Urbanizing China A reflective dialogue , Professor: Jinhua Zhao, TA: Liyan Xu

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Urbanizing China
A reflective dialogue
11.S945, MW9:30-11:00, Professor: Jinhua Zhao, TA: Liyan Xu
Managing Cars in China
Four Cases
• Bidding to Drive: Shanghai’ Auction
• Superficial Fairness: Beijing's Lottery
• Price as a Policy Signal: Gauging the Public
• Purposeful Policy Leakage: Non Local Vehicles
3
Question
Are these patterns also true to other
domains?
housing, education, energy, environment,
health,...
Please offer examples. Overall Acceptance
Frequency)(%)
35"
30"
43% negative
25"
30% neutral
20"
27% positive
15"
10"
5"
0"
'2"
'1.5"
Fully unacceptable
'1"
'0.5"
0"
0.5"
1"
1.5"
2"
Fully acceptable
5
Core policy drivers
Strongly Positive
2.0%
Effect
Affordability
Equity
1.5%
1.0%
0.5%
0.0%
!0.5%
Effectiveness% Affordability%
Private%
vehicle%
auctions%
Government% Comparison% Transparency%
vehicles%
with%other% in%revenue%
cities%
usage%
!1.0%
!1.5%
!2.0%
Strongly Negative
6
Car Owners (18%)
vs.
Non-Car Owners (72%)
Shanghai License (80%) vs. Non-local License (20%)
Acceptance(
Perception(on(government(
vehicles(
Transparency(on(revenue(
usage(
!2#
!1.5#
!1#
!0.5#
0#
0.5#
1#
1.5#
2#
Equity(compare(to(other(
cities(
Acceptance(Change(
Effectiveness(
Affordability(
Equity(in(auction(
7
3.1.1
Rich vs. Poor
3.1.2
Prior vs. New
Current & future car buyers
Prior car buyers
CAR OWNERS
3.1.3 Revenue Transfer
3.1.4 Space
Resource redistribution
Inner vs. Outer City
NON-­‐CAR OWNERS
Future car owners
LOCAL
PRIVATE
MIGRANT
3.2.1 Local vs. Migrant
Different social class
3.2.2 Government Vehicles
PUBLIC
LOOPHOLES
3.3.1 Corruption
3.3.2 Information Asymmertry
8
Shadow Price of Beijing license
9
Policy making in China is Easier?
• Fewer regulatory constraints
• Stronger government power
• Richer resources
Authoritarian decision making
• Elite-driven
• Lack of public participation
• Straightforward
• One-directional
?
10
Do governments gauge the public opinions?
• Lack of mechanism
• Formal public participation
• Consequences
• Implicitly gauging public opinion
– No feedback / ignore feedback
– Over react
– Drama
11
Mechanism of Quota Decision Making
Quota (t) = 1.354 RoadArea +
0.808 Quota (t-1) +
40.4 Price (t-1) + …
Supply à
Quota à
Price
Bidding Price as a Signal for Policy Adjustment
12
Beijing’s
Lottery Policy
• Effectiveness:
Extraordinary
• Efficiency: Disaster
• Equity: Superficial
13
Purposeful Policy Leakage
Legitimacy and Intentionality of Non-Local Vehicles
Consequences of leakage
•
Effectiveness
•
Revenue
•
Traffic management
•
Fairness
•
Trustworthiness of government
15
Effectiveness
• Congestion Management
VS.
Openness
• Shanghai as a global center
16
City State vs. City in a Region
• Singapore
• Shanghai
– No domestic car industry
– Car as pillar industry
– City-state
– City of region
• Closed system with no nonlocal vehicle problems
• Open city allowing non-local
vehicles entering
17
Government Response: Timeline
18
Legitimacy and Intentionality
Government
Public
Legitimacy
• Mixed signals
• Choice to restrict but not completely ban confers implicit legality
• NLL seen as reasonable reaction to policy
• But inconvenient and lower status
Intentionality
• Intentional in general
• Unintentional on specifics
• Maintain current choice
• Potentially more NLL
19
Shanghai’s Policy on Non Local Vehicles
Reserved, Gradual and Strategic
Four Cases
• Bidding to Drive: Shanghai’ Auction
• Superficial Fairness: Beijing's Lottery
• Price as a Policy Signal: Gauging the Public
• Purposeful Policy Leakage: Non Local Vehicles
21
China’s Transportation Policy Making
1. Cocktails of state + market combinations
23
Embracing the market?
Shanghai
Beijing
From early stage motorization
(1994)
Strong
Late + Sudden
(2008,2011)
Strong
Maximum quota
Yes
Yes
Allocation mode
Auction
Lottery
Price based bidding
Time based queuing
Efficiency and equity
More efficiency
More equity
Consequences
Less distortion
Queuing à Price or Power
Financing ability to pay vs. willingness to pay
Mixed of both
Neither
Market and state
State + market
State only
Long term policy intervention
Intervention strength
Allocation mechanism
24
2. Tougher tradeoffs
25
Tougher tradeoffs
• Multiple goals: often conflicting
• Congestion management and city openness
• Efficiency and equity
• Interests of different groups
• Public sentiments and sensible policy choices
26
3. Devolution of decision making
27
Devolution of decision making
• Experiments in Shanghai and Beijing
– Significant
– Significantly different
• 600+ Cities: Each Experiments its Own Transportation Policies
• Tolerance and Encouragement of Diversity and “Try and Error”
Highly centralized politically
Highly decentralized economically and administratively
28
4. Policy Learning, Transfer and Mobility
29
Policy transfer
Time
2012
2011
1994
1990
License Auction
License Lottery
Singapore
Shanghai
Guangzhou
Beijing
Cities
Zhao, J. and Z. Wang (2013) An Interview Based Survey of Transportation Policy
Transfers in China, working paper
30
Policy Experiment and Transfer
Pilot, Evaluate, Codify, Disseminate and Scale up...
31
Formation of Transport Policy Market
Image removed due to copyright restrictions. Table listing the mechanisms behind the transport policy market.
Source: unknown.
32
Broader Policy Transfers
• Singapore à
Shanghai
– Car industry
– City state vs. city in a region
• Shanghai à
Beijing?
– Bidding vs. lottery
– Control use vs. control ownership
– SH+BJ à Guangzhou/Xi’an à 3rd cities
• China à
Borrowing from
the west
Experimenting
within
World?
– China à other developing countries
– China à western cities
– Local context vs. generic human nature
Exporting
knowledge?
33
5. Policy Design ~ Behavioral Response
34
Policy Design ~ Behavioral Response
Increasingly two-way interactive rather than
simply top-down command and control
35
Shanghai
• Embracing market
• Gauging the public
• Learning and adjusting
Grand but nuanced
• Regionally collaborative
• Strategic about leakage
• More open towards migrants
36
Increasing sophistication in China’s policy making
Subtleties in Bold Design
Question
Are these patterns also true to other
domains?
housing, education, energy, environment,
health,...
Please offer examples. MIT OpenCourseWare
http://ocw.mit.edu
11.S945 Urbanizing China: A Reflective Dialogue
Fall 2013
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