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Social networks and technology adoption: Evidence from church mergers in the U.S. Midwest
Abstract
How do social networks impact technology adoption? Exploiting a natural experiment in the mid‐20th century U.S. Upper Midwest, we find that social network expansions, in the form of mergers between congregations of the American Lutheran Church, led to increased rates of agricultural technology adoption among farmers. In counties that experienced a merger, the number of farms using chemical fertilizer increased by over 5%, and the total fertilized acreage increased by over 10% relative to counties without a merger. These effects are consistent with increased information sharing between farmers due to congregational mergers.
... The planting culture nurtured by farming practices and the local culture based on blood and relationships not only molds informal social norms but also affects individual behaviors through personal predilections [24]. Farmers are positioned in an explicit social environment; thus, regarding technology implementation, they often gather technology information and regulate their income prospects through mutual experience exchange and sharing and then alter their production behavior intent, leading to steady production behavior [25,26]. In fact, some studies have rigorously examined the influence of social networks on the uptake of ecofriendly technologies by farmers, including practices like conservation tillage [27] and crop cultivation techniques [28]. ...
... Social networks display features of short pathways and high density in information dissemination, which are pivotal in the rapid and extensive spread of sustainable farming practices [26]. Thus, farmers' decision-making about GPB tends to exhibit homogeneity and consistency through social networks. ...
The green production practices of farmers are essential for sustainable agricultural development. However, studies have mostly overlooked the social factors affecting farmers’ decisions regarding green production behaviors (GPB). Furthermore, the pathways and mechanisms through which social networks modify these behaviors have not been fully validated. Therefore, by examining 1203 farmers from China’s main grain-producing regions, this study aims to empirically investigate both the direct and indirect impacts of social networks on farmers’ GPB, thereby furthering relevant research. First, family social networks in rural areas markedly enhanced farmer engagement in GPB. After assessing the endogeneity issues associated with farmers’ self-selection behaviors using propensity score matching, this effect was found to substantially persist. Analysis of the indirect impact revealed that social networks primarily facilitated farmers’ adoption of green production through channels such as information acquisition, interactive learning, and service support. Second, heterogeneity analyses based on generational differences and crop types demonstrated a distinct, promotional impact of social networks on both “middle-generation” and “older-generation” farmers. Moreover, a comparison between grain crop farmers and cash crop farmers determined the more substantial influence of social networks on encouraging grain crop farmer GPB. Overall, this study emphasizes that rural China’s social networks, especially clan-based ones, can successfully nurture agricultural sustainability by accelerating the propagation of green technologies while offering suitable environments for elderly farmers to “learn by observing” and “learn by doing”. Relevant departments should pay attention to and make full use of farmers’ social network relations in the process of promoting farmers’ adoption of green production behavior and further promoting the green development of agriculture.
... However, it may be argued that FBO membership is endogenous to the adoption of SAPs. The social network literature [22,23] , in particular, argues that social networks, implied in FBO membership, can be influenced by sex, age, and education. However, these variables can also influence the adoption of SAPs [23] . ...
The emerging marketization of agricultural factors allows farmers to expand agricultural production by renting in farmland from land rental market or purchasing services through labor market. Farmers’ transactions in the two markets can stimulate the agricultural transformation in developing countries, but may increase their financial burden in agriculture. Based on the primary data collected from banana farmers in China, this study attempts to clarify farmers categories based on their different choices regarding participation in land rental and labor markets for agricultural production, analyze the determinants of farmers’ simultaneous participation in the two markets, and explore heterogenous effects of farmers’ market participation on agricultural economic performances using the multinomial endogenous switching treatment regression model. The results show that farmer’s tie to retailers is highlighted as an important driving force behind farmers’ simultaneous participation in the two markets, while their participation choices are also varying due to different household head characteristics and family resource endowments. Farmers who participated in the two markets significantly reduce the cost of material input in agricultural production, but the total production cost of participants is higher than that of non-participants due to the high rent of land resource and cost of hired labor for production services. Interestingly, farmers’ participation in land rental market increases the yield of their farms, while participation in labor market has a negative impact on the yield. Farmers’ participation in both land rental and labor markets can significantly increase farm net income by nearly 67 %. In contrast, participation in labor market decreased farm net income by 22 %, whilst participation in land rental market increased farm net income by 186 %, though without statistical significance. The findings underscore the important role of land rental markets in boosting farm economic performance. Policymakers can facilitate land transfer among smallholder farmers in the land rental market and promote agricultural technologies to substitute labor force so as to reduce the adverse effect of labor market, thereby improving agricultural economic performances in developing regions.
We study the causal impact of religiosity through a randomized evaluation of an evangelical Protestant Christian values and theology education program delivered to thousands of ultrapoor Filipino households. Six months after the program ended, treated households have higher religiosity and income; no statistically significant differences in total labor supply, consumption, food security, or life satisfaction; and lower perceived relative economic status. Exploratory analysis suggests that the income treatment effect may operate through increasing grit. Thirty months after the program ended, significant differences in the intensity of religiosity disappear, but those in the treatment group are less likely to be Catholic and more likely to be Protestant, and there is some mixed evidence that their consumption and perceived relative economic status are higher.
Inadequate learning is an oft-cited friction impeding the adoption of improved agricultural technology in the developing world. We provide experimental evidence that farmer field days — an approach used throughout the world where farmers meet, learn about new technology, and observe its performance — alleviate learning frictions and increase adoption of an improved seed by 40 percent. Further analysis demonstrates that these field days are both cost effective and more impactful for poorer farmers. In contrast, we find no evidence that selecting the first adopters of new technology via participatory village meetings has any effect on future adoption.
Within the theoretical framework of social capital, we explore how different religious traditions influence small business activity in U.S. counties. We motivate the analysis by emphasizing the ways in which religious organizations may facilitate social capital, a key factor in business formation and performance. We find that communities with a large concentration of religious congregations have a correspondingly higher level of small business activity. We also find important differences across religious traditions, suggesting that religion should not be treated as a monolithic dimension of social capital. In addition, by exploring different traditions, beliefs, and norms, proxied by religion, finer insights into social capital and community economic development can be gained.
Despite the growing attention to technology adoption in the economics literature, knowledge gaps remain regarding why some valuable technologies are rapidly adopted, while others are not. This paper contributes to our understanding of agricultural technology adoption by showing that a focus on yield gains may, in some contexts, be misguided. We study a technology in Ethiopia that has no impact on yields, but that has nonetheless been widely adopted. Using three waves of panel data, we estimate a correlated random coefficient model and calculate the returns to improved chickpea in terms of yields, costs, and profits. We find that farmers' comparative advantage does not play a significant role in their adoption decisions and hypothesize that this is due to the overall high economic returns to adoption, despite the limited yield impacts of the technology. Our results suggest economic measures of returns may be more relevant than increases in yields in explaining technology adoption decisions. JEL codes: C33, O13, Q16.
OPEN ACCESS LINK: https://academic.oup.com/ajae/advance-article/doi/10.1093/ajae/aay050/5057582
We examine an intervention randomized at the village level in which female farmers invited to a single training session were randomly paired with farmers whom they did not know and encouraged to share new agricultural information throughout the growing season for a recently adopted cash crop. We show that the intervention significantly increased the productivity of all farmers except of those who were already in the highest quintile of productivity, and that there were significant spillovers in productivity to male farmers.
There is an emerging consensus among macro-economists that differences in technology across countries account for the major differences in per-capita GDP and the wages of workers with similar skills across countries. Accounting for differences in technology levels across countries thus can go a long way towards understanding global inequality. One mechanism by which poorer countries can catch up with richer countries is through technological diffusion, the adoption by low-income countries of the advanced technologies produced in high-income countries. In this survey, we examine recent micro studies that focus on understanding the adoption process. If technological diffusion is a major channel by which poor countries can develop, it must be the case that technology adoption is incomplete or the inputs associated with the technologies are under-utilized in poor, or slow-growing economies. Thus, obtaining a better understanding of the constraints on adoption is useful in understanding a major component of growth.
Household-level panel data from a nationally representative sample of rural Indian households describing the adoption and profitability of high-yielding seed varieties (HYVs) associated with the Green Revolution are used to test the implications of a model incorporating learning by doing and learning spillovers. The estimates indicate that imperfect knowledge about the management of the new seeds was a significant barrier to adoption; this barrier diminished as farmer experience with the new technologies increased; own experience and neighbors' experience with HYVs significantly increased HYV profitability; and farmers do not fully incorporate the village returns to learning in making adoption decisions. Copyright 1995 by University of Chicago Press.
First, most adoption research has thus far viewed the adoption decision in dichotomous terms (adoption or nonadoption). But for many types of innovations, the interesting question may be related to the intensity of use. Second, empirical research should recognize that in many cases several innovations which have various degrees of complementarity are introduced simultaneously. It follows that adoption decisions for various innovations are interrelated. Third, many adoption models consider a rather simple economic model where the industry is a price taker in perfect competition with homogeneous inputs. However, price support schemes, food taxes and subsidies, and input and output quotas affect technological choices and diffusion processes. Fourth, the conflicting conclusions sometimes indicated by studies from different regions or countries may in many cases be the result of differing social, cultural, and institutional environments. Finally, differential adoption rates of Green Revolution technology by different socioeconomic groups are often found to disappear once the process is sufficiently advanced. But the early adopters can accumulate more wealth and use the differential in the subjective value of land to acquire more land from the laggards. The acquisition of new wealth enables further adoption and thus affects the dynamic pattern of aggregate adoption. Thus, special attention to changes in landholding patterns and wealth accumulation (as well as tenancy arrangements) is warranted. -from Authors
This paper investigates the role of social learning in the diffusion of a new agricultural technology in Ghana. We use unique data on farmers' communication patterns to define each individual's information neighborhood, the set of others from whom he might learn. Our empirical strategy is to test whether farmers adjust their inputs to align with those of their information neighbors who were surprisingly successful in previous periods. We present evidence that farmers adopt surprisingly successful neighbors' practices, conditional on many potentially confounding factors including common growing conditions, credit arrangements, clan membership, and religion. The relationship of these input adjustments to experience further supports their interpretation as resulting from social learning. In addition, we apply our methods to input choices for another crop with known technology and they correctly indicate an absence of social learning effects.
Lutheran churches in the United States have included multiple ethnic cultures since the colonial era and continue to wrestle with increasing internal variety as one component of their identity. By combining the concerns of social history with an awareness for theological themes, this volume explores the history of this family of Lutheran churches and traces the development from the colonial era through the formation of the Evangelical Lutheran Church in America in 1988. An introduction details the origins of Lutheranism in the European Reformation and the practices significant to the group's life in the United States. Organized chronologically, subsequent chapters follow the churches' maturation as they form institutions, provide themselves with leaders, and expand their membership and geographic range. Attention is given throughout to the contributions of the laity and women within the context of the Lutherans' continued individual and corporate effort to be both authentically Lutheran and genuinely American.
Offering a rich portrayal of the Lutherans' lives and their churches, the social historical approach of this study brings the Lutheran people to the foreground. The dynamic relationship between pietist, orthodox, and critical expressions of the tradition has remained among Lutherans even though they have divided themselves by several factors including ethnicity and confessional stance. Of interest to scholars and researchers of Lutheran history and religion in America, this engaging, multifaceted work balances narrative history with brief biographical essays. A chronological listing of important dates in the development of the Lutheran church is especially helpful.
This book consolidates, refines, advances and grounds recent scholarship that challenges familiar platitudes about family farming and rural life in the United States. Its approach yields a depth of information about farming culture not usually found in the literature on rural America. The book takes the reader on a cultural tour of a cherished American institution and landscape: midwestern farm families and their farms. With attention to detail and knowledge borne of first-hand study over many years, the author reveals the pervasive imprint of ethnicity. The book represents one of those rare studies that enrich our social vision and understanding in extraordinary ways. It contributes to the study of agriculture and culture, and its cross-disciplinary approach will engage scholars in many areas. For historians, it is an illustration that different behaviors between American and immigrant farmers, planted over a century ago in the Middle West, have endured to the present.
Can targeting information to network-central farmers induce more adoption of a new agricultural technology? By combining social network data and a field experiment in 200 villages in Malawi, we find that targeting central farmers is important to spur the diffusion process. We also provide evidence of one explanation for why centrality matters: a diffusion process governed by complex contagion. Our results are consistent with a model in which many farmers need to learn from multiple people before they adopt themselves. This means that without proper targeting of information, the diffusion process can stall and technology adoption remains perpetually low. (JEL O13, O18, O33, Q12, Q16)
Low adoption of agricultural technologies holds large productivity consequences for developing countries. Many countries hire agricultural extension agents to communicate with farmers about new technologies, even though a large academic literature has established that information from social networks is a key determinant of product adoption. We incorporate social learning in extension policy using a large-scale field experiment in which we communicate to farmers using different members of social networks. We show that communicator own adoption and effort are susceptible to small performance incentives, and the social identity of the communicator influences others’ learning and adoption. Farmers appear most convinced by communicators who share a group identity with them, or who face comparable agricultural conditions. Exploring the incentives for injection points in social networks to experiment with and communicate about new technologies can take the influential social learning literature in a more policy-relevant direction.
This paper shows the causal relationship between mutual religious association and the formation of social ties. We analyze dyadic relationships and show that joint attendance at a religious institution (RI) increases the probability of sharing information with and trusting a peer. We use a novel spatial instrumental variable strategy that combines insights from homestead inheritance institutions with triangular distances between peers and RI locations within villages in Kenya. We find that shared attendance at a RI increases the likelihood of receiving advice from a peer by 30 percentage points, demonstrating the strong impact of “weak ties” formed through social spaces.
Adoption of agricultural innovations has been an important factor affecting the welfare of farmers, the productivity of agriculture and the economics of the food sector. This paper reviews the literature on technology adoption in agriculture with a focus on the role of uncertainty and learning. It examines the factors affecting adoption benefits for farmers and their linkages with the innovation process. It also discusses the welfare implications of innovation and adoption for farmers and consumers.
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in the references that are cited.
Expected final online publication date for the Annual Review of Economics Volume 11 is August 2, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
In empirical work in economics it is common to report standard errors that account for clustering of units. Typically, the motivation given for the clustering adjustments is that unobserved components in outcomes for units within clusters are correlated. However, because correlation may occur across more than one dimension, this motivation makes it difficult to justify why researchers use clustering in some dimensions, such as geographic, but not others, such as age cohorts or gender. It also makes it difficult to explain why one should not cluster with data from a randomized experiment. In this paper, we argue that clustering is in essence a design problem, either a sampling design or an experimental design issue. It is a sampling design issue if sampling follows a two stage process where in the first stage, a subset of clusters were sampled randomly from a population of clusters, while in the second stage, units were sampled randomly from the sampled clusters. In this case the clustering adjustment is justified by the fact that there are clusters in the population that we do not see in the sample. Clustering is an experimental design issue if the assignment is correlated within the clusters. We take the view that this second perspective best fits the typical setting in economics where clustering adjustments are used. This perspective allows us to shed new light on three questions: (i) when should one adjust the standard errors for clustering, (ii) when is the conventional adjustment for clustering appropriate, and (iii) when does the conventional adjustment of the standard errors matter.
This paper examines the existence of social learning in agriculture in Ethiopia. To be specific, we use a “random matching within sample” technique to collect data on social networks and to elicit details of the relationships and information exchange between network members. We find that shared kinship or membership in certain groups, informal forms of mutual insurance, and having frequent meetings with network members are all associated with a higher probability of forming an information link with a network member. Furthermore, we find evidence for a statistically significant and positive relationship between networks and the adoption of row-planting as well as yields for both male and female networks. However, the evidence for an inverse U-shaped relationship of social learning, that is, between the number of adopters in the network and the adoption of row-planting, is strongest for female networks. Our results, thus, suggest that extension services and other programs that promote agricultural innovations and seek yield improvement can benefit from social networks but that their success depends on identifying the “right” networks, such as those of female household members in the case of row-planting.
Using newly collected data on association density in 229 towns and cities in interwar Germany, we show that denser social networks were associated with faster entry into the Nazi Party. The effect is large: one standard deviation higher association density is associated with at least 15 percent faster Nazi Party entry. Party membership, in turn, predicts electoral success. Social networks thus aided the rise of the Nazis that destroyed Germany’s first democracy. The effects of social capital depended on the political context: in federal states with more stable governments, higher association density was not correlated with faster Nazi Party entry.
Agricultural technologies typically spread as farmers learn about profitability through social networks. This process can be nuanced, however, when net returns for some farmers may not be positive. We investigate how social learning influences demand for a resource-conserving technology in eastern Uttar Pradesh, India. We identify potential adopters through an experimental auction and randomly select a subset to adopt. We exploit this variation in adoption across networks to estimate network effects on demand for the technology one year later using a second auction. Technology benefits vary, and network effects are completely conditional on benefits. Having a benefiting adopter in one’s network increased demand by over 50 percent, whereas having a non-benefiting adopter had no effect. These effects are strong enough to bring average demand in line with expected benefits. For many farmers, however, demand remains below the market price, suggesting that network effects will lead to increased—but not rapid widespread—adoption.
Church attendance, as reported on three Survey Research Center studies, has been analyzed by sex, race, age, number of children, life cycle, education, occupation, and family income within groups designated as Protestant, Catholic, Jewish, Baptist, and Methodist. Analysis of the Christian groups reveals: (1) women, both in and out of the labor force, attend church more frequently than men; (2) Negroes attend church somewhat more often than whites; (3) the higher the education and occupation levels, the greater the rate of church attendance; (4) no associations between frequency of church attendance and age, number of children, or family income; (5) increased regularity of church attendance by Protestants with children 5 years old and over. The Jewish group shows the effect of its traditional Orthodox pattern in having a greater frequency of synagogue attendance for men than women and for the older Jewish age groups in contrast to the younger age groups. There are no significant associations between church attendance and education, occupation, or income within the Jewish group.
The article examines the effects of a training program, which emphasized the use of social networks and social capital to encourage learning and adoption of a relatively new cash crop, cotton, to female heads of households. The impact of the social network based program is then compared to the impact of a concurrently run standard extension training program of social networks on future labor outcomes. Randomization of the SNP at the village level allows researchers to test the effects of social capital on females' productive outcomes for a new technology without statistical bias. The estimated network effects will not be diluted by potential spillovers of the SNP, because individuals in the treated and control groups are situated in separate villages. In order to capture the effect of a social network intervention, randomization occurred at the village level as researchers would expect externalities from both programs, SNP and TR, between the treated and untreated within a village.
The increased adoption of fertilizer and improved seeds are two key aspects to raising the level of land productivity in Ethiopian agriculture. However, the adoption and diffusion of such technologies has been slow. We use data from Ethiopia between 1999-2009 to examine the role of learning from extension agents versus learning from neighbors for both improved seeds and fertilizer adoption. We combine farmers' spatial networks with panel data to identify these influences, and find that while the initial impact of extension agents was high, the effect wore off after some time, in contrast to learning from neighbors.
As economists endeavor to build better models of human behavior, they cannot ignore that humans are fundamentally a social species with interaction patterns that shape their behaviors. People's opinions, which products they buy, whether they invest in education, become criminals, and so forth, are all influenced by friends and acquaintances. Ultimately, the full network of relationships—how dense it is, whether some groups are segregated, who sits in central positions—affects how information spreads and how people behave. Increased availability of data coupled with increased computing power allows us to analyze networks in economic settings in ways not previously possible. In this paper, I describe some of the ways in which networks are helping economists to model and understand behavior. I begin with an example that demonstrates the sorts of things that researchers can miss if they do not account for network patterns of interaction. Next I discuss a taxonomy of network properties and how they impact behaviors. Finally, I discuss the problem of developing tractable models of network formation.
There is a large and growing literature on peer effects in economics. In the current article, we focus on a Manski-type linear-in-means model that has proved to be popular in empirical work. We critically examine some aspects of the statistical model that may be restrictive in empirical analyses. Specifically, we focus on three aspects. First, we examine the endogeneity of the network or peer groups. Second, we investigate simultaneously alternative definitions of links and the possibility of peer effects arising through multiple networks. Third, we highlight the representation of the traditional linear-in-means model as an autoregressive model, and contrast it with an alternative moving-average model, where the correlation between unconnected individuals who are indirectly connected is limited. Using data on friendship networks from the Add Health dataset, we illustrate the empirical relevance of these ideas.
The investment decisions of small-scale farmers in developing countries are conditioned by their financial environment. Binding
credit market constraints and incomplete insurance can limit investment in activities with high expected profits. We conducted
several experiments in northern Ghana in which farmers were randomly assigned to receive cash grants, grants of or opportunities
to purchase rainfall index insurance, or a combination of the two. Demand for index insurance is strong, and insurance leads
to significantly larger agricultural investment and riskier production choices in agriculture. The binding constraint to farmer
investment is uninsured risk: when provided with insurance against the primary catastrophic risk they face, farmers are able
to find resources to increase expenditure on their farms. Demand for insurance in subsequent years is strongly increasing
with the farmer’s own receipt of insurance payouts, with the receipt of payouts by others in the farmer’s social network and
with recent poor rain in the village. Both investment patterns and the demand for index insurance are consistent with the
presence of important basis risk associated with the index insurance, imperfect trust that promised payouts will be delivered
and overweighting recent events.
To study the impact of the choice of injection points in the diffusion of a new product in a society, we developed a model
of word-of-mouth diffusion and then applied it to data on social networks and participation in a newly available microfinance
loan program in 43 Indian villages. Our model allows us to distinguish information passing among neighbors from direct influence
of neighbors’ participation decisions, as well as information passing by participants versus nonparticipants. The model estimates
suggest that participants are seven times as likely to pass information compared to informed nonparticipants, but information
passed by nonparticipants still accounts for roughly one-third of eventual participation. An informed household is not more
likely to participate if its informed friends participate. We then propose two new measures of how effective a given household
would be as an injection point. We show that the centrality of the injection points according to these measures constitutes
a strong and significant predictor of eventual village-level participation.
Neolithic man probably used fertilizers, but the first fertilizer produced by chemical processes was ordinary superphosphate, made early in the 19th century by treating bones with sulfuric acid. Coprolites and phosphate rock soon replaced bones as the P source. The K fertilizer industry started in Germany in 1861. In North America the K industry started during World War I and expanded with development of the New Mexico deposits in 1931 and the Saskatchewan deposits in 1958. Modern K fertilizers are more the product of physical than of chemical processes. The first synthetic N fertilizer was calcium nitrate, made in 1903 from nitric acid produced by the electric arc process. The availability of synthetic ammonia after 1913 led to many new N fertilizers, but physical quality was poor. In 1933 TVA was formed with a national responsibility to increase the efficiency of fertilizer manufacture and use. More than 75% of the fertilizer produced in the United States is made with processes developed by TVA.
Major fertilizers and fertilizer intermediates introduced by TVA include ammonium nitrate, high‐analysis phosphates, diammonium phosphate, nitric phosphates, ammonium polyphosphate, urea ammonium phosphates, 11‐16‐0 and other liquid base solutions, superphosphoric acid, wet‐process superphosphoric acid, suspensions, granular urea, and S‐coated urea. These have had major impact upon the production of mixed fertilizers, bulk blending, and the fluid fertilizer industry. Future fertilizers not only must be technologically feasible, economical, and agronomically suitable—as have been past fertilizers—but also must meet various air and water pollution standards during production and have reduced total energy requirements.
In a dynamic environment with imperfect information, education contributes to production as an “allocative effect,” arising
from enhanced ability to acquire and process information efficiently, as well as a “worker effect.” This study focuses on
a single dimension of allocative ability: adjustment of Midwestern U. S. farmers to the changing optimum quantity of nitrogen
fertilizer in corn production. Results support the hypothesis that rate of adjustment can be explained by economic variables;
the rate is positively related to education of farmers, availability of information (agricultural extension), and scale incentive
to be informed (acres of corn). Also, education and extension are substitute sources of allocative efficiency.
A unique data-set from Indonesia is analysed to understand what individuals know about the income distribution in their village to test theories such as Jackson and Rogers (2007) that link information aggregation in networks to the structure of the network. The observed patterns are consistent with a basic diffusion model: more central individuals are better informed and individuals are able to better evaluate the poverty status of those to whom they are more socially proximate. [BREAD Working Paper No. 354]. URL:[http://ipl.econ.duke.edu/bread/papers/working/354.pdf].
This is a study of factors responsible for the wide cross-sectional differences in the past and current rates of use of hybrid seed corn in the United States. Logistic growth functions are fitted to the data by states and crop reporting districts, reducing differences among areas to differences in estimates of the three parameters of the logistic: origins, slopes, and ceilings. The lag in the development of adaptable hybrids for particular areas and the lag in the entry of seed producers into these areas (differences in origins) are explained on the basis of varying profitability of entry, "profitability" being a function of market density, and innovation and marketing cost. Differences in the long-run equilibrium use of hybrid corn (ceilings) and in the rates of approach to that equilibrium (slopes) are explained, at least in part, by differences in the profitability of the shift from open pollinated to hybrid varieties in different parts of the country. The results are summarized and the conclusion is drawn that the process of innovation, the process of adapting and distributing a particular invention to different markets and the rate at which it is accepted by entrepreneurs are amenable to economic analysis.
We use recruitment into a laboratory experiment in Kolkata, India to analyze how job networks select individuals for employment opportunities. We present evidence that indi-viduals face a tradeoff between choosing the most qualified individual for the job and the individual who is ideal from the perspective of their social network. The experiment allows randomly selected subjects to refer members of their social networks to subsequent rounds of the experiment and varies the incentive schemes offered to these participants. We find that when faced with performance pay, individuals are more likely to refer co-workers and less likely to refer family members. High ability participants who are offered performance pay recruit referrals who perform significantly better on a cognitive ability task and also prove to be more reliable as evidenced by their choices in the trust game and performance on an effort task.
The least productive agents in an economy can be vital in generating growth by spurring technology diffusion. We develop an analytically tractable model where growth is created as a positive externality from risk taking by firms at the bottom of the productivity distribution imitating more productive firms. Heterogeneous firms choose to produce or pay a cost and search within the economy to upgrade their technology. Sustained growth comes from the feedback between the endogenously determined distribution of productivity, as evolved by past search decisions, and an optimal forward looking search policy. The growth rate depends on characteristics of the productivity distribution, with a thicker tailed distribution leading to more growth.
Information flows are weaker in a heterogeneous population when the performance of a new technology is sensitive to unobserved individual characteristics, preventing individuals from learning from neighbors' experiences. This characterization of social learning is tested with wheat and rice data from the Indian Green Revolution. The rice-growing regions display greater heterogeneity in growing conditions and the new rice varieties were also sensitive to unobserved farm characteristics. Wheat growers respond strongly to neighbors' experiences, as expected, while rice growers do not. Rice growers also appear to experiment more on their own land to compensate for their lack of social information.
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its "effect" as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an "effect" significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a "pre"-and "post"-period and explicitly takes into account the effective sample size works well even for small numbers of states.
This article investigates whether public investments that led to improvements in road quality and increased access to agricultural extension services led to faster consumption growth and lower rates of poverty in rural Ethiopia. Estimating an Instrumental Variables model using Generalized Methods of Moments and controlling for household fixed effects, we find evidence of positive impacts with meaningful magnitudes. Receiving at least one extension visit reduces headcount poverty by 9.8 percentage points and increases consumption growth by 7.1 percentage points. Access to all-weather roads reduces poverty by 6.9 percentage points and increases consumption growth by 16.3 percentage points. These results are robust to changes in model specification and estimation methods
This paper presents the first systematic attempt by economists to analyze the determinants of individuals' participation in religious activities. A multiperiod utility-maximizing model of household behavior is developed which includes among its implications the shape of a house-hold's life-cycle religious-participation profile and the division of religious participation between husband and wife. The theory is empirically tested using statewide church-membership data and survey data on individuals' frequency of church attendance. The paper concludes by discussing several extensions of the model which lead to additional potentially testable hypotheses.
While worldwide investment in agricultural extension is quite substantial, there has been a relatively small body of thorough economic research of extension impact until fairly recently. Section I discusses methodological problems of measuring extension impact. Section II summarizes studies that measured the relationship between extension programs and knowledge, and adoption of particular technologies. Section III reviews studies that have sought to estimate the relationship between extension programs, farm productivity, input demand, and farm profits. Section IV summarizes the computed returns to extension reported in the studies reviewed earlier, and the final section presents conclusions. While there is convincing evidence that extension efforts can have a significant effect on output, there is limited evidence regarding the profitability of investment in extension from a social welfare perspective. -from Authors
I. Introduction, 59.—II. The model, 62.—III. Empirical analysis, 66.—IV. Further implications, 74.—V. Conclusions, 77.—Appendix:
data and sources, 77.
Between 1950 and 1990, the aggregate population of central cities in the United States declined by 17 percent despite population
growth of 72 percent in metropolitan areas as a whole. This paper assesses the extent to which the construction of new limited
access highways has contributed to central city population decline. Using planned portions of the interstate highway system
as a source of exogenous variation, empirical estimates indicate that one new highway passing through a central city reduces
its population by about 18 percent. Estimates imply that aggregate central city population would have grown by about 8 percent
had the interstate highway system not been built.
This paper attempts to identify job networks among Mexican migrants in the U. S. labor market. The empirical analysis uses
data on migration patterns and labor market outcomes, based on a sample of individuals belonging to multiple origin-communities
in Mexico, over a long period of time. Each community's network is measured by the proportion of the sampled individuals who
are located at the destination (the United States) in any year. We verify that the same individual is more likely to be employed
and to hold a higher paying nonagricultural job when his network is exogenously larger, by including individual fixed effects
in the employment and occupation regressions and by using rainfall in the origin-community as an instrument for the size of
the network at the destination.
The relationships between religion and economics are both complex and controversial. In this paper is explored one method for organizing those relationships. Four categories are examined which help identify possible options: economics separate from religion economics; in service of religion; religion in service of economics; and religion in union with economics. The paper begins with a definition of what is included under the headings of religion and economics. Next, each of the four categories is described and discussed. Conclusions close the paper.
The slow diffusion of new technology in the agricultural sector of developing countries has long puzzled development economists. While most of the current empirical research on technology adoption focuses on credit constraints and learning spillovers, this paper examines the role of individual risk attitudes in the decision to adopt a new form of agricultural biotechnology in China. I conducted a survey and a field experiment to elicit the risk preferences of 320 Chinese farmers, who faced the decision of whether to adopt genetically modified Bt cotton a decade ago. Bt cotton is more effective in pest prevention and thus requires less pesticides than traditional cotton. In my analysis, I expand the measurement of risk preferences beyond expected utility theory to incorporate prospect theory parameters such as loss aversion and nonlinear probability weighting. Using the parameters elicited from the experiment, I find that farmers who are more risk averse or more loss averse adopt Bt cotton later. Farmers who overweight small probabilities adopt Bt cotton earlier.
This paper examines the reflection problem that arises when a researcher observing the distribution of behaviour in a population
tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group.
It is found that inference is not possible unless the researcher has prior information specifying the compisition of reference
groups. If this information is available, the prospects for inference depend critically on the population relationship between
the variables defining reference groups and those directly affecting outcomes. Inference is difficult to implossible if these
variables are functionally dependent or are statistically independent. The prospects are better if the variables defining
reference groups and those directly affecting outcomes are moderately related in the population.
We present evidence on how farmers' decisions to adopt a new crop relate to the adoption choices of their network of family and friends. We find the relationship to be inverse-U shaped, suggesting social effects are positive when there are few adopters in the network, and negative when there are many. We also find the adoption decisions of farmers who have better information about the new crop are less sensitive to the adoption choices of others. Finally, we find that adoption decisions are more correlated within family and friends than religion-based networks, and uncorrelated among individuals of different religions. Copyright 2006 The Author(s). Journal compilation Royal Economic Society 2006.