Economics of China
Professor Nelson Mark
University of Notre Dame and NBER
February 1, 2017
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
1 / 23
Today’s Objectives
Li and Yang’s Journal of Political Economy Paper on the Great Leap
Forward
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
2 / 23
Introduction
A good introduction does the following:
1
State the question/problem to be addressed. What is this paper
about?
2
Background information. Motivate why the problem is interesting and
why solution is useful.
Discuss literature.
3
1
2
3
Scholarly demonstration that you are current with the literature, and
you know how your paper fits/contributes to the literature.
Make sure this hasn’t already been done.
Research is supposed to be original.
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
3 / 23
Li-Yang Introduction
1
2
General background on GLF. They say the famine it induced was the
worst loss of life in recorded world history
Problem/question statement: “This paper...:What caused the
collapse in grain outut?”
1
2
3
4
Bad weather (official explanation)
Reduction in grain production inputs (labor and acrage)
Poor communal incentives
Inability to exit communes.
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
4 / 23
Li-Yang Introduction
1
Build a little model that shows
1
2
2
Goal of rapid indstrialization caused resources to be diverted from
agriculture to industry
Excessive procurement (taxation) from peasants combined with output
declines lead to starvation this year. People too weak to work. Next
year output declines even further
Test the theory
1
2
Estimate a production function. Then you can pull the levers on the
various inputs (trace out evolution of the data) and decompose the
output loss into sources
They find
1
2
3
Diversion of resources: 33 %
Excessive procurement (taxation): 28.3%
Weather: 12.9%
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
5 / 23
A Great quote/statement in paper
Central planning introduces systemic risk to the economy, which is the
central planning policies themselves.
Systemic risk is undiversifiable.
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
6 / 23
II. Development Strategy and Rural Institutions
1
2
1949: 90% of the people lived in rural areas, toiled on small plots
using centry old labor intensive technology
To pay for industrialization,
1
2
3
Tax the peasants
Raise agricultural productivity by transforming it to large scale
mechanized farming.
By 1956, only 3.7% of families did not join the collectives
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
7 / 23
II. Development Strategy and Rural Institutions
1
1958: Launch the GLF
1
2
3
4
Collectives turned into communes. There were 26,500 of them, each
consisting of thousands of familes
Local cadres trying to out do each other issued baseless claims about
grain yield. Created illusion that collectivization was super productive.
1958: 16.4 million peasants (2 times industrial workforce) were
re-located to urban areas to work in industry.
1957-58: 100 million peasonats diverted from grain production to
irrigation and land reclamation projects, to operate backyard steel mills.
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
8 / 23
II. Development Strategy and Rural Institutions
Discussion/Significance of Table 1
1
Communes switched some land away from grain to other “cash
crops” production
2
Peasants reduced stock of draft animals (probably ate them rather
than have them confiscated by communes). The most important
agricultural capital.
3
Communal kitchens provided free food, resulting in food waste.
4
Procurement (tax on grain) increased
5
Per capita caloric intake dropped from 2100 (1957) to 1500 (1960).
People too weak to work.
6
1960 reversal. Procurement reduced. 50 million sent back to the
countryside to farm.
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
9 / 23
Table 1 (I converted to plots) This is in levels
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
10 / 23
Table 1 (I converted to plots) This is in logs
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
11 / 23
Table 1 (I converted to plots) This is in logs
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
12 / 23
Theoretical (toy) Model
Basic idea: Two sectors:
1
2
Agriculture, employs only labor
Industry.
Normalize total labor force to 1.
Lt works in agriculture, 1 Lt works in industry.
E↵ective ag. labor Lt⇤ = Lt ht , where ht > 0 is worker’s physical
capacity.
h = f (c ), f 0 > 0, f 00 < 0 where c is consumption. You need to eat in
order to work.
Hence, Lt⇤ = Lt f (ct ).
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
13 / 23
Toy Model: Agriculture production function
Aggregate agriculture production function
Qt = aLt f (ct )
Per capita production function
qt =
Qt
= af (ct )
Lt
Per capita state procurement (taxes) pt Per capita grain retained
gt = af (ct ) pt is what peasants can eat next year. Per capita
consumption,
ct +1 = gt = af (ct ) pt
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
14 / 23
Industry
Inputs are labor and grain in a Leontief (fixed proportions)
technology.
One unit of labor needs m units of grain.
(1 Lt ) units of labor needs m(1 Lt ) units of grain.
Government’s budget constraint
Grain entitlement to city folk: n (1 Lt )
Grain input to production m (1 Lt )
Total procurement pt Lt
Budget constraint is
pt Lt
(m + n)(1
Lt )
Government is best o↵ by forcing an equality, in which case,
Lt =
m+n
pt + m + n
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
(1)
February 1, 2017
15 / 23
Government’s problem
Maximize present value of industrial output,
•
max  bt (1
Lt )
(2)
t =0
subject to (1), where b is the government’s discount factor. Patient means
b is large (close to 1). Impatient means b is low. b = 0 means future is
irrelevant. Substitute (1) into (2).
!
◆
+n
•
• ✓
1 pt m
1 af (ct ) ct +1
+m +n
t
t
max  b
=b Â
pt + m + n
ct + 1 + m + n
t =0
t =0 af (ct )
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
16 / 23
Government’s problem
Look at first two terms,
1 af (c0 ) c1
1 af (c1 ) c2
+b
af (c0 ) c1 + m + n
af (c1 ) c2 + m + n
At t = 0, di↵erentiate with respect to c1 and set result to zero.
Rearranging gives
0
abf (c1 ) =
✓
af (c1 )
af (c0 )
c2 + m + n
c1 + m + n
◆2
This has to hold for all t. The general form sets
t = 0, t + 1 = 1t + 2 = 2. This is a second-order (nonlinear) di↵erence
equation that maps ct into ct +1 and ct +2 .
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
17 / 23
Li and Yang’s experiment
5 years into the new government, Mao suddenly becomes impatient (b
decreases). Simulations show government directs labor to industry (fig 1a)
and increases the procurement (figure 1b).
Point is the outcome of GLF might be what one would expect if the
central planner is really impatient, and cares only about industrial output.
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
18 / 23
great leap forward
851
Fig. 1.—Simulated impact of the GLF: the combined effects of overoptimistic expectation and increased impatience: a, agricultural labor, L(t) ; b, output, consumption, and
procurement per agricultural worker; c, work capacity per agricultural worker, h(t); d,
aggregate agricultural output, Q(t).
output per rural worker, to a permanently higher level than a less patient
one.
In the remainder of this section, we show that the radical GLF policies
be Mark
formulated
by an
increasingly
impatient
central
planner relying
Professor can
Nelson
(University
of Notre
Dame and
NBER)
Economics
of China
February 1, 2017
19 / 23
Test the Theory, Decompose Agriculture Output Decline
The Data
1
Resource diversion: Measured by incremental steel and iron
production. Proxies for diverting rural labor to non agricultural GLF
projects
2
Nutrition: Measured by grain output minus procurement.
3
Weather: Index of weather conditions
4
Communes with exit rights: Ability to exit might give peasants better
incentives to work
5
Size of production units. Bigger communes means less incentive to
work.
6
Communal dining and radicalism. Communal kitchens waste precious
food. Use regional participation rates to get cross-sectional variation
in participation, hence radicalism, hence waste.
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
20 / 23
Empirical specification for agriculture
Cobb-Douglas production Q = K a L(1
They generalize to more inputs:
a)
! ln Q = a ln K + (1
a) ln L.
⇤
⇤
⇤
⇤
ln Qi,t = aL lnLi,t
+ aA ln Ai,t
+ aK ln Ki,t
+ aM ln Mi,t
+ ui + n(t ) + ei,t + Wt
(3)
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
21 / 23
⇤
⇤
ln Qi,t = aK ln Ki,t
+ aM ln Mi,t
+ ui + n (t ) + ei,t + Wt
|{z}
|{z} |{z}
F.E.
CTE
⇤
aL ln Li,t
+
| {z }
ln L⇤ =ln LG +ln R +ln h+gZ ln Z +gE ln E +gS ln S
+
weather
⇤
aA ln Ai,t
| {z }
ln AG +gI ln I
Adjustments: Substitute these adjustments into the production function.
1
Starred variables are e↵ective units. Adjust the data for quality and
efficiency di↵erences.
2
G: superscript is propotion of sown areas allocated to grain.
3
R: radical commune programs
4
Z: size of production unit
5
E: Exit rights
6
S: Surge of steel output
7
I: proportion of sown area irrigated
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
22 / 23
TABLE 5
Estimation of Grain Production Function in China, 1952–77
Dependent Variable: ln(Grain Output)
Explanatory
Variable
ln(sown area)
ln(% acreage irrigated)
ln(% acreage sown
with grain)
ln(fertilizer)
ln(farm capital)
ln(labor)
ln(food availability)
Fixed-Effect Instrumental Variables
Fixed-Effect OLS
(1)
(2)
(3)
(4)
(5)
.206**
(.104)
.448***
(.088)
.131***
(.029)
!.114
(.065)
.033***
(.011)
.224***
(.027)
.532***
(.125)
.479***
(.088)
.129***
(.027)
!.059
(.083)
.022**
(.010)
.160***
(.024)
.311***
(.087)
.180***
(.050)
.474***
(.092)
.127***
(.025)
!.055
(.097)
.019*
(.010)
.138***
(.030)
.284***
(.088)
.267***
(.077)
.442***
(.087)
.110***
(.025)
!.091
(.092)
.025**
(.010)
.158***
(.028)
.343***
(.100)
!.093***
(.027)
!.045
(.031)
.014**
(.007)
!.026
(.019)
.006
(.022)
!.036
(.023)
!.080***
(.026)
!.161***
(.033)
!.018
(.033)
.805
428
!.099***
(.028)
!.076**
(.037)
.013**
(.006)
!.024
(.018)
.011
(.017)
!.034*
(.020)
!.076***
(.023)
!.156***
(.036)
!.002
(.037)
.800
406
.019
(.059)
.012*
(.007)
.245***
(.030)
.578***
(.107)
Tercile 1 (low)
Tercile 2 (middle)
Tercile 3 (high)
ln(steel production)
ln(communal dining)
ln(production unit
size)
No exit (de jure)
Good weather
Average weather
Bad weather
Very bad weather
Time trend
R2
Observations
.704
624
!.083
(.073)
.008*
(.005)
!.006
(.022)
!.016
(.021)
!.048**
(.020)
!.116***
(.027)
!.169***
(.031)
!.107***
(.042)
.764
551
Note.—Numbers in parentheses are robust standard errors adjusted for clustering on year.
* Statistically significant at the 10 percent level.
.271***
(.108)
.246*
(.137)
.332**
(.167)
!.090***
(.028)
!.034
(.039)
.012**
(.005)
!.038
(.023)
!.014
(.026)
!.044*
(.025)
!.081***
(.023)
!.158***
(.035)
!.014
(.034)
.791
406
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
22 / 23
TABLE 7
Contribution of Explanatory Variables to the GLF Grain Output Collapse
and the Post-GLF Recovery
The Collapse (1958–61)
Contributing
Factors
Observed total change
Estimated total change
Procurement/nutrition
Resource diversion
Sown area
Farm capital
Labor
Steel production
Weather conditions
Policy factors
Communal dining/
radicalism
No exit (de jure)
Production unit size
Modern inputs
Fertilizer
% acreage irrigated
Miscellaneous
% acreage sown with
grain
Time trend
Residuals
The Recovery (1961–66)
Changes in
ln(Output)
(1)
% Contribution
to Total Change
(2)
Changes in
ln(Output)
(3)
% Contribution
to Total Change
(4)
!.352
!.232***
(.038)
!.100***
(.029)
!.116***
(.024)
!.023***
(.005)
!.009***
(.002)
!.004***
(.001)
!.080***
(.023)
!.045***
(.008)
.019
(.026)
!.049**
(.024)
!.001
(.001)
!.029**
(.012)
.011**
(.005)
.009*
(.005)
.0014***
(.003)
!.0014
(.0131)
!.0008
(.0014)
!.0006
(.0131)
!.119***
(.038)
!100.0
!66.1
.445
.315***
(.024)
.042***
(.002)
.165***
(.029)
.010***
(.002)
.041***
(.009)
.035***
(.011)
.080***
(.023)
.052***
(.012)
!.013**
(.006)
.000
100.0
70.7
!.001
(.001)
!.012**
(.005)
.055***
(.017)
.028*
(.015)
.026***
(.005)
.001
(.016)
.002
(.004)
!.001
(.015)
.130***
(.024)
!.2
!28.3
!33.0
!6.6
!2.5
!1.2
!22.6
!12.9
5.5
13.9
!.3
!8.1
3.0
2.6
.4
!.4
!.2
!.2
!33.9
Note.—The numbers in parentheses are robust standard errors.
* Statistically significant at the 10 percent level.
9.4
37.1
2.2
9.2
7.8
17.9
14.7
!3.0
.0
!2.8
12.3
6.4
5.9
.3
.5
!.2
29.3
Professor Nelson Mark (University of Notre Dame and NBER)
Economics of China
February 1, 2017
23 / 23