Economics 210c/236a Christina Romer Fall 2016 David Romer L ECTURE 1 Overview of U.S. Macroeconomic History and Data August 24, 2016
I. B EN S. B ERNANKE , “A C ENTURY OF U.S. C ENTRAL B ANKING : G OALS , F RAMEWORK , A CCOUNTABILITY ”
Themes in Bernanke’s Speech? • Different epochs in the history of the Federal Reserve and the U.S. macroeconomy over the past century. • Changes in the monetary regime. • Changes in policymakers’ framework and ideas. • The importance of panics and financial crises.
Epochs in U.S. Macroeconomic/Monetary History over the Past Century • The early Fed (1913–1929). • The Great Depression and subsequent recovery (1929– 1941). • World War II (1941–1945 [or 1951?]) • The early postwar period (1945 [or 1951?]–1964[?]). • The Great Inflation (1964[?]–1979) • The Volcker disinflation and the Great Moderation (1979–2007). • The Great Recession and its aftermath (2007–present).
The Great Depression and Recovery
World War II and the Demobilization
The Early Postwar Period
The Great Inflation
The Volcker Disinflation & The Great Moderation
The Great Recession and Its Aftermath
II. C HRISTINA R OMER : “S PURIOUS V OLATILITY IN H ISTORICAL U NEMPLOYMENT D ATA ”
Conventional GDP Data From Martin Neil Baily, “Stabilization Policy and Private Economic Behavior”
Conventional Unemployment Data 30 25 20 Percent 15 10 5 0 1890 1898 1906 1914 1922 1930 1938 1946 1954 1962 1970 1978 1986 1994 2002 From Historical Statistics of the United States
Lebergott’s Methodology Unemployed = Labor Force – Employed • Labor force is assumed to rise linearly between decadal census estimates. • Employment in some sectors is assumed to move one-for-one with output. • Both assumptions may to exaggerate the cyclical volatility in estimated unemployment.
Romer’s Methodology: “Reverse Alchemy” • Create consistently bad series. • Make replication easier by assuming some components have no errors.
Discussion and Concerns • Might Romer’s approach overestimate, or underestimate, how much Lebergott’s procedures exaggerate cyclical movements in the prewar era? • Two general possibilities: “Structural change.” • Imperfect replication. •
Addressing the Concerns Again, two general possibilities: • Making a case the addressing potential problems would only strengthen the conclusions. • Examining auxiliary evidence.
More Consistent Unemployment Data From Christina Romer, ”Spurious Volatility in Historical Unemployment Data”
From Christina Romer, ”Spurious Volatility in Historical Unemployment Data”
From Christina Romer, ”Spurious Volatility in Historical Unemployment Data”
Implications of Findings • Quality of the data matters. • Depression stands out more. • Why wasn’t there a stabilization? • What changed in the early 1980s?
III. J OSEPH D AVIS : “A N A NNUAL I NDEX OF U.S. I NDUSTRIAL P RODUCTION , 1790-1915”
Davis’s Methodology • Tries to create a consistently good series on industrial production over time. • What are his criteria for including a component series? • What are his sources of data?
Data Sources for Davis’s Index of Industrial Production
Evaluation of Davis
From Joseph Davis, “An Annual Index of Industrial Production, 1790-1915”
Davis Index of Industrial Production 8 7 6 5 Logarithms 4 3 2 1 0 1790 1798 1806 1814 1822 1830 1838 1846 1854 1862 1870 1878 1886 1894 1902 1910 From Joseph Davis, “An Annual Index of Industrial Production, 1790-1915”
From Joseph Davis, “An Annual Index of Industrial Production, 1790-1915”
Percentage Change in Industrial Production 0.2 0.2 0.1 0.1 0.0 -0.1 -0.1 -0.2 -0.2 -0.3 1791 1796 1801 1806 1811 1816 1821 1826 1831 1836 1841 1846 1851 1856 1861 1866 1871 1876 1881 1886 1891 1896 1901 1906 1911 From Joseph Davis, “An Annual Index of Industrial Production, 1790-1915”
Percentage Change in Industrial Production 0.20 0.15 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20 1820 1825 1830 1835 1840 1845 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895 1900 1905 1910 1915 Standard Deviation 1820-1889 0.060 1890-1915 0.089
From Joseph Davis, “An Annual Index of Industrial Production, 1790-1915”
Implications of Findings • Increasing frequency of cycles after 1890 may reflect structural changes, such as the emergence of price stickiness. • Changes in volatility may reflect the emergence of demand-driven recessions. • May affect view of impact of panics in the 19 th c.
From Andrew Jalil, “A New History of Banking Panics in the United States, 1825-1929: Construction and Implications”
Strategies for Dealing with a Lack of Consistent Time-Series Data • Use consistently flawed data. • Be clever in finding more data. • Use a piece of the aggregate that might be consistent over time (pig iron production). • Use an indirect indicator that is measured better (stock prices). • Look at data for other countries that might be better.
IV. R OBERT M ARGO : “T HE M ICROECONOMICS OF D EPRESSION U NEMPLOYMENT ”
The Great Depression and Recovery
Conventional Unemployment Data 30 25 20 Percent 15 10 5 0 1890 1898 1906 1914 1922 1930 1938 1946 1954 1962 1970 1978 1986 1994 2002 From Historical Statistics of the United States
From Michael Darby, “Three-and-a-half Million U.S. Employees Have Been Mislaid: Or, An Explanation of Unemployment, 1934–1941.”
From Robert Margo, “The Microeconomics of Depression Unemployment.”
From Robert Margo, “The Microeconomics of Depression Unemployment.”
From Robert Margo, “The Microeconomics of Depression Unemployment.”
From Robert Margo, “The Microeconomics of Depression Unemployment.”
From Robert Margo, “The Microeconomics of Depression Unemployment.”
Narrative Evidence From Robert Margo, “The Microeconomics of Depression Unemployment.”
Margo’s Simple Empirical Exercise • Margo regresses a 0-1 variable for whether an individual is long-term unemployed (not on work relief) on state employment growth. Does the same thing for a 0-1 variable for whether an individual is long-term unemployed (on work relief). • He wants to interpret it as showing how long-term unemployment (of the two types) changes with aggregate demand.
From Robert Margo, “The Microeconomics of Depression Unemployment.”
Does the Empirical Exercise Make Sense? • Could there be omitted variable bias? (States with more relief jobs had faster employment growth.) • Are the two groups of long-term unemployed otherwise similar? • Are the differences statistically significant?
Implications of Findings • Micro data on unemployment may suggest that people on relief jobs should be considered employed (not unemployed). • This may help explain (some of) the anomalous relationship between unemployment, wages, and output growth in the Depression.
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