Comparing Power Measures of the United States & China

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Introduction

Comparing power in international relations is one of the key topics of research in the world of Political Science. This derives from there not being a set definition of power but rather multiple explanations as to what can be construed as power. Many data sets have been created to being able to quantify levels of power between different countries. This paper will evaluate the Correlates of War Composite index of national capability (CINC), gross domestic product (GDP), GDP per capita, military, and technology measures to evaluate the comparisons of power between countries with the various variables. This paper will also outline the problems of the various methods of evaluating these aspects in how they evaluate power.

Correlates of War - Composite Index of National Material Capability(CINC)

The Composite Index of National Material Capabilities, by the Correlates of War Project, is a data set aimed at tracking different indicators to quantify a country’s material capability. These various indicators include military expenditure, military personnel, energy consumption, iron and steel production, urban population, and total population. This CINC score involves data gathered between the years 1816-2016.

Comparisons of the United States & Russia

nmc60plot <- ggplot(nmc60.USRussia, aes(x=year, y=cinc, color=stateabb)) +
  geom_line() +
  ggtitle("National Capability of Cinc Scores between US and Russia: 1816-2016")
print(nmc60plot + labs(y = "CINC", x = "Year", caption = "Singer, 1972"))

Summary of CINC Score Comparison

The Composite Index of National Material Capability of the United States towers over Russia over the last 100 years. The large dips and gains in the graph demonstrate the country in the two World Wars. The key difference illustrated by the Cinc score shows how the United States was able to ramp up the various variables of the data set, while Russia was not able to keep up with its pre-war capabilities. The other period to note is after the fall of the Soviet Union in 1991 when their Cinc capabilities declined. The only major point of time in which Russia had a higher Cinc score than the United States was a short period during the Cold War.

The World Bank - Gross Domestic Product

Gross domestic product from The World Bank is defined as “GDP at purchaser’s prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for the depreciation of fabricated assets or for the depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single-year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.” (World Bank, 2021). GDP has long been the statistical measurement used in comparing states’ economies and is vital to factor in when creating power rankings internationally. A state’s capability economically can greatly translate to its ability to shape international norms and cooperation.

GDPPLOT <- ggplot(data=gdp.USRussia, aes(x=year, y=gdppp, color=country)) +
  geom_line() +
  ggtitle("Comparison of Gross Domestic Product between US and Russia")
print(GDPPLOT + labs(y = "GDP in US $", x = "Year", caption = "World Bank, 2021"))

Summary of GDP (Current US$)

The comparison between the United States and Russia is very astonishing in terms of gross domestic product. This data does begin in 1990 as The World Bank does not have data before 1990 on Russia. This is likely due to the secrecy of the Soviet Union during its leadership of Russia. Even with the shorter period of data, you can observe a much higher GDP. In 2021 the United States recorded a gross domestic product 13.1 times more than that of the Russian Federation (World Bank, 2021). This truly displays the difference in the economic capacity of the two nations.

The World Bank - Gross Domestic Power per Capita

Gross Domestic Product from The World Bank is defined as “GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for the depreciation of fabricated assets or for the depletion and degradation of natural resources. Data are in current U.S. dollars”(World Bank, 2021). While this metric is similar to the last, it takes into account the number of people the country takes to produce such a boisterous GDP. This is important as the size of a country does matter in terms of measuring power capabilities.

GDPPCPLOT <- ggplot(data=gdppc.USRussia, aes(x=year, y=gdpppc, color=country)) +
  geom_line() +
  ggtitle("Comparison of Gross Domestic Product per Capita between US and Russia")
print(GDPPCPLOT + labs(y = "GDP Per Capita in US $", x = "Year", caption = "World Bank, 2021"))

Summary of GDP per Capita (Current US$)

The per capita scale of GDP between the United States and Russia shows a closer comparison of the GDP gap between the two countries. While the world average GDP per capita sits at 12,236 dollars, Russia sits right below the average at 12,194 dollars and the United States stands at 70,248 dollars(World Bank, 2021). This comparison shows the economic advantage the United States has at such a great scale over the Russian Federation.

The World Bank - Military Power

Military power is difficult to specifically quantify because of the differences in technological advancements between different countries. For this paper’s purposes, the evaluation will be conducted of both “military expenditure (current USD)” and “military expenditure (% of GDP)”. The World Bank defines military expenditure as “Military expenditures data from SIPRI are derived from the NATO definition, which includes all current and capital expenditures on the armed forces, including peacekeeping forces; defense ministries and other government agencies engaged in defense projects; paramilitary forces, if these are judged to be trained and equipped for military operations; and military space activities. Such expenditures include military and civil personnel, including retirement pensions of military personnel and social services for personnel; operation and maintenance; procurement; military research and development; and military aid (in the military expenditures of the donor country). Excluded are civil defense and current expenditures for previous military activities, such as for veterans’ benefits, demobilization, conversion, and destruction of weapons”(World Bank, 2021). The same definition is applied to the “% of GDP” but of course factors in the GDP into the measurement.

milexPLOT <- ggplot(data=milex.USRussia, aes(x=year, y=milexv, color=country)) +
  geom_line() +
  ggtitle("Comparison of Military Expenditure between US and Russia")
print(milexPLOT + labs(y = "U.S. Dollars", x = "Year", caption = "World Bank, 2021"))

Summary of Military Expenditure (Current USD)

The difference in military expenditure between the United States and Russia is also very high. In 2021 the United States spent approximately 800 billion dollars on its military while Russia spent approximately 66 billion dollars(World Bank, 2021). These differences are very stark and paint a picture of military dominance, but do not take in other factors such as the size of the force, technology advancements, military bases, and other important factors to consider when ranking power.

milexgdpPLOT <- ggplot(data=milexgdp.USRussia, aes(x=year, y=milexgdpv, color=country)) +
  geom_line() +
  ggtitle("Comparison of Military Expenditure (% of GDP) between US and Russia")
print(milexgdpPLOT + labs(y = "% of GDP", x = "Year", caption = "World Bank, 2021"))

Summary of Military Expenditure (% of GDP)

The military expenditure (% of GDP) shows the first time the United States is currently below the Russian Federation. The United States paid approximately 3.5% of its GDP for its military while Russia paid approximately 4.1% of its GDP for its military in 2021(World Bank, 2021). This does show that the Russian Federation places more of its resources into its military. However, it has a constrained economic capacity in comparison to that of the United States. This results in the United States’ ability to put almost 12 times the amount of money into it’s military than Russia.

The World Bank - Technology

Measuring total technological advantage over another country is very difficult to quantify as there is not a set measure that can objectively say which state is the most technologically advanced. This section of the paper will utilize the measure of high-tech exports (current US$) to attempt to quantify a technological scale between the United States and Russia.

htechexPLOT <- ggplot(data=htechex.USRussia, aes(x=year, y=htechexv, color=country)) +
  geom_line() +
  ggtitle("Comparison of High Texh Exports between US and Russia")
print(htechexPLOT + labs(y = "Exports in U.S. Dollars", x = "Year", caption = "World Bank, 2021"))

Summary of High-technology exports (Current US$)

The United States has a much higher value of high-technology exports than Russia. In 2021 the United States exported approximately 169 billion dollars of exports while Russia only had approximately 10 billion dollars of high-technology exports (World Bank, 2021). This difference can give a general idea of the worth of both countries’ technology to other countries.

World Power Data Measures

Utilizing the various measures illustrated throughout this paper only gives a partial image of power internationally. While these metrics are incredibly helpful in being able to quantify certain capabilities of each country, there is always another factor that could prove prudent in terms of power. For example, the technology measure utilized in this paper utilizes high-technology exports as a metric for measuring technology. However, in “The Rise and Fall of the Great Powers in the Twenty-first Century”: China’s Rise” by Stephen Brooks and William Wolhlforth, it is stated that “China’s technological capacity should not be measured using high-technology exports give the extent to which foreign companies drive Chinese exports”(Brooks, 1972). This is not the same as America as there is not major ownership outside the United States of the major high-technology exports of the United States. Brooks also argues in how GDP is not the most accurate representation of economic performance as it does not factor in items such as the depreciation of the physical environment and the outside influence of the Chinese economic system(Brooks, 1972). These factors do not necessarily affect the Russian Federation the same way as China, but it shows how these data sets do not account for every variable making them not 100% reliable in predicting world power rankings. It is possible to estimate world power rankings by considering a multitude of various data sets and not relying specifically on a few to predict world power. This paper monitors the comparison between the United States and Russia. According to the metrics utilized, the United States has a substantial hold over the Russian Federation in every data set utilized.

Policy Maker Course of Action

As stated previously, there is discrepancies in which data-sets do not full account for. Therefore, it us up to policymakers to be able to read in between the lines of the studies and monitor gains by adversaries which could threaten the United States’ international power capabilities. It is also important that policy makers utilize multiple sources of data and that they do not rely on individual metrics to base the United States’ position in the world. If absoulutely necessary to use a single data-set, the best course of action for policy makers would be to utilize the multi-factored data sets such as the CINC scores and GDP. Utilizing a single picture data-set would not give you enough background to make an educated policy recommendation.

Conclusion

Data analysis is the key that connects the political science community and the policymakers of the world’s major powers. It is important for not only political scientists but also world leaders to understand the value of and to understand such information. Enacting more data-driven responses in terms of international policy would better suit any country in terms of attempting to gain power status in the world.

Bibliography

Brooks, Stephen & Wohlforth, William. (2016). The Rise and Fall of the Great Powers in the Twenty-first Century: China’s Rise and the Fate of America’s Global Position. International Security. https://www.belfercenter.org/sites/default/files/legacy/files/isec_a_00225.pdf

Singer, J. David, Stuart Bremer, and John Stuckey. (1972). “Capability Distribution, Uncertainty, and Major Power War, 1820-1965.” in Bruce Russett (ed) Peace, War, and Numbers, Beverly Hills: Sage, 19-48.

World Bank, World Bank National Accounts Data. (2021). GDP (Current US$). https://data.worldbank.org/indicator/NY.GDP.MKTP.CD

World Bank, World Bank National Accounts Data. (2021). GDP per Capita (Current US$). https://data.worldbank.org/indicator/NY.GDP.PCAP.CD

World Bank, World Bank National Accounts Data. (2021). Military expenditure (Current USD). https://data.worldbank.org/indicator/MS.MIL.XPND.CD

World Bank, World Bank National Accounts Data. (2021). Military expenditure (% of GDP). https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS

World Bank, World Bank National Accounts Data. (2021). High-technology exports (current US$). https://data.worldbank.org/indicator/TX.VAL.TECH.CD