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\title[Fundamental Causes]{Fundamental Causes of Growth}
\author[Steinsson]{Jón Steinsson}
\institute[]{University of California, Berkeley}
\date[]{Fall 2025}

\begin{document}

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


\begin{frame}{Causes of Growth}
	\begin{figure}
		\centering
		\includegraphics[height = 0.38 \textheight]{FiguresTables/HsiehKlenow2010Page1.pdf}
	\end{figure}
	\begin{itemize}
		\itemsep0.5em 
		\item Development accounting is about right-most arrows
		\item In this lecture, we discuss research on the arrows to the left
	\end{itemize}
	\vspace{4mm}
	{\scriptsize Source: Hsieh and Klenow (2010)}
\end{frame}


\begin{frame}{Cross-Country Growth Regressions}
	\begin{itemize}
		\itemsep1em 
		\item 1990s saw a flourishing of research on correlates of growth
		\item Prominent studies by Barro (1991), Sala-i-Martin (1997)
		\item Some people interpreted these correlations as causal
		\item This gave ``cross-country growth regressions'' a bad name 
	\end{itemize}
\end{frame}


\begin{frame}
	\begin{figure}
		\centering
		\includegraphics[height = 0.9  \textheight]{FiguresTables/Barro1998.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Barro (1998)}
\end{frame}


\begin{frame}{Cross-Country Growth Regressions}
	\begin{itemize}
		\itemsep1em 
		\item Cross-country growth regressions hard to interpret
		\item Reverse causation: Growth may cause other things like \\ democracy / rule of law / government spending
		\begin{itemize}
			\item This is called ``modernization theory''
		\end{itemize}
		\item Omitted variables bias: Both growth and variables of interest \\ may be caused by a third factor
		\item Theory suggests that many factors have level effects as opposed to growth effects (but transition dynamics are slow)
	\end{itemize}
\end{frame}


\begin{frame}{Fundamental Causes of Growth}
	\begin{itemize}
		\itemsep1em 
		\item Many candidate causes:
		\begin{itemize}
			\item Geography, trade, institutions
			\item Luck (e.g., leaders), culture, religion, etc.
		\end{itemize}
		\item To provide convincing evidence on fundamental causes of growth, \\ we need exogenous variation in candidate causes
		\item Subsequent literature includes many creative approaches \\ to this challenging task
		\item Here we will sample a bit from this (large) literature
	\end{itemize}
\end{frame}


\begin{frame}{Institutions as a Fundamental Cause of Growth}
	\begin{itemize}
		\itemsep1em 
		\item Long-standing literature arguing that good institutions cause \\ high levels of income
		\begin{itemize}
			\item Secure property rights, rule of law, constraints on the executive, etc. 
		\end{itemize}
		\item Important work along these lines: {\small North and Thomas (1973), Jones (1981), North and Weingast (1989), North (1990), Engerman and Sokoloff (1997)}
		\item Literature mostly qualitative before late 1990s
		\item Where can we find exogenous variation in institutions?
		\begin{itemize}
			\item A few examples: North vs. South Korea, East vs. West Germany
			\item But something more systematic?
		\end{itemize}
	\end{itemize}
\end{frame}


\begin{frame}{Colonial Origins}
	\begin{itemize}
		\itemsep1em 
		\item Acemoglu, Johnson, Robinson (2001) proposed a theory of \\ institutional differences among countries colonized by Europeans
		\item Conditions in the colonies led colonizers to create different institutions in different colonies
		\item Where colonizers settled, they set up ``good'' institutions \\ (secure property rights, rule of law, constrains on the executive)
		\item Where they didn't settle, they set up ``extractive'' institutions
		\item These institutions have persisted and have affected growth \\ in these countries
	\end{itemize}
\end{frame}


\begin{frame}{Incredibly Controversial Paper}
	\begin{itemize}
		\itemsep0.5em 
		\item Some: Colonial Origins paper shouldn't get A in an undergrad class
		\item Others: Nobel Prize!
		\item Case against: 
		\begin{itemize}
			\item Ideas not original / Data low quality / Identifying restriction implausible / Questionable manipulation of data / Standard errors are wrong
		\end{itemize}
		\item Case for:
		\begin{itemize}
			\item Formal analysis using data and statistical methods constitute huge improvement in discipline imposed on analysis
			\item Traditional historical analysis vague, undisciplined (pick and choose anecdotes to fit the story) and hard to replicate
			\item Fact that it is easy to critique is a consequence of clarity of methods
			\item Pioneered causal historical analysis, which as been very influential
		\end{itemize}
	\end{itemize}
\end{frame}


\begin{frame}{Settler Mortality as an Instrument}
	\begin{itemize}
		\itemsep1em 
		\item To test their theory, AJR propose using settler mortality \\ as an instrument for current institutions
		\item Basic idea: \vspace{5pt}
		\begin{itemize}
			\itemsep0.5em 
			\item Colonizers didn't settle where settler mortality was high
			\item Settler mortality was determined by disease environment \\ (mostly malaria and yellow fever)
			\item Places with malaria and yellow fever got worse institutions
		\end{itemize} 
	\end{itemize}
\end{frame}


\begin{frame}{ARJ's Theory}
	\begin{figure}
		\centering
		\includegraphics[height = 0.6  \textheight]{FiguresTables/AcemogluJohnsonRobinson2001Theory.pdf}
	\end{figure}
	\vspace{-2mm}
	{\scriptsize Source: Acemoglu, Johnson, Robinson (2001)}
\end{frame}


\begin{frame}{Empirical Specification}
	\begin{itemize}
		\item Second stage regression:
		\[ \log y_i = \mu + \alpha R_i + \mathbf{X}'_i \gamma + \epsilon_i \]
		\item First state regression:
		\[ R_i = \zeta + \beta \log M_i + \mathbf{X}'_i \delta + v_i \] \vspace{0pt}
		\item $\log y_i$ is log GDP per capita in 1995 PPP adjusted
		\item $R_i$ is protection against expropriation from Political Risk Services \\ {\small (Is this institutions or policy? Glaeser, et al. (2004)) }
		\item $\log M_i$ is log settler mortality 
	\end{itemize}
\end{frame}


\begin{frame}{Is Settler Mortality a Valid Instrument?}
	\begin{itemize}
		\itemsep1em 
		\item For settler mortality to be a valid instrument, we must have: \vspace{5pt}
		\begin{itemize}
			\itemsep0.5em
			\item Relevance: It must strongly predict current institutions
			\item Exclusion: It must not affect current output through \\ any other channel than current institutions (conditional on controls)
		\end{itemize}
		\item What might be an important threat to the exclusion restriction? \pause
		\item Settler mortality likely correlates with current disease environment which may have a direct effect on GDP per capita today \\ {\small (Bloom and Sachs 98, Gallup and Sachs 98, Gallup et al. 98, \\ Sachs and Malaney 02, Alsan 15)}
	\end{itemize}
\end{frame}


\begin{frame}{Is Settler Mortality a Valid Instrument?}
	\begin{itemize}
		\itemsep1em 
		\item AJR argue that native population had developed immunity to \\ malaria and yellow fever
		\item Settlers died but natives didn't (after early childhood)
		\item Yellow fever has been largely eradicated since 
		\item AJR argue: ``these diseases are therefore unlikely to be the reason \\ why many countries in Africa and Asia are very poor today''
		\item Counterargument: Even if mortality is not high, morbidity is substantial \\ (also TseTse fly)
	\end{itemize}
\end{frame}


\begin{frame}{Is Settler Mortality a Valid Instrument?}
	\begin{itemize}
		\itemsep1em 
		\item Another threat: Settlers brought themselves and their human capital
		\item Hard to distinguish human capital and institutions stories
		\item Glaeser et al. (2004) argue for human capital story
	\end{itemize}
\end{frame}


\begin{frame}{Settler Mortality Variable}
	\begin{itemize}
		\itemsep1em 
		\item Largely from work of Philip Curtin (Curtin 89, 98)
		\item Not actually mortality of settlers
		\item Mostly mortality of soldiers
		\item For Latin American mortality of bishops from Gutierrez (1986) \\ rescaled for comparability with soldiers
		\item More on this in a few slides
	\end{itemize}
\end{frame}


\begin{frame}{IV as a Ratio}
	\begin{itemize}
		\item ``Reduced form'' regression:
		\[ \log y_i = \mu + \lambda \log M_i + \mathbf{X}'_i \gamma + \epsilon_i \]
		\item First state regression:
		\[ R_i = \zeta + \beta \log M_i + \mathbf{X}'_i \delta + v_i \] %\vspace{0pt}
		\item IV estimate is the ratio of $\lambda$ and $\beta$
	\end{itemize}
\end{frame}


\begin{frame}{First Stage}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8  \textheight]{FiguresTables/AcemogluJohnsonRobinson2001Figure3.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Acemoglu, Johnson, Robinson (2001)}
\end{frame}


\begin{frame}{Reduced Form}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8  \textheight]{FiguresTables/AcemogluJohnsonRobinson2001Figure1.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Acemoglu, Johnson, Robinson (2001)}
\end{frame}


\begin{frame}
	\begin{figure}
		\centering
		\includegraphics[height = 0.9 \textheight]{FiguresTables/AcemogluJohnsonRobinson2001Table4.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Acemoglu, Johnson, Robinson (2001)}
\end{frame}


\begin{frame}{Quantitatively Large}
	\begin{itemize}
		\itemsep1em 
		\item Consider two ``typical countries'': Nigeria and Chile \\ {\small (typical in that they are virtually on the regression line)}
		\item Differ by 2.24 in expropriation risk
		\item Fitted difference in $0.94\times2.24=2.06$ in logs or 7-fold in levels
		\item Actual difference: 11-fold in levels
	\end{itemize}
\end{frame}


\begin{frame}
	\begin{figure}
		\centering
		\includegraphics[height = 0.9 \textheight]{FiguresTables/AcemogluJohnsonRobinson2001Table5.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Acemoglu, Johnson, Robinson (2001)}
\end{frame}

\begin{frame}{Albouy (2012) Critique}\label{albouy1}
	\begin{itemize}
		\itemsep1em 
		\item Mortality rates for only 28 out of 64 countries from within country
		\begin{itemize}
			\item Lots of choices as to which neighboring country to use
		\end{itemize} 
		\item Seven countries (all over Africa) get (different) rates from campaigns that occurred in Mali \hyperlink{mali}{\beamergotobutton{Map}}
		\item Bishop rates for 16 Latin American countries based on 4, 5, and 10 deaths out of at-risk populations of 24, 28.5, and 30.5 bishops \\ in three regions
		\begin{itemize}
			\item Rates for three regions not statistically significantly different from each other or different from similar rates in Europe
			\item Multiplied by 4.25 to benchmark with mortality of French soldiers \\ in Mexico in 1862-3
		\end{itemize}
		\item Use campaign rates rather than barracks rates in some cases
	\end{itemize}
\end{frame}


\begin{frame}{Campaign Rates vs. Barracks Rates}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/Albouy2012Figure2B.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Albouy (2012)}
\end{frame}

\begin{frame}{Acemoglu, Johnson, Robinson Reply}
	\begin{itemize}
		\itemsep1em 
		\item Results robust to lots of variation in choices made
		\item Can cap all mortality rates at 250
		\item Results robust to various ways of benchmarking bishop data
		\item Albouy's preferred results largely driven by Gambia
		\item Little difference between activities of soldiers on campaign \\ and in barracks
	\end{itemize}
\end{frame}


\begin{frame}{Does Trade Cause Growth?}
	\begin{itemize}
		\itemsep1em 
		\item Various theoretical reasons why trade may cause growth
		\item Trade is correlated with growth
		\item But reverse causality and omitted variable bias plausible
		\item Frankel and Romer (1999): Propose to use geographical characteristics to instrument for trade
		\item Identifying assumption: Geographical characteristics in question do not have important effects on income except through their impact on trade
	\end{itemize}
\end{frame}


\begin{frame}{International vs. Within-Country Trade}
	\begin{itemize}
		\itemsep1em 
		\item Just as trade with foreigners may affect income, within-country trade may affect income
		\item Larger countries trade more within-country and less internationally (e.g., Germany vs. Belgium)
		\item If within-country trade is correlated with international trade (perhaps negatively) and affects income it will bias estimates of effect of international trade unless controlled for
		\item Frankel and Romer propose to control for country size as a proxy \\ for within-country trade
	\end{itemize}
\end{frame}

\begin{frame}{Gravity Instrument}
	\begin{itemize}
		\item Simple ``gravity'' model of trade between countries $i$ and $j$:  
		\[ \log \left( \frac{\tau_{ij}}{Y_i} \right) = a_0 + a_1 \log D_{ij} + a_2 \log S_i + a_3 \log S_j + e_{ij} \]
		where 
		\begin{itemize}
			\item $\tau_{ij}$ is trade between $i$ and $j$ (e.g., exports plus imports)
			\item $Y_i$ is GDP per person in $i$
			\item $D_{ij}$ is distance between $i$ and $j$
			\item $S_i$ is size of $i$
		\end{itemize} 
		\item Often includes other variables (e.g., colonial relation, language, etc.)
		\item Frankel and Romer only want geographical variables
	\end{itemize}
\end{frame}


\begin{frame}{More Complex Gravity Instrument}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/FrankelRomer1999Gravity.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Frankel and Romer (1999)}
\end{frame}


\begin{frame}{Gravity Instrument}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/FrankelRomer1999Table1.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Frankel and Romer (1999)}
\end{frame}


\begin{frame}{Gravity Instrument}
	\begin{itemize}
		\itemsep1em 
		\item Frankel and Romer aggregate this bilateral gravity relationship \\ to the country level 
		\item Let's rewrite gravity equation as
		\[ \log \left( \frac{\tau_{ij}}{Y_i} \right) = \mathbf{a}'\mathbf{X}_{ij} + e_{ij} \]
		\item Geographic component of overall trade for country $i$:
		\[ \hat{T}_i = \sum_{j\neq i} e^{\hat{\mathbf{a}}'\mathbf{X}_{ij}} \]
		\item $\hat{T}_i$ is the instrument Frankel and Romer use
	\end{itemize}
\end{frame}


\begin{frame}{First Stage}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/FrankelRomer1999Table2.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Frankel and Romer (1999)}
\end{frame}


\begin{frame}{Empirical Specification}
	\[ \log Y_i = a + b T_i + c_1 \log N_i + c_2 \log A_i + u_i \] \vspace{-10pt}
	\begin{itemize}
		\itemsep1em 
		\item $Y_i$ is GDP per person, $T_i$ is exports plus imports over GDP, \\ $N_i$ is population, $A_i$ is area
		\item IV regression with gravity instrument ($\hat{T}_i$) instrumenting for $T_i$
		\item Data from 1985
	\end{itemize}
\end{frame}


\begin{frame}{Trade and Income}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/FrankelRomer1999Table3.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Frankel and Romer (1999)}
\end{frame}


\begin{frame}{Correlated Causes}
	\begin{itemize}
		\itemsep1em 
		\item Three potential ``deep'' determinants of growth: \vspace{5pt}
		\begin{itemize}
			\itemsep0.5em 
			\item Geography
			\item Trade
			\item Institutions
		\end{itemize}
		\item Geography clearly exogenous \\ {\small (but may affect income through trade or institutions)}
		\item Main instruments for trade and institutions based on geography
		\item Seems tricky to tell these apart!
	\end{itemize}
\end{frame}


\begin{frame}{Geography, Trade, and Institutions}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/RodrikSubramanianTrebbi2004Figure1.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Rodrik, Subramanian, Trebbi (2004)}
\end{frame}


\begin{frame}{Institutions Rule?}
	\begin{itemize}
		\itemsep1em 
		\item Rodrik, Subramanian, Trebbi (2004):
		\begin{itemize}
			\item AJR 01 control for geography but not trade
			\item FR 99 control for geography but not institutions
		\end{itemize}
		\item They consider all three together
		\item Conclude that institutions trump other deep determinants
	\end{itemize}
\end{frame}


\begin{frame}{Empirical Specification}
	\[ \log y_i = \mu + \alpha \text{INS}_i + \beta \text{INT}_i + \gamma \text{GEO}_i + \epsilon_i \] \vspace{-10pt} 
	\begin{itemize}
		\item $\log y_i$ is GDP per capita on PPP basis
		\item $\text{INS}_i$ is rule of law measure from Kaufman, Kraay, Zoido-Lobaton 02 \\ {\small (different from expropriation risk measure used by AJR 01)}
		\item $\text{INT}_i$ is ratio of trade to GDP 
		\item $\text{GEO}_i$ is distance to the equator
		\item all regressors are standardized (unit standard deviation)
		\item Use settler mortality and gravity instrument for $\text{INS}_i$ and $\text{INT}_i$
	\end{itemize}
\end{frame}


\begin{frame} \label{RST Main}
	\begin{figure}
		\centering
		\includegraphics[height = 0.9 \textheight]{FiguresTables/RodrikSubramanianTrebbi2004Table3.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Rodrik, Subramanian, Trebbi (2004). T-stats in parentheses.}
	\hyperlink{RST OLS}{\beamergotobutton{OLS}}
	\hyperlink{RST MS}{\beamergotobutton{Market Size Controls}}
\end{frame}


\begin{frame}{Telling Geography Apart}
	\begin{itemize}
		\itemsep1em 
		\item Since instruments for $\text{INS}_i$ and $\text{INT}_i$ are based on geography, it is tricky to tell the effects of these variables apart from direct effects of geography
		\item This depends crucially on the $\text{GEO}_i$ variable (distance to the equator) being different from settler mortality (in the case of $\text{INS}_t$). 
		\item But both are imperfect proxies and not clear that current effects of geography flow through a very different variable than settler mortality (both plausibly about malaria)
	\end{itemize}
\end{frame}


\begin{frame}
	\begin{figure}
		\centering
		\includegraphics[height = 0.75 \textheight]{FiguresTables/RodrikSubramanianTrebbi2004Table6.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Rodrik, Subramanian, Trebbi (2004). T-stats in parentheses.}
\end{frame}


\begin{frame}{Not Robust to Everything}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/DollarKraay2003Table1.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Dollar and Kraay (2003).}
\end{frame}


\begin{frame}{Not Robust to Everything}
	\begin{itemize}
		\item Dollar and Kraay (2003) make institutions statistically insignificant by: \vspace{5pt}
		\begin{itemize}
			\itemsep1em 
			\item Using the large sample
			\item Dropping USA, CAN, AUS, NZL
			\item Adding controls for landlocked and population
			\item Using ``real openness'' \\ (i.e., defining openness somewhat differently from Frankel-Romer)
		\end{itemize}
	\end{itemize}
\end{frame}


\begin{frame}{More Recent Evidence on Trade and Growth}
	\begin{itemize}
		\itemsep1em 
		\item Pascali (AER 2017):
		\begin{itemize}
			\item Uses introduction of steam ships as asymmetric shock to trade \\ (wind patters less important after introduction of steam)
		\end{itemize}
		\item Feyrer (AEJ: Applied 2019):
		\begin{itemize}
			\item Improvement in air travel is time-varying / asymmetric shock to trade
		\end{itemize}
		\item Feyrer (JDE 2021):
		\begin{itemize}
			\item Exploits closing of the Suez canal between 1967 and 1975
		\end{itemize}
	\end{itemize}
\end{frame}




\begin{frame}{Institutions or Just Luck?}
	\begin{itemize}
		\itemsep1em 
		\item Much work on institutions and growth
		\item Prominent example: South Korea vs. North Korea
		\item How convincing is this?
		\item Might this be luck?
	\end{itemize}
\end{frame}


\begin{frame}{South Korea vs. North Korea}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/Acemoglu2012Korea.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Daron Acemoglu.}
\end{frame}


\begin{frame}{South Korea vs. North Korea}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/KoreaNightLight.PNG}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: National Geographic}
\end{frame}


\begin{frame}{South Korea vs. North Korea}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/Glaeseretal2004Figure1.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Glaeser, La Porta, Lopez-de-Silanes, Shleifer (2004) }
\end{frame}


\begin{frame}{Institutions or Luck?}
	\begin{itemize}
		\itemsep1em 
		\item Gleaser et al. (2004) argue that institutions view doesn't work for Korea
		\begin{itemize}
			\item South Korea grew rapidly under one-party dictatorship
			\item Only improved institutions (constraints on executive) \\ \textbf{after} long period of high growth 
		\end{itemize}
		\item Alternative: South Korea was lucky to have a ``good'' leader
		\begin{itemize}
			\item Growth took off under leadership of Park Chung-hee (1961-1979)
			\item He was not constrained by ``good institutions''
		\end{itemize}
		\item Perhaps which countries develop and which don't comes down to \\ luck regarding leaders
	\end{itemize}
\end{frame}


\begin{frame}{Do Leaders Matter?}
	\begin{itemize}
		\item Views differ sharply!
		\itemsep1em Tolstoy: Historical figures mere ex post justifications for events out of any individual's influence
		\item Marxists: ``Materialist dialectic'' holds that social and economic forces \\ trump individuals		
		\item John Keegan: The political history of the twentieth century can be found in the biographies of six men: Lenin, Stalin, Hitler, Mao, Roosevelt and Churchill.
	\end{itemize}
\end{frame}


\begin{frame}{Do Leaders Matter?}
	\begin{itemize}
		\itemsep1em 
		\item Claim: National leaders cause changes in growth
		\item Simple minded evidence: Look at changes in growth \\ when leaders change \pause
		\item But leadership transitions are non-random
		\begin{itemize}
			\item Bad shocks may cause leaders to lose power
			\item Good shocks may shield leaders
		\end{itemize}
	\end{itemize}
\end{frame}


\begin{frame}{Jones and Olken (2005)}
	\begin{itemize}
		\itemsep1em 
		\item Look at leadership transition due to leaders dying in office
		\item Consider cases when leaders die of natural causes or due to accidents (not assassinations)
		\item \textbf{Timing} of transition is random
	\end{itemize}
\end{frame}


\begin{frame}{Leader Transitions}
	\begin{figure}
		\centering
		\includegraphics[height = 0.55 \textheight]{FiguresTables/JonesOlken2005Table1.pdf}
	\end{figure}
	\vspace{-2mm}
	{\scriptsize Source: Jones and Olken (2005) }
\end{frame}


\begin{frame}{Dramatic Cases}
	\begin{figure}
		\centering
		\includegraphics[height = 0.75 \textheight]{FiguresTables/JonesOlken2005Figure1.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Jones and Olken (2005). China: Mao. Mozambique: Samora Machel (nationalized private land). \\ \vspace{-5pt} Guinea: Sekou Toure (totalitarianism/violent purges). Iran: Ayatollah Khomeini (Iran-Iraq war). }
\end{frame}


\begin{frame}{How to Measure Effect?}
	\begin{itemize}
		\itemsep1em 
		\item Timing of death is random, but timing of accession to power is not
		\item Can't use growth during leader's reign 
		\item Calculate growth in $T$ years before and after random transition
	\end{itemize}
\end{frame}


\begin{frame}{Model of Growth}
	\[g_{it} = \alpha_z \text{PRE}_z + \beta_z \text{POST}_z + v_i + v_t + \epsilon_{it} \] \vspace{-10pt}
	\begin{itemize}
		\item $z$ denotes particular leader death
		\item $\text{PRE}_z$: dummy for $T$ years prior to death
		\item $\text{POST}_z$: dummy for $T$ years after death \\ (they exclude year of death)
		\item $v_i$, $v_t$: country and year fixed effects
		\item $\epsilon_{it}$: Other influences on growth 
	\end{itemize}
\end{frame}


\begin{frame}{Test Statistics}
	\begin{itemize}
		\itemsep1em 
		\item Wald test:
		\[ J = \frac{1}{Z} \sum_{z=1}^{Z} \frac{(\widehat{\text{POST}-\text{PRE}}_z)^2}{2\hat{\sigma}^2_{\epsilon z}/T} \]
		\begin{itemize}
			\item Compares change in growth pre and post leader death to \\ usual variation in growth
			\item Under null of no effect, $Z \times J$ will be distributed $\chi^2(Z)$
			\item $\widehat{\text{POST}-\text{PRE}}_z = \alpha_z - \beta_z$ from regression on previous page  
		\end{itemize}
		\item Rank test:
		\[ K = \frac{\sum(y_z - 1/4)}{\sqrt{Z/48}} \]
		\begin{itemize}
			\item $y_z = |r_z - 1/2|$ where $r_z$ is rank of $\widehat{\text{POST}-\text{PRE}}_z$ among all $\widehat{\text{POST}-\text{PRE}}_{it}$
			\item $r_z$ is U[0,1] under null that leaders don't matter. \\ So, $E[y_z] = 1/4$ and $\text{var}[y_z] = 1/48$ 
		\end{itemize} 
	\end{itemize}
\end{frame}


\begin{frame}{Do Leaders Matter?}
	\begin{figure}
		\centering
		\includegraphics[height = 0.72 \textheight]{FiguresTables/JonesOlken2005Table3.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Jones and Olken (2005).}
\end{frame}


\begin{frame}{Magnitude of Effect}
	\begin{itemize}
		\itemsep1em 
		\item POST-PRE is 31 percent higher around leader deaths
		\item A few more assumptions imply that a one standard deviation increase in leader quality increases growth by 1.47 percentage points per year
		\item Huge effect!! \pause 
		\item For autocracies, effect is 2.1 percentage points per year
		\item For democracies, effect is zero
	\end{itemize}
\end{frame}


\begin{frame}{When Do Leaders Matter?}
	\begin{figure}
		\centering
		\includegraphics[height = 0.65 \textheight]{FiguresTables/JonesOlken2005Table5.pdf}
	\end{figure}
	\vspace{-3mm}
	{\scriptsize Source: Jones and Olken (2005). One standard deviation increase in autocratic leader quality \\ \vspace{-5pt} increases growth by 2.1 percentage points per year.}
\end{frame}


\begin{frame}{Macro Development Policy}
	\begin{itemize}
		\itemsep1em 
		\item Very little consensus on how to achieve growth
		\item One view: ``Washington Consensus'' \\ {\small (Williamson, 1990)}
	\end{itemize}
\end{frame}


\begin{frame}{Washington Consensus}
	\begin{figure}
		\centering
		\includegraphics[height = 0.6 \textheight]{FiguresTables/Rodrik2005Table2.pdf}
	\end{figure}
	\vspace{-3mm}
	{\scriptsize Source: Rodrik (2005).}
\end{frame}


\begin{frame}{One Economics, Many Recipes}
	\begin{itemize}
		\itemsep1em 
		\item West didn't follow Washington Consensus when it developed
		\item Asian Tiger's deviated substantially from Washington Consensus
		\begin{itemize}
			\item Highly state directed development
			\item Directed credit, trade protections, export subsidies
			\item Closed capital accounts until 1980s
		\end{itemize}
		\item Rodrik (2005): ``There is no unique correspondence between the \textit{function} that good institutions perform and the \textit{form} that such institutions take.''
		\begin{itemize}
			\item China's liberalization of agriculture \textit{only at the margin} \\ (Lau, Qian, Roland, 2000)
			\item China's township and village enterprises \\ (municipal rather than private property rights)
		\end{itemize}
		\item Local political economy crucial. One recipe does not fit all.
	\end{itemize}
\end{frame}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% APPENDIX
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\appendix

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

\begin{frame}{Assignments from Mali}\label{mali}
	\begin{figure}
		\centering
		\includegraphics[height = 0.8 \textheight]{FiguresTables/Albouy2012Figure1.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Albouy (2012)}
	\hyperlink{albouy1}{\beamerreturnbutton{Back}}
\end{frame}


\begin{frame} \label{RST OLS}
	\begin{figure}
		\centering
		\includegraphics[height = 0.9 \textheight]{FiguresTables/RodrikSubramanianTrebbi2004Table2.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Rodrik, Subramanian, Trebbi (2004). T-stats in parentheses.}
	\hyperlink{RST Main}{\beamerreturnbutton{Back}}
\end{frame}


\begin{frame} \label{RST MS}
	\begin{figure}
		\centering
		\includegraphics[height = 0.9 \textheight]{FiguresTables/RodrikSubramanianTrebbi2004Table8.pdf}
	\end{figure}
	\vspace{-4mm}
	{\scriptsize Source: Rodrik, Subramanian, Trebbi (2004). T-stats in parentheses.}
	\hyperlink{RST Main}{\beamerreturnbutton{Back}}
\end{frame}


\end{document}

