Download The Presidential-Economic Dance: Are “New” Economic Variables

The Presidential-Economic Dance:
Are “New” Economic Variables “In Rhythm” with Traditional Economic
Indicators and Presidential Approval?
Sara Margaret Gubala
University of South Carolina
[email protected]
Nathan Dietz
Research Associate
Department of Research and Policy Development
Corporation for National and Community Service
[email protected]
September 20, 2002
DRAFT
ABSTRACT: This paper examines the relationship between the state of the economy and the popular approval of
the president. Although inflation and unemployment should impact the population in the aggregate, it is unclear
exactly why individuals should attribute blame to the president when inflation and/or unemployment are high. We
hypothesize that while aggregate economic indicators are important determinants of presidential popularity,
variables that have a more direct link or “personal” link to individuals will have the most influence. Therefore,
individuals will condition their opinion of the president’s job performance primarily on these “personal” economic
variables. While expanding upon the work of other scholars, our paper provides a new assessment of presidential
approval from a macroeconomic perspective. In addition, our theory also suggests that fiscal variations and
financial changes (measured through loan rates) will have a greater individual impact on consumer spending and
thus will affect consumer opinions of the president’s job performance. We also consider non-economic variables
such as partisanship and temporal variables (such as rallies) that may help explain and predict the dynamics of
postwar presidential approval. We hypothesize that our improved measurement of macroeconomic performance,
will improve the performance of our model, and our appropriate treatment of the dynamics of presidential
popularity, will enable us to more accurately explain short-term and long-term trends in presidential approval.
Additionally, our improved model should provide insight into the basis of the long “boom” periods of widespread
presidential approval during the Eisenhower and Clinton administrations.
An earlier version of this paper was first presented at the Annual Meeting of the Midwest Political Science Association, Chicago, Illinois, April 25-28, 2002.
The authors would like to thank Joe BaFumi, Roger Carlsson, Michael Colaresci, Christina Corduneanu-Hucci, Tom Durkin, Susan Hammond, William
Jacoby, Susan Johnson, Bill Keech, Stephen Weatherford, and John Williams for helpful comments and advice on this paper. The authors especially thank
William Jacoby for his thoughtful comments, assistance with conversion of the data and last but not least, his advice and encouragement throughout the writing
of this paper. The paper could not have been completed without his help! The analyses presented within this paper are based on presidential approval data
compiled by George C. Edwards III and Alec M. Gallup (1990), and was supplemented by data archived in the Roper Polling Organization
1
(http://www.roperweb.ropercenter.uconn.edu). The economic indicator data was assembled from data archived at the Bureau of Labor and Statistics and
Economy.com (http://www.freelunch.com). The data were analyzed using E-VIEWS 4.0.
1
Introduction
The relationship or careful dance that occurs between the state of the economy and
popular approval of the president has interested scholars over time. Many empirical studies have
demonstrated that macroeconomic performance has an important effect on political support for
elected officials.1 Even though the president does not have complete control over economic
performance, the president, as leader of our nation, is ultimately held responsible for the nation’s
economic health. Declining economic conditions can lead to the demise of a president’s
leadership and at the very least can diminish his approval ratings, which can seriously damage a
presidents prestige and ultimately will affect a president’s power to govern. Presidential
approval (as a measure of political support) is an important political resource that influences
presidential power. Presidents gain or lose power due to changes within the economy and due to
events or shocks that impact the political system, which have a tendency to mobilize support
either for or against the president. The public must decide whether or not they approve of the
president based upon what the current circumstances are.
While economic conditions have been hypothesized to influence presidential approval,
exactly how the economy impacts presidential support has yet to be concluded. For instance, the
typical specification of a model of presidential approval includes inflation and unemployment as
the only measures of economic performance.2 While both inflation and unemployment are
hypothesized to affect presidential support, they have vastly different economic and political
implications. According to Kernell (1978) and Kenski (1977a), inflation causes only minimal
change in approval, while Monroe (1979, 1981) suggests that popularity responds more precisely
to present and lagged changes in inflation. Similarly, most models that include unemployment as
a predictor of approval have typically found that unemployment does influence electoral
outcomes and support for the president. However, Hibbs (1974) found that unemployment
exhibits neither a statistically significant, nor a theoretically important influence on presidential
approval. Trying to compare the impacts of these commonly used predictors, Lau and Sears
(1981) argue that inflation has a greater political impact than unemployment and that the tradeoff
1
See Monroe (1979) and (1984) and Paldam (1981).
Relevant studies that look at the influence that inflation and unemployment has on presidential popularity include:
Mueller (1970); Hibbs (1974); Stimson (1976); Kenski (1977abc); Frey and Schneider (1978); Kernell (1978);
Monroe (1978); Shapiro and Conforto (1980ab); Golden and Poterba (1980); Kenski (1980); Hibbs and Vasilatos
2
2
between the two indicators must be considered. As Norpoth and Yantek summarize: “A president
typically presents the public with an economy in which an improvement in one of those
macroeconomic conditions is brought at the expense of a deterioration of the other. How then
does the public balance the economic seesaw between the two conflicting goals of full
employment and stable prices?” (Norpoth and Yantek 1993, 803). While the economy does
influence presidential approval, conflicting results in the literature make it more difficult to
determine where the influence occurs.
One thing is certain: economic change does influence politics especially through
elections and evaluations of politicians. Contrary to Stimson (1976), who suggested that there are
not “significant economic influences on popularity,” numerous studies have shown that there is a
relationship between the economy and approval. However, methodological differences make if
more difficult to draw general conclusions about the nature of this relationship. For instance,
most empirical studies model presidential approval as a function of leading, lagged and current
economic variables, as well as items from public opinion surveys that tap citizen evaluations of
the economy. These studies are also theoretically divided on whether or not voters are interested
in national (sociotropic3) or personal (pocketbook) economic conditions, or whether they are
motivated primarily by retrospective or prospective concerns.4 The result is that although the
perceived state of the economy is clearly an important indicator of presidential approval, it is not
clear whether factors outside inflation and unemployment matter to the public. “Support for the
president fluctuates with changes in general economic conditions. A president skilled enough, or
fortunate enough, to preside over a healthy economy is rewarded with public popularity” (Kinder
1981)5.
Furthermore, although inflation and unemployment should impact the population in the
aggregate, it is unclear exactly why and when individuals should attribute blame to the president
as these economic indicators change over time. The underlying assumption of this “blame-game”
implies that the electorate perceives and understands economic trends and that the electorate is
(1981); Monroe (1981); Chappell (1983); Monroe and Levi (1983); Norpoth and Yantek (1983); Monroe (1984);
Chappell and Keech (1985ab); Ostrom and Simon (1985); and Michales (1986).
3
See Kinder and Kiewiet 1979, 1981; Feldman 1982; Weatherford 1983
4
See MacKuen, Erikson and Stimson (1992) for the stark conclusion that citizens appear to use only prospective
evaluations, and Clarke and Stewart (1994) for a re-analysis that shows that other variables also affect popularity.
5
Despite the fact that Kinder (1981) finds support for the idea that a healthy economy leads to rewards in terms of
approval, Mueller finds quite the opposite result. Presidents are only punished when the economy slumps and are not
credited when the economy improves.
3
able to factor this economic information into their assessment of the president. This assumption
is rather naïve because it implies that there is homogeneity in information acquisition within the
electorate.6 People differ in both the amount and type of economic information that they have
and these differences contribute to variation in economic judgments. How people evaluate
economic performance does differ but we can assume that the evaluations are often based upon
similar criteria. The conditions under which an individual might approve of the president may be
based upon personal conditions, both satisfactions and dissatisfactions. “If one is sufficiently
pleased or displeased with a situation in which one finds oneself, if one’s pleasure or displeasure
is sufficiently salient, and if one sees an outside agent as having contributed to that pleasure or
displeasure, then feelings about one’s personal situation may be carried over to the outside
agent” (Sigelman and Tsai 1981, 372).
Model Specification
In our model, this agent is presumed to be the president and support for the president
should rise and fall with changes in the economy. However, we improve on the simple
specifications of earlier models of approval in several ways. First, we explore the possibility that
citizens respond to other indicators of economic activity besides unemployment and inflation.
By adding variables that measure changes in the macro economy, such as short-term and longterm consumption indicators, productivity, and monetary policy, we explore the possibility that
citizens are more attuned to less familiar changes in the national economy than earlier models
have presumed. Second, we unpack the commonly used summary measure of inflation to see
what types of price increases exert the most impact on presidential approval. Finally, we allow
for the possibility that the inflation and unemployment variables do not exert a constant impact
on the popularity of every president, but rather that the popularity of Democratic presidents may
depend more on unemployment than on inflation, for instance, while Republican presidents may
worry more about inflation. We estimate this improved model using a technique first used by
Hibbs (1974) to control for serial correlation in the unmeasured “shocks” to the president’s
approval rating. As we argue below, this model is appropriate even though more sophisticated
6
The naïve assumption is important to consider but is really not a problem for our model. The real problem is that
we do not know why these economic indicators tend to affect presidential approval. The conflicting results found in
previous studies do not help understand exactly why citizens respond the way that they do to changes in inflation
and unemployment.
4
models that assume a more complicated dynamic structure to the approval time series have come
into vogue in political science in the last decade7.
The “Personal” Link
Economic conditions have consistently been found to be related to presidential
popularity, especially when the public evaluates the performance of the national economy.
“Economics is the fate of politicians. It is an article of faith that success or failure of
governments in dealing with the economy decides whether or not they survive politically”
(Norpoth 1984, 253). Within the literature, there are ongoing debates over the extent to which
macroeconomic indicators influence presidential approval and vote choice. This literature
assumes that people evaluate presidents based upon their expectations about what the president
should do and what he should be like. The president should embody the spirit of the nation and
be a symbol for all people. Symbolically, the president is held responsible for all of the problems
and conflicts within our nation. Poor economic conditions, as exhibited through periods of high
inflation and unemployment, indicate that the president is not fulfilling his duty to maintain
prosperity and consequently should have disparate effects on presidential approval (Brace and
Hinckley 1991).
Previous studies have concluded that national economic perceptions affect the
vote/approval of the president more than personal economic circumstances (see Kinder and
Kiewiet 1979; Sears et al, 1980). Weatherford (1983b) suggests one reason for this: that
personal economic circumstances are somewhat removed from the overall economic policy
judgment. “Declines in personal financial condition do not automatically acquire political
meaning. Even if the individual suffers an income decline, he might remain generally satisfied
with his standard of living and income relative to that of others similarly situated” (Weatherford
1983a). In the aggregate, citizens appear to be sensitive to the possibility that their own
7
We tested one of these “sophisticated” models found within the approval literature. Williams (1990) uses a vector
auto-regressive (VAR) model, where the lagged values of monetary factors, interest rate, unemployment, CPI,
federal expenditures and GNP are all included in the approval equation and similar equations are estimated where
the dependent variable is each of the economic series. Williams’ equation (using our data): Approval = β1 + β2
(GDP) + β3 (CPI) + β4 (Unemployment) + β5 (bank prime rate) + β6 (currency), AR(1) model. We tested for
redundant variables and differenced all of the variables and re-tested again for redundant variables. The results show
that the AR(1) specification explains everything until the political factors are controlled for. By not controlling for
autocorrelation, the model is jeopardized and it is untrustworthy. Our model and philosophy are completely different
from Williams so it is not surprising that our conclusions are significantly different. Additionally, Ostrom and Smith
argue that Williams does not take the possible cointegrating relationships into account and that his results are
suspect until someone does that.
5
individual economic circumstance might not be generalizable to the nation as a whole, even
though an individual unemployed worker might erroneously judge the nation’s unemployment
rate to be rising or a housewife strapped for grocery money might mistakenly infer that inflation
is going up. Most empirical findings imply that blame for economic troubles is based on a
concern for national economic problems rather than personal experiences.
However, the use of individual-level survey data to test these hypotheses makes it
difficult to understand what citizens consider when evaluating the economy. Feldman (1982)
asserts that attribution of blame for personal economic circumstances depends upon where
responsibility is assigned for political problems. Thus, unless there is a perception of
governmental responsibility for economic well being then the public may not attribute blame to
the president. While studies that find support for the sociotropic voting hypothesis may be
correct, the question of what types of aggregate economic variables drive citizen evaluations
remains open. We hypothesize that in the aggregate, the blame that citizens assign to the
president will stem from measures of macroeconomic variables that have personal consequences.
These include, but are not limited to, commonly used measures of economic health such as
inflation and unemployment, variables whose use in past models has shed little light on the
reasons why they are significantly associated with presidential approval. We believe that
aggregate measures of personal economic conditions, such as the price of clothing and food and
other necessities, will be most closely linked to presidential approval, since they are most closely
linked to the “everyday life” of citizens. Modeling approval as a function of aggregate changes
in the economy is sensible, but we must consider aggregate measures that are presumed to be
most closely linked to individual attitudes and beliefs.
In this study, we approach the question of whether personal economic variables matter
from a different perspective. Generally speaking, we define “personal” economic variables to be
those which individuals have the most exposure to, those that are the most salient and are often
those which affect individual pocketbooks. The idea that people act on the basis of changes in
their personal well-being has been a widespread conclusion since Downs’s seminal work (1957).
Tufte’s (1978) evidence also suggests that not only do people act on the basis of personal
conditions but that politicians also believe that people vote their pocketbooks. Presumably, the
idea that people vote their pocketbooks can be extended to approval, such that individuals who
approve of the president do so with their pocketbooks in mind. From a theoretical perspective,
6
this argument is substantiated by the idea that individual evaluations of the performance of the
government/ president should depend upon the economic context in which they live (Palmer &
Whitten 1999). Individuals should condition their opinion of the president’s job performance
primarily as a result of “personal” economic variables that may specify individual memory
patterns through which the economy influences presidential approval. These memory patterns are
thought to be influenced by experience and knowledge. “People’s knowledge of how they are
being affected by economic conditions is acquired on the basis of daily experiences: buying
food, clothing, and other goods; keeping track of budgets and savings; shopping for a new house,
and so forth “(Feldman 1984, also see Popkin et al., 1976). We argue that these “individual”
indicators will have a greater effect on an individual’s pocketbook immediately than will broadly
defined economic indicators such as unemployment and the inflation rate, which represent much
more extensive and less salient concepts that are harder to define and recognize.
There is some evidence within the literature supporting our initial hypothesis that
“personal” economic indicators influences public opinion8. Changes in prices, wage rates and
hours worked, which are all components of short-run changes in real income, influence voting
behavior (and possibly public opinions). For example, a brief increase in electronic prices may
not be as noticeable as long-run changes in food costs, which increase the cost of living and the
flow of money out of Americans wallets each day. While the public may be responsive to
changes in inflation, Conover, Feldman and Knight (1986) argue that the public is more
responsive to changes in unemployment than they are to changes in inflation. This is simply due
to the salience of the indicator in the media and among family and friends (Hibbs 1979).
Although unemployment is understood by most Americans to be something that is bad, the
measure is imperfect because it includes people who have just left the job force and those who
have been out of a job for longer periods of time. While it may be true that inflation is less
salient than unemployment9, inflation has a far greater meaning than is often conceptualized by
most people. According to Nordhaus (1975), inflation can have a domino effect because inflation
may lead to balance of payments problems, inefficient resource allocation, and an arbitrary
redistribution of income. Individuals in society do not typically notice these effects but the
8
See Campbell et. al (1960) suggests that there are political consequences to economic changes that effect personal
circumstances, the result is that there is increased criticism of the administration.
7
outcomes of these effects can have a direct effect on individual circumstances. As a result, the
component factors that go into the determination of inflation may paint a better picture of
national conditions than the aggregated measure. This is hypothesized to be the case because the
public is most likely to respond to their own personal economic situations. “The sorts of
economic conditions and events the individual knows and understands best are those closest to
home. There is no question that citizens have realistic, firmly grounded impressions of their own
personal-financial condition, and surely we are justified in having faith in their ability to sacrifice
in maintaining or improving it” (Weatherford, 1983a).
Conover, Feldman and Knight (1986) provide some evidence for our theoretical
argument by stating that inflation is an ambiguous measure. Most people do not understand the
nature of inflation (Peretz 1983). According to Keech (1995), “inflation is a general increase in
the money prices of goods and services and a decline in the purchasing power of currency”
(114). The consumer price index (CPI) is the most common measure of inflation. CPI measures
the retail price of a fixed “basket” of goods and services typically purchased by households.
“CPI is an explicit price index in the sense that it directly measures the movements in the
weighted average of the prices of the goods and services in the market basket through time”
(Froyen 1999, 25). Per Capita CPI represents another major index of consumer prices. Slightly
different goods go into this measure which weights the current cost relative to the costs in the
reference period
The problem with inflation is that it is often misrepresented because it is often thought to
mean higher prices rather than increasing prices. This misunderstanding of inflation makes the
inclusion of this variable within a model of explaining presidential approval somewhat
problematic because most people are making assessments of inflation based upon experience
with items that are not reflected in the government’s package of indicators for inflation. The
inherent misunderstanding surrounding inflation is troublesome because many people do not see
the link between increasing real incomes and increasing prices. Increased wages are symbols of
hard work not hard times for most people but the reality is that these two indicators are tied
together.
9
Unemployment is included within the model because it is an important indicator that is most often used to measure
presidential economic performance. The unemployment rate represents the percentage of workers in the labor force
(those that are working and seeking work) who are unemployed.
8
By unpacking the traditional CPI measure and using its components – food, education,
medical, housing, etc. – as predictors of presidential popularity, our study provides a test of this
hypothesis. For example, one hypothesis would suggest that increased housing costs will affect
individuals more than national unemployment because most individuals would “feel” the effects
of these changes within their own pocketbooks, whereas aggregate unemployment changes may
or may not affect individual levels of consumption and expenditure.
Housing starts10 are also included as a personal economic variable that are presumed have
an impact on presidential approval. This variable is a measure of consumer confidence, which
should provide important cues about the economy. The number of housing starts may be an
indication that the economy is good and that the general public is relatively satisfied with the
current conditions such that the number of loans or proposals to build homes increases. Housing
starts, as a measure of consumer confidence, should be related to presidential approval. As
housing starts increase, approval for the president should also increase.
The Fiscal/Monetary Link – “Real Dollar Value of Approval”
While expanding upon the work of other scholars, our paper provides a new assessment
of presidential approval from a macroeconomic perspective. Unlike Feldman’s (1984) work,
which measures the change in financial well-being through a survey question, we operationalize
the fiscal/monetary link through financial variables. We argue that variables measuring fiscal or
monetary change may trigger aggregate changes in presidential approval because of the impact
that they have on American’s pocketbooks. As stated previously, individuals should be
influenced by variables that capture their individual spending and financial situations11.
Variations in fiscal policy, measured through the bank prime rate, will affect consumer spending
and thus will affect consumer opinions of the president’s job performance. Bank prime rates are
the percent of interest charged by major banks to their largest customers. It also can be seen as a
10
This indicator reports the number of housing units on which construction has started. This indicator implies
consumer confidence that the economy will be strong, that money will be available for construction financing and
housing purchases.
11
Monetary variables, such as currency in circulation, may contribute to economic change. For example, if people
are holding onto more money, meaning here that the flow of currency is decreasing within the economic system, we
might assume that individuals are either worried about the economic situation (if they are thinking about the future)
or they could be responding to current conditions. Money supply, overall, is an important indicator for the economy
because changes in this supply influence interest rates, which can in turn stimulate housing purchases and
investments in capital goods. .Models that included a measure of currency and replaced Per Capita CPI with GDP
had no more explanatory power than models that excluded this variable. Thus, we excluded the currency measure.
9
measure of perceived risk in the economy. Interest rates are presumed to be influential because
when interest rates are lower, people are more likely to spend money, which in turn stimulates
the economy. Although general economic well-being is considered to be the determining factor
for predicting whether or not expenditures will occur, interest rates are also a measure of
economic health. Bank prime rates should be correlated with presidential approval. As prime
rates increase, presidential approval should decrease due to the burden that increasing interest
rates have on the population at large.
General Measures of Economic Well-Being
We also consider variables that are often characterized as general/ typical indicators of
economic health or well-being. In this category, we include Gross Domestic Product (GDP) as a
measure of the nation’s economic situation, unemployment as a measure of economic health, and
growth of the stock market as measured through the Dow Jones Industrials 30 industries average.
The Dow Jones Industrials Average is the most reported stock index whose values have
been compiled daily since the late nineteenth century. The stock market is an important indicator
for economic health. ). Volatility within the stock market, while influencing personal economic
circumstances, also plays a role in the stability and health of the national economy. “The public,
through the stock market, registers its optimism or pessimism about the future performance of
companies through the quantities of stock it supplies and demands” (Carroll 1995, 120).
Additionally, the stock index measures “expected profitability since markets price stocks on
profit potential rather than solely on past earnings” (Rogers 1994, 248). We include an
independent variable capturing the stock prices because the stock market tends to discount in
advance the ups and downs in the economy (Lewis 1962, 49). Stock market values tend to be
salient within the media and the general public. As a result, it seems probable that that
significance of the indicator will influence presidential approval because as stock prices increase,
this is symbolic that the economy is improving and is expanding, which will have a positive
effect on approval for the president. It is presumed that the president may be “rewarded” for
positive changes within the economy and the stock market price increases are presumed to have
positive effect on the economy.
GDP is included within this model because it is a measure of all final goods and services
in the economy. It is important to consider because approximately 70% of GDP represents
10
consumption and consumer expenditures (Froyen 1999). GDP is the measure of all currently
produced final goods and services evaluated at market prices (Froyen 1999, 14).
GDP accounts for all goods and services produces in the United States in a given year and is
probably the most reported economic statistic (Carroll 1995, 39). GDP is the best-known
standard of living indicator and to truly understand presidential economic performance GDP
really needs to be considered. GDP may be seen by some as the “bottom line” as far as
economic performance is concerned, “the components of GDP and other driving economic
indicators (e.g., employment rate, productivity) that are the building blocks of economic growth
and more closely measure the impact of economic policies” (Carroll 39).
GDP has two components, consumption and investment. For our purposes, consumption
is the most important because it describes the component of GDP that is explained by the
household sector’s purchases of goods and services (that are currently produced). Consumption
is really durable and non-durable goods and consumer services and this accounts for
approximately 70% of GDP. Therefore, the fact that GDP has a direct link to the consumer
makes this variable a likely candidate for inclusion in our model (however though we must
consider the fact that GDP is highly sensitive to changes in the average price level and thus
probably correlated with CPI). Finally, GDP is a standard measure used to assess the economy
and consequently it may have a direct influence on approval of the president. We expect that
increases in GDP to be positively associated with the aggregate support for the president because
an increase in GDP symbolizes that the economy is growing, which should have a positive
impact on support for the president.
Other studies have used the misery index, M, as a measure of extremely harsh economic
times:
M=U+π
U = unemployment and π = inflation. However, according to Keech (1995), “the fact that the
misery index is an weighted sum of the rates of inflation and unemployment [it] is an arbitrary
simplification.” It seems that Democrats should value unemployment differently than
Republicans such that 1 additional point of unemployment may be twice as painful as one point
of inflation.
In order to capture this relationship, we could weight the variables in the misery index so
that Democrats are more susceptible to unemployment and Republicans are more susceptible to
11
inflation. One potential bias in doing so is that we do not know the extent of the bias and by
arbitrarily placing a weight on this value, we may be misrepresenting the true but unknown
underlying relationship between the economic variables. Thus, we test the hypothesis that
Democratic and Republican presidents are affected differently by inflation and unemployment by
including two interaction terms: one each to capture the effects of inflation and unemployment
during a Democratic administration. The complete effect of a one-point increase in inflation
and/or unemployment for a Democrat is thus the sum of the coefficient of the interaction term
and the coefficient of the relevant indicator. We expect that inflation and unemployment will
both be negative and significant in the equation, but that the interaction term for unemployment
under Democrats will be negative, while the interaction term for inflation under Republicans will
be positive. Finally, to capture the interactive effects of truly miserable economic circumstances,
when inflation and unemployment are both high, we include a third interaction term, “misery”,
which is simply the product of inflation and unemployment, which we also expect to be negative.
Thus, the full impact of inflation (for instance) under Democrats will be the sum of the
coefficient for inflation, plus the interaction term, plus the product of the “misery” coefficient
and the current unemployment rate.
Exogenous Links
Finally, we also consider salient non-economic variables such as rallies and partisanship
that may help explain and predict the dynamics of postwar presidential approval. Economic
conditions are not the sole determinant of presidential approval variations. However, “people
deal with economics every day, and the economy has always been a concern in national politics,
while events and issues, those that bring ‘good news’ and ‘bad news’, may be ephemeral or
depend on particular actions or conditions by public officials. Consequently, the economy may
determine the ‘baseline’ for explaining popular evaluation of presidential performance and
certain election outcomes” (Shapiro and Conforto 1980, 64). While the economy provides
“baseline” approval or disapproval of the president, other factors such as rallies also play an
important role in influencing approval.
John Mueller (1970) defined a rally point as an incident that is international; involves the
United States, and particularly the President, directly; and is specific, dramatic, and sharply
focused. We use an expanded definition of rally events, which also includes significant domestic
events as well as presidential honeymoons. Rallies may influence the approval rating of the
12
president through shocks, which reverberate through the political system and temporarily affect
the approval dynamics. Like most shocks, rapid approval increases typically correspond to
adjoining decreases in approval as a sense of normalcy is restored in the political system. We
control for rally effects with a dummy variable, RALLY12, where 1 = rally occurred during the
month and 0 = no rally occurred. We use this dummy variable to construct a variable that
represents the impact of the rally on presidential approval by estimating the number of months
since a rally has occurred. The rally variable is important for our analysis because while an
increase in unemployment or an increase in prices may cause a president’s approval ratings to
decline, these fluctuations may be related to other factors such as international crises that also
may influence presidential approval. As the number of months between a rally event increases,
we expect the impact of a new rally event to be greater than say if a rally occurred in January and
then another occurred in March of the same year.
Partisanship is thought to be an important determinant of policy outcomes. Scholars such
as Alesina and Hibbs have argued that “partisanship is the most fundamental basis for political
influence over macroeconomic policy and outcomes” (Keech 66). Hence, partisan strategies are
often contingent on economic conditions. Because partisanship may influence the type of
policies passed during a president’s administration, it may be necessary to include a measure of
partisan ties in the House and Senate that could help us explain and predict why and when
policies that could cause long run shifts in the natural rate of unemployment or the natural rate of
consumption will be proposed. Divided government should affect policy outcomes because of
the competitiveness that exists between the two political parties, which may lead incumbents to
seek policies with short-run advantages at the cost of long-run problems. Additionally, partisan
differences stem from the dissimilar outlooks that each has toward the economy – social class,
prosperity, distributional issues, etc.
Instead of simply measuring the president’s party, we include a dummy variable for each
presidential administration except the Kennedy administration (the reference category). We
hypothesize (as do others such as Golden & Poterba) that popularity is a function of the
12
The RALLY variable data from 1972-2000 was obtained by coding rally events from the Gallup Opinion Index
chronological list of dates. According to Gallup “this chronology is provided to enable the reader to relate poll
results to specific events, that may have influenced public opinion” (See any addition of the Gallup Opinion Index).
Rally events prior to November 1971 were coded from various other sources and events after January 1993 events
coded using Gallup’s list of chronological events influencing public opinion during the year. The decision as to
13
individual characteristics of each president, the non- economic events that occur during the
presidency as well as the economic events. Beyond these individual-specific administration
effects, the partisanship of the president may also influence approval through the interaction
between economic variables such as unemployment and inflation. This is hypothesized to be true
because of the partisan theory of economics, which suggests that Democrats/Liberals are more
susceptible to unemployment than are Republicans who are more susceptible to inflation.
However, the tradeoff between unemployment and inflation is difficult to explain. On one hand,
the costs of unemployment are so great in terms of loss of output and lower incomes, but on the
other hand, inflation costs are just as troubling in terms of the effect that inflation has on the
economic system.
Models of presidential support should include both economic and political factors in
order to avoid misspecification (Frey and Schneider 1978). According to Kenski (1977), those
approval models that fail to include noneconomic variables may artificially inflate the salience of
economic factors. However, it may also be the case that these non-economic variables act as
suppressors attenuating the effect of economic conditions on presidential approval (Shapiro and
Conforto 1980).
The Dynamics of Presidential Approval: Modeling Issues
A. Unit roots, Cointegration, and Error-correction models
There has been significant evidence that the economic conditions contribute to government
popularity and electoral choice (see Arcelus and Meltzer 1975; Bloom and Price 1975; Goodman
and Kramer 1975; Kenski 1977abc; Frey and Schneider 1978; Kernell 1978; Hibbing and Alford
1981). In their survey of the time-series models of presidential approval, Ostrom and Smith
(1994, Table 1, 135-139) show that the most of these studies have relied upon standard
assumptions about the lasting effects of economic change, political events, and unmeasured
“shocks” to presidential popularity. As in these studies, the dependent variable in our analysis is
which rally events were to be included is rather arbitrary, which runs the risk of biasing the estimates for the
economic variables.
14
presidential approval13: the percentage of those surveyed who approve of the job that the
president is doing averaged within monthly responses to the Gallup poll question, “Do you
approve of the way in which Mr.________ is handling his job as president?”.
Ostrom and Smith’s survey indicates the substantive conclusions drawn by these studies
depend critically on the independent variables included in the model. Earlier studies closely
follow Mueller’s (1970) specification, relying on a now-canonical list of independent variables:
unemployment, inflation, an indicator for the cumulative effects of war, indicators for rally
events, and a variable that measures the deterministic trend that Mueller observed for every
president except Eisenhower: as time goes by, controlling for all other factors, the president
experiences a secular decline in his popularity. Mueller argues that this trend reflects the
cumulative effects of the “coalition of minorities” that forms during every president’s term: the
unmeasured reasons why an ever-growing segment of the population disapproves of the
president’s job performance.
Mueller’s treatment of these unmeasured sources of discontent, along with various other
unmeasured determinants of presidential approval, has driven many of the recent approval
models (Hibbs 1974; MacKuen, Erikson and Stimson 1992; Beck 1991; Clarke and Stewart
1994; Ostrom and Smith 1994; etc.). Several of these models hypothesize, and confirm, that
changes in economic indicators like inflation and unemployment have both immediate and
lagged effects on the president’s popularity. Modeling these lagged effects of these variables is
straightforward if the effects of economic change are assumed to wear out after a specific
number of months. In contrast, Monroe (1981) argues that these changes can have a persistent
impact on approval well into the future, with the effect of a change eventually approaching
insignificance many months later. Her (Almon) distributed-lag model relies on the assumption
that every variable has effects that can be immediate and/or long-lasting, theoretically taking a
very long time to diminish.
The most sophisticated approval models (Ostrom and Smith 1994; Clarke and Stewart
1994) pay particular attention to the long-term effects of the unmeasured variables in the
statistical model, which are contained in the error term. Drawing on econometric time-series
13
The presidential approval data was obtained from the Edwards and Gallup (1992), Roper Center Presidential
Archive at http://www.roperweb.ropercenter.uconn.edu and from Gallup Polls published in the Gallup Monthly Index.
Approval data points that were missing (all except those from the first month of a presidents term) were linearly
15
studies of macroeconomic change, they begin by testing to see whether the dependent variable is
stationary, or whether it is integrated (whether it has a unit root, in other words).14 The
distinction is not innocuous: in a time series that has a unit root, the effects of a “shock” never
diminish, causing unpredictable aggregate behavior as the cumulative effects of past shocks keep
reverberating forever at full strength. If true, the presence of a unit root would seriously hamper
inference about the effects of changes in the individual economic variables measured in the
model. In the long run, the variance of a variable with a unit root will “explode,” theoretically
becoming infinitely large. Thus, it is imperative that time-series models of presidential approval
accurately determine the degree to which shocks to the dependent variable persist over time. If
approval has a unit root, inferences drawn from t-t-statistics and F-tests are potentially spurious,
especially when the independent variables appear to share a common trend (Maddala and Kim
1998, 28-29). Even if approval is stationary, modeling its dynamic properties is crucial, since a
failure to account for autocorrelation in the disturbances biases t-statistics and goodness-of-fit
measures by inflating them.
However, the presence of economic indicators, many of which have been demonstrated to
have unit roots, may actually alleviate the problems caused by the unit root in the approval time
series, if one actually exists. If two variables are cointegrated, they each have a unit root, but the
relationship between them is not spurious: they “travel together” over time in an established
equilibrium relationship, and an OLS regression will give valid estimates of the causal impact of
one variable on the other. The key to estimating a relationship between two cointegrated
variables is to account for the reequilibration mechanism that keeps the two variables moving
together through time. This is the motivation behind the “error correction” model (ECM)
estimated by several scholars (Beck 1991; Ostrom and Smith 1994; Clarke and Stewart 1994;
Durr 1994; Zorn and Caldeira 2000).
The intuition behind the ECM should be appealing to those who study the links between
citizen attitudes, governmental actions, and economic changes. In an ECM, there is a
interpolated to fill in the gaps. The missing data points from the first month of a president’s term were acquired
through backward extrapolation of the data.
14
The term “unit root” refers to the solution to the dynamic equation for a variable like yt, where yt = ρyt-1 + εt, some
random disturbance. In the event that ρ=1, the value of the dependent variable y at time t is a function of the last
realized value (yt-1) plus some normally distributed disturbance with mean zero (εt). A random shock εt felt at time t
will never dissipate over time, whereas in a stationary series, when |ρ| < 1, the time series is mean-reverting, since
the effects of εt eventually wear off.
16
mechanism that determines the “correct” level of approval for a given set of economic
circumstances:
Implicitly, we are assuming that for every “mix” of observed outcomes, there is a
corresponding level of approval […]. The level of approval associated with a given
distribution of environmental outcomes reflects rewards and punishments levied on the
president by the public in response to deviations of observed from institutionally
expected outcomes. (Ostrom and Smith 1994, 131)
The question ECM’s can answer is how quickly the public reacts to these deviations by
establishing a new set of expectations and rewarding or punishing the president as the situation
warrants. It should be noted that theory can guide our expectations about why such a long-term
equilibrium pattern might exist, but that cointegration is purely a statistical concept, and
cointegrating relationships between variables in a statistical model can be shown to exist whether
or not they have any theoretical interpretation. The most common ECMs that have been
estimated start from the assumption that the dependent variable in question is presidential
approval, and that economic variables may or may not be in a long-term equilibrium relationship
that changes over time, but that there is no “feedback” so that, say, the economy responds to
unexpected changes in presidential approval by increasing or decreasing unemployment (Beck
1994).15
B. Is there a unit root in presidential approval?
As appealing as the ECM is for theoretical reasons, there is no need to estimate it if the
approval time series is stationary – that is, if it does not have a unit root. The most common tests
for unit roots – the augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test – share
a common flaw: that the null hypothesis is that the series has a unit root. Critical values for both
these tests must be specially generated since the test statistics do not have standard distributions
(Maddala and Kim, Chapter 3) but nevertheless, the power of each test can be limited by small
sample sizes, and the statistical burden is to demonstrate that the time series is stationary – that
15
Beck argues that such a feedback mechanism is theoretically implausible, but Williams (1990) finds support for
the idea of a political business cycle by demonstrating that changes in approval tend to predict changes in
macroeconomic policy. Testing for several cointegrating relationships among, for instance, various interest rates
makes sense if the analyst has no reason to suspect that one rate is certainly the cause of reequilibration in another
rate. However, a true test of a cointegrating relationship between macroeconomic indicators and presidential
17
is, that it is relatively well-behaved in the long run. Clarke and Stewart (1994) also estimate an
ECM after using the ADF test as a pretest for the existence of a unit root, but this model may be
inappropriate if approval is in fact stationary, and the presumption of the test is that a unit root
exists. Ostrom and Smith use the ADF test along with another test, the KPSS test, in which the
null hypothesis is stationarity, but cannot confidently conclude that, for the Reagan
administration, the approval series has a unit root. However, as Beck (1994) points out, the
small sample size in Ostrom and Smith’s model may be driving this conclusion; considering the
longer series starting with the Eisenhower administration, his ADF tests reject the null
hypothesis that there is a unit root in the approval time series. He concludes that the use of an
ECM when the dependent variable is “near-integrated” may be appropriate, but that the issue is
unresolved (1994, 24).
More recent work suggests plausible reasons why the approval time series should be
stationary and why tests for a unit root in presidential approval may be biased toward the
conclusion that a unit root exists. Alvarez and Katz (2000) present two arguments for why a unit
root is not present in presidential approval. One is technical: that since the approval variable, as
usually measured, is bounded by 0 and 1 below and above, it is impossible for the variance to
“explode” as an integrated time series must do eventually. They argue that if the time series has
a unit root, we should expect to see approval hit either the lower or upper boundary and stay
there for an appreciable period of time. The criticism is similar to that posed by John Cochrane
(quoted in Maddala and Kim, 127), who wondered: “Interest rates are the same now as in
Babylonian days. How can there be a unit root in interest rates?” One general answer is that the
variance of a time series with a unit root will eventually “explode,” but that its failure to do so in
a finite time frame is not in and of itself evidence that the variable is stationary. Another is given
by Ostrom and Smith, who argue that the “real” approval variable is latent, since citizens are
constrained by the possible survey responses of yes/no/don’t know. Thus, the aggregate series,
in which the observed proportion of supporters is measured, may have a unit root if the latent
approval variable does, since the measured approval variable is a nonlinear transformation of the
approval – which has not been attempted, as Ostrom and Smith (1994) point out in their critique of Williams’ study
(1994, 171-174) – should probably remain agnostic about the direction of causality, as Williams does.
18
“real” one. However, in practice, a variable that fails to exhibit the characteristics of a unit root
makes one wonder about the potential harms that may result from treating it as stationary.16
The other criticism Alvarez and Katz make is that there is no plausible reason why a
“shock” to the president’s approval rating should persist forever. Beck (1994) and Durr (1994)
agree that shocks may die off slowly, but that, for instance, a shock that affected Franklin
Roosevelt’s popularity in 1933 would probably not exert the same undiminished impact in 1944.
Alvarez and Katz make the even more sensible argument that when a new president is elected,
his popularity cannot be affected by the unmeasured shocks experienced while his predecessor
held office. While exceptional cases suggest that this may not always be true – Ford’s popularity
was certainly predicated on the notion that he was not Richard Nixon, while the same may have
been true of George W. Bush and Bill Clinton – the relationship between the public and the
president changes fundamentally when a new president is elected.
Recent advances in the study of time series that undergo such structural breaks suggest
that standard tests for unit roots are biased in favor of concluding that unit roots exist. Building
from the seminal paper of Perron (1989), recent econometric work suggests that the effects of
outliers in the time series can distort tests for unit roots, frequently but not always causing
behavior that may be mistaken for evidence of nonstationarity. In particular, outliers that reflect
level changes, as opposed to one-time extreme values that reflect unusual circumstances, cause
tests for unit roots to be biased in favor of nonstationarity. Working from another modeling
tradition, Wood (2000) shows that the effects of lagged approval are not constant over the course
of modern history, which makes sense, and when we allow its impact to vary over time, behavior
that is consistent with the existence of a unit root is very rarely observed. This conclusion is
consistent with the actual graph of the approval time series from 1948 to 2002, as displayed in
Figure 1.
[Figure 1 about here]
16
The proliferation of unit root tests, and the various complaints against them, almost argue for a simpler approach
to time series analysis. Before the advent of Dickey-Fuller tests, the Box-Jenkins (or ARIMA) approach simply
looked at the correlograms that indicated the correlation between present and lagged values of a variable for various
lag lengths. If these correlations were significant over many lags, the variable was treated as integrated of order 1,
or I(1) – that is, as if it had a unit root – and differenced until it became stationary, i.e., until the correlogram
19
Unit root tests look for sharp changes in the level of popularity, as was observed when the thenunpopular Harry S Truman left office and national hero Dwight Eisenhower was inaugurated, as
evidence of the instability that characterizes nonstationary processes.
Nevertheless, we begin our analysis with a pretest for a unit root in the presidential
approval time series by applying three versions each of the augmented Dickey-Fuller (ADF) and
Phillips-Perron (PP) tests to the approval time series; the results are summarized in Table A (in
the Appendix).
[Table A about here]
Except when the assumption is that approval is a pure random walk – that is, that the only
change in approval from month to month is caused by a random shock – both tests allow us to
reject the hypothesis of a unit root at p = 0.01. This result, obtained in spite of the objections that
a unit root probably does not exist and the biases in the tests against a finding of stationarity,
leaves us confident that our analysis can proceed under the assumption that the dependent
variable is stationary – that is, that “shocks” to the president’s popularity eventually die off,
however slowly. The presence of autocorrelation in the time series, however, must still be
accounted for, since our OLS estimates will be biased toward statistical significance if we fail to
do so. Still, this finding simplifies our analysis considerably, and yet leaves open the question of
the long-term equilibrium relationship between approval and macroeconomic variables. If the
approval series is stationary but “long-memoried,” as Beck (1994) argues and Box-Steffensmeier
and Smith (1998) later confirm statistically, an error correction model may still be useful, but our
decision not to estimate an ECM is justifiable. Thus, we leave that question to future research.
Measurement Issues
By modeling economic change with real economic indicators, rather than citizen
evaluations of economic performance, past, present and/or future, we are able to explain why the
now-traditional survey-research indicators of citizen economic evaluations behave as they do.
Our independent variables were collected monthly, except for GDP, per capita CPI, CPI and
reflected insignificant correlations. We choose not to do so, choosing instead to develop a model of presidential
approval in levels and controlling for autocorrelation conventionally.
20
medical, which were all quarterly measures that were linearly interpolated in SAS to coordinate
this data with the approval data17. Other scholars have conducted similar analyses using
different time spans. Stigler (1973) modeled economic changes on a two-year cycle, while
Kramer (1971) relied on yearly fluctuations, and Shapiro and Conforto (1980) structured
economic changes in terms of quarterly figures. There does not appear to be a right or wrong
way to measure these economic indicators but it must be stated that conflicting results may occur
through the different conceptualizations of economic time. The economic variables used within
this analysis are: aggregate rate of unemployment; Consumer Price Index for All Urban
Consumers; Interest Rate; Dow Jones Industrial Average for 30 leading industries; Housing
Starts; Component Measures of CPI: Medical Care; Food Expenditures; Transportation; Apparel;
Fuels and Utilities; Gas and Electric; and Gross Domestic Product .
Economic conditions are not likely to be the sole source of deviations in approval, other
factors are also important. However, stating that, we must be careful to state that a distinction
can be made between economic indicators as a cause for fluctuations in approval and other
factors that influence presidential approval. Economic conditions provide the initial assessment
of the job performance of the president and the other variables should simply add to the
explanatory power of the model. Shapiro and Conforto (1980) have found evidence that
“changes in unemployment and the rate of inflation are components of a baseline model applied
to the assessment of presidential performance” (64). This assumes, however, that the public in
the aggregate can conceptualize employment and inflation and that the variations within these
indicators affect the assessment of the president. The following two equations represent the
models within our analysis:
Approval 1 = Unemployment + CPI + (Democrat indicator*UE) + (Democrat indicator *CPI) + (misery) + GDP +
Housing Starts + Bank Prime Rate + Dow Jones Average + Presidential Dummies + Rally Change
Approval 2 = Unemployment + CPI Component Measures (Apparel +Food +Gas and Electric + Medical
+Transportation) + (Democrat indicator*UE) + (Democrat indicator *CPI) + (misery) + GDP + Housing Starts +
Bank Prime Rate + Dow Jones Average + Presidential Dummies + Rally Change
17
We converted the quarterly data to monthly data because most of the economic data as well as all of the approval
data is released monthly which gave us additional data points to look at for each year.
21
Thus, Model 1 refers to the model with CPI included as the measure of inflation, while Model 2
disaggregates CPI into its constituent elements.
Hypotheses
We hypothesize that more sophisticated and more “personal” indicators of
macroeconomic performance, especially the disaggregated CPI measures used in Model 2, will
improve the performance of our model, enabling us to more accurately explain short-term and
long-term trends in presidential approval. The inclusion of the personal economic variables into
the model should provide insight into the basis of the long “boom” periods of widespread
presidential approval during the Eisenhower and Clinton administrations.
We expect the following relationships to be present for our independent variables on our
dependent variable, presidential approval. Table B (in the Appendix) contains descriptive
statistics for our independent variables.
Independent Variables
Independent Variable Direction
Approval
Unemployment Rate
Increase
decrease
Democrat *Unemployment
Increase
decrease
Housing Starts
Increase
increase
CPI
Increase
decrease
CPI Apparel
increase
decrease
CPI Food
increase
decrease
CPI Gas/Electric
increase
decrease
CPI Medical
increase
decrease
CPI Transportation
increase
decrease
Misery Index
increase
decrease
Democrat*Inflation
increase
decrease
GDP
increase
increase
22
Bank Prime Rate
increase
decrease
Dow Jones Industrials Index
increase
increase
Rally Change
increase
increase
Results and Conclusions
The results for the estimation of the full model are contained in Tables 1 and 2 (in the
Appendix). In these models, we control for serial correlation in the dependent variable by
estimating a first-order autoregressive [AR(1)] model. The estimated first-order autocorrelation
parameter, ρ, is the last parameter listed in the table; the fact that ρ is significantly less than 1 in
both Tables is further justification of our decision to treat approval as if it were stationary. The
Akaike Information Criterion Values are 5.656 and the Schwartz Criterion are 5.811 and 5.838.
These information criterion values are rather small, which implies that there is parsimony within
our models. The large number of variables within our models are justified by the fact that these
values are low, which suggests that the variables we have included are necessary and that they do
not change the overall explanatory power of the model.
For comparison, the results in Tables 3 and 4, in which we estimate Models 1 and 2 using
OLS without the AR(1) correction, show how faulty our inferences can be if we estimate OLS
models and fail to account for autocorrelated disturbances. The Durbin-Watson statistics in
Tables 3 and 4 of .416 and .472 are even less than the R-squared and Adjusted R-squared values,
suggesting very strong positive autocorrelation and even a “spurious regression.” Although the
consequence of serial correlation in an OLS model is inflated t-statistics and R2 values, many of
our variables in the specifications in Tables 1 and 2 remain statistically significant. Tables 1 and
3 contain the results for a model with the summary CPI measure of inflation, while Tables 2 and
4 disaggregates the CPI index into its five component items: apparel, food, medical care, gas
and electric, and transportation costs. We also estimated (but do not present) a model where we
use the same disaggregated measures of inflation, but take first-differences of these and the other
independent variables that contain unit roots. This is not necessary, as valid inferences from an
equation in levels can still be made if the regressors have unit roots as long as the dependent
23
variable is stationary (Maddala and Kim 1998, 249-251).18 However, it is commonly done in
dynamic models where the dependent variable is suspected of being integrated, and few of our
substantive results change.
In our preferred specification, which is found in Table 2, the first thing to realize is that
the R2 value is very high, indicating that the predictive accuracy of the model exceeds 90% of the
variation in approval over time. This is due to the fact that the lagged residuals, which are
assumed to be part of the explanation in an AR(1) model, possess a tremendous amount of
explanatory power – as do many of our independent variables. Thus, whether or not the model is
perfectly specified, the fact that the autoregressive component is well controlled in the AR(1)
models leaves us confident of the model’s substantive results. Inflation, as measured by CPI, is
significant and negative as predicted in Table 1, but in Table 2 we can see the reasons why
inflation affects presidential approval: Consumers respond much more to changes in apparel and
food prices than to any other types of price increases. The fact that these two factors were most
influential demonstrates that our hypotheses are indeed correct. “Personal” characteristics do in
fact matter and have a significant effect. Additionally, of the five components of the summary
CPI measure, the ones that exert the most explanatory power are the ones that are most closely
tied to basic human needs: food and clothing, if not shelter. This suggests that neither the
sociotropic nor the “pocketbook” voting metaphors has the story exactly right. On the one hand,
citizens are responding to aggregate economic trends; on the other, they are most responsive to
aggregate trends that affect their daily lives the most. Further evidence that Table 2 provides the
best model specification is found in the scores for the Akaike and the Schwartz information
criteria. Because the specification in Table 2 gives the lowest values of these scores, it can be
said to fit the data the best.
The fact that the coefficient on gas and electric is not significant is not surprising. Hibbs
(1987, 160) demonstrates that the OPEC oil price shocks did not effect presidential approval
ratings because presidents were not held responsible for the acceleration of prices that occurred
because the increases were imposed externally and were for the most part out of the president
control. Perhaps a similar story can be told over time for gas and electric prices. Much of the
increase in prices can be attributed to factors that the president does not control and hence should
18
The modeler can realize some efficiency gains can be realized if cointegrating relationships among the regressors
is present and accounted for (Maddala and Kim, 250-251). We leave the investigation of this complicating factor
24
not effect presidential approval. However, the fact that the coefficient is negative suggests that
the public did hold presidents responsible for the deterioration of the economy brought upon by
changes within the gas and electrical industries.
Economic conditions are often used as a litmus test for evaluating presidents and other
public officials, assuming that they are held responsible for economic fluctuations. Despite the
fact that economic circumstances influence presidential approval, economic effects vary from
president to president and the situations and conditions under which a president governs also
varies (Monroe 1984). However, in Table 2, the signs of the interaction terms between the
Democratic presidential indicator and the inflation and unemployment variables exhibit some
truly striking results about the relative impact of unemployment increases for Democrats and
Republicans. When this interaction is not included, unemployment is insignificant and positive,
suggesting the perverse result that presidents benefit when unemployment increases, controlling
for all other present and past explanatory factors.
This result echoes Hibbs (1974), but our interaction term shows that the story is even
more striking. For Republicans, unemployment has a significant and positive impact on
approval, while for Democrats, the effect is nullified by the coefficient on the interaction term,
which is negative and nearly as large in magnitude. Thus, these results suggest that Democrats
are neither hurt nor helped by rising unemployment, while Republicans unambiguously benefit
when the jobless rate increases. Even more surprising, the misery index indicates that, contrary
to common belief, the combined effects of high unemployment and high inflation tend to
decrease the amount of punishment meted out by voters. The coefficient on the
Democrat/inflation interaction term is not significant, but the other two interaction terms suggest
a truly remarkable conclusion about the reaction of voters to the tradeoff experienced – or chosen
– by the president between inflation and unemployment. Presidents of both parties appear to
enjoy the sympathy of citizens, at the margins, when both unemployment and inflation are high,
while Republican presidents appear to benefit by pursuing economic policies that leave more
workers unemployed. This result alone suggests that more research is necessary on the longterm equilibrium pattern of rewards and punishments that voters give out in response to
economic states of the world.
for future work.
25
Foreign-policy events, which have always been shown to positively influence presidential
approval by rallying voters “around the flag,” exert the same impact here: every additional
month that elapses after a rally event causes the president’s popularity to drop 1.7 points, on
average. However, citizens appear to be unresponsive to changes in monetary policy, aggregate
consumption, the stock market, and aggregate expenditures, since none of the non-traditional
economic indicators are significant. If these changes are associated with particular
administrations, the president-specific dummy variables may be tapping the variance that these
variables explain. From these results, we can rank the administration-specific popularity levels,
controlling for all other factors, from least to greatest, as follows. Truman was the least popular
president, followed by four other Democrats: Kennedy, Johnson, Carter and Clinton, the latter
two of whom have similar administration-specific effects. Eisenhower and Nixon have slightly
greater coefficients, but beginning with Ford, the last four Republican presidents appear to enjoy
very large un-modeled levels of popularity that cannot be explained by other variables. Even
compared to the presidency of George W. Bush, who is currently riding the effects of the largest
rally effect ever recorded, the administration-specific effects for Reagan and George H.W. Bush
stand alone among modern presidents.
We conclude that “personal” economic variables do in fact influence support for the
president. As predicted, the components of CPI that are the most influential are those things that
are needed for basic survival: food and clothing. Housing starts do not have a significant effect
on explaining approval for the president, which is somewhat surprising because this variable
represents a measure of consumer confidence which should be positively related to presidential
approval. However, if we rely on Mueller’s (1970) theory, then this result is not that surprising
because presidents are presumed to be blamed for negative economic conditions but are not
credited for positive conditions, so housing starts as a measure of economic confidence does not
appear to be something that is rewarded by the general public.
The results for bank prime rate and GDP were exactly as predicted. As interest rates
increased, the approval rate decreased, which helped prove that increasing interest rates help
define a declining economy and deteriorating economic conditions. As GDP increases, the
economy is growing so it is not surprising that the president reaps the rewards (even though very
small) of increases in approval. The Dow Jones Industrial Index was also expected to be an
indicator of economic success. While this was the case in two out of four models, we cannot
26
conclude that this will always be true because approval actually decreased in the models
containing the AR(1) specifications. Perhaps approval decreased because of the fact that
increasing stock market prices may also be associated with increasing prices and costs in other
areas and sectors, which may actually harm the president in terms of the fact that too much
growth too fast is not healthy for an economy. This result is also evident through the CPI values,
especially the disaggregated cases where the results are somewhat mixed for increases in medical
and transportation costs, which actually increase the approval values, rather than decrease them.
This just goes to show that the public are able to differentiate between different types of costs
and assign blame where it is necessary.
The strangest result presented in all of the tables is that of unemployment. While
traditional economic and presidential approval literature would lead us to believe that increases
in the rate of unemployment are bad, we have an alternative explanation that may help clarify
this very “strange” result. The Phillips Curve is based on the assumption that inflation and
unemployment move in tandem, so that a movement in one variable should cause a
corresponding movement in the other variable in the same direction. Phillips Curve theories
suggests that increases in unemployment will lead over time to a decrease in inflation. There is a
clear tradeoff in both economic and political terms between these two variables. Clearly though,
the Phillip’s Curve is based on the theory that this stabilization will occur; however,
macroeconomists are skeptical as to the time frame in which the stabilization will occur. As we
move from one steady state to the next we can expect that instead of an inverse relation between
the two variables, there may actually be a direct positive relation between them so that an
increase in an inflation may also cause (in the very short-run) an increase in inflation.
So for a Republican president, who is more sensitive to inflationary tendencies, increases
in inflation will hurt their approval ratings. However, in order to stabilize the inflation or
decrease the inflation, unemployment may have to rise in the short-run as well. What we end up
with is an increase in inflation and an increase in unemployment but yet , we could still have an
increase in presidential approval. This can be explained by the fact that a president, in an effort
to return the economy to the steady state or move to a new steady state, may have to shift
policies which will actually make it appear that an increase in unemployment leads to an increase
in approval. The raw data would lead us to believe that this is the case; however, prior economic
theory does not suggest that a negative shift in the economy will be rewarded by the general
27
public. Rather, the true explanation probably lies within the fact that economists cannot tie down
the time frame for Phillip’s Curve shifts. The short-run may be as short as a few months or could
even be as long as one to two years and these unknown factors make it really difficult for us to
reject these unemployment results as being false or to even accept them as being true. We need
to consider this issue in greater depth in a future paper so that we can determine whether the
Phillips Curve can be blamed for the results or whether there is something very strange going on
here.
While rallies and other non-economic variables do have some impact on presidential
approval, they cannot be used to explain a significant amount of the variance in approval scores.
These occasional shocks to the political system provide only temporary periods of destabilization
and change that ultimately shift back to the pre-disturbance levels. Consequently, economic
indicators are more influential measures and greater predictors of the ebb and flow of
presidential support. “Political life, then, is far more than an occasional random shock to a selfcontained, isolated economic system; rather, economic life vibrates with the rhythms of politics”
(Tufte 1978, 137). The “new” economic indicators that we have employed within our paper
provide evidence for the Tufte’s rhythm in politics and demonstrate that presidential approval
responds to these “personal” or “new” measures of CPI in a very similar way to more
“traditional” economic indicators. Economic indicators, both “new” and “old”, do truly dance
with presidential approval. The rhythm of politics has remained the same over time, even though
the “beat” has changed. New presidents bring different challenges to the political system but the
end result over time has been the same. Approval changes are most responsive to economic
indicators that have more of a direct relationship to the people. The careful dance that occurs
between the economy and approval is something that must continue to be studied. There are
many other interesting relationships that need to be studied and assessed. We have only just
begun to “waltz” our way down this research pathway, but from what we have discovered, we
know that there is much more ground to be covered. Our next step is to assess whether or not
“personal” economic variables influence individual-level assessments of the president. While at
the aggregate level, we have demonstrated this to be the case, we can not be certain that the same
results will be found at the individual-level.
28
Future Directions for Research
One future direction for this research would be to look at the link between public
policymaking and presidential approval. Policymakers, especially presidents have the ability to
systematically manipulate policy to help meet their short-run economic and political goals.
Keynesian economists argue that “policy instruments under the control of the government might
be manipulated systematically in order to achieve given target values for outcomes such as
inflation and unemployment” (Keech 32). Building on the work of Chappell (1983) and Chappell
& Keech (1985a), we could examine the effect that changes in the natural rate of unemployment
(as measured through both endogenous and exogenous policy changes, such as changes in the
minimum wage rates and changes in the population levels) have on aggregate presidential
approval. These endogenous and exogenous policy changes may be important if we assume that
policymakers can systematically affect economic outcomes in the short run. Incumbents
interested in maximizing their approval have the ability to manipulate macroeconomic policies to
enhance the probability of success in the next election or to maintain their legacy. Although
output may be increased or unemployment decreased in the short-run, at the cost of higher
inflation, a similar tradeoff is not likely to occur in the long run. The long run Phillips Curve has
a natural rate of unemployment or output that can be indefinitely associated with any rate of
inflation. Thus, policymakers with expansionary monetary and fiscal policies may be able to
move the rate of unemployment below its natural rate at the cost of higher inflation or vice-versa
in the short run, which may result in a long run shift in the natural rate of unemployment, which
could in turn influence the popularity ratings of the president. The point at which policymakers
achieve “optimum” policy is the point at which they should cease manipulation of the economy.
However, predicting/ determining where this level exists is virtually impossible to do.
Nevertheless, manipulative incumbent politicians may be able to successfully elicit voter support
by manipulating aggregate demand and producing unsustainable booms. Incumbent presidents
have the ability to enhance their approval ratings by manipulating monetary and fiscal tools
(Golden and Poterba, 1980).
Politicians often attempt to exploit popular discontent within the American political
system by proposing what may be seen as “symbolic policies” as a means to solve the problems
inherent within the economy. The problem is that many of these reforms such as the GrammRudman-Hollings deficit reduction acts of 1985 and 1987 fail to solve the economic problems.
29
Policy acts such as lengthening the terms of the Federal Reserve Board would be an example of a
non-symbolic economic policy that in the short-run may not affect the economy but in the longrun could change the way that policies are made.
Additionally, we would like to include a measure of group effects. We could do this the
years in which Gallup has disaggregated the presidential approval measure into approval by
political party. By looking at political party in this manner, we would be able to hold constant the
partisanship of the public to determine how the personal economic variables effect the approval
of the president.
30
References
Alesina, Alberto, and Howard Rosenthal. 1995. Partisan Politics, Divided Government, and the
Economy. Cambridge: Cambridge University Press.
Alesina, Alberto., Nouriel Roubini., and Gerald D. Cohen. 1997. Political Cycles and the
Macroeconomy. Cambridge, Massachusetts: The MIT Press.
Alvarez, R. Michael, and Jonathan N. Katz. 2000. “Aggregation and Dynamics of Survey
Responses: The Case of Presidential Approval.” Unpublished, California Institute of
Technology.
Arcelus, Francisco., and Allen H. Meltzer. 1975. “The Effect of Aggregate Economic Variables
On Congressional Elections.” American Political Science Review 69: 1223-1239.
Beck, Nathaniel.1994. “The Methodology of Cointegration.” Political Analysis 4: 237-247.
Bloom, Howard S., and Douglas H. Price. 1975. “Voter Response to Short-Run Economic
Conditions: the Asymmetric Effect of Prosperity and Recession.” American Political
Science Review 69: 1240-1254.
Box-Steffensmeier, Janet M., and Renee M. Smith. 1998. “Investigating Political Dynamics
Using Fractional Integration Methods.” American Journal of Political Science 42(2):
661-689.
Brace, Paul., and Barbara Hinckley. 1991. “The Structure of Presidential Approval:
Constraints within and across Presidencies.” Journal of Politics 53(4):993-1017.
Caldeira, Gregory A., and Christopher J.W. Zorn. 1998. “Of Time and Consensual Norms in the
Supreme Court.” American Journal of Political Science 42(3): 874-902.
Campbell, Angus., Philip E. Converse., Warren E. Miller., and Donald E. Stokes. 1960.
The American Voter. New York: Wiley.
Carroll, Richard J. 1995. An Economic Record of Presidential Performance: From
Truman to Bush. Westport, Connecticut: Praeger Publishers.
Chappell, Henry W., and William R. Keech. 1985a. “A New View of Political Accountability for
Economic Performance.” American Political Science Review 79: 10-27.
Chappell, Henry W., and William R. Keech. 1985b. “The Political Viability of a Rule-Based
Monetary Policy.” Public Choice 46: 125-140.
Chappell, Henry W. Jr., and Motoshi Suzuki. 1993. “Aggregate Vote Functions for the
U.S. Presidency, Senate, and House.” Journal of Politics 55(1):207-217.
31
Chappell, Henry W. Jr. 1983. “Presidential Popularity and Macroeconomic Performance:
Are Voters Really So Naïve?” Review of Economics and Statistics 65:385-392.
Clarke, Harold D., and Marianne C. Stewart. 1994. “Prospections, Retrospections, and
Rationality: The ‘Bankers’ Model of Presidential Approval Reconsidered.” American
Journal of Political Science 38(4): 1104-1123.
Conover, Pamela Johnston., Stanley Feldman., and Kathleen Knight. 1986. “Judging
Inflation and Unemployment: The Origins of Retrospective Evaluations.”
Journal of Politics 48(3):565-588.
Darnay, Arsen J. Editor. 1994. Economic Indicators Handbook. 2nd edition. Washington,
D.C.: Gale Research Inc.
Downs, Anthony. 1957. An Economic Theory of Democracy. NY: Harper & Row.
Durr, Robert H. 1994. “An Essay on Cointegration and Error Correction Models.”
Political Analysis 4: 185-227.
Edwards, George C. III., and Alec M. Gallup. 1992. Presidential Approval: A Sourcebook.
Baltimore, MD: The Johns Hopkins University Press.
Fair, Ray C. 1978. “The Effect of Economic Events on Votes for President.” Review of
Economics and Statistics 60(2): 159-173.
Feldman, Stanley. 1982. “Economic Self-Interest and Political Behavior.” American Journal of
Political Science 26(3): 446-466.
Feldman, Stanley. 1984. “Economic Self-Interest and the Vote: Evidence and Meaning.”
Political Behavior 6(3): 229-251.
Fiorina, Morris. 1978. “Economic Retrospective Voting in American National Elections: A
Microanalysis.” American Journal of Political Science 22(2): 426-443.
Frey, Bruno S., and Friedrich Schneider. 1978. “An Empirical Study of Politico-economic
Interaction in the United States.” The Review of Economics and Statistics 60:174-183.
Froyen, Richard T. 1999. Macroeconomics Theories and Policies. 6th ed. Upper Saddle River,
NJ: Prentice Hall.
Garrett, Geoffrey. 1998. Partisan Politics in the Global Economy. Cambridge: Cambridge
University Press.
Golden, David G., and James M. Poterba. 1980. “The Price of Popularity: The Political
Business Cycle Reexamined.” American Journal of Political Science 24(4):696-714.
32
Hibbing, John R., and John R. Alford. 1981. “The Electoral Impact of Economic Conditions:
Who Is Held Responsible?” American Journal of Political Science 25: 423-439.
Hibbs, Douglas A., Jr. 1987. The American Political Economy: Macroeconomics and
Electoral Politics. Cambridge, MA: Harvard University Press.
Hibbs, Douglas A., Jr. 1974. “Problems of Statistical Estimation and Causal Inferences in TimeSeries and Regression Models.” In H.L. Costner. Editor. Sociological Methodology
1973-1974. San Francisco, CA: Jossey-Bass.
Hibbs, Douglas A., Jr., with the assistance of R. Douglas Rivers and Nicholas Vasilatos. 1982.
“The Dynamics of Political Support for American Presidents Among Occupational and
Partisan Groups.” American Journal of Political Science 26(2): 312-332.
Hibbs, Douglas A. 1979. “The Mass Public and Macroeconomic Performance: The
Dynamics of Public Opinion Toward Unemployment and Inflation.”
American Journal of Political Science 23(4): 705-731.
Keech, William R. 1995. Economic Politics: The Cost of Democracy. Cambridge: Cambridge
University Press.
Keech, William R. 1980. “Elections and Macroeconomic Policy Optimization.” American
Journal of Political Science 24:345-367.
Kenski, Henry C. 1980. “Economic Perception and Presidential Popularity: A Comment.”
Journal of Politics 42:68-75.
Kenski, Henry C. 1977a. “Inflation and Presidential Popularity.” Public Opinion Quarterly
41:86-90.
Kenski, Henry C. 1977b. “The Impact of Unemployment on Presidential Popularity from
Eisenhower to Nixon.” Presidential Studies Quarterly 7: 114-126.
Kenski, Henry C. 1977c. “The Impact of Economic Conditions on Presidential Popularity.”
Journal of Politics 39: 764-773.
Kenski, Henry C. 1980. “Economic Perception and Presidential Popularity: A Comment.”
Journal of Politics 42: 68-75.
Kernell, Samuel. 1978. “Explaining Presidential Popularity.” American Political Science
Review 72:506-522.
Kiecolt, K., Jill. 1987. “Group Consciousness and the Attribution of Blame for National
Economic Problems.” American Politics Quarterly 15(2): 203-222.
Kiewiet, D. Roderick. 1983. Macroeconomics and Micropolitics. Chicago: University of
33
Chicago Press.
Kinder, Donald R., and D. Roderick Kiewiet. 1979. “Economic Grievances and Political
Behavior: The Role of Personal Economic Discontents and Symbolic Judgments in
Congressional Voting.” American Journal of Political Science 23: 495-527.
Kinder, Donald R. 1981. “Presidents, Prosperity, and Public Opinion.” Public Opinion
Quarterly 45:1-21.
Kramer, Gerald H. 1983. “The Ecological Fallacy Revisited: Aggregate-versus Individual-level
Findings on Economics and Elections, and Sociotropic Voting.” American Political
Science Review 77(1):92-111.
Kramer, Gerald. 1971. “Short-Term Fluctuations in U.S. Voting Behavior, 1896-1964.”
American Political Science Review 65:131-143.
Krause, George A. 1997. “Voters, Information Heterogeneity, and the Dynamics of Aggregate
Economic Expectations.” American Journal of Political Science 41(4): 1170-1200
Lanoue, David J. 1988. From Camelot to the Teflon President: Economics and Presidential
Popularity Since 1960. NY: Greenwood Press.
Lau, Richard R., and David O. Sears. 1981. “Cognitive Links Between Economic Grievances
And Political Responses.” Political Behavior 3(4): 279-302.
Lewis, Wilfred, Jr. 1962. Federal Fiscal Policy in the Postwar Recessions. Washington, DC:
The Brookings Institution.
MacKuen, Michael B. 1993. “Political Drama, Economic Conditions, and the Dynamics of
Presidential Popularity.” American Journal of Political Science 27: 165-192.
MacKuen, Michael B., Robert S. Erikson, and James A. Stimson. 1992. “Peasants or Bankers?
The American Electorate and the U.S. Economy.” American Political Science Review 86:
597-611.
Maddala, G.S., and In-Moo Kim. 1998. Unit Roots, Cointegration, and Structural Change. New
York: Cambridge University Press.
Markus, George B.1988. “The Impact of Personal and National Economic Conditions on the
Presidential Vote: A Pooled Cross-Sectional Analysis.” American Journal of Political
Science. 32:137-154.
Michales, R. 1986. “Reinterpreting the Role of Inflation in Politico-Economic Models.”
Public Choice 48: 113-124.
Monroe, Kristen R. 1979. “Econometric Analyses of Electoral Behavior: A Critical Review.”
34
Political Behavior 1(2): 137-173.
Monroe, Kristen Renwick. 1978. “Economic Influences on Presidential Popularity.”
Public Opinion Quarterly 42: 360-369.
Monroe, Kristen Renwick. 1979. “Inflation and Presidential Popularity.”
Presidential Studies Quarterly 9(3):334-340.
Monroe, Kristen Renwick. 1981. “Presidential Popularity: An Almon Distributed Lag Model.”
Political Methodology 7: 43-69.
Monroe, Kristen Renwick. 1984. Presidential Popularity and the Economy. NY:
Praeger Publishers.
Monroe, Kristen Renwick., and M.D. Levi. 1983. “Economic Expectations, Economic
Uncertainty, and Presidential Popularity.” In Kristen Renwick Monroe. Editor.
The Political Process and Economic Change. NY: Agathon Press.
Mueller, John E. 1970. “Presidential Popularity from Truman to Johnson.” American
Political Science Review. 64:18-34.
Mueller, John E. 1973. Wars, Presidents and Public Opinion. New York: Wiley and Sons.
Nadeau, Richard., Richard G. Niemi., David P. Fan., and Timothy Amato. 1999.
“Elite Economic Forecasts, Economic News, Mass Economic Judgments, and
Presidential Approval.” Journal of Politics 61(1):109-135.
Nordhaus, William D. 1975. “The Political Business Cycle.” Review of Economics and
Statistics 42: 169-190.
Norpoth, Helmut. 1984. “Economics, Politics, and the Cycle of Presidential Popularity.”
Political Behavior 6(3): 253-273.
Norpoth, Helmut., and Thom Yantek. “Macroeconomic Conditions and Fluctuations of
Presidential Popularity: The Question of Lagged Effects.” American Journal of
Political Science 27: 785-807.
Norpoth, Helmut. 1996. “Presidents and the Prospective Voter.” Journal of Politics
58(3): 776-792.
Ostrom, Charles W., Jr., and D.M. Simon. 1985. “Promise and Performance: A Dynamic
Model of Presidential Popularity.” American Political Science Review 79: 334-358.
Ostrom, Charles W., Jr., and Renee M. Smith. 1994. “Error Correction, Attitude Persistence,
And Executive Rewards and Punishments: A Behavioral Theory of Presidential
Approval.” Political Analysis 4: 127-183.
35
Paldam, Martin. 1981. “A Preliminary Survey of the Theories and Findings on Vote and
Popularity Functions.” European Journal of Political Research 9: 181-189.
Palmer, Harvey D., and Guy D. Whitten. 1999. “The Electoral Impact of Unexpected Inflation
And Economic Growth.” British Journal of Political Science 29(4):623-639.
Peffley, Mark., and John T. Williams. 1985. “Attributing Presidential Responsibility for
National Economic Problems.” American Politics Quarterly 13:393-425.
Perlman, Morris. 1976. “Party Politics and Bureaucracy in Economics.” In Gordon Tullock
The Vote Motive. London: Institute of Economic Affairs.
Perron, Pierre. 1989. “The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis.”
Econometrica 57: 1361-1401.
Popkin, Samuel., John W. Gorman., Charles Phillips., and Jeffrey A. Smith. 1976. “Comment:
What Have You Done For Me Lately? Toward an Investment Theory of Voting.”
American Political Science Review 70(3): 779-805.
Rogers, R. Mark. 1994. Handbook of Key Economic Indicators. Burr Ridge, Illinois: Irwin
Professional Publishing.
Sears, David O., Richard Lau., T.R. Tyler., and H. M. Allen, Jr. 1980. “Self-Interest
Versus Symbolic Politics in Policy Attitudes and 1976 Presidential Voting.”
American Political Science Review 74: 670-684.
Shapiro, Robert Y., and Bruce M. Conforto. 1980a. “Economic Perception and Political
Behavior: Reply to Professor Kenski.” Journal of Politics 42:76-81.
Shapiro, Robert Y., and Bruce M. Conforto. 1980b. “Presidential Performance, the Economy,
and the Public Evaluation of Economic Conditions.” Journal of Politics 42(1):49-67.
Shienbaum, Kim Ezra., and Ervin Shienbaum. 1982. “Public Perceptions of Presidential
Economic Performance: From Johnson to Carter.” Presidential Studies Quarterly
12(3):421-427.
Sigelman, Lee. 1979. “The Dynamics of Presidential Support: An Overview of Research
Findings.” Presidential Studies Quarterly 9:206-216.
Sigelman, Lee., and Yung-Mei Tsai. 1981. “Personal Finances and Voting Behavior: A
Reanalysis.” American Politics Quarterly. 9(4): 371-399.
Spulber, Nicolas. 1989. Managing the American Economy from Roosevelt to Reagan.
Bloomington: Indiana University Press.
Stigler, G. 1973. “Micropolitics and Macroeconomics General Economic Conditions and
36
National Elections.” American Economic Review 63:160-167.
Stimson, James A. 1976. “Public Support for American Presidents: A Cyclical Model.”
Public Opinion Quarterly 40(1): 1-21.
Suzuki, Motoshi. 1991. “The Rationality of Economic Voting and the Macroeconomic Regime.”
American Journal of Political Science 35(3):624-642.
Tufte, Edward R. 1978. Political Control of the Economy. Princeton: Princeton, University
Press.
Weatherford, M. Stephen. 1978. “Economic Conditions and Electoral Outcomes: Class
Differences in the Political Response to Recession.” American Journal of Political
Science 22:917-938.
Weatherford, M. Stephen. 1983a. “Economic Voting and the ‘Symbolic Politics’ Argument:
A Reinterpretation and Synthesis.” American Political Science Review 77(1):158-174.
Weatherford, M. Stephen. 1983. “Evaluating Economic Policy” A Contextual Model of the
Opinion Formation Process.” Journal of Politics 45(4):866-888.
Williams, John T. 1990. “The Political Manipulation of Macroeconomic Policy.” American
Political Science Review 84(3): 767-795.
Wood, B. Dan. 2000. “Weak Theories and Parameter Instability: Using Flexible Least Squares to
Take Time-Varying Relationships Seriously.” American Journal of Political Science
44(3): 603-618.
Zeidenstein, Harvey G. 1980. “Presidential Popularity and Presidential Support in Congress:
Eisenhower to Carter.” Presidential Studies Quarterly 10(2): 224-232.
1
Figure 1: Presidential Approval from Truman to Clinton
90
80
70
60
50
40
30
20
50
55
60
65
70
75
80
APPROVAL
85
90
95
00
1
Table A: Unit root tests for presidential approval
1. Augmented Dickey-Fuller (ADF) Test – Trend + intercept
ADF Test Statistic
-4.175104
1% Critical Value*
5% Critical Value
10% Critical Value
*MacKinnon critical values for rejection of hypothesis of a unit root.
-3.9768
-3.4189
-3.1317
2. ADF test – Intercept only
ADF Test Statistic
-4.176137
1% Critical Value*
5% Critical Value
10% Critical Value
-3.4430
-2.8664
-2.5693
1% Critical Value*
5% Critical Value
10% Critical Value
-2.5689
-1.9399
-1.6159
3. ADF test – Neither trend nor intercept
ADF Test Statistic
-0.508863
1. Phillips-Perron (PP) test – Trend + intercept
PP Test Statistic
-4.583031
1% Critical Value*
5% Critical Value
10% Critical Value
-3.9768
-3.4189
-3.1316
-4.576560
1% Critical Value*
5% Critical Value
10% Critical Value
-3.4429
-2.8663
-2.5693
1% Critical Value*
5% Critical Value
10% Critical Value
-2.5688
-1.9399
-1.6159
2. PP – Intercept only
PP Test Statistic
3. PP – Neither trend nor intercept
PP Test Statistic
-0.670115
2
Table B: Descriptive Statistics for Independent Variables
Variable
Unemployment Rate
Democrat
Unemployment
Housing Starts
CPI
CPI Apparel
CPI Food
CPI Gas/Electric
CPI Medical
CPI Transportation
Misery Index
Democrat*Inflation
GDP
Bank Prime Rate
Dow Jones Industrials
Index
Rally Change
Mean
Median Maximum Minimum Std. Dev. N
5.666
5.600
10.8
2.5
1.560
636
8.934
7.850
21.6
2.5
4.405
636
1502.987 1483.500
76.103
53.750
80.870
72.400
76.733
59.750
62.063
40.000
88.552
47.600
71.213
49.800
448.701 397.575
115.861
82.450
3045.958 1612.567
7.236
6.935
2065.768 894.555
3.950
3.000
2494
178.1
134.9
174.9
147.3
278.4
157.9
1092.78
356.2
10253.2
20.5
11497.12
23
798 300.833
23.51
51.062
39.7
34.704
24.3
49.507
19
42.871
14.7
80.881
21.6
45.222
66.75 318.852
23.51
83.955
265.6 2956.053
2
3.536
167.42 2681.222
1
3.350
636
636
636
636
636
636
636
636
636
636
636
636
636
3
Table 1: Results from AR(1) model, CPI not disaggregated
Variable
Coefficient
Standard Error
Constant
71.480
24.160***
Unemployment Rate
1.837
2.291
Democratic*Unemployment
-3.007
1.404**
Housing Starts
.0003
.001
CPI
-.391
.892
Misery Index
.048
.018***
Democratic*Inflation
-1.231
.356***
GDP
.029
.009***
Bank Prime Rate
-.380
.378
Dow Jones Industrial Index
-.001
.001*
Truman
-49.778
6.694***
IKE
46.363
4.033***
LBJ
16.929
4.046***
Nixon
85.031
10.648***
Ford
120.078
11.372***
Carter
37.535
9.220***
Reagan
183.209
21.217***
Bush
174.181
21.675***
Clinton
-17.663
25.596
G.W. Bush
206.534
36.342***
Rally Change
-.160
.068***
AR(1)
.979
.008***
R-squared:
.910
Adjusted R-squared:
.904
Standard Error of Regression: 4.024
Durbin Watson Statistic:
2.224
*** p<.01
** p<.05
* p<.10
Mean Dependent Variable: 54.808
F-Statistic:
283.722
Prob (F-statistic):
.0000
Inverted AR Roots:
.98
(one-tailed tests for all except presidential dummies)
N=635
Sample: 1949:02 2001:12
4
Table 2: Results from AR(1) model, CPI disaggregated
Variable
Coefficient
Standard Error
Constant
118.950
13.419***
Unemployment Rate
6.653
2.032***
Democratic*Unemployment
-6.256
1.165***
Housing Starts
.0001
.001
CPI Apparel
-1.011
.340***
CPI Food
-.650
.452*
CPI Gas & Electric
-.242
.242
CPI Medical
.550
.400*
CPI Transportation
-.200
.299
Misery Index
.044
.018***
Democratic*Inflation
-.299
.274*
GDP
.011
.011
Bank Prime Rate
-.372
.378
Dow Jones Industrial Index
-.001
.001*
Truman
-37.850
5.521***
IKE
46.689
3.826***
LBJ
11.564
3.762***
Nixon
55.592
8.007***
Ford
89.716
9.025***
Carter
37.677
8.754***
Reagan
128.229
16.510***
Bush
121.757
17.210***
Clinton
35.965
21.462*
G.W. Bush
111.010
27.895***
Rally Change
-.172
.070***
AR(1)
.921
.017***
R-squared:
.910
Adjusted R-squared:
.904
Standard Error of Regression: 4.010
Durbin Watson Statistic:
2.177
*** p<.01
** p<.05
* p<.10
Mean Dependent Variable: 54.808
F-Statistic:
240.263
Prob (F-statistic):
.0000
Inverted AR Roots:
.92
(one-tailed tests for all except presidential dummies)
N=635
Sample: 1949:02 2001:12
5
Table 3: OLS Estimates of Model in Table 1
Variable
Coefficient
Standard Error
Constant
78.529
7.604***
Unemployment Rate
13.020
1.057***
Democratic*Unemployment
-7.454
.632***
Housing Starts
.0002
.002
CPI
-.957
.384***
Misery Index
-.012
.011*
Democratic*Inflation
-.386
.123***
GDP
.014
.005***
Bank Prime Rate
-1.133
.270***
Dow Jones Industrial Index
.002
.001***
Truman
-30.972
1.843***
IKE
44.931
2.527***
LBJ
-5.124
1.829***
Nixon
47.980
3.678***
Ford
69.777
4.457***
Carter
12.595
4.451***
Reagan
139.745
8.699***
Bush
165.090
10.365***
Clinton
57.799
11.474***
G.W. Bush
165.380
14.707***
Rally Change
-.0558
.099***
R-squared:
.676
Adjusted R-squared:
.666
Standard Error of Regression: 7.492
Durbin Watson Statistic:
.4161
*** p<.01
** p<.05
* p<.10
Mean Dependent Variable: 54.830
F-Statistic:
64.202
Prob (F-statistic):
.0000
(one-tailed tests for all except presidential dummies)
N=636
Sample: 1949:02 2001:12
6
Table 4: OLS Estimates of Model in Table 2
Variable
Coefficient
Standard Error
Constant
128.780
8.598***
Unemployment Rate
14.733
1.001***
Democratic*Unemployment
-9.570
.627***
Housing Starts
.0001
.002
CPI Apparel
-1.929
.225***
CPI Food
-.250
.264
CPI Gas & Electric
-.212
.159*
CPI Medical
.079
.192
CPI Transportation
.301
.201*
Misery Index
-.003
.011
Democratic*Inflation
.155
.126*
GDP
.003
.006
Bank Prime Rate
-1.112
.318***
Dow Jones Industrial Index
.001
.001
Truman
-31.680
1.818***
IKE
40.365
2.420***
LBJ
.894
1.834
Nixon
55.871
3.861***
Ford
85.592
4.803***
Carter
45.988
5.416***
Reagan
135.066
8.858***
Bush
162.277
9.924***
Clinton
112.487
12.211***
G.W. Bush
123.058
13.903***
Rally Change
-.571
.092***
R-squared:
.722
Adjusted R-squared:
.711
Standard Error of Regression: 6.970
Durbin Watson Statistic:
.4719
*** p<.01
** p<.05
* p<.10
Mean Dependent Variable: 54.830
F-Statistic:
65.973
Prob (F-statistic):
.0000
(one-tailed tests for all except presidential dummies)
N=636
Sample: 1949:02 2001:12
7