Some congregations were even conducting multiple services, each in a different language ; Murray County. Cross-branch mergers between local congregations were large shocks to churchgoers’ social networks, since the congregants were not likely to have interacted frequently prior to the merger.We acquired a unique dataset on mergers between Lutheran congregations based on the archives of the ELCA. Church archivists compiled a database of mergers between Lutheran congregations, the earliest of which occurred in 1810. These data are derived from two main sources: annual national church yearbooks, which in turn were compiled from reports congregations made to their governing bodies; and The American Lutheran Church , a record of all of the details surrounding the TALC merger. This dataset includes all mergers between congregations of the UELC, ALC, and ELC, which merged to form TALC in 1960, including mergers between congregations which no longer exist. For each congregation involved in a merger, the dataset records the state, county, local post office, location, synod , congregation name, founding date, merger date, details on which other congregations were involved in the merger, hydroponic bucket and additional historical notes. In our main analysis, we use the mergers that took place between 1959 and 1964. 

A total of 41 mergers occurred during this time period in Minnesota, North Dakota, and South Dakota. In our main study period, between 1959 and 1964, 34 counties experienced one merger, two counties experienced two mergers, and one county experienced three mergers. We consider counties that experienced at least one merger between 1959 and 1964 to be treated, and use counties that did not experience a merger during this time as controls. Figure 2.4 displays the spatial distribution of treated and control counties. The counties in blue experienced at least one merger between 1959 and 1964; the counties in white did not; and the counties in gray included a major urban area and were excluded from the sample.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, 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.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 , we include both state-by-year fixed effects and weather controls, though given our small sample, we expect this specification to be underpowered. 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 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, stackable planters 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. The total tonnage of fertilizer applied to corn increases by 484.45 tons, an 26.6 percent increase, statistically significant at the 1 percent level. The impact congregational mergers have on corn fertilizer use is not only statistically significant, but also represents an economically meaningful change. In sum, the information in Table 2.3, Table 2.4, and Table 2.5, demonstrates that congregational mergers increase fertilizer use, in keeping with Proposition 2. Next, we test Proposition 3: that congregational mergers will not affect technology adoption when all potential adopters are fully informed. Strip cropping, irrigation, and orchards are technologies for which we expect no effect. Table 2.6 demonstrates the impact congregational mergers have on the number of farms using strip cropping and the acres under strip cropping; the number of farms reporting irrigation use and the total number of irrigated acres; and the total number of acres in fruit orchards, groves, vineyards, and nut trees. Table 2.6 confirms our hypothesis: we see no statistically significant impacts of congregational mergers on strip cropping, irrigation, or orchard acreage. Furthermore, the magnitudes of the coefficients we do see are small, and in the case of irrigation, have negative signs. The absence of results in this table further supports the fact that congregational mergers are driving the changes in input uses observed above, because mergers are affecting farmers on the dimensions we would expect, but we are not seeing impacts on agriculture that should not be affected by mergers.With any regression analysis, it is important to ensure that the results are robust. We use three pieces of evidence to demonstrate that the results we present in this paper hold up to robustness checks. First, we saw impacts of congregational mergers where we expected them: on fertilizer and lime use and on corn. We did not see impacts where we expected them not to be: on strip cropping, irrigation, and orchards, as presented above. In addition, our results are robust to a variety of different land use variables provided in the census of agriculture data: the outcomes measured in number of farms do not change significantly if instead of total farms, we use commercial farms or cash-grain farms; the outcomes measured in acreage and tonnage do not change if we use acres in the county or acres harvested rather than acres in farms.