Climate Econometrics

I have a new working paper out reviewing various methods, models, and assumptions used in the econometrics literature to quantify the impact of climatic conditions on society.  The process of writing this was much more challenging than I expected, but rereading it makes me feel like we as a community really learned a lot during the last decade of research. Here's the abstract:

Climate Econometrics (forthcoming in the Annual Reviews) 

Abstract: Identifying the effect of climate on societies is central to understanding historical economic development, designing modern policies that react to climatic events, and managing future global climate change. Here, I review, synthesize, and interpret recent advances in methods used to measure effects of climate on social and economic outcomes. Because weather variation plays a large role in recent progress, I formalize the relationship between climate and weather from an econometric perspective and discuss their use as identifying variation, highlighting tradeoffs between key assumptions in different research designs and deriving conditions when weather variation exactly identifies the effects of climate. I then describe advances in recent years, such as parameterization of climate variables from a social perspective, nonlinear models with spatial and temporal displacement, characterizing uncertainty, measurement of adaptation, cross-study comparison, and use of empirical estimates to project the impact of future climate change. I conclude by discussing remaining methodological challenges.

I summarize several highlights here.

We're hiring @Berkeley!

The Global Policy Lab at UC Berkeley is now hiring multiple positions for a major research project at the nexus of environmental resource management, economic development, and econometric modeling. 

All job postings are open and applications will be under review immediately.

Positions available:

1. Post doc - for applicants with a PhD

2. Project manager - for applicants with a Masters or PhD

3. Research analyst - for applicants with a Bachelor's degree 

All positions are full time, start date approx.: 5 or 6/2016

See job descriptions and application instructions at: http://globalpolicy.science/jobs

PROJECT: MEASURING SUSTAINABLE DEVELOPMENT

The Global Policy Lab is beginning a two year research program bringing rigorous quantitative analysis to bear on the empirical measurement of sustainable development at industrial scale in a real world setting.

Sustainable development is a well-established theoretical concept in environment and resource economics, requiring that a population invest new capital resources at least as rapidly as they are removed or damaged from a system, however it has yet to be determined if this condition is met in any real world scenarios.

Achieving sustainable development requires that we are able to quantitatively monitor economic and environmental conditions and decisions in real time, so that the costs and benefits of management choices can be evaluated as they arise.

Our team will design, develop, and deploy a system to quantify and monitor management decisions at a full-scale mixed agricultural-industrial site in New Zealand. Our findings and innovations will advance our understanding of how sustainable development can be effectively achieved at the firm level, with the goal of similar systems being developed and deployed around the world.

The five member team will be based at  UC Berkeley and will be led by Principle Investigator Solomon Hsiang.

Learn more and apply at: http://globalpolicy.science/jobs

US health inequality

"The report by Chetty et al also suggests that geography and income percentiles interact in previously unknown ways. For instance, the percentile gradient for life expectancy at 40 years of age is steeper in Detroit, Michigan, than in San Francisco, California, or New York, New York, almost entirely because being in the bottom income percentile is worse in Detroit. However, this outcome in Detroit cannot be entirely related to income because this same income percentile in Detroit has more real purchasing power than in New York. (The adjustment for race and ethnicity may be an issue here.) Beyond Detroit, it is generally true that it is at the bottom of the income distribution, not at the top, where geography matters. It is as if the top income percentiles belong to one world of elite, wealthy US adults, whereas the bottom income percentiles each belong to separate worlds of poverty, each unhappy and unhealthy in its own way. The life expectancy at 40 years of age in the top income percentile of the United States is better than the mean in any other country for life expectancy at 40 years of age. However, not by a lot, and likely not better than the top percentile in Sweden or the Netherlands. In contrast, the life expectancy at 40 years of age in the bottom income percentile of the United States is located between the mean for Pakistan and Sudan for life expectancy at 40 years of age."

That's from Angus Deaton's editorial in the latest issue of JAMA, referencing Chetty et al.'s The Association Between Income and Life Expectancy in the United States, 2001-2014.