Computational Methods for Economics
Whether employed by theorists, policymakers or developers, practical applications of the computational models and analytical devices can be used on all levels of economic assessment. The methods themselves are myriad — they range from general algorithms and formulae, to comprehensive state-level modeling, to virtually microscopic sector-based analyses. The results are equally extensive and necessary for truly understanding the systems that build and maintain a macroeconomy.
Keywords Algorithm; Cost Benefit; Empirical; Macroeconomy; Modeling; Sector Analysis
Economics: Computational Methods for Economics
Samuel Johnson once said that "The use of traveling is to regulate imagination by reality, and instead of thinking how things may be, to see them as they are" (Bartlett, 1919). Indeed, it is true that there are many concepts that, when applied in different analytical environments, act or appear significantly different from their other incarnations. In the case of the Age of Exploration, there were those who viewed the world as flat, ending at the edge of the visible horizon. Of course, for those who set out in ships to the New World (and for humanity as a result of their pioneering), the world became a vastly different place.
In the study of any discipline, there are three methods to employ. The first is the theoretical, one in which hypotheses and conceptual themes are given light. In Johnson's metaphor, theoretical adherents use their imagination to view an environment.
The second is the empirical method, which uses data from experiments, interviews, polls and tests to prove or disprove theories. Empiricists are more rooted in the earth, accepting new ideas only if the evidence of those concepts is truly verifiable.
Bridging the gap between theory and reality is computation. Again using Samuel Johnson's comment, computation is the traveler, inspired by what is theoretically possible and willing to strike out to irrefutably verify or repudiate the idea at hand. Computation rests at the center of the oft-conflicting camps of theory and empiricism. The methods employed using computation result in a new theory, or they may provide evidence that clarifies or discounts previously garnered empirical data. In short, computation's place in an analytical situation is critical and essential for identifying the best possible information.
There are countless forms of methods to employ when assessing trends in economics. Among them are algorithmic formulae, linear modeling and sector-specific modeling. Economists will utilize any of these methods, tailoring them in order to better encompass the topic of study much in the same way that a chemist will add or subtract varying volumes of compounds in order to achieve an experimental result. In this paper, many of the types of computational assessment are, in a general mode, discussed within the broader context of how certain trends and issues are studied.
In economics, the need for both educated theory and empirical data is exceptional. In an ever-changing global economy, many previously established theories and positions have been discounted, and others have yet to be borne. This paper takes a close look at some of the aforementioned computational methods used in the study of economics. Using examples from both the theoretical and empirical arenas of macroeconomics, this author highlights the links built between the two in the practice of economic analysis.
Settling the Debate
Since the latter 20th century and the change into the Millennium, academics, policymakers, business forecasters and observers in general have taken great pains to grasp the world's economic trends. One of the international economic stage's big stars, globalization, has received a particular amount of attention, as links between national economies are becoming more extensive by the day. In some countries, manufacturing has declined as globalization has become the norm. Experts debated the notion that globalization and this trend of "deindustrialization" were linked — that greater free trade, enhanced transportation and communications systems caused disinvestment in national manufacturing industries, which were becoming unnecessary in light of inexpensive imports.
Some studies, hinged on theoretical analyses, pointed to economic development and productivity, not globalization, for the decline in manufacturing in certain systems. However, empiricists, seeing holes in such hypotheses, gave a more careful analysis of this decline, focusing attention on the workforces of each manufacturing industry. By employing a curvilinear model as opposed to one of the more traditional "U-shaped" models, one study revealed a connection; one that is more subtle and therefore more revealing. Globalization, the model determined, causes differentiation between manufacturing sectors, which in turn creates a saturated market. This saturation is the culprit in the decline of manufacturing jobs (Brady, 2006).
The example above provides an illustration of the bridge formed between theory and empirical data. The authors, reviewing a theory that purported a lack of linkage between globalization and manufacturing declination and summarily forming an alternative hypothesis, were able to use the data found in "real-world" systems to verify their position.
Helping to Make Effective Policy
In a world in which linkages are being formed not only among long-standing trade partners and industrialized nations but between so-called "northern" and "southern" states as well, aid for developing nations has become a tremendous component of international diplomacy and relations. Of course, development monies do not come without strings. In fact, any nation that contributes international aid funds to a developing country or government seeks a return on that investment. Hence, tracking the effectiveness of an international development investment is an important part of government policymaking.
On one side of the debate over this issue are those who assert that international development funds do little more than create dependency rather than help generate economic growth. On the opposite end are those who believe that many developing nations do not reach their potential because the funds invested in their growth are insufficient. It is in the analysis of the effectiveness of international aid programs in which an economic method of computation may be useful.
An effective method of analysis in this debate is a sector-specific approach. In other words, economists may focus on the very elements into which international aid is infused: Educational institution-building, financial infrastructure development, environmental protection and other arenas. By studying the growth (or lack thereof) of a sector and the time in which that growth occurs, and factoring in periods of stagnancy (which could be periods in which war, civil unrest or natural disaster occurred), an accurate picture of each sector can become manifest. One study following this methodology applied American aid, in varying forms (such as conditional grants and unrestricted aid) to this sector analysis. The authors' results paint an extremely interesting illustration of the most effective forms of international aid for nations of varying size, economic status and geo-political status (Dovern, 2007).
One of the timeliest of issues facing municipalities in the United States is whether or not to embrace casinos and gaming. In recent years, the number of states to allow casinos and/or casino-style gambling has jumped to 40, with several others presently considering following suit (Cauchon, 2007). The potential economic benefits that states see are sizable — millions of dollars in revenues that could potentially serve as a boon for economically distressed cities and regions. Here too, computational methods may be employed to study not only the monetary potentials (or losses), but the social consequences as well, of legalized gambling.
Using a sector analysis approach, one study reviewed several economic factors. One of the most significant elements studied was that of unemployment in...
(The entire section is 3624 words.)