Economic and Statistical Analysis Budget
Total Page:16
File Type:pdf, Size:1020Kb
Load more
										Recommended publications
									
								- 
												
												Curious About Cryptocurrencies? Investors Need to Make Sure They Separate “Investing” from “Speculation” by Don Mcarthur, CFA®
Curious About Cryptocurrencies? Investors Need to Make Sure They Separate “Investing” from “Speculation” By Don McArthur, CFA® Bitcoin and other cryptocurrencies have received plenty of media coverage lately, and it is natural for investors to wonder about them. Even celebrities have become associated with Bitcoin publicity through social media. Interest has piqued to a point where there are even Exchange Traded Funds (ETFs) that invest in Bitcoin, giving investors the means to invest in the Futures market. After having performed in-depth research on Bitcoin and other cryptocurrencies, our position at Commerce Trust Company is that they should not currently play a role in client portfolios. As part of that research, Commerce Senior Vice President and Investment Analyst Don McArthur, CFA, put together a primer on the topic of cryptocurrencies in general. In the following commentary, he explains why Bitcoin at this stage is more about speculating than investing in something with intrinsic value. He also touches on how Blockchain networking technology not only supports cryptocurrencies, but many other industrial applications as well. We thought you would enjoy this commentary as McArthur shares his thoughts in a mind-opening Q&A. Q . What is Bitcoin and how did it start? A. Bitcoin is one of hundreds of digital currencies, or cryptocurrency, based on Blockchain technology. As an early mover, Bitcoin is by far the largest digital currency. Bitcoin was launched in 2009 by a mysterious person (or persons) known only by the pseudonym Satoshi Nakamoto. Unlike traditional currencies, which are issued by central banks, Bitcoin has no central monetary authority. - 
												
												Fritz Machlup's Construction of a Synthetic Concept
The Knowledge Economy: Fritz Machlup’s Construction of a Synthetic Concept Benoît Godin 385 rue Sherbrooke Est Montreal, Quebec Canada H2X 1E3 [email protected] Project on the History and Sociology of S&T Statistics Working Paper No. 37 2008 Previous Papers in the Series: 1. B. Godin, Outlines for a History of Science Measurement. 2. B. Godin, The Measure of Science and the Construction of a Statistical Territory: The Case of the National Capital Region (NCR). 3. B. Godin, Measuring Science: Is There Basic Research Without Statistics? 4. B. Godin, Neglected Scientific Activities: The (Non) Measurement of Related Scientific Activities. 5. H. Stead, The Development of S&T Statistics in Canada: An Informal Account. 6. B. Godin, The Disappearance of Statistics on Basic Research in Canada: A Note. 7. B. Godin, Defining R&D: Is Research Always Systematic? 8. B. Godin, The Emergence of Science and Technology Indicators: Why Did Governments Supplement Statistics With Indicators? 9. B. Godin, The Number Makers: A Short History of Official Science and Technology Statistics. 10. B. Godin, Metadata: How Footnotes Make for Doubtful Numbers. 11. B. Godin, Innovation and Tradition: The Historical Contingency of R&D Statistical Classifications. 12. B. Godin, Taking Demand Seriously: OECD and the Role of Users in Science and Technology Statistics. 13. B. Godin, What’s So Difficult About International Statistics? UNESCO and the Measurement of Scientific and Technological Activities. 14. B. Godin, Measuring Output: When Economics Drives Science and Technology Measurements. 15. B. Godin, Highly Qualified Personnel: Should We Really Believe in Shortages? 16. B. Godin, The Rise of Innovation Surveys: Measuring a Fuzzy Concept. - 
												
												Economics 329: ECONOMIC STATISTICS Fall 2016, Unique Number 34060 T, Th 3:30 – 5:00 (WCH 1.120) Instructor: Dr
University of Texas at Austin Course Outline Economics 329: ECONOMIC STATISTICS Fall 2016, Unique number 34060 T, Th 3:30 – 5:00 (WCH 1.120) Instructor: Dr. Valerie R. Bencivenga Office: BRB 3.102C Office hours (Fall 2016): T, Th 11:00 – 12:30 Phone: 512-475-8509 Email: [email protected] (course email) or [email protected] The best ways to contact me are by email and in person after class or in office hours. If I don’t answer my phone, do not leave a phone message – please send an email. Use the course email for most emails, including exam scheduling. COURSE OBJECTIVES Economic Statistics is a first course in quantitative methods that are widely-used in economics and business. The main objectives of this course are to explore methods for describing data teach students how to build and analyze probability models of economic and business situations introduce a variety of statistical methods used to draw conclusions from economic data, and to convey the conceptual and mathematical foundations of these methods lay a foundation for econometrics COURSE DESCRIPTION SEGMENT 1. The unit on descriptive statistics covers methods for describing the distribution of data on one or more variables, including measures of central tendency and dispersion, correlation, frequency distributions, percentiles, and histograms. In economics and business, we usually specify a probability model for the random process that generated the data (data-generating process, or DGP). The unit on probability theory covers the set-theoretic foundations of probability and the axioms of probability; rules of probability derived from the axioms, including Bayes’ Rule; counting rules; and joint probability distributions. - 
												
												New Insights on Retail E-Commerce (July 26, 2017)
U.S. Department of Commerce Economics Newand Insights Statistics on Retail Administration E-Commerce Office of the Chief Economist New Insights on Retail E-Commerce Executive Summary The U.S. Census Bureau has been collecting data on retail sales since the 1950s and data on e-commerce retail sales since 1998. As the Internet has become ubiquitous, many retailers have created websites and even entire divisions devoted to fulfilling online orders. Many consumers have By turned to e-commerce as a matter of convenience or to increase the Jessica R. Nicholson variety of goods available to them. Whatever the reason, retail e- commerce sales have skyrocketed and the Internet will undoubtedly continue to influence how consumers shop, underscoring the need for good data to track this increasingly important economic activity. In June 2017, the Census Bureau released a new supplemental data table on retail e-commerce by type of retailer. The Census Bureau developed these estimates by re-categorizing e-commerce sales data from its ESA Issue Brief existing “electronic shopping” sales data according to the primary #04-17 business type of the retailer, such as clothing stores, food stores, or electronics stores. This report examines how the new estimates enhance our understanding of where consumers are shopping online and also provides an overview of trends in retail and e-commerce sales. Findings from this report include: E-commerce sales accounted for 7.2 percent of all retail sales in 2015, up dramatically from 0.2 percent in 1998. July 26, 2017 E-commerce sales have been growing nine times faster than traditional in-store sales since 1998. - 
												
												E-Commerce Integration and Economic Development: Evidence from China∗
E-Commerce Integration and Economic Development: Evidence from China∗ Victor Couture,y Benjamin Faber,z Yizhen Gu§ and Lizhi Liu{ July 2017 –Preliminary and incomplete– Abstract The number of people buying and selling products online in China has grown from practically zero in 2000 to more than 400 million by 2015. Most of this growth has occurred in cities. In this context, the Chinese government recently announced the expansion of e-commerce to the countryside as a policy priority with the objective to close the rural-urban economic divide. As part of this agenda, the government entered a partnership with a large Chinese e-commerce firm. The program invests in the necessary transport logistics to ship products to and sell prod- ucts from tens of thousands of villages that were largely unconnected to e-commerce. The firm also installs an e-commerce terminal at a central village location, where a terminal manager assists households in buying and selling products through the firm’s e-commerce platform. This paper combines a new collection of survey and transaction microdata with a randomized control trial (RCT) across villages that we implement in collaboration with the e-commerce firm. We use this empirical setting to provide evidence on the potential of e-commerce inte- gration to foster economic development in the countryside, the underlying channels and the distribution of the gains from e-commerce across households and villages. Keywords: E-commerce, trade integration, economic development, rural-urban divide JEL Classification: F63, O12, R13 ∗We are grateful to CEGA, the Clausen Center, the Haas School of Business, the Weiss Family Fund and the Bill and Melinda Gates Foundation for providing funding for this study. - 
												
												Eliminating Barriers to Internal Commerce to Facilitate Intraregional Trade
Eliminating Barriers to Internal Commerce to Facilitate Intraregional Trade Olumide Taiwo and Nelipher Moyo, Brookings Africa Growth Initiative ncreased trade between African countries holds Roads account for 80 to 90 percent of all freight and promise for shared growth and development in passenger movement in Africa. Road density is an ef- the region. However, before African countries can fective proxy of how well connected areas of a country Ifully exploit the benefits associated with increased are. Africa has a road density of only 16.8 kilometers trade with each other, they must first address the bar- per 1,000 square kilometers, compared with 37 kilo- riers to the movement of goods and people within meters per 1,000 square kilometers in other low-in- their countries. It is difficult to imagine how Africa come regions (table 1). Likewise, rail density in Africa will be able to move goods from Cape Town to Cairo is only 2.8 kilometers per 1,000 square kilometers— when it is unable to move goods from one city to an- much lower than the 3.4 kilometers per 1,000 square other within the same country. Take the case of Ke- kilometers in other low-income regions. Air travel nya: while parts of northern Kenya were experiencing within Africa continues to be more expensive per mile major food shortages in January 2011, farmers in the than intercontinental travel. Africa’s inland waterways Rift Valley had food surpluses and were imploring the present an excellent opportunity to connect cities and government to buy their excess crops before they went countries. - 
												
												The Macro-Economic Impact of E-Commerce in the EU Digital Single Market
INSTITUTE FOR PROSPECTIVE TECHNOLOGICAL STUDIES DIGITAL ECONOMY WORKING PAPER 2015/09 The Macro-economic Impact of e-Commerce in the EU Digital Single Market Melisande Cardona Nestor Duch-Brown Joseph Francois Bertin Martens Fan Yang 2015 The Macro-economic Impact of e-Commerce in the EU Digital Single Market This publication is a Working Paper by the Joint Research Centre of the European Commission. It results from the Digital Economy Research Programme at the JRC Institute for Prospective Technological Studies, which carries out economic research on information society and EU Digital Agenda policy issues, with a focus on growth, jobs and innovation in the Single Market. The Digital Economy Research Programme is co-financed by the Directorate General Communications Networks, Content and Technology It aims to provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. JRC Science Hub https://ec.europa.eu/jrc JRC98272 ISSN 1831-9408 (online) © European Union, 2015 Reproduction is authorised provided the source is acknowledged. All images © European Union 2015 How to cite: Melisande Cardona, Nestor Duch-Brown, Joseph Francois, Bertin Martens, Fan Yang (2015). The Macro-economic Impact of e-Commerce in the EU Digital Single Market. Institute for Prospective Technological Studies Digital Economy Working Paper 2015/09. JRC98272 Table of Contents Abstract ............................................................................................................... 3 1. Introduction .............................................................................................. 4 2. Online trade in goods in the EU ................................................................... - 
												
												GLOSSARY of INTERNATIONAL TRADE TERMS 2016 Guide
CALIFORNIA FASHION ASSOCIATION 444 South Flower Street, 37th Floor · Los Angeles, CA 90071 ·ph. 213.688.6288 ·fax 213.688.6290 Email: [email protected] Website: www.californiafashionassociation.org GLOSSARY OF INTERNATIONAL TRADE TERMS 2016 Guide Sponsored By: Prepared by: CALIFORNIA FASHION ASSOCIATION 444 South Flower Street, 37th Floor, Los Angeles, CA 90071 Phone: 213-688-6288, Fax: 213-688-6290 [email protected] | www.californiafashionassociation.org 1 CALIFORNIA FASHION ASSOCIATION 444 South Flower Street, 37th Floor · Los Angeles, CA 90071 ·ph. 213.688.6288 ·fax 213.688.6290 Email: [email protected] Website: www.californiafashionassociation.org THE VOICE OF THE CALIFORNIA INDUSTRY The California Fashion Association is the forum organized to address the issues of concern to our industry. Manufacturers, contractors, suppliers, educational institutions, allied associations and all apparel-related businesses benefit. Fashion is the largest manufacturing sector in Southern California. Nearly 13,548 firms are involved in fashion-related businesses in Los Angeles and Orange County; it is a $49.3-billion industry. The apparel and textile industry of the region employs approximately 128,148 people, directly and indirectly in Los Angeles and surrounding counties. The California Fashion Association is the clearinghouse for information and representation. We are a collective voice focused on the industry's continued growth, prosperity and competitive advantage, directed toward the promotion of global recognition for the "Created in California" - 
												
												Pandemic 101:A Roadmap to Help Students Grasp an Economic Shock
Social Education 85(2) , pp.64–71 ©2021 National Council for the Social Studies Teaching the Economic Effects of the Pandemic Pandemic 101: A Roadmap to Help Students Grasp an Economic Shock Kim Holder and Scott Niederjohn This article focuses on the major national economic indicators and how they changed within the United States real GDP; the over the course of the COVID-19 pandemic. The indicators that we discuss include output from a Ford plant in Canada is not. Gross Domestic Product (GDP), the unemployment rate, interest rates, inflation, and Economists typically measure real GDP other variations of these measures. We will also present data that sheds light on the as a growth rate per quarter: Is GDP get- monetary and fiscal policy responses to the pandemic. Graphs of these statistics are ting bigger or smaller compared to a prior sure to grab teachers’ and students’ attention due to the dramatic shock fueled by the quarter? In fact, a common definition of pandemic. We will explain these economic indicators with additional attention to what a recession is two quarters in a row of they measure and the limitations they may present. Teachers will be introduced to the declining real GDP. Incidentally, it also Federal Reserve Economic Database (FRED), which is a rich source of graphs and measures total U.S. income (and spend- information for teacher instruction and student research. Further classroom-based ing) and that explains why real GDP per resources related to understanding the economic effects of the COVID-19 pandemic capita is a well-established measure used are also presented. - 
												
												Occasional Paper 03 November 2020 ISSN 2515-4664
Public Understanding of Economics and Economic Statistics Johnny Runge and Nathan Hudson Advisory team: Amy Sippitt Mike Hughes Jonathan Portes ESCoE Occasional Paper 03 November 2020 ISSN 2515-4664 OCCASIONAL PAPER Public Understanding of Economics and Economic Statistics Johnny Runge and Nathan Hudson ESCoE Occasional Paper No. 03 November 2020 Abstract This study explores the public understanding of economics and economics statistics, through mixed-methods research with the UK public, including 12 focus groups with 130 participants and a nationally representative survey with 1,665 respondents. It shows that people generally understand economic issues through the lens of their familiar personal economy rather than the abstract national economy. The research shows that large parts of the UK public have misperceptions about how economic figures, such as the unemployment and inflation rate, are collected and measured, and who they are produced and published by. This sometimes affected participants’ subsequent views of the perceived accuracy and reliability of economic statistics. Broadly, the focus groups suggested that people are often sceptical and cynical about any data they see, and that official economic data are subject to the same public scrutiny as any other data. In line with other research, the survey found consistent and substantial differences in economic knowledge and interest across different groups of the UK population. This report will be followed up with an engagement exercise to discuss findings with stakeholders, in order to draw out recommendations on how to improve the communication of economics and economic statistics to the public. Keywords: public understanding, public perceptions, communication, economic statistics JEL classification: Z00, D83 Johnny Runge, National Institute of Economic and Social Research and ESCoE, [email protected] and Nathan Hudson, National Institute of Economic and Social Research and ESCoE. - 
												
												ONS Economic Statistics and Analysis Strategy
Economic statistics and analysis strategy HOTEL September 2016 www.ons.gov.uk Economic statistics and analysis strategy, Office for National Statistics, September 2016 Economic Statistics and Analysis Strategy Office for National Statistics September 2016 Introduction ONS published its Economic Statistics and Analysis Strategy (ESAS) for consultation in May 2016, building upon the National Accounts Strategy1, but going further to cover the whole of Economic Statistics Analysis in the light of the recommendations of the Bean Review. This strategy will be reviewed and updated annually to reflect of changing needs and priorities, and availability of resources, in order to give a clear prioritisation for ONS’s work on economic statistics, but also our research and Joint working agendas. Making explicit ONS’s perceived priorities will allow greater scrutiny and assurance that these are the right ones. ESAS lays out research and development priorities, making it easier for external experts to see the areas where ONS has and would be particularly keen to collaborate. The strategy was published for consultation and ONS thanks all users of ONS economic statistics and other parties, including those in government, academia, and the private sector who provided comments as part of the consultation. These comments have now been taken on board and incorporated into this latest version of the ESAS. Overview The main focus of ONS’s strategy for economic statistics and analysis over the period to 2021 will be in the following areas: 1 Available at Annex 1. 1 Economic statistics and analysis strategy, Office for National Statistics, September 2016 • Taking forward the programme set out in the latest national accounts strategy and work plan to meet international reporting standards according to the agreed timetable; and to improve the measurement of Gross Domestic Product (GDP) through the introduction of constant price supply and use balancing, the adoption of double deflated estimates of gross value added and improved use and specification of price indices. - 
												
												Digital Economy 2002
Digital Economy 2002 ECONOMICS AND STATISTICS U.S. DEPARTMENT OF COMMERCE ADMINISTRATION Economics and Statistics Administration DIGITAL ECONOMY 2002 ECONOMICS AND STATISTICS ADMINISTRATION Office of Policy Development AUTHORS Chapter I ................................................................................................................................ Lee Price [email protected] George McKittrick [email protected] Chapter II..................................................................................................................... Patricia Buckley [email protected] Sabrina Montes [email protected] Chapter III ........................................................................................................................... David Henry [email protected] Donald Dalton [email protected] Chapter IV ................................................................................................................... Jesus Dumagan [email protected] Gurmukh Gill [email protected] Chapter V ....................................................................................................................... Sandra Cooke [email protected] Chapter VI .................................................................................................................... Dennis Pastore [email protected] Chapter VII ........................................................................................................ Jacqueline Savukinas [email protected]