Leveraging Digital Tools and Crowdsourcing Approaches to Generate High-Frequency Data for Diet Quality Monitoring at Population Scale in Rwanda

Diet quality is a critical determinant of human health and increasingly serves as a key indicator for food system sustainability. However, data on diets are limited, scattered, often project-dependent, and current data collection systems do not support high-frequency or consistent data flows. We piloted in Rwanda a data collection system, powered by the principles of citizen science, to acquire high frequency data on diets. The system was deployed through an unstructured supplementary service data platform, where respondents were invited to answer questions regarding their dietary intake. By combining micro-incentives with a normative nudge, 9,726 responses have been crowdsourced over 8 weeks of data collection. The cost per respondent was < $1 (system set-up, maintenance, and a small payment to respondents), with interactions taking <15 min. Exploratory analyses show that >70% of respondents consume tubers and starchy vegetables, leafy vegetables, fruits, legumes, and wholegrains. Women consumed better quality diets than male respondents, revealing a sex-based disparity in diet quality. Similarly, younger respondents (age ≤ 24 years) consumed the lowest quality diets, which may pose significant risks to their health and mental well-being. Middle-income Rwandans were identified to have consumed the highest quality diets. Long-term tracking of diet quality metrics could help flag populations and locations with high probabilities of nutrition insecurity, in turn guiding relevant interventions to mitigate associated health and social risks.

Gradient Boosting Machine and Deep Learning Approach in Big Data Analysis

Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposes the processing and analytics of stochastic high-frequency stock market data using a modified version of suitable Gradient Boosting Machine (GBM). The experimental results obtained are compared with deep learning and Auto-Regressive Integrated Moving Average (ARIMA) methods. The results obtained using modified GBM achieves the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.

Statistical inference of prehistoric demography from frequency distributions of radiocarbon dates: a review and a guide for the perplexed

The last decade saw a rapid increase in the number of applications where time-frequency changes of radiocarbon dates have been used as a proxy for inferring past population dynamics. Although its simple and universal premise is appealing and undoubtedly offers some unique opportunities for research on long-term comparative demography, practical applications are far from trivial and riddled by challenges. Here I review: 1) the most common criticisms concerning the nature of radiocarbon time-frequency data as a demographic proxy; 2) the statistical nature of the problem; and 3) three classes of inferential approaches proposed so far in the literature.

Costa Rica’s economy looks set for a strong recovery

Significance GDP posted growth of 9.4% year-on-year in the second quarter, the highest rate in 23 years. According to high-frequency data, economic recovery appears to have continued between July and September albeit at a slightly slower pace. Impacts Low inflation will allow the Central Bank to maintain an accommodative stance in the short term; any rate hikes next year will be gradual. Banks’ profitability and credit quality may deteriorate in 2022 as loan restructuring measures expire and lagged pandemic effects kick in. The exchange rate may further depreciate amid uncertainty over the country’s fiscal prospects and the outcome of the 2022 elections. While tourism appears to be on a strong trajectory, the spread of Omicron in Europe and the United States could reverse its recovery.

How Can High-Frequency Sensors Capture Collaboration? A Review of the Empirical Links between Multimodal Metrics and Collaborative Constructs

This paper reviews 74 empirical publications that used high-frequency data collection tools to capture facets of small collaborative groups—i.e., papers that conduct Multimodal Collaboration Analytics (MMCA) research. We selected papers published from 2010 to 2020 and extracted their key contributions. For the scope of this paper, we focus on: (1) the sensor-based metrics computed from multimodal data sources (e.g., speech, gaze, face, body, physiological, log data); (2) outcome measures, or operationalizations of collaborative constructs (e.g., group performance, conditions for effective collaboration); (3) the connections found by researchers between sensor-based metrics and outcomes; and (4) how theory was used to inform these connections. An added contribution is an interactive online visualization where researchers can explore collaborative sensor-based metrics, collaborative constructs, and how the two are connected. Based on our review, we highlight gaps in the literature and discuss opportunities for the field of MMCA, concluding with future work for this project.

Essays on modern economic issues in international trade, exchange rates and housing

<p>The common theme of the three papers in this thesis is the focus on the impact of data choices on empirical research in Economics. Such choices can be about the source of data; should we source the data from country A or country B in a bilateral trade relation? Is there a way to reconcile the discrepancies in international trade data? In investigating the impact of exchange rate on trade, should we choose high-frequency or low-frequency data? What does the choice of a certain frequency imply for the econometric analysis? In assessing the impact of housing wealth on household consumption, what are the benefits of choosing household-level data? How can we take advantage of aggregate data on house prices to circumvent the endogeneity arising from household-specific confounding factors? This thesis shows that data choices can strongly affect our conclusions regarding several modern economic issues. The first paper is titled ‘Reconciling International Trade Data.’ International trade data are filled with discrepancies–where two countries report different values of trade with each other. We develop an index for ranking countries’ data quality based on the following notion: the more a country’s reports on bilateral trade differ from the corresponding reports of its partners, the more likely it is a low-quality reporter. We calculate the comparative quality for each country’s imports and exports separately for every year from 1962 to 2016. We reconcile international trade data through picking the value reported by the country with higher quality in every bilateral flow. The findings include: (a) global trade was under-reported by roughly 5% over the past five years as countries with low data quality under-report both, their imports and exports; (b) erroneous reporting is prevalent among low-quality reporters; (c) importers’ data are less likely to be in error; (d) the level of development and corruption are possible determinants of trade data quality; (e) low-quality reporters are 14% more open to trade using reconciled data than using self-reported data (f) China tends to under-report its exports and over-report its imports, while there is only a small difference between US self-reported and reconciled data. The reconciled trade dataset is made freely available for future studies to use. The second paper is titled ‘Why You Should Use High Frequency Data to Test the Impact of Exchange Rate on Trade.’ The paper suggests that testing the impact of exchange rate on trade should be done using high frequency data. Using different data frequencies for identical periods and specifications between the US and Canada, the paper shows that low frequency data suppresses and distorts the evidence of the impact of exchange rate on trade in the short-run and the long-run. The third paper is titled: ‘Housing Leverage and Consumption Expenditure: Evidence from New Zealand Microdata.’ The paper investigates how household debt affects the marginal propensity to consume out of housing wealth. The paper uses New Zealand household-level data on spending, income, and debt over the period 2006–2016. The main empirical challenge is to identify exogenous variation in house prices to determine how consumption evolves with movements in household wealth. This identification problem is complicated by the presence of unobserved household characteristics that are correlated with housing wealth. The paper uses a detailed house sale dataset to derive local average house prices and use it as an instrument. The empirical results show that the estimated elasticity of consumption spending to housing wealth is about 0.22%. In dollar terms, the average marginal propensity to consume out of a one-dollar increase in housing wealth is around 2.2 cents. The empirical results confirm that household indebtedness, especially mortgage debt, acts as a drag on consumption spending, not only through the debt overhang channel, but also through influencing the collateral channel of the housing wealth effect.</p>