Starting in the early 2000s, a boom in demand for agricultural commodities displaced cattle ranching out of the most productive areas of the Pampas’ prairie. The crossbreeds between Angus and Hereford with Brahman, i.e., Brangus and Braford, have been successfully adopted across Argentina. However, little is known about the specific bulls’ traits that drive the demand for cattle genetic selection outside the Pampas. Obtaining the economic value of traits would help to identify the demand for adapting livestock production to different ecosystems while preserving the meat quality of Angus and Hereford cattle. We estimated hedonic price models using Brangus bull sales data from two cattle breeding ranches in the north of Cordoba province. Cattle ranchers prefer observed traits such as weight, coat color, and age, while genetic indicators such as Expected Progeny Differences (EPDs) have secondary importance. We argue that stronger preferences for read-coated bulls, as opposed to black-coated bulls, could be associated with the demand for reducing heat stress; the lack of association between EPDs and prices may be related to unobservable variables such as ranchers’ characteristics and that the value of genetics is implicit in the studs’ reputation.

Over the past decade, genomic testing has transformed the U.S. dairy cattle genetics market by allowing breeders to identify high-performing bulls at birth, sharply reducing generation intervals and increasing the rate of genetic improvement in milk production traits. Using data on dairy bull genetics, national and state-level milk yields, and herd characteristics from 2000 to 2020, this article documents a clear structural break around 2010, the predicted genetic potential for milk yield of dairy bulls more than doubled in its rate of growth following the adoption of genomic testing, while the number of bulls on the market tripled and the age at which bulls were first sold fell dramatically. Despite this rapid acceleration in genetic potential, realized milk yields at the national level have continued to grow at roughly the same pace as before genomic testing and have even slowed in recent years. Disaggregated evidence shows modest gains concentrated in states with smaller average herd sizes, suggesting heterogeneous returns to genetic improvement across production systems. We discuss several explanations for this divergence between genetic potential and realized outcomes, including genotype-by-environment interactions, differences in farm management and scale, data limitations in genetic evaluations, and environmental stressors. The findings highlight an important puzzle in agricultural productivity growth and underscore the need for better data on genetic adoption, costs, and farm-level responses to fully understand the impacts of genomic innovation in dairy production.

This paper explores the effect of joint labor decisions on the study of wage regression models. The estimation of Mincer equations suffers from numerous sources of bias, including the sample selection problem generated by the fact that agents’ decision to work is not independent of their wage levels. Most of the papers correct for this bias using a model of individual labor participation. However, recent trends in the labor market show greater participation of women in the labor force and seem to indicate that the joint decision of the spouses is increasingly relevant in determining the selection mechanism. A bivariate version of Heckman’s method appears as an interesting alternative to solve this problem. Although the estimates are in line with the previous literature, the results indicate that the joint decision of the couple is a relevant factor in the selection bias.

We estimate the impact of winning an award at a cattle show on the price of cattle genetics and that of their relatives. Dairy farmers choose from various dairy bulls for breeding. These bulls possess genetic traits that reflect their productivity, resiliency to disease, and physical characteristics. Another relevant attribute of dairy bulls is their pedigree prestige, which dairy farmers can use as a proxy for quality. We test the importance of pedigree prestige in determining dairy bull prices by examining the winners and runners-up of “Premier Sire” at the annual World Dairy Expo. Using an event study framework, we find that bulls that win Premiere Sire experience a 10 percent in their price compared to the second-place winner. This impact is also transmitted to their relatives, meaning the effects of prestige spillover into their genetic network.

We use a differences-in-differences with a matched control group method to estimate the long-term impacts of genomic selection in the American market for dairy cattle genetics. Genomic selection is an application of big data that uses the entire genome of an animal to test for the presence of a set of traits. Unlike pre-existing technologies that require several years of data from a bull’s daughters, an animal can be tested as soon as it is born, allowing breeders to identify the “best” animals much faster. Using a data set of all bulls marketed in the US from 2000 to 2020, we find that genomic selection significantly increased genetic gains for all measured traits, particularly milk production, protein, and fat yields, but also increased levels of inbreeding depression, a reduction in the performance of animals whose parents have a high degree of relatedness, as a consequence of genetics companies breeding more animals from established lines to respond to an increased “brand” loyalty towards such lines. Our estimation shows that the increased inbreeding rate of American bulls caused a loss of between 2.5 to 6 billion dollars to the entire industry from 2011 to 2019. Solving this externality will require either a mechanism to internalize the harmful effects, such as paying a much higher price for more inbred sires, or a collective action mechanism to select which lines will be bred in the next generation.

What are the effects of floods on reporting likelihood and observable outcomes? I examine this question in the context of a Randomized Control Trial (Shukla and Baylis, 2019) aimed at adopting a specific new technology for small-scale farmers in Bihar, India. I study two effects; first, to which extent adaptation to a regular rainfall pattern (the South Asian Monsoon) makes farmers under-report the impact of floods/heavy rainfall. To do so, I use inundation maps from satellite-measured floodwater to compare observed and reported floods. Second, given that I can determine which household lives near flooded areas, I measure their impact on food security outcomes. On the one hand, there is significant evidence in favor of under-reporting bias, but I also find little evidence of impacts on food security outcomes.

This paper uses the decomposition method of (Greenwood et Al, 2014) to assess the contribution of Assortative Mating to household income inequality in Argentina. For this purpose, the Gini coefficient and contingency tables for spouses’ educational attainment are estimated to simulate the outcomes of random mating. Unlike Greenwood, we do not use IPUMS data due to the lack of availability of income data in the Argentine census; we resort instead to household surveys for 21 years. We find that Assortative Mating plays a minor role in the determination of household income inequality; despite explaining about 5% of household income inequality, results have very little robustness to changes in parameters and are also at odds with values of several sorting indicators which show a very modest increase.