Allegedly the most empirical civilization of all time, our Information Age would no doubt serve its audience righteously in their attempts to obtain knowledge. But take a look at the N-gram, a Google search engine that charts the frequency of a word in printed sources over time, for the word epistemology:

Google N-gram Viewer of ‘epistemology’

As the study of ‘how we know’, epistemology distinguishes justified beliefs from opinion. Since the dawn of the Information Age in the 1990s and the advent of the Internet, the use of this word, and implicitly its application to our lives, has been in decline. But what does this trend mean for the news media industry in terms of how news firms compete?

Firstly, considering audience trends in the US, newspapers have decreased in circulation by 7%, while the average viewership for prime-time news has increased by 8% [1]. Competition in the Cable TV market has increased because of the reduction of regulatory controls during the 1980s, subsequently incentivizing news firms to enter this market [2]. This raised much appraise with academics and professionals in the field who hold that the ‘persuasion game’, between firms in the market who bout for news scoops and larger readerships, will always yield the truth. Given that at least one news source propagates the truth and consumers read all sources, the truth will be known by all readers as all firms eventually bend to the most empirical facts and information over time as presented in the truthful news source, since each firm’s reputation is on the line [3]. For example, a Democrat newspaper reveals a scandal concerning a Republican, and a Republican newspaper initially denies it. However, assuming the Democrat newspaper has the best facts, the Republican newspaper eventually concedes to some of the allegations because their reputation is at stake as their readership, who also reads the Democrat newspaper, begins to know of the truth.

Now, let’s complicate our ‘persuasion game’ by introducing a bias on the supply-side of the news market. Naturally, news firms are incentivized to be the first to find and publish ‘scoops’, news stories that are desirable to the public. However, a firm might be suppressed as a result of government intrusion. Consider the following variables: government bribe B, firm revenue for story circulation R, the number of firms N, and value to government of suppression V. The bribe must be B ≥ R. Further, B ≤ V/N, since the value of suppression will be distributed between the number of firms. Therefore, the suppression equilibrium is V/N ≥ R, which indicates that a greater number of firms, or increased competition, will decrease the likelihood that the story is suppressed. Additionally, as firms drop out and avoid a particular story, remaining firms have a growing incentive to publish as their potential audience grows. Human rights violations in Iraq’s Abu Ghraib prison and the leak of the ‘Pentagon Papers’ are examples of stories that were suppressed by government intrusion after their initial publication [3].

More often in our Information Age, a bias is introduced on the demand-side of the news market. Consumers have a preference for news sources that confirm their prior beliefs [4]. When the main source of news was newspapers, readers could pick up multiple papers with different biases to get an objective view of all sides of an issue, thus the success of the ‘persuasion game’ in yielding truth. However, with the rise of prime-TV news coverage, and readers turning to other sources on the Internet, like Facebook, it became simple and easy to appease your own bias. Given that consumers have a psychological urge to confirm and fall further in their beliefs [5] and that news quality is increasingly being associated with whether or not their belief is confirmed [3], it comes as no surprise that news firms cater to their audience by bias-targeting. Thus, considering the N-gram presented above, a decline in empiricism can be causally related to the advent of Internet news and the drinking of the Kool-Aid, en masse.

Bias-targeting is ever present in the strategy of prime-TV news firms who hope to satisfy their audience. With the Information Age, such a formula has unfurled itself farther as the news industry’s competition increases with evermore rapid forms of ingestion: Websites, mobile apps and social media posts. Just in case such conveniences weren’t courtly enough, Facebook’s news feed algorithm prioritizes what a user is likely to click on and browse through [6]. However, this may only reinforce false biases. Further, as a user’s online traffic becomes more prevalent, it paves the way for bias-targeting on a political level.

Cambridge Analytica is a Big Data company that worked for the ‘Brexit’ campaign in its primal stages and Trump’s Presidential campaign [7]. Their accurate modelling of people’s digital footprints gives particular persons an edge as they confirm those biases at that right place, at the right time, to the right people. And the irony that seeps through is that the populist movement, so unempirical and unscientific in their diatribes and nationalistic jargon, was thrust forth unto the steeple because of the modern work of statisticians and scientists of the day.

[1] http://www.journalism.org/2016/06/15/state-of-the-news-media-2016/
[2] Hamilton, James T. 2004. All the News that’s Fit to Sell. Princeton, NJ: Princeton University Press.
[3] https://web.stanford.edu/~gentzkow/research/jepmedia.pdf
[4] https://web.stanford.edu/~gentzkow/research/BiasReputation.pdf
[5] Nisbett, Richard, and Lee Ross. 1980. Human Inference: Strategies and Shortcomings of Social Judgment. Englewood Cliffs, NJ: Prentice-Hall, Inc.
[6] https://www.bloomberg.com/view/articles/2017-02-17/mark-zuckerberg-s-manifesto-for-facebook-offers-a-social-dystopia
[7] https://motherboard.vice.com/en_us/article/how-our-likes-helped-trump-win

After Donald Trump’s surprise U.S. election victory and the Republicans’ full control of the Congress, the markets have reacted and the U.S. Dollar (dollar) has been continually surging – catching companies and investors off guard. The new U.S. administration seems to believe that this is a sign of “global confidence in Trumpism”, but there are many concerns for U.S. exporters towards an overly strong exchange rate [1].

A strong dollar is defined as one that can purchase more foreign currency relative to a weak dollar. This means that U.S. consumers will pay less for imports but foreign consumers will pay more for U.S. exports [4]. This is good for U.S. consumers as the appreciation of the dollar against other currencies makes foreign goods and foreign travel cheaper, both of which American consumers enjoy. However, this negatively affects tourism as the United States becomes a less affordable travel destination [3].  

A second consideration is the impact of a rising dollar on the earnings of U.S. companies with large foreign operations [5]. In 2012, for companies in the S&P 500 that provided foreign sales details, 47% of total sales came from abroad, mainly Europe and Asia. Clearly, a stronger dollar would have a negative effect on net exports produced domestically, thus creating a drag on potential earnings. Interestingly, one can consider that “truly global U.S. –based” companies involved in exports do not produce within the U.S., but rather internationally [6]. The effects of globalization in the past decades have allowed companies the ability to purchase materials and set up factories abroad, which means that the rising dollar does not have a huge negative relationship with production as initially understood [2]. The real issue is when the earnings in foreign currencies are converted back to the domestic currency, as companies will feel the full brunt of the reduced returns.

As an example, Apple, the world’s most valuable company and a company known for their international dominance, faces some of the greatest foreign exchange exposures with 22% of their sales from China and 23% from Europe [1]. In the past quarter, Apple reported its biggest hit to its margins in China, about 3% in revenue growth, due to the weakness of the Chinese RMB against the dollar. Luca Maestri, Apple’s finance chief, has suggested the company has been preparing for further dollar strength but has come to realize that “at some point, the strong dollar becomes the new normal and we need to work with that” [1].

Unemployment Rate in the United States averaged 5.81 percent from 1948 until 2017. The unemployment rate is currently at 4.8% in January 2017.

On the positive side, a higher dollar effectively transfers demand from the U.S. economy to other economies around the world [5]. The U.S. unemployment rate is currently below its 50-year average and is showing signs that it will continually decrease. By contrast, other economies, notably in Japan and emerging Asia countries, would benefit greatly from a boost to their exports as a result of a higher dollar. In the long run, this will develop a stronger and more balanced global economy [5].

The strong dollar will remain a concern in the coming years as President Trump moves to revive domestic production. As it currently stands, having a stubborn stance for domestic development may harm the U.S. in the long run with reduced export potential; however, the strong exchange rate will be hugely favoured by American consumers. The rise of the dollar in 2016 will have impacts well into 2017, and those impacts should be considered positive on a global scale in the U.S. and around the world [5].

GMOs have been the centre of a political debate for a long time. Now, a product made in Canada will serve as a major indicator to where that debate is in the public conscience. This debate could open up the floodgates to the GMO market, and result in a major shakeup to the food industry as a whole. If the Arctic Apple succeeds, many other products in other industries may be opened up to GMOs as well.

The Arctic Apple underwent limited release in midwestern markets on February 1. The company believes that the apple could be available in Canada in the form of slices by 2019.

The attraction to the Arctic Apple is that it will not brown. The hope for the Okanogan company is that this feature will compel people to try the product, and then hope they will like the product. In fact, they see it more as a matter of convenience, rather than an issue of GMOs. The argument for them, is that every consumer will want an apple that doesn’t brown.

The idea came to the company after realizing if baby carrots can become as popular as it had, because of convenience, then apples should be able to do the same. The company also hopes they can help reverse declining apple consumption.

Historically, GMO-style products have failed in the market. GMO products have been greatly limited in specific markets like corn, wheat, tomatoes, and more because of efforts from Anti-GMO groups.

Despite, the nine years of testing, Anti-GMO groups say the apple is understudied, and believe that consumers will not have any interest in modified apples – citing inability to measure freshness of apples without natural browning.

There is major hurdles that all GMO companies must overcome. In a poll conducted by ABC News, 52% of Americans believe that GMOs are unsafe to eat [1]. That is the environment that the Arctic Apple will walk into.

It should be noted that both the World Health Organization [2] and the National Academies in Sciences [3] have said there is no danger to human health from genetic modification.

Also, after three years of review [4] by Canadian Food Inspection Agency and Health Canada, CFIA said “[Arctic Apples] are as safe and nutritious as traditional apples, while Health Canada said the apple is safe to consume, and has the same nutritional value.

The big test for this particular apple, is whether or not the convenience of the product can overcome negative connotations of GMOs. If the apple can overcome the negativity surrounding GMOs, it will be a major turning point in the GMO industry. Which, in turn, will result in a big shakeup in the entire food industry.

In the previous article, we introduced the motivation behind econometrics and the role it plays in the field of economics. We also briefly discussed the concept of an econometric model, which was essentially an equation that captures the relationship between variables. Today we will leap further into the econometric discussion by examining the most fundamental model: Simple Linear Regression (SLR).

Suppose we have collected data for two variables, and we want to use a Simple Linear Regression model to estimate their relationship. The equation to link the two variables (let’s call them x and y) would be as follows (Wooldridge 21):

where:

• is the independent variable, or regressand
• is the dependent variable, or regressor
• is the intercept parameter
• is the slope parameter
• is the error term

Immediately, you may realize that this formula is near identical to the slope-intercept form of a line. Indeed, the idea behind the SLR model is produce a line that best represents the collection of data points (i.e. the regression line). We can denote the estimated line (also called a line of fitted values) as follows:

Notice that while , and are being estimated here, is not. This means we can plug in any arbitrary value for and the model will estimate a value for given our slope-intercept parameters. Another value we are particularly interested in is the residual. This is essentially the distance between a data point and our fitted line.

Source: Introductory Econometrics, A Modern Approach

We have yet to discuss how to obtain the estimates for the slope and intercept parameters. Though we have formulas for and , we still require and to calculate them. We will not divulge too much into the mathematical derivation, but it is important to understand the idea behind achieving the estimates.

Often, you will see the term OLS or ordinary least squares used in conjunction with simple linear regression. This refers to the method of estimating and . The idea behind this method revolves around the residual. Recall that the residual is the distance between a data point and the fitted value (on the line). The goal is to minimize the sum of squared residuals with respect to and , that is (Wooldridge 27):

We will need to take the partial derivatives with respect to and and set them to zero. The solution to the system of equations will minimize the sum of squared residuals. We call these following equations the first order conditions:

(1)  

(2)  

With some algebra, one can see that (Stock and Watson 115):

At this point, you may be overwhelmed with the theory and lack of practicality of the SLR model. Let us now consider an application of regressions in Finance using the capital asset pricing model:

captures the sensitivity of stock returns to changes in returns of the market portfolio (Brealey et al 386). For example, Apple’s current is reported at 1.32 (Google Finance) with respect to NASDAQ. If is bigger than 1, we know the stock has higher risk than that of the market portfolio. Conversely, is less than 1 suggests that the stock is less risky. The market portfolio always has a beta of 1.  As we can see, the capital asset pricing model looks similar to that of a simple linear regression model. If we include an error term, we can use OLS to estimate the parameter by regressing the equity risk premium on the market premium (Stock and Watson 118).

The Simple Linear Regression Model serves as a building block for many more complex models. In future articles, we will study the underlying assumptions in which the linear regression model depends upon. If those conditions fail, we will explore strategies to mitigate the potential issues that may arise during our regression analysis. Furthermore, we will see that this model can be extended to more than just one regressor.