While this may appear counter intuitive at first, two insights help to explain this result. First, blueberry farmers have already adapted to hot temperatures: pickers generally finish picking around 3:00 p.m. and avoid the hottest parts of the day. This means that I do not observe how workers would perform under temperatures above 100–105 degrees.And looking at the temperature response function in figure 1.14, it is easy to imagine due to its overall inverse-parabolic shape that there would be even larger productivity losses at such high temperatures. Second, blueberry picking is a highly dextrous job requiring workers to use their bare hands to pick only ripe berries from the bush. At cooler temperatures, berry pickers lose finger dexterity and find it uncomfortable to maintain the same levels of productivity as at warmer temperatures.Indeed, Enander and Hygge note that manual dexterity can start to be impaired at temperatures in the range of 12–15 degrees Celsius .In agriculture – as in many other industries – labor is a primary input, pay is tied to worker output, and firms cannot completely control important workplace environmental conditions like temperature. How do agricultural workers respond to changes in their piece rate wage? How does temperature affect this wage responsiveness? And what are the net effects of temperature on agricultural labor productivity? This paper addresses these questions in the context of California blueberry farmers and provides the following answers: on average, blueberry pickers’ productivity is very inelastic with respect to wages; workers seem to face binding constraints on effort at moderate to hot temperatures,commercial racks but display an elastic response to wages at cool temperatures; and both very hot and cool temperatures have negative direct effects on berry pickers’ productivity. This paper makes a meaningful contribution to the empirical understanding of how wages affect worker productivity. While the basic theoretical prediction is straightforward , previous studies have struggled to test this hypothesis directly.
Doing so is difficult since, in settings where piece rates vary over time, their variation is endogenous to worker productivity. To isolate wages’ effect on productivity, I instrument for blueberry pickers’ piece rate wage using the market price for California blueberries. I find that on average, pickers’ productivity is very inelastic with respect to piece rate wages, and I can reject even modest elasticities of up to 0.7. However, this finding hides important heterogeneity in the relationship across different temperatures. In particular, only at cool temperatures do higher wages have a statistically significant and positive effect on worker productivity. This result suggests that at most temperatures and wages, blueberry pickers face some sort of binding constraint on effort and cannot be incentivized to increase their productivity. This research raises questions for future research both about firms’ responses to changing temperatures and their choice of an optimal payment scheme. For instance, it would be helpful to analyze a different industry to see how temperature response functions differ across tasks. It would also be interesting to analyze, both theoretically and empirically, a varying wage scheme tied directly to exogenous factors such as market prices, resource abundance, and environmental conditions. With the advent of cheap, sophisticated monitoring technology, more and more industries are candidates for adopting piece rates, raising the importance for economists to deepen our understanding of the forces at work in such wage schemes. 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. 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%, 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,greenhouse rolling benches 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. 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. In addition to the local mergers that were precipitated by national church changes, a number of congregational mergers resulted from other plausibly random events. Several congregations initiated mergers after natural disasters destroyed congregation buildings ; St. Mark’s Lutheran Church. Other congregations merged due to difficulties hiring full-time clergy.