An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers.

Usually, official and survey-based statistics guide policymakers in their choice of response instruments to economic crises. However, in an early phase, after a sudden and unforeseen shock has caused unexpected and fast-changing dynamics, data from traditional statistics are only available with non-negligible time delays. This leaves policymakers uncertain about how to most effectively manage their economic countermeasures to support businesses, especially when they need to respond quickly, as in the COVID-19 pandemic. Given this information deficit, we propose a framework that guided policymakers throughout all stages of this unforeseen economic shock by providing timely and reliable sources of firm-level data as a basis to make informed policy decisions. We do so by combining early stage 'ad hoc' web analyses, 'follow-up' business surveys, and 'retrospective' analyses of firm outcomes. A particular focus of our framework is on assessing the early effects of the pandemic, using highly dynamic and large-scale data from corporate websites. Most notably, we show that textual references to the coronavirus pandemic published on a large sample of company websites and state-of-the-art text analysis methods allowed to capture the heterogeneity of the pandemic's effects at a very early stage and entailed a leading indication on later movements in firm credit ratings. While the proposed framework is specific to the COVID-19 pandemic, the integration of results obtained from real-time online sources in the design of subsequent surveys and their value in forecasting firm-level outcomes typically targeted by policy measures, is a first step towards a more timely and holistic approach for policy guidance in times of economic shocks.

Changing climate - Changing livelihood: Smallholder's perceptions and adaption strategies.

Experts expect that climate change will soon have a severe impact on the lives of farmers in the region surrounding Kerala, India. This region, which is known for its monsoon climate (which involves a distinct temporal and spatial variation in rainfall), has experienced a decrease in annual rainfall over the last century. This study is aimed at investigating how smallholder farmers perceive climate change and at identifying the methods that these smallholders use to adapt to climate change. We use data collected from a survey of 215 households to compare the climate vulnerability of three watershed communities in Kerala. We find that the farmers perceive substantial increases in both temperature and the unpredictability of monsoons; this is in accordance with actual observed weather trends. The selection of effective adaptation strategies is one of the key challenges that smallholders face as they seek to reduce their vulnerability. The surveyed households simultaneously use various adaptation methods, including information and communication technology, crop and farm diversification, social networking through cooperatives, and soil and water conservation measures. The results of a binary regression model reveal that the household head's age, education and gender, as well as the farm's size and the household's size, assets, livestock ownership, poverty status and use of extension services, are all significantly correlated with the households' choices regarding adaptations to cope with climate change.

Measuring the diffusion of innovations with paragraph vector topic models.

Measuring the diffusion of innovations from textual data sources besides patent data has not been studied extensively. However, early and accurate indicators of innovation and the recognition of trends in innovation are mandatory to successfully promote economic growth through technological progress via evidence-based policy making. In this study, we propose Paragraph Vector Topic Model (PVTM) and apply it to technology-related news articles to analyze innovation-related topics over time and gain insights regarding their diffusion process. PVTM represents documents in a semantic space, which has been shown to capture latent variables of the underlying documents, e.g., the latent topics. Clusters of documents in the semantic space can then be interpreted and transformed into meaningful topics by means of Gaussian mixture modeling. In using PVTM, we identify innovation-related topics from 170, 000 technology news articles published over a span of 20 years and gather insights about their diffusion state by measuring the topic importance in the corpus over time. Our results suggest that PVTM is a credible alternative to widely used topic models for the discovery of latent topics in (technology-related) news articles. An examination of three exemplary topics shows that innovation diffusion could be assessed using topic importance measures derived from PVTM. Thereby, we find that PVTM diffusion indicators for certain topics are Granger causal to Google Trend indices with matching search terms.

New Methods in Exploring Old Topics: Case Studying Brittle Diabetes in the Family Context.

BACKGROUND: In questing for a more refined quantitative research approach, we revisited vector autoregressive (VAR) modeling for the analysis of time series data in the context of the so far poorly explored concept of family dynamics surrounding instable diabetes type 1 (or brittle diabetes). METHOD: We adopted a new approach to VAR analysis from econometrics referred to as the optimized multivariate lag selection process and applied it to a set of raw data previously analyzed through standard approaches. RESULTS: We illustrated recurring psychosomatic circles of cause and effect relationships between emotional and somatic parameters surrounding glycemic control of the child's diabetes and the affective states of all family members. CONCLUSION: The optimized multivariate lag selection process allowed for more specific, dynamic, and statistically reliable results (increasing R(2) tenfold in explaining glycemic variability), which were derived from a larger window of past explanatory variables (lags). Such highly quantitative versus historic more qualitative approaches to case study analysis of psychosomatics surrounding diabetes in adolescents were reflected critically.

Family system dynamics and type 1 diabetic glycemic variability: a vector-auto-regressive model.

Statistical approaches rooted in econometric methodology, so far foreign to the psychiatric and psychological realms have provided exciting and substantial new insights into complex mind-body interactions over time and individuals. Over 120 days, this structured diary study explored the mutual interactions of emotions within a classic 3-person family system with its Type 1 diabetic adolescent's daily blood glucose variability. Glycemic variability was measured through daily standard deviations of blood glucose determinations (at least 3 per day). Emotions were captured individually utilizing the self-assessment manikin on affective valence (negative-positive), activation (calm-excited), and control (dominated-dominant). Auto- and cross-correlating the stationary absolute (level) values of the mutually interacting parallel time series data sets through vector autoregression (VAR, grounded in econometric theory) allowed for the formulation of 2 concordant models. Applying Cholesky Impulse Response Analysis at a 95% confidence interval, we provided evidence for an adolescent being happy, calm, and in control to exhibit less glycemic variability and hence diabetic derailment. A nondominating mother and a happy father seemed to also reduce glycemic variability. Random shocks increasing glycemic variability affected only the adolescent and her father: In 1 model, the male parent felt in charge; in the other, he calmed down while his daughter turned sad. All reactions to external shocks lasted for less than 4 full days. Extant literature on affect and glycemic variability in Type 1 diabetic adolescents as well as challenges arising from introducing econometric theory to the field were discussed.