My new website for a quick-look on the U.S. Treasury Yield Curve

Hello everyone,

I apologize for my extended absence. It turns out that I don’t write many essays that aren’t for work now that I’m no longer in graduate school; who would’ve thought?

I have in fact been working on a project as of late, however. The problem that I sought to address was the lack of any good yield curve visualizer for U.S. Treasury bonds. You can always go to the Treasury’s website and look at the latest interest rates, yes, but that’s not a great solution for people that feel they could better understand where the economy and business cycle are at with a visualization of the yield curve as opposed to just parsing the raw data. I’m one of those people.

So, I bought the domain and began learning JavaScript.

Screen Shot 2018-07-25 at 11.12.50 AM

I want this website to be a no-nonsense, lightning fast resource for those that need to know which way the yield curve is moving on a day-to-day basis. Not only this, I’ll be taking some time to write extensively on to help people understand what a yield curve is, why its shape matters, and the various factors that influence the movements of interest rates.

As always, please let me know if you have any feedback, it would be much appreciated!



Haiti has the lowest coverage of electricity in the Western Hemisphere, with only 37.9% of the population having regular access.[i] The energy sector in Haiti is broken by any modern standard, with frequent interruptions in service making it impossible to rely on the public utility to provide enough power to even maintain a freezer to preserve food. Something relatively unique to Haiti, however, is the rampant theft of electricity, with half of all residents being connected to the grid illegally; a problem that a public utility rarely has to contend with.[ii]

It is concluded that these problems stem from a failure of the government of Haiti: a complete lack of the institutional capacity needed to provide a public utility to the country. This problem results in much of the population, including hospitals and other major institutions, relying on generators which are highly inefficient, negatively affect the environment, and make the country as a whole more susceptible to volatile oil prices. Moreover, a decrepit electricity generation and distribution network creates the conditions that allow for 54% of the electricity that is distributed to be stolen or lost, while the world average is only 8%.[iii]



            Not only contained within the energy sector, the Haitian government can be characterized by an astonishing lack of institutional capacity. This is largely because Haitian politics have historically been so unstable, with coups being a regular occurrence and widespread corruption being an accepted part of every day life. Dictators have regularly stolen millions of dollars from the Haitian treasury, straining the Haitian fiscal situation even more than it already is. Thus, one can see that government failure in the energy sector is not unique in the context of the broader governmental apparatus. Insofar as this is true, the shortcomings of Haiti’s past have largely shaped the present, and Haiti is still playing catch-up to the rest of the world. Institutional weakness as it pertains to the energy sector is as follows:

  • Loss of technical know-how: In 2005, the Secretary for Energy, Mines, and Telecommunications (SEEMT) was eliminated, with the Ministry of Public Works, Transport, and Communications (MTPTC), as well as the Bureau of Mines and Energy (BME) supposedly taking its place. The SEEMT was previously tasked with the creation of energy policy, enhancing the electrical grid, and maintaining current systems, thus, the SEEMT worked in a policy and technical capacity. Because there was no effort on the part of the Haitian government to integrate some of the human capital possessed within the SEEMT with the institutions that were assuming its responsibilities, a great deal of technical know-how was lost in the transition.[iv] The has led to a total and complete failure of the MTPTC to manage and maintain electrical infrastructure, much less advance and improve it.
  • Diffusion of responsibilities: There is no institution or agency tasked with regulating the energy sector in Haiti, ironically creating a problem that government, by its very definition, is supposed to solve. In theory, the MTPTC and BME both work with the state-owned electric utility Electricité d’Haïti (EDH) to advance national energy policy, but progress has been excruciatingly slow due to the fragmentation of responsibility. MTPTC, BME, and EDH have been working on a national energy policy since 2006, releasing a draft in 2012, but have not yet implemented any of it.[v]



Because the Haitian economy is so under-developed, with a GDP (PPP) per capita of only $1,800, real GDP growth rates of less than 2%, an unemployment rate of over 40%, and a fiscal budget deficit, nearly everyone in Haiti uses wood or charcoal for lighting and cooking. Those that are well off will instead use diesel generators as they cannot rely on a constant source of electricity from EDH.[vi]

  • Public health: 77% of energy usage in Haiti comes from the use of wood and charcoal for primary energy use, while accounting for 93% of the fuel used for household cooking.[vii] Like many other developing nations that use similar fuels, this has serious public health implications, as these fuels are often used indoors and emit harmful respirable toxins.
  • Environmental impact: The reliance on wood and charcoal fuels has resulted in a tremendous demand for timber in Haiti. Due to the relative size of the population to the country’s land area, this demand has caused complete deforestation of the entire country. Deforestation at this scale also creates a cascade of other problems, e.g. the displacement of topsoil from higher elevations to waterways which reduces agricultural output and has been documented to reduce certain river flows (and thereby impacting drinking water sources) by 80%.[viii] This is an unsustainable practice, and at this point, it will take decades to restore the environmental damage that has already been inflicted on the Haitian landscape.



            Lastly, perhaps Haiti’s most obviously apparent problem in the energy sector is the widespread theft of electricity, with an estimated 54% of all electricity produced being stolen or lost in transmission. The root causes that create the conditions for rampant theft are EDH’s inability to bill and collect payments, an unregulated electrical infrastructure that has been cobbled together and not maintained, and a lack of commercial customers that shifts the revenue burden to those that are poorest.

  • Billing and collections: EDH, consistently suffering from the institutional capacity problems that also afflict the wider Haitian government, has shown a consistent inability to implement efficient billing and collection practices. Underscoring inefficient bureaucratic procedures are a lack of electricity meters that would otherwise determine how much to bill each customer. A recent USAID project installed proper connections and meters to over 8,000 households, and found that collection rates improved from 25% to above 90%.[ix] A central principle to collecting payment is first ensuring that the customer and utility can agree on the level of service consumption.
  • Poor infrastructure: The electrical infrastructure of Haiti is severely under-maintained. This is most easily attributed to a lack of financial resources, a lack of technical know-how, and a complete lack of regulation of the infrastructure itself. Because the energy grid is so frequently down (with most customers only receiving around ten hours of electricity per day[x]), this provides ample time for individuals to make illegal connections to distribution systems, as a majority of the time there is no associated danger. Moreover, a dilapidated grid that provides such poor service has the effect of creating a culture of non-payment, as consumers don’t feel like they are receiving a quality of service that would justify compensation.
  • Inflated prices: As previously mentioned, because businesses and organizations cannot rely on EDH to provide electricity around-the-clock, these organizations turn to diesel generators for their energy needs. The effect that this has on the economics of the public utility are far more impactful than one may initially assume. These firms are the would-be customers that would be best positioned to actually pay for the electricity service, but in their absence, the burden to pay falls on those with the least amount of resources. This means that the final cost of electricity is shaped by a customer base in which many aren’t paying, incentivizing EDH to raise their prices in an attempt to recoup the lost revenue from customers that are In sum, this artificially increases electricity prices, leading to a situation where electricity costs as much as $0.34/kWh in Haiti; far more than nearly every other country on Earth, and about triple the costs that one would encounter in the contiguous United States.[xi] This further incentivizes theft of electricity.



It is well documented that widespread, reliable access to electricity is a key for economic growth, thus, if the nation of Haiti ever wishes to become a legitimate player in the global economy, it must first solve this fundamental problem of electricity generation, distribution, and access. It is concluded here that the government of Haiti has failed in its totality for decades to build the institutional capacity needed in order to accomplish these goals, and the spillover effects include but are not limited to a failure to produce any national energy policy or decide who is to regulate the sector, widespread reliance on charcoal and generators for cooking and lighting that have detrimental public health implications, deforestation of the entire country, artificially and insurmountably high electricity prices for consumers, an exacerbated fiscal deficit due to EDH subsidies to recoup lost revenue, and the extremely prevalent theft of electricity. These findings show that, despite the efforts of numerous countries to aid the people of Haiti in ways that increase the resilience and efficiency of their electrical grid, the ultimate responsibility to advance the cause of the Haitian people falls squarely on their own government.



[i]    The World Bank. Access to electricity (% of population). 2012. 1 March 2017.


[ii]   USAID. Haiti Energy Fact Sheet – January 2016. Fact Sheet. Washington: USAID, 2016.

[iii]  The World Bank. World Development Indicators: Power and Communications. 2014. 27

February 2017. <;.

[iv]   The World Bank. Project Information Document: Haiti Electricity Loss Reduction Project.

Report. Washington: The World Bank, 2006.

[v]   Energy Transition Initiative. Energy Snapshot: Haiti. Report. U.S. Department of Energy. Washington: U.S. Department of Energy, 2015.

[vi]   U.S. Central Intelligence Agency. The World Factbook: Haiti. 2017. 2 March 2017.


[vii]   Worldwatch Institute. Haiti Sustainable Energy Roadmap. Report. Washington: Worldwatch

Institute, 2014.

[viii]   McClintock, Nathan. Agroforestry and Sustainable Resource Conservation in Haiti. Case

Study. North Carolina State University. Raleigh: NC State, 2004.

[ix]   USAID. Haiti Energy Fact Sheet – January 2016.

[x]   Worldwatch Institute. Haiti Sustainable Energy Roadmap.

[xi]   Friedman, Lisa. Can Haiti Chart a Better Energy Future? 17 April 2013. 3 March 2017.






There are a multitude of factors that dictate how someone votes in an election, and for a large share of individuals, whether they even vote at all. Political Science has a long history of attempting to define which characteristics influence the electorate to vote for a particular party; sometimes for academic purposes, but in the election industry, these defining characteristics are very valuable information.

There are internal factors and external factors that decide an individual’s vote. Internal factors are characteristics inherent to the person that increases their predisposition to vote for a particular political party, e.g. race, sex, or age. External factors are things that occur out in society that influence an individual’s political leanings, e.g. the salience of a social issue at the time of the election, economic growth or shrinkage, or the response of an administration to a national security threat. The effects of these events on the electorate are more difficult to quantify, but that is the aim of this paper, at least for one specific type of external factor: economic growth.

Do voters hold government officials, more specifically executives, accountable to economic performance during their tenure? What about the party of the executive; do voters punish the political party of the executive when economic performance is poor and reward it when it exceeds expectations? At first the answer seems like an obvious one. In fact, if there were any factor that did dictate the electorate’s vote, it would seem that most would say economic performance was it. But, the interesting part of this lies in the specifics, and implications of the question. If it is found that economic performance has little to no bearing on the electorate’s aggregate vote for the political party that is in power at the executive level, then this would indicate that voters do not make their decision in the context of political parties. Perhaps it would mean that they place more emphasis on the personal characteristics of the candidate. Alternatively, it could indicate that the partisan divide is so strong that voters, on the aggregate level, vote on party lines despite how good or bad the perceived economic leadership of the president or governor is during their tenure. And lastly, it could even indicate that campaigns are so effective at pushing a specific narrative that voters forget the reality of economic conditions.

But what results do you get when you examine the relationship between state economic conditions and presidential vote share? Do voters hold the president’s party accountable, and furthermore do they hold the president’s party accountable for state economic conditions that are outside of the national government’s control? In this paper we will discover whether or not a relationship exists, and determine whether or not accountability stays in its appropriate sphere. Building on this as well, we can make some determinations of other factors that affect presidential and gubernatorial success in elections.


            Nearly every publication in political science regarding this subject looks at election results through the lens of two different voting behavior models. The first is the national referendum model, which “suggests that voters in subpresidential elections express their approval or disapproval of the sitting president and his policies with their vote.” The other model is the economic voting hypothesis, which “suggests that voters in these elections express support or dissent for the performance of the incumbent based upon how well the economy is doing,” (Atkeson and Partin, 1995). At its core, this last model indicates that voters who are financially better off than they were before the candidate took office will reward the incumbent, and conversely will punish them if that does not hold true. The economic voting hypothesis is what we are primarily taking a look at here, with a couple of variables that would shed some meaningful light on the applicability of the national referendum model in the gubernatorial analysis.

Institutions, The Economy, and the Dynamics of State Elections takes a deep look into the aforementioned topic at the state legislative and gubernatorial levels (Chubb, 1984). Chubb considers the relative effects of presidential coattails, the common backlash against the party of the president during mid-term elections, and state and national economic conditions. He believes that “. . . when it comes to assigning responsibility for economic performance, state voters have generally and increasingly looked outside of the state to the national economy and the president’s imputed performance in managing it,” thus a poorly performing national economy would influence the outcome of a state election, despite a state’s hypothetical independently strong economic performance. The analysis of this paper has shown Chubb’s claim to be inconsistent with voting behavior in the last two decades, but perhaps there has been a genuine change in behavior between the time period he analyzed and now. Lastly, Chubb says that at the state level, “the factors that account for variations in normal partisan voting across the states include idiosyncrasies of culture and history that subvert general explanations,” which corroborates the findings of the analysis here. Gubernatorial electoral outcomes are the product of far more variables than presidential electoral outcomes.

James King, of the University of Wyoming, in 2001 performed a similar analysis in his publication titled Incumbent Popularity and Vote Choice in Gubernatorial Elections. Studying gubernatorial elections in four states, his results found that “. . . voters use the ballot for governor to express approval or disapproval of current economic conditions and the president’s job performance, or as an easy means of evaluating candidates.” This would square with Chubb’s publication that retrospective economic voting is a reality, and that voters hold the governor accountable to state economic performance.

Atkeson and Partin, in Economic and Referendum Voting: A Comparison of Gubernatorial and Senatorial Elections come to similar conclusions. They find that “. . . governors, as chief executives of their respective states, are held responsible for the health of their state economies and are not generally shown to be liable for fluctuations in presidential approval.” This further affirms the existence of economic retrospective voting at the gubernatorial level. Atkeson and Partin also “. . . find something of an in-party incumbency effect whereby incumbent governors of the president’s party suffer more from a perceived worsening of state economic conditions than incumbents of the out party,” and finally they claim, “. . . governors . . . escape from these national-level evaluations of presidential performance and are instead held liable for state economic conditions.” This would suggest that national economic performance has no bearing on governors, regardless of political affiliation.

In a very interesting article written by Robert M. Stein in 1990, the argument is made that economic conditions affect presidential vote share, but not gubernatorial, saying “. . . state and local incumbents are less likely to be affected by voters’ retrospective economic evaluations than their national counterparts.” Stein’s usage of the word “incumbents” would indicate that voters do not punish or reward the incumbent party when an incumbent is term limited and the election is open-seat. Stein’s findings do confirm his aforementioned hypothesis, and he goes on to state that “Voters hold their governor neither responsible nor accountable for the state’s economic conditions and their voting behavior reflects this perception . . . This evidence of economic voting for governor, however, varies with the partisan affiliation of the incumbent candidate,” thus when economic effects do matter in an election, voters will punish or reward each party by different amounts for the same economic performance. Stein’s research shows that voters differentiate between the impact state and federal policies can have on their personal finances, wrapping up his article by saying that “voters recognize that their personal financial condition is more closely tied to federal policies and actions than to the state’s,” (Stein, 1990).

A different approach to testing the executive economic accountability theory is taken by David Samuels and Timothy Hellwig. They cited the controversy of trying to define the dependent variable in the accountability model; whether to measure accountability as number of seats in a legislature swings in vote share of an incumbent, or in its most stringent sense, partisan control of the office in question. They found that accountability can be observed when measuring it as seats in a legislature or vote share of an incumbent, saying “When we conceive accountability in terms of sensitivity of swings in incumbent vote shares . . . and when we use seats as the dependent variable, we find that incumbent performance is sensitive to economic performance.” However, when measuring accountability as partisan control of the office, the results were inconclusive, saying “. . . when we conceive of accountability as partisan control over government . . . our findings temper Cheibub and Przeworski’s (1999) pessimism.” The authors ultimate conclude that “citizen control of politicians is . . . imperfect because particular political contexts limit voters’ ability to hold incumbents to account by obscuring responsibility for economic performance,” (Samuels & Timothy, 2010).

Johnson and Ryu look to other countries to test these models, but with the inclusion of broken campaign promises in the analysis. They sought to determine if economic performance, broken promises, or some combination of the two were what voters cared most about in executive elections. Their findings indicated that neither of the two factors had any substantive effect alone, but an interaction between both variables resulted in statistically significant results, going on to say that “the relationship between broken campaign promises and incumbent vote change is affected by economic conditions.” This means that regardless of economic performance, as long as no campaign promise was broken, the executive was not rewarded nor were they punished for it; and alternatively, when a campaign promise was broken, voters held the executive accountable for economic performance, likely due to increased scrutiny (Johnson & Ryu, 2007).


Data was gathered from official and private sources (deferring to official whenever possible), from the years 1987 to 2013.

The hypothesis for the state level is as follows:

 State Level H1: People vote to retain the governor, or party of the governor when he/she is term limited, when the state in question experiences strong economic growth.

State Level H2: On average, a governor will be positively affected when the governor is the same political party as the sitting president and their elections coincide.

The overall theorized model of the analysis is specified by the following regression equation:

 %VoteShareGovParty = β0 + β1 % StateGrowth + β2 % NationalGrowthSamePres – β3 %NationalGrowthOppPres + β4 PartisanControl + β5 GovIsIncumbent + β6 GovSamePartyElectionasPres + ε

The following are variable definitions:

%VoteShareGovParty: The percentage of vote share received by the party of the incumbent governor, whether the governor was running for reelection or not.

%StateGrowth: Measured as the percentage change in Per Capita Real Gross State Product over the tenure of the governor.

%NationalGrowthSamePres: An interaction between a dummy variable that turns on when a governor is the same party as the president, and the percentage change in Real Gross Domestic Product

%NationalGrowthOppPres: An interaction between a dummy variable that turns on when a governor is the opposite party as the president, and the percentage change in Real Gross Domestic Product

PartisanControl: Serves as a baseline for the predicted vote share of a governor. This is measured by taking the average of the vote share of the Democrat Party for every gubernatorial election between 1987 and 2013, and providing that number if the incumbent party is Democrat, or 100 minus that number if the Incumbent party is Republican. The coefficient of this variable will be difficult to interpret, and is not meaningful for our sake.

GovIsIncumbent: This is a dummy variable that turns on if the governor is an incumbent.

GovSamePartyElectionasPres: This is a dummy variable that turns on if the governor is the same party as the president, and their elections fall on the same year.

Gubernatorial election results and information on gubernatorial incumbency were aggregated from Wikipedia, which has curated lists that source the data from each state’s secretary of state or equivalent. I am aware that Wikipedia is frowned on to use as a source in an academic context, but there was simply no other source that had the data in a remotely useful format. National RDGP growth, as well as state per capita RGDP growth figures were obtained from the Bureau of Economic Analysis. The rest of the variables were computed through Excel functions to prepare the dataset. Observations were discarded if the incumbent party was not Democrat or Republican, and after the completion of the dataset, there were 327 total observations being analyzed. For the regression analysis, Stata 14 was used.

The national level model is much the same, with two hypotheses as well:

National H3: People vote to retain the president, or party of the president when he is term limited, when the country as a whole experiences strong economic growth.

National H4: People vote to retain the president, or party of the president when he is term limited, when their state experiences strong economic growth.

The overall theorized model of the analysis is specified by the following regression equation:

%VoteSharePresParty = β0 + β1 % StateGrowth + β2 % NationalGrowth + β3 PartisanControl + β4 PresIsIncumbent + ε

The following are variable definitions:

%VoteSharePresParty: The percentage of vote share received by the party of the incumbent president, whether the president was running for reelection or not.

%StateGrowth: Measured as the percentage change in Per Capita Real Gross State Product over the previous presidential term.

%NationalGrowth: Measured as the percentage change in Real Gross Domestic Product

PartisanControl: Serves as a baseline for the predicted vote share of a president. This is measured by taking the average of the vote share of the Democrat Party for every presidential election between 1987 and 2013 and for every state, and providing that number if the incumbent party is Democrat, or 100 minus that number if the Incumbent party is Republican. Like the gubernatorial model, the coefficient of this variable will be difficult to interpret, and is not meaningful for our sake.

PresIsIncumbent: This is a dummy variable that turns on if the president is an incumbent.

Presidential election results and incumbency information were aggregated from The American Presidency Project. National RDGP growth, as well as state per capita RGDP growth figures were obtained from the Bureau of Economic Analysis. The partisan control variable was computed through excel functions. Observations were adjusted if there was a third party candidate to exclude that impact on the results.


Screen Shot 2015-05-07 at 8.27.16 PM

After running the regression analysis on the data and ensuring that there were no omitted variables, we are presented with some very interesting results. Three different models were created for the gubernatorial level, with each subsequent model adding controls in an attempt to isolate the real effects that state GDP growth and national GDP growth have on the vote share for the political party that is in power at the executive level in state government. Though no heteroscedastic problems were foreseen, every model was controlled for heteroscedasticity by using the robust command in Stata. Refer to Table 1 for the results of all three models.

Screen Shot 2015-05-07 at 8.27.06 PM

After producing a correlation matrix, we find no possible issues with multicollinearity in the model. Notice the low correlation between the PartisanControl variable and %VoteShareGovParty. The PartisanControl variable takes historical voting averages for the Democrat Party in each state and uses that number as a baseline to predict vote share in the year in question. However, because aggregate state partisanship is much more volatile than the country as whole, and is susceptible to much quicker political ideology shift, historical voting averages are not nearly as good of a baseline at the state level as they are for the national level. In an ideal world, this correlation number would be equal to 1.000, which would allow us to perfectly predict the outcome of each election with only one piece of data, but unfortunately this is not the case. As you will soon see, the presidential model correlation between these two variables was much higher, leading to a much more accurate model.

Neither state nor national economic performance has any effect on gubernatorial vote, and we cannot accept H1. The closest either variable gets to any significance is with a p-value of 0.102, but even this is in Model 1 when the other controls are not active. This is a surprising result, and though the p-values for the state, same-party-national and different-party-national variables are 0.335, 0.995 and 0.736 respectively in the complete model, the R2 is 0.429. This means that there are other variables out there that will account for the other 57.1% of the variance in vote share for the political party in power. If those were controlled for, there is a chance that state and national economic performance could come back down into significance. However, the challenge with this is that there is no shortage of unobservable variables that have some bearing on the outcomes of elections.

To those familiar with the field of Political Science, it should come as no surprise that incumbents enjoy a tremendous advantage over their opponents. In this analysis, it was found that, when controlling for the other previously mentioned variables, simply being an incumbent gives a gubernatorial candidate a 10.646% (though we must consider the constant forces us to start below zero, at -3.343, so it’s not exactly 10.646 but rather a touch below) vote share boost; with a p-value of 0.000. This is also an underestimate, due to some states only requiring a plurality of the vote to win the governorship; e.g. some gubernatorial results in this data show a governor winning with as little as approximately 40% of the vote. These observations could have been removed, but they comprised a significant portion of the data, and incumbency advantage was not the aim of this research.

Presidential coattails were also confirmed by this analysis, for better or for worse. When a governor shared the same political party as the incumbent president and their elections coincided, the governor enjoyed a modest vote share boost; with a p-value of 0.003, allowing us to accept our H2. This could be attributed to the president running a hard fought campaign during his election year, in an attempt to increase his approval rating before Election Day; and when the incumbent president could not run for reelection, perhaps those conditions did not necessarily hurt a same-party governor either. Presidential campaign efforts, on average, apparently have a positive effect on not only his party members at the federal level but governors as well. This may no longer be the case today, but on average from 1987 to 2013, it was.

These results call into question the rationality of the votes cast in gubernatorial elections, and force us to ask ourselves, if these results are an accurate representation of reality, why voters do not hold governors and their political party accountable to state economic performance? Perhaps this is a result of state economic conditions being to some extent a product of the national economy, and also a result of state economic conditions being vaguely defined in the context of the bigger national picture. Rarely are voters exposed to economic performance data, and when they are it is highly unusual to be state data. However, even this does not square with voters not being mindful of national economic performance either. There is a chance that voters view the governor as isolated from any decisions that would affect the state economy and insulated from decisions that affect the national economy, and because of this, state and national economic conditions do not hold any bearing on their vote in gubernatorial elections.

Screen Shot 2015-05-07 at 8.27.40 PM

The presidential model produces far more predictable results. Three different models were created for the presidential level as well, with each subsequent model adding controls in an attempt to isolate the real effects that state GDP growth and national GDP growth have on the vote share for the political party that is in power at the executive level in the federal government. State economic growth is not significant in the complete model, however, and has a p-value of 0.918; preventing us from accepting H4 that hypothesizes that state growth has a positive impact on presidential vote share. Unlike the gubernatorial results that showed that state economic performance had no effect on general election outcomes, the president’s party is indeed accountable to national economic performance, with a p-value of 0.000; allowing us to accept our H3. Like the gubernatorial model, incumbency matters. Simply being an incumbent resulted in a significant vote share boost for the incumbent president.

Screen Shot 2015-05-07 at 8.27.32 PM

After producing a correlation matrix, we find no possible issues with multicollinearity in the model. The PartisanControl variable correlates highly with %VoteSharePresParty, 0.8909, but this is to be expected. In these models we are using voting averages for the Democrat Party in years past to estimate current Democrat vote share. What this means is that we seek a high correlation with the PartisanControl variable, and if it were not correlated highly this would mean that partisan trends and a state’s propensity to vote more for a specific political party were non-existent. We know this is false.

Perhaps most interesting is the performance of the included quadratic national GDP growth variable. The inclusion of this variable is only due to the very high level of significance it attained, and it admittedly makes little theoretical sense within the context of the model. Because OLS has estimated a negative coefficient, this indicates that GDP growth has a diminishing effect with respect to vote share of the incumbent party of the president, to the extent that at a certain point, higher growth actually reduces it. Theoretically, voters are eager to kick out the party in leadership when there is very low GDP growth. Voters are eager to retain the party in leadership when there is a satisfactory amount of growth. And lastly, perhaps voters are enjoying an economic prosperity so much with very high GDP growth that they don’t care to turnout for an election and keep their favored party in power.

The performance of the presidential model squares with the economic voting hypothesis. Voters notice economic growth (or the lack thereof) and attribute this to the performance of the president. Though the president has no constitutional basis to manage the national economy, voters have given this responsibility to him.

The gubernatorial model does not square with the economic voting hypothesis. Governors are not beholden to any economic performance. However, the model does square with the national referendum hypothesis, as evident in the statistical significance of the “Governor Same Party as President” variable. Voters express their discontent or satisfaction with the sitting president by voting against or for same-party governors.


Though these results indicate that economic performance holds little significance in the context of elections in modern times, this may not have always been the case. There is a stark juxtaposition between the strong anti-federalist sentiment of the 19th century, and today’s interconnected and unified country. Voters 150 years ago placed much more emphasis on state-level government and politics, and this is just simply not the case anymore. There is a (nearly) century long trend of giving more and more state power to the national government, and I believe that in this, the significance of state level economic performance has been largely lost. However, this is not to say that this trend will continue, or that the results of this analysis will even hold true in the future. Perhaps we are yet to witness the imminent state politics renaissance, but for now, economic performance doesn’t matter for governors.

Hope you all enjoyed,



Atkeson, L. R., & Partin, R. W. (1995). Economic and Referendum Voting: A Comparison of Gubernatorial and Senatorial Elections . American Political Science Review , 89 (1), 99-107.

Chubb, J. E. (1988). Institutions, the Economy, and the Dynamics of State Elections. American Political Science Review , 82 (1), 133-154.

Johnson, G. B., & Ryu, S.-R. (2007). Campaign Promises, Economic Performance, and Electoral Accountability in Latin America . Annual Meeting of the American Political Science Association, (pp. 1-23). Chicago.

King, J. D. (2001). Incumbent Popularity and Vote Choice in Gubernatorial Elections. The Journal of Politics , 63 (2), 585-597.

Samuels, D., & Timothy, H. (2010). Elections and Accountability for the Economy: A Conceptual and Empirical Reassessment. Journal of Elections, Public Opinion and Parties , 20 (4), 393-419.

Stein, R. M. (1990). Economic Voting for Governor. Journal of Politics , 52 (1), 30-53.

U.S. Department of Commerce. (2015, April). Regional Data. Retrieved April 20, 2015, from Bureau of Economic Analysis:

Wikipedia. (2014, December 2). United States gubernatorial elections, 1990. Retrieved

April 15, 2015, from Wikipedia:,_1990

Woolley, J. T., & Peters, G. (2015). Presidential Elections Data. Retrieved April 1, 2015, from The American Presidency Project:

Should the Keynesian Consumption Function be Non-Linear?

To those that follow this blog, sorry for not posting anything lately. I have been busy with this project and several others.

Those familiar with the macroeconomic theory of John Maynard Keynes know that his consumption function is defined as follows:

C = a + bYD

Where C is aggregate consumption, a is autonomous consumption, b is the slope (which in effect measures how much consumption increases with a one unit change in income), and YD is income.

As one is able to see, the consumption function closely resembles the basic point-slope equation that many of us learned in middle school mathematics class, and this consumption function is completely identical in how it operates. Thus, those that have expanded their knowledge of mathematics into the Calculus realm know that the derivative of the consumption function is referred to as the slope’s rate of change. In this case, that is called the marginal propensity to consume. The marginal propensity for individuals to consume measures how much of the next dollar in income will be utilized by the owner of the dollar. Real economic factors are largely what dictates the value of the MPC, with income level and general consumer confidence being far and away the most important factors. Because there is no exponents in Keynes’ consumption function, this means that the derivative (the MPC) is simply a constant, and when graphed is a flat line. We can interpret this to be the average MPC at the aggregate level.

Why is this important? What struck me as odd is that we are accepting that the MPC is a single value for all consumers, even despite income difference, when we know that wealthier consumers spend a smaller portion of their income. This means that the MPC must be variable, which is impossible without a quadratic term (a “squared” exponent) in the original consumption function. What I have done is started with the end in mind, i.e. developed a simple equation for an MPC that could be still linear but not a constant, and integrated it back into a consumption function. The variable MPC equation looks like this:

MPC = b – 2xYD

Where b is the intercept on the Y-axis of the MPC, x is the slope, and Yd is still the original income. When we integrate it back into a new consumption function, we have this (any new constant from integration can be assumed to be lumped into a):

C = a + bYDYD2

Now that our new consumption function is quadratic, this means that our consumption function is now non-linear, thus the C curve is literally now a curve; and in a regression it could potentially better account for variance and “fit” better.

I found the economic data needed to test this at the Bureau of Labor Statistics (BLS). The BLS administers a Consumer Expenditure Survey every year, in which they survey thousands of average citizens and instruct them to list all of their expenditures and income in fine detail. They make this data available for public use and years worth of survey results are available on their website. I chose 2013 data because it was the most recent. Also it is important to note that every observation in this CE Survey data was measuring a consumer unit, i.e. a family/household.

The first step was to prepare the data set. Because many of the observations had very questionable income/expenditure values, such as negative numbers or extraordinarily high expenditures coupled with zero income, I had to create some rules for inclusion in the data set and in effect, remove outliers. After all, this experiment was to look at data for average households that have  at least someone employed that generates a steady income, thus I removed any observations that had an income under $500/month and over $35,000/month. I also removed any observations that reported expenditures of less than $750/month (as this would indicate they are far under what we accept as autonomous consumption, or the minimal amount that people must spend to have the absolute minimal level of necessities; this would make observations at this level or lower very questionable and unrealistic), and removed expenditure observations of more than $35,000/month.

After using Stata to estimate the coefficients for the new consumption function, we are presented with this:

C = 4389 + 0.332YD – 8.9e-7YD2

R2 = 0.23

Screen Shot 2015-04-11 at 8.15.33 PM

As compared to the consumption function based on Keynes’ original equation, which produces the following results:

C = 4949 + 0.2727YD

R2 = 0.228

Moving on, the new non-linear consumption function produces the following derivative/MPC equation:

MPC = 0.33218 – 17.8e-7YD

While the original consumption function produces this derivative/MPC equation:

MPC = 0.2727

With the new consumption function and MPC curve plotted on top of each other, we get the following results:

Screen Shot 2015-04-13 at 12.47.15 PM

So how can we interpret these results? The new robust and non-linear consumption function accounts for the diminishing utilization of new income across income levels. Also, it intercepts the expenditure-axis at $4389 expenditures/quarter for those hypothetically making $0 income. What this means is that autonomous consumption a, the smallest amount that people must spend to have the absolute minimal level of necessities, is estimated by this model to be $17,556/year/household. The original consumption function, because it was lacking this quadratic term, fit the data in a slightly different manner. This pushed its intercept higher, and would lead us to believe that autonomous consumption is actually $19,796/year/household. Because we are better able to estimate this now, we know that the $19,796 figure is an overestimate. Consumers are actually able to live off of $2,240 less goods and services than the original consumption function would have us believe.

Theory has a necessary confrontation with the mathematics behind the theory here, due to the R2s being essentially identical, 0.23 and 0.228. This means that both models fit the data just as good as the other, but in this case we have an important choice to make. Do we choose the linear model because of its simplicity? Or do we choose the non-linear model because it gives us a variable MPC? I will choose the latter. It is disingenuous for economic textbooks to almost universally teach students that the consumption function is a linear equation because this relegates the MPC equation to only a single constant term, and this cannot be true.




“In the period between about 1935 and 1980, America became steadily more equal. This just happened to be the period of our most sustained economic growth. In that era, more than two-thirds of all the income gains were captured by the bottom 90 percent, and the bottom half actually gained income at a slightly higher rate than the top half. By contrast, in the period between 1997 and 2012, the top 10 percent captured more than 100 percent of all the income gains. The bottom 90 percent lost an average of nearly $3,000 per household.”

When public policy favors the lower and middle classes, genuine economic growth occurs that benefits both the rich and the poor. Demand drives and economy, and when policy shifted to the supply-side during the Reagan administration, suddenly all of the income gains happened at the top, and continue to do so!


People remain fearful of a regulated free-market because they view it as an infringement on liberty, but they ignore the times when free markets or deregulation produced a market failure or severe consequences for a majority of America. Look to the financial crisis of 2008, the negative externality of global warming, or income inequality for your evidence.

The point I’m getting at is that some regulation can be a good thing. The right policies can leave everyone better off, and that’s usually achieved by enacting policy that favors the majority of America and not just those at the top.