\pdfminorversion=6
\documentclass[10pt]{beamer}
%\documentclass[handout, 10pt]{beamer}

\mode<presentation>
\usetheme{Madrid}
%\definecolor{lightblue}{HTML}{3498DB}
\definecolor{lightblue}{HTML}{5DADE2}
\definecolor{foundersrock}{HTML}{3B7EA1}
\definecolor{goldenbear}{HTML}{FDB515}
\definecolor{berkeleyblue}{HTML}{003262}
%\definecolor{colblue2}{HTML}{0072CE} % Juan's blue
\usecolortheme[named=lightblue]{structure}
\setbeamercolor{button}{fg=white, bg=lightblue}

%\usecolortheme{seahorse}
%\usecolortheme{rose}
\usefonttheme[onlylarge]{structuresmallcapsserif}
%\usefonttheme[onlymath]{serif}

\definecolor{alertcolor}{rgb}{0.8,0,0} % dark red
%\definecolor{alertcolor}{HTML}{5DADE2} % light blue
\newcommand{\hl}[1]{\textcolor{darkred}{#1}}
\definecolor{beamer@darkred}{rgb}{0.8,0,0}
\setbeamercolor{alerted text}{fg=beamer@darkred}

\usenavigationsymbolstemplate{}

\renewcommand{\baselinestretch}{1.3}   
\setbeamersize{text margin left=0.5cm}
\setbeamersize{text margin right=0.5cm}

\usepackage[english]{babel}
\usepackage[latin1]{inputenc}
\usepackage{times}
\usepackage[T1]{fontenc}
\usepackage{pdfpages}
\usepackage{time}
\usepackage{graphicx}
\usepackage{amsmath, amssymb}
\usepackage{amsfonts}
\usepackage{url}
\usepackage{dsfont}
\usepackage{color}
\usepackage{rotating}
\usepackage{bigstrut}
\usepackage{multirow}
\usepackage{tikz}
\usepackage[customcolors]{hf-tikz}

\definecolor{lightblue}{rgb}{0.9,0.9,1}

\setbeamertemplate{itemize items}[boadilla]
\setbeamertemplate{enumerate items}[default]

% If you wish to uncover everything in a step-wise fashion, uncomment
% the following command: 
%\beamerdefaultoverlayspecification{<+->}

%\newtheorem{fact}[theorem]{Fact}
\newtheorem{question}[theorem]{Question}

\title[Consumption Risk]{Business Cycles and Productivity: \\ Is this Time Different?}
\author[]{Emi Nakamura}
\institute[]{UC Berkeley}
\date[]{September 2025}

\begin{document}

\begin{frame}
  \titlepage
\end{frame}

\begin{frame}{Business Cycles: Is this Time Different?}
	\begin{itemize}
		\item Great Recession vs. earlier recessions
		\begin{itemize}
			\item Stock and Watson (2012)
		\end{itemize} 
		\item Slow/jobless recoveries
		\item How do we measure productivity?
		\begin{itemize}
			\item Simple methods (e.g., Solow Residual)
			\item More sophisticated methods (Basu, Fernald and Kimball, 2006)
		\end{itemize}
		\item Is productivity still procyclical?
	\end{itemize}
\end{frame}

\begin{frame}{Stock and Watson (2012)}
	Often taken for granted that Great Recession was ``different''
	\begin{itemize}
		\item Recovery was puzzlingly slow
		\item Financial shocks were more important
		\item (Many papers started with this contention, and moved onto theory)
		\item e.g., Mitman-Rabinovich 14; Jaimovich-Siu 14; Restrepo 15; Berger 16; Hall 16; Begnino-Fornaro 17; Schmitt-Grohe-Uribe 17
	\end{itemize}
\end{frame}	


\begin{frame}{Slow Recoveries: Prime Age Employment}
\begin{figure}
	\centering
	\includegraphics[height=0.85   \textheight]{FiguresTables/epop1_actual_stacked_0.pdf}
	Additional slowing due to aging
\end{figure}
\end{frame}

\begin{frame}{Slow Recoveries}\label{slow_bar}	
\vspace{-0.4cm}
\begin{figure}
\centering
\includegraphics[height=0.80   \textheight]{FiguresTables/barfigure_total.pdf}
\end{figure}	
\vspace{-0.6cm}
\hyperlink{slow_table}{\beamerbutton{Table}}
\end{frame}


\begin{frame}{Stock and Watson (2012)}
	Reassess the ``conventional wisdom'' 
	\begin{itemize}
		\item Were the shocks fundamentally different?
		\item Were the dynamics fundamentally different?
		\item Was the recovery slower? 
	\end{itemize}
	Basic answer: No!
	\begin{itemize}
		\item But the shocks were bigger
		\item And the trend is changing
	\end{itemize}
\end{frame}

\begin{frame}{Stock and Watson (2012)}
	Methodology 
	\begin{itemize}
		\item 6-factor, 200-variable dynamic factor model
		\item Estimate using data through 2007Q3 
		\item Feed in realized values of factors post-2007 (but using pre-2007 loadings)
		\item Do not allow for any ``extra'' factors
		\item Apply to detrended data!
	\end{itemize}
\end{frame}

\begin{frame}{Stock and Watson (2012): Detrending}
	\begin{figure}
		\centering
		\includegraphics[height =0.8\textheight]{FiguresTables/stockandwatson2012_fig1.pdf}
	\end{figure}
\end{frame}

\begin{frame}{Detrending Procedure}
	\begin{itemize}
		\item  Series were detrended to eliminate very low frequency variation
		\item Deviations from ``local mean'' estimated using a biweight kernel
		with a bandwidth of 100 quarters
		\begin{itemize}
			\item Approximately same as taking averages over a centered moving window of +-30 quarters
			\item Kernel avoids the sharp cutoff of a moving window
		\end{itemize}
		\item Series also transformed to be stationary (e.g., log difference), mean zero
	\end{itemize}
\end{frame}


\begin{frame}{Dynamic Evolution of Factors}
	\begin{figure}
		\centering
		\includegraphics[width =1\textwidth]{FiguresTables/stockandwatson2012_0.png}
	\end{figure}
\end{frame}


\begin{frame}{What Are Factor Models?}
Key intuition:
\begin{itemize}
	\item If there is only a single factor: 
	\begin{itemize}
		\item Factor can be estimated by cross-sectional average, up to scale
		\item Can estimate factor loadings by regressing on average series
		\item With enough series, ``generated regressor'' problem goes away
	\end{itemize}
	\item Can generalize with principal components analysis for multiple factors
	\item Note: Only space spanned by factors is identified
	\item Factors themselves can be arbitrarily ``rotated''
\end{itemize}
	
\end{frame}

\begin{frame}{Data and Estimation}
\begin{itemize}
	\item Data: 200 macro time series $X_{t}$ (1959-2011), \\ drop aggregated series -- yields 132 series
	\item Principal components analysis for 1959-2007 
	\item Generate 6 ``old factors'': \\ Pre-2007 factor loadings applied to $X_{t}$ over full 1959-2011 sample
	
	\item Thought experiment: 
	\textit{Suppose forecaster was magically given a sneak preview of the six ``old'' factors for 2007-2011 (using actual values)  and computes predicted values for all 200 series. How well would these predicted values track the actuals over the recession and recovery?}
	\begin{itemize}
		\item If Great Recession was associated with new factors (financial crisis etc.) then $R^{2}$ of old factors would fall 
	\end{itemize}
	
\end{itemize}
	
	
\end{frame}


\begin{frame}{Stock and Watson (2012): \\ Dynamic Factor Model}
	\vspace{-7pt}
	\begin{figure}
		\centering
		\includegraphics[width =1\textwidth]{FiguresTables/stockwatson2012_1.png}
	\end{figure}
\end{frame}


\begin{frame}{Stock and Watson (2012): \\ Dynamic Factor Model}
	\vspace{-7pt}
	\begin{figure}
		\centering
		\includegraphics[width =1\textwidth]{FiguresTables/stockwatson2012_1b.png}
	\end{figure}
\end{frame}


\begin{frame}{Stock and Watson (2012)}
	Findings
	\begin{itemize}
		\item Factor loadings did not break (Formal break tests)
		\item No extra factor needed (e.g. financial factor) to explain ``slow'' recovery
		\begin{itemize}
			\item Employment: Recovery actually \textit{faster} than would have been predicted based on historical estimates 
		\end{itemize}
		\item Underpredicts interest rate response during Great Recession
		\begin{itemize}
			\item No surprise given model abstracts from ZLB
		\end{itemize}
		\item But: Larger shocks needed
	\end{itemize}
\end{frame}



\begin{frame}{Interpreting the Shock}
\begin{itemize}
	\item Dynamic factor methodology is agnostic about interpretation of shocks
	\item Use ``external instruments'' to interpret
	\begin{itemize}
		\item Mechanically: Regress instruments on VAR ``innovations''
		\item More later: Intuitively, error terms in stacked linear regressions
		\item Converts non-structural error terms into structural shocks \\ 
		(up to scale/sign normalization)
	\end{itemize}
	\item Nice list of instruments used in macro (as of this paper)
	\item Many instruments should be taken with a grain of salt
\end{itemize}
\end{frame}

\begin{frame}{Stock and Watson (2012): External Instruments R-squared in GDP growth}
	\begin{figure}
		\centering
		\includegraphics[height =0.6\textheight]{FiguresTables/stockandwatson2012_tab6.png}
	\end{figure}
	2007Q4: High R-squared for uncertainty, liquidity-financial shocks! 
\end{frame}

\begin{frame}{Stock and Watson (2012): External Instruments}
	Concerns raised:
	\begin{itemize}
		\item Many weak instruments
		\item Many instruments for the same thing uncorrelated
		\item Many instruments for different things correlated
	\end{itemize}
\end{frame}



\begin{frame}{Slow Recovery}
	\begin{figure}
		\centering
		\includegraphics[height =0.6\textheight]{FiguresTables/Atlantic2010SlowRecovery.png}
	\end{figure}
	Atlantic (2010): Derek Thompson- High corporate pay to blame?
\end{frame}

\begin{frame}{Slow Recovery?}
\begin{itemize}
	\item Many observers saw recovery from Great Recession as slow!
	\item How to reconcile with Stock and Watson's (2012) finding that the Great Recession was ``normal''
	\begin{itemize}
		\item  Recovery was actually \textit{faster} than DFM predicted
		\item Shock configuration did not predict particularly slow recovery

	\end{itemize}
	\item
	\item Bigger shocks, but ``typical'' dynamics
	\item Recall the data pre-processing step! 
	\item Huge role for ``trend'' as opposed to ``cycle'' in explaining slow recovery 
\end{itemize}	
\end{frame}
	
\begin{frame}{Stock and Watson (2012): Trend vs. Cycle}
	\begin{figure}
		\centering
		\includegraphics[width =0.9\textwidth]{FiguresTables/stockwatson2012_3.png}
	\end{figure}
	Post 2009Q2 growth: Great Recession vs. Earlier Recessions
\end{frame}

\begin{frame}{Stock and Watson (2012): Trends}
	\begin{figure}
		\centering
		\includegraphics[height =0.8\textheight]{FiguresTables/stockandwatson2012_fig5.png}
	\end{figure}
\end{frame}

\begin{frame}{Stock and Watson (2012): Trends}
\begin{quotation}
	...we estimate that slightly less than half of the difference between the recovery in
	employment since 2009Q2 and [earlier recoveries] is attributable to cyclical factors.... 
	Instead, most of the slowness...is attributable
	to a long-term slowdown in trend employment growth. Indeed, that slowdown
	has been dramatic:...trend annual employment
	growth slowed from 2.4 percent in 1965 to 0.9 percent in 2005. 
	The explanation ...we find most compelling
	rests on changes in underlying demographic factors, primarily the plateau
	in the female labor force participation rate (after a sharp rise during the
	1970s through the 1990s) and the aging of the workforce..this slower
	trend growth in employment translates into slower trend GDP growth...
    suggests that future recessions
	will be deeper and longer, and will have slower recoveries, than has been
	the case historically.
\end{quotation}

\end{frame}

{  \setbeamercolor{background canvas}{bg=lightblue}
	\begin{frame}
	\addtocounter{framenumber}{-1}
	\thispagestyle{empty}
	
	\begin{center}
		{\Large The Trend vs. Cycle and Gender Convergence}
	\end{center}
	
\end{frame}
}

\begin{frame}{Recall: Slow Recoveries, Prime Age}
	\begin{figure}
		\centering
		\includegraphics[height=0.85   \textheight]{FiguresTables/epop1_actual_stacked_0.pdf}
		Additional slowing due to aging
	\end{figure}
\end{frame}

\begin{frame}{What Slowed Down? \\
		 Employment, LFP, Unemployment, Productivity}
	\begin{figure}
		\centering
		\includegraphics[width =\textwidth]{FiguresTables/Fukuietal1.png}
	\end{figure}
\end{frame}





\begin{frame}{Slow Recoveries: Men versus Women}
	\vspace{-7pt}
	\begin{figure}
		\centering
		\includegraphics[height=0.9   \textheight]{FiguresTables/epop_mf_stacked_0.pdf}
	\end{figure}
\end{frame}



\begin{frame}{Five Recessions: Prime Age Employment}\label{slow_bar_gender}
	
	\vspace{-0.4cm}
	\begin{figure}
		\centering
		\includegraphics[height=0.80   \textheight]{FiguresTables/barfigure_gender.pdf}
	\end{figure}
	
	\vspace{-0.6cm}
	\hyperlink{slow_table_gender}{\beamerbutton{Table}}
	
\end{frame}


\begin{frame}{Slow Recoveries: Men versus Women}
	Strikingly different patterns for men vs. women:
	\begin{itemize}
		\item For men, recoveries have been slow since (at least) the 1970's
		\item For women, recoveries were fast and have slowed sharply
	\end{itemize} 
	\vspace{10pt}
	20th century saw a ``Grand Gender Convergence'' (Goldin 06, 14):
	\begin{itemize}
		\item Rate of convergence peaked for employment in 1970s
		\item Has slowed sharply since, to virtual plateau after 2000
	\end{itemize}
\end{frame}

\begin{frame}{Trend vs. Cycle: Female Convergence}
	\begin{itemize}
		%\addtolength{\itemsep}{0.5\baselineskip}
		\item If you superimpose a recovery on an upward trend, it will look fast
		\item If you superimpose a recovery on a downward trend, it will look slow
		\item[]
		\item Rapid rise in female employment in 1970s-80s may have \\ contributed to fast recoveries
		\item Slower growth of female employment since 1990 may have \\ contributed to slower recoveries 
	\end{itemize}
	\vspace{10pt}
	{\small (Juhn-Potter 06, Albanesi 17, Kreuger 17, CEA 17)}
\end{frame}


\begin{frame}{Female Employment Converging to Male}\label{epop_time}
	\begin{figure}
		\centering
		\includegraphics[height=0.85   \textheight]{./FiguresTables/overtime_epop_lfp_2019.pdf}
	\end{figure}	

\end{frame}


\begin{frame}{Longer Horizon}\label{long_time}
	\begin{figure}
		\centering
		\includegraphics[height=0.8   \textheight]{./FiguresTables/epop_long.pdf}
	\end{figure}
	\vspace{-1cm}
	\hyperlink{epop_time}{\beamerbutton{Back}}
\end{frame}

\begin{frame}{Longer Horizon: Age Over 24}
	\begin{figure}
		\centering
		\includegraphics[height=0.8   \textheight]{./FiguresTables/epop25above_long.pdf}
	\end{figure}
	\vspace{-1cm}
	\hyperlink{epop_time}{\beamerbutton{Back}}
\end{frame}

\begin{frame}{Accounting vs. Economics}\label{acc_econ}
	\begin{itemize}
		\itemsep1em 
		\item Gender Revolution is a big macro shock
		\item Can't assume that all else is held constant
		\begin{itemize}
			\item Entry of women may have affected men
			\item Accounting exercise assumes zero effect on men
		\end{itemize}
		\item Magnitude of GE effects crucial in determining
		whether \\ gender convergence can explain slowing overall recoveries
	\end{itemize}
\end{frame}

\begin{frame}{Crowding Out as a Sufficient Statistic}
	\begin{itemize}
		\item Identity:
		\[ L = \frac{1}{2} L_f + \frac{1}{2} L_m \]
		\item Effects of a ``female-biased shock'':
		\[ \frac{dL}{d\theta} = \frac{1}{2} \frac{dL_f}{d\theta} + \frac{1}{2} \frac{dL_m}{d\theta} \]
		\smallskip \vspace{-10pt}
		\[ \frac{dL/d\theta}{dL_f/d\theta} = \underbrace{ \hspace{12pt} \frac{1}{2} \hspace{12pt}}_{\text{Accounting}} + \frac{1}{2} \underbrace{\frac{dL_m/d\theta}{dL_f/d\theta}}_{\text{Crowding Out}} \]
		\item Effect of Gender Revolution on total employment \\ differs from accounting exercise by crowding out
	\end{itemize}
\end{frame}

\begin{frame}{Cross-State Convergence}
	\begin{figure}
		\centering
		\includegraphics[height=0.85   \textheight]{FiguresTables/epop_gap_convergence_female.pdf}
	\end{figure}
\end{frame}

\begin{frame}{No Relationship for Male Employment}
	\begin{figure}
		\centering
		\includegraphics[height=0.85 \textheight]{FiguresTables/epop_gap_convergence_male.pdf}
	\end{figure}
\end{frame}

\begin{frame}{Pass Through to Total Employment}
	\begin{figure}
		\centering
		\includegraphics[height=0.85 \textheight]{FiguresTables/epop_gap_convergence_total.pdf}
	\end{figure}
\end{frame}


\begin{frame}{Implications}
	\begin{itemize}
		\item Gender revolution led to dramatic growth in female employment in 1970's, followed by substantial slowdown
		\item Crowding out sufficient statistic for aggregate effects
		\item Cross-state analysis suggests crowding out is small!
		\item Nakamura, Fukui and Steinsson (2021)
			\begin{itemize}
			\item Gender convergence explains 60-75\% of slowdown in recoveries
			\item Consistent with Stock and Watson (2012) analysis of trends
		\end{itemize}
	\end{itemize}
\end{frame}


\begin{frame}{The Tyranny of Demographics: Fertility}
	\begin{figure}
	\centering
		\includegraphics[height=0.75 \textheight]{FiguresTables/Goldin_TFR.png}
	\end{figure}
Goldin (2025): Presentation at Jackson Hole Economic Policy Symposium
\end{frame}

{  \setbeamercolor{background canvas}{bg=lightblue}
	\begin{frame}
		\addtocounter{framenumber}{-1}
		\thispagestyle{empty}
		
		\begin{center}
			{\Large Measuring Productivity}
		\end{center}
		
	\end{frame}
}


\begin{frame}{Measuring Productivity}
Cobb-Douglas Production Function:
\[Y = AN^{\alpha}K^{1-\alpha}\]
\begin{itemize}
	\item Can measure A as Solow residual
	\item[] \[\log Y - \alpha \log {N} - (1-\alpha) \log{K} \]
	\item What could go wrong? \pause
	\begin{itemize}
		\item Wrong production function (e.g., due to aggregation)
		\item Mismeasurement
		\item Variable factor utilization
	\end{itemize}
\item Which way would the bias go? 
\end{itemize}

\end{frame}


\begin{frame}{Basu, Fernald and Kimball (2006)}
	Incorporates features of (extensive) previous literature:
	\begin{itemize}
		\item Basu and Fernald (1997) emphasize  aggregation issues
		\item Basu (1996) emphasizes variable factor utilization
		\item Basu, Fernald and Kimball (2006) puts these together
	\begin{itemize}
	\item Productivity measure is weighted average across industries \\
	$\rightarrow$ Looks beyond manufacturing
	\item New application: How do productivity shocks affect economy?
\end{itemize}		
	\end{itemize}
\end{frame}


\begin{frame}{Model}
	\begin{itemize}
		\item Y = F(AK, EHN, M, Z)
		\item A is capital utilization, E is effort, H is hours, Z is technology
		\item Many unobserved margins of adjustment
		\begin{itemize}
			\item Capital and employment (\# workers) both face adjustment costs
			\item Firms can freely vary A, E,and H without adjustment cost
			\item Can use H to proxy for utilization! 
		\end{itemize}
	\end{itemize}		
\end{frame}


\begin{frame}{Key Equation}
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =0.9   \textwidth]{FiguresTables/BNKeq1.png}	
	\end{table}
	\begin{itemize}
		\item Can show unobserved factor utilization proportional to hours of work (assumed to be flexible input)
		\item Can use $\beta dh_{i}$ to control for unobserved factor utilization
	\end{itemize}
\end{frame}


\begin{frame}{Identification}
\begin{itemize}
	\item Need variation in inputs uncorrelated with productivity
	\item Otherwise estimates of productivity will be biased (which way?)
	\item Use ``standard'' instruments in this literature
	\begin{itemize}
		\item Oil prices 
		\item Growth in government spending 
		\item Monetary shocks
	\end{itemize}
\end{itemize}


\end{frame}


\begin{frame}{Basu, Fernald and Kimball (2006)}
``Purifying'' term in production function: 
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =1   \textwidth]{FiguresTables/BFK1.png}	
	\end{table}
Adjustment is substantial!
\end{frame}

\begin{frame}{Solow Residual vs. Output and Hours}
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =1   \textwidth]{FiguresTables/BFK3.png}	
	\end{table}
\end{frame}


\begin{frame}{``Purified'' Technology vs. Hours}
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =1   \textwidth]{FiguresTables/BFK4.png}	
	\end{table}
Hours covaries negatively with purified tech shocks!
\end{frame}


\begin{frame}{Solow Residual vs. Purified Tech. Shock: Volatility}
\begin{table}
	\setcounter{table}{0}
	\centering
	\includegraphics[width =1   \textwidth]{FiguresTables/BFK2.png}	
\end{table}
\end{frame}



\begin{frame}{Response to ``Purified'' Technology}
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =0.8   \textwidth]{FiguresTables/BFK5.png}	
	\end{table}
\end{frame}

\begin{frame}{Dynamic Response of Hours}
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =0.7   \textwidth]{FiguresTables/BFK6.png}	
	\end{table}
\end{frame}



\begin{frame}{Was Marx Right (in the short run?)}
What happens to employment in response to a positive technology shock? 
\begin{itemize}
	\item RBC model: It rises! 
	\item Marx: It falls! (He talked about the long-run too) 
	\item Keynesian model: TFP improvements can also be ``bad'' for employment \\
	(In the short-run)
	\begin{itemize}
		\item What is the logic for this?
	\end{itemize}
\end{itemize}
\end{frame}


\begin{frame}{Keynesian Interpretation}
	\begin{itemize}
		\item Positive productivity shock does not lead to immediate price fall in sticky price model
		\item Hence, demand does not increase immediately
		\item Instead labor, utilization etc. falls
		\begin{itemize}
			\item Requires sub-optimal monetary policy in simple models
		\end{itemize}
	\end{itemize}		
	Other explanations:	
	\begin{itemize}
		\item Argue are less plausible
		\begin{itemize}
		\item Sectoral shifts: controlling for technological dispersion measure (for productivity shocks / sectoral shifts) makes little difference
		\item Firm-level evidence suggests ``cleansing'' mechanism less likely
	\end{itemize}
	\end{itemize}
	
\end{frame}

\begin{frame}{The Vanishing Procyclicality of Productivity}
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =0.7   \textwidth]{FiguresTables/Gali1.png}	
	\end{table}
Gali and van Rens (2021)
\end{frame}

\begin{frame}{Time Series of Labor Productivity}
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =0.6   \textwidth]{FiguresTables/SFFed1.png}	
	\end{table}
FRBSF Economic Letter, Fernald et al. (2024)
\end{frame}


\begin{frame}{The Vanishing Procyclicality of Productivity}
	\begin{table}
		\setcounter{table}{0}
		\centering
		\includegraphics[width =0.7   \textwidth]{FiguresTables/Gali2.png}	
	\end{table}
	Gali and van Rens (2021)
\end{frame}


\end{document}






















