Lecture 21: Institutions II Dave Donaldson and Esther Duflo 14.73 Challenges of World Poverty
Institutions II: Plan for the lecture Discussion of assigned reading (Acemoglu, Johnson and Robinson)
Causes of Long-Run Development Authors draw distinction between proximate and fundamental causes Where do the topics we ve covered so far in this course (food, education, health, technology, finance) fit in to this distinction?
Fundamental Causes Authors draw distinction between geography and institutions as two candidates for fundamental causes of long-run development. What could go wrong with drawing this distinction? What else might matter for long-run development?
Why Might Geography Matter? 1. Montesquieu: Geography affects humans (and their capacity for work, thinking and learning) directly. 2. Sachs: Geography affects other living organisms (plants, diseases, disease vectors...) directly. 3. Diamond: Geography affects the types of technologies (e.g. domesticated animals) that can be used, and these may have long-run consequences (e.g. the agricultural revolution). 4. Diamond: Geography affects ability of humans to migrate. Latitudinally-oriented continents (Eurasia) mean a large migratory range at similar climates (so can take appropriate technology with you, and will face familiar diseases wherever you go). Longitudinally-oriented continents (eg the Americas) are different. Similar argument: a large group of people facing similar environments can take advantage of economies of scale in knowledge-production.
But What are Clear Counter-examples to Geography?
Do We See Geography at Work Here?
Do We See Geography at Work Here? From Paul Romer s Charter Cities blog
Geography and the Reversal of Fortune Geography is (largely) persistent. So if geography is a key fundamental determinant of development, development should also be persistent. By contrast, institutions change (and changed big time around colonization, c 1600).
Figure 3 Reversal of Log Fortune income I per capita in 1995vs. Urbanization as a proxy for GDP today urbanization in 1995 USA 10 CAN AUS NZL SGP HKG Log GDP per capita, PPP, 1995 8 6 KNA BRB MUS ZAF GABMYS CRI PANBWA NAM LCA GRD BLZ DZA ECU TUN GTM FJI VCT DOM PRY JAM SWZ IDN MAR PHL EGY CPV GUY SLV SUR BOL LKA AGO ZWE HND GIN CIV CMR NIC COM GHA COG LSOIND PAK SEN MRT VNM GMB SDN TGO HTI CAF LAO KEN BEN NPL UGA BFABGDTCDMDG ZAR NGAZMB NER BDI ERI MLI RWA MWI MOZ TZA SLE ETH MEX TTO COL BRA DMA PER CHL BHS VEN ARG URY 0 50 100 Urbanization in 1995
Reversal of Fortune II Figure 4 Only ex-colonies plotted Log income per capita in 1995 vs. urbanization USA SGP HKG in 1500 10 CAN AUS NZL Log GDP per capita, PPP, 1995 9 8 7 CHL ARG URY VEN BRA PRY GUY DOM JAM PHL HTI MYS COL PAN CRI ECU BLZ PER GTM IDN SLV BOL LKA HND NIC PAK VNM IND LAO BGD TUN MEX DZA EGY MAR 0 5 10 15 20 Urbanization in 1500
Figure 5 Reversal of Log Fortune income III per capita in 1995 vs. log Only ex-colonies plotted population density in 1500 SGP USA 10 CAN AUS HKG NZL Log GDP per capita, PPP, 1995 8 6 ARG BWA BRA NAM SUR GUY CHL BHS BRB VEN ZAF MYS KNA GAB MEX COLTTO PAN CRI LCA DOM GRDECU TUN DMA PER BLZ DZA VCT GTM PRY JAM SWZ PHL IDN CPV MAR BOL SLV AGO ZWE HND LKA CMR NIC GIN COG MRTCOM CIV LSO GHA SEN GMB SDN PAK IND HTI CAF TGOVNM LAOKEN BEN UGA NPL ZMB TCD MDG ZAR NGA BFA BGD NER MLI ERI BDI MWI MOZ RWA TZA SLE ETH -5 0 5 Log Population Density in 1500 EGY
Two Important Qustions 1. Is the reversal of fortune necessarily evidence against the geography hypothesis? 2. Is the reversal of fortune necessarily evidence in favor of the institutions hypothesis?
Reversal of Fortune Timing Industrial revolution: industry is particularly institution-sensitive (huge up-front costs of innovating and investing) Figure 6 Timing of the Reversal Urbanization in excolonies with low and high urbanization in 1500 (averages weighted within each group by population in 1500) 25 20 15 10 5 0 800 1000 1200 1300 1400 1500 1600 1700 1750 1800 1850 1900 1920 low urbanization in 1500 excolonies high urbanization in 1500 excolonies
The Industrial Revolution Industrial revolution is key to long-run Figure development 7 Reversal, Industrialization and Divergence Industrial Production Per Capita, UK in 1900 = 100 (from Bairoch) 400 350 300 250 200 150 100 50 0 1750 1800 1830 1860 1880 1900 1913 1928 1953 US Australia Canada New Zealand Brazil Mexico India
Colonialization and the Reversal of Fortune I Colonialization was process that changed institutions a great deal. Who created the new institutions? The colonizers. What institutions did they create? (Good or bad?)
Colonialization and the Reversal of Fortune II The institutions the colonizers created depended on what was in their own self-interest. Self-interest depended on whether settlers present or not. If settlers: A bit like quasi-democracies back home need to keep the median voter happy. If no settlers: Attempt to extract as much as possible from the colony. Exploit native populations. Coerce labor into slavery. So what determined whether there were settlers or not? Could settlers survive there? Disease, etc. Were settlers needed there? Some resources just needed loads of unskilled labor, and only a few settlers to manage the process.
Settler Mortality Thinking in this manner generates another experiment in long-run development: Suppose that whether settlers were killed by the disease environment when they arrived or not (we call this: settler mortality ) was basically random. Then places with high settler mortality should have received fewer settlers, and hence received bad institutions. Then, if (for whatever reason) institutions are persistent (bad institutions beget bad institutions), then these places will have bad institutions today. Then, if bad institutions (PR) are bad for development (Y ), places with high settler mortality (SM) should have low levels of development today. Acemoglu, Johnson and Robinson (2001) pursue this logic empirically.
Settler Mortality as an Instrumental Variable We are interested in the question: Does PR cause Y? The correlation between PR and Y is strongly positive. But we are worried that this correlation does not prove causation. Suppose that SM affects PR through the incentives for colonizers to build good institutions (and the fact that institutions are persistent). Suppose further that SM affects Y only because SM affects PR and PR affects Y. Then SM can be used as an instrumental variable for PR in the regression of Y on PR.
Figure 1 The Question: Log income Does per PR capita affect in Y? 1995 vs. perceived protection against expropriation risk, 1985-95. Log GDP per capita in 1995 10 8 6 ZAR. SDN HTI MLI LUX AUS BEL CAN CHE USA SGP HKG DNK AUT JPN FRA NOR ITA ISL ARE FIN GBR KWT SWE NLD ISR NZL IRL QATBHR ESP MLT PRT GRC KOR BHS CHL OMN SAU CZE ARG URY VEN MEX CRICOL BWA GAB PAN ZAF MYS TTOTHA HUN BRA IRN TURPOL TUN ECU PER BGR DOM DZA ROM RUS GTM JORPRY JAM PHL SUR SYR MAR IDN SLV BOLGUY EGY CHN AGO HND ZWE LKA. NIC CMR COG SEN CIV PAKGHA GIN VNM MNG GMB IND TGO KEN UGA MDG BFA BGD NGA ZMB NER YEM SLE ETH MOZMWI TZA 2 4 6 8 10 Average Protection Against Expropriation Risk, 1985-1995
Figure 8 Perceived protection against expropriation The Instrument (First Stage): SM affects PR risk, 1985-95 vs. log settler mortality. Average Protection Against Expropriation, 1985-95 10 8 6 4 NZL AUS USA CAN SGP HKG MYS ZAF PAK GUY ETH MMR SUR IND CHL BRA MEXBHS TTO COL MAR VEN URY PRY CRI EGY TUN ECU DZA ARG LKA PER BOL HND GTM BGD SLV SDN IDN PNG JAM VNM DOM KEN PAN SEN NIC GNB HTI GAB TZA CMR AGO COG UGA BFA ZAR TGO CIV GIN GHA SLE NER MDG GMB NGA MLI 2 4 6 8 Log Settler Mortality
Figure 9 The Instrument (Reduced Form): SM affects Y Log income per capita in 1995 vs. log settler the reduced form slope to the first stage slope is the causal effect of PR on Y. mortality If SM affects Y only because SM affects PR, then SM is a valid IV. Further, the ratio of. 10 AUS NZL USA SGP HKG CAN Log GDP per capita in 1995 8 6 FJI ZAF MYS ETH MUS SUR GUY PAK IND CHL BRB BHS ARG URY VEN MEX COLCRI BRATTO TUN ECU PER DZA GTM PRY MAR EGY BOL SLV LKA HND BGD SDN DOM JAM PAN 2 4 6 8 Log Settler Mortality BLZ IDN NIC SEN VNM HTI LAO KEN GAB AGO CMR COGMRT CAF BEN UGA ZARTCD BFA BDI RWA TZA NER GIN MDG SLE CIV GHA TGO GMB NGA MLI
What Could Violate this Logic?
Application to Development Policy Today? How useful is the geography vs institutions debate for contemporary development policy? How useful is the AJR empirical approach for contemporary development policy? Go back in time and save more settlers? Right the wrongs of past colonial injustice? If institutions matter, what can be done to make them better?
MIT OpenCourseWare http://ocw.mit.edu 14.73 The Challenge of World Poverty Fall 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.