Appropriate use of Data in Economics
Economic data, also economic statistics mean data the describes the actual economy through history. This type of data is found in a time-series format. In other words, it covers more than a single period; for instance, the monthly rate of unemployment for the past ten years. It can also be represented as cross-sectional, as in the consumption and income rate of selected households. Data can be connected from different sources, including a survey of variables like individuals and firms, or aggregated sectors to sectors and markets of a single economy. Data can also go beyond the border into the international economy. These data can be represented in a table, which will then comprise data sets.
Economists rely on data to create hypotheses and find solutions on various issues within an economy. Both individuals and firms are faced with decision-making situations that cannot be avoided. In any economic phenomenon, the relationship between consumers and producers is eminent. Hence, decisions each of them makes may have a huge impact on the other, and on the general business environment. A single entity may not have such a significant impact, but collectively, they can make a huge change.
Besides companies and markets, governments have to make decisions too. If there is an issue within an economy, policymakers come together to try and find the most appropriate cause of action and avoid getting into worse conditions. Consider the 2008 Great Recession; for instance, it was one of the hardest times for the global economy. Governments rushed to create fiscal policies that could shield the market from falling further.
Economies keep fluctuating. Since we exist in an imperfect market setup, there will always burbles and recessions. Economic cycles are part of the natural ecosystem, and there is nothing anyone can do about it.
And this is why data is very crucial in economic studies and processes. We are living in the information era where everything depends on the availability of data. Hence, it is easy to assume data is the new oil. Data has taken over as the most valuable aspect of the digital economy. It seems as though business would stop without the availability of data. Over the past ten years, companies that have data at their core have come to dominate the world. They are among the world's most valuable corporations because they have something that everyone else seems not to own. The literature that focuses on the economics of data has become an asset to the world, spanning separately growth, privacy, competitions, inclusiveness, and the financial health aspects of the global economy.
Types of data
There are various types of data used, especially in the estimation of a model. They include:
- Time-series data.
Time series data is the data that offers information on numerical values of variables for period to period. Such data is collected over a specified time frame. For instance, and changes in monthly income between 1985 and 2005, is a time frame data. This information may include different factors that affect the economy. It leads to the discovery of ways to make better decisions concerning economic growth and minimum wages.
- Cross-section data.
When you need information on variables concerning individual agents, like consumers or producers, you go for cross-section data at a certain point in time. Consider a sample of family budget representing expenditures on various products and services on an individual family at a certain point in time. Or perhaps information on family income, family composition, and other demographic, social, and/or financial aspects of the family, can also be considered in cross-section data. It is all about picking on variable and using it as a point of reference.
- Panel data.
Data that comes from panel data is referred to as panel data. It is the representation of a cross-sectional in various periods of time.
- Dummy variable data.
There are variables defined as qualitative in nature. In this case, the data can be recorded as an indicator function. The values of the variable are do not show the intensity of the data. They only reflect the presence/absence of a characteristic. When collecting demographic data like sex, there are only two values, male and female. Or when you are describing taste, you only use values-like or dislike. These values are represented by the dummy variable. For instance, these values can be denoted as '1' for male/like, and '0' for female/dislike.
Data use and application
Data has changed today, now requiring policymakers to think twice about its economic implication. Two modern technological trends have been recognized widely as having influenced an explosion in data's economic relevance. The first trend concerns how data is collected and stored. It was very costly to do this in the past, but modern technology has made things much easier and cheaper. The widespread digitalization of data creates a chance for the creation and duplication of more data. Hence, data has become a byproduct of economic and social activities. Different aspects of human interactions and experiences have been greatly impacted, too, thanks to the digitalization of data. The second aspect is the advances in analytic techniques, which have made it possible for better processing, hence extracting more value from data. Many people in the past who had to access data did not have the best approaches to use and get the most of it because there were too many obstacles. General-purpose technologies like artificial intelligence and machine learning have pumped data through the roof and spread it to all sectors. The deployment of the prediction algorithm helps in the development of autonomous cars, creating new drugs that promise better results, delivery of targeted adverts, and improved market operations. Manty of valuable traded world firms heavily rely on data for profitable business models.
These trends have become essential for modern economies. They have changed the way consumers, companies, and policymakers, measure, and analyze economic activities. A previous study by IMF has revealed the implications of big data and digitization to firms and individual consumers within an economy.
Characteristic of data
In order to use data effectively, and in a manner that produces the best results, it important to first draw out what makes data unique. We have already seen that firm growth, and indeed market and economic development highly depend on the presence and applicability of data. Over the years, data literature has emphasized two critical functions. One, that data is input in today's function of production. Firms combine it with other factors labor, capital, land, and oil in the production of various goods and services. It has become a great asset for innovation, and one that is included the production costs for companies. Secondly, data initiate the change of information across various economic agents. In this case, its implication of efficiency, equity, and competition can be clearly seen in its operation.
Data can be argued to have three characteristics that build important aspects of public policy.
- Data is nonrival
Consider when one is using oil; it means that others can no longer use it at the same time. But data can be used by many people at any given time. For instance, if a new idea comes into the market, it will benefit the most when it is shared. More users will use the same to increase efficiency and innovation, increase its application, and benefit to the wider society. There is no limit to what data can do, and the extent it can reach when used in the right manner.
However, whereas technology makes data nonrival possible, its application is a different thing altogether. Policies and private decisions determine whether it will have the same effect in practice. In the modern imperfect market, every firm wants to be at the top; hence it is quite unlikely that they will share the information with their rivals. Hence, data monopoly can be a liming factor for contestability in the markets. Even the social benefits that can be derived from data are hidden from those who need it the most.
- Data involves externality
One of the biggest issues faced with data is how it is used. Personal data is collected, shared, and processed by a particular agent and can impose costs on others when it touches their privacy. Among the implications is that the data market lacks user-controlled rights, which can cause conflicts if they involve a party that does not like how it used. Also, if data collectors are not pleased with the collected, it can lead to an excessive collection. In such a case, there is too little privacy for the participants. As if that is not enough, there are the issues of clarity on whether the rights and duty of the participants in the data market intact.
- The data is only partially excludable
Data is stored and secured on interconnected systems, which means controlling access to it calls for continuous investment. And computer technology magnifies cybercriminals and hackers are becoming smarter, finding better ways of tapping into stored data to steal or delete it. Luckily cybersecurity has also increased with better but more expensive ways of keeping it safe. This means data owners have to invest more security measures and keep updating their security until it is clear. An important concern is the extent private collectors and processors have to go in protecting the data. Whether they have enough incentives to protect, especially data from about others, should be looked at very carefully. The emerging consensus that private reputation impacts are not sufficient has been a major point of reference. Also, policy measures required for proper protection of this data is necessary for assuring adequate lock to this sensitive data.
Decisions that determine market data production and use
When we look at data critically, we are looking at the real representation of characteristics, in action, or natural happening. We have already seen that data can be either quantitative or qualitative in nature, which means can be stored as a digital or analog format
Data is described as a type of information, and there is a vast literature on the economics of information. A strong emphasis is often placed on incomplete, or imperfect information; hence it can provide useful insights for considering economic data.
The decision to produce data involves different players, who are responsible for ensuring data safety and completeness. The economic individual who collects and stores data is called a data collector. When collecting data, they incur costs, which is transferred to storing the same too. Some of the costs are fixed, for instance, when one installs sensors or other tech gear to keep their information safe. Other costs translate to a function of the quantity of the collected data, like storage capacity, paying off any labor involved in keeping the data safe as well as collecting additional information to update.
The data subject is the person whose data has been recorded. When selecting data, it is required the subject to be a willing participant, and the collector compensates for the data subject. A supermarket, for instance, may give users of the loyalty card a discount on their shopping. There is a cost for obtaining the consent of the subject before collecting personal, which closely tied to the type of information and privacy preferences.
In other words, creating and collecting data is expensive. The main reason for this is the demand for information, which is determined by how the information is used in the economy. There are two roles of economic data, which modern literature has focused on. The first is that data is an input into production and plays a vital role in innovation and efficiency. The second role is that data build and change information within economic agents, which affects strategic interactions.
There is no denying that data is a vital aspect of economic growth and development. Hence, it is a valuable asset to modern society. Based on the discussion above, we can draw a conclusion for the proper use of data; we can summarize a few takeaways on the proper use of economic data.
- Data is crucial for economic development. It is the backbone of decision-making processes.
- Data should be collected and stored appropriately. The data collector should be sensitive to the privacy of the subject.
- Proper use of data calls for intelligence. It can either help a firm grow or destroy its reputation.
- Data is universal.
Author: James Hamilton