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In the early 17th century, the cumulative result of 30 years of gold and silver coin production by the Spanish Mint was almost double our baseline estimates, and the village himself pointed out that a large part of the coins minted during this period were reserved for military operations in the Spanish financial lowland countries.
However, the more enduring economic reason behind the export of Spanish coins was their status as an internationally recognized means of payment, and as a result, the Spanish peso was used by many European trading companies for their East Asian trade. An estimate of the stock in 1775 was given, equivalent to 563 tons of silver.
This estimate is based on the production of Spanish mints within the empire from 1772 to 1778. It is important to note that recycling is not mandatory for private holders and, therefore, not all money is reminted. For example, in New Spain (Mexico) and New Granada (Colombia), only 28 to 50 percent of the local currency stock has been recovered, which could explain why the value of Tortella shares in 1775 was significantly lower than our baseline value.
The 1775 GDP estimate provides another justification for this year's higher valuation of the money stock. The value of 563 tons means a fairly high speed of 14.5.
In conclusion, contrary to previous stock estimates, our money supply sequence implies a more reasonable pace. In addition, our money supply series links the initial stock estimate in 1492 to the more reliable stock estimate of the second half of the 19th century.
This is based on a meticulous synthesis of available data on mining and international monetary metal flows in the early modern era.
We can use our money supply series to explain a long-debated question about the history of money circulation: To what extent did monetary growth explain the rise in price levels in early modern Europe? According to monetarists, the rise in the price level is mainly the result of the rise in the stock of money, which is highlighted by another view of precious metals from the United States: the growth of the rate of urbanization in the early modern era promoted a large number of economic transactions in any given period, that is, the speed of money circulation accelerated, pushing up the price level.
The accounting mechanisms listed in the application require data on price, actual output, currency, and velocity. Current best practice estimates for the first two come from their data, which, combined with new money supply estimates, allows us to remove velocity from the exchange equation.
We use the 11-year moving average GDP series of Á Álvarez-Nogar and Prado de la Escosusa. We generate equivalent moving averages for all other variables to avoid giving too much weight to individual annual observations at the beginning and end of the sample. This is particularly relevant for prices, which experienced double-digit growth rate fluctuations after the outbreak of the Napoleonic Wars.
The following breakdown results reflect the data uncertainty of currency, real GDP, and price series within the 95% probability interval. In particular, 10,000 random draws from the distribution of money supply at the beginning and end of the sample reflect the uncertainty of the money supply sequence.
Similarly, we illustrate the uncertainty of actual output growth by randomly sampling from a uniform distribution at the beginning and end of the sample period. These two evenly distributed ranges were determined in preparation for 1492 and 1810 based on the minimum-maximum ranges of the three GDP estimates.
Finally, taking into account the uncertainty of the price level in the early modern period in Spain, we use by and unfortunately these different price series did not overlap in 1492 and 1810.
Therefore, instead of using the minimum-maximum range for these two years, we multiply the value of the price levels in 1492 and 1810 by 8% standard deviation of the normal distribution error scalar with a mean of 1 for Á Á Alvarez-Nogar and Prado de la Escosusa.
To what extent does Spain's monetary growth explain the rise in its early modern price levels? The decomposition results are shown.
The first line reports the actual changes in prices, money, velocity and real GDP. Prices increased by 4.95 times and real GDP by 2.8 times. This is due to a 15.6-fold increase in the currency, while the velocity of circulation has decreased by 11%. According to the measure of importance described in , the increase in the money supply accounts for 70% of the increase in the price level in Spain. The 95% probability interval for this importance measure extends from 62% to 71%.
In contrast, the 11% rate decline represents only 3% of the price change (95% range: 0% to 14%). Real GDP growth of 2.8 times greatly reduced the pressure on the price level, which correspondingly accounted for 26% of the price change (95% range: 21% to 30%).
Thus, the growth of money more than the speed of circulation explained the rise in the price level in early modern Spain. In summary, this result is consistent with the monetary account of the increase in the price level in Spain in the early modern period, and the level of the Spanish velocity of money at the end of the early modern period is similar to the level at the beginning of it, so it accounts for a relatively small proportion of the price level increase in the entire sample.
This paper presents a new long-term estimate of the Spanish money supply between 1492 and 1810. The influx of precious metals from the United States makes this period a unique and interesting episode for monetary historians.
We arrived at an estimate of the Spanish money supply by combining data on the early modern production of precious metals and their international flows, as well as data on the initial money stock. This estimate suggests that Spain's money supply is growing at an annual rate of between 0.7% and 1.1%.
From the point of view of the exchange equation, the resulting increase in the money supply was the main reason for the rise in the price level in early modern Spain. Economic data in the early modern era carries uncertainty.
We use stochastic simulations to generate probability distributions of Spain's monetary stock that reflect this uncertainty. More specifically, for uncertain input variables, we specify a probability distribution that reflects the type and degree of uncertainty we face in the data source.
Then, based on the randomly selected input variable distribution, we repeatedly calculate that the Spanish currency stock is the time-varying distribution of the Spanish currency stock, reflecting the uncertainty of the data. This method also allows us to report the probability intervals for all outcomes.
An overview of the distribution of input variables can be obtained below. The rest of this section discusses the specifications for this distribution.
We illustrate the uncertainty of the initial stock of money by defining a minimum-maximum range that corresponds to the range of initial stock estimates in the literature. These estimates are summarized and discussed. The initial stock value appears by and as the lower and upper limits that define a reasonable initial stock value. The upper limit of 565 tonnes is about 250% higher than the lower limit of 228 tonnes. We are randomly selected from a well-defined distribution to reflect the uncertainty of the initial inventory.
To illustrate the uncertainty of Pacific flows, we estimate how many million pesos Manila galleons carried, using the range of a specific period.
The range is estimated to be wide, with the upper limit often exceeding the lower limit by 100%.
Discusses the underlying data and lists the ranges for a specific period.
In the absence of prior information on how Pacific flows are distributed over these ranges, we employ a uniform distribution over a specific period of time.
Each sub-cycle is extracted independently.
To reflect the uncertainty of the precious metals flowing out of Europe, we randomly draw an error scalar from the normal distribution, the standard deviation of which reflects the dispersion seen in the literature. Our European Outflow Baseline Series uses data from:
In particular, we used Attman's estimate of precious metal flows in the Baltic Sea and the Levant, and de Vries' correction of direct flows to East Asia via the Cape route. A series of European outflows was also compiled.
35 Barrett's collection is very similar to that proposed by de Vries and Attman until the mid-18th century. After that, the Barrett series missed the Cape route traffic of several European trading companies, thus underestimating direct traffic from Europe to East Asia.
The standard deviation of the difference between Artman's European outflow sequence and our baseline outflow sequence was 7.6%. Regarding the Barrett outflow series, it was not until the mid-18th century, when the Barretts series began to systematically underestimate the Cape route traffic, which was equivalent to 8%.
Therefore, we set the standard deviation of the normal distribution scalar to 8% of the baseline outflow. Error terms are plotted independently for each observation, i.e. a period of 25 years. For the outflow rate in Spain, we added a normal distribution error term with a standard deviation of 5%. This reflects the great uncertainty surrounding the data on capital outflows from Spain.
More specifically, our baseline outflow rate series and outflow views provided by individuals were 4.7 percentage points. The available data is listed in .
Error terms are plotted separately for each observation, that is, for each constituent subperiod shown. The calculation of the interpolation is then based on the current randomly drawn group for each subperiod.
Transport loss data are uncertain due to unregistered goods. Private wealth was taxed upon arrival in Spain, so there was a smuggling motive. The data collected show that, on average, smuggling accounted for 30 per cent of registered shipments in the 16th century, 67 per cent in the 17th century and 47 per cent in the 18th century.
The standard deviation of the smuggling rate reaches 7 percentage points (based on 27 observations in the 17th century, therefore, we multiply the smuggling rate lost per transport by a normal-distributed scalar centered on 1 with a standard deviation of 7%. We plot this scalar separately for each loss event. The main source of uncertainty about U.S. precious metals production is related to unregistered production. A detailed discussion of this topic and available data are provided.
To illustrate the uncertainty created by unregistered production, we multiply U.S. production data (including average estimates of illicit production) by a normally distributed error scalar whose standard deviation reflects differences between production series. In particular, we set the standard deviation of the error scalar before 1640 and after 1720 to 10%.
Between 1640 and 1720, we used a higher standard deviation of 15% to reflect the higher uncertainty of unregistered production during this period.
The standard deviations for these specific periods reflect differences between production series, which include illegal production, and our baseline production series, which adds estimates of illegal production to comprehensive official production data by error scalars plotted independently in the three sub-cycles 1492 to 1639, 1640 to 1720, and 1721 to 1810.
With regard to European precious metal production, estimates have been accepted by much later literature, and only data from before 1600 have been revised substantially upward. However, the lack of this discrepancy in the literature cannot be explained by the uncertainty of European production data. Therefore, we multiply the European production data by an error scalar with a standard deviation of 10% and an average of 1.
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Data and Codes: The Reconstruction of the Spanish Money Supply