World-System (1960-2100) A Stable Geopolitical Alignment for Argentina?

Argentina just entered what Paul Krugman has called (here) a Classical Monetary-Financial Crisis. The Peso is collapsing, the Central Bank is trying to defend the currency (but is running out of money) and the US is ready to support Argentina with a $20 Billion Swap Line (Bailout). What triggered the crisis? According to Google AI:

After discussing the economics of the crisis, Paul Krugman concludes:

In this post, I'm going to argue that the "alternative strategy" involves Geopolitical Realignment within the World-System. The forecast plot at the beginning of this post shows the ARL20 model being driven by three alternative input systems: (1) the Random Walk (RW, no input, muddle through), (2) the USL20 model (Hegemonic Dominance) and (3) Latin American Integration from the LAC20 model. US attempted dominance of Argentina has been going on since after World War II and it is about to fail (ARL20 model US Input [96.74 < AIC = 106.6 < 117.3]). 

The best Geopolitical model for AR1 (Growth in the Argentinian Economy, see the Measurement Model in the Notes) involves integration with other Latin American countries [63.72 < AIC = 123.9 < 154.1].

Not only is the future forecast for Argentinian growth better under Latin American Integration, but also the system response to shocks is better than the ARL20 BAU model and the integrated system is stable. Under the BAU model (an unstable system), positive shocks to the system reduce growth; under the LA Integration input model (see the Notes), positive shocks increase growth as would be expected.

Latin American Integration, however, has been tried before and has a history of failure. After World War II  the US tried to drive the movement but simply ended up as the dominant Hegemon. A question I will investigate in a future post is whether Latin American Integration would benefit other countries in the region. Until stable Integration does benefit enough countries (a long time in the future?), the Economy of Argentina will likely continue lurching from one crisis to another.

You can experiment with the LA20 BAU model here. Suggestions are given in the code for how to stabilize the model.

Ex. 1.0 Can you find a way to eliminate cycles once the model has been stabilized? 

The solution to this Exercise can be found in the LA_TECHP model which I will describe in a future post. 

Descriptions of how the Dynamic Component State Space models are constructed are given in the Boiler Plate.

Notes

Reading:

• Why is Trump Bailing Out Argentina -- Paul Krugman (2025)
• The Bubbly Economy of Argentina
• Seven Futures for Argentina
• Austerity and Anti-Systemic Protest (Argentina)
• Unstable Systemic Cycles in Latin America

Please see the Boiler Plate for a description of the DCM State Space Models.

 ARL20 model  Measurement Model:

The ARL20 State Space includes three Historical Feedback Controllers: AR1=(Growth-EF) Overall Growth balanced against the Ecological Footprint (EF). AR2=(LU+EF+KOF-Q) an historical feedback controller balancing Unemployment (LU), Ecological Footprint (EF) and Globalization (KOF) against Output (Q). AR3=(EF+CO2-KOF-LU) an historical controller balancing Ecological Footprint (EF) and CO2 Emissions against Globalization (KOF) and Unemployment (LU). Together, the components explain 98.8% of the variation in the indicators.

 ARL20 model  LA Input Model:

Compare the LA Integration System matrix (above) with the  ARL20 BAU model. Notice that (1) all the coefficients in the System Matrix (F)  are reduced in size and (2) the largest effects in the Input matrix (G) are from the LAC Unemployment (LU) controller (LU-Q-EG=1.57) and the LAC Labor Force Controller LA3=(N+L-CO2-Q=1.6)  on AR2 (Argentina's unemployment Controller).

 ARL20 model  LA Shock Input:

The shocks presented above are from the LACL20 Model (see below): (1) A shock to growth of the Latin American Region increases growth in Argentina. (2) A shock to the Unemployment (LU) controller (LU-Q-EG) reduces growth. (3) A Shock to the Populaton-Labor Force Controller (N+L-CO2-Q) increases growth. An explanation of Historical Feedback Controllers can be found in the Boiler Plate.

LACL20 Model Measurement Model:

The LAC State Space contains one Overall growth component (LA1, all indicators weighted positive and approximately equal) and two Historical Feedback Controllers (see the Boiler Plate): LA2=(LU-Q-EG) an Historical Unemployment controller balancing Output (Q) and Energy Use (EG) against Unemployment (LU), explaining 2% of the variation in the indicators. LA3=(N+L-CO2-Q), an Historical Labor Force controller balancing Population Growth (N) and Labor (L) Force with CO2 Emissions and Output.

World-System (1960-2010) The Globalization Controller and the UK Labor Market

 

UK3 = (LU+L+N+EF - KOF-HDI)

Notes

UKL20 Measurement Matrix:

World-System (1960-2010) What Explains Labor Force Growth in the UK?

Notes

UKL20 Measurement Matrix:

EUL20 Measurement Matrix:

AIC (Akaike Information Criterion) statistics:

• EU [1720 < AIC = 1725 < 1729]
• RW [1441 < AIC = 1455 < 1471]
• BAU [1789 < AIC = 1798 < 1807] 

 

World-System (1960-2015) How is the French Labor Market Controlled?

 

In an earlier post describing how the Economy of France works (here) I showed that the Historical Labor Market Feedback Controller was described by:

FR3=(LU-L-N)

Where LU=Unemployment, L=Labor Force and N=Population. What the controller says is that Unemployment is monitored relative to Population and the overall Labor Force. The historical FR3 controller which explains 3% of the variation in the FR State Space (see the Measurement Matrix in the Notes Below) peaked between 1985 and 2000 and then declined after that. Starting in the 1970s, this was a very high period for French Unemployment which took almost 30 years to correct. This historical bulge in Unemployment gave the Economy of France a reputation of having persistent high Unemployment which is not supported by the data. But what caused the Unemployment Bulge?

The Unemployment Bulge from 1975 to 2010 in France was caused by the World System, specifically by shocks to Agricultural and Oil Markets.

In this post I will show results from the FRL20 Model to explain the conclusion. The graphic above also shows the attractor path for FR3 (dashed red line) driven by the World System (WL20 Model). The attractor path suggests that this source of World System shock is over for the immediate future. However, the system is sensitive to World Agricultural and Oil Markets. It seems unreasonable to forecast that these markets will adjust less radically in the future.

Keep in mind that the FRL20 BAU model is approaching a steady state around 2035 (graphic of the FR1 Growth Component above). In a steady state economy, there is a constant stock of capital and people. The only variability in the FR3=(LU-L-N) historical feedback controller is essentially Unemployment (LU) and possibly Labor Force participation, L. Unemployment might also stabilize, but that is not a certainty (Goldman-Sachs predicts that AI could replace 300 million full-time jobs, here, between 2025 and 2030). On the other hand, current AI Energy Demands might not be sustainable (here). And, needless to say, we are dealing with the predictions of a model--we will have to wait for the Future to know what actually happens.

You can experiment with FR3 in the FRL20 Model here.

Notes

The Measurement Matrix for the Economy of France is presented above. All indicators (columns of the matrix) are taken from the World Development Indicators. FR1=(Overall Growth), FR2=(Historical Environmental Controller) and FR3=(Labor Market Controller). For information about how the models were constructed, see the Boiler Plate.

World-System (1950-2010) Has Austerity Helped or Hurt the French Economy?

 

Notes

The data for the AUST index is taken from the World Development Indicators (WDI). The indicators and definitions are listed in the table above. NOTE: AUST is entirely measured by budgetary categories as percentages; the cyclical nature of the index is a result of percentages hitting up against limits [0%,100%].

The AUST index contains three components that explain 94% of the variation in the indicators. 

AUST1 = (0.433 GED + 0.4571 MIL - 0.4477 G - 0.393 GE - 0.4701 GH)  

AUST2 = (0.822 GHE - 0.357 GED - 0.377 GE) 

AUST3 = (Overall Growth) 

AUST1 and AUST2 are historical feedback controllers for the budgetary categories defining Austerity. AUST1 focuses on controlling Education, Military expenditure, Overall Government Expenditure and Health Expenditure. AUST2 focuses on controlling Health and Education Expenditure.

In the Economy of France, Austerity, Debt and Globalization (KOF) are closely related. The relationship can be seen from the Measurement Matrix above when DEBT and WorldGlobal (KOF) are added to the model. In future posts, I will investigate all the indicators of Neoliberalism in France.

The state space of the French Economy is dominated by three components explaining 98% of the variation in the underlying indicators: 

FR1=(Overall Growth)

FR2= (CO2+EF-KOF)

FR3=(LU-L-N) 

FR2 and FR3 are Historical Feedback Controllers regulating Environmental Impacts of Globalization and Unemployment, respectively. EF is the Ecological Footprint and KOF is the Index of Globalization.

Boiler Plate

 

State Space Model Estimation

The Measurement Matrix for the state space models was constructed using Principal Components Analysis with standardized data from the World Development Indicators. The statistical analysis was conducted in an extension of the dse package. The package is currently supported by an online portal (here) and can be downloaded, with the R-programming language, for any personal computer here. Code for the state space Dynamic Component models (DCMs) is available on my Google drive (here) and referenced in each post.

Atlanta Fed Economy Now

My approach to forecasting is similar to the EconomyNow model used by the Atlanta Federal Reserve. Since the new Republican Administration is signaling that they would like to eliminate the Federal Reserve, the app might well not be available in the future.

While the app is still available, there have been some interesting developments. In earlier forecasts, the Atlanta Fed was showing GDP growth predictions outside the Blue Chip Consensus. Right now, after unorthodox economic policies from the Trump II Administration, the EconomyNow model is predicting a drastic drop in GDP (the Financial Forecast Center is only predicting a slight drop here).

Climate Change

Another comparison for what I have presented above are the IPCC Emission Scenarios. These scenarios are for the World System. Needless to say, (1) the new Right-Wing Republican administration plans on withdrawing the US from all attempts to study or ameliorate Climate Change and (2) the IPCC does not produce any RW modes for the World System (but seem my forecasts here).

World System

The longest running set of data we have for the World-System is the Maddison Project based on the work of Angus Maddison (more information is available here). Data on production (Q) and population (N) for most countries and regions runs from years 0-2000. More data becomes available as we near the year 2000. 

Available data were entered in a spreadsheet (see Population above, double click to enlarge). Missing data were interpolated with nonlinear spline smoothing using the R programming Language.

In cases where initial values were not available (see GDP above), the E-M Algorithm was used to estimate initial conditions.

From the graph of GDP above (W_Q) for the World System, it can be seen that economic growth from the year 0-1500 was basically flat. The period of British Capitalism (after 1500) had a small plateau of growth. Takeoff does not happen until the Nineteenth Century.

From a system's perspective, the only model that can be tested for the entire period is Kenneth Boulding's Malthusian Systems Model [Q,N] = f[Q,N].

When developed as a State Space model (measurement matrix above) there are two components: W1=Growth and W2=(Q-N), the Malthusian Controller. When more data is available, the Malthusian Controller can be generalized to other SocioEconomic theories.

What the Malthusian Controller shows (plotted as Q-N above) is that a long-developing Malthusian Crisis (Q<N) started in the Late Middle Ages and accelerated through the period of British Capitalism (Dark Satanic Mills) and was reversed spectacularly during the Nineteenth Century.  Takeoff in response to a deepening Malthusian Crisis would not be an unreasonable way to view Modern Economic Growth.

Error Correcting Controllers (ECC)

In another post (here), I presented Leibenstein's Malthusian Error Correcting Controller (ECC). It can be generalized to the dominant ECCs in most theoretical economic models (above). These controllers can be further generalized. For example, (X-U) and (L-U) can be generalized to (N-U), a more general Urbanization Controller which describes market expansion for economic growth. In countries and periods with limited data, (N-U) might subsume all these processes. ECCs describe important feedback processes in SocioTechnical System that are typically not recognized as such in academic literature.

Kaya Identity

The basic theoretical model underlying all the World-System models I crate is the Kaya Identity. There are a number of advantages to starting theoretical development with the Kaya Identity: (1) An "identity" is true by definition Adding other variables to the model ensure that theory construction is on a solid footing. (2) The Kaya Identity is also used as the foundation for the IPCC Emissions Scenarios allowing a linkage between World-Systems Theory and the work of the IPCC.

World Development Indicators (WDI)

After WWII, extensive data sets on all countries in the World-System became available from the World Bank (here). The indicators above where chose to construct the state space for each WDI-based model. Addition indicators can be added for specific forecasts and analyses.

About

 

This blog separates the macro-policy space from the growth and feedback space of SocioEconomic Systems. My argument (here) is that both the Classical, Neoclassical and Neoliberal economists have been too quick to jump from trying to understand the macro-economy to offering policy proscriptions. For example, just because you can write an ImPAcT Model that has markets for Labor, Commodities, Financials, Energy and Carbon (CO2) it does not mean that the SocioEconomic System has adequate feedback loops to control shocks (as most countries have found out in the inflation that resulted from the COVID-19 Pandemic).

To be specific, at least for the present, here are some policy models that I think should be kept separate from state-space models of the SocioEconomic System:

• AD-AS Market Model
• IS-LM Model
• Phillips Curve
• Arms Race Model
• Demographic Transition Model
• Monetary Models
• Taylor Rule Models
• Keynesian Model

Each of these models and others that I will discover over time make up the Policy Space that is distinct from the State Space of the SocioEconomic Systems I will be presented in following posts.