Behind the Economics: Social Uprisings
On 9 June 2020 hundreds of protestors in Seattle, Washington had filled the Seattle City Hall main entrance, setting up a community microphone in the dim lights as people, each representing a unique voice and concern, nervously milled the hall, now in their third week of protesting. As the clock was hitting 2100, the building had been opened by City Councilor Kshama Sawant. Only a tight circle knew that the building would be opened, leaving protestors confused why they were being urged to City Hall instead of the Amazon building they had initially set out for. Throughout those next hours, several arguments between speakers would breakout; even Sawant had critics accusing her of co-opting the Black Lives Matter movement to push the city legislation core to her platform: socialist economic reform. As a non-surprising consequence, much of that evening in Seattle City Hall was not focused on the Capitol Hill Autonomous Zone (CHAZ), taken by protestors two days before, or police reform. Instead, protestors used the opportunity to voice deep displeasure with growing economic inequality in Seattle.
As protests continued throughout the week, and are continuing past the time of publishing this, a not-insignificant portion of protestors throughout the United States are beginning to discuss economic inequality as much as they are discussing other social forms of inequality. Some protestors, specifically those in CHAZ, are calling for more radical forms of economic reform such as pure communism or anarchy, many inspired by the millions of past Marxists seeking to turn nations into havens for the working class. Less radical protestors are approaching economic reform from an angle that their basic needs be provided for – Universal Basic Income, assistance for the poor, expanded Temporary Assistance for Needy Families (TANF) or Supplemental Nutrition Assistance Program (SNAP) benefits.
An often-forgot fact is that many protestors taking to the streets with economic demands are not versed in the intricacies of professional economic theory or even radical Marxist economics – they are simply protesting from the pain of inequality. Unfortunately, for most an interest in economics begins only once they begin to participate in calls for reform because they are suffering from inequality. Most historic revolutions have been sparked, in part, by economic concerns and circular feeding policies. The French Revolution of 1789 came on the back of the Ancien Regime nearing bankruptcy and baking their books; the 1848 revolutions which swept Europe were catalyzed by growing economic inequality precipitating from 1846’s putrid weather and subsequent famine, progressives blamed on government maleficence; the 1905 and 1917 Russian Revolutions were both sparked by domestic policies which merely bandaged the economy; although Hong Kong 2019 was sparked by a proposed Extradition Bill, many protestors stayed out in the streets due to growing inequality; poorly structured and corrupt economic systems in Lebanon and Chile 2019 brought the wave of protests to both countries.
There are ample reasons to protest, but the economy is a touchpoint that often pushes people into the streets and keeps them there. Inequality in the United States is only growing, and the situation is bluntly even more dire for black houses, who are now at a USD 30,000 gap between their white counterparts. The economic pain is amplified as more than 40 million people have now filed for unemployment, with real unemployment floating around 20 to 25% and at least 1.1 million Americans planning to begin long-term unemployment. The stock market may not be in free-fall, but personal savings and financial planning are certainly are on a trajectory of unfettered chaos.
Hence, there is an underlying academic urgency to research, study, and diagnose economic concerns in the United States. Further, while many countries are sitting on the embers of protests left unfinished from 2019, there is an urgency to understand how COVID-19’s economic recession may have been part of the precipice for the current wave of protests, and why the overarching storyline for 2020 may merely begin with a once in a century pandemic. For many, inept and/or corrupt COVID-19 responses are not the cause, rather, the symptoms of something deeper.
Predicting a pandemic is impossible; preparing for, or at least factoring in socioeconomics to a situation report, however, is a very realistic task for most opensource intelligence (OSINT) researchers. From a practical point of view for this article, OSINT can be broken down into retrospective studies, ongoing situation reports, and status monitoring. A retrospective study of a situation is the most common – event X happens; therefore, OSINT technicalities are needed to analyze X event. Ongoing situation reports are what many researchers are conducting in Syria, Libya, the Indian-China Line of Actual Control (LAC) conflict, and other conflicts. Status monitoring may include monitoring Russian-state actors or Iranian-US conflict off the Persian Gulf. Status monitoring, by no means a defined technical term, also includes socioeconomic analysis and will be the emphasis of the remainder of the article.
The Philosophy and Tangible Practice of Socioeconomic OSINT
The elaboration on the first part is to emphasize that at a certain point in time, based on an observable pattern in modern history, an unequal system will lead to mass civil disobedience. OSINT research has a strong stake hold in researching and exploring tangible products – military weaponry and movement, cyberespionage, cybercrime, and even public corruption once there is a tangible product. OSINT research, at least to this point, has not focused on an intangible appreciation of assets, or lack thereof, in most cases where a situation is developing.
To make the philosophical case for why OSINT research should focus on socioeconomics, a classical historiographical debate needs to be quickly mentioned. There are two main positions about who drives history (with plenty of derivatives that I cannot address now) with historians focusing on either the governing leaders driving history or people driving governing leaders. The most common argument regards Alexander the Great, Julius Caesar, and Napoleon – were they “Great men” or did history merely welcome them to be great because of the setting in their time, which would have been equivocally created by the people. Thus, the second position, that people under these leaders willing to categorically organize society and lead sections under these “Great Men”. This debate is sure to never end about, and those actors merely serve as an introduction to this debate. More serious historians analyze historiography with the entire spectrum in mind, attempting to build a model for how it ought to be analyzed. Historians such as E.P. Thompson (who focuses on the creation, destruction, and re-creation of class) and Barbara Tuchman (see Guns of August, The Proud Tower, and Stillwell and the American Experience in China) focuses on how people are not aimlessly dragged along by the governing body, and instead drive the culture, leading to a reaction from the governing body, and the momentous counteraction from the people. Karl Marx and Friedrich Engels argued for historical materialism (history is driven by material needs, and thus the needs given or deprived of by leaders), Thomas Carlyle the Great Man Theory, Machiavelli for realpolitik, and Adolphe Thiers for the ‘chief god’ of law and order (French Revolutionary historian and First President of the Third Republic). Thus, in mature research there must be a philosophical cornerstone: are leaders driving historical momentum, or are the people driving leaders to react? If the first, then economic decisions being made are driving maladies that people will, inevitably react to, and thus study ought to be on policies being the driving force of history. If the latter, then there is an assumption socioeconomic signals that there are disparities will lead towards the people demanding change from politicians.
Both policy and socioeconomic indicators should be studied but deciding the philosophy behind which has a greater force on the other – the classical chicken or the egg question – is key to developing research questions.
Beyond the philosophical question and to the practical, there are ample sets of tools and datasets to analyze socioeconomic indicators. At the most basic level, an introductory course in economics and/or economic theory should be taken. If not at the university level, users on YouTube have posted Yale economic lectures that cover basics and a side series focusing on Game Theory. This basic understanding and introduction is key to foremost remove false suppositions and biases regarding economic systems, while also avoiding critical misunderstandings when concluding later research. More bluntly – socioeconomic research cannot be taken lightly.
Introduction to Finding Tools and Socioeconomic Data
There are plenty of tools and methods for studying and understanding socioeconomics in a region. A government’s websites and data center are usually the best places to begin, with great examples being Hong Kong’s Census Data, General Statistics Office of Vietnam, or the United States Bureau of Labor Statistics. The most intricate resources, however, can be found in independent or academic research, 15 of which are linked and explained below.
Mapping America might be one of the most robust tools in the United States, mapping details of Sustainable Development, Human Development, Housing, Education, and other socioeconomic indicators. A repeating theme will be maps – the importance of visually digesting socioeconomics in maps cannot be understated.
Never be afraid to expand what influencers are included in socioeconomic research. Here is an example of a climatology study on Australian Wheat, which reflexively affects food sustainability.
Vulnerability indexes might be my favourite socioeconomic indicator. Here is an example of an Australian study that analyzed the effect of COVID-19 influenced economic recessions on Australians.
In yet another vulnerability analysis example, here is an example of a graphic adapted from research on climate change affecting elderly populations and skiing in the Nordic Region into a user-based climate change impact. The user can go to the website, change the exposure, sensitivity, and adaptive capacities, which will then change how vulnerable these populations will be in the future. Not only is this tool fantastic at showing the implications of climate change, but it is also a great way to begin understanding vulnerability index studies.
Another vulnerability index, this one from the Vermont Department of Health, focusing on social vulnerability derived from several different factors. This is a great place to begin understanding the multiple factors that go into socioeconomic research.
Developing country socioeconomic conditions are also important for OSINT research, with this integrating adaption study for social return on investment in Indonesia being a great start to understanding the academic undertakings in this realm.
For the time being Google and Apple are reporting on mobility trends within communities. These are great OSINT tools to understand how people are moving right now. Warning: do not use these to predict the spread of COVID-19. Please leave that to the epidemiologist. The worst epidemiologist is an armchair one.
Another COVID-19 related factor is hospital utilization, funding, and resources. This study from the Kaiser Family Foundation focuses on ICU beds, revealing the troubling trend more than half of the countries have no hospital ICU beds.
Yet again, a vulnerability index. This is a great example of a holistic study throughout Australia on employment vulnerability from the Centre of Full Employment and Equity, or CofFEE, one of the best agency acronyms.
Even the simplest statistics are essential to understand. Here, for example, is a map of GDP per Capita by county, which, surprisingly was not released by the US Bureau of Economic Analysis until December 2018.
Another example of a simple, but a key statistic, is summary census data, then using that to break apart what statistics are needed for the specific area of research. Here are the summary data files from the United States Census Bureau.
Foot traffic and human behaviour is not a key part of socioeconomics, but understanding human movement is key to understanding community movement, what businesses are patronized frequently, and possible shopping (thus spending and revenue hypotheses) patterns. Here is the Bicycle and Pedestrian Count Portal from the Washington State Department of Transportation.
A Working Example: COVID-19, Vulnerability Analysis, and Socioeconomics
For an example of this in action, I wrote three analyses of labour force vulnerability earlier this year, the first analyzing the potential for labour forces in Washington State and California State to recover after the pandemic, the second analyzing Hong Kong labour forces and the potential economic fallout from a retracted economy, the third analyzing almost economic vulnerability to an economic recession in almost every United States county.
To begin this analysis, I took the vulnerability analysis equation of Exposure + Sensitivity – Adaptive Capacity = Vulnerability and developed a unique equation for each situation. I will leave out the technical math on the first two regarding the use of natural log and why I decided to divide this by that. The chief focus here is on what I included in the analysis.
The first equation ran [Cases of COVID-19 per 100,000] + [Unemployment Rate] – ln ((Income per Capita/1000) * ICU Capacity)) = vulnerability. The focus for this was answering how COVID-19 will affect the population and effectiveness of workers. Hence, cases of COVID-19 and unemployment rate established the condition affecting the labour force, and the current status of unemployment. The income per capita was intended to weigh the potential personal savings that might cushion a community from a recession. The ICU Capacity was included to reflect the potential fact no amount of income per capita will save a labour force from mass death due to the lack of ready ICU units. This analysis, while beneficial for the beginning of the pandemic, turned out to be the worst. COVID-19 has shown to be almost a non-factor on unemployment while methods of treating COVID-19 outside of the ICU have dramatically improved.
The second equation applies to Hong Kong, includes several other factors, and runs [Total cases per building] + (Industry Factor/Labour Force Participation) – ln ((Median House Income Factor/1000) * Hospital Beds per 100k (or 453.4) = Vulnerability. Due to the reporting method, Hong Kong reported cases per building in the district, instead of how many cases were within a district, chiefly due to the small size of Hong Kong. The industry factor is a score I established per district, which includes income in HKD by a weighted risk factor. Certain industries, such as tourism, are going to carry more risk due to COVID-19 compared to an essential industry that can thrive in this new normal. The Median House income factor is the income bracket multiplied by a factor that emphasizes how wealthy or low income a district is. Overall hospital beds are counted instead of ICU units as responses proved that hospitals can be turned into effective COVID clinics.
The third equation focuses on the entire United States, less several counties that did not report data in the same manner or simply did not report data. The equation runs that [[(Avg. Capita income) / (Per capita personal income)] + Severe Housing Costs)] + [[(Employees per industry)/(Total Employment) * Industry Factory] + Food Insecurity + Unemployment] – [[(HUD Assistance/Population)/County] + [(TANF/Population)/County] + [(PPP Loans/Businesses)/County] + [(SNAPS Benefits/Population)/County]] = Vulnerability. This reflects both socioeconomic factors that are driving the conditions and then policies that can adapt and assist to recover the economy. This was the most beneficial way to understand how pre-existing conditions were going to affect people while analyzing the effectiveness of policies.
Socioeconomics and OSINT are a unique intersection that has not been entirely reviewed or explored before. One of the most compelling parts of this is allowing OSINT research to dive deeper into the human condition and be able to add one of the most compelling parts of a story. There is a wealth of research and ideas to be untapped here, and after reading this article I truly hope this article encourages further research into some economic or socioeconomic areas for application to some level of OSINT research. Understand the theory behind what you are researching, and then dive into the never-ending statistics and analytics available through each nation, waiting to become another layer of modern history.