How much Government Expenditure is Too Much in Argentina?

 

In a prior post (here), I looked at President Javier Milei's attempt to impose Shock Therapy on the Argentine Economy. A central argument of Milei's policies is that government expenditure must be cut quickly because, given Neoliberal Theory, government policies and expenditures interfere with the growth of a free-market economy. But, Shock Therapy is based on an untested assumption: How Much Government Expenditure is Too Much?** In this post, I'll look at the question from the standpoint of Systems Theory.

From the perspective of Systems Theory, government expenditure in Argetnina is out of control.

At the same time, increased government expenditure (G) would have helped Argentina grow but was inhibited by Regional Latin American forces (LAC). 

Typically, the How-Much-is-Too-Much debate is conducted in terms of percentages, for example, 90-100% of GDP is too much debt. ChatGPT summarizes the recommendations above and concludes that 55-60% of Govt. Spending/GDP is "Too Much". However, in a qualifying sentence, ChatGPT concludes:

And, I would add, Latin American Regional Conditions (LAC). In other words, Milei's "Chainsaw" is a bit too much of a blunt instrument.

System theory has more general answer to the  How-Much-is-Too-Much question and it involves: (1) constructing alternative attractor paths for Overall Growth in the Economy (AR1 in Argentina, see the Measurement Model below in the Notes, AR1=(Growth-EF) where EF is the Ecological Footprint) and (2) investigating which path (to include Government Expenditure driven) is best (using the AIC criterion). 

Two paths for AR1 are presented in the graphic at the beginning of this post: (1) AR1 driven by Government Expenditure and (2) AR1 driven by the Latin American Regional Economy (LAC, the best). In other words, more Government Expenditure would create more growth for the Argentine Economy but that growth is limited by the Latin American Regional Economy.

And, the relationship between growth in Argentine Government Expenditure and the LAC Regional Economy is unstable. In other words, government expenditure will keep growing unchecked, exponentially, forever (see the forecast above and System model in the Notes where the dominant eigenvalue is greater than 1.0, F[1,1]=1.089).

So, although more government expenditure might increase growth of the Argentine Economy, growth in Government Expenditure is out of control. Whether Milei can get it under control is another question. And, as is often the conclusion from Systems Models (see the Limits to Growth), slowing down growth rates, not slashing budgets, is what will bring spending under control.

Notes

** Readers familiar with the This Time is Different Controversy will recognize the "How Much is Too Much" debate as a familiar theme in Economics: How much Debt is too much? How much Inflation is too much? How much Financialization is too much? etc. etc. etc.

AR State Space:

System Matrix and Input Matrix for Government Expenditure:

World-System (1960-2010) Argentina Takes a Random Walk

 

In a prior post (here), I investigated the Geopolitical Policy Space for the Economy of Argentina and explored President Javier Milei's use of Shock Therapy to transition Argentina to a Free-Market Economy without Government Planning. But, is there evidence that Shock Therapy actually works or is it just a pretext to destroy institutions and create anarchy? In this post, I explore the question for the Economy of Argentina.

In general, the ChatGPT AI System doesn't report that Shock Therapy has been very successful (above). But what happens when it is unsuccessful? My argument is that Shock Therapy takes the country on a Random Walk (RW) where the outcomes are unpredictable (graphic at the beginning of this post). For Argentina, using the ARL20 BAU model,  there is a 3 in 10 chance of a positive result and a 70% chance of failure (declining systemic growth)--not very good odds.

You can experiment yourself with the  ARL20 BAU model creating various types of Random Walk Models with the instructions in the code.

Notes

Examples:

• Is the US Presidential Election a Random Walk?
• Trump Takes the US On a Random Walk
• Mexico Takes a Random Walk
• What We've Learned about Economic Bubbles
• A Random Walk Among the Undead
• The Drunkards Walk and the Moment of Truth
• What Caused the Financial Crisis
• Policy Models, Randomness and Free Will
• The Deficit and the 2012 Federal Budget Overview
• The Edge of Chaos in Switzerland

Should The Random, Drunkards Walk Policy Model be Taken More Seriously?

It's hard to look at the current policy environment in the US or in Argentina and seriously think about Rational Policy Models. In the current Trump II Administration, events seem to happen randomly: Tariffs being imposed and withdrawn; Immigrants being expelled and then brought back by the courts; Universities having research funding withdrawn for political reasons and then given back after a shake-down; National Guard Troops being deployed to the streets of states and cities with democratic Governors and Mayors and then being withdrawn; the Supreme Court supporting some radical policy measures and not others, etc. etc. What Rational Policy Models can explain all of this?

Q(t) = Q(t-1) + E(t-1)

The Random Walk model is really simple (equation above) and easy to estimate from a statistical standpoint (there is only one parameter in the model and its value is given as 1.0): Tomorrow is like today except for Random Error or History is One Damned Thing After Another or The Drunkards Walk: How Randomness Rules Our Lives.

When using Multimodel Inference (MMI) and selecting models based on the Akaike Information Criterion (AIC), the Random Walk (RW) model should always be one of the competitors. Any model I present will always be tested against the RW. For many countries and time periods, especially using year-to-year data, the RW is often the best short-term model. Yet, we continue to hold on to the belief that there is (rational?) causal structure in macro-social systems.

In Systems Models, there are many kinds of RW models: RW with drift, RW with Feedback, Partial RW (some diagonal elements of the System Matrix are 1.0), in addition to the pure RW model:

The pure RW model above is from Argentina. The only distinguishing feature of the model (from any other country) is the initial state:

If the on-diagonal elements of F take on values other than 1.0 or the off-diagonal elements of F take on values other than 0.0 or if there is a fourth column to F (a constant vector capturing drift) we have the other RW models. To decide which one is most appropriate in a given historical period, I use the AIC criterion.

To supplement Universal Growth Theory in Evolutionary Economics (Random Walk -> Malthusian Growth Model -> Neoclassical Growth Model), I would add an initial RW stage (above) that can be revised at any time in history when the Environment changes and a random search for new approaches is needed.

Or, in Marxist Economics, as the initial stage prior to Feudalism.

Or in Post-Modernist variants (above) with a feedback loop returning to RW when the Environment Changes.

Below in the Notes are some concrete examples. 

Notes

Examples:

• Is the US Presidential Election a Random Walk?
• Trump Takes the US On a Random Walk
• Mexico Takes a Random Walk
• Argentina Takes a Random Walk
• What We've Learned about Economic Bubbles
• A Random Walk Among the Undead
• The Drunkards Walk and the Moment of Truth
• What Caused the Financial Crisis
• Policy Models, Randomness and Free Will
• The Deficit and the 2012 Federal Budget Overview
• The Edge of Chaos in Switzerland

World-System (1960-2010) Controlling the Argentine Economy.

 

Economists* should probably admit that they don't know how to control the Economy. When an economist and politician such as Javier Milei gets elected as president of Argentina in 2023 and starts waving a chainsaw around as a symbol of cutting government, critics start to get nervous.

In a prior post (here) I found that Latin American Integration could stabilize the economy of Argentina. Unfortunately, Latin American Integration has been tried before and mostly failed, probably because Latin American has it's own problems with instability. The problem leaves me searching for other Geopolitical Alignments. In this post, I'll look more carefully at the ARL20 BAU model, that is, turning inward and concentrating on Business as Usual.

Why all the hand-wringing over Argentina? Millie has become the poster boy for the US Right Wing after giving a speech (with Elon Musk) at CPAC in 2025. The current Trump Administration and it's Department of Government Efficiency (DOGE), originally chaired by Elon Musk, seems intent on copying Milei's shock therapy. Unfortunately, or predictably, it seems that Milei's shock therapy has failed and will require a Bailout from the IMF and the US. So, it seems important to ask the general question about how (if at all) an unstable economy such as Argentina can be controlled?

The argument of Shock Therapy is that if we get the Government out of the economy, the Free-Market will take over and ensure prosperity. In other words, the free market will control the economy. If you have problems, it is because the market is not free of government interference. The "free market" assertion can be proven wrong (here).

From the standpoint of Systems Theory (where we have the best understanding of how to control systems), the first step is to establish an attractor path** among the competing Geopolitical models.

The attractor path (AP) for the ARL20 LAC Input model is presented above (dashed line) with the actual historical data (solid line). There are few serious deviations from the attractor path, except for AR3 (the definitions for the state variables are given in the Measurement Matrix below in the Notes) around 1975 and after 2000. However, AR2 and AR3 are Environmental-Unemployment-Globalization controllers and should be relatively stable over time.

The attractor path (AP for AR2 and AR3) for the ARL20 BAU model is presented above. Notice that it differs from the ARL20 LAC Input model AP. The period from 1980 onwards shows departures for both historical feedback controllers. For AR2=(LU+EF+KOF-CO2), unemployment, Ecological Footprint (EF) and Globalization (KOF) departures were very large relative to Emissions (CO2). For AR3=(EF+HDI+CO2-KOF-LU), departures for Globalization (KOF) and Unemployment (LU) dominated. 

The difference between the two time plots above shows that Latin American forces caused the departures and that the two historical feedback controllers (AR2 and AR3) were unable to correct the system with a period of decades (see the Eigen Modes.

Also, in the DCM model (see the Notes below), these two historical feedback controllers interact: shocking AR2 increases AR3=(EF+HDI+CO2-KOF-LU);  shocking AR3 decreases AR2=(LU+EF+KOF-CO2). In other words, Globalization, Unemployment and Environmental degradation are used as historical feedback mechanisms to control the Economy. 

The effects take decades to work out. The historical feedback controller coefficients are weak (the off-diagonal elements in the System matrix below). The feedback effects in the full ARL20 BAU model (including growth components) are also weak.

Exercise 1: Strengthen the feedback coefficients in the ARL20 BAU model and see if you can better control the system.

Controlling how the system responds to Unemployment, Globalization, Environmental degradation will be a great deal more challenging than cutting Government spending, but better system control is needed in Argentina and a free market will not accomplish everything that is needed (here) while Latin American Integration is a long way off in the future.

Notes

* Part of my Interdisciplinary degree at the University of Wisconsin--Madison (1981) was in Economics, so I should probably include myself in this criticism!

** The attractor path of a Dynamics Components State Space Model (DCM) can be computed with a free simulation starting from historical initial conditions (see Pasdirtz 2007). The free simulation that minimizes the AIC among competing Geopolitical models is considered the "best" attractor path, using some historical judgment when competing attractor paths are not well separated.

ARL20 model AIC summary:

ARL20 model Measurement Matrix AR1=(Overall Growth), AR2=(LU+EF+KOF-CO2), AR3=(EF+HDI+CO2-KOF-LU):

ARL20 model State Space Time Plot:

ARL20 BAU model Historical Feedback Controllers System Matrix:

ARL20 BAU model Historical Feedback Controllers Shock Decomposition: 

ARL20 BAU model modes:

ARL20 LAC Input model modes: