Economic impact of unemployment and inflation on output growth in Bihar during 1990–2019

The effects of unemployment and inflation on output growth based on time series data of Bihar (India) over the period 1990–2019 has been examined in this paper. The physical capital expansion in terms of infrastructure development along with skill development to provide employment opportunities to the youth appears to be the major determinant of boosting the potential productivity of physical and human capital and affecting positively the economic growth. The results indicated that there are significant and certain benefits from the increased supply and improvement in the quality of physical capital which increases labor productivity as well as investment in human capital. Thus, it is recommended that Bihar makes large-scale investments in infrastructure and skill development and carry-on renewal at opportune moments to keep steady the positive trend of economic growth over the years. The investments may be in terms of mechanized technologies, supporting infrastructure and appropriating the knowledge relating to their management; and adopting new technologies and practices involving better innovation in agriculture, forestry, manufacturing, and relevant skill development to sustain the growth of value-added.

The Research and Development Survey (RANDS) during COVID-19

The National Center for Health Statistics’ (NCHS) Research and Development Survey (RANDS) is a series of commercial panel surveys collected for methodological research purposes. In response to the COVID-19 pandemic, NCHS expanded the use of RANDS to rapidly monitor aspects of the public health emergency. The RANDS during COVID-19 survey was designed to include COVID-19 related health outcome and cognitive probe questions. Rounds 1 and 2 were fielded June 9–July 6, 2020 and August 3–20, 2020 using the AmeriSpeak® Panel. Existing and new approaches were used to: 1) evaluate question interpretation and performance to improve future COVID-19 data collections and 2) to produce a set of experimental estimates for public release using weights which were calibrated to NCHS’ National Health Interview Survey (NHIS) to adjust for potential bias in the panel. Through the expansion of the RANDS platform and ongoing methodological research, NCHS reported timely information about COVID-19 in the United States and demonstrated the use of recruited panels for reporting national health statistics. This report describes the use of RANDS for reporting on the pandemic and the associated methodological survey design decisions including the adaptation of question evaluation approaches and calibration of panel weights.

Automatic extraction of land cover statistics from satellite imagery by deep learning1

In this paper, we address the challenge of producing fully automated land cover estimates from satellite imagery through Deep Learning algorithms. We developed our system according to a tile-based, classify-and-count design. To implement the classification engine of the system, we adopted a cutting-edge Convolutional Neural Network model named Inception-V3, which we customized and trained for scene classification on the EuroSAT dataset. We tested and validated our system on two Sentinel-2 images representing quite different Italian territories (with an area of 751 km2 and 443 km2, respectively). Because no genuine ground-truth is available for the land cover of these sub-regional territories, we built a pseudo ground-truth by integrating land cover information from flagship European projects CORINE and LUCAS. A critical and careful analysis shows that our automatic land cover estimates are in good agreement with the pseudo ground-truth and offers extensive evidence of the remarkable segmentation ability of our system. The limits of our approach are also critically discussed in the paper and possible countermeasures are illustrated. When compared with traditional projects like CORINE and LUCAS, our automatic land cover estimation system exhibits three fundamental advantages: it can dramatically reduce production costs; it can allow delivering very timely and frequent land cover statistics; it can enable land cover estimation for very small territorial areas, well beyond the NUTS-2 level. As an additional outcome of land cover estimation, our system also automatically generates moderate resolution land cover maps that might be used in cartography projects as an agile first-level tool for map update or change detection purposes.

A quality framework for statistical algorithms

As national statistical offices (NSOs) modernize, interest in integrating machine learning (ML) into official statisticians’ toolbox is growing. Two challenges to such an integration are the potential loss of transparency from using “black-boxes” and the need to develop a quality framework. In 2019, the High-Level Group for the Modernisation of Official Statistics (HLG-MOS) launched a project on machine learning with one of the objectives being to address these two challenges. One of the outputs of the HLG-MOS project is a Quality Framework for Statistical Algorithms (QF4SA). While many quality frameworks exist, they have been conceived with traditional methods in mind, and they tend to target statistical outputs. Currently, machine learning methods are being looked at for use in processes producing intermediate outputs, which lead to a final statistical output. Therefore, the QF4SA does not replace existing quality frameworks; it complements them. As the QF4SA targets intermediate outputs and not necessarily the final statistical output, it should be used in conjunction with existing quality frameworks to ensure that high-quality outputs are produced. This paper presents the QF4SA, as well as some recommendations for NSOs considering the use of machine learning in the production of official statistics.

New recommendation to predict export value using big data and machine learning technique

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

Web scraping technique for producing Iranian consumer price index

Data pervasiveness was made possible by the advent of new technologies such as the Internet and the World Wide Web in every human and non-human activity. This created an exponential increase or data explosion in data generation, coined under the term Big data. Alternatively, Big Data sources can contribute to the reduction of the response burden or they can be used only to study some economic or social phenomena before designing a statistical survey which is inherently expensive to pilot. Also, incorporating Big Data sources into official statistics means maintaining a net competitive advantage and relevance of the official statistics products compared to those provided by a plethora of commercial players, with reference to large corporations that are active in the field of information technology. In this paper, the web scraping technique was used to extract the daily prices of the food and drinks products in order to replace them with conventional prices which had been used for price indices. Moreover, these sorts of new datasets enable us to calculate the indices in smaller time scales like weekly or daily basis in comparison to the conventional approach which is possible only on monthly basis. Although web scraping has its own problems, it is more economically friendly, accurate, and time-saving, especially in urban areas. Findings revealed that the web scraping technique can be applied as an effective alternative to conventional methods for CPI. Also, this technique can be used for other price statistics.