Constant Elasticity of Substitution

CES could be either production or utility function. It provides a clear picture of how producers or consumers choose between different choices (elasticity of substitution).

CES Production

The two factor (capital, labour) CES production function was introduced by Solow and later made popular by Arrow.

$$Q=A\cdot(\alpha K^{-\rho}+(1-\alpha)L^{-\rho})^{-\frac{1}{\rho}}$$

  • \(\alpha\) measures the relative proportion spent across K and L.
  • \(\rho=\frac{\sigma-1}{\sigma}\) is the substitution parameter.
  • \(\sigma=\frac{1}{1-\rho}\) is the elasticity of substitution.

While identical producers maximise their profits and markets get competitive, Marginal Product of Labour and Marginal Product of Capital follow,

$$MP_L=\frac{\partial Q}{\partial L}=w$$

$$MP_K=\frac{\partial Q}{\partial K}=r$$

So we get,

$$ \frac{w}{r}=\frac{1-\alpha}{\alpha}(\frac{K}{L})^{\rho+1} $$

$$\frac{K}{L}=(\frac{\alpha}{1-\alpha}\frac{w}{r})^{\frac{1}{1+\rho}}$$

Here, we get the substitution of K and L is a function of the price, w & r. As we are studying the elasticity of substitution, in other words how W/L is affected by w/r, we take derivatives later. We denote \(V=K/L\), and \(Z=w/r\). Then,

$$V=(\frac{\alpha}{1-\alpha}Z)^{\frac{1}{1+\rho}}$$

The Elasticity of Substitution (the percentage change of K/L in terms of the percentage change of w/r) is,

$$ \sigma=\frac{dV/V}{dZ/Z}=\frac{dV}{dZ}\frac{Z}{V}=\frac{1}{1+\rho} $$

Therefore, we get the elasticity of substitution becomes constant, depending on \(\rho\). The interesting thing happens here.

  • If \(-1<\rho<0\), then \(\sigma>1\).
  • If \(0<\rho<\infty\), then \(\sigma<1\).
  • If \(\rho=0\), then, \(\sigma=1\).

Utility Function

Marginal Rate of Substitution (MRS) measures the substitution rate between two goods while holding the utility constant. The elasticity between X and Y could be defined as the following,

$$ Elasticity=\frac{\%\Delta Y}{\% \Delta X}=\frac{\Delta Y/Y}{\Delta X/X}=\frac{X/Y}{\Delta X/\Delta Y} $$

The elasticity of substitution here is defined as how easy is to substitute between inputs, x or y. In another word, the change in the ratio of the use of two goods w.r.t. the ratio of their marginal price. In the utility function case, we can apply the formula,

$$\sigma=\frac{\Delta ln(X/Y)}{\Delta ln(MRS_{X,Y})}=\frac{\Delta ln(X/Y)}{\Delta ln(U_x/U_y)}= \frac{\Delta ln(X/Y)}{\Delta ln(U_x/U_y)} $$

$$\sigma=\frac{\frac{\Delta(X/Y)}{X/Y}}{\frac{\Delta (p_x/p_y)}{p_x/p_y}}$$

  • \(U_x=\frac{\partial U}{\partial X}=p_x\)
  • \(MRS_{X,Y}=\frac{dy}{dx}=\frac{U_x}{U_y}=p_x/p_y\) marginal price in equilibrium.

In the

$$ u(x,y)=(a x^{\rho}+b y^{\rho})^{1/\rho} $$

$$\sigma=\frac{1}{1-\rho}$$

If \(\rho=1\), then \(\sigma\rightarrow \infty\).

If \(\rho\rightarrow -\infty\), then \(\rho=0\).

Two common choices of CES production function are (1) Walras-Leontief-Harrod-Domar function; and (2) Cobb-Douglas function (P.S. but CES is not perfect, coz sigma always equal one).

As \(\rho=1\), the utility function would be a perfect substitute.

As \(\rho=-1\), the utility function would be pretty similar to the Cobb-Douglas form.

Later, the CES utility function could be applied to calculate the Marshallian demand function and Indirect utility function, and so on. Also, easy to show that the indirect utility function \(U(p_x,p_y,w)\) is homogenous degree of 0.

Reference

Arrow, K.J., Chenery, H.B., Minhas, B.S. and Solow, R.M., 1961. Capital-labor substitution and economic efficiency. The review of Economics and Statistics43(3), pp.225-250.

Causal Inference in Statistics

Base: Correlation does not mean casualty.

  • If X and Y are statistically dependent, X does not necessarily cause Y (or Y cause X). 相关性不代表有因果性
  • If X causes Y, then X & Y are very likely to be statistically dependent (but not always, there is extreme condition). 但是因果性代表相关性

Study 1. V Structure:

  • Chain

$$ X\rightarrow Y\rightarrow Z $$

Z and X are likely dependent. However, Z and X are independent, conditional on Y.

$$ P(Z=z|X=x,Y=c)=P(Z=z|Y=c) $$i.e.

i.e.

\(f_x: X=u_x\)

\(f_y: Y=84-X+u_Y := c\)

\(f_z: Z=100\underbrace{Y}_{c}+u_z\)

Now, Z and X are independent.

Therefore, we know, in the Chain:

$$ X\equiv Z$$

$$ X\bot Z|Y $$

  • Folk

$$ Y\leftarrow X\rightarrow Z $$

Y and Z are likely dependent. However, Y and Z are independent conditional on X.

$$P(Z=z|Y=y, X=c)=P(Z=z|X=c)$$

While conditioning on intermediate node X, then Z and Y are independent.

$$Y\bot Z|X$$

  • Collider

$$ X\rightarrow Z\leftarrow Y $$

X and Y are independent. However, X and Y are dependent conditional on Z.

$$ P(X=x|Y=y, Z=c)\neq P(X=x|Z=c) $$

i.e.

If we know \(Z=X+Y+u_Z:=c\), then \( X=c-Y-u_Z\), and thus X and Y become dependent conditional on \(Z=c\). Otherwise, \(X=u_X\) and \(Y=u_Y\).

Once, conditioning on \(Z\), the way gets connected. Otherwise (unconditional), we get independent.

P.S. Descendent of Z:

$$ X (or\ Y)\rightarrow Z\rightarrow W $$

Similarly, we get in the Collider:

$$ X\bot Y $$

$$ X\equiv Y|Z $$

$$ X\equiv Y |W $$

  • See notes for further studies.

Reference

Pearl, J., Glymour, M. and Jewell, N.P., 2016. Causal inference in statistics: A primer. John Wiley & Sons.

中央经济局会议 2021.12.08-10

会议指出在充分肯定成绩的同时,必须看到我国经济发展面临需求收缩、供给冲击、预期转弱三重压力

会议要求,明年经济工作要稳字当头、稳中求进,各地区各部门要担负起稳定宏观经济的责任,各方面要积极推出有利于经济稳定的政策,政策发力适当靠前。

  • 1. 宏观政策要稳健有效。要继续实施积极的财政政策和稳健的货币政策。积极的财政政策要提升效能,更加注重精准、可持续。要保证财政支出强度,加快支出进度。实施新的减税降费政策,强化对中小微企业、个体工商户、制造业、风险化解等的支持力度,适度超前开展基础设施投资。党政机关要坚持过紧日子。严肃财经纪律。坚决遏制新增地方政府隐性债务。稳健的货币政策要灵活适度,保持流动性合理充裕。引导金融机构加大对实体经济特别是小微企业、科技创新、绿色发展的支持。财政政策和货币政策要协调联动,跨周期和逆周期宏观调控政策要有机结合。实施好扩大内需战略,增强发展内生动力。

通过fiscal policy:减tax增加Government spending着重刺激小企业=>目的为刺激地方经济发展,且为带动就业(需考虑提高市场主体带动就业的能力)。

通过monetary policy:释放流动性,支持实体经济小微企业进行科技创新。

  • 2. 微观政策要持续激发市场主体活力。要提振市场主体信心,深入推进公平竞争政策实施,加强反垄断和反不正当竞争,以公正监管保障公平竞争。强化知识产权保护,营造各类所有制企业竞相发展的良好环境。强化契约精神,有效治理恶意拖欠账款和逃废债行为。

微观层面增强市场的竞争水平,反垄断(此点需要结合支持小企业发展才能支持企业之间进行竞争)。提高社会主体及企业公信度,减少违约行为发生。

  • 3. 结构政策要着力畅通国民经济循环。要深化供给侧结构性改革,重在畅通国内大循环,重在突破供给约束堵点,重在打通生产、分配、流通、消费各环节。要提升制造业核心竞争力,启动一批产业基础再造工程项目,激发涌现一大批“专精特新”企业。加快形成内外联通、安全高效的物流网络。加快数字化改造促进传统产业升级。要坚持房子是用来住的、不是用来炒的定位,加强预期引导,探索新的发展模式,坚持租购并举,加快发展长租房市场,推进保障性住房建设,支持商品房市场更好满足购房者的合理住房需求,因城施策促进房地产业良性循环和健康发展。

支持供应链改革优化,提高资源产出效率。

支持专精特新,提高我国企业在国际范围的竞争力。

加强数字化转型:提高企业内生产效率

房主不炒,避免房地产市场吸纳过多资金,发展长租房市场

  • 4. 科技政策要扎实落地。要实施科技体制改革三年行动方案,制定实施基础研究十年规划。强化国家战略科技力量,发挥好国家实验室作用,重组全国重点实验室,推进科研院所改革。强化企业创新主体地位,深化产学研结合。完善优化科技创新生态,形成扎实的科研作风。继续开展国际科技合作。

推动科技发展,同时保证产学研结合,将研究的科技产出应用到企业生产。

  • 改革开放政策要激活发展动力。要抓好要素市场化配置综合改革试点,全面实行股票发行注册制,完成国企改革三年行动任务,稳步推进电网、铁路等自然垄断行业改革。调动地方改革积极性,鼓励各地因地制宜、主动改革。扩大高水平对外开放,推动制度型开放,落实好外资企业国民待遇,吸引更多跨国公司投资,推动重大外资项目加快落地。推动共建“一带一路”高质量发展。

要素市场资源分配。推动电网、铁路等大国企市场化。吸引外资企业注入,吸纳国际资金,解决国内就业。

  • 区域政策要增强发展的平衡性协调性。要深入实施区域重大战略和区域协调发展战略,促进东、中、西和东北地区协调发展。全面推进乡村振兴,提升新型城镇化建设质量。

区域发展不均衡,推动乡村发展

  • 社会政策要兜住兜牢民生底线。要统筹推进经济发展和民生保障,健全常住地提供基本公共服务制度。解决好高校毕业生等青年就业问题,健全灵活就业劳动用工和社会保障政策。推进基本养老保险全国统筹。推动新的生育政策落地见效,积极应对人口老龄化。

就业问题,公共服务问题,社保问题。养老问题。

  1. 把握共同富裕目标。合理资源分配制度,加强社保、税收、转移支付等政策力度;推动高质量发展要以就业为导向;完善公共服务制度体系,教育、医疗、养老、住房等。完善第二次分配和第三次分配的方向。
  2. 加强资本监管,防止资本野蛮生长。
  3. 节约资源利用,推进资源全面节约、集约、循环利用。
  4. 防范风险:行业、企业。
  5. 碳达峰碳中和:技术攻关。早实现能耗“双控”向碳排放总量和强度“双控”转变。保供稳价。

Say’s Law

Say’s Law argues that the ability to purchase something depends on the ability to produce and the wealth. In order to have something to buy, the buyer has to get something to produce and sell. Thus, the source of demand is production, not the money itself. Therefore, production drives economic growth.

Say drew four main conclusions.

  1. More producers would boost the economy.
  2. If members of the society do not produce would drag the society.
  3. Business entities with trading are benefitial when they near each other.
  4. Encouraging consumption is harmful. Production adn accumulation of goods constitutes prosperity, but consuming without producing eat away the economy.

The implication is that government should support and control production rather than consumption.

REITs “不动产投资信托基金”

Real Estate Investment Trusts (REITs) 是一种向投资者发放信托凭证,募集资金投资于不动产,并向投资者分配投资收益的一种投资基金。

不动产包括:写字楼、购物中心、高速公路、产业园区等。REITs起源于美国历史近60年,全球资产管理规模已超过2万亿美元。

REITs的意义:可以帮助企业快速回笼资金,盘活存量资产,给市场注入动能。此外,降低了投资者对不动产投资的门槛,具有流动性较高、收益相对稳定、安全性较强等特点。

2020年4月30日,中国证监会和国家发改委联合发布重要通知,境内基础设施领域公布REITs试点正式起步。这意味着,个人投资者也可以通过REITs分享国家经济高质量发展的红利。但应了解相关知识,知晓投资风险,理性投资。

REITs可分为公募型和私募型

2020年8月7日,中国证监会发布《公开募集基础设施证券投资基金指引(试行)》,聚焦基础设施领域,开展公墓REITs试点,试点项目具有权属清晰、信用稳健、投资回报良好等特点。宏观上,有助于落实深化金融供给侧结构性改革政策,为基础社会建设提供直接融资支持。微观上,可以降低企业杠杆率,实现新资产模式转型。同时,共募REITs是一种区别于股票和债券等大类金融产品,填补了我国资本市场空白,可以为投资者提供中等收益、中等风险资产。

公募REITs试点首先基础设施领域的原因

因为我国拥有巨大的基础设施存量,而且很多项目收益率和现金流都不错,可以为REITs提供理想的资产来源。整体来看,我国发展基础设施REITs的优势为:

  1. 存量大。我国基础设施存量超过100万亿元,REITs的发展空间充足。据统计,仅2003-2017年,中国累计的基础设施投资规模高达108万亿元。考虑一定的折旧率后,即按1%的证券化比例计算,转化为REITs的规模也有往达到万亿级别。
  2. 增量多。未来发展新基金,所以将来可选的底层资产会越来越丰富。我国每年新增的基建投资规模超过15万亿元。统计局测算,我国基础设施存量水平相当于发达国家的30%,所以还有较大的空间。随着产业政策向新基建倾斜,包括5G通信设施、城市轨道交投、数据中心、特高压、新能源车充电设施等领绿的投资也有望为REITs提供新的可选资产。
  3. 收益稳定。很多成熟的基础设施,如电力、水利、高速公路等项目,都有稳定的现金流,适合作为REITs的底层资产。
公募REITs与普通公募基金的区别

区别来自:1. 投资标的;2. 发行定价; 3. 市场波动。

  1. 投资标的方面:REITs限定主要投资于不动产的基金。试点的报考:仓储物流、收费公路、信息网络、产业园区等,不包括住宅和商业地产。基础设施公募REITs可以让投资者用较少的资金参与到大型基建项目中,从而分享设施等基础收益与资产升值。
  2. 和普通公募基金着重在二级市场交易不同,公募REITs要求基金管理人参与到一级市场中,基金管理人会代替投资者对他们所投资的项目进行监督或管理,从而让项目较好的运行起来,保障收益实现。

基础设施公募REITs采用“公募基金+基础设施资产支持证券”的产品结构,是一种风险回报比中等的权益类投资品种。根据国际经验,公募REITs可以提供相对稳定的分红收益,其总的长期复合回报也相当可观。另外,公募REITs与股票、债券等其他金融资产相关性低,风险分散效果较好。REITs试点主要集中于重点地区的优质资产,试点政策强调,鼓励信息网络等基础设施以及国家战略性新兴产业集群等。项目应具有成熟的经营模式及市场化运营能力

中国国际金融协会年会

2022 世界经济与中国 – 聚焦全球经济金融体系的未来

– 中国银行总行 2021.12.07

刘连舸 (中行行长)

全球经济复苏与产业链。G20国家经济已复苏,贸易水平超疫情前。但是流动性差,经济体脆弱fragile,所以需要全球产业链网络合作深化。区域间多边贸易增加 => 经济体在全球产业链中获利。

但是2008 crisis及covid-19使全球化脚步放缓。全球Tariff以及贸易保护使得全球化合作减缓

Post-Covid展望:

全球产业链合作 <= 全球化趋势。原因:

  1. 应对stagflation
  2. 内在动力
  3. 生产要素及技术进步融合
  4. 深化区域合作(区域自贸合作)

仍存在的问题

  1. Covid带来的Uncertainty i.e. Omicron
  2. 意识形态对抗 i.e. US 单边主义+保护主义 (如芯片行业)
  3. 传统经贸规则无法跟上Green Finance的步伐

刘明康 (原银监会主席)

应对疫情中的国际金融

疫情影响:

  1. Death & Infection
  2. Pandemic is repetitive i.e. 预计本年增长6%不足,实际预计5%
  3. 股市\(downarrow\),油价\(downarrow\),流动性\(downarrow\),最终导致金融市场unfunctional金融市场运作失效

To face the Covid

  1. CB conduct 1. QE; 2. Negative or zero interest rate.
  2. Fed & Treasury stimulate the economy by releasing extra liquidity救市
  3. Extra liquidity results in negative growth of Net worth of assets 超额流动性导致资产净值减少
  4. 企业融资成本下降
  5. 2008 crisis后 CB+Gov合作的模式形成

未来的五个问题

  • Problem 1: Future: Inflation becomes a problem in the future 10 years. Problems:
  • Problem 2: 全球供应链重塑
  1. Supply chain interruption呈现两种形式。1. short-run 阶段性,如:集装箱缺少及司机短缺。2. Structural & Permanent 永久性及结构性,如:三星、LG、微软、台积电大笔资金重塑供应链。(我国chips & semi-conduct有供应链重塑及合作需求)
  2. 要解决数据(大数据方向)渠道问题。Balance企业数据共享+隐私保护
  3. 国际金融要跟上Supply Chain Resilience的步伐。用 多节点多个供应链 替代 单个供应链的模式。打破Just-in-time零库存以降低储存成本的经营模式(日本)
  4. 各国需要深化改革:海关控制及Tariff限制。
  • Problem 3: 高新科技带来的变化
  1. i.e. 5G带来工业+通讯互联的同时 需要金融支持1. Cyber Security. 2. Risk control.
  2. Creative Construction. 国际大型制药公司进行公司拆分 1.避免臃肿; 2.Specialist & focus on
  3. 同时对于高新+数据的监管需要跟上(如限制ratio标准)
  • Problem 4: 研究国际金融如何服务绿色发展
  1. Target目标:Net of zero carbon, Needs trillion of dollors supports per year.
  2. 通过CCER(碳交易平台)+碳捕捉+碳储存 技术协助解决net zero 的目标
  3. 国际交流协作
  • Problem 5: 国际资产: 1. 重估revaluation; 2. 重组restructure; 3. 重分配reallocation
  1. 目前投资考虑两个维度:宏观+微观。但是现在可能需要考虑第三个点:对Index Fallacy的纠正。纠正经济金融指数带来的谬误。
  2. 周期转变 (新经济 v.s. 旧经济)、氢能源的 产->利->用 导致对传统供应链的冲击
  3. 银行:用fin-tech+big data应对新经济模式的冲击

五个时代的呼唤

  1. 当下 & 今后的时机+抱负
  2. Face New tech
  3. regulation & cooperation
  4. ESG + CSR
  5. Human Development

Joseph Stiglitz (2001 Nobel Prize winner)

About the Post-Pandemic

  • Uncertainty
  • Global Cooperation (Prof. Stiglitz is upset about the global cooperation): unable to supply vaccine, cannot egt the low cost of production
  • Country’s Government conduct government purchases (about 25% U.S. GDP), But developing countries and emerging markets do not have ability of finance the economy recovery by “G”.
  1. SDR of CB: about to move money from CB to Treasury
  2. Make funds recyclable
  3. Problems of debts. We nned deep or restructure and cooperate between private and public sectors to solve the problems of extra debts.
  • Climate Risks
  1. Weather uncertainty. Extreme weather.
  2. Transformation to neclear energy.
  3. Maket not works well. Asset price changes as well.
  • Systematic Consequence.
  1. Wrong valuation of financial sectors
  2. Banks need to ensure not over-exposure to value risks
  3. Gov needs to response by policy that not allow risks to be tranfered to private sectors. i.e. ensure all mortgages are green mortgage.
  4. Financial Secors commisions to allocate.
  5. Companies also need to allocate and disclose financial risks.
  • China – Debt Finance + Real Estate

The economy relies highly on real estate, so highly exposed to debts problems. Therefore, alternative modes can be chosen.

  1. more equity finance than debt finance. However, equity finance needs more information, and then better regulation. Equity based finance is better to absorb risks.
  2. Focus on more small business lending. Use small business loan to stimulate the economy.
  3. More public investment. Private sectores are polluted (driven by tendency of profitability). Thus, public sectors need to work and help movement from rural to city and further make city better. (common prosperity).
  4. A transformation to service dominant mode.
  5. Public investment is the engine of economy.

A country with rapid tranformation would be the winner.

朱民 (清华大学国家金融研究院院长,前IMF副总裁,央行副行长,中行副行长)

2022通胀之剑+央行的挑战

p.s. 参考Dalio经济weather取决于1. 经济增长; 2. 通胀。

  • Facts:
  1. 通胀呈现国际国与国之间不同,国内产业间不同的趋势。
  2. 全球:大宗商品价格上升 PPI上升,能源价格上升. PPI+CPI剪刀差导致企业利润被严重压缩。
  • 问题:通胀,暂时性 or 持久性?— Ideas:持久性
  1. Demand side: 需求持续上升,由于Fiscal & Monetary Policy的传递最终传向demand side
  2. Supply side: cannot be stable coz covid continues + fluctuations of econ
  3. 大宗商品价格上升 (主要 油+能源+稀有金属),价格上升的来源为:1. supply structure changes;2. Targets of carbon neturality
  4. Labour Market: 劳动力参与率低 low skill workers 导致unemployment。但是demand高,i.e. 卡车司机+港口集装箱问题。 最终导致low skill workers wage 上升 => 导致 all wages 上升。
  5. Real Estate Price 上升。

综上导致persistent increase in inflation。

  • 央行对通胀的政策:Fed 提前加息。但是U.S. stock market 的tail risks 提升。

Summary: structural inflation becomes persistent because both supply and demand sides changes. Fed are less affected to the inflation.

  • Future

U.S. debts 提升 interest rate下降 => achieve a balance。 但是当Fed要提高 interest rate收缩经济时,U.S. unable to pay back debts because of higher cost of interest payment. 最终导致 interest 难以提升的问题。

未来可能出现 1. 高inflation;2. 高interest rate;3. 高debt。但 低economic growth的情况。

余伟文 (香港金融管理局总裁)

  • 三个风险
  1. Pandemic
  2. High inflation risks,Fed降低G增速
  3. Emerging market growth降低。资金外流风险,但是此风险可控
  • 香港的机会
  1. 股票通+债券通+离岸人民币(推动人民币国际化)
  2. 金融科技
  3. 绿色金融

梁新桥(新加坡金融管理局副行长)

  1. Finance need to tranform to meet the economy.
  2. Singapore has robust banking system to meet the need
  3. Deepen financial connection between Singapore & China

Green finance – help to achieve low carbon emission,

  1. Taxation helps reformatiom.
  2. Help corporate achieve cheaper bonds & notes
  3. Work together to build green finance expertises

Techonoly & Innovation help shape the economy. Resillence of economy.

李扬 (国家金融与发展实验室理事长)

近期(2021.12.6)政治局会议结束 -> 经济局会议

  • 1. 中国经济增长率下行
  1. 产出缺口
  2. 投资减少
  3. 消费减少 – 分配问题:收入一次分配在 公共+居民 存在不合理。需要增加居民收入
  • 2. 物价 (通胀)

中国CPI PPI在2012年已分道扬镳,因为两者对应的产业不同。

  1. CPI:电商,互联网,物流,etc 消费品市场是有效市场 高度竞争
  2. PPI:上游呈现寡头垄断的趋势

CPI & PPI 无直接传导机制,因为两市场分离。Friedman words “inflation is everywhere a monetary phenomenon” 在中国不适用,因为两市场分离,且市场不是有效市场(不竞争)。

  • 3. 对外部门

净出口大幅支撑GDP。但是远期未来 必须要提振 内循环 – 双循环机制。

  • 4. 就业

就业出现结构性问题 Unemployment多存在于20-25岁劳动力中。问题:

  1. 创业对就业的边际带动能力。i.e. 2015年一个市场主体创造1.9个人就业,但是2020年仅0.5个。(可能会有measurement error2015年统计数据真实性问题。若无,则)说明边际创造就业能力差。且需要数据考量带动的是何种就业(地摊or企业)。
  2. 劳动力市场面临多重失衡+压力
  • 5. 中国债务+杠杆率
  1. Firms: 企业去杠杆 => 导致问题:企业不借钱+不经营。生产意愿下降。矛盾点:杠杆率大=>风险大;杠杆率小=>生产意愿小
  2. Households: 不愿意通过borrowing增加消费。

Summary:仅调整资产负债表让其好看(low risk),但是同时降低了“意愿”带来了灾难性后果!

Public:财政政策态度消极,地方杠杆率+增长率同时下降。 尽管市场说“紧信用,款货币”,但是反映到终端生产者or消费者最终仍然是无支持效果(money 仍在金融体系中流转,因为没有优质资产投资及放贷)。所以从结果考虑是紧缩的货币政策。

  • 6. 银行要找的项目,资金出口:

已从之前 大企业+房地产 的模式转向 小企业+小微企业 的模式。但是此模式的优质资产也即将耗尽。=> 要开辟新的路径 去实现 金融服务实体经济

但是对于小企业来说,借钱融资的 财务成本高=> 实体部门less profitable,因为对小企业放贷的利率过高。

  • 7. 流通成本高
  • 8. 全要素生产率水平有待提高
  • 综上得到结论:
  1. 由于之前“实际”monetary policy紧,=> 可以将其实际宽松,刺激经济发展 => 空间大
  2. 11.22发文帮助小企业,说明此前没有做好。未来支持小企业对经济发展空间大。原因: Adv:1. 小企业服务大企业;2.小企业帮助就业。Disadv:缺少优质小企业(不能多为仅带来0.5个就业的企业)。
  3. 当前准备金率过高 8.4%,世界他国多为0%准备金时代及趋势 => 空间大

Eric Maskin (2007 Nobel Prize winner)

Mechanism Design: start with the outcomes, then work backward to find what mechanism can create that outcomes.

E.G. Seperate a cake to two kids. Aims to make those kids think they get same size. Ideal way to do so is halve the cake. However, kids may not consider sizes are same. We the mechanism design does is 1. as one kid to cut, and then 2. ask the other kid to choose a piece.

Two Realistic Problems can be solved by mechansim design.

  • 1. Supply Chain Disruption: Economy is complex that there are goods and inputs, and producers get multi-sources.

Problems: producer does not seek multi-sources, coz they would prefer protect itself & its downstream by using single source supply chain. => supply side market is inefficient.

Solve: Government conduct i.e. 1. government subsidies to achieve multi-sources; 2. government encourage producers through other ways.

By government is the mechanism

  • Climate changes: Human emission gas into atmosphere. A firm that uses coal to generate electicity has no incentives to switch to clean energy, coz 1. high cost of tranformation; 2. CO2 has less effects on that certain firm.

Solution: conduct carbon tax. 1. reduce carbon uses; 2. high cost of coal encourage firms to switch to clean power.

Carbon Tax is the mechanism

  • Chinese Government did good:
  1. carbon trade system (,which is similar to carbon tax). Also, what banks can do higher rate for high carbon emission firms (my simple idea).
  2. ban Cryptocurrency (加密货币). Cryptocurrency would reduce the effects of monetary policy, coz people would instead use Cryptocurrency.

陆磊 (国家外汇管理局副局长)

PPI 上涨来自 supply shocks,上游利润上升,CPI上涨来自demand shocks,下游利润下降(利润被上游收割)。

高质量发展: 工具+动力

工具:

  1. 需求侧:宏观调控需要continuous & persistent & transparent
  2. 供给侧:增强competitivity,竞争性由technology带来。
  3. 结构:优化分配(见李扬)。
  4. Green Finance。

动力:

  1. 改革创新 – 灵活有效的fiscal policy
  2. 发展源泉激励
  3. Focus on also the liquidity risk (, which exceeds the credit risks already)

Jeffrey Sachs

In the long-term future, there is a fundamental changes of the realtionship between China and U.S.. Need strengthen the cooperation.

  1. At the end of North Atlantic dominant, Asia takes doninant instead. Geographic driven by economic divergence,
  2. Environmental Crisis – Fragile ecosystem
  3. Demographic changes. Massive urbanisation.
  4. Common prospertiy – demand for social inclusion.
  5. Smart machines and digital socity
  6. Wealth and well-being. How to shift the focus from wealth to well-being.

All countries, governments need to consider the global environment regulation goal.

Smart chiens and technology declines labour demands => reallocation of wealth (conflicts exist that the wealthy people rejects to pay higher tax)

Risk Aversion

The Arrow-Pratt coefficient of absolute risk aversion

Definition (Arrow-Pratt coefficient of absolute risk aversion). Given a twice differentiable Bernoullio utility function \(u(\cdot)\),

$$ A_u(x):=-\frac{u”(x)}{u'(x)} $$

  • Risk-aversion is related to concavity of \(u(\cdot)\); a “more concave” function has a smaller (more negative) second derivative hence a larger \(u”(x)\).
  • Normalisation by \(u'(x)\) takes care of the fact that \(au(\cdot)+b\) represents the same preferences as \(u(\cdot)\).
  • In probability premium

Consider a risk-averse consumer:

1. prefers \(x\) for certain to a 50-50 gamble between \(x+\epsilon\) and \(x-\epsilon\).

2. If we want to convince the agent to take the gamble, it could not be 50-50 – we need to make the \(x+\epsilon\) payout more likely.

3. Consider the gamble G such that the agent is indifferent between G and receiving x for certain, where

$$G= \begin{cases} x+\epsilon, & \text{with probability $\frac{1}{2}+\pi$}.\\ x-\epsilon, & \text{with probability $\frac{1}{2}-\pi$ } \end{cases}$$

4. It turns out that \(A_u(x)\) is proportional to \(\pi/\epsilon\) as \(\epsilon \rightarrow 0\); i.e., \(A_u(x)\) tells us the “premium” measured in probability that the decision-maker demands per unit of spread \(\epsilon\).

 

ARA.

Decreasing Absolute Risk Aversion. The Bernoulli function \(u\cdot)\) has decreasing absolute risk aversion iff \(A_u(\cdot)\) is a decreasing function of \(x\). Increasing Absolute Risk Aversion… Constant Absolute Risk Aversion – Bernoulli utility function has constant absolute risk aversion iff \(A_u(\cdot)\) is a constant function of \(x\).

Relative Risk Aversion

Definition (coefficient of relative risk aversion). Given a twice differentiable Bernoulli utility function \(u(\cdot)\),

$$ R_u(x):=-x\frac{u”(x)}{u'(x)}=xA_u(x) $$

There could be decreasing/increasing/constant relative risk aversion as above.

Implication: DARA means that if I take a 10 gamble when poor, I will take a10 gamble when risk. DRRA means that if I gamble 10% of my wealth when poor, I will gamble 10% when rich.

The R.C.K. Model

Ramsey (1928), followed much later by Cass (1965) and Koopmans (1965), formulated the canonical model of optimal growth for an economy with exogenous ‘labour augmenting technological progress. The R.C.K model (or called. Ramsey (Neo-classical model) can be considered as an extension of the Solow model but without an assumption of a constant exogenous saving rate.

Assumptions

Firms

  1. Identical Firms.
  2. Markets, factors markets and outputs markets, are competitive.
  3. Profits distributed to households.
  4. Production fucntion with labour augmented techonological progress, \(Y=F(K,AL)\). (Three properties of the production: 1. CRTS; 2. Diminishing Outputs, second derivative<0; 3. Inada Condition.)
  5. \(A\) is same as in Solow model, \(\frac{\dot{A}}{A}=g\). Techonology grows at an exogenous rate “g”.

Households

  1. Identical households.
  2. Number of households grows at “n”.
  3. Households supply labour, supply capital (borrowed by firms).
  4. The initial capital holdings is \(\frac{K(0)}{H}\)., where \(K(0)\) is the initial capital, and \(H\) is the initial number of households.
  5. Assume no depreciation of capital.
  6. Households maximise their lifetime utility.
  7. The utility fucntion is constant-relative-risk-aversion (CRRA).

The lifetime utility for a certain household is represented by,

$$ U=\int_{t=0}^{\infty} e^{-\rho t}\cdot u(C(t))\cdot\frac{L(t)}{H} dt$$

Behaviours

Firms

By CRTS, \(\frac{\partial F(K,AL)}{\partial K}=\frac{\partial f(k)}{\partial k}\).

$$ F(K,AL)=AL\cdot F(\frac{K}{AL},1) \quad \text{By CRTS} $$

$$ \frac{\partial F(K,AL)}{\partial K}= AL \cdot \frac{\partial F(K/AL,1)}{\partial K}\cdot \frac{\partial K/AL}{\partial K}=F'(K/AL,1)\\=f'(k) $$

For the firms’ problem, the market is competitive, firms maximise their profits, then we get capital earns its marginal products,

$$ r(t)=\frac{\partial F(K,AL))}{\partial K}=f'(k(t)) $$

Similarly, labours earns its marginal products,

$$ W(t)=\frac{\partial F(K,AL)}{\partial L}=\frac{\partial F(K,AL)}{\partial AL}\cdot \frac{\partial AL}{L} $$

$$ =A \frac{\partial F(K,AL)}{\partial AL} =A \frac{\partial ALF(K/AL,1)}{\partial AL} $$

Apply the chain rule and replace \(F(K/AL,1)=f(k)\) and \(F'(K/AL,1)=f'(k)\). Then, we would get,

$$ W(t)=A[f(k)-kf'(k)]\ or\ W(t)=A(t)[f(k(t))-k(t)f'(k(t))]$$

We denote \(w(t)=W(t)/A(t)\) as the efficient wage rate, then we get,

$$ w(t)=f(k(t))-k(t)f'(k(t)) $$

Another key assumption of this model is,

$$ \dot{k}(t)=f(k(t))-c(t)-(n+g)k(t) $$

, which represents the actual investment (outputs minus consumptions), \(f(k(t))-c(t)\); and break-even investment, \((n+g)k(t)\). The implication is that population growth and technology progress would dilute the capital per efficient work.

The difference with the Solow model is that we do not assume constant saving rate “s” in \(sf(k)\) now, instead we assume the investment as \(f(k)-c\).

Households

The budget constraint of households is that: the PV of lifetime consumption cannot exceed the initial wealth and the lifetime labour incomes.

\int_{t=0}^{\infty} e^{-R(t)}\cdot C(t) \cdot \frac{L(t)}{H} dt \leq \frac{K(0)}{H}+\int_{t=0}^{\infty} e^{-R(t)}\cdot W(t) \cdot \frac{L(t)}{H} dt

, where \(R(t):=\int_{\tau=0}^ t r(\tau) d\tau\) to represent the discount rate overtime. When \(r\) is a constant, \(R(t)=r\cdot t\) and \(A(t)=A(0)e^{rt}=A(0)e^{R(t)}\).

The budget constraint can be rewritten as,

$$ \frac{K(0)}{H}+\int_{t=0}^{\infty} e^{-R(t)}\cdot[W(t)-C(t)]\cdot \frac{L(t)}{H} dt \geq 0 $$

And easy to know,

$$ \lim_{s\rightarrow \infty} [ \frac{K(0)}{H}+\int_{t=0}^{s} e^{-R(t)}\cdot[W(t)-C(t)]\cdot \frac{L(t)}{H} dt ] $$

$$ e^{-R(s)}\frac{K(s)}{H}= \frac{K(0)}{H}+\int_{t=0}^{\infty} e^{-R(t)}\cdot[W(t)-C(t)]\cdot \frac{L(t)}{H} dt $$

We get the non-Ponzi condition,

$$ \lim_{s\rightarrow \infty} \frac{K(s)}{H}\geq 0 $$

Then,

$$ \frac{K(s)}{H}=e^{R(s)}[ \frac{K(0)}{H}+\int_{t=0}^{\infty} e^{-R(t)}\cdot[W(t)-C(t)]\cdot \frac{L(t)}{H} dt] $$

Maximisation of Households’ Problem

We plug the \( \begin{cases} C(t)=A(t)c(t)\\L(t)=L(0)e^{nt}\\A(t)=A(0)e^{gt} \end{cases} \) into households’ lifetime utility function (objective function), and then get the,

$$ U=B\cdot \int_{t=0}^{\infty} e^{-\beta t} \frac{c(t)^{1-\theta}}{1-\theta} dt $$

, where \(B:=A(0)^{1-\theta} \frac{L(0)}{H}\) and \(\beta:=\rho-n-(1-\theta)g\) (we need \(\beta>0\) to make the utility function convergence), and the utility function is, \(u(c_t)=\frac{c_t^{1-\theta}}{1-\theta}\).

The budget constraint is the households’ lifetime budget constraints divided by \(A(0)\) and \(L(0)\),

$$ k(0)+\int_{t=0}^{\infty} e^{-R(t)}\cdot[w(t)-c(t)]\cdot e^{(g+n)t} dt \geq 0 $$

Lagrangian

$$ \mathcal{L}=B\cdot \int_{t=0}^{\infty} e^{-\beta t}\frac{c_t^{1-\theta}}{1-\theta} dt\\ +\lambda [k_0+\int_{t=0}^{\infty} e^{-R(t)}(w_t-c_t) e^{(n+g)t}dt ]$$

F.O.C.

$$B \cdot e^{-\beta t} c_t^{-\theta}=\lambda \cdot e^{-R(t)+(n+g)t} $$

Take logritham,

$$ ln(B)-\beta t-\theta ln(c_t)=ln(\lambda)-R(t)+(n+g)t $$

$$ ln(B)-\beta t-\theta ln(c_t)=ln(\lambda)-\int_{\tau=0}^t r(\tau) d\tau+(n+g)t $$

Thus, we get the relationship between consumption, \(c_t\), and \(R(t)\). Later, we take differentiation w.r.t. \(t\), and we would get,

$$ -\beta -\theta \frac{\dot{c_t}}{c_t} =-r(t)+n+g$$

So, (by replacing in the \(\beta\))

$$ \frac{\dot{c_t}}{c_t}=\frac{r(t)-n-g-\beta}{\theta}=\frac{r(t)-\rho-\theta g}{\theta}$$

Therefore, we get the time-path of consumption.

Dynamics

The dynamics of consumption

Combine the firms’ problems and households’ problems.

$$ \begin{cases} \frac{\dot{c_t}}{c_t}=\frac{r(t)-\rho-\theta g}{\theta} \\ r(t)=f'(k(t)) \end{cases} \Rightarrow \frac{\dot{c_t}}{c_t}=\frac{ f'(k(t)) -\rho-\theta g}{\theta} $$

Therefore, we find the time-path of consumption depends on \(f'(k)\). We define \(k*\) is the solution when \(f'(k)=\rho+\theta g\). So, at \(k^*\), the numerator of RHS equals zero.

As \(f(k)\) is an increasing function but with diminishing returns, so \(f”(k)<0\) and that means \(f'(k)\) is a decreasing function in k. Thus,

  • at \(k<k^*\), \(f(k)>\rho+\theta g\) and \( \frac{\dot{c_t}}{c_t} >0\);
  • at \(k>k^*\), \(f(k)<\rho+\theta g\) and \( \frac{\dot{c_t}}{c_t} <0\).
Figure 1

The dynamics of k

We recall the assumption,

$$ \dot{k}(t)=f(k(t))-c(t)-(n+g)k(t) $$

At \(\dot{k}=0\), consumption, \(c(t)=f(k(t))-(n+g)k(t)\), equals outputs minus break-even investment.

We now consider the Solow model without the depreciation term. Recall the difference between the RCK model and the Solow model is that we do not assume a constant saving rate over time, but other things keep similar. Thus, the term \(c(t)\) is “equivalent” to \(sy\) in Solow model.

Figure 2

In the Solow model, changes in saving rate would change the magnitude of \(sy\) curve. The Golden Rule saving rate is “s” that maximises consumption (the difference between Y and the interaction between \(sy\) and \(k(g+n)\)). The shape of the production function determines the property of the Golden Rule saving rate.

An equilibrium level of consumption is determined in mainly two steps. 1. the interaction between saving \(sy\) and \((n+g)k\) determines the \(k^*\). 2. plug \(k^*\) back to \(sy\) and find the difference between outputs and savings to get consumption.

We here focus on the second step, the equilibrium level of \(k^*\) determines consumption and thus consumption is a function of \(k^*\). At a lower saving rate (see Figure 2), \(k^*\) is too small, so there is less consumption. At a higher saving rate, \(k^*\) is too large, so there is also less consumption. Therefore, we can find that consumption is in a quadratic form w.r.t. \(k^*\).

Figure 3. \(c(t)=f(k(t))-(n+g)k(t),\ \dot{k}=0\)

$$ (s\downarrow) \Leftrightarrow k \downarrow \to c \downarrow$$

$$ (s\uparrow) \Leftrightarrow k \uparrow \to c \downarrow$$

$$ (s_{GoldenRule}) \Leftrightarrow k^*\to c_{max}$$

Or, we can consider consumption as the difference between \(y\) and \((n+g)k\). The wedge like area gets large and then shrinks.

Overall, the above facts make the \(\dot{k}=0\) curve.

Recall \( \dot{k}(t)=f(k(t))-c(t)-(n+g)k(t) \).

Above the curve where \(c\) is large, then \(\dot{k}<0\) so \(k\) decreases. Below the curve where \(c\) is small, then \(\dot{k}>0\) so \(k\) increases. Implication is that if less consumption, then more saving, \(\dot{k}\) increases.

Phase Diagram.

Figure 4

Combining Figure 1 and Figure 3, we get the above Phase Diagram. The equilibrium is shown in the figure above.

P.S. We can prove that the equilibrium is less than Golden Rule level \(k^*_{GoldenRule}\) (which is the maximum point in the quadratic shaped curve). The proof is the following,

The value of k at \(\dot{c}=0\) is \(f'(k)-\rho-\theta g=0\), and the Golden Rule level is \(c=f(k)-(n+g)k\) (as we illustrated before), and take f.o.c. w.r.t. k to solve the Golden rule k. \(\frac{\partial c}{\partial k}=0 \to f'(k)=n+g\). Therefore, we get,

$$ \begin{cases} f_1:=f'(k_{equilibrium})=\rho+\theta g \quad\text{equilibrium in phase diagram}\\ f_2:=f'(k_{GoldenRule})=n+g\quad\text{golden rule level}\end{cases}$$

$$ \rho+\theta g>n+g \quad \text{by our assumption of \beta convergence}$$

So, we get,

$$ f_1>f_2 $$

$$ k_{equilibrium}< k_{GoldenRule} $$

Thus, we find the equilibrium level capital per efficient workers, \(k_{equilibrium}\), must be less than the Golden Rule level \( k_{GoldenRule} \).

From the phase diagram, we can get the saddle path that can achieve equilibrium.

BGP

At the Balanced Growth Path, the economy is in equilibrium. So the time-paths satisfy \( \frac{\dot{c}}{c}=0, and \frac{\dot{k}}{k}=0 \). Therefore, we can get the BGP of others,

$$y=k^{\alpha}\to lny=\alpha ln(k)\to \frac{\dot{y}}{y}= \alpha\frac{\dot{k}}{k}=0 $$

$$y=\frac{Y}{AL}\to lny=lnY-lnA-lnL\to \frac{\dot{y}}{y}=0= \frac{\dot{Y}}{Y}- (g+n)\\ \Rightarrow \frac{\dot{Y}}{Y}= g+n $$

$$ \frac{\dot{c}}{c}=0\to c=\frac{C}{AL} \to \frac{\dot{c}}{c} =g+n $$

Similarly (also as the similar method in Solow model), we can get,

$$ \begin{cases} \frac{\dot{Y}}{Y}=n+g \\ \frac{\dot{C}}{C}=n+g \\ \frac{\dot{K/L}}{K/L}=g \\ \frac{\dot{Y/L}}{Y/L}=g \\ \frac{\dot{C/L}}{C/L}=g \end{cases} $$

Fiscal Policy

Assumption: government conducts government purchases, \(G(t)\). The government purchases do not affect the utility of private sectors, and future outputs. Government finances, G(t), by lump-sum taxes.

We can consider the crowding-out effect. Under full employment, government purchases take away part of the consumptions. In our case, government spending takes away some of the savings. Therefore, the dynamics of capital per efficient workers become (the minus government spending term shifts the curve downward by G(t)),

$$\dot{k}(t)=f(k(t))-c(t)-G(t)-(n+g)k(t)$$

In short, government purchases would make the economy achieve a new equilibrium where there is less consumption but the same capital (investment from the private sector) level. Also, the saddle path moves downward.

\(\rho\) Changes

A fall in \(\rho\) can be considered as the effect of monetary policy. A fall in \(\rho\) would result in a movement of \(\dot{c}=0\) curve to the right by the equality, \(\frac{\dot{c}}{c}= \frac{r(t)-\rho-\theta g}{\theta} =\frac{f'(k)-\rho-\theta g}{\theta}\).

P.S. \(\dot{c}=0 \Leftrightarrow f'(k)=\rho-\theta g \to k=f’^{-1}( \rho-\theta g )\)

As \(\rho\) changes, a new path generates. The economy is at \(E^1\), and then follows the new path moving to \(E^{new}\). We would finally end up with a new equilibrium with higher consumption.

Convergence

See Romer (2018) find the speed of adjustment.

Recall two paths in the R.C.K model.

$$ \dot{k}=f(k(t))-c(t)-(n+g)k(t) $$

$$ \frac{\dot{c}}{c}=\frac{f'(k)-\rho-\theta g}{\theta} $$

We replace this non-linear equation with the linear approximation, so we take the first order Taylor approximation around the equilibrium \(k^*\) and \(c^*\).

$$ \dot{k} _{approx} \approx \dot{k}|_{k^*,c^*}+\frac{\partial \dot{k}}{\partial k}|_{ k^*,c^* }(k-k^*) \\+ \frac{\partial \dot{k}}{\partial c}|_{ k^*,c^* }(c-c^*) $$

As in the equilibrium \( \dot{k}|_{ k^*,c^* } =0\) and \( \dot{k}|_{ k^*,c^* }=0 \), so

$$ \dot{k}_{approx}\approx \frac{\partial \dot{k}}{\partial k}|_{ k^*,c^* }(k-k^*)+\frac{\partial \dot{k}}{\partial c}|_{ k^*,c^* }(c-c^*) $$

Similarly, (after we doing a bit transformation \( \dot{c}=\frac{f'(k)-\rho-\theta g}{\theta}c \) ).

$$ \dot{c} _{approx} \approx \frac{\partial \dot{c}}{\partial k}|_{ k^*,c^* }(k-k^*)+\frac{\partial \dot{c}}{\partial c}|_{ k^*,c^* }(c-c^*) $$

We then replace \( \dot{c}=\frac{f'(k)-\rho-\theta g}{\theta}c \) ) and \( \dot{k}=f(k(t))-c(t)-(n+g)k(t) \) into \( \dot{k}_{approx} \) and \( \dot{c}_{approx} \). Also, we denote \(\tilde{c}=c-c^*\) and \(\tilde{k}=k-k^*\).

P.S. \(\partial \frac{\dot{c}}{\partial k}|_{ k^*,c^* }=\frac{c^*}{\theta}f”(k^*)\) and \(\frac{\partial \dot{c}}{\partial c}|_{ k^*,c^* }= \frac{f'(k)-\rho-\theta g}{\theta} |_{ k^*,c^* }=\frac{\dot{c}}{c}|_{ k^*,c^* }=0\).

And, \(\frac{\partial \dot{k}}{\partial k}|_{ k^*,c^* }=f'(k^*)-(\delta+n) \) and \(\frac{\partial \dot{k}}{\partial c}|_{ k^*,c^* }=-1\).

Then, we get,

$$ \dot{k}_{approx}\approx [f'(k^*)-(\delta+n)] \tilde{k}-\tilde{c} $$

In equilibrium, \(\dot{c}=0 \Leftrightarrow f'(k)=\rho+\theta g\), so,

$$ \dot{k}_{approx}\approx [\rho+\theta g-(\delta+n)] \tilde{k}-\tilde{c} =\beta \tilde{k}-\tilde{c} $$

$$ \dot{c} _{approx} \approx \frac{c^* f”(k^*)}{\theta } \tilde{k}$$

Then, we divide both side by \(tilde{k}\) or \(\tilde{c}\), respectively. We would get,

$$ \frac{\dot{k}_{approx}}{\tilde{k}}\approx \beta -\frac{\tilde{c}}{\tilde{k}} $$

$$ \frac{\dot{c} _{approx}}{\tilde{c}} \approx \frac{c^* f”(k^*)}{\theta } \frac{\tilde{k}}{\tilde{c}} $$

From the above two equations we can find growth rate of \(\dot{\tilde{c}}_{approx}\) and \( \dot{\tilde{k}}_{approx} \) depend only on the ratio, \(\frac{\tilde{k}}{\tilde{c}}\).

Later, we apply a very strong assumption that \(\tilde{c}\) and \(\tilde{k}\) changes at the same rate, and also the rate make LHS of two equations equal. By this assumption, we denote,

$$ \frac{\dot{c} _{approx}}{\tilde{c}}= \frac{\dot{k}_{approx}}{\tilde{k}} =\mu $$

Then, solving those three equations by making above two equations equal, we would get,

$$\mu^2-\beta \mu +\frac{ f”(k^*) c^* }{\theta}=0$$

$$\mu_1,\mu_2=\frac{\beta\pm[\beta^2-4f”(k^8)c^*/\theta]^{1/2}}{2}$$

Figure 2.7 (Romer)

We can see \(\mu\) must be negative, otherwise the economy cannot converge (see the path BB). If \(\mu<0\), the economy would be in the path AA instead. The path is the saddle path of R.C.K. model.

Applying such as the Cobb-Douglas form production, we can plug second derivatives of the production into \(\mu\) and get the speed of adjustment.

Something More about Solow Model

The current mostly used Solow model always have a depreciation term, and thus the law of motion becomes, \(\dot{K}=I-\delta K\).

The mainstream model has different assumptions about the production function as well. For example, technological progress is generally added. 1. \(Y=AF(K,L)\) in which technology is exogenous, and it could be called Hicks-neutral; 2. \(Y=F(K,AL)\) that can represent the efficient workers, labour-augmented, or Harrow-neutral; 3. \(Y=F(AK,L)\) in which the technological progress is capital augmented.

Applying for example the labour-augmented technology and \( \frac{\dot{A}}{A}=g\) , we can simply solve the Solow model as the following,

$$k=\frac{K}{AL}$$

$$ \frac{\dot{k}}{k}= \frac{\dot{K}}{K}- \frac{\dot{A}}{A}- \frac{\dot{L}}{L} $$

$$ \frac{\dot{k}}{k}= \frac{sY-\delta K}{K}- \frac{\dot{A}}{A}- \frac{\dot{L}}{L} $$

$$ \frac{\dot{k}}{k}= \frac{sY}{K}-\delta-g- n $$

$$ \dot{k}=sy-(\delta+g+n)k $$

, where \(y=\frac{Y}{AL}\) and \(\frac{K}{AL}\) represent the output/capital per efficient works. Therefore, if \(\dot{k}=0\), then \(sy=(\delta+g+n)k\).

The stable point of k is \(k^*\) in which \(sf(k)=(\delta+n+g)k\).

We always the Cobb-Douglas function to represent the production function, because it satisfies CRTS, increasing and diminishing assumptions, and the Inada conditions (\(\lim_{k\rightarrow0}f'(k)=\infty; \lim_{k\rightarrow \infty}f'(k)=0\), Inada, 1963 ).

In the following, we would all analyse the model using efficient works to do analysis.

Balance Growth Path

All the following is assuming the economy is at the steady state or stable point.

For \( \frac{\dot{K}}{K} \),

$$ k=\frac{K}{AL} $$

By taking logritham,

$$ ln(k)=ln(K)-ln(A)-ln(L) $$

By taking differentiation and set \(\dot{k}=0\) (based on our previous derivations of finding the steady state condition).

$$ \frac{\dot{K}}{K} = \frac{\dot{A}}{A} + \frac{\dot{L}}{L} $$

$$ \frac{\dot{K}}{K} = g+n $$

For \( \frac{\dot{Y}}{Y} \), similar as the original Solow’s one.

$$ln(Y)=ln(F(K,AL))$$

Differentiate w.r.t. \(t\),

$$ \frac{\dot{Y}}{Y}=\frac{ \dot{K}F_1’+\dot{A}LF_2’+ A\dot{L}F_2′ }{F(K,AL)} $$

By Euler’s Theorem to the demoninator (see math tools),

P.S. differentiate \(tY=F(tK,tAL)\) w.r.t. \(t\), then we get \(Y=F’_1 K+F’_2 AL\).

$$ \frac{\dot{Y}}{Y}=\frac{ \dot{K}F_1’+\dot{A}LF_2’+ A\dot{L}F_2′ }{ F’_1 K+F’_2 AL } $$

Devide both numerator and demoninator by \(KAL\),

\frac{\dot{Y}}{Y}=\frac{ \frac{\dot{K}}{KAL}F_1’+\frac{\dot{A}L}{KAL}F_2’+\frac{ A\dot{L}}{KAL}F_2′ }{ \frac{F’_1 K}{KAL}+\frac{F’_2 AL}{KAL} }

\frac{\dot{Y}}{Y}=\frac{ \frac{\dot{K}}{K}\frac{F_1′}{AL}+\frac{\dot{A}}{A}\frac{F_2′}{K}+\frac{ \dot{L}}{L}\frac{F_2′}{K} }{ \frac{F’_1 }{AL}+\frac{F’_2 }{K} }= \frac{ \frac{\dot{K}}{K}\frac{F_1′}{AL}+(\frac{\dot{A}}{A}+\frac{\dot{L}}{L})\frac{F_2′}{K} }{ \frac{F’_1 }{AL}+\frac{F’_2 }{K} }

\frac{\dot{Y}}{Y}= (n+g)\frac{ \frac{F_1′}{AL}+\frac{F_2′}{K} }{ \frac{F’_1 }{AL}+\frac{F’_2 }{K} } = (\frac{\dot{K}}{K})\frac{ \frac{F_1′}{AL}+\frac{F_2′}{K} }{ \frac{F’_1 }{AL}+\frac{F’_2 }{K} }

$$ \frac{\dot{Y}}{Y}=n+g = \frac{\dot{K}}{K} $$

For \( \frac{\dot{y}}{y} \), (as \(y=\frac{Y}{AL})

$$ln(y)=ln(Y)-ln(A)-ln(L)$$

$$ \frac{\dot{y}}{y}= \frac{\dot{Y}}{Y}- \frac{\dot{A}}{A}- \frac{\dot{L}}{L} =(n+g)-n-g $$

$$ \frac{\dot{y}}{y} =0$$

Similarly, for per capita terms,

For \( \frac{\dot{K/L}}{K/L} \), per capita capital,

$$\frac{\dot{K/L}}{K/L}=\frac{ \frac{\dot{K}L-K\dot{L}}{L^2} }{K/L}$$

$$\frac{\dot{K/L}}{K/L}=\frac{\dot{K}L-K\dot{L}}{KL}= \frac{\dot{K}}{K}- \frac{\dot{L}}{L} $$

\frac{\dot{K/L}}{K/L} =(g+n)-n=g

For \( \frac{\dot{Y/L}}{Y/L} \) (per capita output) we apply the same transformation as K/L,

\frac{\dot{Y/L}}{Y/L}= \frac{\dot{Y}}{Y}- \frac{\dot{L}}{L} =g

In summary, the BGP is a situation in which each variable of the model is growing at a constant rate. On the balanced growth path, the growth rate of output per worker is determined solely by the rate of growth of technology.

P.S. Technology Independent of Labour And Capital

Applying for example the Type 1 case and \( \frac{\dot{A}}{A}=g\) , we can simply solve the Solow model as the following,

We would not use capital per efficient worker here, because labour is not technology-augmented by assumption. Instead, we simply assume capital per capita, \(k=\frac{K}{L}\). We can easily get the relationship,

\frac{\dot{k}}{k}= \frac{\dot{K}}{K}- \frac{\dot{L}}{L}

By setting \(\dot{k}=0\), we can find \( \frac{\dot{K}}{K}=\frac{\dot{L}}{L}=n \), which is same as Solow’s original works.

However, the difference is when we deal with the output. As the output is now \(Y=AF(K,L)\), so the changes in outputs (numerator) are,

$$ \dot{Y}=\dot{A}F(K,L)+AF’_1\dot{K}+AF’_2\dot{L} $$

We expand output per se (demoninator) by Euler’s Theorem \(Y=AF’_1K+AF’_2L\) (A is now outside the production function), and then calculate the percentage changes of outputs,

$$ \frac{\dot{Y}}{Y}=\frac{ \dot{A}F(K,L)+AF’_1\dot{K}+AF’_2\dot{L} }{ AF’_1K+AF’_2L } $$

Devided both demoninator and numerator by AKL,

$$ \frac{\dot{Y}}{Y}=\frac{ \dot{A}F(K,L) }{ AF(K,L) }+\frac{\frac{F’_1}{L}\frac{\dot{K}}{K}+\frac{F’_2}{K}\frac{\dot{L}}{L} }{ \frac{F’_1}{L}+\frac{F’_2}{K} } $$

$$ \frac{\dot{Y}}{Y}= \frac{\dot{A}}{A}+ \frac{\dot{L}}{L}=g+n $$

Saving Rates

We now consider first how does changes in the saving rate affect those factors.

The determinants of saving rate are, for example, uncertainty or decrease in expected income, and required pension rate.

See the following figures,

An increase in the saving rate would result in an increase in the investment curve. \(\dot{K}=I-\delta K\) tells that there would be a huge increase in \(\dot{K}\) initially, and by the shape of production function, the difference diminishes until achieving the new stable point \(k^*_{new}\).

As \(\dot{k}\) is a derivative of \(k\) w.r.t. \(t\), we can easily get the time path of \(k\) as the following,

Another important factor is the growth rate of output per capita,

Also \(ln(Y/L)\),

For this one, we can prove that the slope of \(ln(Y/L)\) is \(\dot{ln(Y/L)}=\frac{\partial}{\partial t}[ln(Y)-ln(L)]=(g+n)-n=g\), so it grows constantly at rate “g” before \(t_0\). Later growth rate of Y jumps makes the slope of \(ln(Y/L)\) increases, but \(ln(Y/L)=g\) when achieves a new steady state and \(ln(Y/L)\) keeps growing at “g” in the long run.

The Speed of Convergence

Way 1

We follow our Solow model with labour-augmented technology. The time path of changes of capital per efficient works is,

$$ \dot{k}=sy-(\delta+n+g)k$$

$$ \dot{k}=sy-(\delta+n+g)k$$

At the steady state, \(\dot{k}=0\), so \( sy-(\delta+n+g)k \). We then plug in the Cobb-Doglas production function and denote \(y=\frac{Y}{AL}=\frac{K^{\alpha}(AL)^{1-\alpha}}{AL}=k^{\alpha}\), we can find the \(k^*\),

$$ k^*=(\frac{s}{\delta+g+n})^{\frac{1}{1-\alpha}} $$

And get the path of k,

$$ \frac{\dot{k}}{k}=sk^{\alpha-1}-(\delta+g+n) :=G(k)$$

To find the speed of convergence, we would focus on the time path of k around \(k^*\). Or approximate the time-path by taking first-order Taylor expansion around \(k^*\) to approximate,

$$ G(k)\approx G(k^*)+G'(k^*)(k-k^*) $$

As \(G(k^*)=0\) by our proof of steady state condition, thus,

$$ G(k)\approx (\alpha-1)s {k^*}^{\alpha-1}\frac{k-k^*}{k^*} $$

We plug the steady state \(k^*\) back into the above equation and get,

$$ G(k)=-(1-\alpha)(\delta+g+n)\frac{k-k^*}{k^*} $$

Therefore, we find the mathematic expression of the convergence speed, \( (1-\alpha)(\delta+g+n) \). It is the measure of how quickly k changes when k diviates from \(k^*\). Also, we find that the growth rate \( G(k)=\frac{\dot{k}}{k} \) depends on both the convergence speed and \( \frac{k-k^*}{k^*} \), which is how far k deviates from its steady state level.

Take also a Taylor expansion to \(ln(k)\) at \(k^*\), we would get,

$$ G(k)=-(1-\alpha)(\delta+g+n)(ln(k)-ln(k^*)) :=g_k$$

Then, to find the convergence speed of outputs, we apply \(y=k^{\alpha}\) and take logritham \(ln(y)=\alpha ln(k)\). Differentiate w.r.t. \(t\),

$$ \frac{\dot{y}}{y} =\alpha\frac{\dot{k}}{k} $$

$$ g_y:=\frac{\dot{y}}{y} =\alpha( -(1-\alpha)(\delta+g+n)(ln(k)-ln(k^*))) \\= -(1-\alpha)(\delta+g+n)(ln(y)-ln(y^*)) $$

So we get \(g_y=\alpha g_k\), and \(\beta= (1-\alpha)(\delta+g+n) \) is the speed of convergence. It measures how quickly \(y\) increases when \(y<y^*\). The growth rate of y depends on the speed of convergence, \(\beta\), and the log-difference between \(y\) and \(y^*\).

Way 2

We take first order Taylor approximation to \(f(k)=\dot{k}\) around \(k=k^*\).

$$ \dot{k} \approx \dot{k}|_{k=k^*}+\frac{\partial \dot{k}}{\partial k}|_{k=k^*}(k-k^*) $$

By definition of steady state condition, the first term of RHS is zero. So,

$$ \dot{k}\approx -\lambda \cdot (k-k^*) $$

We denote \(-\frac{\partial \dot{k}}{\partial k}|_{k=k^*}\:=\lambda\) as the speed of convergence. As \(\dot{k}=sy-(\delta+g+n)k=sk^{\alpha}- (\delta+g+n)k\), so,

$$ \lambda=-s\alpha {k^*}^{\alpha-1}- (\delta+g+n) $$

Plug \(k^*\) into, we get,

$$ \lambda=(1-\alpha)(\delta+g+n) $$

To see why we denote \(\lambda\)as the speed of convergence, solve the differential equation, \( \dot{k}\approx -\lambda \cdot (k-k^*) \), by restrict time from 0 to t.

$$ \dot{k}=\frac{\partial k}{\partial t} \approx -\lambda \cdot (k-k^*) $$

$$ \frac{1}{k-k^*} dk=-\lambda dt $$

$$\int_{k(0)}^{k(t)} \frac{1}{k-k^*} dk=\int_{0}^t -\lambda dt $$

$$ [ln(k-k^*)]|^{k(t)}_{k(0)}=-\lambda t|_0^t $$

$$ ln(k(t)-k^*)=-\lambda t|_0^t+ ln(k(0)-k^*) $$

Finally,

$$ k(t)=k^*+e^{-\lambda t}[ k(0)-k^* ] $$

Or in other form,

$$ ln(\frac{k(t)-k^*}{k(0)-k^*})=-\lambda t $$

Solow Residuals

Recall our labour-augmented production function, \(Y(t)=F(K(t),A(t)L(t))\).

$$ \dot{Y}=\frac{\partial Y}{\partial t}=F’_1\dot{K}+ F’_2\dot{A}+ F’_2\dot{L} $$

$$\frac{ \dot{Y}}{Y}=\frac{\partial Y(t)}{\partial K(t)}\dot{K(t)}+ \frac{\partial Y(t)}{\partial L(t)}\dot{L(t)}+ \frac{\partial Y(t)}{\partial A(t)}\dot{A(t)} $$

Then, applying the replacement equation into the above equation,

$$ \frac{\partial Y(t)}{\partial L(t)}=\frac{\partial Y(t)}{\partial A(t)L(t)}\cdot A(t) $$

$$ \frac{\partial Y(t)}{\partial A(t)}=\frac{\partial Y(t)}{\partial A(t)L(t)}\cdot L(t) $$

Then we get,

$$ \frac{ \dot{Y}}{Y}=\frac{Y(t)}{K(t)}\frac{\dot{K(t)}}{K(t)}\frac{K(t)}{Y(t)}+ \frac{Y(t)}{L(t)}\frac{\dot{L(t)}}{L(t)}\frac{L(t)}{Y(t)}+ \frac{Y(t)}{A(t)}\frac{\dot{A(t)}}{A(t)}\frac{A(t)}{Y(t)}\\=\epsilon(t)_{Y,K}\frac{\dot{K(t)}}{K(t)}+ \epsilon(t)_{Y,L}\frac{\dot{L(t)}}{L(t)}+R(t) $$

,where we denote \(R(t)\) as the Solow Residuals.

$$ R(t) = \frac{Y(t)}{A(t)}\frac{\dot{A(t)}}{A(t)}\frac{A(t)}{Y(t)} $$

Solow Residuals represent the residuals unexplained by growth of capital and labours.

Golden Rule Saving Rate (Phelps)

To be continued.

Reference

Math Tools

1. Homogenous of Degree \(k\)

Definition (Homogeneity of degree \(k\)). A utility function \(u:\mathbb{r}^n\rightarrow \mathbb{R}\) is homogeneous of degree \(k\) if and only if for all \(x \in \mathbb{R}^n\) and all \(\lambda>0\), \(u(\lambda x)=\lambda^ku(x)\).

$$f(\lambda x_1,…,\lambda x_n)=\lambda^kf(x_1,…,x_n)$$

Property

  1. Constant Return to Scale: CRTS production function is homogenous of degree 1. IRTS is homogenous of degree \(k>1\). DRTS is homogenous of degree \(k<1\).
  2. The Marishallian demand is homogeneous of degree zero. \(x(\lambda p,\lambda w)=x(p,w)\). (Maximise \(u(x)\) s.t. \(px<w\). “No Money Illusion”.
  3. Excess demand is also homogeneous degree of zero. Easy to prove by the Marshallian Demand.

$$CRTS:\quad F(aK,aL)=aF(K,L) \quad a>0$$

$$IRTS:\quad F(aK,aL)>aF(K,L) \quad a>1$$

$$DRTS:\quad F(aK,aL)<aF(K,L) \quad a>1$$

2. Euler’s Theorem

Theorem (Euler’s Theorem) Let \(f(x_1,…,x_n)\) be a function that is homogeneous of degree k. Then,

$$ x_1\frac{\partial f(x)}{\partial x_1}+…+ x_n\frac{\partial f(x)}{\partial x_n} =kf(x) $$

or, in gradient notation,

$$ x\cdot \nabla f(x)=kf(x) $$

Proof: Differentiate \(f(tx_1,…,tx_n)=t^k f(x_1,…,x_n)\) w.r.t \(t\) and then set \(t=1\).

P.S. We use Euler’s Theorem in the proof of the Solow Model.

3. Envelop Theorem

Motivation:

Given \(y=ax^2+bx+c, a>0, b,c \in \mathbb{R}\), we need to know how does a change in the parameter \(a\) affect the maximum value of \(y\), \(y^*\)?

We first define \(y^*=\max_{x} y= \max_{x} ax^2+bx+c \). The solution is \(x^*=-\frac{b}{2a}\), and plug it back into \(y\), we get \(y^*=f(x^*)=\frac{4ac-b^2}{4a}\). Now, we take derivative w.r.t. \(a\). \(\frac{\partial y^*}{\partial a}=\frac{b^2}{4a^2}\). We would find that,

$$\frac{\partial y^*}{\partial a}= {\frac{\partial y}{\partial a}}|_{x=x^*} $$

A Simple Envelop Theorem

$$v(q)=\max_{x} f(x,q)$$

$$=f(x^*(q),q)$$

$$ \frac{d}{dq}v(q)=\underbrace{\frac{\partial}{\partial x}f(x^*(q),q)}_{=0\ by\ f.o.c.}\frac{\partial}{\partial q}x^*(q)+\frac{\partial}{\partial q} f(x^*(q),q) $$

$$ \frac{d}{dq}v(q) =\frac{\partial}{\partial q}f(x^*(q),q) $$

Think of the ET as an application of the chain rule and then F.O.C., our goal is to find how does parameter affect the already maximised function \(v(q)=f(x^*(q),q)\).

A formal expression

Theorem (Envelope Theorem). Consider a constrained optimisation problem \(v(\theta)=\max_x f(x,\theta)\) such that \(g_1(x,\theta)\geq0,…,g_K(x,\theta)\geq0\).

Comparative statics on the value function are given by: (\(v(\theta)=f(x,\theta)|_{x=x^*(\theta)}=f(x^*(\theta),\theta)\))

$$ \frac{\partial v}{\partial \theta_i}=\sum_{k=1}^{K}\lambda_k \frac{\partial g_k}{\partial \theta_i}|_{x^*}+{\frac{\partial f}{\partial \theta_i}}|_{x^*}=\frac{\partial \mathcal{L}}{\partial \theta_i}|_{x^*} $$

(for Lagrangian \(\mathcal{L}(x,\theta,\lambda)\equiv f(x,\theta)+\sum_{k}\lambda_k g_k(x,\theta)\)) for all \(\theta\) such that the set of binding constraints does not change in an open neighborhood.

Roughly, the derivative of the value function is the derivative of the Lagrangian w.r.t. parameters, \(\theta\), while argmax those unknows (\(x=x^*\)).

4. Hicksian and Marshallian demand + Shepherd’s Lemma

To be continued.

https://www.bilibili.com/video/BV1VJ411J7ZL?spm_id_from=333.999.0.0

5. KKT

6. Taylor Series

A Taylor series is a series expansion of a function about a point. A one-dimensional Taylor series is an expansion of a real function \(f(x)\) about point \(x=a\) is given by,

$$ f(x)=f(a)+f'(a)(x-a)+\frac{f”(a)}{2!}(x-a)^2\\+\frac{f^{(3)}(a)}{3!}(x-a)^3+…+\frac{f^{(n)}(a)}{n!}(x-a)^n+… $$

Taylor expansion is a way to approximate a functional curve around a certain point, by taking derivatives. We focus the function around this point.

For example, we approximate \(f(x)=x^3\) around \(x=2\).

$$ f(x)\approx f(2)+\frac{f'(2)}{1!}(x-2)+\frac{f”(2)}{2!}(x-2)^2\\+frac{f^{(2)}(1)}{3!}(x-2)^3+… $$

$$ f(x)\approx 8+\frac{12}{1}(x-2)+\frac{12}{2}(x-2)^2+\frac{6}{3\times2}(x-2)^3 $$

Simplifying it, we get \(f(x)\) around \(x=2\) is,

$$ f(x)=x^3 $$

That is a coincidence that the original function and the Taylor polynomial are exactly the same if \(f(x)=x^3\).

Another Example, we take first order Taylor approximation to \(f(k)=ln(k)\) at \(k^*\),

$$ ln(k)\approx ln(k^*)+\frac{1}{ln(k^*)}(k-k^*) $$

Thus, we know,

$$ ln(k)- ln(k^*)\approx\frac{k-k^*}{k^*} $$

New Study and Idea of Taylor Expension

Taylor Expansion aims to use polynomial to approximate a certain function.

For example, in order to describe the shape of function \(cos(x)\) at x=0, we would first construct a polynomial.

(P.S. We let \(c_0=1\) as we need to pin the polynomial equal to 1 at x=0.)

$$ P(x)=c_0+c_1x+c_2x^2 \ and\ at\ x=0\ P(0)=c_0 $$

, where those coefficients are free to change, and the magnitude of those coefficients would affect how the approcimated curve looks like.

To get a better approximation, we would adjust those coefficients. Thus, we consider using different orders of derivatives to simulate our target function.

We need the first order derivate of \(cos'(x)|_{x=0}=sin(x)|_{x=0}\) to be zero, so we set the first-order derivative of our polynomial function to equal to zero as well!

$$\frac{\partial P(x)}{\partial x}|_{x=0}=c_1 \times 1 |_{x=0}=c_1$$

Therefore, \(c_1\) must be zero.

Let’s go one more step. As the second derivative of \(cos^{(2)}(x)=-1\), we need the second derivative (, which is also the second derivate of the second-order term of our constructed polynomial function) of our polynomial function to be also -1.

$$\frac{\partial^2 P(x)}{\partial x^2}|_{x=0}=2\times c_2$$

We adjust that to be negative one, so \(c_2=-\frac{1}{2}\).

Therefore, we get,

$$cos(x)|_{x=0}\approx P(x)=c_0+c_1 x +c_2 x^2 = 1-\frac{1}{2}x^2$$

Great! If we need a more accurate approximation, then we keep on going to more derivates and calculate the coefficient of the higher-order term. However, I would do that, so I just simply add a term \(O(x^3)\) to represent there are other terms that are less equal than \(x^3\). (There are accurate descriptions that I will update in later posts).