Economic Impact Group, LLC.

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1 Tracking Your Region s Economic Performance Dr. Alfie Meek Economic Impact Group, LLC. June 3,

2 8:30 9:00 9:00 9:30 9:30 10:00 10: : :15 10:45 10:45 12:00 12:00 1:00 1:00 2:30 2:30 2:45 2:45 4:00 Introduction Economic Indicators Data Sources & Tracking Break Data Issues The Math! Lunch Building an index Break Building an index (cont.) Agenda 2

3 What is an Index? A statistical measure of change in an economy or market. It has its own calculation methodology and is usually expressed in terms of a change from a base value. Therefore, the percentage change is more important than the actual numeric value. Introduction 3

4 What is an Economic o Index? A measure of the alternating waves of economic expansion and contraction (a.k.a. business cycle ) in an economy. Leading Index Coincident (Current) Index Lagging Index Other types of indices? Innovation Index Entrepreneur or Small Business Index Introduction 4

analytical, operational, or informational.

5 What is a dashboard? d Provides at-a-glance views of Key Performance Indicators (KPIs) relevant to a particular objective or business process. Can be broken down according to role and are either strategic, analytical, operational, or informational. Analytical & Informational dashboards often include more context, comparisons, and history, along with subtler performance evaluators. Introduction 5

6 Why ydoa an Economic o Index? Reflects the direction of current and future economic activity in a given area. A gauge of where an economy has been, where it is, and where it is headed. Gain an understanding of what drives the local economy. Introduction 6

7 Economic Indicators 7

8 National a Leading Coincident (current) Lagging Local Leading Coincident (current) Lagging Economic Indicators 8

9 Criteria: Economic significance Statistical adequacy Timing at recessions and revivals Conformity to historical business cycles Smoothness Timeliness Economic Indicators 9 Source: Victor Zarnowitz, Business Cycles: Theory, History, Indicators, and Forecasting.

10 U.S. US Leading Economic o Index Average weekly hours, manufacturing Average weekly initial claims for unemployment insurance Manufacturers new orders, consumer goods and materials Vendor performance, slower deliveries diffusion index Manufacturers new orders, non-defense capital goods National Economic Indicators 10

11 U.S. US Leading Economic o Index (cont.) o Building permits Stock prices, 500 common stocks Money supply (M2) Interest rate spread, 10-yr. Treasury bond less federal funds Index of Consumer Expectations National Economic Indicators 11

12 U.S. US Current (Coincident) Economic o Index Employees on nonagricultural payrolls Personal income less transfer payments Industrial production Manufacturing and trade sales National Economic Indicators 12

13 U.S. Lagging g Economic Index Average duration of unemployment Inventories to sales ratio, manufacturing and trade Labor cost per unit of output, manufacturing Average prime rate Commercial and industrial i loans Consumer installment credit to personal income ratio Consumer price index for services National Economic Indicators 13

14 Possible Leading Economic o Indicators: Initial unemployment claims New residential building permits (single family) Number Value New automobile registrations Stock index Consumer expectations (proxy) Manufacturing average weekly hours (proxy) Local Economic Indicators 14

15 Possible Coincident Economic o Indicators Retail sales Durable goods Sales tax receipts Hotel/Motel tax receipts Domestic airport passengers Employment Household Establishment Consumer confidence (proxy) Local Economic Indicators 15

16 Possible Lagging g Economic o Indicators Average duration of unemployment benefits Prime rate Bankruptcies Unemployment rate Local Economic Indicators 16

17 Data Sources & Tracking 17

18 Internal data a Local government data State/Federal government data Non-government public data Private data Data Sources/Tracking 18

19 Your organization a o may have developed d some internal indicators and/or variables that track some aspect of the local economy. Number of new businesses Customer/client calls Procurement appeals Internal Data 19

20 Tax collections o Income tax collections Sales tax collections Hotel/Motel tax collections Building permits Business licenses Local Government Data 20

21 State: Sa State Labor Department county level labor force, weekly hours, initial claims, duration of unemployment, etc. Auto registrations Federal: CPI ( Bankruptcies Administrative Office of U.S. District Courts State/Federal Government Data 21

22 Bloomberg Stock Indexes & interest rates ( Freelunch ( Conference Board Consumer Confidence ( Public Data 22

23 Data aavendors dos Example: Economy.com s Data Buffet ( Private Data 23

24 Break 15 minutes 24

25 Data Issues 25

26 Availability/Timeliness ab Seasonality Trading day adjustments Inflation Data Issues 26

27 The number one requirement for an economic indicator is that it is available in a timely manner. The best variables for an economic index are: 1. monthly series that t are 2. available with short lags and are 3. not subject to large revisions Data Issues Availability 27

28 Most economic data contain seasonal patterns. The seasonal pattern must be removed before any underlying economic trend can be identified. For monthly data, seasonal adjustment procedures estimate effects that occur in the same calendar month with similar magnitude and direction from year to year. Data Issues - Seasonality 28

29 Examples: Retail sales Christmas, back-to-school Construction Northeast in January Note: Seasonal adjustment does NOT account for anomalies in the data. Seasonal factors are estimates based on present and past experience. Future data may show a different seasonal pattern. Data Issues - Seasonality 29

30 Example: Sales Tax Revenue $15 $14 $13 (Millions) $12 $11 $10 $9 $8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec. Actual Sales Tax SA Sales Tax 30

31 Y = C + S + T + I (additive) Y = C S T I (multiplicative) C = Trend cycle S = Seasonality index T = Trading day index I = Irregular component You can use the Census X-13 ARIMA Seasonal Adjustment Program which can be found here: / / / / Data Issues - Seasonality 31

32 Recurring effects associated with individual days of the week are called trading-day d effects. Calendar months are of different lengths and begin on different days In a 28 day month, each day is represented exactly 4 times. However, in a 30 day month, two days are represented 5 times. Some economic data might be impacted by the presence of an additional weekend or weekday. Data Issues Trading Days 32

33 For example, building permit offices are usually closed on Saturday and Sunday. Thus, the number of building permits issued in a given month is likely to be higher if the month contains a surplus of weekdays and lower if the month contains a surplus of weekend days. Data Issues Trading Days 33

34 1,500 1,450 1,400 1,350 1,300 1,250 1,200 1,150 1,100 1,050 Example: Building Permits 1,000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec. Actual Permits SA Permits SA & TDA Permits 34

35 Example: Sales Tax Collections: [Actual data / (seasonal factor x trading day factor)] = Seasonally Adjusted Data Month Actual Seasonal Factor Trading Day Factor Adjusted Data November $11,545, $12,685,323 December $13,751, $12,491,019 Data Issues Trading Days 35

36 Economic o data a reported in dollars ($) has an inflation component As with seasonality, and trading day influences, inflation must be removed before any underlying economic trend can be identified. Data Issues - Inflation 36

37 Example: Sales Tax Revenue $15 $14 $13 (Millions) $12 $11 $10 $9 $8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec. Nominal Sales Tax SA & TDA Nominal Sales Tax Real SA Sales Tax 37

38 Example: Sales Tax Revenue $15 $14 $13 $12 (Millions) $11 $10 $9 $8 $7 $ Nominal Sales Tax SA & TDA Nominal Sales Tax Real SA Sales Tax 38

39 Which inflation number do you use? Consumer Price Index Personal Consumption Expenditures Price Index p// / / _ nipa.cfm Table GDP deflator Table Data Issues - Inflation 39

40 How do you convert nominal data a into inflation-adjusted data? Base year index: [Nominal data / (price index B / 100)] = Constant Data B Where B is the base year index for the current period. To change the base year: [Nominal data / (price index B / price index N )] = Constant Data N Where B is the base year index for the current period and N is the base year index for the new base year. Data Issues - Inflation 40

41 Nominal GDP Deflator Real GDP Real GDP Year GDP (2009 = 100) (2009 $s) (2013 $s) 2007 $14, $14, $15, $14, $14, $15, $14, $14, $15, $14, $14, $15, $15, $15, $16, $16, $15, $16, $16, $15, $16, [Nominal data / (price index B / 100)] = Constant Data B [14, / (97.3/100)] = 14, Inflation Adjustment Example 41

42 Nominal GDP Deflator Real GDP Real GDP Year GDP (2009 = 100) (2009 $s) (2013 $s) 2007 $14, $14, $15, $14, $14, $15, $14, $14, $15, $14, $14, $15, $15, $15, $16, $16, $15, $16, $16, $15, $16, [Nominal data / (price index B / price index N )] = Constant Data N [14, / (97.3/106.6)] = 15, Inflation Adjustment Example 42

43 The Math 43

44 1. Calculate a month-to-month o o changes 2. Standardize the changes to remove volatility 3. Sum the changes Optional: equalize the leading & lagging index 4. Compute the index using symmetric change 5. Re-base the index The Math 5 Steps 44

45 Step 1: Calculate month-to-month changes If the variable is not already in a percent change form, use a symmetric alternative to the conventional percent change formula. ensures symmetrical treatment of positive and negative changes e.g., when a one percent increase is followed by a one percent decrease, the variable has returned to its original value. Use simple arithmetic differences if the variable is: already in a percent change form is an interest rate contains zero or negative values The Math Step 1 45

46 Data 100 Conventional Percent Change Symmetric Percent Change % + 9.5% % - 9.5% % - 5.1% % 5.3% +51% 5.1% The Math Step 1: Example 46

47 Symmetric percent change: x t = 200 * (X t X t-1 )/(X t + X t-1 ) The Math Step 1 47

48 Step 2: Standardize to remove volatility Compute the standard deviation of the changes of each variable (v x ) Invert each standard deviation (w x = 1/v x ) Sum the inverted standard deviations (k = w x ) Re-state the inverted standard deviations so that they sum to 1 (r x = 1/k * w x ) The adjusted change in each variable is the month-to-month change multiplied by the corresponding component standardization factor (m t = r x * x t ) The Math Step 2 48

49 Var. 1 Chg. Standardized Chg. (M t ) Jan 7.02 v x = 0.11 Feb Mar Apr w x = 1/v x 0.22 May 4.30 = Jun Jul k = w x 0.41 Aug Sep r x = w x /k 0.75 Oct = Nov Dec x.0156 = The Math Step 2: Example 49

50 Step 3: Sum the changes Sum the adjusted d changes for each component (i t = m t ) For the coincident index, no further adjustment For the leading and lagging indexes.... Each it is multiplied by an index standardization factor f f is the ratio of the standard deviation of the percent changes for the coincident index to the standard deviation of the percent changes of the composite indexes. f lead = (v coin /v lead ) f lag = (v coin /v lag ) The Math Step 3 50

51 Var. 1 Var. 2 Var. 3 Var. 4 Sum (i t ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec The Math Step 3: Example 51

52 Step 4: Compute the index using symmetric change Make the first month index value 100 I 1 = 100 Use the symmetric change for the following months I 2 = I 1 * (200 + i 2 )/(200 - i 2 ) I 3 = I 2 * (200 + i 3 )/(200 i 3 ) etc. The Math Step 4 52

53 Sum (i t ) Index Formula: Jan Feb = 100 * {[200 + (-.55)] / [200 (-.55)]} Mar = * {[200 + (.35)] / [200 (.35)]} Apr May Jun Jul Aug Sep Oct Nov Dec The Math Step 4: Example 53

54 Step 5: Re-base the index Take the average of the index for the 12 months of the base year. b y = ( I ty )/12 Divide each I t by b y and then multiply by 100 Re-based Index = I tr I tr = (I t /b y ) * 100 The Math Step 5 54

55 Lunch 55

56 Building an Index 56

57 First, deal with the data a issues: s Seasonality Trading day adjustments Inflation Download the raw data file from: Download the seasonal factors file from: Data Issues 57

58 Raw Unadjusted Data 58

59 Seasonal & Trading Day Adjustment Import the seasonal factors and the trading day factors into the data file. Seasonal & Trading Factors 59

60 Seasonal & Trading Day Adjustment Use the formula to get the adjusted data: Data / (SF * TDF) Do this for all four variables Seasonally Adjusted Data 60

61 Inflation Adjustment Import the CPI data. Inflation 61

62 Inflation Adjustment Use the formula to adjust for inflation: SAD / (CPI/100) or SAD / (CPI/CPI average of new base year) Completely Adjusted Data 62

63 Step 1: Calculate month-to-month changes using the symmetric percent change formula: x t = 200 * (X t X t-1 )/(X t + X t-1 ) Do this for all four variables Building an Index Step 1 63

exercise, calculate the standard deviation

64 w x Step 2: Standardize the changes to remove volatility by computing the inverted standard deviation for each variables symmetrical percent change where: standard deviation is v x then invert so that w x = 1/v x For this exercise, calculate the standard deviation through January 2013 using function. Building an Index Step 2 64

65 k w x r x w x r x w x r x w x r x sum of r x Step 2: Continue to remove volatility by: Sum the inverted standard deviations (k = w x ) Re-state the inverted standard deviations so that they sum to 1 (r x = 1/k * w x ) Building an Index Step 2 65

k Step 2: Continue to remove w x r x volatility

component standardization di i factor (m t = r x

66 k Step 2: Continue to remove w x r x volatility by: x t Calculating the adjusted change m t in each variable by multiplying the the month-to-month change by the corresponding component standardization di i factor (m t = r x * x t ) Do this for all four variables Building an Index Step 2 66

67 Break 15 minutes 67

68 i t m t m t m t m t Step 3: Sum the adjusted percent changes (i t = m t ) Building an Index Step 3 68

69 I t Step 4: Compute the index Make the first month index value 100 I 1 = 100 Use the symmetric change for the following months I 2 = I 1 * (200 + i 2 )/(200 - i 2 ) Building an Index Step 4 69

70 b y Itr Step 5: Re-base the index Take the average of the index for the 12 months of the base year. b y = ( I ty )/12 Divide each I t by b y and then multiply by 100 Re-based index = I tr I tr = (I t /b y ) * 100 Building an Index Step 5 70

71 Complete Current Index 71

72 Complete Current Index 72

73 1. Every month, check for recent revisions to the previous months data. 2. Every yy year, check for major benchmark revisions to previous years data. 3. Every year, add 12 more months to the inverted standard deviation calculation. 4. Every two years, update the seasonal and trading day factors. Index Maintenance 73

74 Innovation o Index Small Business Index Entrepreneurship Index Other Types of Indexes 74

75 Alfie Meek, Ph.D. Economic Impact Group, LLC. ( LOCI Fiscal Analysis Model ( Phone: Questions? 75

FOR RELEASE: 10:00 A.M. ET, Thursday, May 20, 2010

FOR RELEASE: 10:00 A.M. ET, Thursday, May 20, 2010 The Conference Board U.S. Business Cycle Indicators SM THE CONFERENCE BOARD LEADING ECONOMIC INDEX (LEI) FOR THE UNITED STATES AND RELATED COMPOSITE ECONOMIC