China’s financial system is based on banks: 80% provided by banks or bank-like lenders; 20% from stocks and bonds. 90% of banks are owned or controlled by central or regional government.

Key: this system is build for stability / control.

1. unusually high bank dominance
2. AMC became permanent and control much of the economy
3. stock markets dominated by state entities, unusually limited foreign participation
4. IPO funding shared among different stock type
5. lockup of state shares which devalues companies

Definitions

Capital flows through stocks and bonds. Within China, although the government exert a certain degree of control; a bank based system gives banks a directive, they can control capital flow much easier.

What did we learn thus far?

1. unitary political system—extension of party organization into every part of government (agencies, banks, schools, etc.)
2. unusually large state ownership and assets; unusually high barriers
3. asset transfer to AMCs

taxation setup

• banks are an arm of the state
• China’s fiscal system based heavily on taxation of enterprises
  • VAT on manufacturing: 34%
  • corporate income tax: 17%
  • social security contributions: 18%

compare that to China

• corporate taxes: US: 4%; China: (^)
• payroll taxes: US: 16%; China 25%

fiscal system

• china: taxes on housholds are only 10-11 percent of revenue
• private wealth, capital gains are not taxed
• no inheritance tax
• 20% or so should pay some income tax, actual number is largely lower

distinct feature

1. strongly reliance on enterprises
2. heavily VAT-based
3. central and local government misalignment

History

Taxation 1.0

• tax farming
• each level given a quota of tax to become the higher levels
• central government thus had a smaller share—localities had too much power

Taxation 2.0

• 1994 tax reform
• no more tax farming
• central government got more revenue
• only Beijing tax: everyone else shared a bit of the revenue and can put into special banks

Adverse Incentives

• even if a company is loosing money, its still paying a lot in taxes which is good for government
• …companies are therefore being propped up just to keep them operational

New Fiscal Source for Localities

• Beijing asserted ownership on land
• use rights sold to developers
• ground rent charged for fees

Impact of 2008

• exports dropped 20%
• tried to turn to domestic sources of wealth; but consumers can’t spend enough
• China instead decided to go New Deal and just stimulate economy by building things

China’s Stimulus

• underfunded local mandates (i.e. “we’ll fund half, and you will match, grow your GDP by n%”)
• bank loans to governments (this is not possible in US because then US collapsed) to match the above

BUT: local governments can’t make new taxes and can’t make bonds; they can’t borrow from banks either; so they make a local state enterprise via a “local government financing vehicle.”

For two systems of inequality and equality constraints.

1. weak alternatives: if no more than one system is feasible
2. strong alternatives if exactly on of them is feasible

The theorem of alternatives says two inequalities are either weak or strong alteratives.

• \(x > a\), \(x < a - 1\) are weak alteratives,
• \(x > a\), \(x <a\) are strong alteratives

Tractable optimization problems.

• Linear Programming
• quadratic programming
• etc.

We can solve these problems reliably and efficiently.

constituents

• where \(x \in \mathbb{R}^{n}\) is a vector of variables
• \(f_{0}\) is the objective function, “soft” to be minimized
• \(f_{1} … f_{m}\) are the inequality constraints
• \(g_{1} … g_{p}\) are the inequality constraints

requirements

Generally of structure:

\begin{equation} \min f_{0}\qty(x) \end{equation}

subject to:

\begin{align} &f_{i} \qty(x) \leq 0, i = 1 \dots m \\ &Ax = b \end{align}

CVXPY allows us to cast convex optimization tasks into OOP code.

\begin{align} \min \mid Ax - b \mid^{2}_{2} \end{align}

object to:

\(x \geq 0\)

import cvxpy as cp

A,b = ...

x = cp.Variable(n)
obj = cp.norm2(A@x - b)**2
constraints = [x >= 0]

prob = cp.Problem(cp.Minimize(obj), constraints)
prob.solve()

How it works

1. starts with the optimization problem \(P_{1}\)
2. applies a series of problem transformation \(P_{2} … P_{N}\)
3. final problem \(P_{N}\) should be one of Linear Program, Quadratic Program, SOCP, SDP
4. calls a specialized solver on \(P_{N}\)
5. retrieves the solution of the original problem by reversing transformations