizer applied on all crops, though this is likely to be driven in part by noise involved in measuring tonnage of fertilizer used. We do find the expected positive effects of congregational mergers on the number of farms using lime: 9.2 additional farms use lime in the treatment group relative to the control group, a large increase of 21.1 percent, statistically significant at the 5 percent level. We find corresponding increases in the number of acres limed and the tons of lime used, with acreage limed increasing by nearly 24 percent; and tons of lime used increasing by close to 22 percent. These effects are statistically significant at the 5 and 10 percent level, respectively. Taken together, these results suggest that congregational mergers led to an economically meaningful and statistically significant increase in fertilizer and lime use, as predicted by our model. Next, we look at corn. In the Census of Agriculture data, there is information about tonnage of both wet and dry commercial fertilizer applied for corn, so we will be able to separate the impact on the different types of inputs. In addition, there is information on fertilized acreage. Table 2.5 displays the results. In column , the dependent variable is corn acres fertilized; in column , the dependent variable is tons of dry commercial fertilizer used; column looks at tons of liquid commercial fertilizer used, and column looks at the total tonnage of commercial fertilizer used. We expect to see most of the positive effect on dry, rather than wet, tons. The results from Table 2.5 are in line with Proposition 2, suggesting that fertilizer used on corn increases as a result of congregational mergers. Acreage fertilized increases by 5,300.54, a change of 24.2 percent. This is statistically significant at the 1 percent level. Columns , , and demonstrate that there is an increase in tonnage of fertilizer used on corn, and that this increase is driven by dry fertilizer use. We find an increase of 391.41 tons of dry fertilizer, statistically significant at the 5 percent level, which represents a 24.7 percent increase over the mean; and no statistically significant increase in the tonnage of wet fertilizer applied.