Economic Data Lit iteracy Edi Tebaldi, Ph.D. Professor of Economics, Bryant University Hassenfeld Institute for Public Leadership
• “Practical men, who believe themselves to be quite exempt from any intellectual influence, are usually the slaves of some defunct economist.” (John Maynard Keynes) • “Practical men, who believe themselves to be quite exempt from mistakes interpreting data are often wrong or are pawns of data analysts and economists.”
Outline • Data analysis in the era of big data • Assessing economic conditions • Common mistakes in data analysis • The DO’s and DON’TS of data analysis
Data analysis in in the era of big ig data Hassenfeld Institute for Public Leadership
Why bother about economic data literacy? • Meet public administration’s modernization goals; • Make the decision-making process data-driven; • Data are been produced in unfathomable quantity: • Can you make sense of it? • What good are the data if you cannot analyze the information collected? • Are you asking your data the right/wrong questions? • Data scientists now how to process data, but are they the ones who know best what is the right question to be asked? • Should we start with data-first answers and then works backward to find the questions that should have been asked?
Data Literacy Data Collection Data Analysis & Application • Data Discovery and Collection • Evaluating & Ensuring Quality of Data and Sources • Data Tools • Basic Data Analysis • Data Interpretation • Data Visualization • Identifying Problems Using Data Data Management • Presenting Data (story) • Data-Driven decision Making • Data Organization • Evaluating decisions based on data • Data Manipulation • Data Conversion • Metadata Creation and Use • Data Curation, Security, & Re-Use • Data Preservation
Analyzing Data Descriptive Stats Data Mining & Regression Big Data Analysis Machine Learning Computational algorithms Presentation of basic The practice of examining Extremely large data sets that provides the ability to statistics data in order to generate are analyzed (hopefully) automatically new information computationally to reveal learn from the data and patterns, trends, and improve from experience associations related to without being explicitly human behavior and programmed. Prediction is interactions; a key output of machine learning algorithms.
Assessing Economic Conditions Hassenfeld Institute for Public Leadership
Assessing economic conditions • Leading economic indicators: often change prior to large economic adjustments, thus can be used to predict future trends. • Lagging economic indicators: reflect the economy’s historical performance and changes to these are only identifiable after an economic trend or pattern has already been established.
Lagging Economic In Indicators National & Local Global • Current Economic Conditions • Exchange Rates: US Indicator: RIPEC/Bryant & FED • Balance of Trade: US • Gross Domestic Product (GDP) • Libor (Interest Rate): UK • US, RI • Japan Stock market - NIKKEI • Unemployment Rate: US, RI • EURO-Zone Stock Market: EURO • Income: US, RI • Wages: US, RI • Consumer Price Index (Inflation): • US, New England
Warning: Do not let unemployment statistics fool you Alternative Measures of Unemployment Labor Force Participation • Discouraged workers: would like to have a job but they have not looked for work in the past four weeks: • Counted as out of the labor force ; • Could be counted as unemployed but they are not. • Involuntary part-time workers: people who would like to work full-time but cannot find a full-time job. • Counted as employed . BLS: https://www.bls.gov/lau/stalt.htm FRED: U6UNEM6RI
Leading Economic In Indicators Key Data Source: : https:/ ://fred.stlouisfed.org/ • FED Leading Economic Indicators • Corporate Business Profits • USSLIND, RISLIND, • A446RC1Q027SBEA • State Dataset: https://www.philadelphiafed.org/rese • Inventory Levels arch-and-data/regional- economy/indexes/leading/ • TOTBUSMPCIMSA • RIPEC/Bryant Leading Economic • Retail Sales Indicator • RI Briefing • RRSFS (Local: RIERET, RIWRET ) • Stock Market • Housing Market • SP500, DJIA, NASDAQCOM, VIXCLS • S&P/Case-Shiller (local: RISTHPI ) • Manufacturing Activity • Consumer Sentiment • IPMAN, MANEMP (local: RIMFG, SMU44000003000000011, • UMCSENT PROV244MFG)
Economic theory based Leading Indicators Natural Unemployment rate (NROU) The Yield Curve (T10Y2Y)
Common Mis istakes in in Data Analysis Hassenfeld Institute for Public Leadership
Data consumers often interpret the data incorrectly
Nominal versus Real • Example 1: Wage growth • Fred dataset: https://fred.stlouisfed.org • Wages: Average Hourly Earnings of Production and Nonsupervisory Employees: Manufacturing 𝑋𝑏𝑓𝑡 • 𝑆𝑓𝑏𝑚 𝑋𝑏𝑓𝑡 = ∗ 100 𝐷𝑄𝐽 • CPI: Consumer Price Index for All Urban Consumers: All Items, Index 1982-1984=100 • How to change the base year? Multiply real wages by: 𝑫𝑸𝑱 𝒄𝒃𝒕𝒇 𝟐𝟏𝟏 • where 𝐷𝑄𝐽 𝑐𝑏𝑡𝑓 if the CPI of the base year
Levels versus Change (Growth rate) • It is important to look at both the level and the growth rate of a variable. • The decision whether to use one or the other in economic analysis depends on the question a researcher wants to answer. • Use levels to compare things that are measured in the same scale or are of similar in size • Cannot directly compare population in TX to that of RI; • Cannot directly compare GDP (US$) to Population (number of people); • Use Growth Rates to compare things that are measured using different scales or to identify how fast or slow an indicator is changing
Levels versus Change (G (Growth rate) Example 2: Population Example 3: GDP • Fred dataset: • RI GDP https://fred.stlouisfed.org • Choose “ right” indicator • Plot • Change Axis from “US$” to: • Pop TX: “Resident Population in Texas” • Percentage change • Pop RI : “Resident Population in Rhode • Percentage change from year ago Island” • Change Axis from “number of people” to: • Percentage change • Percentage change from year ago
US Export Index (A) 100 150 200 250 Tre 100 150 200 250 50 50 0 0 rends and 1980 1980 1982 1982 1984 1984 1986 1986 1988 1988 and Corr 1990 Correlation= 0.945 Correlation= 0.945 1990 1992 1992 1994 1994 B 1996 B 1996 1998 A 1998 A 2000 2000 2002 rrelation 2002 2004 2004 2006 2006 2008 2008 2010 2010 2012 2012 66 68 70 72 74 76 78 80 82 Australian males' life expectancy 66 68 70 72 74 76 78 80 82 US Export Index (A) - % Change -20.0% -15.0% -10.0% 10.0% 15.0% 20.0% 25.0% -5.0% 0.0% 5.0% 1980 1982 1984 1986 1988 Correlation= -0.07 1990 1992 1994 B 1996 1998 A 2000 2002 2004 2006 2008 2010 2012 0.0% 0.5% 1.0% 1.5% 2.0% Australian males' life expectancy - % Change
Trends and Correlation • Time Series Data might have TRENDS • May cause severe statistical problems • Trending variables may produce unreliable estimates • Spurious Correlations • Examples: http://tylervigen.com/spurious-correlations What to do: • Do not let the data analyst fool you; • Demand figures/tables with “variation in ” or “ % change in ”
Time series: “% change” is the way to go Correlation=0.975 Correlation=0.12 500 10,000 0.0% 14.0% 450 9,000 12.0% 0.0% 400 8,000 10.0% 0.0% R&S Spending (billion) 350 7,000 R&S Spending (billion) Suicide - Suffocation Suicide - Suffocation 8.0% 0.0% 300 6,000 6.0% 250 5,000 0.0% 4.0% 200 4,000 0.0% 2.0% 150 3,000 0.0% 0.0% 100 2,000 0.0% -2.0% 50 1,000 0 0 0.0% -4.0% 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 R&D Spending Sucide - Suffocation R&D Spending Sucide - Suffocation
Correlation versus Causation Should we propose legislation to foster ice- cream consumption among children as a tool to increase academic performance?
Correlation versus Causation • Policy making depends on your understanding of the difference between correlation versus causation; • Correlation can assist examining what has happened, but finding the cause of something gives you the opportunity to change it. • Correlations may happen by accident, but causal relationships are the outcome of structural economic forces;
How to identify fy Causation? Causation, most of the times, cannot be identified only through graphical analysis or work with 2 variables. 2. Design an experiment to make sure 3. Conduct graphical 1. Ask the right policy question that biases are eliminated or deploy and statistical analysis appropriate statistical methods
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