The downside of such models is that they fail to provide comparative statics that can be tested with the data I observe in this setting.A growing literature has rigorously documented the non-linear impact of temperature on everything from corn yields to cognitive performance , but has not focused specifically on how temperature affects agricultural workers. Nevertheless, several recent papers in this literature seem particularly relevant to my findings. One strand of research has investigated how temperature affects labor productivity in a variety of different industries. Adhvaryu et al. show that factory workers in India produce more output when heat-emitting conventional light bulbs are replaced LED lighting, especially on hot days. Sudarshan et al. find similar evidence that temperature reduces worker productivity in a variety of Indian manufacturing firms. Finally, Seppänen et al. show that temperature even has large effects on the productivity of office workers. Other researchers have asked broader questions about how temperature affects aggregate production or labor decisions at the county- or country-level. The growing consensus is that weather shocks – particularly exposures to extreme heat – reduce aggregate production in a wide variety of settings. For instance, Hsiang exploits natural variation in cyclones to find negative impacts of high temperatures in both agricultural and non-agricultural sectors at the country-level.
Deryugina and Hsiang and Park find similar county level effects of daily temperature in the United States, plastic pots 30 liters despite widespread adoption of air conditioning. Heal and Park document relevant findings throughout the economics literature and provide a useful theoretical link between heat’s physiological effects and aggregate economic activity. The first possibility, discussed at length in the previous paragraph, is that employees are less productive while working at high temperatures. Another possibility is that employees may choose to work fewer hours when temperatures are particularly high. In other words, there may be a labor supply response to temperature on the extensive margin. Graff Zivin and Neidell provide support for this hypothesis by analyzing data from the American Time Use Survey. They find that at high temperatures, individuals reduce the time they spend working and increase the time they spend on indoor leisure. Finally, temperature can affect even broader aspects of the labor market like aggregate demand for agricultural labor in India , or the composition of labor in urban vs. rural regions of Eastern Africa . While this paper examines how a particularly salient environmental condition, temperature, affects labor productivity, previous research has shown that other environmental factors matter as well. Chang et al. , for instance, find that outdoor air pollution negatively affects the indoor productivity of pear packers. The same authors conduct a similar exercise using data from Chinese call-centers and find comparable results. Adhvaryu et al. find a steep pollution-productivity gradient in the context of an Indian garment factory, and Graff Zivin and Neidell find large damages from ozone in an agricultural context somewhat similar to my own.
In an older case study, Crocker and Horst, Jr. study seventeen citrus pickers in southern California and find negative effects of both high temperatures and air pollution. It is useful to think of temperature not as a single sufficient statistic to describe environmental quality, but rather as one condition among many that is relevant for understanding labor productivity. This paper makes several important contributions to the literature discussed above. First, because I observe berry-pickers’ productivity multiple times during a single day, the variation I observe in both productivity and temperature is much more temporally precise than in many previous studies. Additionally, since I use temperature observations that are taken hourly, and sometimes more frequently, I do not need to interpolate temperature over time. Second, I study a setting where both very hot and cool temperatures have negative effects on productivity, highlighting the particularities of different production processes when it comes to temperature impacts. Third, and most importantly, I look at how how environmental conditions and incentive schemes interact. Technology adoption is an essential component of economic growth ; Foster and Rosenzweig ; Perla and Tonetti. In 2015 alone, the World Bank committed over eight billion dollars to projects encouraging people to adopt new technologies. Over the past decade, economists and policymakers have begun to recognize that social networks can facilitate technology adoption. In particular, information barriers hinder the take-up of new technologies; social networks can spread information and reduce these frictions.
Understanding the ways in which these networks impact the take-up of new technologies is relevant for policymakers across the developed and developing world. Economists face a fundamental challenge when trying to study social networks, since these networks are endogenously formed: people choose their own friends. Though there is a broad theoretical literature on social networks1 , endogenous network formation poses a significant challenge for empirical research ; Goldsmith-Pinkham and Imbens ; Jackson ; Choi et al.. In response to these difficulties, recent work in economics has relied on randomized experiments that act on or through existing social networks in field settings. Other work uses detailed data on network structures to study how information moves within existing networks.These papers represent a major development in our understanding of how information is transmitted through social networks. What they are unable to do, however, is analyze how naturally-arising changes in these networks affect economic activity. A small literature exists that attempts to address this issue by estimating the effects of plausibly exogenous shocks to existing social networks on economic outcomes. The majority of these papers in this focus on how social networks affect labor market outcomes , Edin et al. , and Beaman. Though none of these papers studies technology adoption, there is a rich literature in economics studying the diffusion and take-up of new technologies, particularly in agricultural settings. Our work is most closely related to several recent papers which study the role of social networks in agricultural technology adoption. Foster and Rosenzweig and Munshi study the network determinants of technology adoption during India’s Green Revolution. Conley and Udry study farmer learning about fertilizer use and pineapple in Ghana. Bandiera and Rasul find that family and religious communities matter for technology adoption in Mozambique. Vasilaky and Vasilaky and Leonard randomly connect women with agricultural extension agents, and find that this dramatically improves productivity. In this paper, we are able to directly estimate the causal effects of increases in network size and composition on technology adoption in agriculture. We take advantage of a unique natural experiment to isolate exogenous shocks to social networks. In particular, these shocks take the form of mergers between rural congregations of the American Lutheran Church between 1959 and 1964 in the Upper Midwest of the United States. These mergers were caused by national-level church mergers, church building fires, and pastoral employment constraints, all of which were beyond the control of individual congregations. Using county-level data from the American Census of Agriculture, we employ a difference-in-differences approach to study how these mergers affected farmers’ adoption of inorganic nitrogen fertilizer – at the time, a relatively new yield-improving technology. We demonstrate that congregational mergers had an economically meaningful effect on technology adoption among farmers. The number of farms using nitrogen fertilizer increased by over 7%, and the total fertilized acreage in these counties increased by over 13%, round plastic pots in counties with merging congregations, relative to those without. These increases were most pronounced on the region’s major commercial crop: counties with mergers used 26% more fertilizer on corn. We perform a randomization inference test and a placebo exercise to demonstrate that our results are caused by congregational mergers and not other factors. Our results are consistent with a model where information sharing is the primary mech-anism through which social networks facilitate technology adoption. Mergers only affected use of fertilizer, a new technology, and its complements. In contrast, congregational mergers did not lead to increases in the use of existing technologies. We find no effects of mergers on durable goods with high fixed costs, suggesting that mergers did not ease capital constraints.
The remainder of this paper is organized as follows: Section 2.2 describes the context in more detail. Section 2.3 presents a simple model of social networks and technology adoption. Section 2.4 details our data, and Section 2.5 describes our empirical strategy. Section 2.6 reports our results. Section 2.7 provides a discussion. Section 2.8 concludes.We study the effects of social networks on the adoption of a new technology in the Upper Midwest of the United States during the 1950s and 1960s: commercial fertilizer. Between 1940 and 1970, the use of commercial fertilizer increased dramatically. Figure 2.1 displays the sharp increase in usage of chemical fertilizer for corn production in the United States. Between 1940 and 1949, average annual consumption of commercial fertilizer in the United States was 13.6 million tons; between 1950 and 1959, this number rose to 22.3 million tons; and between 1960 and 1969, use had increased further to 32.4 million tons . This increase in usage had tangible results: between 1950 and 1975, agricultural productivity in the United States increased faster than ever before or since. In 1950, the average American farmer supplied the materials to feed and clothe 14 people; by 1960, he was sustaining 26. While today, over 95 percent of corn acres are fertilized, and fertilizer is well-known to increase yields, during the 1950s and 1960s, farmers were far from being fully informed about optimal fertilizer usage and its benefits. Communication between farmers in different social circles was infrequent ; Amato and Amato ; Cotter and Jackson, but information sharing within farmers’ social networks was a major means of spreading professional knowledge. Religion was an important driver of farmers’ social connections ; Azzi and Ehrenberg ; Swierenga ; Cotter and Jackson. The Upper Midwest had a high rate of religious adherence: according the Association of Religion Data Archives, in 1952, 64%, 62%, and 58% of the population of Minnesota, North Dakota, and South Dakota, respectively, were religious. We focus on these three states, because they contained large Lutheran populations: 51%, 48%, and 33% of religious Minnesotans, North Dakotans, and South Dakotans belonged to a Lutheran church. Figure 2.2 demonstrates the prevalence of religion in the United States in the 1950s, as well as the concentration of Lutheranism in Minnesota, North Dakota, and South Dakota.In the 1950s and 1960s, national Lutheran church bodies underwent significant institutional consolidation. At an April 1960 meeting in Minneapolis, Minnesota, three of the largest national Lutheran church bodies – the American Lutheran Church , the United Evangelical Lutheran Church , and the Evangelical Lutheran Church – voted to merge and form The American Lutheran Church . This merger officially took effect on January 1, 1961. A similar merger between the United Lutheran Church in America, the Finnish Evangelical Lutheran Church of America, the American Evangelical Lutheran Church, and the Augustana Evangelical Lutheran Church created the Lutheran Church in America in 1962. In 1963, the Lutheran Free Church , composed largely of congregations that originally opted out of the 1960 TALC merger on theological grounds, decided to join TALC as well, extending the scope of this major Lutheran branch. Figure 2.3 depicts the major mergers between Lutheran church bodies in the United States since the 1950s. For historical context, we focus primarily on TALC for two reasons. First, congregations of TALC were geographically clustered in the upper midwest whereas congregations of the LCA were more disperse throughout the country. Second, we have access to yearbooks from TALC detailing congregational-level statistics throughout the 1960s. National-level mergers, arranged by the constituent churches’ theological and institutional leadership, had far-reaching impacts. The TALC merger was reported in local newspapers across the Upper Midwest ; Dugan ; Press. National mergers forced local congregations to adopt new constitutions, bringing them into alignment with the newly-formed national church. Prior to the mergers, many towns had congregations from multiple church branches. As a result of the merger, these congregations suddenly found themselves in the same national denomination. This frequently led to mergers between local congregations that were previously impossible ; United Lutheran Church Laurel. These mergers brought previously socially disparate groups of people into contact with one another. Each of the merging national-level church bodies were linked to a different ethnic group: the ALC had German roots, the ELC had a Norwegian background, and the UELC was historically Danish. Especially in the early parts of the twentieth century, this often meant that congregations across the street from one another were holding services in different languages.