Inferring informal risk-sharing regimes: Evidence from rural Tanzania

This paper studies informal risk-sharing regimes in a unified framework by examining intertemporal consumption behavior of rural households in Tanzania. We exploit  a theoretically-consistent link between interest rates and cross-sectional consumption moments to test alternative risk-sharing models without requiring data on interest rates or assuming a restriction to eliminate the need for such data, which are often unavailable in developing economies. We specify tests that allow us to distinguish among models even with temporal dependence in income shocks. Our analysis shows that the consumption pattern in rural Tanzania is consistent with the self-insurance regime, and that risk aversion varies substantially across districts. Imposing a strict condition on interest rates, as often done in prior literature, misses their intertemporal heterogeneity and biases the estimation of risk aversion

Inferring Informal Risk-Sharing Regimes: Evidence from Rural Tanzania

This paper studies informal risk-sharing regimes in a unified framework by examining intertemporal consumption behavior of rural households in Tanzania. We exploit  a theoretically-consistent link between interest rates and cross-sectional consumption moments to test alternative risk-sharing models without requiring data on interest rates or assuming a restriction to eliminate the need for such data, which are often unavailable in developing economies. We specify tests that allow us to distinguish among models even with temporal dependence in income shocks. Our analysis shows that the consumption pattern in rural Tanzania is consistent with the self-insurance regime, and that risk aversion varies substantially across districts. Imposing a strict condition on interest rates, as often done in prior literature, misses their intertemporal heterogeneity and biases the estimation of risk aversion

Effects of Transparent Performance Data on Employee Performance: Evidence from a Field Experiment

There is a growing trend of continuously tracking performance metrics and providing them to employees via digital means without supervisor intermediation. Using a field experiment at a service organization, we examine how employees respond to transparent performance data previously available only to supervisors (i.e., daily performance metrics of employees in the same work group). We find that, compared with the pre-intervention mean value, the treatment group experienced an 11-percent decrease in strictly nonproductive time relative to the control group. The effect on reducing strictly nonproductive time seems greater than that on increasing strictly productive time. Performance improvements are greater in certain employee subsamples: those who previously perceived their supervisors as less-supportive, those with low intrinsic motivation, and those with high extrinsic motivation. We find inconclusive evidence on the moderating effects of social comparison orientation, suggesting that the main effect is unlikely to be driven by access to relative performance information

Transversals in a collections of trees

Let $\mathcal{S}$ be a fixed family of graphs on vertex set $V$ and $\mathcal{G}$ be a collection of elements in $\mathcal{S}$. We investigated the transversal problem of finding the maximum value of $|\mathcal{G}|$ when $\mathcal{G}$ contains no rainbow elements in $\mathcal{S}$. Specifically, we determine the exact values when $\mathcal{S}$ is a family of stars or a family of trees of the same order $n$ with $n$ dividing $|V|$. Further, all the extremal cases for $\mathcal{G}$ are characterized.Comment: 16pages,2figure

NetSec: Real-time and Scalable Malware Traffic Detection within IoT Networks

Detecting malicious network traffic in real time has become a crucial requirement at smart communities for elderly care and medical facilities with the prevalence of Internet-of-things (IoT) devices. Existing machine learning based solutions for network traffic malware detection often fail to scale with the exponential increase of IoT devices at the facility and to detect malicious traffic with desirable low latency. In this paper we seek to fill the gap by designing a scalable end-to-end network traffic analyzing system that permits real-time malware detection. By leveraging distributed systems such as Apache Kafka and Apache Spark, the system has demonstrated scalable performance as the number of IoT devices grow. Using Intel’s oneAPI software stack for both machine learning and deep learning models, the model inference speed is boosted by three-fold