Winners and Losers in the Global Financial Crisis Ben Tengelsen - BYU Macroeconomics and Computational Laboratory Working Paper #2012-03
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BYU Macroeconomics and Computational Laboratory
Working Paper #2012-03
Winners and Losers in the Global Financial Crisis
Ben Tengelsen
April 2012
keywords: Global Financial Crisis, Fiscal Policy, Recession Length.
JEL classification: E62, E63, F02BYU Macroeconomics and Computational Laboratory
Working Paper #2012-03
∗
Winners and Losers in the Global Financial Crisis
Benjamin Tengelsen†
May 22, 2012
Abstract
I compare the performance of 30 OECD countries over the years of 2008-
2010 based on (1) cumulative growth in output and (2) the length of their
recessionary periods. Based on these measures, I compare the average
fiscal stimulus measures and the pre-recession conditions among high/low
performing countries using both summary statistics and an iterated re-
gression model as used in Leamer (1985). I find that no fiscal response or
pre-recession variable to be significantly correlated with either measure of
performance, with the exception of government spending on investment.
keywords: global financial crisis, fiscal stimulus, recession duration.
JEL classifications: E62, E63, F02
∗
This research benefitted from the computing resources of the Brigham Young University Macroe-
conomics Laboratory. Thanks to Tom Isern, Chris Brown, and Western Social Science Conference
participants (2011) for helpful comments.
†
Brigham Young University, Department of Economics, 121b FOB, Provo, Utah 84602, (801)
422-3580, b.tengelsen@byu.edu.1 Introduction
The global financial crisis of 2008 was not kind to any country. The US experienced
its worst recession since the great depression, the global strain uncovered economic
instability for several European governments, and year-to-year percent change in real
GDP reached its lowest point in decades for several countries including France, Ger-
many, Japan, and Sweden.1 Although all developed countries were affected by the
global crisis to some degree, the severity of economic decline varied significantly from
country to country. Countries such as Australia, Poland, and South Korea endured
only brief periods of economic slowing. Other countries such as Ireland, Iceland, Hun-
gary, and Japan had failed to regain 2008Q1 levels of output by the end of 2010. This
paper seeks to identify common elements between countries that were affected the
most/least by the global economic downturn, particularly in the composition of their
respective fiscal responses and in the health of their economies prior to the recession.
The question—Why did Country A do so well while Country B did so poorly?—
has been abundantly examined in a country-by-country fashion. Bordo, Redish, and
Rockoff (2011) claim the centralized nature of Canada’s banking industry protected
Canada from a recession as severe as that of the US. Lim, Chua, Claus, and Tsiaplias
(2010) and Tiernan (2010) attribute Australia’s success during the crisis to timely
stimulus and booming demand from Asian markets. Nabli (2011) claims that Poland’s
sound monetary policy and largely domestic economy made it less susceptible to global
downturns. Research such as this is well deserved, as the unique structural differences
1
Based on Federal Reserve Economic Data. Federal Reserve Bank of St. Louis. FRED Real
GDP datasets FRARGDPR, DEURGDPR, JPNRGDPR, SWERGDPR.
1between different countries likely play a significant role in deciding which countries
thrive or fail during times of economic stress. These single country comparisons,
however, are limited in their application to economic policy in general. Furthermore,
to say that Country A flourished and Country B did not due to some policy that
Country A used and Country B did not use is difficult to test empirically while
controlling for ceteris paribus conditions.
Broader empirical studies on economic performance during the global financial cri-
sis, such as Berkmen, Gelos, Rennhack, and Walsh (2009), Rose and Spiegel (2009),
and Claessens, Dell’Ariccia, Igan, and Laeven (2010) consider a broader panel of
countries and perform some cross-country comparison. While these studies lack the
fine analytical detail of the single country studies, their findings could be applied to
economies throughout the world with greater confidence, due to the larger sample size
and improved statistical techniques. My study aims to follows this comprehensive ap-
proach. Berkmen, Gelos, Rennhack, and Walsh (2009) compare the revision of GDP
growth forecasts as an indicator of economic performance during the global recession.
They find that countries with leveraged domestic financial systems and rapid growth
in lending to the private sector were “financially vulnerable” and consequently expe-
rienced deeper downward revisions to their growth forecasts. Rose and Spiegel (2009)
use a sample of 85 countries to examine how both trade with the US and holdings
of US assets correspond to economic performance during the crisis. They find (sur-
prisingly) no credible evidence that these international linkages impacted countries
negatively during the crisis and, in fact, that they may have had a positive impact.
Claessens, Dell’Ariccia, Igan, and Laeven (2010) similarly examine international links
2in foreign-asset holdings and conclude that initial conditions are a poor predictor of
economic performance during a crisis and that how to quantitatively describe the
spread of economic crisis between countries remains an enigma in most respects.
Similar to these studies, this paper aims to explain how countries that relatively
flourished during the crisis differed in their policies from countries that struggled over
the same time period. To do this, I use two approaches on two different measures
of economic performance. The first method simply compares the summary statistics
of countries with extremely high and extremely low economic performance. The sec-
ond method considers all countries in an iterated regression model as introduced by
Leamer (1985). Economic performance is measured by (1) cumulative growth from
2008 to 2010 relative to 2008Q1 output levels and (2) the length of the recession-
ary period as measured in quarters over the same time frame. This time frame is
optimal for several reasons. First, although the US began its economic decline in
2007, most other countries did not follow until 2008 or later (Claessens, Dell’Ariccia,
Igan, and Laeven (2010)). Next, even though the US recession began in 2007, most
counter-cyclical policies that intended to reverse or mitigate declines in output were
not enacted until 2008 or later. Such is the case with the two largest pieces of US
legislation: the Economic Stimulus Act of 2008 and the American Recovery and
Reinvestment Act of 2009.
In addition to answering how high/low performing countries differed in their fiscal
response and pre-recession conditions, this paper also adresses the ongoing question
of what kinds of fiscal stimulus provide the largest boost to output. This related
question has recently been examined empirically in different ways by both Alesina
3and Ardagna (2009) and Taylor (2011), among others. This paper is a useful ad-
dition to this body of literature through both its unique econometric approach and
its focus on a narrow window of time in which larger structural features of individ-
ual economies remain fixed. Additionally, in comparing the pre-recession state of
economies that fared well/poorly in terms of growth, this paper adds to the academic
discussion surrounding the importance of “fiscal space” as examined by Ghosh, Kim,
Mendoza, Ostry, and Qureshi (2011) and Blanchard, Dell’Ariccia, and Mauro (2010)
and reinforces the research by Rose and Spiegel (2009) and Claessens, Dell’Ariccia,
Igan, and Laeven (2010) regarding “initial conditions” and their ability to predict
the depth of an ensuing recession. Finally, this paper documents useful statistics on
cumulative growth and recession duration for a sizable panel of OECD countries.
Generally speaking, differences between high- and low-performing countries are
minimal when considering pre-recession conditions. The composition of fiscal re-
sponse, however, differs notably—especially in terms of spending on investment projects.
The average stimulus plan among high-growth countries directed over 40% of spend-
ing toward investment projects, while low-growth countries spent only about 10%
of their stimulus funds on investment. Government transfers also differ between the
two groups, though by a smaller amount. The highest-performing countries trans-
fered more funds to businesses than the lowest-performing countries. Transfers to
individuals/households are considerably lower among the high-performing countries,
which agrees with a theory posed by Taylor (2011) but runs contrary to the prevailing
mood of Oh and Reis Oh and Reis (2011). My regression results similarly point to
investment spending as the only variable in the study with a strong correlation to
4short-run economic performance. No other fiscal policy or pre-recession variable in
my study is strongly correlated with either measure of economic performance, which
agrees with Taylor (2011), Rose and Spiegel (2009) and Claessens, Dell’Ariccia, Igan,
and Laeven (2010).
In section two I explain my data and methodology. In section three I present
my results, first for cumulative growth and then for recession duration. Section four
concludes.
2 Data & Methodology
2.1 Data
To compare the economic performance of the different countries, I use two variables:
cumulative growth and the length of the recessionary period. Here, cumulative growth
is defined to be the sum of quarterly output from 2008Q2 to 2010Q4 relative to output
in 2008Q1 (2008Q1 is omitted from the sum as it is the base period). This approach
trumps any analysis that examines only the post-recession period for each country
as it gives no preference to a deep recession with a rapid recovery versus a shallow
recession with a long and slow recovery.2 Cumulative growth is used as a similar
means of cross-country comparison in Coelli and Rao (2005) and Moreno (2001).
To determine the length of the recessionary period, I use a peak-to-trough method
based on quarterly percent changes in output, measured from the previous quarter.
2
These recession/recovery patterns are not always deep recession/rapid recovery or shallow re-
cession/slow recovery. See Bordo and Haubrich (2011), Cerra and Saxena (2005), and Blanchard
(1993).
5The recessionary period begins with the first quarter of negative growth and continues
until the country has two subsequent quarters with positive quarterly growth. If the
country has more than one “recession” as I have defined it during the 2008–2010 time
frame, the sum of the separate recession lengths is used.3
The explanatory variables include both measures of fiscal response and various
economic indicators from the pre-recession time period. Fiscal response variables
include several kinds of tax measures, spending measures, and transfer payments and
are given as percents of total fiscal stimulus over 2008–2010. To avoid unwanted
causality arguments, I do not use fiscal variables as a percent of GDP. If a high-
performing country spent less on stimulus than a low-performing country, it may be
that the stimulus hampered growth for the low-performing country, or it may be that
the high-performing country spent less simply because it did not need as much of
a boost. Conversely, if a high-performing country spent more on stimulus than a
low-performing country, it may be that the stimulus generated a boost in output,
or it may be that the high-performing country could afford to spend more. A fairer
comparison comes from how a country divided its stimulus resources between specific
kinds of spending and tax-measures. I briefly describe these variables in Table 1.
The data are collected from various OECD publications, including Economic Out-
look No. 91, Quarterly National Accounts, and the OECD Factbook 2010 (for pre-
recession variables). I include the 30 OECD countries of Australia, Austria, Belgium,
3
My numbers for recession lengths may differ from official statements regarding their respective
recession lengths for two main reasons. First, I consider only 2008–2010, and some countries such as
the US were already in a state of recession prior to 2008. Next, while this definition resembles other
commonly used methods for determining the beginning and end of a given recession, it should be kept
in mind that not all recession dates are determined by predetermined rules or may be determined
by rules other than this. I assume this rule to make my comparison consistent.
6Table 1: Explanatory variables
Pre-Recession Variables Year*
Tax: household Tax-based stimulus measures: households 2008–2010
Tax: business Tax-based stimulus measures: business 2008–2010
Tax: consumption Tax-based stimulus measures: consumption 2008–2010
Tax: socal Tax-based stimulus measures: social contributions 2008–2010
Spending: consumption Gov. spending: consumption 2008–2010
Spending: investment Gov. spending: investment 2008–2010
Transfers: households Gov. transfers: households 2008–2010
Transfers: business Gov. transfers: business 2008–2010
Transfers: state Gov. transfers: sub-national government 2008–2010
Pre-Recession Variables Year
Debt Stock of Debt as a % of GDP 2006
Disability Benefits Gov. spending on disability benefits as a % of GDP 2006
Secondary Education % of population aged 25-34 with secondary education 2004
Tertiary Education % of population aged 25-34 with tertiart education 2004
Health Spending Gov. spending on health as a % of GDP 2006
Employment Protection OECD Index for employment protection 2003–2004
Product Market Regulation OECD Index for product market regulation 2003–2004
Population Total country population 2006
Pre-recession variables are averaged over given years. Fiscal response variables are sums over given years.
Canada, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary,
Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Mexico, the Netherlands, New
Zealand, Norway, Poland, Portugal, the Slovak Republic, Spain, Sweden, Switzer-
land, Turkey, the United Kingdom, and the United States.
2.2 Methodology
To identify the common elements among high/low-performing countries, I consider
two methods for each of the metrics used to rank the performance of the respective
countries. The first of these is simply a comparison of summary statistics between
high/low-performing groups, where “groups” are the five countries with the best
or worst economic performance as measured by cumulative growth and recession
duration. In this part of the analysis, I exclude countries that decreased spending
7and/or increased tax revenues, as opposed to the usual stimulus of increased spending
and decreased taxing. This removes confusion from comparing an increase in spending
as a fraction of total stimulus with a decrease in spending as a fraction of negative
stimulus. The second method uses an iterated regression scheme with the pool of
explanatory variables.
The iterated regression scheme was first posed by Leamer (1985) and is also de-
scribed in detail by Levine and Renelt (1992) andSala-I-Martin (1997). This ap-
proach is intended to overcome the effect of multicollinearity between macroeconomic
variables and to provide a robustness check for variables that may demonstrate an
impressive relationship with the dependent variable in some regressions but not oth-
ers (depending on what other variables are included in the regression). While this
approach is used most frequently in growth literature, I apply it to this short-run sce-
nario for its benefits in reducing the effects from collinearity and to maintain sufficient
degrees of freedom to analyze a large number of independent variables with a limited
number of observations. While Leamer (1985) gives the theoretical underpinnings for
this approach, I pattern my methodology after the specific model in Sala-I-Martin
(1997), with some variation.
Sala-I-Martin (1997) proceeds by estimating a model with a seven independent
variables as given in equation 2.1
y = βy Y + βx1 + βx2 + βx3 + βx4 (2.1)
where the xi variables are drawn from a larger pool of explanatory variables, and Y is
8a 3×n matrix with observations for three variables present in all regressions (βY is the
corresponding coefficients). Regressions are estimated for every unique combination
of variables, and the coefficients and standard errors are averaged over all the results.
The variables included in Y are known a priori to have a strong correlation with the
dependent variable. I differ from Sala-I-Martin by not including any variables fixed
in all regressions, as there is too much controversy/political debate surrounding what
variables are “most” effective as stimulus in the short-run. I have 17 explanatory
variables, which corresponds to a total of 2,380 regressions. Each individual variable
is included in 560 regressions.4
3 Results
3.1 Cumulative Growth
Cumulative growth over 2008–2010 is given in Table 2 for all included countries.
The countries with the five highest cumulative growth rates are Poland, Australia,
Korea, New Zealand, and the Slovak Republic. The mean cumulative growth for these
countries was 11.17%, representative of an average quarterly growth rate of about 1%
improvement in output over 2008Q1 for each successive quarter. The countries with
the lowest cumulative growth are Ireland, Iceland, Hungary, Japan, and Finland.
The mean cumulative growth for these countries was 10.32%, which corresponds to
average quarterly growth of about .93% of 2008Q1. The path that these groups
4
The total numbers of regressions
and the number
of regressions for a single variable are given
17 16
by simple probability expressions and respectively.
4 3
9follow from 2008 to 2010 differ most notably in their recovery, as shown in Figure 1.
Regardless of their trajectory at the beginning of 2008, all groups enter a period of
decline in the middle of 2008. The low performing group maintains quarterly GDP
levels well below that of 2008Q1 through the end of 2010, while the average high
performing country exceeds 2008Q1 levels around mid-2009. Other countries fall just
below 2008Q1 output levels at the end of 2010.
Table 2: Cumulative Growth by Country 2008Q2–2010Q4
Country Cumulative Growth
Poland 11.388
Australia 11.253
Korea 11.172
New Zealand 11.049
Slovak Republic 10.992
Switzerland 10.943
Canada 10.901
Norway 10.873
Belgium 10.818
Czech Republic 10.788
Portugal 10.773
United States 10.745
France 10.723
Netherlands 10.712
Austria 10.690
Greece* 10.679
Sweden 10.669
Turkey 10.643
Mexico 10.626
Spain 10.619
Germain 10.609
Denmark 10.537
Luxembourg 10.513
Italy* 10.479
United Kingdom 10.429
Finland 10.406
Japan 10.387
Hungary* 10.376
Iceland* 10.308
Ireland* 10.135
*These countries are excluded from the high/low-
performance comparison due to a negative fiscal
response.
Table 3 gives the composition of stimulus spending for the high- and low-performing
10groups. For reasons given in section 2.1, the bulk of my analysis is focused on individ-
ual fiscal measures as a percent of total stimulus, rather than on total stimulus as a
percent of GDP. It is interesting to note, however, that total stimulus does not differ
notably between the two groups, and neither does the division between the aggregate
categories of taxing and spending.
For the high-performing group, stimulus consists primarily of investment spend-
ing, household tax measures, and transfers to businesses and households. Of these,
the largest two components are by far spending on investment and household tax
measures. The low-performing group also has large amounts of spending in these
same areas, but with a much smaller emphasis on investment spending. The low per-
forming group aims more stimulus toward consumption tax and social tax measures,
transfers to sub-national governments, and spending on consumption.
Pre-recession economic indicators differ only slightly between the two groups, with
the exception of the debt/GDP ratio. There is some uncertainty surrounding outlier
effect in this analysis. One of the low-performing countries, Japan, has a very high
debt/GDP ratio (about 160%), while another low-performing country, Luxembourg,
has a very low outstanding debt (less than 2%). Australia, one of the high-performing
countries, also has a very low debt/GDP ratio (about 6%). When these outlying
observations are removed, the debt/GDP ratios for the high- and low-performing
groups are about 31.90% and 40.14% respectively. Also, the average high-performing
country spends slightly less on disability benefits and healthcare and has slightly
higher employment protection and product market regulation scores. The significance
of such differences, however, is dubious considering the size of the standard deviations.
11Table 3: Fiscal Response and Pre-recession Variables by Cumulative Growth
High Performing Low Performing
Variable Obs Mean Std.Dev Obs Mean Std.Dev
Cumulative Growth 5 11.171 0.159 5 10.454 0.067
Duration 5 1.800 1.789 5 5.800 1.095
Total Stimulus 5 3.546 2.262 5 3.378 1.036
Total: spend 5 0.482 0.336 5 0.493 0.336
Total: tax 5 -0.518 0.336 5 -0.507 0.336
Tax: household 5 -0.381 0.415 5 -0.261 0.261
Tax: business 5 -0.076 0.066 5 -0.075 0.087
Tax: consumption 5 -0.044 0.084 5 -0.084 0.137
Tax: social 5 -0.009 0.020 5 -0.036 0.059
Spending: consumption 5 0.003 0.007 5 0.065 0.119
Spending: Investment 5 0.407 0.454 5 0.188 0.076
Transfers: households 5 0.079 0.145 5 0.106 0.089
Transfers: business 5 0.134 0.183 5 0.074 0.139
Transfers: state 5 0.009 0.020 5 0.025 0.056
Debt 5 26.796 14.132 5 56.497 60.702
Disability Benefits 3 3.427 2.894 4 5.828 2.954
Seconday Education 5 82.438 14.848 5 82.698 10.337
Tertiary Education 5 30.150 13.244 5 38.032 8.011
Health Spending 5 7.188 1.360 5 8.224 0.681
Employment Protection 5 1.988 0.379 4 1.698 .444
Product Market Regulation 5 1.774 0.736 5 1.238 0.260
Population (millions) 5 23.300 19.600 5 39.700 54.900
High-Performing Countries: Poland, Korea, Australia, New Zealand, Slovak Republic
Low-Performing Countries: Japan, Finland, United Kingdom, Luxembourg, Denmark
The regression results are given in Table 4. The coefficients and standard errors
are the averages of the respective statistics over all regressions that included the given
variable. The statistic by the name of t-stat(1) is the average of the t-statistics, and
t-stat(2) is the t-statistics of the average coefficients and standard errors. Statistics
sig90, sig95, and sig99 are the percent of regressions for which the variable tested
positively for significance at the 90%, 95%, and 99% levels respectively.
Findings from the regression analysis tell much of the same story as the summary
tables. The fraction of stimulus committed to investment spending is significantly
correlated with growth at the 99% level in about 78% of inclusive regressions. This
is the only variable with a notable correlation with growth at high significance levels.
12The variable with the next highest significance frequency is transfers to businesses,
significant at the 99% level in only 5% of inclusive regressions. At the 90% level, the
OECD’s product market regulation index is significant in 67% of regressions. Debt
as a percent of GDP is the only other variable that demonstrates significance with
more than 50% frequency. The coefficient for investment spending indicates a positive
relationship between growth and the fraction of stimulus committed to investment
spending. It is important to note that this approach is not sufficiently precise to
demonstrate any kind of causality.
Table 4: Regression: 3-yr cumulative growth
CUMGROWTH coeffs sterrs t-stat(1) t-stat(2) sig90 sig95 sig99
Tax: household 0.1333 0.2037 0.2468 0.6545 0.079 0.034 0.000
Tax: business -0.8950 0.5748 -1.6428 -1.5572 0.323 0.138 0.041
Tax: consumption 0.2755 0.2416 1.1972 1.1402 0.329 0.221 0.068
Tax: social 0.1074 0.4264 0.1886 0.2518 0.000 0.000 0.000
Spending: consumption -0.0985 0.1208 -0.1446 -0.8152 0.263 0.180 0.039
Spending: Investment 0.6654 0.2128 3.2865 3.1265 0.929 0.845 0.777
Transfers: households 0.0137 0.1605 0.2584 0.0855 0.196 0.132 0.039
Transfers: business 0.1347 0.2910 0.8627 0.4629 0.211 0.170 0.050
Transfers: state -0.1119 0.7274 -0.1046 -0.1538 0.000 0.000 0.000
Debt -0.0027 0.0015 -1.8885 -1.7846 0.543 0.296 0.071
Disability Benefits -0.0333 0.0209 -1.5888 -1.5926 0.307 0.059 0.000
Seconday Education 0.0008 0.0037 0.3693 0.2180 0.136 0.077 0.025
Tertiary Education -0.0070 0.0065 -1.1402 -1.0888 0.100 0.057 0.002
Health Spending 0.0231 0.0342 0.7481 0.6764 0.163 0.057 0.002
Employment Protection -0.0063 0.0797 -0.1227 -0.0788 0.020 0.018 0.000
Product Market Regulation 0.3044 0.1647 1.8923 1.8482 0.677 0.345 0.025
Population 0.0000 0.0000 -0.4794 -0.4770 0.041 0.018 0.002
3.2 Duration of Recession
The duration of the recessionary period for each country is given in Table 5. Some
countries, such as the US, were in official recessions prior to 2008, hence these numbers
may not align with official recession-length figures for “great recession” since those
13figures span a wider time-frame. The high-performing countries are Australia, Poland,
Korea, the Czech Republic, and the Slovak Republic. In the event of a tie between
countries, I resort to the cumulative growth rate. The high-performing group for
recession duration is the same as it was for cumulative growth, with New Zealand
replacing the Czech Republic. The countries with the longest recessions are Sweden,
Portugal, Norway, Luxembourg, and Spain. The average recession length for this
group is 6.4 quarters. Luxembourg is the only country within this group that is
also in the low-performing group for cumulative growth. This suggests that low
cumulative growth is not synonymous with a long recessionary period, although very
high cumulative growth implies a short recessionary period.
Comparing these two groups yields many of the same findings as before. As
shown in Table 6, the summary statistics for both groups differ notably for fiscal
response variables and differ much less in terms of pre-recession indicators. The high-
performing group conducted most of their fiscal stimulus in the form of investment
spending and household tax measures. The stimulus among the low-performing group
consisted less of investment spending. Instead the largest components were transfers
to households and consumption related spending.
The pre-recession indicators between the groups are mostly the same. Debt as a
percent of GDP is more than ten percentage points lower among the high-performing
group. The effect of outliers is felt in both directions for the low-performing group.
Greece has a high debt/GDP ratio of about 108%, while Luxembourg has a debt/GDP
ratio of just over 1% (the lowest of all countries in the sample). The fraction of popu-
lation aged 25–34 with secondary education is about ten percentage points higher for
14Table 5: Length of Recessionary Period by Country (2008-2010)
Country Duration
Korea 1
Slovak Republic 1
Australia 1
Poland 1
Belgium 3
Czech Republic 3
Canada 3
Denmark 4
Austria 4
Switzerland 4
United States 4
Germany 4
France 4
Turkey 4
Mexico 5
Netherlands 5
New Zealand 5
Italy* 5
Norway 6
Japan 6
Sweden 6
Hungary* 6
United Kingdom 6
Finland 6
Portugal 6
Spain 7
Iceland* 7
Luxembourg 7
Greece* 9
Ireland* 12
*These countries are excluded
from the high/low group compar-
ison due to a negative fiscal re-
sponse.
the high-performing group. Government spending on disability benefits and health-
care is again smaller among the high-performing group as it was with cumulative
growth. Most of these differences are within one standard deviation, and all are
within two standard deviations.
As before, I extend my analysis beyond the summary statistics with an iterated
regression approach, this time with the length of the recession as the dependent
15Table 6: Fiscal Response and Pre-recession Variables by Recession Length
Winners Losers
Variable Obs Mean Std. Dev. Obs Mean Std.Dev
Cumulative Growth 5 11.118 0.234 5 10.689 0.139
Duration 5 1.400 0.894 5 6.400 0.548
Total Stimulus 5 3.358 2.283 4 3.056 1.278
Total: spend 5 0.523 0.251 4 0.559 0.156
Total: tax 5 -0.477 0.251 4 -0.441 0.156
Tax: household 5 -0.166 0.171 4 -0.299 0.200
Tax: business 5 -0.124 0.091 4 -0.121 0.111
Tax: consumption 5 -0.072 0.089 4 -0.004 0.005
Spending: social 5 -0.107 0.215 4 -0.014 0.025
Spending: consumption 5 -0.008 0.018 4 0.108 0.157
Spending: Investment 5 0.394 0.464 4 0.170 0.101
Transfers: households 5 0.112 0.081 4 0.107 0.103
Transfers: business 5 0.148 0.173 4 0.067 0.078
Transfers: state 5 0.009 0.020 4 0.087 0.141
Debt 5 26.903 14.094 5 33.098 25.541
Disability Benefits 4 4.128 2.747 4 5.056 4.585
Seconday Education 5 84.230 15.696 5 72.424 22.585
Tertiary Education 5 27.148 15.384 5 33.838 9.490
Health Spending 5 6.888 1.100 5 8.796 0.977
Employment Protection 5 2.310 0.656 4 3.060 0.824
Product Market Regulation 5 1.942 0.645 5 1.542 0.112
Population (millions) 5 24.600 18.300 5 13.800 17.400
Winners: Poland, Korea, Australia, Slovak Republic
Losers: Sweden, Portugal, Norway, Luxembourg, Spain
variable (results given in Table 7). The pool of explanatory variables is the same
as before, as well as the number of regressions run in total and for each variable.
Unlike the regressions run for cumulative growth, not a single variable demonstrates
significance at the 99% level in more than 10% of regressions. The most significant
variable at this threshold is transfers to sub national governments, but this only
significant in only 7.5% of all inclusive regressions (42 of 560). At lower significance
levels, both investment spending and the percentage of the population aged 25–34
with tertiary education are significant with notable frequency, with significance at the
90% level in about 75% percent of all inclusive regressions, but this is still not frequent
enough to consider robust. Transfers to sub-national governments are significant at
16this level with a much lower frequency (about 38%). This reinforces the findings drawn
from the summary statistics, in which tertiary education and investment spending
differed the most between the two groups.
Table 7: Regression: Length of recessionary period
DURATION coeffs sterrs t-stat(1) t-stat(2) sig90 sig95 sig99
Tax: household -1.8910 1.9228 -0.7601 0.0000 0.004 0.000 0.000
Tax: business 5.9391 5.6736 1.0828 0.0304 0.082 0.030 0.002
Tax: consumption -0.9874 1.9618 -0.4947 0.0750 0.104 0.075 0.002
Spending: social -0.8512 3.9672 -0.1753 0.0000 0.000 0.000 0.000
Spending: consumption 1.1209 0.9726 0.5610 0.0625 0.118 0.063 0.005
Spending: Investment -3.6832 1.8920 -2.0307 0.4750 0.748 0.475 0.029
Transfers: households -0.6526 1.2688 -0.2229 0.0304 0.073 0.030 0.000
Transfers: business -2.6064 2.4949 -0.9909 0.0768 0.132 0.077 0.004
Transfers: state 6.5371 4.7244 1.5090 0.2536 0.382 0.254 0.075
Debt 0.0035 0.0147 0.2978 0.0036 0.016 0.004 0.000
Disability Benefits 0.2358 0.1938 1.2096 0.0179 0.041 0.018 0.000
Seconday Education -0.0206 0.0274 -0.7840 0.0339 0.071 0.034 0.002
Tertiary Education 0.1088 0.0545 2.0381 0.3893 0.750 0.389 0.025
Health Spending -0.1970 0.2969 -0.6874 0.0000 0.027 0.000 0.000
Employment Protection 0.2067 0.7619 0.4219 0.0143 0.038 0.014 0.007
Product Market Regulation -1.7443 1.1453 -1.6445 0.1821 0.525 0.182 0.061
Population 0.0000 0.0000 -0.1650 0.0000 0.002 0.000 0.000
4 Conclusion
The global financial crisis of 2008 was very harmful to the economic performance
of several countries. Fortunately, the compact time frame in which many countries
engaged in counter-cyclical policy provides an opportunity to empirically answer im-
portant economic questions. The goal of this study was to identify, if possible, com-
monalities between countries that performed exceptionally well or poorly during the
years of 2008–2010. To do so, I compared countries based on two measures: cumula-
tive growth and the length of the recessionary period. Analysis under both of these
measures suggests that high-performing countries devoted a larger fraction of their
17stimulus to investment than low-performing countries. Other variables that differed
between the summary statistics failed to extend their correlation when additional
countries were considered with regression analysis. Pre-recession variables are not
significantly correlated with performance of either kind, though debt had higher sig-
nificance frequencies than other pre-recession variables with cumulative growth as the
dependent variable.
Comparing countries based on the length of their recessionary periods is consider-
ably less fruitful, but reinforces the superior correlation between investment spending
and economic performance relative to other variables. All variables fail to regularly
show statistical significance at the highest levels when all countries are considered.
My findings also reinforce the idea that pre-recession conditions are a poor predictor
of economic performance during a recession.
It is important to understand that I have not attempted to show any causal re-
lationship between fiscal activity and short-term economic performance. The use of
cumulative variables and simple statistical techniques preclude this type of analysis.
My study does, however, provide cause for additional research on the relative efficacy
of different kinds of stimulus measures such as transfers, consumption spending, and
tax measures. Continued research on these subjects is especially important consider-
ing a growing spirit of fiscal activism as described in Taylor (2011) and the increasing
popularity of non-investment stimulus measures, as detailed in Oh and Reis (2011).
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20APPENDIX
Figure 1
High-Performing Countries: Slovak Republic, New Zealand, Korea, Australia, Poland
Low-Performing Countries: Ireland, Iceland, Hungary, Japan, Finland
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