Operations managers clearly play a critical role in targeting plant-level investments toward environment and safety practices. In principle, a “rational” response would be to align this investment with senior management's competitive goals for operational performance. However, operations managers also are influenced by contingent factors, such as their national culture, thus creating potential tension that might bias investment away from a simple rational response. Using data from 1,453 plants in 24 countries, we test the moderating influence of seven of the national cultural characteristics on investment at the plant level in environment and safety practices. Four of the seven national cultural characteristics from GLOBE (i.e., uncertainty avoidance, in-group collectivism, future orientation and performance orientation) shifted investment away from an expected “rational” response. Positive bias was evident when the national culture favored consistency and formalized procedures and rewarded performance improvement. In contrast, managers exhibited negative bias when familial groups and local coalitions were powerful, or future outcomes—rather than current actions—were more important. Overall, this study highlights the critical importance of moving beyond a naïve expectation that plant-level investment will naturally align with corporate competitive goals for environment and safety. Instead, the national culture where the plant is located will influence these investments, and must be taken into account by senior management.

We examine the relation between high frequency quotation and the behavior of stock prices between 2009 and 2011 for the full cross section of securities in the US. On average, higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading. We also explore market resiliency during periods of exceptionally high low-latency trading: large liquidity drawdowns in which, within the same millisecond, trading algorithms systematically sweep large volume across multiple trading venues. Although such large drawdowns incur trading costs, they do not appear to degrade the price formation process or increase the subsequent cost of trading. In an out-of-sample analysis, we investigate an exogenous technological change to the trading environment on the Tokyo Stock Exchange that dramatically reduces latency and allows co-location of servers. This shock also results in prices more closely resembling a random walk and a sharp decline in the cost of trading.

This article develops welfare-consistent measures of the employment effects of environmental regulation. Our analysis is based on a microeconomic model of how households with heterogeneous preferences and skills decide where to live and work. We use the model to examine how job loss and unemployment would affect workers in Northern California. Our stylized simulations produce earnings losses that are consistent with empirical evidence. They also produce two new insights. First, we find that earnings losses are sensitive to business cycle conditions. Second, we find that earnings losses may substantially understate welfare losses once we account for the fact that workers may have to commute further or live in a less desirable community after losing a job.

With the advent of high-dimensional stored big data and streaming data, suddenly machine learning on a very large scale has become a critical need. Such machine learning should be extremely fast, should scale up easily with volume and dimension, should be able to learn from streaming data, should automatically perform dimension reduction for high-dimensional data, and should be deployable on hardware. Neural networks are well positioned to address these challenges of large scale machine learning. In this paper, we present a method that can effectively handle large scale, high-dimensional data. It is an online method that can be used for both streaming and large volumes of stored big data. It primarily uses Kohonen nets, although only a few selected neurons (nodes) from multiple Kohonen nets are actually retained in the end; we discard all Kohonen nets after training. We use Kohonen nets both for dimensionality reduction through feature selection and for building an ensemble of classifiers using single Kohonen neurons. The method is meant to exploit massive parallelism and should be easily deployable on hardware that implements Kohonen nets. Some initial computational results are presented.

Based on considerable neurophysiological evidence, Roy (2012) proposed the theory that localist representation is widely used in the brain, starting from the lowest levels of processing. Grandmother cells are a special case of localist representation. In this article, I present the theory that grandmother cells are also widely used in the brain. To support the proposed theory, I present neurophysiological evidence and an analysis of the concept of grandmother cells. Konorski (1967) first predicted the existence of grandmother cells (he called them “gnostic” neurons) - single neurons that respond to complex stimuli such as faces, hands, expressions, objects, and so on. The term “grandmother cell” was introduced by Jerry Lettvin in 1969 (Barlow, 1995).

This study is an attempt to use group information collected on climate change from farmers in eastern Uttar Pradesh, India to address a key question related to climate change policy: How to encourage farmers to adapt to climate change? First, we investigate farmers’ perception of and adaptation to climate change using content analysis and group information. The findings are then compared with climatic and agriculture information collected through secondary sources. Results suggest that though farmers are aware of long-term changes in climatic factors (temperature and rainfall, for example), they are unable to identify these changes as climate change. Farmers are also aware of risks generated by climate variability and extreme climatic events. However, farmers are not taking concrete steps in dealing with perceived climatic changes, although we find out that farmers are changing their agricultural and farming practices. These included changing sowing and harvesting timing, cultivation of crops of short duration varieties, inter-cropping, changing cropping pattern, investment in irrigation, and agroforestry. Note that these changes may be considered as passive response or adaptation strategies to climate change. Perhaps farmers are implicitly taking initiatives to adapt climate change. Finally, the paper suggests some policy interventions to scale up adaptation to climate change in Indian agriculture.

Using a natural experiment (Regulation SHO), we show that short selling pressure and consequent stock price behavior have a causal effect on managers’ voluntary disclosure choices. Specifically, we find that managers respond to a positive exogenous shock to short selling pressure and price sensitivity to bad news by reducing the precision of bad news forecasts. This finding on management forecasts appears to be generalizable to other corporate disclosures. In particular, we find that, in response to increased short selling pressure, managers also reduce the readability (or increase the fuzziness) of bad news annual reports. Overall, our results suggest that maintaining the current level of stock prices is an important consideration in managers’ strategic disclosure decisions.