The ‘poor, angry, white man’ [1], as some of Trump’s critics would proclaim, was the reason his popularity parades him to electoral victory. If so, what made him poor, what made him angry? As economic uncertainty and political distrust become more prevalent subjects in common dinner table discussion, Right-Wing movements and populist approaches to politics continue to hit new heights in Europe.

In order to compete on the global level, notably with American and Japanese markets, European member states need stable collaboration towards a Common Market. Since its conception in 1957, the European Economic Community has established measures to regulate trade and promote stable growth and integration amongst its members. However, as protectionism becomes more prevalent [2], a trend the European Commission reports is now present more than ever, EU member states will find collaborative exporting more difficult with limits to free trade. Since the Financial Crisis of 2008, there has been a rising number of policies enacting product import restrictions, requiring import licenses for various products, an increase in tariffs, and general regulation and oversight capability of importing parties. Between July 2014 to December 2015, 31 EU trade partners introduced 201 of these aforementioned measures, whilst only 16 of them were withdrawn. This added market inefficiency [4] only encourages Euroscepticism [5], a doubt in the ability of EU, and is further fueled by the consequences of a Brexit, the process for which is likely to begin this Tuesday the 17th of January when British Prime Minister Theresa May triggers Article 50 of the Treaty of Lisbon.

In recent years the Right-Wing has found momentum in Europe [6], with the European Parliament elections of 2014 allocating 25% of the seats to Eurosceptic parties. Its dogma against the liberal economic mainstream bodes well with the middle and lower classes, whose employment potential has been most hampered by globalization, outsourcing and automation. Such Right-Wing parties have sprung up across Europe, some of the most popular of which include: France’s Front National (polling at 22%), Germany’s Alternative fur Deutschland (12%), UK’s Independence Party (12%), and Austria’s Freedom Party (46% of 2016 election). A common ideological feature of these parties is one which insinuates that the mainstream body politic of today is disillusioned in its support for the EU. Populist figures claim that an unregulated, free market has shown to be untrustworthy, whereas this assertion is allegedly epitomized by the Financial Crisis of 2008 [7] and the gross gains made by the private lenders and subprime underwriters, respectively. And thus, following the same Rightist line of logic, the resultant increase in protectionist measures is desirable if the national market and its associated labor force is to be preserved.

It comes as no surprise that some governments may choose to fully shelter themselves from an increasingly protectionist EU market by exiting the EU. If British Prime Minister Theresa May is successful in negotiating a favorable Brexit [8] for her nation, the fracture will still detrimentally affect EU members who will now turn to Germany [9] for chief financial support and guidance. Not only is the direct effect of Brexit significant, but it has also caused the formalization of the constitutional procedure to do so through an amendment of the Treaty of Lisbon [10]. Thus, the anti-EU, pro-exit doctrine of Right-Wing parties is given legitimacy, and with this legitimacy comes political weight.

To answer the original questions, with increasing economic uncertainty after the Financial Crisis of 2008 and a growing distrust of the mainstream body politic, man became frustrated and put up barriers around its borders. As a result, this same man saw the effects of a disjointed EU and became poorer in a more disjointed market. Upon seeing such errs, the man, now angry, turns to the Right-Wing and looks up at figures like Trump, Le Pen or Petry with a resolute hope.

Today, we live in a world where information is at one’s disposal with the single click of a button. The result of this is a growing demand for methods in understanding, analyzing, and modelling data. In economics, we refer to the development and usage of statistical techniques as econometrics.

Econometrics begins with an economic question: a relationship in which we are interested in studying. This could be a theory we wish to prove, or a policy’s effects we are trying to understand. Once we have posed a question, we can hypothesize a model that we believe would capture the relationship (Wooldridge 2). For example, consider the economic question, “What affects a person’s wage rate?”. Suppose that we believe education is a factor, and relationship is captured with the equation:

Perhaps to most of us, this follows quite naturally, despite not having done any form of analysis. However, an econometrician will often tell you this is not the case and a deeper study of this question is required.

From an econometric perspective, the above equation fails to answer two crucial questions:

• What is the magnitude in which education affects wage?
• Is education the only factor in determining wage?

Luckily, we can quickly transform our equation into an econometric regression model.

By collecting enough data (sets of information), we are able to conduct an empirical analysis to determine the estimates for the parameters and . The result is a relationship explained through a numerical equation, backed by a set of observations. With statistical tests, we can also assess the strength of our model. If it is weak, we know that some important factors may be missing, such as age. We can continue this process until an optimal model is achieved.

While econometric methods do carry enormous predictive and analytical power, they cannot be used indiscriminately. In economics, we are highly interested in causality through ceteris paribus: Keeping all other things equal (Wooldridge 12). However, it is often difficult to create controlled experiments to achieve this concept. Thus, when creating econometric models, it is imperative that we preserve ceteris paribus when selecting our sample of data. For example, assume the government made a policy change so that families with under $50,000 household income would receive a subsidy for their child’s education. We could measure the effects of this subsidy by observing the same group of families through the policy change. However, if different samples of families were drawn before and after the subsidy, we may inadvertently include the variation across households in addition to the effects of the policy change.

Another area to consider is the interpretation of statistical correlation. Sometimes, econometric models may report substantial correlation between two variables. In particular, time-series data (variables that change over time) often exhibit correlation if not corrected for when estimating. Indeed, statistical correlation does not imply causation. For example, consider number of pregnancies and number of doctors in a city. There may be data that shows strong correlation between the two variables, but we know that this is likely a coincidence and not a causal effect.

Econometrics uses a set of statistical tools in economic settings to derive conclusions and empirical results. With the prolific demand for studying data, it is important for us as economists to understand this field of study. In a society surrounded by data, econometrics is our key to better understand and observe the world.

One of Canada’s biggest news stories in 2016

The original stance from the provincial governments was to do nothing. This was clearly not a politically stable position for them to take, as many families were completely priced out of the market – especially first-time buyers.

The increase in prices can be explained by a few factors. First, foreign investment has been increasing greatly. Further, the population is increasing in both cities while the size of households is going down. In addition, mortgage rates has been low which making borrowing much easier. All of these factors creates a much greater demand. Specifically in Vancouver, supply is naturally restricted as it is surrounded by sea and mountains. In fact, it is the most densely populated city in Canada, and fourth in North America. This means that if demand increases, supply cannot increase naturally increase with it, and would raise prices.

Economics 101 explains that pricing is determined by two things: supply and demand. Thus, there are two ways to approach this problem.

The BC government has decided to approach this problem from the demand side. BC introduced a 15% tax on foreign buyers. Theoretically, this should shift the demand curve to the left, therefore reduce the price of housing. This, roughly, has worked in practice. Foreign investment has shrunk in the past few months in Metro Vancouver, though it is still too early to tell how effective the policy has been to discourage foreign buyer speculation.

In addition, the provincial governments in BC and Ontario have decided to give help to first-time home buyers. Basic economics would suggest this is a poor idea. A tax rebate would shift the demand curve to the right, and increase housing prices, theoretically.

Meanwhile, on the supply side, Economics 101 would tell us that building more homes would also help to solve this problem. However, neither BC nor Ontario have decided to approach the problem this way. One way to do this, is to make building homes easier as current laws make it difficult to build homes easily. For example, current regulations in Ontario result in long and uncertain building permits, effectively slowing the building of new homes. In addition, costly fees and opposition by local citizens slows construction. Building more homes and loosening regulations would shift the supply curve to the right, and decrease the equilibrium price.

Clearly, housing prices will be a hot-button topic in 2017 and beyond – and will impact you throughout your lifetime. It goes to show that even the complexities of the evolution of housing prices can be understood applying basic economics.