A traditional economy is generally defined as: “An economy is the large set of inter-related production and consumption activities that aid in determining how scarce resources are allocated. In an economy, the production and consumption of goods and services are used to fulfill the needs of those living and operating within it.”  Resources generally referring to natural resources such as oil, gas, minerals, and forests to name a very small sample. The dominant type of economy that is in place across the globe is the market- based economy, which follows the principles of the free market. The production of goods and services is symbiotic to the demand and supply. If there is a high demand but a limited supply, this generally raises the price of the good. In reverse, too much supply available then lowers the cost to the consumer and also impacts the producer.
In this global market-based economy, following their principles of efficiency and lowering costs in order to make the most profit, global value chains are established. In many instances, the least developed and developing countries where the natural resources are present, are at the bottom at this global value chain. Then, as the resource is harvested in its raw form and exported to another country that has the capacity, technology and know-how to process the raw form, the product is then more valuable and profitable. It goes further up the chain until the raw material has reached its intended final version that can then be sold for much, much more than the cost of the raw material purchased in the bottom of the value chain. It is, to say the least, an exploitative chain that keeps those in the low value parts of the chain unable to break free. Many of these origins are usually considered as cheap labor and have little to no capacity nor the infrastructure to process the raw material.
The free market based economy is massive in that all jobs, economic activities including free trade, whether formal or informal and any activity relating to how money is used whether saved, spent or invested, all contribute to the economy.
A significant point to highlight is that the free market based economy is supposed to be monitored carefully, using metrics and indicators in order to avoid any run- away inflation or economies crashing which do have very real consequences for the people. This is not to say that the free market based economy has implemented these safeguards as witnessed in the 2008 financial crisis and more recently, the COVID 19 pandemic, where in both occasions, large banks and transnational corporations were prioritized instead of workers and the people at large. Recessions and the massive negative impacts on the people have exacerbated the conflicts and problems of an unfair free market economy that puts profits of large corporations ahead of everything and everyone else.
There are also policies from national to global, free trade agreements that lay out the rules to ensure the free trade flows in a supposedly fair rules-based system. This includes a multilateral free trade agreement under the World Trade Organization (WTO) that not only lays out rules and obligations, it also has the authority to hear trade disputes, deliberate and come to rulings by the judges hearing the case. Penalties for the countries found guilty of not following WTO rules can suffer economic sanctions amongst other things. It has to be stated though that these WTO rules have largely favored larger economies and transnational corporations. A stark example evidenced by Big Pharmaceutical corporations intentionally profiting on vaccines during the COVID 19 pandemic using the WTO rules on trade related intellectual property rights. In the face of millions dying in poor countries, trade rules were used to benefit the already wealthy Big Pharma.
As opposed to the well-established and unimaginably vast traditional economy upon which most of the countries’ economic stability rely upon, the digital economy is relatively new, not fully understood and is ever changing as the technology it depends on rapidly progresses. There is no formal definition of the digital economy but a generally accepted concept is that the digital economy in general terms is understood to be economic activities using digital technologies to do transactions on the internet. But this is not to say that the growth of the digital economy has not been at a fast pace. Some definitions and scopes with regard to the digital economy have some differences. Not so different from how digital trade or electronic commerce still have debates on reaching a universally agreed definition.
In general, there are three main layers to the digital economy. First, there needs to be good and reliable infrastructure that will be the base hardware for providing high-speed and constant internet. This is the hardware to the digital which include fundamental elements such as semiconductors, processors, high power computers, telecommunication tools and miles and miles of cables to enable high speed internet and telecommunications services. And although not mentioned, this level of hardware obviously entails financing, engineers and the technology experts who can make this all happen. The cost and manpower needed to build this massive kind of infrastructure and then ensure uninterrupted powerful high-speed internet day and night that can smoothly support video, graphics, and otherwise very heavy files across, makes it absolutely clear that the poorer countries will not be able to afford nor keep up. This top of the line hardware and software can also be used as a tool in sending encrypted information whether on free trade, competition, or security, some of these are referred to as cross border data flows.
The second layer is digital services and the platform economy. In the introduction earlier in this publication, some examples of digital services were mentioned. One is a consumer service in the form of transportation, for which you have several competing providers such as Uber, Lyft or Grab. There is also a digital service of streaming movies to your electronic gadget via a membership fee such as Netflix or Amazon Prime. Platforms on the other hand, will be discussed in detail in the next chapter. However, just to give a brief idea of what the platform economy is: as the UNCTAD defines it, “Digital platforms are technology- enabled operations that facilitate interaction and exchange between various groups, built on a shared and interoperable infrastructure and driven by data. They operate over a range of activities. Transaction platforms enable interaction between individuals who would otherwise not find each other; innovation platforms provide technological building blocks enabling innovators to develop complementary services or products.” 
To make that less abstract, here are some examples of such platforms: Apple, Amazon, Google, Facebook and many more, that as mentioned earlier, will be delved into more deeply in the next chapter. The platform economy is a crucial and critical part of the digital economy. It is enabling the improvement and range of mobile applications, technological advances, and completely online payment services.
Many sectors that are able to branch out into the digital economy, are doing so, predicting that this will be giving their business a much-needed boost. In this second layer of digital services and the platform economy, they are building upon the core hardware needed to operate and conduct business online. Digital services are booming. “With more devices accessing the Internet, an ever-increasing number of people using digital services and more value chains being digitally connected, the role of digital data and technologies is set to expand further. As a result, access to data and the ability to transform data into digital intelligence have become crucial for the competitiveness of companies. Producers and exporters are becoming increasingly dependent on data analytics as operations get more digitized, and because they use support services that require access to data such as shipping and transportation, retail distribution and finance.” 
To make a simpler explanation together with a visual, this definition is best understood with the visual below. As mentioned earlier in this text, there is a global data value chain where the equivalent of a raw material in the traditional economy in the digital economy is raw data. Mining, harvesting and transforming this raw data into a monetized form is what has fueled the development of the now massive digital platform economy, which will be delved deeper into in the following chapter. Suffice to say, digital platforms form an economy of its own and plays a critical role in the rise of the digital economy.
What this definition and breakdown of the global data value chain do not however show is that there is a “natural resource” that makes this new economy happen. Just as how the traditional economy relies on extracting, mining, harvesting and exploiting nature to get the raw material needed for their economic chain to work. The digital economy has too its own natural resource that it mines, extracts and exploits to even get the wheels in motion, that is data. Data is the new oil. The value of data will be discussed in depth in a following chapter as it too, like the platform economy, is of great and critical value to the digital economy.
The third layer is composed of several digitalized elements, digital economic activities, online businesses and “the fourth industrial revolution” or industry 4.0 for short. The list in this third layer include: e-business, e-commerce, algorithm economy, the sharing economy, and the gig economy, precision agriculture and maybe one that should be a layer unto itself: industry 4.0.
E-business and e-commerce although, technically, not interchangeable, in their definitions, they are quite similar in their goals and operations. These have also been referred to as digital trade. These two are the bedrock for commercial transactions that happen online. Both enable online transactions which
cover the sale, delivery and consumption of digital goods. Digital trade is not new. As far back as 1998, the World Trade Organization already had this issue of e-commerce in their sights. Electronic commerce being relatively new at the time, the consensus was to agree on a moratorium on imposing tariffs on any digital trade, until further negotiations could be held. This moratorium has gotten renewed at every WTO Ministerial. At this 2022 WTO Ministerial, that moratorium was again upheld, albeit with a clause that this issue will be tabled for negotiations to reach a consensus agreement. It is understandably very different policies that need to be established for something digital versus a physical good. However, these delays in the negotiations have been pointed out by countries like South Africa and India as tactics of the developed countries to deny the potential profits from these tariffs.
Buying and selling of goods, and although services was already discussed in the previous layer, it is more cogent to discuss the two: digital goods and digital services together. Digital trade, which covers both digital goods and digital services, simply put is the sale and purchase of goods and services via the internet of which the digital good or digital services are delivered. And during the pandemic, as mentioned earlier, digital trade inadvertently received a boom. As the UNCTAD’s analysis of trade data shows, “While total services exports declined by 20% (an unprecedented drop since records began in 1990), worldwide exports of digitally deliverable services fell by only 1.8%. This reflects an increasing reliance on digital delivery to continue services’ trade despite restrictions on movement implemented due to the pandemic. With Information and Communications Technology (ICT) services exports increasing and digitally deliverable services exports holding relatively steady in 2020, their share in the greatly reduced overall services exports increased significantly across all regions.” 
The algorithm economy refers to the development of the ability for data analytics to be fed into algorithms, making the targeting of a certain audience, advertising, marketing, even social media posts, all more effective. It sounds quite benign at the beginning, with, for example, Spotify showing off that they are keeping track of what you listen to, how many times you listen to it and therefore what songs and advertisements to send your way. Facebook also uses algorithms to make decisions. Depending on how much personal information you decided to share with Facebook, that valuable data is then used for several things. First, Facebook uses this to determine what advertisements to show you, which recommendations for people you may know, groups that share the same likes, and so on. This data is also stored and analyzed and from the data analytics findings, the sky is the limit in what these tech corporations can do with those findings.
The algorithm economy though has opened an avenue for algorithm based surveillance that can be used for several things from the more aggressive targeted marketing to the invasion of privacy of an individual or group of individuals or even public or private establishments.
The algorithm economy though has opened an avenue for algorithm based surveillance that can be used for several things such as the more personalized and targeted marketing. This is done as the platform/s you use track your social and economic activities online to then make a profile of you and be able to discern which advertisements to show to you or which job opportunities may be fit to your qualifications. Advertisement has never been so effective and not always with the consumer knowing it. You may not realize that you are being targeted. For example, your online history shows that you had been looking to buy a new mobile phone. The algorithm in the platforms then get triggered to keep showing you various brands of mobile phones on whichever platform you get on, so say, you checked it on google, now there will be ads in your Facebook page, then other platforms, keeping the product in your mind and maybe even convincing you to go and purchase it on an online store. That is effective advertising that is stepping right on the line of whether or not that was a fair use of your data and privacy. There are of course people who are completely fine with sharing their personal data and do not find this kind of surveillance algorithm economy to be intrusive.
Some companies have now gone further in developing even more sophisticated surveillance technology in the name of personalizing the consumers’ experience of the application. A concrete example that is causing controversy is the technology that Spotify has developed. Spotify has recently been granted a patent for their new technology that can monitor the users’ speech and collect data to personalize music recommendations. But there is even more, “Spotify say the technology would work by retrieving audio, including voice signals and background noise, to understand “content metadata” about users such as their emotional state, gender, age and accents. The patent goes on to say that the platform would rely on information such as “intonation, stress, rhythm and the likes of units of speech” to determine if a user is feeling “happy, angry, sad or neutral”.  This new technology will also be able to hear where you are, if you’re in a park, your house or anywhere really, as the technology picks up the sounds around you from cars passing to sounds in a restaurant.
Despite the warnings and open letters by musicians, civil society and activists, of the dangers of such an intrusive, privacy invading and potentially harmful technology, Spotify was still granted the patent. Spotify does not need all this metadata they plan to collect and can be misused to harm or violate human rights. “Access Now, Fight for the Future, Union of Musicians and Allied Workers, and a coalition of over 180 musicians and human rights organizations from around the world sent a letter to Spotify calling on the company to make a public commitment to never use, license, sell, or monetize its new speech-recognition patent technology.”  The coalition along with other organizations are going to continue campaigning against this dangerous technology, raise awareness and pressure Spotify to not use this invasive technology.
Algorithms can also be used in an invasion of privacy of an individual or group of individuals or even public or private establishments by buying, hacking or stealing their data and then analyzing it and applying the algorithm to the intended goal. For example, data of users of a platform has been taken without their consent by a group of hackers, who then use the data to turn it from raw data to analyzed data. If the goal of those hackers was to use that data to then design algorithms for those people and manipulating them into voting a certain way. The loss of privacy from the use of surveillance is a serious issue and can be used to harm people. And with the help of other technologies such as facial recognition and facial recognition surveillance, an activist or a persecuted individual for example, would have no chance with the amount of technology that can be used to either detain or even harm them. The dangers of these new technologies will be discussed in a later chapter.
The sharing economy is defined by the Oxford Languages dictionary as “an economic system in which assets or services are shared between private individuals, either free or for a fee, typically by means of the internet.: i.e. “thanks to the sharing economy you can easily rent out your car, your apartment, your bike, even your Wi-Fi network when you don’t need it”” In this capitalist system, “sharing” is a far cry from what sharing used to mean when sharing occurred amongst peers and in a non-capitalist system or community, where sharing means giving something of your own to another person with no expectation of a fee. These sharing for a fee versus genuine sharing definitions are not to be confused for the other. The sharing economy as it is currently defined in the scope of the digital economy, has a veneer of platforms bringing together a whole variety of people such as businesses, service providers, sellers, consumers, and the whole gamut. The ride sharing application called Uber does this. They use a digital platform to connect consumers and drivers through the Uber digital application. This has been hailed as a great example of a sharing economy model at work. It is seen as sharing as the drivers share their car to Uber depending on their availability and then consumers use the Uber application to then connect them to a car that is being “shared” with them, but with a fee.
Airbnb came onto the scene promoting a new and more innovative way of getting accommodation. As with Uber, Airbnb uses a digital platform and claims that it is part of the sharing economy. The platform connects people who have houses with a room or rooms to “share” with people who are traveling to that city and need accommodation. These rooms are not being shared with the guests. The guests are expected to pay fees to rent the room and maybe get a couple of amenities. Just like a hotel. It was interesting and quaint but there were scams of places not actually existing, or even worse, that it’s a property development corporation that owns the house/ room/ condominium and not the idealized person who wanted to take part in the sharing economy by posting their room/s availability on the Airbnb site.
The most devastating effect however has been the “Airbnb effect”. “The Economic Policy Institute , a non-profit, non-partisan American think tank, found that the economic costs of Airbnb likely outweigh the benefits: ‘While the introduction and expansion of Airbnb into cities around the world carries large potential economic benefits and costs, the costs to renters and local jurisdictions likely exceed the benefits to travelers and property owners.’” In simple terms, because apartments, houses or condominiums are earning more money from Airbnb tourists, without the hassle of the owner ensuring the tenant is treated well, the place maintained and the monthly rent at times are less than a few nights by an Airbnb customer who agrees to pay a couple hundred dollars per night. “The ‘Airbnb effect’ is to some extent remarkably similar to gentrification in that it slowly increases the value of an area to the detriment of the indigenous residents, many of whom are pushed out due to financial constraints. Cities, popular ones especially, seem to fare the worst. In major cities such as Amsterdam, Barcelona, Edinburgh, and Los Angeles, studies on the ‘Airbnb effect’ have found that over-tourism facilitated by platforms such as Airbnb negatively impacts on house prices and communities.” 
According to Investopedia, “in a gig economy temporary, flexible jobs are commonplace and companies tend to hire independent contractors and freelancers instead of full-time employees. A gig economy undermines the traditional economy of full-time workers who often focus on their career development. Furthermore:
– the gig economy is based on flexible, temporary, or freelance jobs, often involving connecting with clients or customers through an online platform.
– the gig economy can benefit workers, businesses, and consumers by making work more adaptable to the needs of the moment and the demand for flexible lifestyles.
– at the same time, the gig economy can have downsides due to the erosion of traditional economic relationships between workers, businesses, and clients.” 
The word gig comes from the slang referring to when a musician or a band is scheduled to play at a bar or a similar venue. The band has a gig there at 8pm, which connotes the two things about the way gig is used in the gig economy: it is a job set and for the short term.
The prospect of being able to work your own hours, at your own home or a flexibility of the venue of work, appeals to many who fear the 8 to 5 daily grind in a stuffy office. And although freelance and “gig” jobs were already available even before the digital platforms, the rise of the digital economy boosted the diversity of choices for work in the gig economy. “According to the most recent and reliable data, in the US economy, there are more than 57 million gig workers, which equates to 36% of all US employees.”  Working flexible hours and possibly at home would lead one to think that the gig economy would be if not immune, at least not so hard hit by the pandemic, however, that would be a wrong assumption. During COVID, “Worldwide, 62% of gig economy workers have lost their job because of the coronavirus pandemic. While 26% of global gig economy workers say their working hours have decreased.” 
Add this to the massive layoffs across industries, many were seeking whether in person or online on the platform economy, for any kind of gig jobs, to help get through the crisis and afford basic needs such as housing and food.
Amazon, one of the top 20 global companies by market capitalization, had gigs to offer. Amazon started the “Flex program” in 2015 to handle the surge of orders during peak seasons such as holidays. This is an application you install in your gadget, apply for a slot and hope that the AI bot slots you in. As this article explains, “Flex hirings, performance reports, and firings are all handled by software, with minimal intervention by humans. Drivers sign up and upload required documents via a smartphone app, through which they also sign up for shifts, coordinate deliveries, and report problems. It’s also how drivers monitor their ratings, which fall into four broad buckets—Fantastic, Great, Fair, or At Risk. Flex drivers are assessed on a range of variables, including on- time performance, details like whether the package is sufficiently hidden from the street, and a driver’s ability to fulfill customer requests.”  These Flex drivers are not the regular Amazon delivery drivers who have set wages, set schedules and the general benefits of being a regular employee. Flex drivers on the other hand, especially during the pandemic as cash was tight, would wait anxiously for the application to give them a slot and a number of packages they need to deliver. The Flex driver then goes to the Amazon pick up, scans the packages they receive, deliver the packages and scan as they go, with some even having to take pictures of the package by the door as proof and a selfie as proof to Amazon that only one driver is using this app and not multiple drivers on one account. At average, Flex drivers reportedly earn 18 USD – 30 USD an hour but have to pay for their own gasoline, have their own vehicle, and pay for their own repairs in case they get a flat or engine trouble on the road. Not to mention the fact that the AI is counting your delay in delivering packages against you, lowering your rating, and thereby lowering your chances of getting a slot the next day or ever even.
It is really incredulous that a company worth billions of dollars would have in place an exploitative system that relies on a population that is desperate to work and earn to pay for basic needs. While it’s true that a part of gig workers do get gigs in addition to their more regular job or part time job, during the pandemic, many were relying on these gig jobs as their sole source of income. Vice news  did a feature on this to show what a disaster this is to workers and labor rights. This trend needs to be stopped and addressed before it becomes the norm and replacing hard won labor rights for regular employment.
Although a job you find on the platform economy in the gig economy has a veneer of being better than a minimum wage job at a factory, it may be a case of not all that glitters is gold. As with the example of the Amazon Flex program where an AI bot can decide your ranking and effectively fire you by not giving you any more slots, the gig job just might as be as exploitative as a non-gig job. And though there is nothing wrong with doing a gig job as a side hustle to add to your regular income, and there might be the couple of featured success stories of gig jobs becoming so successful that it becomes the person’s main job, it is still important to be aware that while some gigs can be good, there will also be gigs that will exploit you, just like in a traditional non-online short-term job.
Precision agriculture is in this third layer of the digital economy but further review of other literature has shown that this has had much resistance to it. “Precision agriculture (PA) is an approach to farm management that uses information technology (IT) to ensure that crops and soil receive exactly what they need for optimum health and productivity. The goal of PA is to ensure profitability, sustainability and protection of the environment.”  Additionally, precision agriculture envisions “In the future, multinational agriculture companies might collect raw data from farmers, then use them to develop a system of rules that optimize productivity and output, and subsequently sell the data back to those same farmers who provided the raw data.”  This may sound like it has intentions of improving agriculture but this is very problematic in many ways in the real world.
This precision agriculture idea proposes that data and digitalization adds value to the food, farmers, the land, agriculture and the consumers themselves. Indeed, some sections of the food systems can be mechanized or digitalized, however, that will more likely be more about adding value to the land owners and large agro-industries profit share as the additional monetized digitalization only happens in the higher end of the global value chain, hence the owners and corporations.
Agriculture has and is a way of life for decades, it feeds the world and has in many places, local systems in place to maximize the relationship of seasons, nature and the land with the aim of growing food while ensuring to restore the land so that it can regenerate sustainably in the next season. As seen below, this is supposedly how digitalization would add value. However, it is not clear how digitalization would add value to agriculture, especially that since many food and crops require unique care at every step of the process, entailing farmers, not machinery.
Adding value with digitalization?
Industry 4.0 became a buzzword back in 2011 at the World Economic Forum as the new frontier of industrialization. It’s also referred to as the fourth industrial revolution. In essence, this new stage claims to have transformed industrialization through the use of new and advanced digital technologies and software that can intelligently process and communicate with one another to further advance an even more efficient and productive way of industrializing.
As shown below, the first stage was mechanization, followed by electrification then automation and then digitalization. IBM and other technology corporations find the leap to Industry 4.0 as an exciting development. “Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products. Manufacturers are integrating new technologies, including Internet of Things (IoT), cloud computing and analytics, and AI and machine learning into their production facilities and throughout their operations.” 
One of the main reasons the technology industry and avid technology people are excited at this Industry 4.0 is that it has made a giant leap from the three past revolutions. While the three were all revolutionary in their own right at the time they were made, they more or less had one thing in common which was that they added an element to make the process easier, faster, more efficient and workers needed to learn new skills to keep up with the technology they were using.
This fourth industrial revolution goes above and beyond the past three. Industry 4.0 is a system of smart technologies collaborating, communicating and working together, in a way none of the past industries
could have done. Industry 4.0 uses and collates big data, analyzes it, communicates with the rest of the system and completes tasks with great speed efficiently. The system also learns and then includes those into their algorithms so as not to make those mistakes and even to foresee, warn and prevent any major mishaps from happening. The engineers are alerted and the disaster is averted. Technology corporations are already talking about “smart factories” where they reduce the manpower and replace them with these smart systems.
As IBM describes it, “Characterized by increasing automation and the employment of smart machines and smart factories, informed data helps to produce goods more efficiently and productively across the value chain. Flexibility is improved so that manufacturers can better meet customer demands using mass customization—ultimately seeking to achieve efficiency with, in many cases, a lot size of one. By collecting more data from the factory floor and combining that with other enterprise operational data, a smart factory can achieve information transparency and better decisions.” 
Interestingly, the UNCTAD has doubts and has been analyzing the digital technologies and the productivity paradox.
Digital technologies and the productivity paradox
Usually, ICTs have been considered a driver of productivity and economic growth… (OECD, 2012b; Stanley et al., 2018). However, the rapid process of digitalization during the past decade does not seem to have translated into strong productivity growth; on the contrary, that growth has slowed (Crafts, 2018). This slowdown appears to be more of an issue in developed countries, but has also been observed in developing countries (APEC, 2018).
This is known as the productivity paradox, as Solow (1987: 36) put it: “You can see the computer age everywhere but in the productivity statistics”. Updating this by changing the word “computer” for “digitalization” would better de ne the productivity paradox in the digital economy.
Different reasons for this paradox have been provided. Those with a more pessimistic view about the effects of technology on productivity (e.g. Gordon, 2016) see the evolving digital technologies as having much less impact than the technological advances that characterized previous technological revolutions. A more optimistic perspective attributes the slow productivity growth to the time lags before the effects of digital technology uptake kick in. It is likely that when these technologies are adopted in wider segments of the economy, there will be more visible impacts on productivity (OECD, 2019b; Remes et al, 2018)
Difficulties in measuring the digital economy (see chapter III) have been considered an additional factor to explain the productivity paradox. The fact that activities in that economy are not properly recorded in overall GDP statistics could also explain the slow productivity growth. If properly measured, these would be rejected in higher output, and therefore higher productivity.
Moreover, other factors not related to digital technologies may also be responsible for the slowdown in productivity growth. A notable example is the low aggregate demand and limited investment that characterized the period following the 2008 global financial crisis. Slow productivity growth in developed countries has also been attributed to demographic factors related to an ageing population (Maestas, 2016).
The jury is still out on the causes of this paradox, but most of the explanations cited above probably hold some truth. However, the productivity paradox seems to be more of a feature in those countries – mostly developed countries – that are close to the digital technology frontier. Therefore, it is likely that for developing countries that are far from the technological frontier, the scope for productivity gains from an increasing use of digital technologies is still significant.