Factory Orders (Comprehensive Measure of Manufacturing Orders and Sales Activity) Web: Major revisions covering prior 2 months as late respondent data comes in. Census Bureau surveys 3,500 manufacturers covering 89 industry groups Factory new orders includes previously released durable goods orders data (hard products like aircraft, cars, stoves) which is around 53% of all factory orders and adds nondurable goods orders data (soft items life food, clothing and fuel which have less than 3 years useful existence). Value of manufacturers’ new orders is an excellent measure of the underlying pace of demand for manufactured goods. Durable goods orders are an excellent leading indicator of future economic (production) activity for the next 3-6 months. Non-durable goods orders (products essential for living) data possess little predictive value because the category tends to rise at a fairly stable rate every month regardless of the state of the economy. New Orders – Legally binding agreements to purchase a product for immediate or future delivery (not options on new orders). Order cancellations are subtracted out. Total orders can be distorted by surge in spending on civilian aircraft and defense. It is prudent to look at orders excluding transportation and defense. Use a 3-month moving average to smooth out wild monthly data swings. Value of Manufacturers’ Unfilled Orders (order backlog) – factory orders that have yet to be filled and shipped. Good leading indicator of future manufacturing activity and good barometer of overall health of economy. Good indicator of manufacturing resources strain.  Unfilled Orders = New Orders – Shipments. The unfilled orders-to-shipments ratio measures the logjam between new orders and shipments. Shipments – ordered products that are being delivered. Coincident economic indicator – measure of current economic activity. Values are computed net of discounts but before freight charges and excise taxes. Shipments = production –  inventory. Value of Manufacturers’ Inventory – are calculated on a current cost basis. Data foretells future economic activity. Factory inventories are typically 1/3 of all business inventories (manufacturer, wholesale, retail). One major economic concept is that  inventories is a function of the relative pace of supply and demand changes. Data are in nominal (current) dollars. To adjust for inflation, use the intermediate materials Producer Price Index. If economy is expanding at an increasing rate, inventories rise as firms keep stockrooms filled to meet expected sales. When the economy moves past the “inflection point”, where the economy is expanding at a decreasing rate, the decline in sales growth can be greater than the decline in production growth (imbalance where QS > QD) leading to a rise in factory inventories. Inventory-to-sales ratio gives a good relative perspective of production dymanics. Inventories: Important with John Maynard Keynes Aggregate Expenditure Model If production > new orders, then either  unfilled orders or  unplanned inventories =>  future production/output =>  (  Y/Y) t+1 Production Dynamics – (supply and demand imbalances of products and materials) If Y > Y POT (no slack capacity) and new orders surge, then production capacity will be constrained => production can’t keep up with pace of new orders (  new orders >  production) =>  unfilled orders (order backlog) => delay of customers deliveries =>  bottlenecks and inflation =>  investment,  labor, and  overtime =>  production capacity =>  economic activity Market Analysis: Bonds:  new orders and unfilled orders (excluding defense & aircraft) =>  manufacturing sector =>   Y/Y =>   P/P =>  D Bonds =>  i Bonds Stocks:  new orders and unfilled orders =>  profits =>  P Stocks Dollar:  new orders =>  manufacturing sector =>   Y/Y =>   P/P =>  D Bonds =>  i Bonds =>  dollar New Orders Unfilled Orders Shipments = Production –  Inventory

Why is the Aggregate Demand Curve Downward Sloping? Y = C + I + G + NX THE WEALTH EFFECT:  P =>  W/P =>  C =>  AD THE INTEREST-RATE EFFECT:  P =>  D M =>  S,  D loanable funds =>  r =>  I Res, I Bus, C Dur =>  AD THE INTERNATIONAL-TRADE EFFECT:  (PL U.S. / PL ROW ) =>  D X,  D M =>  X,  M =>  (X-M) =>  AD w I S

The Variables That Shift the Aggregate Demand Curve  GOVERNMENT POLICIES  r =>  I Res, I Bus, C Dur => AD   G => AD   t =>  DPI =>  C => AD   EXPECTATIONS OF HOUSEHOLDS AND FIRMS  optimism of Y t+1 =>  C,  I => AD   FOREIGN VARIABLES  Exchange rate (Euro/$) =>  X,  M => AD   (  Y/Y) U.S. / (  Y/Y) ROW =>  X,  M =>  (X-M) => AD   Wealth  P Assets =>  Wealth =>  C => AD  F W O G

Chicken Vs Egg  labor market =>  HH confidence =>  spending Or  spending =>  business confidence =>  labor market

Euro/$ $/Euro

Full capacity = 82-84%

23 Unemployed Involuntarily working part-time Marginally attached (want jobs but haven’t searched in a month) Frictional Unemployment Structural Unemployment Cyclical Unemployment

Variables That Shift the Short-Run Aggregate Supply Curve  LABOR FORCE,…  CAPITAL STOCK,…  TECHNOLOGY EXPECTED CHANGES IN THE FUTURE PRICE LEVEL ADJUSTMENTS OF WORKERS AND FIRMS TO ERRORS IN PAST EXPECTATIONS ABOUT THE PRICE LEVEL UNEXPECTED CHANGES IN THE PRICE OF AN IMPORTANT NATURAL RESOURCE G I P S

Consumer Credit Outstanding (Measures consumers’ willingness and ability to spend) Web: Large revisions Consumer credit outstanding, CCO, is debt not secured/collateralized by real estate. Seasonally adjusted, not annualized data (actual outstanding loan balance at month end). Large revisions require evaluating data over 3-6 month time frame to determine trend. Annualized percent change numbers reveal whether pace of indebtedness is accelerating, decelerating or falling. 2 Components: 1. Revolving credit (40% of total) – credit card, retail cards, gasoline cards 2. Non-revolving credit (60% of total) - auto, boat, mobile homes, vacation, home improvement, and education loans Net change in indebtedness:  CCO = Loan originations – Loan repayment  CCO = F [  spending,  income,  prices,  consumer confidence] Loan originations and consumer spending tend to be highly correlated but don’t have origination data. Change versus Level Analysis:  debt =>  spending =>   Y/Y High debt levels (relative to income) =>  spending &  repayments =>   Y/Y High debt levels with  interest rates or  jobs =>  non-revolving loan applications =>  spending High debt levels with  interest rates or  jobs =>  revolving credit (to maintain living standards) Market Analysis: Bonds: Unexpected  debt =>   Y/Y =>  (  P/P) E t+1 =>  D Bonds =>  i Bonds Stocks: Sustained  debt =>  spending =>  sales=>  revenues=>  profits=>  P Stocks Dollar: Unexpected  debt =>   Y/Y =>  (  P/P) E t+1 =>  D Bonds =>  i Bonds =>  P $