The Great Escape: Shocks, Learning-by-doing, and Modern Growth --Modeling the Industrial Revolution

The Great Escape: Shocks, Learning-by-doing, and
Modern Growth
--Modeling the Industrial Revolution
Xun (Henry)
Zhou
0
I. Introduction
The economic history of the last several thousand years can simply be divided
into two distinct phases: 1. prolonged stagnation in Malthusian trap since the emergence
of agricultural economies; 2. abrupt industrializations initialized by the Industrial
Revolution, followed by ongoing economic divergence around the globe.
Compared with the “dismal” first phase, the second is probably more intriguing
and mysterious, especially the starting point—the Industrial Revolution: starting from the
mid 18th century, Britain had doubled its GDP per capita in a century amid rapid
population growth; its labor productivity growth rate had increased to a sustaining rate of
1% per year in early 19th century (Craft 2005); and a large variety of far-reaching
inventions, such as the Spinning Jenny and the Steam engine, debuted in England one
after another during that era. Compared with other European or Asian economies, Britain
seemed to be not so different in many aspects. But why did the Industrial Revolution
happen in England in 18th century?
Economists have long been interested in this question. “Institutionlists” argue
that the legal changes such as the consequences of the Glorious Revolution of 1688
created a favorable climate of investment, which made the industrial revolution possible
(North and Weingast (1989), Acemoglu, Johnson, and Robinson (2005)). Joel Mokyr
(2003) suggests that the Enlightenment was the exogenous shock that changed the
fundamental dynamic of the economy. Multi-equilibrium theorists, such as Becker,
Tamura and Murphy (1990), Lucas (2002), describe the Industrial Revolution as a
transition from one equilibrium to another. Some exogenous shocks drive the economy
from a low level equilibrium of Malthusian state to a modern growth equilibrium;
Endogenous theories represented by Kremer (1993) , Prescott and Hansen (2002), and
Galor and Weil (2002) try to explain not only how the Industrial Revolution happened,
but its timing. In Kremer’s model, each person has an equal probability of producing new
ideas, and the growth rate of knowledge is a positive function of the size of population.
Thus larger population produces more ideas and faster growth, and the increasing scale is
the driver behind the modern economic growth. In Hanson and Prescott (2002), the
economy consists of two sectors: a Malthusian sector with slower TFP growth rate, and a
1
Solow sector with a faster TFP growth rate. The difference in the TFP growth rate
inevitably drives the Industrial Revolution.
Economic historians look into broad historical evidence and single out a couple of
unique advantages England owned: Pomeranz (2000) indicates that easy access to
abundant industrial materials such as coal and iron ore, establishment of colonies that
endowed Britain with an almost insatiate market for industrial products. The effect of
geographical factors on the industrial revolution has been emphasized by Jones (1981)
and Diamond (1997), who suggest that more favorable geographic conditions make
Europe less vulnerable to the risk associated with climate and disease, leading to the
economic take-off.
The literature above provides reasonable explanations in many aspects, but
neglects some important facts. In history we can find a few aborted early technical
progresses (Aiyar, Dalgaard, Moav (2005)) and industrializations in different regions: for
example, Italy from 10th century to mid 13th century, and China in its prosperous Sung
dynasty around 14th century. In these early industrializations witnessed efficient
mechanical inventions, increasing industrial productions, and improvements of economic
systems. But none of these early industrializations sustained. Then again why England?
Can we explain both the successful industrializations and the aborted ones with one
unified model?
The Industrial Revolution was phenomenal and successful more because it was a
sustained process. From 1760 to 1830, Britain had only a mild GDP per capita growth
rate short of 0.5% per year (Crafts(2005)), yet, over a span of 70 years, the English in
1830 were 50% richer than their ancestors in 1760, and afterwards the growth process
continued and accelerated. The economic transition from one state to another should
never be completely smooth and worry-free. The forces keep the economy balanced in
one equilibrium act as the resistance when the economy tries to “escape”. If the driving
force behind the industrializations are strong enough, or the resistance weak enough, the
transition will be successful, otherwise the attempts fail.
The factor that directly contributed to the industrialization and the ever-growing
income gap around the world is labor productivity. Clark (1987) points out that the
differences in labor productivity could explain most of the discrepancies economic
2
performances. To explain the Industrial Revolution, we have to give convincing answers
to why productivity growth differed in history, and what the driving force and mechanism
are behind labor productivity growth in England.
Uncertainty is also an important aspect to be incorporated in a model explaining
the Industrial Revolution. Bad news is the increase of complexity, but the good news is
that we can model the long run growth better: the Malthusian economy is greatly subject
to shocks such as weather, plagues, or wars. Inventions such as Spinning Jenny and
Steam engine can be regarded as positive shocks to the industrial sector, triggering the
industrialization. There are already attempts to address the uncertainty problem in the
long run growth. Voth and Voigtlander (2006) focus on the interplay of random events
and structural factors that facilitated industrialization. Their model includes two types of
consumers—rich and poor—and three production sectors—agriculture, manufacturing
and intermediate products. In the beginning, poor consumers can only afford food
consumption, while the rich have access to both the manufactured goods and food
products. Random, positive shocks to productivity in agricultural sector raises incomes
and the possibility that the poor to consume manufactured goods. The increase of demand
for manufactured goods then makes a sink-cost investment profitable and feasible,
triggering industrialization.
This paper constructs a feedback control framework to model industrialization as
transition processes: shocks trigger transferring of labor between sectors;
learning-by-doing effect kicks in when people start to work in the industrial sector; and
changes in income introduce demographic dynamics that further impact the growth
process. The model can illustrate the mechanism behind industrializations, explain how
the Industrial Revolution succeeded, and propose possible reasons behind the aborted
industrializations. Dynamics of a successful industrialization in the model fit the facts
during a transition from pre-industrial economy to modern world. This model also
incorporates important economic variables carefully studied by economic historians, such
as the land area, demographic changes, and term of trade. Parameters are calibrated, and
the model simulated. The main conclusions are the following: First, great inventions
provide potentials, while learning-by-doing make the take-off happen. Second, openness
is important in that it keeps the price of manufactured good from falling too fast. Third,
3
misfortunes such as lower productivity in household manufacturing or negative shocks to
agricultural sector can be conducive to modern economic growth.
The next section states historical facts, divided by different economic aspects and
region. The third section presents the model. The fourth section calibrates and simulates
the model. The fifth section follows the predictions from the simulation to infer the real
causes of the Industrial Revolution. Last section concludes.
II. Industrializations in history.
China in the 14th century
Before the Industrial Revolution, Europeans regarded China as the richest and
most powerful nation in Asia, if not in the world. In 1684, Louis XIV of France sent
Jesuit missionaries to China bearing precious gifts from the collections of the Louvre and
of the Versailles palace. Ironically, around 170 years later, the Anglo-French force
captured the capital of Qing China, looted back tons of precious and burnt down the
Summer Palace.
Compared with the elusive rise of the west, the stagnation of china is at least as
mysterious. As Joseph Needham pointed out, China, in her early days, had many
advantages to originate the Industrial Revolution: inventions that could lead to large-scale
production, capitalist workshops in several handicraft industries, large market, and a
sophisticated education system allegedly producing human capital. “The mystery lies in
China’s failure to realize its potential.” (Landes 1999)
Among China’s great potentials, the most impressive is its sophisticated
technology, compared with its contemporary European counterparts. Even the British
state-of-the-art technology in the early stage of the Industrial Revolution was no more
advanced than its counterpart in early China. Taking the textile industry as an example, it
is confirmed by archeologists that Chinese started to use the spinning wheel in the
Warring State Period (480BC-221BC). In the beginning, the Spinning Wheel was
operated by hand only: one hand held the fiber, and the other hand rotated the handle to
let the wheel spin. Several hundred years later, in the West Han Dynasty (207BC-9AD),
the invention of the pedal spinning machine freed the rotating hand, greatly improved
4
efficiency and product quality. By the end of the West Han Dynasty, the Pedal Spinning
machine had widely taken place with the hand spinning wheel. The Pedal Spinning
machine was first made of wooden materials; iron machine had been manufactured since
Ming Dynasty. The machine had 4 spindles for cotton spinning, 5 or more spindles for
flax spinning. It was the most popular spinning machine in China: even in the early last
century, it was still widely used in remote areas.
A more efficient machine, the “Big Spinning Machine” was first described in
documentation written in Yuan Dynasty (1260AD-1368AD). The “Big Spinning
Machine” was particularly interesting to scholars in that it was water-driven, fully
automatic, and able to spin as many as 32 spindles at same time. It was really big—7
meters long, 2 meters wide, made from a combination of wood and iron; its driver
adopted a lot of gears and pulleys, configured efficiently; the friction was handled exactly
well; its driving power could be either water or animal. A Pedal Spinning machine could
produce 1-3 pounds of yarn per day, while the “Big Spinning Machine” could produce
110 pounds per day. Invented 400 years earlier than Arkwright’s Water-Frame, the “Big
Spinning Machine” could be seen throughout the Southern China in Yuan Dynasty. But
this machine disappeared mysteriously right before Ming Dynasty, when cotton textile
was prevailing.
Arkwright’s Water Frame
The “Big Spinning Machine”
Another candidate: Italy’s Early Urbanization during the last millennium
In the early 14th century, Italy had a more advanced economy than England.
Cipolla (1982) states
5
‘in the early years of the fourteenth century Florence represented a dominant and
developed economy, while England and the kingdom of Naples were two decidedly
underdeveloped countries: the periphery, to use Wallerstein’s expression’.
The Italian economic advantages are reflected in its high urbanization rate. As
Britnell (1989) points out, the larger urban share of population in northern Italy implied
that a larger percentage of the population was employed outside agriculture. In 1500 the
share of north Italy's population in cities of over 10,000 inhabitants was over 16 percent,
while the England had only three percent.
Table 1. Urbanization rates in Western Europe, Central and Northern Italy, England and Wales and the
Netherlands, 1500-1850 (inhabitants > 10,000)
Western Europe
Centre-North Italy
England and Wales
The Netherlands
1500
1600
1700
1800
1850
5.6
7.6
9.2
10
16.7
16.4
14.4
13
14.2
13.3
3.2
6.1
13.4
24
40.8
15.8
24.3
33.6
28.8
29.5
Source: Malanima (2005) de Vries (1984), Wrigley (1986), Klep (1992), de Vries (1986 and 1994)
The Italian urbanization rate in the last millennium has experienced three distinct
epochs that were observed by economic historians (Malanima 2005): from 10th century
to mid 14th century; from 1300-50 to 1860-70; and from 1860-70 to 2000. The first and
last periods witnessed fast urbanizations, while the middle is characterized by a long
setback.
Between the 10th century and 1350, new industrial activities and productivity had
risen in the cities. Wool, silk and cotton industries experienced technical progresses that
made labor more productive. Banking and financial innovations increased the efficiency
of mercantile activities. This inspiring trend reversed from 1300 to 1900. Figure (1)
illustrates the prolonged setback of urbanization. The trend was reversed only after the
English industrial production pattern reached Italy.
6
Figure (1) Urbanization rates in Central-Northern Italy 1000-1951.
50
40
30
20
10
0
1000
1200
1400
1600
1800
2000
Source: Malanima (2005)
Italy’s early urbanization had reached a relatively high level. Fifteenth-century
Florence was a prosperous city—in 1425 it had a population of 60,000 and was a
self-governed, independent city-state. Twelve artist guilds that regulated the trades
contributed to Florence's commercial success. The most powerful guilds were those that
represented textile workers. Much of Florence's wealth was dependent on the
manufacture or trade of cloth, primarily wool. Florentine textile workers cleaned, carded,
spun, dyed, and wove the unfinished and untreated wool from England and Iberia. These
cloths of superb quality were sold in Italy, northern European cities, and even in eastern
countries. Other textile experts purchased inferior cloth from northern cities and
refinished it to create a superior product.
Urbanization and productivity
Malanima (2005) investigates the relation between Urbanization and productivity
by dividing the Italian economy into a rural and an urban sector, respectively
characterized by the production of food and raw materials, and of secondary goods and
services. E. A. Wrigley and K. G. Persson have developed two models linking the
agrarian productivity to urbanization. Wrigley argues that agricultural productivity is
related to the ratio of urban to rural population, which is equal to the total population
minus urban inhabitants and rural inhabitants not employed in agricultural activities.
Persson later takes into account the urban-rural gap in income and the urban marginal
propensity to consume rural goods. Persson’s estimates of agricultural labor productivity
in the Middle Ages are higher than Wrigley’s. Persson estimated that a 5 to 20 percent
7
rise in urbanization implies a doubling of labor productivity in the countryside.
The three phases of Italian urbanization accompanied well with the movement of
labor productivity, which rose from the tenth to the fourteenth centuries, and even more
after 1870, but declined in the intermediate phase, in both the urban and the rural sector.
In the period from 1000 to 1350 new industrial activities prospered the cities and
productivity rose in the countryside. A rise in income and employment followed, and
then a growth in urban production for the internal market. Technical advances in the wool,
silk, and cotton industries were making labor more productive, and improvements in road
and canal technology may have helped reduce transportation costs to the cities, thus
allowing agricultural products to be distributed over a wider area. From the second half
of the thirteenth century, importation of agricultural goods from less densely populated
southern areas to northern cities increased, whereas raw materials such as wool, cotton
and silk continued to be supplied by distant regions easily reachable by sea.
III The Industrial Revolution
Output and Productivity growth
Economists used to believe that the output and productivity growth during the
Industrial Revolution ear was abrupt and rapid (Deane and Cole (1962)). “Gradualists”
such as Crafts (1985) and Harley (1982) significantly tune down the numbers.
Table 2. Growth of the British Economy, 1700-1831 (Annual percentage growth rate)
A. Real GDP per head
Deane and Cole
Crafts
1700-1760
0.45
0.31
1760-1780
-0.04
-0.05
1781-1801
1.08
0.41
1801-1831
1.61
0.45
1700-1760
0.97
0.75
0.75
0.74
1760-7170
1.06
1.48
1.12
1.36
1770-1801
2.22
2.66
1.27
2.61
1801-1831
3.44
2.74
2.72
3.18
B. Industrial production
Deane and Cole
Hoffmann
Crafts and Harley
Cuenca Esteban
Source: Crafts and Harley (1992, 715);
Crafts (1985, 45), Cuenca Esteban (1994, 88).
In Crafts and Harley’s view, the productivity growth rate was rather small
throughout the 18th century until it reached a consistently significant level by the second
8
and third quarters of the 19th century. Even the downward revision to the output and
productivity growth entails a more gradual growth process, by the second quarter of the
19th century the sustained growth rate was unprecedented in human history. Moreover,
population increased with real wage growth, and declaring the end of Malthusian ear in
Britain.
Demographic changes:
Fertility rate in the European countries started to decline in 1870s. By 1920, the
fertility rate had been reduced by 30%. Andorka (1978) reported that over the period
1875-1920, the Crude Birth Rates declined by 44% in England, 26% in France.
Clark (2005) points out the four features characterized all pre-industrial societies:
high fertility rates, little education, dominance of physical over human capital, and low
rates of productivity growth. In comparison, modern developed economies are the
opposite: low fertility, high education, human capital as an important source of income,
and high rates of productivity growth. These facts suggest a connection between the
decline of fertility, the increase of human capital and the sustaining economic growth.
Figure 2.
Fertility rate in England 1760-1831
3.5
3
2.5
2
1.5
1
0.5
0
1750
Gross
Reproduction
Rate
Net
Reproduction
Rate
1800
Year
Source: Wrigley (1997)
Dichotomy of British Economy during the Industrial Revolution
Mokyr (1993) regards Britain during the period of the Industrial Revolution as a
dual economy in which two economies coexisted. One was the gradually developing,
traditional economy, with slow productivity and slowly rising capital-labor ratios; the
9
other is the modern sector, characterized by fast growth and modernization. This
dichotomy has gained favor from other economic historians such as Crafts (1985) and
McCloskey (1985).
As the industries in the modern sector grow, the traditional economy shrinks,
while dualism existed within as well as between various products for a long period. The
average size of agriculture and “all others” between 1780 and 1860 was 79 percent of the
British economy. McCloskey (1985) estimates that the productivity growth in the
traditional sector was about 0.6 percent per annum, while productivity in the modern
economy grew at a rate of 1.8 percent per annum during the same period.
The dichotomy of British economy during the Industrial Revolution provides a
framework depicting the Industrial Revolution: The modern sector of the economy,
which was small in the beginning, enjoyed dramatically fast technological growth.
Consequently the modern sector grew must faster than the traditional sector so that its
share in the whole economy increases steadily. Mokyr (1993) further suggests that the
rapid technological change in the modern sector could penetrated the “membrane” of the
traditional sector so that parts of the traditional sector eventually became modernized.
Inventions
Inventions are different in sizes and impacts, and this is why Mokyr divide
inventions into Microinventions and Macroinventions.
The Macroinvention of enormous economic importance during the early years of
the Industrial Revolution was the invention of mechanical spinning in the textile
industrial. Water frame, invented by Richard Arkwright in 1769, which used two pairs of
rapidly turning rollers to mimic the human fingers. Hargreaves invented Spinning Jenny
in 1765, using metal bars to guild the spun yarn onto the spindle. In 1779, Samuel
Crompton, combine the above two inventions into a hybrid of the two, called the Mule. In
the next century, the mule remained the backbone of the British cotton industry. The
economic importance of the inventions was that they provide methods to produce a yarn
of far higher quality than contemporary products at a cost that is just a small fraction
compared to the previous technique.
10
Besides the famous inventions, most inventions during the Industrial Revolution
were small, incremental improvements to know technologies. Such small-step inventions
are often the result of on-the-job learning-by-doing or of attempts to reduce the
production costs. Over time, a long sequence of such microinventions may lead to
substantial gains in productivity, major advances in quality, fuel and material saving, etc.
One well-known example is the sailing ship. Since the emergence of the fully rigged,
three-masted ship in the fifteenth century, the art of shipbuilding had been improving
steadily: Ships were cheaper to build and maintain, more durable in 1800 than in 1450.
Planking or rigging had not been changed radically, and there was no discontinuous leap
in ship design since 1500. The same is true for technologies as diverse as the cultivation
of grains, the smelting of iron ore, the printing of books, and the making of guns.
Negative shocks to British economy around the Industrial Revolution:
The early 1800’s were a period of substantial upheaval for the British economy.
Britain was fighting a long and expensive series of wars with France that drained
resources and disrupted trade. Government spending was high, and a substantial fraction
of the labor force was drawn into military service. The interruptions to foreign trade
reduced food supplies and drive up the prices. In addition, there were several years of
devastating crop failures in the early 1800’s.
IV. Model:
In this model economy, a traditional sector produces both agricultural good and
manufactured good. In the new sector, only the manufactured good is produced.
Production in the traditional sector is captured by a Cobb-Douglas production function of
agricultural good, as well as a constant function of household manufacture good. The
new sector has a linear production function, in which human capital is the only input.
Many identical agents are both workers and consumers, living for two periods. In
the first period of their life, they are fed by their parents, and receive human capital
investment, if any. In the second period, they become adults, working, consuming, and
raising their own children.
11
Adults have two economic roles in the second phase of their life. As producers, they
choose jobs either in the new sector or in the traditional sector; as consumers, they
optimize their own consumption, number of their children, and the education investment
of their children.
Production:
An agent could be either a farmer or a worker as she is endowed with one unit of
somatic capital and one unit of human capital, the latter of which represents the basic
skills in any normal human being. Income arbitrage determines the labor division ratio,
given all predetermined variable quantities and the realization of uncertainties.
In the traditional sector, land is regarded as a public property: in the beginning of
every period, the total land is distributed evenly among farmers, who will appropriate all
the output from their agricultural production. In the new sector, human capital is the only
factor, and similarly a worker reaps all she produces.
Production in the old sector has two features: 1. Economies of scope: a farmer
produces not only food, but household manufactured good. 2. Different functional form
for different productions: the food production function is of Cobb-Douglas form, while
the household manufacturing function is a constant function.
ytF = at xtα
where
xt =
X
θt Nt
ytM = A o
(1)
(2)
where at is the stochastic productivity in period t with mean μ and
variance σ 2 . θt is the percentage of population working in traditional sector in period t.
N t is the population at time t. X is the total area of land.
The agricultural productivity of the traditional sector is stochastic because the
traditional sector is easily affected by random shocks—flood, draught, policy shocks. The
household manufactured good can be viewed as a by-product of the agricultural good
production, a fruit of economies of scope.
The production function in the new sector is an increasing and concave function
of the only input—human capital. The productivity is a dynamically predetermined
12
variable, consisting of three parts: the productivity of last period, the “depreciation” of
productivity over generation, and the increase of productivity from learning-by-doing
effect.
ytM = Atn .(1 + ht )π ,
(3)
where Atn = Atn−1 − δ .( Atn−1 − ABn ).θ t −1 + γ .(1-θ t −1 ). Atn−1 ,
where, 0 < π < 1 , a parameter associates with human capital. ABn denotes the
basic technology, representing the major inventions that open up a whole field of
possibilities. γ .(1-θt −1 ) captures the learning-by-doing effect: γ is the parameter
controlling the speed of learning-by-doing; (1-θ t −1 ) implies the percentage of labor
force working the manufacturing sector has positive effect on the productivity next period.
δ .( Atn−1 − ABn ).θt −1 is the forgetting part: an intuitive explanation is that the more skill the
current generation possesses, the more the technological loss is associated with the death
of this generation. But the forgetting effect diminishes as higher and higher fraction of
labor force switching to the new sector. Similarly δ is the parameter adjusting the
speed of the forgetting effect. Parameters such as δ and γ can be different for different
technologies represented by different ABn .
Wage Income Arbitrage and Labor Division
The output of the traditional production is appropriated solely by individuals—the
income from this sector is
I o, t = at xtα + Pt M Ao
,
(4)
where Pt M is the relative price of the manufactured good, given the price of
agricultural good being normalized to 1.
Labor income in industrial sector depends only on the output of the manufactured
good and its relative price
I n,t = Pt M Atn (1 + ht )π
(5)
At the beginning of each period, a person chooses to be a farmer or a worker. If the
income in the new sector, Pt M Atn (1 + ht )π , is higher than the farmer’s income,
13
at xtα + Pt M A o , people will move from the old sector to the new sector. As a result, the
income level in the old sector will keep rising as less and less farmers remaining, until the
income levels in the two sectors eventually equalize.
at xtα + Pt M Ato = Pt M Atn (1 + ht )π
where xt =
(6)
X
θt Nt
As long as the new sector is running, the incomes in the two sectors should be
equal. Let at* denotes the minimum values of at corresponding to θt = 1
Pt M Atn (1 + ht )π − Pt M Ao
a =
,
xtα
*
t
xt =
X
Nt
(7)
When part of the population is working in the new sector, labor division ratio is
lesser than 1. In that case, we know the income arbitrage equation still holds. As we solve
for θ t , we find that
1
⎛ a ⎞α
θt = ⎜ t* ⎟
⎝ at ⎠
if at < at* .
In summary,
1
⎧
α
⎛
⎞
⎪⎪ at
θt = ⎨ ⎜ a* ⎟
⎝ t ⎠
⎪
⎪⎩ 1
if at < at*
(8)
if at* ≤ at
Proposition 1. In a successful industrialization, as labor force shifts from the traditional
sector to the new sector, θt → 0 , the industrial productivity grows at a constant rate, γ .
Proof:
Atn = Atn−1 − δ .( Atn−1 − ABn ).θt −1 + γ .(1-θt −1 ). Atn−1
= Atn−1 − δ . Atn−1.θt −1 + δ .ABn .θt −1 + γ . Atn−1 − γ .θt −1. Atn−1 ,
as θt −1 → 0, Atn ≈ Atn−1 + γ .Atn−1 ,
Atn − Atn−1
≈γ
Atn−1
14
Figure 3: Illustration of the dynamics of Atn :
Atn
Atn = f ( Atn−1 ) curve
450 line (Atn = Atn−1 )
Atn−1
Proposition 2. Given an existing potential industrial sector, the probability of
industrialization decreases with the productivity of the household manufacturing, and
increases with the productivity of the basic technology in the industrial sector.
Proof:
1
P(at < a ) = Φ(a ) =
2πσ
*
t
*
t
∫
at*
−∞
e
−
( x − μ )2
2σ 2
dx
⎛ Pt M Atn (1 + ht )π − Pt M Ao ⎞
∂
⎜
⎟
xtα
⎛ (at* − μ ) 2 ⎞ ⎝
∂Φ(at* ) ∂at*
1
⎠
=
−
exp
.
⎜
⎟
2
o
∂at* ∂Ao
∂
2
A
σ
2π .σ
⎝
⎠
=
⎛ (at* − μ ) 2 ⎞ ⎛ Pt M .Nt
1
exp ⎜ −
⎟ .⎜ −
2σ 2 ⎠ ⎝
X
2π .σ
⎝
⎞
⎟<0
⎠
⎛ Pt M Atn (1 + ht )π − Pt M Ao ⎞
∂
⎜
⎟
xtα
⎛ (at* − μ ) 2 ⎞ ⎝
∂Φ (at* ) ∂at*
1
⎠
=
exp ⎜ −
⎟.
n
n
*
2
∂at ∂AB
∂AB
2σ
2π
⎝
⎠
⎛ (at* − μ ) 2 ⎞ ⎛ Pt M (1 + ht )π .N t ⎞
1
=
exp ⎜ −
⎟.⎜
⎟>0
2σ 2 ⎠ ⎝
X
2π
⎝
⎠
15
Consumption:
The consumer side introduces the dynamics of price change as well as
demographic transitions. Agents are given, under budget constraint, choices of goods
consumptions, number of children, and children’s human capital investment. The overall
objective utility function consists of two parts: utility from goods consumption and utility
from the expected total income of children.
ρ
ρ
1
U t = U1, t + U 2, t = ⎡⎣ μ1.cF , t + μ2 .cM , t ⎤⎦ + nt +1.Et ( I t +1 )
ρ
Assume parents spend a fixed fraction, φ , of their income on their children.
Consequently the two parts of utilities are independent to each other. The utility function
of goods consumption is in the form of Constant Elasticity of Substitution.
1
U1, t = ⎡⎣ μ1.cFρ , t + μ2 .cMρ , t ⎤⎦ ρ
(9)
The coefficients μ1 , μ2 are share parameters satisfying μ1 + μ 2 = 1 , and ρ is
the elasticity of substitution. cF , t and cM , t are respectively the food consumption and
manufacture good consumption. The consumption goods ci are perfect complements
when ρ approaches infinity and perfect substitutes when ρ = 1 . When ρ = 0 , we have
the case of unit elasticity of substitution with the familiar Cobb-Douglass function.
The utility function of the total expected income of children is:
U 2, t = nt +1.Et ( I t +1 )
(10)
where nt +1 is the total number of children, and Et ( I t +1 ) is the expected income
of each child. Agents base their expectation on the current values of variables.
Et ( I t +1 ) = θ t Et ( I o , t +1 ) + (1 − θ t ) Et ( I n , t +1 )
(11)
where θt and (1 − θ t ) are the current labor division ratios, perceived by the
parents as the probabilities of their offspring taking different careers. I o , t +1 and I n , t +1 are
defined by equation (4) and (5)—the income levels of labors in the old sector and the new
sector respectively.
16
The specification of the expected income indicates that, before industrialization,
θt = 1 , Et ( I t +1 ) = Et ( I o , t +1 ) . The choice of number of children effects the expected
income of each children.
Et ( I o , t +1 ) = Et (at +1 xtα+1 + Pt +M1 Ao )
= at .xtα+1 + Pt +M1 Ao
(12)
α
⎛ X ⎞
M o
= at . ⎜
⎟ + Pt +1 A
⎝ nt +1.N t ⎠
As industrialization happens,
θt < 1
Et ( I t +1 ) = θt Et ( I o , t +1 ) + (1 − θt ) Et ( I n , t +1 ) = Et ( I n , t +1 ) + θt . ( Et ( I o , t +1 ) − Et ( I n , t +1 ) )
= Et ( I n , t +1 ) = Pt +M1 Atn+1 (1 + ht +1 )π
(13)
Budget constraint of the consumer states that the sum of the expenditure on goods
consumption and the expenditure on raising children does not exceed the consumer’s
income.
ctF + Pt M ctM + Φ (nt +1 , ht +1 ) ≤ I t
(14)
Since the income spent on children is assumed to a fixed fraction of total income,
the budget constraint can also be split into two independent parts.
Φ (nt +1 , ht +1 ) ≤ φ .I ,
(15)
ctF + Pt M ctM ≤ (1 − φ ).I
(16)
Assume the consumer can either raise unskilled children at a cost of τ for each
child, or to raise skilled children at an additional cost of human capital investment, ht +1 ,
on each child.
So the budget constraint for this trade-off distribution specifies equation (13) as:
nt +1 (τ + ht +1 ) ≤ φ .I
(17)
The cost of raising an unskilled children, τ , is assumed to be an increasing
function of income, and an increasing function of parents’ human capital level. The
specific functional form of τ ( I t ) is assumed to be:
17
τ ( ht , I t ) = [ (1 + ht ).I t ] ,
ω
where 0 < ω < 1
(18)
This functional form captures two aspects of the cost of child-raising. The
higher the income or the higher the parents’ human capital, the higher the cost of raising
children. The cost of raising unskilled children increases at slower pace as income and
human capital rise.
Optimization of goods consumption:
Set up the Lagrangian function for optimization of goods consumption:
1
L1,t = ⎡⎣ μ1.cFρ ,t + μ2 .cMρ ,t ⎤⎦ ρ + λt [ I t − (1 + φ ).ctF − (1 + φ ) Pt M ctM ]
(19)
First order conditions are the following:
1
−1
U1,ρ t μ1.cFρ ,−1t = λt (1 + φ )
1
(20)
−1
U1,ρ t μ2 .cMρ −,1t = λt .Pt M (1 + φ )
(21)
I t = (1 + φ ).ctF + (1 + φ ) Pt M ctM
(22)
When there is no trade, price will change to clear market after consumer
optimization. Combining equation (17) and (18), we have:
μ .⎛ y
P = 2 ⎜ F, t
μ1. ⎜⎝ yM , t
M
t +1
1− ρ
⎞
⎟⎟
⎠
1− ρ
⎞
θt .at xtα
μ ⎛
= 2⎜
o
n
π ⎟
μ1 ⎝ θt . At + (1 − θ t ). At .(1 + ht ) ⎠
Figure 4. Price changes in Pre-industrial economies
yM
(23)
Price changes during industrializations
yM
u (c F , c M )
u (c F , c M )
yM
2
yM
y1M
P2
P1
P1
P1
y1F
yF2
P2
yF
y F2
yF
y1F
18
Demographics:
Population and human capital accumulation dynamics comes from the
optimization problem:
s.t. nt +1 (τ + ht +1 ) ≤ φ .I t , ht +1 ≥ 0 , nt +1 ≥ 0
max nt +1.Et ( I t +1 )
(24)
Since the budget constraint is non-linear, the Lagrangian method fails here.
In the pre-industrial world θ t = 1
α
⎛ X ⎞
M o
Since Et ( I t +1 ) = a . ⎜
⎟ + Pt +1 A , we can rewrite the problem as:
⎝ nt +1.N t ⎠
o
α
1−α
max a .nt+1
⎛ X ⎞
M o
⎜ ⎟ + Pt +1 A .nt +1
⎝ Nt ⎠
s.t. nt +1 (τ + ht +1 ) ≤ φ .I t , ht +1 ≥ 0 , nt +1 ≥ 0
α
FOC:
−α
a .(1 − α ).nt +1
⎛ X ⎞
M o
⎜ ⎟ + Pt +1 A > 0
N
⎝ t⎠
So in the pre-industrial world, people always have incentive to have more
children. Consequently the human capital investment is always zero.
⎧
⎪
⇒⎨
⎪⎩
ht +1 = 0
nt +1 =
φ .I t
τ
As industrialization happens, θt < 1
max nt +1.Et ( I t +1 ) = nt +1.Pt +M1 . Atn+1 (1 + ht +1 )π
s.t. nt +1 (τ + ht +1 ) ≤ φ .I t , ht +1 ≥ 0 , nt +1 ≥ 0
⎧
⎪
⎪
⎪
⎪
⇒ ⎪⎨
⎪
⎪
⎪
⎪
⎪⎩
⎧
⎪⎪ 0
ht +1 = ⎨
⎪ π .τ − 1
⎩⎪ 1 − π
⎧ φ .I t
⎪⎪ τ
nt +1 = ⎨
⎪φ .I . (1 − π )
⎪⎩ t τ − 1
if
τ≤
if
τ>
1
π
1
π
if
τ≤
if
τ>
1
π
1
π
19
Thus the fertility rate increases with income level. When the income level
arrives at the level that π .τ ( I t ) − 1 = 0 , the human capital investment starts, and the
fertility rate reaches its peak. Since now ht +1 =
∂ht +1
π .τ − 1
π ∂τ
and
=
.
> 0 , human
∂I t
1 − π ∂I t
1− π
capital investment increases with income; and as nt +1 = φ .I t .
(1 − π )
∂τ
, and
> 1 , the
∂I t
τ −1
fertility rate decreases with income.
Figure 5. Illustration of demographic transitions:
I
start of human
capital
t
n
start of the IR
t
h
t
The Dynamical Transition from Malthusian Era to Industrial Era
20
Malthusian Era:
A significant characteristic of Malthusian Era is the stationarity of individual
income. The agricultural sector and industrial sector co-exist, but the latter is not viable
because the income in the industrial sector, which is solely determined by the industrial
technological level, is relatively low. People have no incentive to work in the industrial
sector, thus θt = 1 .
During the Malthusian Era, agricultural income is determined by several factors:
the stochastic agricultural productivity, per capita land, the household manufacture
productivity. Since the latter two factors are constant, the income level in the Malthusian
era is stochastically dependent on each period’s agricultural productivity.
In good years, people harvest relatively more agricultural goods, and the whole
economy is relatively more abundant in agricultural goods. At the pre-determined price,
people’s income level is relatively higher. Even though everyone owns and consumes the
same amount of agricultural and manufacture goods, optimization of consumption will
suggest a lower relative price level for next period.
With higher income, people have more budgets to breed their children. As long as
the income level is not too high, the cost of raising unskilled children remains in the
range that the optimal choice for parents is to raise unskilled children only, and the
human capital investment is zero.
Proposition 3 : The population in a Malthusian economy is determined by the total land
area and systematic parameters including the agricultural productivity and the household
manufacture productivity.
(Proof to come)
In bad years, the income level is relatively lower, and the dynamical changes are
reverse compared with dynamics in the good years. Thus, as the agricultural productivity
being stochastic and stationary around a certain level, income level, fertility rate, and
price all fluctuate around their respective means.
Aborted Industrializations:
21
At some points of time, the positive shocks in industrial productivity or negative
agricultural shocks are large enough so that the fixed income in the industrial section is
relatively higher than the income in agricultural sector. A certain portion of farmers will
simultaneously shift into the new sector; the wage in the traditional sector rise due to
reduced labor input; the wages in the two sectors equalizes.
As people start to work in the new sector, the productivity of the new sector will
be higher in the next period—the learning-by-doing effect kicks in. The increased
productivity raises the industrial wage and the probability of industrial production in the
next period. The process of industrialization, however, may not be smooth at all: first,
depreciation of the new productivity exists, so we need sustained negative shocks to keep
labor in the new sector to counteract the depreciation effects; second, if the price of the
manufactured good decrease too fast, then in the next period or soon later the potential
income from the new sector will be too low to attract any labor. Thus industrialization
will be choked off. Third, during a transition, negative shocks to population, for example,
the Black Death, blowing off majority of labor force in the economy, will make the
traditional sector more attractive than before, because now each person can inherit a large
land and income in the traditional sector is higher. Thus labor force shifts back into the
traditional sector; the industrial productivity is lacking of the learning-by-doing growth
and suffering from depreciation as nobody is working in the new sector anymore.
Aborted Industrialization is characterized by a sudden increase of industrial
production that is followed by a prolonged period of setback due to depreciation effect.
The theoretical process echoes many periods in different economies in history, including
the two examples of Italy and China mentioned before.
Successful Industrialization—the Onset of the Modern Era
A successful Industrialization is characterized by sustained increases of industrial
activities and consequent improvements of productivity and income.
In the beginning, sustained negative shocks to agricultural industry or large
positive shocks to the industrial sector triggers the industrialization process. As more
manufactured goods are produced, its relative price tends to decline. If the rate of
decrease is slow, possibly due to high preferences parameter ρ or openness to trade, a
22
portion of labor force stay in the industrial sector, and keep driving up the industrial
productivity. As long as the industrial productivity rise up to the level that makes at*
close to the mean of at , industrialization will happen more probable at every point of
time onwards—the modern era begins.
The budgets for breeding children increase with income level. In the beginning
the income level is not too high, the cost of raising unskilled children remains in the
range that the optimal choice for parents is to raise unskilled children only, and the
human capital investment is zero. So the fertility rate increases as the industrialization
goes on. At some point, the income level reaches the threshold that predicts a large
enough cost for raising unskilled children—the cost that make the return to investment in
unskilled children relatively lower than the return to human capital investment. Then
parents start to invest a portion of the budget in human capital investment. This portion
increases with the rising income level, while the fertility rate keeps declining, because the
investment in breeding unskilled children is less and less attractive. This dynamical
process is smooth due to the decreasing return to human capital and nice functional form
of the child-breeding cost.
V. Simulation:
Calibration:
Calibrations for the Industrial Revolution started with Stokey (2001) and Crafts
(2000). Stokey (2001) calibrated an aggregative model with data from Great Britain in
1850, to study the role of growing foreign trade, the declining cost of power, and
technical change in manufacturing over the period 1780-1850. She concludes that
technical changes contributed significantly to growth, while the growth in trade was
important in redistributing income. Crafts and Harley (2000) used a CGE model to
examine the importance of broad-based technological change, and suggest that slow,
sector-specific improvements in Total Factor Productivity are compatible with the
observed pattern of trade. An probabilistic approach to calibrate the Industrial Revolution
was brought out in Lagerlöf (2003), indicating that mortality fluctuations eventually lead
to a transition to self-sustaining growth. Voth (2006) adopts a probabilistic model in the
spirit of Lagerlöf (2003), and calibrate it to match the main characteristics of transition of
23
the English economy from 1700 to 1850.
Apps and Rees (2000) find that in households with two children and the traditional
market/household division of labor, the overall cost of both children’s consumption of
market goods is estimated to be around 23 and 34 per cent of that of the household,
depending on the choice of distribution. These estimates increase to around 40 and 47 per
cent in non-traditional households (in which the female partner works an average of q508
hours per year). When the costs of parental time devoted to child care and domestically
produced goods are added in, the value of both children’s consumption allocation is
estimated to be around 51 and 56 per cent of total consumption in the first kind of house,
and around 49 and 54 per cent in the second.
Per capita land:
Land was the key factor of production in the pre-industrial era with fixed supply. In
accordance with the Law of Diminishing Return, output per worker fells as the labor
supply increases, given a constant technological level. Consequently the average amount
of material consumption available per person fell with population increases—the fact that
characterizes the Malthusian Trap.
According to Qing Shi Lu, a historical documentation of Qing Government, the
arable land in China was 87,310,568 acres in 1662; 140,026,382 acres in 1722; this
number increased a little afterward and reached an estimate of 164,736,900 acres of total
arable land including the unregistered in late 18th century. Comparatively the current
Chinese arable land area is 245,458,010 acres. From the same source, the total population
was 20-30 million in 1722, some 300 million in 1797, over 400 million in 1834.
Because of lacking more detailed data, some primitive estimate is used to calculate
the per capita land. I simply use the estimate, 164,736,900 acres, as the land area in 1797
to divide by the estimate of total population. The result is 0.55 acres per person.
In contrast, in England, population was roughly 9.2 million and farmland area was
about 26 million acres. Thus the ratio in England is 2.8 acres per capita--5 times higher.
24
Table 3
Farmland and population in England relative to Western Europe
England
Population (m)
Farm Area (m. ac.)
Acres/N
Western Europe
Population (m)
Farm Area (m. ac.)
Acres/N
1800-9
1860-9
9.2
26
2.8
21
26
1.2
103
317
3.1
152
317
2.1
Source: Clark(2006)
The agricultural stochastic shock is assumed to be normally distributed with
mean one and standard error 0.4( μ = 5, σ 2 = 0.16 ). The mean may need to be calibrated
to fit history, while the standard error is chosen based on historical observations (Clark
(2001)).
Generating Stochastic productivity in the traditional sector.
Figure (2) An example of the stochastic shock:
( μ = 5,
σ = 0.16 ), smoothed as MA(2).
2
Generated as Normal Distribution
Number of observations=100.
The variance σ 2 is set to 0.16 throughout this sector because this
magnitude can well simulate the actual productivity in agricultural sector. Since the
originally generated data is still choppy, I smooth it by implementing MA(2). Some
economic literatures use AR(p) for stochastic shock Voth(2006), which implies that
current shock has influences on the infinite future. But the traditional productivity in my
model is stochastic due to shocks such as flood or draught, which can not be
appropriately modeled as AR processes.
25
Total land is set to be X=10000. Population is initialized to be 5000. Thus we
have a per capita land of 2 units in Malthusian Era. Voth(2006) documents that the shares
of labor, land and capital in the Malthusian economy are respectively 0.4, 0.4, 0.2. Since
the capital share is missing in my model, the land’s share in income, alpha, is set to be
0.5;
δ , the depreciation of productivity, is set to be 0.05. The parameter controlling the
speed of learning-by-doing, γ , is set to be 0.04.
( Work in progress here )
Simulation 1: Dynamics in a Malthusian economy.
26
The Malthusian economy is characterized by stationarity in most economic variables. The
fluctuation is introduced by the randomness in agricultural productivity. The relative
price and income move in the same direction, reflecting the relative abundance of the
household manufactured goods. The fertility rate, which is a function of the income level,
converges to and fluctuates around 1, indicating that each generation, on average, only
reproduce themselves. As a result, there is no sustained population growth in the long
run.
Simulation 2: Industrialization triggered by positive shocks to industrial productivity
27
Simulation 3: Industrialization triggered by negative shocks to agricultural production.
28
Simulation 2 and 3 illustrate two examples of successful industrializations. The economy
in Simulation 2 takes off because of a large positive shock to the industrial sector,
attracting a portion of labor into the industrial sector. Consequently the industrial
29
productivity is driven up further by the learning-by-doing effect. After At surpasses the
Malthusian mean income level in the traditional sector, a further increase of At becomes
routine, and we are in the modern growth era.
In simulation 3, the traditional productivities are exceptional low for two
generation, pushing some people into the new sector in each period. Consequently the
productivity in the new sector rises due to learning-by-doing. After At surpasses the
Malthusian mean income level in the traditional sector, a further increase of At becomes
routine, and the industrialization process sustains.
Simulation 4: Aborted Industrialization—positive shocks not high enough to generate a
sustained growth
30
Simulation 5: Aborted Industrialization—negative shocks not large or persistent enough
to generate a sustained growth
The traditional productivities are exceptional low for two generation, pushing some
people into the new sector in each period. Consequently the productivity in the new
sector rises due to learning-by-doing. Unfortunately the upward trend is snapped as the
traditional productivity moves back to average level soon. Again all people work in the
traditional sector, and there are no more increments in the new productivity, which slides
back to its initial level due to the depreciation effect.
31
VI. Why not China or Italy
The curse of high household manufacturing productivity---British industrial
textile V.S. Chinese household textile
Since late 18th century, British industrialized textile sector had swept traditional
textile industries not only domestically but around the world. Interestingly it was upset by
China’s traditional textile industry when it first tried to enter this immense market.
Before the Opium War, a large share of China’s textile production is in the
household-based handicraft industry. The technologically inferior textile production was
always associated with farming that constitutes small economic units of great vitality.
After the Opium Wars, it was this household handicraft production that gave a hard time
to the foreign machine-made textiles.
When the British textiles first arrived in China, the sales record was dismal in such a
huge market of 360 million consumers. The total consumption of British textile goods in
China was 1,129,799 pounds, 0.75 pennies per capita—a figure that was way below
average consumption in the rest of the world. Haiti was consuming 1 shilling and 9.25
pennies per capita; Honduras, a British colony with a population of 14,600, was
consuming 3 pounds 17 shilling and 10.5 pennies. The reason of Britain’s early defeat in
Chinese textile market is that the comparative cost of Chinese native textiles was very
low.
The native cloth or yarn was mainly produced in rural areas. The production was
distinguished from foreign contemporaries in that almost every average household owned
their own spinning and weaving machines, which were not expensive to buy or to make.
A household could finish the whole process of cloth-making by themselves. Male adults
usually took farming job when it was the season; in the winter, male adults would stay at
home helping spin or weave. Females, including female children, spent most of their time
spinning or weaving at home.
This type of combined production generated a certain level of economies of scope,
keeping the cost of textile low. A farmer and other family members, who otherwise
almost unemployed, could utilize a large amount of spare time to produce.
32
As show in Table 1, the export of Chinese native textiles in fact had increased in the
first half of 19th century, and then lost steam as cheaper British textile goods started to
flood in China’s market.
Table 4 Guang Zhou’s Textile Trade with the Great Britain; Unit: Liang(31.3 grams of Silver)
Year
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
Britain's Export to China
0
0
0
0
9,807
0
0
0
1,895
36,144
124,983
183,338
215,373
246,189
360,521
337,646
451,565
China's Export to Britain
395,237
488,640
265,987
433,734
367,651
337,264
451,434
321,162
336,750
145,172
467,876
469,432
355,295
386,364
115,878
61,236
16,304
China's Surplus
395,237
488,640
265,987
433,734
357,844
337,264
451,434
321,162
364,855
109,028
342,893
286,094
139,922
140,175
-244,643
-276,410
-435,261
Source: Yen, Chung-ping. 1963 (Originally From H. B. Morse, Chronicles, Vol 2)
British manufacturers reduced the production cost at a very fast pace (See Table 2
and Table 3). The major source of saving came from the increase of labor
productivity—price of cotton did not vary much; Interest Rate almost stayed put. The
only contributing factor is increasing productivity.
Table 5 : Thomas Ellison’s Data (pp61)
Yarn 40 hanks to the lb.
Year
1779
1784
unit
s.
d.
s.
Selling Price
16
0
10
Cost of Cotton (18oz)
2
0
Return to Labor and Capital 14
0
Source: Yen, Chung-ping. 1963
1799
d.
1812
1830
s.
d.
1860
s.
d.
1882
s.
d.
s.
d.
s.
11
7
6
2
6
1
2.5
0
11.5
0
10.5
2
0
3
4
1
6
0
7.75
0
6.875
0
7.125
8
11
4
2
1
0
0
6.75
0 4.625
0
3.375
33
d.
Table 6: Zhongpin Yan’s estimate of British Production Cost of Yarn.
Year
Cost per Pound
Source:
1775
10 Shilling
1792
4 Shilling
1793
3 Shilling and 1 penny
1795
8 penny
1826
6 and a half penny
Yen, Chung-ping. 1963
Chinese people gradually embraced British textile products as they became cheaper
and cheaper. By 1860, ninety percent of consumers in Fujian Province purchased foreign
cloth, denying Yangzi Delta cloth merchants this once lucrative market.
Compared with cloth, British machine-spun yarn was more successful in Chinese
markets, and had a greater impact on Chinese textiles production. In provinces with no
cotton production, imported thread was often cheaper than Chinese raw cotton. Chinese
weavers first adopted machine-spun yarn as warp, then as weft, and stopped using native
yarns.
Taking into account the above facts, it’s obvious that in the 18th century China had
little chance to industrialize. Britain was lucky for one reason the productivity in its
traditional sector was not high.
The plague and the snapped Italian industrialization.
The Black Death reached the southern shores of Italy in early 1348, and spread all over
Europe in merely 5 years. It is estimated that 25% to 50% of Europe's population had
succumbed to the plague.
Figure 6.
Population in Central-Northern Italy 1300-1700
10000
8000
6000
4000
2000
13
00
-1
13 0
30
-4
13 0
6
13 0-7
90 0
-1
4
14 00
20
-3
14 0
50
-6
14 0
80
-9
15 0
10
-2
15 0
40
-5
15 0
70
-8
16 0
00
-1
16 0
30
-4
16 0
60
16 -7 0
90
-7
00
0
Year
34
As large numbers of the labor force died, those who had skills became even more
valuable than the rich people. The peasants and artisans enjoyed higher wages, and the
standard of living rose for those survivals. Because of labor shortage, much land could no
longer be cultivated. Serfs seeking liberation from cultivating their lord's land were
ordered by decree to return to their master's duties. Free tenants also demanded better
terms from their landlords and that the nobles had no choice but to comply. If the land
lords would be more lenient, serfs simply fled to areas where wages were higher or land
rental terms lower.
Figure 7.
160
140
120
100
80
60
40
20
0
Gross
Per capita
13
10
13 20
60
14 70
10
14 20
60
15 70
10
-2
0
15
60
16 70
10
16 20
60
17 70
10
17 20
60
18 70
10
-2
0
Output
Agricultural output in CN Italy
Year
Source: Malanima (2005)
35
Simulation 6: Industrialization snapped by negative shocks to population.
36
VII. Conclusion:
In the economic history of last millennium, we can see not only the Industrial
Revolution, but several aborted industrializations. Moreover, the Industrial Revolution
brought along not only changes in production, but fundamental changes in demography.
What is the story behind all these industrializations? How to model the Industrial
Revolution in a unified and general way?
This paper models the role of uncertainties such as weather, demographic shocks,
and inventions in a formal growth model in allowing for sustained growth through
learning-by-doing and human capital accumulation. It shows the conditions in which
successful industrializations produce sustained growth, or aborted industrializations leads
back into Malthusian trap. Besides positive shocks to the industrial sector, negative
shocks, and low manufacturing productivity in the traditional sector are friendly to
industrializations.
Including the trade effect is one of the future directions. Trade openness could
help sustain growth from early industrializations, which helps explain the success of the
British in the eighteenth century. Openness is important in that it keeps the price of
manufactured goods from falling too fast, choking off early growth.
37
References:
Acemoglu, D., Johnson, S., and Robinson, J. (2005). “The Rise of Europe: Atlantic Trade, Institutional
Change and Economic Growth” American Economic Review Vol. 95, 546-579.
Allen, Robert. (1998) Agricultural Output and Productivity in Europe, 1300-1800. Discussion paper.
Barro, R. J., and Sala-I-Martin, X. 1995. Economic Growth. New York; McGraw-Hill.
R. H. Britnell. (1989) “England and Northern Italy in the Early Fourteenth Century: The Economic
Contrasts” Transactions of the Royal Historical Society, 5th Ser., Vol. 39., pp. 167-183.
Becker, G., Murphy, K., and Tamura R. 1990. “Human Capital, Fertility and Economic Growth.” Journal
of Political Economy, 98, 12-37
Clark, Gregory. A Farewell to Alms: A Brief Economic History of the World. 2006
Clark, Gregory. “The Condition of the Working-Class in England, 1209-2004” , The Journal of Political
Economy Vol. 113 December 2005
Clark, Gregory. “The Long March of History: Farm Wages, Population and Economic Growth, England,
1209-1869” (February, 2005)
Clark, Gregory. The Conquest of Nature: A Brief Economic History of the World. 2004.
Clark, Gregory. “The Great Escape: The Industrial Revolution in Theory and History” Working paper
(2002).
Clark, Gregory. “Human Capital, Fertility and the Industrial Revolution” Journal of the European
Economic Association, 3 (2-3) (2005): 505-515.
Crafts, Nicholas. “The First Industrial Revolution: Resolving the Slow Growth/Rapid Industrialization
Paradox”. Journal of European Economic Association. 2005. 3(2-3). 525-534.
Gregory Clark, “The Agricultural Revolution?
England, 1500-1912” (2001)
Clark, Gregory. “Why isn’t the Whole World Developed, Lessons from the Cotton Mills.” The Journal of
Economic History. 1987. Vol 47. 141-173.
Cliver, Robert “350 Years of Chinese Textiles”
University, Boston, USA
National Overview China, Textile Conference, Harvard
Crafts, N.F.R. 1985. British Economic Growth During the Industrial Revolution. New York: Oxford
University Press
Diamond, J. (1997), Guns, Germs, and Steel: The Fates of Human Societies. Norton, New York.
Dornbusch, Rudiger; Fischer, Stanley and Startz, Richard. Macroeconomics 7th Ed. Irwin McGraw-Hill
Edwards, Michael M. The Gorwth of the British Cotton Trade 1780-1815, 1967 New York, Augustus M.
Kelly. Publishers.
Galor, Oded. “The Transition form Stagnation to Growth”, Handbook of Economic Growth, North Holland,
2005
38
Galor, Oded and Moav, Omer. “Das Human Kapital”. (2002) NBER
Galor, Oded and Mountford, Andrew. “Trade, Demographic Transition, and the Great Divergence: Why
are a Third of People Indian or Chinese?”. Working Paper. January 2003.
Hajnal, J. (1965), "European Marriage Patterns in Historical Perspective," in Glass, D.C. and D.E.C.
Eversley (eds.), Population in History.
Jones, E.L. (1981), The European Miracle: Environments, Economies and Geopolitics in the History of
Europe and Asia. Cambridge University Press, Cambridge.
Landes, David. The Wealth and Poverty of Nations—Why some are so rich and some so poor. W. W.
Norton & Company, Inc. 1999
Lucas, Robert E, Jr, “On the Mechanics of Economic Development”, Journal of Monetary Economics, 22
(1988) 3-42.
Lucas, Robert E. 2002. “The Industrial Revolution: Past and Future.” Lectures on Economic Growth.
Cambridge, Harvard University Press.
Malanima, Paolo. “Urbanization and the Italian economy during the last millennium” European Review of
Economic History Volume 9, Part 1 (April 2005) 97-122.
McCloskey, Donald. 1994. “1780-1860: A Survey” in Floud, R. and D.N. McCloskey (1994), The
Economic History of Britain since 1700 (2nd ed), Vol. I, 242-270
Mokyr, Joel. “Technological Inertia in Economic History” The Journal of Economic History, Vol. 52, No.
2 (Jun. 1992), 325-338
Mokyr, Joel. “Demand V.S. Supply in Industrial Revolution” The Journal of Economic History, Vol. 37,
No. 4. (Dec., 1977), pp. 981-1008.
Mokyr, Joel. The British industrial revolution : an economic perspective. Boulder, Colo. :Westview
Press, c1993.
Luca Mola. The Silk Industry of Renaissance Venice. Baltimore: Johns Hopkins University Press, 2000.
North, D.C. and Weingast, B.R. (1989). “Constitutions and Commitment: Evolution of Institutions
Governing Public Choice in Seventeenth Century England” Journal of Economic History, Vol. 49,
803-832.
Parente, Stephen L. and Prescott, Edward C. “Barriers to Technology Adoption and Development”. The
Journal of Political Economy, Vol. 102, No. 2 (Apr., 1994), 298-321.
Peretto, Pietro F. “Sunk Costs, Market Structure, and Growth.” International Economic Review, Vol. 37,
No. 4 (Nov, 1996), 895-923.
Pomeranz, K. (2000) The Great Divergence: China, Europe, and the Making of the Modern World.
Princeton: Princeton University Press.
Romer, David, Advanced Macroeconomics. 2nd Ed. McGraw-Hill
Romer, Paul M., “Endogenous Technological Change”, The Journal of Political Economy, V98 (Oct 1990),
S71-S102
39
Romer, Paul M. “Increasing Returns and Long-Run Growth.” Journal of Political Economy, (1986):
1002-37
Romer, Paul M. “Why, Indeed, in America? Theory, History, and the Origins of Modern Economic
Growth” The American Economic Review, Vol. 86 , (May, 1996), pp. 202-206.
Shapiro, Seymour. 1967. Capital and the Cotton Industry in the Industrial Revolution. Ithaca, New York,
Cornell University Press
Hans_Joachim Voth, Nico Voigtlander, Why England? Demographic Factors, Structural Change and
Physical Capital Accumulation during the Industrial Revolution. 2006 Working paper
Yen, Chung-ping. 1963. Zhongguo Mian Fang Zhi Shi Gao 1289-1937 (The History of Chinese Textiles
1289-1937) Beijing Science Press
40