Past Baker Lowe Scholar Research

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Predicting Presidential Elections & Approval Ratings
By Sarah Baker ‘17

For my Baker-Lowe research project, I worked in conjunction with Professor Manfred Keil to evaluate presidential election prediction models. After the 2016 election, it was evident that these models were flawed—almost every respected analyst had incorrectly predicted a Hillary Clinton victory. Through my examination of these models and the data surrounding them, I determined a few possible explanations for the models’ inaccuracies. Some contributing factors include low response rates to polls and gender discrimination. In addition to examining these models, I forecasted President Trump’s approval ratings using time series analysis and concluded that his approval ratings will require significant improvement to make reelection a possibility.

Of the models examined, only one predicted a Republican victory. This model, the Ray Fair model, uses mainly economic indicators to determine the outcome of a presidential election (“it’s the economy, stupid”). Fair famously uses a “good news quarter” variable, which allows the incumbent candidate a bonus for every quarter in which real GDP per capita grows at or above 3.2%. Fair’s model predicted a solid republican victory—his final estimate allotted the democratic candidate 44% of the popular vote. But therein lies the problem: Clinton won the popular vote with a definitive 51.1% share. And other models, including that of the famous Nate Silver, employed similar economic variables and drew entirely different conclusions. Thus, we can’t quite chalk this election up to the economy, stupid.

So what made this election so difficult to predict? One issue lies with the polling itself—the response rate for polls is ~2%. As a result, election modelers must correctly predict the demographic breakdown of the voting population to achieve accurate results. Demonstrating the difficulty of this task, an article in The Economist revealed that the higher the percentage of non-educated whites in a district, the higher the polling error in that district. Put another way, most modelers did not expect so many uneducated whites to vote. This presidential race was also unusual in the fact that a woman was running for the first time in American history. Established gender roles and sexist insults may have hurt Clinton more than we are willing to admit. For instance, it seems that Clinton was held to an entirely different ethical standard due to her gender, since her private email server seemed to be more harmful a scandal than Trump’s outwardly sexist remarks.

With Trump elected, however, what can we say about his first months in office? At the time I presented my research, Trump’s approval ratings were particularly low by historical standards; by my forecast, they were set to remain low. My prediction has held true and Trump continues to have historically low approval ratings. Though this could change with an unpredictable event, (for example, a war or terrorist attack that causes a “rally around the flag” effect), Trump needs to drastically improve his approval ratings in order to stand a chance for reelection. In fact, his approval ratings probably need to reach the accepted threshold of 50% or greater to allow for a Republican victory in 2020.


Why Do We Care About Inequality?
By Xinzhu (Nancy) Li ‘15

From the Occupy Wall Street Movement to the Capital of 21st Century, income inequality has recently generated broad interest among economists and politicians. There are various schools of thought in explaining why inequality is not desirable. Piketty and Saez (2013) argue that widening income inequality could pose a potential threat to democracy through increasing rent-seeking behaviors. Corak (2011) and Krueger (2012) demonstrate that income inequality results in a lack of intergenerational mobility. Ostry, Berg, and Tsangarides (2014) present evidence that income inequality leads to slower growth for countries, and that redistribution does not necessarily negatively impact growth. Wilkinson and Pickett (2011) show that health and social problems are positively related with the level of inequality across countries. One possible conclusion of this literature is that sustainable growth can be achieved coincidentally with political stability. For the U.S., this implies that policy makers need to get a better understanding of the consequences of inequality. Moreover, it will be important that academic researchers will continue to work on concepts such as optimal levels of income distribution and their political feasibility.

Monetary Policy Expectations and the Zero Lower Bound
By Rachel Doehr ’16 and Enrique Martínez-García, Federal Reserve Bank of Dallas

Using a panel of survey-based measures of future interest rates from the Survey of Professional Forecasters, we study the dynamic relationship between monetary policy expectations shocks and fluctuations in output and inflation. We propose a structured recursive vector autoregression (VAR) model using orthogonalized impulse-response functions to identify the macroeconomic effects of changes in expectations about monetary policy. We find that when interest rates are away from the zerolower bound, a perception of higher future interest rates leads to a significant rise in current measures of inflation and economic activity. However, when interest rates approach zero, the perception of higher future interest rates leads to the opposite effect, with modest declines in current inflation and economic activity. The impact of changes in expectations about monetary policy is qualitatively robust when we control for changes in long-term interest rates and oil price shocks, as well as incorporate other channels of monetary policy (e.g., quantitative easing and the exchange rate channel). Our findings emphasize the role of forward guidance as a tool for monetary policy and provide new evidence on the role it plays in expectations-driven business cycles when interest rates approach the zero-lower bound.

Six Californias
By Ji Young Huh ‘15

Tim Draper, a Silicon Valley venture capitalist, submitted a proposal for a ballot initiative to split California into six separate states last year. Even though the proposal failed to go on the 2016 ballot due to an insufficient number of valid signatures, Draper has indicated that he will continue the battle given his firm belief that California is too big to be governed in its current state. Apart from the proposal itself, I was fascinated by his reasoning, that it is “too big” to be governed. There were three main questions that motivated my research: 1) how would California economically and politically look according to the proposal? 2) does California currently underperform because of the way it is politically organized? and if so, 3) is size really the cause of the problem? For his arguments to hold, dividing the state into less populous regions should result in more efficient governments. I looked at the question from an economic perspective and specifically used economic growth as a measure of government efficiency to assess the validity of Draper’s argument. Using regression analysis, I found that population size does not have a statistically significant impact on economic growth when controlling for other factors. Furthermore, lower population size actually results in a higher share of government spending, suggesting lower government efficiency. These regression results oppose Draper’s argument that California’s size is the cause of government inefficiency. While there may be other measures of government efficiency than economic growth, in order to pursue the proposal, Draper should know that size does not necessarily bring about faster economic growth. Given Draper’s lack of empirical evidence, to attract more supporters, Draper should present arguments on how his proposal will benefit the economy.


Why do we are about inequality?
Xinzhu Li ‘15

From the Occupy Wall Street Movement to the Capital of 21st Century, income inequality has recently aroused broad interest among the Economists and politicians. There are various schools of thought in explaining why inequality is not desirable. Piketty and Saez (2013) argue that widening income inequality could put democracy in threat income through increasing rent-seeking behaviors.  Corak (2011) and Krueger (2012) think income inequality results in intergenerational mobility. Ostry, Berg, and Tsangarides (2014) find robust and consistent evidence that income inequality is positively correlated with slowing growth and redistribution do not necessarily hurt growth. Wilkinson and Pickett (2011) show that health and social problems are positively correlated with the level of inequality across countries. In order to keep sustainable growth while maintaining political stability in the U.S., policymakers need to get a better understanding of not only the consequences of inequality, but more importantly the perceptions of inequality among the academia field and the mass to capture the intersection of the economically optimal and the political feasible solution for the long run.


Eagle and Bear – An Analysis of California’s Significance for the American EconomyKanupriya Rungta ‘14

If California were a country, it would be the 10th largest economy in the world when measured by nominal GDP, ahead of Russia and India. However, this represents a relative decline. In 1990, it was the 6th largest global economy.

When we look at GDP per capita, California does even better. In fact it is the highest of the big economies. Of course, as we know, the per capita GDP of the Scandinavian countries are the highest in the world. Yet, these charts show that California has high productivity. So how important is California to the US economy? Is it the iron to the Rest of the US, or should I say RUST? Have structural problems in California led to any (or all) of the recent recessions?

We do know that in terms of employment, California suffered the most in the last three recessions. 1990 marked the end of the Cold War, which led to the decline of the aerospace industry. This hit California, particularly Southern California the hardest. The blue solid line is the US employment recovery and the blue dotted line is the California employment recovery, months after the start of the recession. As can be seen, until month 13, the recession hits them similarly. Yet, in month 14, the US begins it recovery and has recouped all lost jobs by month 30. California, on the other hand, sees a further drop in employment, which results in it taking 60 months – or 5 years, to recover. The 2001 recession, also known as the Dotcom recession, should hit California more strongly than the US. Yet, as can be seen, the recession and recovery were almost identical. This is because the recession started in March 2001, and 9 months into the recession, or September 2001 was 9/11. All of the US suffered a decline in consumer confidence, and so California was not specially impacted. The Great Recession of 2007 hit California harder. At the 25 month mark, California has a 2 percentage point higher unemployment rate than the US. Yet, California’s recovery has also been faster (WHY?). I used the Current Population Survey data for this analysis, according to which recovery is not yet complete. However, other data suggests that the US and California have both recovered.

So where does California lie on the national spectrum? Its Real GDP increase is amongst the highest in the country. Only Texas, Oregon, Washington, and North Dakota had higher GDP increases in 2012. North Dakota, which saw a 13.4% increase in GDP, has been in the news a lot lately for almost completely removing unemployment in the state.

In fact, there was a 3 percentage point increase in employment in North Dakota from 2011 to 2012. That is a lot… or is it? Any guesses on how many unemployed people do you think found employment in North Dakota in 2011?

The number is 11,000. On the other hand, over 300,000 Californians and 250,000 Texans found jobs in that same period.

When we look at unemployment in California, though, it presents a dismal picture. Currently, about 2 million people are unemployed in California. We can see that the US has recovered a significant proportion of its job, but California’s recovery has been jobless.

Part of the reason for the gain in employment in the US, however, is that many people have left the labor force. As can be seen from this graph, there was a sharp decline in 2007, when people exited the labor force. These discouraged workers were mostly young adults – who continued to study for a longer time. It is also interesting to note that the employment – population ratio for California was higher than the national ratio – until 1990. As I mentioned before, that was the Aerospace recession – which particularly impacted Southern California. So if California were not a part of the US, would this mean the American unemployment rate goes down? No.

California has 10% of the population. So in 100, 10 are from California. Lets assume that the national unemployment rate is 10% so 90 are employed, 10 are not. When we take California out of the equation, we have a population of 90. In California, the rate of unemployment is higher, lets say 15%. So 1.5 in every 10 Californians is unemployed. This means 8.5 of 90 Americans are unemployed, which gives us an unemployment rate of 9.44%. Here, we’re assuming California’s unemployment rate is 50% more than that of the US. In reality, its closer to 14% higher. So the impact on the American unemployment rate will be minimal.

Part of California’s unemployment struggles have been because of its sectoral recovery. This map shows the main industries in California. They are arranged from left to right on the basis of the industry that employs the most to the industry that employs the least number of people in 2012. So Trade, Transportation, and Utilities is the industry that employed the most people in California in 2012. The lengths of the bars (pink plus green) denote the percent change in employment from the peak in that specific industry. The green area shows the recovery – so how many jobs have industries recouped since the peak. And finally the pink area shows the jobs that are yet to be recouped. So manufacturing, construction, financial services, and information were the worst hit. Yet, they only employ 20% of the population while Trade Transportation and Utilities employs about 19%.

How does all of this line up for output? We see that real GDP growth in California has mirrored that of the US in terms of time. However, it’s much more extreme – the peaks are higher than national averages and troughs are lower. Again, this has limited impact on the national GDP growth rate. As can be seen – the US without California (the green line) is performing similar to the US.

When we look at absolute values – California starts to matter more. Importantly, however, California’s GDP increase has been fairly flat – as compared with the rest of the US.

It would make sense that with 16% of the American GDP and 10% of the population, California will have a higher per capita real GDP than the average for the country. The gap between the two has only been widening – with Californian’s producing almost $5000 per person more per year. This also points to the higher margins industries that California operates in. Professional and business services, its third largest industry, includes the technology sector.

The largest employed, as I mentioned before, is Trade & Transportation. In order to understand the significance of this better, it is important to look at the share of port activity of the 3 largest Californian ports, compared with that of the US as a whole. Currently, the ports of Long Beach, Los Angeles, and Oakland, manage over 38% of the total imports and exports to the US. That number, however, has significantly reduced from its high of 44% in 2006. This is also telling of the fact that the 2007 recession hit California harder than the US.

Finally, we look at housing starts – the main cause of the 2007 recession. Housing permits are a leading indicator of housing starts. The blue represents the US while red is California. The dotted lines are permits while the solids are starts. As can be seen, both California and the US see a severe decline in permits starting in 2003, while the decline in housing starts only becomes visible in 2005. The US has started its recovery, although California still lags behind.

So would the US have escaped a recession had California not been a part of it? It depends on the recession. The 1990 and 2007 recessions definitely point to greater structural damage in California, which impacted America as a whole. Part of the reason for that is California’s reliance on specific industries. The rest of the US is much more diversified.

The Effects of Margin Rates on the Stock Market Crash of 1929
Eliana Rachel Merle POM ’14
  • Rapid economic growth in the 1920’s led to continued euphoria and momentum (Galbraith, 1954).
  • Fast growth of the securities market in the 1920’s led to inexperienced investors taking large risks
  • The bull market was furthered by easy access to credit, specifically brokers’ loans, which encouraged further leverage and speculation (White, 1990; Galbraith, 1954).
  • Most agree that margin purchases and ‘easy access to credit’ contributed to speculation and inflated asset prices (White, 1990; Kindleberger, 1978).
  • Lack of data on margin lending makes it hard to quantify this effect; however, there is data available on margin rates
  • Higher margin rates decreased volatility and decreased returns
  • This is consistent with the literature’s expectations: Access to credit increases returns, but increases risk


Historical Evidence for the Necessity of Margin Requirements: Examining the Effects of Unrestricted Credit Availability on the 1929 Stock Market Crash
Igor Tischenko ‘13

Federal regulation of securities margins, in the form of initial and maintenance margin requirements, was mandated by Congress in the Securities and Exchange Act of 1934.1 The purpose of these margin requirements was to limit excessive stock market volatility, which was to be accomplished by requiring investors to collateralize a certain portion of the value of a stock purchase from their own capital. By restricting the amount of credit that brokers and dealers can extend to their customers for the purchase of new stocks, Congress aimed to reduce credit-financed speculation, which was highly detrimental to the economy.

The impetus for these margin requirements policies arose from the stock market experience of the late 1920s, which culminated in the infamous “Stock Market Crash of 1929”. The 1929 crash ushered in the Great Depression and is considered to be the most devastating stock market crash in the history of the United States.2 The rampant speculative excesses of the 1920s led Congress to conclude that a policy constraining the amount of borrowing by optimistic investors with relatively low degrees of risk aversion would preclude the disastrous consequences of that period.

By constraining funds available for stock speculation, Congress aimed to lessen price increases that could not be justified by economic fundamentals. If not controlled, these price increases would fuel themselves by enabling speculators to employ their increased wealth to borrow more funds for the purchase of more stocks, thus driving the stock price ever higher. That is, until less optimistic investors would begin to sell when they felt that the market was overbought. Such a price decrease would feed on itself as creditors would ask for more collateral on their loans to speculators, which if not provided would lead the creditors to liquidate the collateral stocks and drive the price down further. This process of pyramiding and depyramiding of stock prices was perceived as one of the main contributors to excessive market volatility of the 1920s, thus justifying the imposition of margin requirements.3

The consensus among economists is that higher margins, imposed through margin requirements, would restrict margin credit, and thus stock trading.4 Yet the relationship between margin rates and stock market volatility during the years surrounding the stock market crash of 1929, the event which led to the creation of margin requirements, has not been fully studied. As such, the purpose of this research paper will be to quantify and better understand this relationship during that crucial period in time.