What Does The Velocity Of Money Measure

Monetary Views: Part II

Victor A. Canto , Andy Wiese , in Economic Disturbances and Equilibrium in an Integrated Global Economy, 2018

Abstract

Now it is time to explore the left side of the equation of exchange to see what insights can be derived as we consider different assumptions regarding the control of the quantity of money, the behavior of the monetary aggregates, and velocity of money. As already mentioned, an assumption explicitly made in the textbook representation of the monetarist views is that the Fed controls the quantity of money, say M2 or Money of Zero Maturity (MZM). In doing so, the monetarists use one of the three degrees of freedom afforded by the equation of exchange. A second assumption or degree of freedom used by the monetarists is that demand for money is "stable." In the old days, when we were in grad school, the narrowest definition of stability was assumed. The textbooks assumed a constant velocity. The assumptions that the Fed controls the quantity of money and of a constant velocity of money leaves the left side of the equation determined and completely controlled by the Fed.

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The M-Pesa Technological Revolution for Financial Services in Kenya: A Platform for Financial Inclusion

Njuguna Ndung'u , in Handbook of Blockchain, Digital Finance, and Inclusion, Volume 1, 2018

3.3.2 Financial Inclusion Has Improved the Environment for Monetary Policy in Kenya

The role of the Central Bank is to conduct monetary policy which works more efficiently when financial markets are fairly developed. In Kenya, perhaps the starting point is participation in the bank-dominated financial sector. Most of the population were financially excluded and over time with declining economic opportunities, most commercial banks had withdrawn their branch networks from the rural areas and poor peri-urban centers. Secondly, most transactions were taking place in cash and a large proportion of currency outside the banking system. The introduction of M-Pesa platform changed the traditional holding of currency outside of banks and the preference of cash. These developments affected the velocity of money and the money multiplier, the basic pillars of monetary policy framework at the time.

As soon as M-Pesa hit the ground, currency outside the banking system started to decline and the velocity of money started to decline. Since 2009, velocity of money and proportion of currency outside banks have declined significantly reflecting financial deepening and increased financial innovation. The declining currency outside banks and the significant velocity decline reflect changes in behavior of holding cash – people are keeping less and less money outside of banks and prefer less cash in their daily transactions. The monetary policy framework at the time relied on the assumption that velocity of money was constant and the relationship between base money and broad money, the multiplier, was stable and predictable. From 2007, the picture changed as Figs. 3.4–3.6 show and the monetary policy framework had to be revised to incorporate these changing dynamics.

Figure 3.4. Declining velocity of money.

Source: Source: Central Bank of Kenya

Figure 3.5. Decline in cash outside bank in relation to broad and reserve money.

Source: Source: Central Bank of Kenya

Figure 3.6. Rising money multiplier.

Source: Source: Central Bank of Kenya

In addition, due to innovations taking place in the banking sector through the M-Pesa technological platform, the money multiplier has been rising, see Fig. 3.6.

Declining velocity and rising money multiplier reflects the fact that money demand function has shifted and thus became unstable. This provided a chance to revise the monetary policy framework to a forward-looking framework. This has created an environment for monetary policy signals to work through the market effectively and efficiently. Monetary policy works through signals in the market: these signals cannot be processed by market agents if they are not included in the financial system. In this case, financial inclusion allows access and participation in the financial system and this has provided a better environment for monetary policy to influence the market.

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Macroeconomic Policies and Exchange Rate in the Short Run

Cristina Terra , in Principles of International Finance and Open Economy Macroeconomics, 2015

Exercise 3

Consider the Mundell–Fleming model to determine the nominal exchange rate. Imagine now that the aggregate demand of the domestic and foreign economies obeys the following quantitative equation:

$\begin{array}{ll}{M}^{\mathrm{d}}\hfill & =kPY\hfill \\ M^{{\mathrm{d}}^{*}}\hfill & =k^{*}{P}^{*}{Y}^{*}\hfill \end{array}$

where $M^{\mathrm{d}}$ represents the demand for money, $k>0$ is a constant associated to the velocity of money, $P$ is the level of prices, and $Y$ is the output level, in real terms. The variables signed with * represent the foreign economy and have an identical definition.

a.

Using purchasing power parity, find the nominal exchange rate as a function of the exogenous variables of the model.

b.

Assume now that a positive, exogenous shock in productivity increases the real, long-run output in the domestic and in the foreign markets alike, namely, $Y_{\mathrm{f}}^{*}-Y^{*}=Y_{\mathrm{f}}-Y$ . How does the nominal exchange rate in the domestic economy react to this shock?

c.

Now assume that the shock from the previous item affects the domestic and foreign economies in distinct ways. How does the nominal exchange rate in the domestic economy react to this shock if $Y_{\mathrm{f}}^{*}-Y^{*}>Y_{\mathrm{f}}-Y$ ? How does the nominal exchange rate react to this shock if $Y_{\mathrm{f}}^{*}-Y^{*}<Y_{\mathrm{f}}-Y$ ?

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Reshaping the Financial Order

Christopher Dula , David LEE Kuo Chuen , in Handbook of Blockchain, Digital Finance, and Inclusion, Volume 1, 2018

1.6 Weapons of Mass Consumption

New means of payments are leading to innovative new business models, most notably in the sharing economy – where network intermediaries peddle in 'trust', 'reliability' and 'convenience'.

Cash is expensive to manage and access to a digital payments network unlocks new markets and increases the velocity of money, all of which are critical to economic expansion. This is why emerging market countries often consider digital payment networks as co-infrastructure in their development strategies.

Consider Alibaba in China: it has empowered millions of SMEs to act globally by linking buyers and sellers from all over the world. Facilitating all these transactions is a digital payments network. Such access simplifies accounting procedures, lowers banking costs and reduces the risks associated with handling cash. It also enables electronic money transfers – remittances – which is the single largest FDI flow for some countries. But these networks directly accelerate local economic activity as well, especially in retail.

Plastic cards create a frictionless payment experience that is fast and convenient. It helps merchants to minimize queues; speeds up cashier efficiency and makes them less prone to error. This improves the shopping experience of customers, and that drives loyalty. Contactless cards take this experience even further.

However, credit card penetration is a lot lower in emerging markets versus their more developed counterparts, but in almost every market, rich or poor, smartphone penetration is nearly complete. It's not a privilege for the affluent anymore. So, while Visa may have around two billion cards in circulation, there are some 7–8 billion smartphones actively being used. What we are witnessing is a democratization of access.

Consumers that were previously unbanked can now receive banking services through their smartphones via mobile communications networks. And with NFC technology, these mobile devices can be used as a contactless card, an e-commerce platform, or a location-based payments service, à la Uber, which uses a combination of mobile broadband and GPS to facilitate digital payments transaction in ride-sharing – a business model that is disrupting traditional taxi services in irreversible ways.

There is an active drive to create an effortless consumer experience that is indistinguishable to face-to-face and remote commerce. And it is the role of players in the digital payments industry to create an ecosystem that connects banks, merchants and consumers through a real-time and secure global network that makes all this possible.

Merchants and consumers that rely only on cash are penalized to just one, single limited form of payment. However, cash does have certain advantages. It is universally accepted (practically), anonymous and versatile. And for companies like Visa, MasterCard and American Express – a viable cash replacement continues to be their greatest market opportunity in the small transactions and P2P payments space, which is growing in-step with the rapid growth of the peer-to-peer sharing economy.

Moreover, the peer-to-peer sharing economy is quickly making traditional corporate models obsolete. Crowdfunding (Kickstarter), ridesharing (Uber), apartment/house sharing (Airbnb), co-working (The Coop), reselling and trading (Craigslist), knowledge and talent sharing (TaskRabbit) are just a handful of companies doing revolutionary work that is driving change across the spectrum. These companies are transcending national boundaries and innovating faster than regulation can contain them.

In the new economy trust is the currency that matters more than ever.

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The Optimal Rate of Inflation

Stephanie Schmitt-Grohé , Martín Uribe , in Handbook of Monetary Economics, 2010

3.2 Optimality of the Friedman rule with distortionary taxation

The Ramsey problem consists of choosing a set of strictly positive sequences ${\{c_t,h_t,v_t\}}_{t=0}^{\infty }$ to maximize the utility function (1) subject to Eqs. (14), (16), v_t ≥ v, and $v_t^{2}s\prime (v_t)<1$ , given R ₋₁ B ₋₁ + M ₋₁ > 0 and P ₀. We fix the initial price level arbitrarily to keep the Ramsey planner from engineering a large unexpected initial inflation aimed at reducing the real value of predetermined nominal government liabilities. This assumption is regularly maintained in the literature on optimal monetary and fiscal policy.

We now establish that the Friedman rule is optimal (and hence the optimal rate of inflation is negative) under the assumption that the production technology is linear in hours; that is, F(h_t ) = Ah_t , where A > 0 is a parameter. In this case, wage payments exhaust output and firms make zero profits. This is the case typically studied in the related literature (e.g., Chari et al., 1991). With linear production, the implementability constraint (16) becomes independent of money velocity, v_t , for all t > 0. Our strategy to characterize optimal monetary policy is to consider first the solution to a less constrained problem that ignores the requirement $v_t^{2}s\prime (v_t)<1$ , and then to verify that the obtained solution indeed satisfies this requirement. Accordingly, letting ψ_t denote the Lagrange multiplier on the feasibility constraint (14), the first-order condition of the (less constrained) Ramsey problem with respect to v_t for any t > 0 is

(17) ${\psi }_t{c}_{t}s\prime (v_t)(v_t-\underset{\_}{v})=0;v_t\ge \underset{\_}{v};{\psi }_t{c}_{t}s\prime (v_t)\ge 0.$

Recalling that, by our maintained assumptions regarding the form of the transactions cost technology, s′(v) vanishes at v = v , it follows immediately that v_t = v solves this optimality condition. The omitted constraint ${\underset{\_}{v}}_t^{2}s\prime ({\underset{\_}{v}}_t)<1$ is also clearly satisfied at v_t = v , since s′( v ) = 0.

From the liquidity preference function (5), it then follows that R_t = 1 for all dates t > 0.

Finally, because the Ramsey optimality conditions are static and because our economy is deterministic, the Ramsey-optimal sequences of consumption and hours are constant. It then follows from the Fisher equation (7) that the inflation rate π_t − 1 is negative and equal to β − 1 for all t > 1.

Taking stock, in this section we set out to study the robustness of the optimality of negative inflation to the introduction of a fiscal motive for inflationary finance. We did so by assuming that the government must finance an exogenous stream of government spending with distortionary taxes. The main result of this section is that, in contrast to Phelps's conjecture, negative inflation emerges as optimal even in an environment in which the only source of revenue available to the government, other than seignorage revenue, is distortionary income taxation. Remarkably, the optimality of the Friedman rule obtains independently of the financing needs of the government, embodied in the size of government spending, g_t , and of initial liabilities of the government, (R ₋₁ B ₋₁ + M ₋₁)/P ₀.

A key characteristic of the economic environment studied here that is responsible for the finding that an inflation tax is suboptimal is the absence of untaxed income. In the present framework, with linear production and perfect competition, a labor income tax is equivalent to a tax on the entire GDP. The next section shows, by means of three examples, that when income taxation is incomplete in the sense that it fails to apply uniformly to all sources of income, positive inflation may become optimal as a way to partially restore complete taxation.

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Modeling Corporate Assets

Donald W. Boyd , in Systems Analysis and Modeling, 2001

7.3.3 Simulation Tests

Each model (at whatever level of articulation) must be verified by the analyst and his or her peers, and validated to the satisfaction of the domain expert(s) before the model is used to solve problems. The more scenarios for which a model is validated, the greater the confidence that can be placed in the model. The urgency of validation is related to the value of the problem being solved. For example, it is nonsense to spend $10,000 validating a model that will solve a problem worth $1,000. On the other hand, it may well be worth spending $100,000 to validate a model that solves a problem worth $1,000,000. The ability to specify which and how many validation tests comes only from modeling experience. Three illustrative simulation tests are presented for the L _2,4 model.

7.3.3.1 Data Base Generation

Initially, the value for raw material procurement coefficient, c ₁₁, in Equation 12, X ₁₆ = C ₁₁ X ₅, was set equal to 0.6 (approximately, 1/1.74). A "better" value for c ₁₁ was imputed through iterative solutions of the model, guided by the following system-based heuristic:

All four trends established by IT solution pairs must be positive to accommodate growth but must be kept relatively small.

Justification:

•

Sales forecast data exhibit a positive trend.

•

Inventory and WIP levels must be minimized and no monthly level can be negative.

•

Backlogging must not occur.

The model was iteratively solved for secondary data using this criterion, yielding c ₁₁ = 0.585 and a standard data base for all 19 variables. Data base values for selected months are listed in Table 7.14 of Appendix E. Selective plots of secondary data are included in Figure 7.3 of Appendix E, where small, positive trends are apparent. See Exercise 8 for the effect of trend on β. IT trends and empirical β's (turnover rates) for each subsystem are presented in Table 7.7 ; they were computed by Equations 8.1 and 6.5, respectively. For example, for the asset pool

Table 7.14. Data Base for Corporate Assets Planning Model

Month	X 1	X 2	X 3	X 4	X 5
undefined	X 6	X 7	X 8	X 9	X 10
undefined	X 11	X 12	X 13	X 14	X 15
undefined	X 16	X 17	X 18	X 19	undefined
1	0.040000	0.049988	0.050987	0.040999	0.049450
undefined	0.012237	0.074755	0.060000	0.089031	0.045724
undefined	0.003726	0.021519	0.000987	0.850000	0.844290
undefined	0.028928	0.050000	0.028928	0.050000	undefined
2	0.049988	0.031544	0.029710	0.048144	0.057500
undefined	0.007130	0.068178	0.089031	0.104549	0.052661
undefined	0.004839	0.012903	0.000782	0.844290	0.849627
undefined	0.033637	0.028928	0.033637	0.028928	undefined
3	0.031554	0.024282	0.034555	0.031827	0.067988
undefined	0.008293	0.054765	0.104549	0.096348	0.062966
undefined	0.005022	0.014645	0.000918	0.849627	0.857257
undefined	0.039773	0.033637	0.039773	0.033637	undefined
12	0.045614	0.045925	0.045957	0.045645	0.076000
undefined	0.011030	0.076201	0.111966	0.117914	0.070253
undefined	0.005747	0.019527	0.001116	0.914437	0.919587
undefined	0.044460	0.044840	0.044460	0.044840	undefined
13	0.045925	0.045702	0.045680	0.045903	0.090850
undefined	0.010963	0.076284	0.117914	0.109021	0.085177
undefined	0.005673	0.019417	0.001220	0.919587	0.930979
undefined	0.053147	0.044460	0.053147	0.044460	undefined
22	0.071495	0.069661	0.069477	0.071312	0.115275
undefined	0.016675	0.117541	0.083457	0.090062	0.110937
undefined	0.004338	0.029555	0.001690	1.054552	1.066808
undefined	0.067436	0.067787	0.067436	0.067787	undefined
23	0.069661	0.069165	0.069116	0.069611	0.114450
undefined	0.016588	0.115589	0.090062	0.095840	0.109802
undefined	0.004648	0.029382	0.001680	1.066808	1.078596
undefined	0.066953	0.067436	0.066953	0.067436	undefined
24	0.069165	0.068660	0.068609	0.069115	0.112000
undefined	0.016466	0.114747	0.095840	0.103572	0.107015
undefined	0.004985	0.029166	0.001656	1.078596	1.089268
undefined	0.065520	0.066953	0.065520	0.066953	undefined

Figure 7.3. Selected Plots of Secondary Data (Millions of Dollars)

Table 7.7. Trends and Betas for the Corporate Assets Planning Model

Subsystem	Percent Trend	Beta
Raw Inventory	1.17	0.99
Manufacturing	2.15	0.98
Finished Inventory	2.15	1.06
Asset Pool	1.06	0.14

$\beta =\frac{{\overline{X}}_{11}+{\overline{X}}_{12}+{\overline{X}}_{13}+{\overline{X}}_{16}}{{\overline{X}}_{15}}=\text{0}\text{.143890}\text{.}$

Observe the following:

•

Small positive trends were realized.

•

Turnover rates for raw inventory, manufacturing, and finished inventory subsystems approximate to β = 1.0, as for the ideal inventory system.

•

Delay for X ₁₀, finished product output, is inferred from D = 1/β

$\frac{1}{1.06}=0.943month.$

•

The economists' velocity of money for the asset pool is 1/0.14, or approximately one turnover of assets each seven months. Thus, velocity should be increased by moving some of the assets to an external investment portfolio (see Exercise 10).

Total asset growth for the two years is obtained from the sum of the terminal asset levels, X ₂ + X ₉ + X ₁₅ + X ₁₈ = 1.327020. After 24 months, the assets were distributed as shown in Table 7.8 . Subtracting the 1.0 million of initial assets:

Table 7.8. Asset Redistribution After 24 Months

Subsystem	Initial Percent	Final Percent	Asset Level
Manufacturing	4	5.2	0.068660
Finished Inventory	6	7.8	0.103572
Asset Pool	85	82.1	1.089268
Raw Inventory	5	4.9	0.065520
Total	100	100.0	1.327020

$\text{Growth}\text{=}\text{1}\text{.327020}-\text{1}\text{.0 = 0}\text{.327020, or \$327,020, or 32}\text{.70\%}\text{.}$

Asset growth is directly proportionate to X ₃. Compared to $317,048 of the L _1,1 model, growth is 3.1% higher, due to the difference in the way production input, X ₃, was calculated: Markup for raw inventory storage cost was included in the L _2,4 model but was absent from the L _{1 1} model because the raw inventory subsystem was nonexistent.

7.3.3.2 Perturbation of Forecast

For the first scenario of Phase Two, the sales forecast was perturbed by doubling the slope of the linear trend. New forecast data, displayed in Table 7.9 , were obtained by changing the slope from 0.00375 to 0.00750 in Equation 7.1. Seasonal indices remained the same. These data replaced the X ₅ data in the standard data base of Table 7.14 in Appendix E. Again, the L _2,4 model was iteratively solved using the previous criterion for selecting a "best" value for c ₁₁. A new value for c ₁₁ was found to be 0.620.

Table 7.9. New Forecast Data in Millions of Dollars

Month	Forecast	Month	Forecast
1	0.052900	13	0.135700
2	0.065000	14	0.155000
3	0.080475	15	0.180375
4	0.097600	16	0.207400
5	0.109375	17	0.221875
6	0.106400	18	0.207200
7	0.104550	19	0.196350
8	0.104500	20	0.190000
9	0.105750	21	0.186750
10	0.108750	22	0.187050
11	0.111300	23	0.186900
12	0.112000	24	0.184000

Table 7.10 presents the solution for the final period (month 24), where "Sum" is cumulative algebraic value of change, accumulated over the 24 periods. Average monthly change due to perturbation in X ₅ is readily obtained by dividing "Sum" for each solution variable by 24 months. For example, terminal finished inventory, X ₉, averaged −0.143409/24 = −0.005975, or $5,975, lower.

Table 7.10. Perturbation Test: Month 24

Variable	Value	Change	Sum
X 2	0.118763	0.050103	0.726444
X 3	0.118752	0.050143	0.731456
X 4	0.118862	0.049747	0.681349
X 6	0.028501	0.012035	0.175548
X 7	0.197834	0.083087	1.167019
X 9	0.163326	0.059754	−0.143409
X 10	0.176370	0.069355	1.107262
X 11	0.007630	0.002645	−0.008662
X 12	0.050471	0.021305	0.310117
X 13	0.002874	0.001218	0.018432
X 15	1.106400	0.017132	0.186470
X 16	0.114080	0.048560	0.761586
X 18	0.114080	0.048560	0.761586
X 19	0.115878	0.048925	0.713026

The asset trend, level, and distribution for each subsystem for the 24 months are shown in Table 7.11 .

Table 7.11. New Asset Distribution after 24 Months

Subsystem	Percent Trend	Percent Total	Asset Level
Manufacturing	3.93	7.9	0.118763
Finished Inventory	5.65	10.9	0.163326
Asset Pool	1.13	73.6	1.106400
Raw Inventory	3.14	7.6	0.114080
Total	undefined	100.0	1.502569

$\text{Growth = 1}\text{.502569}-\text{1}\text{.0 = 0}\text{.502569, or \$502,569, or 50}\text{.26\%}\text{.}$

Compared to the first trend slope, the new trend slope produces 53.68% greater growth in total assets. If the two trends represent upper and lower bounds, asset growth of between 32.70% and 50.26% can be projected for the immediate time fence of 24 months.

7.3.3.3 MRP Offset for Lead Time

In the previous two tests, the current month's sales forecast was used in Equation 12 of the model to determine the order rate for the current month. However, this practice ignores the average total delay (lead time) in transforming raw material into finished product. To better utilize sales forecast data, this next scenario employs an MRP (material requirements planning) feature [43]: that of offsetting the forecast by an assumed lead time of two months. (See Exercise 8 for computation of average total delay.) Equation 12 (order policy) was replaced by X ₁₆ = c ₁₁ X ₂₀, where X ₂₀ is a two-month offset of the X ₅ series, an additional exogenous variable extending the number of months to 26 and the number of variables to 20. The exogenous data series is shown in Table 7.12 .

Table 7.12. Sales Forecast and Two-Month Offset

Month	X 5	X 20
1	0.000000	0.049450
2	0.000000	0.057500
3	0.049450	0.067988
4	0.057500	0.079300
⋮	undefined	undefined
22	0.118750	0.115275
23	0.115875	0.114450
24	0.115275	0.112000
25	0.114450	0.000000
26	0.112000	0.000000

For comparison to the standard data base, c ₁₁ was reset to 0.585. No assets were distributed as initial inventories, so that $X_1^0=X_8^0=X_{17}^0=0.$ A new data base reflecting the MRP offset was generated. Data for selected months are presented in Table 7.15 of Appendix E. Because demand X ₅ = 0 zero during the first two-month start-up period, no revenue was generated. However, transient response to X ₁₆ (see Equation 12) produced a small trickle of finished product inventory (X ₈, X ₉), incurring small storage costs by Equation 8: X ₁₁ = $7 and $587. As a result, transient, negative values were obtained for X ₁₀ for months one and two, as readily seen by examining Equation 7: X ₁₀ = X ₅ − X ₁₁. Moreover, these values, X ₁₀ = −$7 and −$587, added $594 to finished product inventory by Equation 6. Thus, by X ₁₁ and Equation 11, $594 was subtracted from the asset pool but was subsequently returned to the pool as revenue during month three via Equations 6, 7, and 11.

Table 7.15. MRP Offset Data Base for Corporate Assets Planning Model

Month	X 1	X 2	X 3	X 4	X 5
undefined	X 6	X 7	X 8	X 9	X 10
undefined	X 11	X 12	X 13	X 14	X 15
undefined	X 16	X 17	X 18	X 19	X 20
1	0.000000	0.000329	0.000362	0.000033	0.000000
undefined	0.000087	0.000268	0.000000	0.000275	−0.000007
undefined	0.000007	0.000149	0.000362	1.000000	0.970555
undefined	0.028928	0.000000	0.028928	0.000000	0.049450
2	0.000329	0.027039	0.029710	0.003000	0.000000
undefined	0.007130	0.022356	0.000275	0.023219	−0.000587
undefined	0.000587	0.012226	0.000782	0.970555	0.923321
undefined	0.033637	0.028928	0.033637	0.028928	0.057500
3	0.027039	0.033872	0.034555	0.027723	0.049450
undefined	0.008293	0.050599	0.023219	0.025589	0.048230
undefined	0.001220	0.014583	0.000918	0.923321	0.916277
undefined	0.039773	0.033637	0.039773	0.033637	0.067988
⋮	undefined	undefined	undefined	undefined	undefined
13	0.045925	0.045702	0.045680	0.045903	0.076650
undefined	0.010963	0.076284	0.049676	0.051848	0.074112
undefined	0.002538	0.019417	0.001220	0.975675	0.976002
undefined	0.053147	0.044460	0.053147	0.044460	0.090850
14	0.045702	0.053756	0.054561	0.046508	0.076000
undefined	0.013095	0.082670	0.051848	0.061348	0.073170
undefined	0.002830	0.023068	0.001414	0.976002	0.964728
undefined	0.059962	0.053147	0.059962	0.053147	0.102500
⋮	undefined	undefined	undefined	undefined	undefined
24	0.069165	0.068660	0.068609	0.069115	0.115275
undefined	0.016466	0.114747	0.109384	0.114452	0.109679
undefined	0.005596	0.029166	0.001656	1.052902	1.066238
undefined	0.065520	0.066953	0.065520	0.066953	0.112000
25	0.068660	0.066550	0.066339	0.068449	0.114450
undefined	0.015921	0.112596	0.114452	0.118420	0.108628
undefined	0.005822	0.028226	0.000819	1.066238	1.145822
undefined	0.000000	0.065520	0.000000	0.065520	0.000000
26	0.066550	0.006050	0.000000	0.060500	0.112000
undefined	0.000000	0.061407	0.118420	0.072603	0.107224
undefined	0.004776	0.000907	0.000000	1.145822	1.252139
undefined	0.000000	0.000000	0.000000	0.000000	0.000000

Table 7.13 shows the distribution of assets after 26 months. Subtracting the 1.0 million of initial assets:

Table 7.13. Asset Distribution After 26 Months

Subsystem	Initial Percent	Final Percent	Asset Level
Manufacturing	0	0.45	0.006050
Finished Inventory	0	5.46	0.072603
Asset Pool	100	94.09	1.252139
Raw Inventory	0	0.00	0.000000
Total	100	100.0	1.3330792

$\text{Growth = 1}\text{.330792}-\text{1}\text{.0 = 0}\text{.3330792, or \$330, 792, or 33}\text{.08\%}\text{.}$

Compared to $327,020 of the standard data base of Table 7.14, growth is 1.15% higher even though c ₁₁ was the same. Thus, an additional $3,772 of assets were generated by incorporating an MRP offset.

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Economics, History of

M. Schabas , in International Encyclopedia of the Social & Behavioral Sciences, 2001

1 Historical Background

While insights on economic phenomena can be found in the works of Plato (c. 427–347 BC), Aristotle (384–322 BC), and the medieval commentaries their work spawned, it was only in the early modern period, coincident with the Scientific Revolution, that a full-fledged discourse emerged. Nicolaus Copernicus (1473–1543), Jean Bodin (1530–96), Thomas Mun (1571–1641), William Petty (1623–87), and John Locke (1632–1704) are some of the more prominent figures of the sixteenth and seventeenth century who wrote on the subjects of money and trade. Copernicus and Bodin articulated the quantity theory of money, that a growth in the money stock results in a rise in prices, a phenomenon observed in Europe since the influx of gold and silver from overseas. Mun, a leading mercantilist and advocate of net exports as the key to England's prosperity, recognized that market forces transcend legal and institutional arrangements, and that genuine wealth comes from the growth of domestic production and the enrichment of the land. Petty was one of the first to devise quantitative measurements of economic phenomena, including per capita output. Locke refined the quantity theory of money, noting the velocity of money, and devised a labor theory of value. He also wedded the historic right to private property with the virtue of industry, and thus launched the liberal doctrine of political economy which subsequently played a profound role in the American constitution.

In the eighteenth century, the most distinguished treatises and essays on political economy were issued by Richard Cantillon (c. 1680–1734), David Hume (1711–76), FrançZois Quesnay (1694–1774), Ferdinando Galiani (1728–87), James Steuart (1712–80), and Adam Smith (1723–90), but there were hundreds of lesser known contributors to the subject. Hume and Quesnay were the most influential thinkers prior to Smith. Hume's Treatise of Human Nature (1739–40) argued that property claims were artificial and not natural, and thus had no deeper links to justice. Promise-keeping and contracts were privileged as the original steps in the formation of human society. In a series of Political Essays (1750), Hume made a number of brilliant insights on money, including his celebrated specie-flow mechanism whereby the gold supply and price level equilibrate via international trade. Hume also recognized that a sudden and unanticipated growth in the money stock can have real effects on output, at least in the interim between the influx of gold and the adjustment of prices. Hume also celebrated the advent of commerce as the means to bring peace and civility. Whereas Locke had highlighted the advent of money in prehistoric times as the critical moment when the accumulation of wealth became ethical, Hume pointed to the much more recent ubiquitous use of money, 'where no hand is empty of it,' as the more critical transition in human history.

Quesnay founded the first school of economics, known as Physiocracy, or the rule of nature. The Physiocrats or les économistes maintained that wealth only comes from the gifts of nature, from the fact that one plants one seed in the spring and obtains two in the fall. Wealth thus only truly grows in the agrarian sector, and so all policy measures should be taken to promote the cultivation of the land. All manufacturing activities were deemed sterile, merely transmuting materials but incapable of augmenting them. Quesnay devised one of the first models in economic theory, his celebrated tableau économique, which depicted the economy as a circular flow of money and goods between three sectors, the farmers, the artisans, and the landowners. The tableau adumbrated a nascent version of the multiplier via the 'zig-zag' transference of wealth from one sector to another over the course of a year. Physiocracy was widely influential, particularly on the work A. R. J. Turgot (1727–81) and Smith.

Adam Smith's Wealth of Nations (1776) canvassed the entire field of economics, including the theories of value and distribution, trade and development, public finance, even economic history and the history of economics. Smith rightly is admired for his concept of the 'invisible hand,' that highlighted the unintended and beneficial consequences of self-interested behavior. The idea that private vices might have public benefits had already been articulated by Bernard Mandeville (1670–1733), in his parody of human society, the Fable of the Bees (1714). Smith explored at length the problem of virtuous action in his Theory of Moral Sentiments (1759), which grounded human action in a natural capacity for sympathy and the desire for the approval of others. For all his emphasis on the pursuit of wealth, Smith saw it as a hollow dream, the futile pursuit of trinkets and baubles. But because we are deceived into thinking that wealth brings happiness, we work hard and thus keep the world going. Arguably no one has surpassed Smith in weaving such a penetrating and cynical view of human nature into such an expansive theory of material well-being.

Smith's ideas were extended and refined by, most notably, Thomas Robert Malthus (1766–1834), Jean-Baptiste Say (1767–1832), David Ricardo (1772–1823), and John Stuart Mill (1806–73), a tradition now known as the Classical school of political economy (see Political Economy, History of ). The Classical economists emphasized a cost-of-production (labor) theory of value and the competing claims of the three economic classes (laborers, landowners and capitalists) over the annual product, most aptly depicted as the fall harvest of corn (wheat). Landowners and their servants were engaged in unproductive labor insofar as they did not produce tangible goods for consumption or investment. Malthus advanced a theory of population growth that reinforced the view that capital accumulation would reach a plateau in the not too distant future. Say popularized many of Smith's principles and also argued against the possibility of gluts in production.

Ricardo devised a novel theory of rent, as a return to the original and indestructible powers of the soil. Rent was a pure residual, and did not enter into the formation of prices. Ricardo highlighted the inverse tendencies of wages and profits, and recognized the importance of transfers of capital between different sectors of the economy. He also worked out the principle of comparative advantage in trade, predicated on the international immobility of capital. Mill extended the analysis by introducing nation-specific patterns of demand and rigidities in foreign exchange rates.

For the Classical economists, pricing came about after distribution, which was in turn determined by natural laws, for example, Malthusian laws governing the growth of population and the diminishing returns of the soil. Because of this emphasis on the natural order, the Classical economists tended to view their laws as inexorable, the result of forces deeper then any human action or process of deliberation. Even money was seen as having a motion of its own that transcended human agency. Scarcity, doom, and gloom were prevalent motifs to that set of writings, notwithstanding the remarkable growth rate of the European economy throughout the nineteenth century. Economists worried about the onset of the stationary state, whereby the profit rate fell and capital accumulation came to a halt; overpopulation resulted in a world of 'standing room only.' It is now apparent that they underestimated the potential for technological invention and innovation, both in the form of labor-saving and capital-saving procedures. Even if they did not comprehend the full ramifications of the industrial revolution of the late eighteenth century, there had already been many indicators in the agrarian sector that new techniques of plant breeding, crop rotation, and fertilization could have dramatic consequences for the accumulation of wealth. The reasons for their pessimism are still not fully understood.

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Global Investing: The Balance of Payments

Victor A. Canto , Andy Wiese , in Economic Disturbances and Equilibrium in an Integrated Global Economy, 2018

Scenario 1: The Textbook Scenario Where Each Country Exclusively Uses Its Own Currency in All Domestic Transactions

In this scenario, we assume that each country only uses its own currency within its borders. That is the peso is only used in Lakeland and the notes are only used in Westland. This assumption greatly simplifies the analysis and yields some alluring and testable insights.

If the peso is only used on transactions effected in Lakeland, then it follows there is no reason for the people of Westland to hold pesos and vice versa there is no reason for the people of Lakeland to hold notes. Therefore the global demand for pesos consists solely of Lakeland's demand for pesos while the global demand for notes consists solely of Westland's demand for notes. The respective changes in global demand for pesos and notes are described by the following equations:

(23.12) $\text{Ψ}{\text{d}}_{\text{p}}={\pi }_{\text{p}}+\eta \text{l}-\upsilon \text{l}$

and

(23.13) $\text{Ψ}{\text{d}}_{\text{n}}=\pi \text{w}+\eta \text{w}-\upsilon \text{w}$

where ψd_p and ψd_n denote the percent change in the global demand for pesos and notes respectively, π _p and π_n the peso inflation rate, that is, Lakeland's inflation rate and the notes inflation rates respectively. Also, ηl and ηw denote the income elasticity of demand for money in Lakeland and Westland respectively while υl and υ w denote the percentage change in the velocity of money in each of the two economies. The latter is intended to capture shifts in the demand for money.

We also assume that the respective countries central banks are the only ones allowed to print the local currency. That is the Lakeland Central Bank is the only printer of pesos while Westland Central Bank is the only issuer of notes. Therefore the world supply of pesos is solely determined by Lakeland's Central Bank while the world supply of notes is solely determined by Westland's Central Bank. Again, the percentage change in the quantities of pesos and notes supplied by the respective central banks can be expressed as:

(23.14) $\text{Ψ}{\text{s}}_{\text{p}}=\omega \text{l}+\mu \text{l}$

(23.15) $\text{Ψ}{\text{s}}_{\text{n}}=\omega \text{w}+\mu \text{w}$

where ψs_p and ψs_n denote the percent change in the world supply of pesos and notes respectively and ωl and ωw the growth in the peso and notes monetary bases while μl and μw denote the percent change in the money multiplier in Lakeland and Westland respectively (see Eq. (23.7)).

Equilibrium requires that the changes in demand, Eq. (23.12), match the changes in supply, Eq. (23.14). Hence the equilibrium conditions provide us with enough information to solve for an expression regarding the underlying peso inflation rate.

(23.16) ${\pi }_{\text{p}}=\omega \text{l}-\eta \text{l}+\upsilon \text{l}+\mu \text{l}$

Similarly Eqs. (23.13) and (23.15) allow us to solve for the equilibrium inflation rate in Westland's currency.

(23.17) ${\pi }_{\text{n}}=\omega \text{w}-\eta \text{w}+\upsilon \text{w}+\mu \text{w}$

We have already established that the nominal exchange rate is the price of the two currencies measured as the ratio of the two countries' CPI. The rate of exchange rate appreciation/depreciation can be expressed as:

(23.11) $\in ={\pi }_{\text{p}}-{\pi }_{\text{n}}$

Implications: The assumption that each country uses its own currency means that the world demand for pesos is solely determined by Lakeland demand for pesos. Put another way the world demand for pesos and Lakeland demand for pesos are one and the same. Similarly the world demand for notes is nothing more than Westland demand for notes and the world supply of notes is nothing more than Westland supply of notes.

An important result, which is clearly dependent on these assumptions is that for this scenario the price level and or inflation rate for each locality, Eqs. (23.16) and (23.17), is determined solely by local demand and supply conditions. Lakeland's inflation rate is positively related to Lakeland's Central Bank peso printing, ωl, and Lakeland's real GDP growth rate, ηl. Notice that in Eq. (23.16), there is no term relating to Westland.

The allure of this analysis is the conclusion that according to these assumptions as we have shown, that the adoption of a floating exchange rate system can isolate an economy from external monetary shocks. The domestic central bank can pursue a monetary policy solely based on meeting domestic objectives without having to worry about the impact of other countries' monetary policies on the underlying inflation rate.

Taking a Lakeland perspective, it is clear that under these conditions, the exchange rate would absorb the impact of any external monetary shock. If the rest of the world increased its inflation rate, say Lakeland in this case, Eq. (23.11) shows that the notes/peso exchange rate would appreciate and no impact would be felt on Westland's domestic inflation rate. Eq. (23.17) for Westland's inflation rate has no term relating to Lakeland. Hence if the conditions assumed for this scenario hold, Westland's exchange rate would appreciate and would not be adversely impacted by a Lakeland monetary mismanagement. The other side of this argument is that Westland would get no benefit from a superior Lakeland monetary policy either. In that case, Westland's exchange rate would depreciate and prevent any of the benefits of the foreign monetary policy from filtering through.

Counterarguments: The insights developed with the assumption that each country only uses its own currency are very seductive. They hold the promise of isolating the domestic economy from other central bank mistakes.

A large number of economists who advocate free markets make the case in favor of a flexible exchange rate [2]. These economists pose an interesting argument that nothing could be better than a market-determined exchange rate. The alternative being a controlled or government-determined fixed exchange rate system. Presumably the market would provide a better signal and better policy design under the freely floating system.

While persuasive, the implications however have not quite borne out by the facts. The argument for a floating exchange rate system was made during the late 1960s and early 1970s. The floating exchange rate movement gained steam during the 1970s when the Bretton Woods gold exchange standard was dismantled. Yet the results were not as expected. Most of the economies of the world experienced a sharp increase in their underlying inflation rate and the global economy slowed down. This leads one to ask what happened to the promise of no contagion. Were all the central banks printing too much money? Clearly the unhinging of the gold standard and adoption of floating exchange rates did not turn out as auspicious as promised. The fact that, qualitatively, most countries had similar experiences suggests that either independently they all made similar policy mistakes or that the floating exchange rate did not isolate the economies as promised, or maybe both. Either way, the floating exchange rate experience of the 1970s was a disappointing one. Many countries experienced a bout of stagflation, which is not what they had signed up for when they adopted the floating exchange rate system.

Let us next examine the possible reasons for each country to exclusively use its own currency. How can that be possible in a global economy with free trade in goods and services? One possibility is that the government uses its powers and imposes legal tender requirements that force the local transactions to be effected in local currencies.

To prevent the use of foreign currencies, transaction costs related to the use of foreign currencies have to be truly prohibitive. If that is the case, then the transaction costs effectively reduce the volume of trade of monies across national borders to zero. Hence forcing the use of only local currencies is tantamount to a prohibitive tariff that eliminates trade in money and forces each economy into monetary autarky. If that is the case, it is ironic that the economists who promote free markets and free trade are the ones who promote floating exchange rates and implicitly the use of legal tender provision that force local people to use the domestic currency. In effect, they are money protectionists. A floating exchange rate that prevents the use of foreign money in domestic transaction is analogous to a tax on the use of foreign money. To the extent that choices are reduced, society will be worse off and the economic efficiency thereby reduced.

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What Does The Velocity Of Money Measure

Source: https://www.sciencedirect.com/topics/economics-econometrics-and-finance/velocity-of-money

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