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EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
Digitized Maps
Digitized Maps
Digitized Maps
Digitized Maps
Geo-referenced Digitized Map
Geo-referenced Digitized Map
Geo-referenced Digitized Map
Geo-referenced Digitized Map
Geo-referenced Digitized Map
Geo-referenced Digitized Map
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
Specifics of our RR database
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
Specifics of our Midwestern database
Specifics of our Midwestern database
Specifics of our Midwestern database
Specifics of our Midwestern database
Specifics of our Midwestern database
Specifics of our Midwestern database
Rivers and Canals…
Rivers and Canals…
Railroads
Railroads
Railroads
Railroads
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
EC 2333: Transportation: Topic 4
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1EC 2333: Transportation: Topic #4. 31DELETE. Currently have mappings for 1911,
Professor Robert A. Margo Harvard 1887, 1882, 1870, 1860, and 1851
University Spring 2014. (copyright dates); 1840, and 1830
2Outline. Social Savings of the (Hallberg DBF off 1851 mapping).
Railroad: Fogel Impact of RR on local 32
economies: Atack, et. al. Re-assessment of 33Measuring Transportation Access.
Fogel: Donaldson and Hornbeck (student Original version uses dummy variables.
presentation) If time: Guy Michaels on the Railroad = 1 if county has a railroad.
Interstate Highway System. Ditto for navigable river or canal. Does
3Fogel (1964). One of the most famous NOT cross county boundaries. Latest
(perhaps most famous) of all cliometrics version has % of county within Z miles of
studies. Originally a JEH paper, then a transportation. Crosses county boundaries.
dissertation, then a book. Book is about Not yet in general use, still
more (much) than the “social savings” of experimenting with Z parameter.
the RR but, for better or worse, it is the 34
social savings chapter that received the 35
most attention. HUGE follow-up literature, 36
some critical, some applying the same (or 37Did RR “cause” Midwestern urbanization
modified) method to other countries. + population growth? Midwest is of
4Definition of the Social Savings. interest because rapid RR diffusion occurs
Social savings (percent): savings in in the 1850s. Percent urban and pop
transportation costs in a given year by density increase. Is this “because” of the
shipping the same amount of goods by the RR? According to Fishlow, RRs were built
next best alternative/national income As where conditions already favored
defined, SS is an upper bound to the true development, including urbanization and
resource cost (because the true demand for density. HOWEVER, stops short of
transportation is NOT perfectly estimating RR effect (not computationally
inelastic). Application is to the RR. Next feasible at the time). Atack, et. al. use
best alternative is some combination of DID and IV: economically significant,
water and wagon transportation. Concept positive effect of RR on urbanization but
does not originate with Fogel but rather not pop density.
with engineers (and economists) in C19 38
Europe. 39
5Fogel’s Algebra. Two sectors, 40Specifics of our Midwestern database.
Transportation (T) and all other things Building on our 1860 mapping from Colton
(A). Households consume A and some portion Midwest 1848-60 from Paxson, “Railroads of
of T as a final good. T can be produced in the Old Northwest” Transactions of the
two different ways, by R or by W. Overall, Wisconsin Academy… (1914).
R is more efficient than W. Holding T 41Specifics of our Midwestern database.
fixed, if R is no longer available, then Annual new mileage open for rail service
some resources (L and K) will have to be Supplemented with map data for Iowa and
shifted from the production of A to the Missouri Extensions to 1861 and railroad
production of T. The effect of this gauges from Taylor & Neu (1956)
re-allocation on national income is, to a Additionally, rivers used by steamboats,
first approximation, the additional cost canals in operation and Great Lakes.
associated with W (i.e. it is the wage x 42Rivers and Canals…
labor reallocated + rental price of 43Data. TR database restricted to seven
capital x capital reallocated). Because T Midwestern states. Link to Haines-ICPSR.
is held fixed, this is an upper bound to Basic sample consists of 278 counties
the true SS. Also note that resources (L which (a) existed in 1840-60 (b) had fixed
and K) are fully employed. Note that SS county boundaries (c) had all data needed
can also be calculated as ? x dZ, where dZ for regressions (d) got rail in the 1850s
= efficiency gain of the RR in percentage or did not have rail before the Civil War.
terms (percent effect on costs) and ? = We need fixed boundaries because
transportation’s share of national income. Haines-ICPSR data only very recently
6 corrected for boundary changes. Got rail
7Results. Calculation performed in two in 1850s = treatment group. No rail =
stages: inter-regional and intra-regional control group. Counties in sample with RR
Inter-regional: between major shipping access in 1850 probably got railroads very
points (eg. New York-Chicago) Four late in 1840s. Not the best control sample
commodities are considered: pork, beef, for 1850s. Two outcomes: percent urban and
wheat, and corn, comprise 75 percent of log of population density
commodities shipped by rail in 1890 Big (population/square miles). Percent urban
surprise: initial calculation of is ? 2,500. Later look at 1 (percent urban
inter-regional is negative, turns positive > 0), which is, does the county have at
after adjustment is made for lower least one urban area?
insurance costs (cargo losses smaller 44Railroads. Sample counties: 278
under rail) and speedier service counties that had stable borders 1840-1860
(inventory costs lower) Intra-regional: (for controls and linkage) ACCESS to
farmers would have used more wagon transportation: county boundary defined by
transportation. This is the main effect. transportation route or county traversed
Total for four commodities is around 117 by one Alternative metrics in works.
million, Fogel inflates by ratio of value 45Research Template So Far: Effects of
added = 4.7 percent of GNP in 1890. 7. RR. Measure the “treatment” (causal)
8Follow-up Literature. Book had a HUGE effect of TR on economic outcomes at the
impact. Narrow: applications of SS concept county level. Different from previous work
to transportation improvements in other which was aggregate (e.g. “social
settings. Probably the most important is savings”). Recent studies in urban
Coatsworth on Mexico (SS is around 30 (Baum-Snow) and development (Donaldson on
percent). SS concept also applied to other India; Bannerjee, et. al. on China). We
innovations (steam engine, tractor) but adopt a “natural experiments” (reduced
not many. Broad: accelerated the shift to form) econometric approach to measure the
quantitative methods in economic history. treatment effect of RR. Popular in applied
SUBSTANTIAL critical literature at the microeconomics, gaining ground in economic
time and shortly after: McClelland, David, history circles. Ideal natural experiment:
Lebergott, Williamson, among others. Fogel treatment vs. control group, treatment is
(1979) is the response. “as good as” random. Link TR database to
9Lebergott: SS too small because RR county level outcomes, typically published
system had large economies of scale and census or IPUMS using FIPS codes.
was never even close to capacity. Use Completed papers to date mainly using
minimum of LR AC curve instead Fogel’s Midwest sample. Start with a “balanced”
response: transport costs that were panel of counties, 1850-1860 and, if
technologically feasible were never possible, 1840. Balanced: constant land
economically feasible because the system area, 1850-60. Approximately 280 counties.
always carried a mixture of goods to many Strategy #1: simple
places. Major Criticisms at the Time (1): difference-in-differences. Control group:
Technological versus Economic Definition. no RR access before Civil War. Treatment
10 group: no rail in 1850, got rail by 1860.
11Major Criticisms at the Time (2): Strategy #2: DD w/covariates (including
Perfectly Inelastic Demand and Marginal pre-existing trend if possible).
Social Rate of Return. Fogel assumes price Covariates statistically related to coming
elasticity of demand for RR is zero. of the RR and (when data relevant)
Clearly false, but especially for individual/household characteristics.
passenger transportation. How large is the Primary robustness check: instrumental
bias? Assume log-linear demand. Answer: variable. Congress authorized 60+
VERY. Measure marginal social rate of transportation surveys, 1824-1838. Draw
return (Lebergott; Nerlove). Captures straight line between starting and ending
impact of the last dollar invested, not location of survey. IV = 1 if county lies
the total impact (BUT if you could measure on this straight line. Good “first stage”.
this for every dollar invested, would be Some evidence (in SSH paper) that IV
useful). satisfies the exclusion restriction. Other
12 straight-line instruments.
13 46TR and Urbanization. Coming of RR
14Major Criticisms at the Time (3): increases trade. Midwest has comparative
Water Rates. Water rates too low advantage in agriculture. Trade has to
(McClelland and others). BUT Fogel used take place somewhere. Central place =
water rates appropriate to average urban area. Population growth occurs
distance traveled. Canals were subsidized within economically feasible vicinity of
and subsidy not reflected in SS transportation hubs. Rising farm incomes
calculation. NOT true, Fogel allows for increase demand for Midwestern
this and not that important manufactures, which take place in urban
quantitatively. Canals were monopolies and areas due to agglomeration economies. When
would have charged monopoly price in rail comes to one central place, others
absence of RR. TRUE in British case but nearby grow in anticipation of future RR
not true in US case. Also, effect is expansion. Return to this (more general)
offset by assumption of perfectly point later.
inelastic demand which is economically 47Tables 1-3. Table 1 shows distribution
inconsistent under monopoly pricing. BUT of basic sample by state Table 2 shows
even if this were maintained, effect would sample means in 1850 and 1860. Table 3,
be small because canals did not provide Panels A and B, show base
much of the water-borne transportation: differences-in-differences. Weight is land
maximum effect is around $39 million, area in 1850 for density, average
which adds about 0.3 percentage points of population for percent urban. Results for
GNP to the SS. Canals had rising LRMC. percent urban are very similar if land
Regressions in the paper suggest to the area is weight. Treatment effects are
contrary, that MC was decreasing in positive, small for population density.
tonnage shipped and if more tonnage had Table 3, last column is consistent w/
been shipped by water, canals would have Fishlow . Treatment counties became more
been designed to carry more traffic urban and densely populated in the decade
(similar to widening a modern highway). (1840s) before arrival of railroad.
15 “Demand ahead of building” rather than the
16 reverse.
17Major Criticisms of the Time (4): 48
Substitution Possibilities. Lebergott: RR 49
and water were perfect substitutes. False, 50
even on routes where the two ostensibly 51
competed. Example: Grain shipped from 52Dif-in-Dif Regression Estimates.
Chicago to NY could go by train or water. Pre-treatment differences in outcomes more
Cross-elasticity of substitution of grain general phenomenon: RR did not arrive
demand for water transportation with randomly (Fishlow). Table 4 shows
respect to rail rate is about 1. observable correlates of gaining rail
18Major Criticisms at the Time (5): RR access: high agricultural yields, growing
and Long-Run Growth. Probably the most urbanization, presence of navigable river
important criticism of SS is that it is a (-) , state dummies also matter. BTW:
SR measure not a LR measure. In LR, Table 4 specification gives good
resource costs will compound. Possible propensity score results for matching
effects on factor supply (see below). BUT estimator. Matching very close to DD.
offsetting this is possibility of faster Columns 3, 5 of Table 5 shows DID with
tech change in the next best alternative. Table 3 controls x (year = 1860
David argues that SS misses scale interactions). No change in percent urban
economies. Fogel argues back that these treatment effect, population density
cannot be at the level of the firm effect is larger and nearly significant at
(because these would already have been 5 percent level.
captured) and a reasonable assessment 53
suggests they must be small. 54
19 55IV estimation. Conditional DID may not
20 be enough if treatment correlated with
21Major Criticisms at the Time (6): error term. Need an instrumental variable.
Williamson GE model. Williamson 1974 “Congressional Survey” IV. Between 1824
presents CGE model of C19 American and 1838 Congress authorizes approximately
economy. Chapter on RR social savings. 60 transportation surveys. Location
Claims the effect is around 20 percent of (starting and end points) of most are
GNP. Charles Kahn reassesses. Finds errors reported in American State Papers. Many in
in computer program, reduces to 12 Midwest. IV = 1 if county is on straight
percent. Allows for resource costs of line between starting and end point of
transportation, reduces to 6.8 percent. survey. Idea is that shortest distance
Williamson does not measure RR, but rather between two points is a straight line and
effect of improvements in RR and water low cost construction is preferred. If we
transportation captured by inter-regional regress pre-trend (1840-50) of
convergence (MW and NE) in grain prices. urbanization or log density on survey IV
VERY large impact on regional production, w/controls (1840 urbanization, density,
industrial composition of output. water transport + state dummies),
22Lessons: The C19 Transportation coefficients on IV are very close to zero
Revolution. Mistake to think that one and insignificant. Suggests exclusion
technology just replaced another: water restriction may be ok. First stage pretty
transport experienced great gains in TFP good. Second stage coefficients are
in C19 and in C20, of course, the “wagon” positive but imprecise enough such that
became the truck The key advances prior to are not significantly different from DID
the C20 were in medium and long-haul coefficients. Results shown in Table 6.
transportation Water had advantages in the 56
very long haul but rail did better in 57
medium haul; water + rail had an enormous 58Table 7. Use Table 4 (columns 3,5) DID
advantage over wagon transportation. In coefficients to predict ? in urbanization
countries without adequate water and density. RR can “explain” a little
transportation, SS of railroad could be more than half of growth in urbanization
very high Example: Mexico (30 percent of but only a small amount of population
GDP in early 20th century). 22. growth. Implications for Fogel SS. If (1)
23Railroads Re-visited. Work on RR and RR “caused” urbanization (2) effect of RR
other aspects of transportation went out on urbanization > canals (or other
of favor by the 1980s. Return to favor in water transportation improvements) (3)
mid-2000s with studies of the interstate aggregate TFP increases because of
highway system (Baum-Snow; Michaels). urbanization, then Fogel SS is downward
Important predecessor in economic history bias. (2) and (3) are speculative at this
is Craig and Palmquist (1996) which uses point.
county level data and historical maps. 59
Atack-Margo project: uses GIS applied to 60Robustness Check (1). We keep county
digitized historical maps to generate boundaries fixed from 1850-60 but require
county-level panel database on the TR. ONLY that county existed in 1840. What if
Linked to economic outcomes typically from we fix county boundaries over 1840-60?
census data. Basic idea is to use “natural Sample size much smaller (188) counties.
experiment” econometrics: RR is a DD very similar for urbanization but
“treatment”. Similar projects underway in impact on population density is larger and
other countries (Germany, England, China, now significant. Explanatory power of
Sweden). gaining rail access for population density
24Motivation. Long tradition in ?’s 1850-60 doubles (about 10 percent) but
economics of studying the local impact of still small.
transportation improvements. Social 61
savings not especially well-suited to 62Robustness Checks (2). Alternative
this. Local effects may be useful in measure of urbanization: 1(percent urban
assessing certain outcomes not captured by > 0). This variable = 1 if percent
Fogel. Examples include technological urban is positive, 0 otherwise.
change (Sokoloff 1988 on patents and Erie Potentially useful because there are a lot
Canal), urbanization, agricultural of zeros. See below. Base DD: positive
improvements, mortality. Direct treatment effect, fairly large and
predecessor is Haines and Margo (2008). precisely measured. Ditto with controls,
Used Craig-Palmquist-Weiss (CPW) IV results similar. Rail explains 62
transportation database (1850-60) matched percent of increase in 1(percent
to published census and IPUMS. New TR data urban>0) between 1850 and 1860
set is a significant improvement over CPW. (predicted = 0.115, actual = 0.173).
Atack, Margo, and co-author studies focus 63
primarily on Midwest in 1850s. Discussion 64
today is of the primary paper (Social 65Robustness Checks (3). Useful to
Science History). compare DD with lagged dependent variable
25New Data, Part One: Background. Large (LDV). Economic reason is reverse
body of nineteenth century transportation “Ashenfelter” dip. Counties that grow
maps stored at various archives/libraries. rapidly in 1840s are more likely to get a
Previously used by scholars to measure RR BUT because experience slower growth in
change over time and construct data sets 1850s because of more rapid growth in
via visual matching (eg. Craig, Palmquist, 1840s. Fishlow missed this BUT doesn’t
and Weiss 1996). Very costly to access matter much quantitatively (DD is close to
maps on-site and many opportunities for LDV, see next table). Note that DD for
subtle and even gross error. Libraries and percent urban > LDV. Indicates that
archives have embraced the digital age. percent urban is an explosive time series.
On-line maps can be manipulated via GIS 1(percent urban > 0) is ok, however.
software to create numerical databases Because percent urban has so many zeros,
more cheaply and (potentially) more good idea to try quantile regression. Need
accurately than visual methods. GIS can to go far up the distribution (90th and
also be used to create data from scanned 95th percentiles). Pooled TSCS, with
paper maps. dummies for state, water transportation.
26New Data, Part Two: Original Version. Treatment effect of rail access is
Used in all papers to date except Atack, 0.12-0.16 (depending on weights),
Margo and Perlman (in progress). significant at 5 percent level. Still
Constructed by moving “forward” in time: significant if standard errors are
1850 uses an 1850 map, 1860 uses and 1860 bootstrapped.
map, etc. Problems with this approach but 66
apparent only after we were done. Time 67Other Results: Impact of RR. In
frame is 1850-1880, covers entire county manufacturing (1850-70): positive effect
(BUT work to date focuses primarily on of RR on % “factory” (firms with 16+
Midwest, 1850-60). Census year frequency. employees). Consistent with Adam Smith
Panel data at the county level. Archival (division of labor limited by the extent
version is “unbalanced” but mostly we work of the market). Atack, Haines, Margo
with a “balanced” sample (see below). RR (2011). Midwest: positive effect on % of
access is a 0-1 dummy (is there a RR in or acres improved and average farm values.
bordering the county at date t?). Other Atack and Margo (JTLU, 2011) Midwest:
types of transportation (e.g. canal, negative effect on ownership of land,
navigable waterway) coded as dummy possibly because of increase in minimum
variables as of 1850. PROBLEM: ignores efficient scale of farms (or credit
change over 1850s. Easy to link to census constraints). Atack and Margo (2012).
data (published) or IPUMS using county Entire country (1850-70): positive effect
FIPS code. BUT a major issue: changes in on school enrollment. Atack, Margo, and
county boundaries. So far have either (a) Perlman (in progress).
restrict sample to counties with constant 68Michaels. Recent PhD from MIT. Work
boundaries (b) ignored the problem. focuses on impact of transportation
27New TR Data Set (2). Locate and improvements and factor endowments.
download digital TR maps and/or create Motivating framework is international
digital maps from paper copies. Overlay trade. Paper on reading list focuses on
digital TR maps on digital county boundary impact of the US federal highway system.
maps. Data set starts in 1850 and System was proposed in 1940s. Idea was to
currently ends in 1880 at census year facilitate national defense, connect major
frequencies. At present GIS overlay is metro areas, and the US to Mexico and
sufficiently accurate to create 0-1 access Canada. BUT to do this, it was necessary
variables (eg. is there a rail line in a to go through rural areas. What was the
county as of a specific census date?) effect in such areas?
Water transportation access: Great Lakes 69Data. Focuses on approximately half
frontage = 1, Ocean frontage = 1, the mileage of the interstate highway
Navigable river = 1, canal = 1. Measured system, basically long highways (n = 18)
as of 1850. Given county boundaries as of built between 1959-1975. Eg: I-40, I-95.
year t, no change over time so impact Unit of observation is county. Has to be
cannot be assessed (except maybe IV). 50 percent rural in 1950 and essentially
Railroad access = 1 if railroad line no county boundary changes. Two
passes through county. Also plan to instruments: (a) 1944 plan (b) orientation
compute total mileage, number of nodes, of the county towards major urban areas
etc. Current version was constructed Key results: positive effects on retail
working forward from 1850. New version of trade, earnings from transportation,
database in progress, working backwards likelihood of working outside county of
from 1911 map, removing lines. Fixes some residence (statistically insignificant,
measurement error in access dummy and however).
should be superior for refined measures 70
like mileage (1911 map overlays on county 71
boundary maps very well). 72
28Digitized Maps. 73
29Geo-referenced Digitized Map. 74
30Geo-referenced Digitized Map. 75
31Specifics of our RR database. From 76
1911 New Century mapping, we progressively
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