Improving Agronomic Structure in Econometric Models of Climate Change Impacts

Economists are relying on agronomic concepts to construct weather or climate independent variables and improve the reliability and efficiency of econometric models of climate change impact on U.S. agriculture. The use of cumulative heat measures in agronomy (growing degree-days), has recently served as a basis for the introduction of plurimonthly calendar heat variables in these models. However, season-long weather conditions seem at odds with conventional agronomic wisdom that emphasizes crucial differences in crop stage sensitivity to environmental stress. In this paper I show that weather variables matched to key corn development stages provide an enhanced and more stable fit than their calendar counterparts. More importantly, the proposed season-disaggregated framework yields very different implications for adaptation than its calendar counterparts as it indicates that most of the projected yield damages are accounted during the flowering period, a relatively short period in the crop cycle. This should open the door to more advanced yield models that account for additional possibilities of adaptation and thus provide a more nuanced outlook on the potential impacts of climate change on crop yields.agriculture, climate change, corn, degree-days, phenology, proxy, yield, Production Economics, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, Q54, C23,

Essays on Climate Change Impacts and Adaptation for Agriculture

Over the past twenty years economists have developed econometric approaches for estimating the impacts of climate change on agriculture by accounting for farmer adaptation implicitly. These reduced-form approaches are simple to implement but provide little insights into impact mechanisms, limiting their usefulness for adaptation policy. Recently, conflicting estimates for US agriculture have led to research with greater emphasis on mechanisms including renewed interest in statistical crop yield models. Findings suggest US agriculture will be mainly and severely affected by an increased frequency of high temperatures with crop yield suggested as a major driver. This dissertation is comprised of three essays highlighting methodological aspects in this literature. It contributes to the ongoing debate and shows the preeminent role of extreme temperature is overestimated while the role of soil moisture is seriously underestimated. This stems from issues related to weather data quality, the presence of time-varying omitted weather variables, as well as from modeling assumptions that inadvertently underestimate farmers' ability to adapt to seasonal aspects of climate change. My work illustrates how econometric models of climate change impacts on crop production can be improved by structuring them to admit some basic principles of agronomic science. The first essay shows that nonlinear temperature effects on corn yields are not robust to alternative weather datasets. The leading econometric studies in the current literature are based on a weather dataset that involves considerable interpolation. I introduce the use of a new dataset to agricultural climate change research that has been carefully developed with scientific methods to represent weather variation with one-hour and 14 kilometer accuracy. Detrimental effects of extreme temperature crucially hinge upon the recorded frequency at the highest temperatures. My research suggests that measurement error in short amounts of time spent at extreme temperature levels has disproportionate effects on estimated parameters associated with the right tail of the temperature distribution. My alternative dataset suggests detrimental temperature effects of climate change over the next 50-100 years will be half as much as in leading econometric studies in the current literature. The second essay relaxes the prevalent assumption in the literature that weather is additive. This has been the practice in most empirical models. Weather regressors are typically aggregated over the months that include the growing season. Using a simple model I show that this assumption imposes implausible characteristics on the technology. I test this assumption empirically using a crop yield model for US corn that accounts for differences in intra-day temperature variation in different stages of the growing season. Results strongly reject additivity and suggest that weather shocks such as extreme temperatures are particularly detrimental toward the middle of the season around flowering time, which corrects a disagreement of empirical yield models with the natural sciences. I discuss how this assumption tends to underestimate the range of adaptation possibilities available to farmers, thus overstating projected climate change impacts on the sector. The third essay introduces an improved measure of water availability for crops that accounts for time variation of soil moisture rather than season-long rainfall totals, as has been common practice in the literature. Leading studies in the literature are based on season-long rainfall. My alternative dataset based on scientific models that track soil moisture variation during the growing season includes variables that are more relevant for tracking crop development. Results show that models in the literature attribute too much variation in yields to temperature variation because rainfall variables are a crude and inaccurate measure of the moisture that determined crop growth. Consequently, I find that third of damages to corn yields previously attributed to extreme temperature are explained by drought, which is far more consistent with agronomic science. This highlights the potential adaptive role for water management in addressing climate change, unlike the literature now suggests. The fourth essay proposes a general structural framework for analyzing the mechanisms of climate change impacts on the sector. An empirical example incorporates some of the flexibilities highlighted in the previous essay to assess how farmer adaptation can reduce projected impacts on corn yields substantially. Global warming increases the length of the growing season in northern states. This gives farmers the flexibility to change planting dates that can reduce exposure of crops during the most sensitive flowering stage of the crop growth cycle. These research results identify another important type of farmer adaptation that can reduce vulnerability to climate change, which has been overlooked in the literature but which becomes evident only by incorporating the principles of agronomic science into econometric modeling of climate change impact analysis

Speaking the Same Language within the International Organizations; A Proposal for an Enhanced Evaluation Approach to Measure and Compare Success of International Organizations

It is currently difficult for Member States to assess and compare the success or performance of UN organizations despite recent movements towards results-based approaches. Efforts in the implementation of logical frameworks have been too independent and uncoordinated and left at the discretion of agencies. This has led to different and deficient implementations of the same theoretical approach making it almost impossible to draw any conclusions. The lack of a common approach is perceptible across agencies in the diversity of evaluation standards and terminology used to describe the same concepts, the unevenness and diversity of staff training as well as in the way intentions and results are presented. The myriad of organizations with some different sort of evaluation role may be seen as an additional symptom of the lack of coordination within the UN system. The establishment of a useful and reliable evaluation process in the UN system requires three main elements: 1- a common and enhanced evaluation framework, 2- the human and organizational capacity to ensure the accurate implementation of the framework, and 3- the commitment of Member States and agencies to implement the approach. This report mainly discusses the common evaluation framework and methodological issues, although it also provides significant insight regarding how to build the human and organizational capacity of the UN to carry out this approach. Assessing the success of an organization entails the determination of three elements: mandate or mission relevance, effectiveness, and efficiency. The report provides insight into these three components of success but its primary focus is on effectiveness. Measuring effectiveness entails establishing precise targets to be reached by agencies and collecting actual results in order to assess if intended targets are being met. Indeed, assessing effectiveness encompasses comparing intentions (provided by targets) to actual achievements (collected through monitoring). The UN Secretariat itself does not provide targets to be met by the organization. Additionally, it over-emphasizes outputs (output implementation rates) and disregards the “big picture” provided by outcomes. Under the proposed approach, subprograms meeting most of their targets are the most effective. Programs (agencies) with a large share of effective subprograms (programs) may be considered effective themselves. As a way to simplify and give an intuitive sense of effectiveness, subprograms could be attributed a category or color following a “traffic light” methodology (green for satisfactory, amber for average, red for below expectations) according to the share of targets satisfactorily met. The same could be done for programs according to their share of satisfactory subprograms. Program and subprogram performance data of every agency could be centralized (by a coordinating body) in a comprehensive webpage that would facilitate comparison between similar functions or themes across the UN system [Please refer to pg. 27 for an elaborate illustration]. The report also suggests the possibility of complementing this objective approach with a perception survey. Despite significant limitations of this type of subjective approach, it is still widely used and gives an idea of which organizations are best regarded by their peers. Contrasting actual performance data and perception indicators could be revealing, and could shed light in areas where the objective methodology may fall short. One of the most important recommendations concerns the organizational capacity ensuring the accurate implementation of the evaluation approach. This capacity should be embodied by a centralizing coordinating body (perhaps under the CEB) that would 1-ensure a common evaluation training and support of UN staff and uniformity of standards (terminology, methods, etc.), 2- centralize performance data gathered from agencies in a common database and present results in a user-friendly manner where programs and agencies could be compared and 3- verify the validity of the data submitted by the agencies (performance auditing)

Understanding Temperature and Moisture Interactions in the Economics of Climate Change Impacts and Adaptation on Agriculture

Growing econometric and statistical evidence points to high temperature as the main driver of large negative effects of climate change on US agriculture. This literature also suggests a limited role for precipitation in overall impacts. This paper shows this finding stems from the widespread use of calendar precipitation variables, which poorly represent water availability for rainfed crops. I rely on a state-of-the art dataset with very high spatial (14km) and temporal (1h) resolution to develop a statistical model and unpack the effects of temperature and drought stress and analyze their interactions. Using a 31-year panel of corn yields covering 70% of US production, I account for nonlinear effects of soil moisture with varying effects throughout the growing season, in addition to nonlinear temperature effects. I show that yield is highly sensitive to soil moisture toward the middle of the season around flowering time. Results show that omission of soil moisture leads to overestimation of the detrimental effects of temperature by 30%. Because climate change affects intra-seasonal soil moisture and temperature patterns differently, this omission also leads to very different impacts on US corn yields, with a much greater role for water resources in overall impacts. Under the medium warming scenario (RCP6), models omitting soil moisture overestimate yield impacts by almost 100%. The approach shows a more complete understanding that climate change impacts on agriculture are likely to be driven by both heat and drought stresses, and that their relative role can vary depending on the climate change scenario and farmer ability to adapt

Improving Agronomic Structure in Econometric Models of Climate Change Impacts

Economists are relying on agronomic concepts to construct weather or climate independent variables and improve the reliability and efficiency of econometric models of climate change impact on U.S. agriculture. The use of cumulative heat measures in agronomy (growing degree-days), has recently served as a basis for the introduction of plurimonthly calendar heat variables in these models. However, season-long weather conditions seem at odds with conventional agronomic wisdom that emphasizes crucial differences in crop stage sensitivity to environmental stress. In this paper I show that weather variables matched to key corn development stages provide an enhanced and more stable fit than their calendar counterparts. More importantly, the proposed season-disaggregated framework yields very different implications for adaptation than its calendar counterparts as it indicates that most of the projected yield damages are accounted during the flowering period, a relatively short period in the crop cycle. This should open the door to more advanced yield models that account for additional possibilities of adaptation and thus provide a more nuanced outlook on the potential impacts of climate change on crop yields

The Economic Impacts of Climate Change on Agriculture: Accounting for Time-invariant Unobservables in the Hedonic Approach

I propose a strategy of measuring the long-run economic impact of climate change on farmland values that tackles the elusive problem of time-invariant spatially-dependent unobservables in the hedonic approach. The strategy exploits that a county’s agricultural productivity is primarily influenced by its own climate, and the fact that climate assignment appears random conditional on average county-neighborhood characteristics. Results suggest that large impacts of climate change on US agriculture seem unlikely. Findings are robust to multiple checks and cannot be attributed to measurement error. Ignoring such confounders considerably overstates long-run climate change impacts on the sector

On the Timing of Relevant Weather Conditions in Agriculture

A growing empirical literature is analyzing the effects of weather fluctuations on a variety of economic outcomes with the goal of better understanding the potential impacts of climate change. In agricultural studies, constructing weather variables typically requires researchers to define a “season”, a time period over which weather conditions are considered relevant to the agricultural outcome of interest. While researchers often have the background knowledge to make reasonable assumptions about seasonality in crop-specific analyses, these modeling choices are less obvious when dealing with aggregate agricultural data encompassing multiple crops or livestock. In this article, we explore the consequences of assuming an incorrect season in such analyses. We first provide a conceptual framework to show that imposing an incorrect season essentially introduces non-classical measurement error in weather regressors, causing unknown biases in weather impacts. We confirm this finding in simulations. We then propose a tractable data-driven approach to recover the “true” underlying season. The approach consists of a grid search with cross-validation that evaluates the fit of models based on a wide range of season definitions. In simulations, we find the approach is effective at recovering the “true” season under certain data generating processes. Finally, we apply our approach to a US state-level panel of agricultural Total Factor Productivity. We find, unsurprisingly, considerable differences in seasonality across regions. Importantly, our empirical findings suggest that imposing arbitrary seasons lead to substantially different estimates of weather effects in either direction, in line with our theoretical and simulated results. This work contributes to the development of more robust empirical studies of climate change impacts on agriculture and beyond