The Effect of the €uro on Income: A panel data analysis of the individual economic ramifications of common currency

Patrick Hess

University of California, San Diego Graduate School of International Relations & Pacific Studies

MPIA Candidate 2008

Abstract

The following study analyzes the economic impacts of the adoption of a common currency; namely, the effect of the euro on the average income of those inheriting the currency. The principal hypothesis builds from the fact that countries adopting the euro not only lose control of monetary policy but they also incur rigid fiscal standards put forth by the Stability and Growth Pact1 – a requirement of Eurozone countries. It’s hypothesized that the Eurozone governments’ tremendous loss of economic stimuli presumably hinders the economic prosperity of their citizens; in turn, average income is hurt by the adoption of the euro. The hypothesis is tested using a two-way fixed effects regression on panel data acquired from Global Financial Data and the World Bank (World Development Indicators). The results from the analysis in many ways confirm the hypothesis, given the number of years and countries observed. Lastly, policy recommendations are provided to the euro currency union.

1 For more information on the Stability and Growth Pact, visit the European Commission’s website: http://ec.europa.eu/index_en.htm

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Introduction

"The history of fiat money is little more than a register of monetary follies and inflations. Our present age merely

affords another entry in this dismal register."

- Hans F. Sennholz

The effects that common currency has on income have been estimated before. For instance,

Frankel and Rose wrote such a research paper in the Quarterly Journal of Economics in May 20022.

Their findings favored the creation of currency unions due to the predicted positive impacts on

trade, which has traditionally generated wealth on the individual level. Moreover, they inferred

optimism for a common currency based on the assumption that increased trade leads to higher

income, which for the most part has been true. It is no secret that countries that trade on a large

scale with each other can benefit tremendously from a common currency (due to all currency

transaction costs being avoided). This paper does not refute this finding, nor does it propose that

trade doesn’t increase income – in fact,

the contrary is found (see graph to the

right generated from this study).

Nonetheless, the benefits of trade can be

impacted severely if governments are

incapable of adjusting to ever-changing

economic conditions using both

monetary and fiscal instruments. As long

as the proposed common currency area is extremely homogeneous and labor and capital are

extremely mobile, these ever-changing economic conditions should not pose too great a threat, for

2 Frankel, Jeffrey & Rose, Andrew. “An Estimate of the Effect of Common Currencies on Trade and Income”. The Quarterly Journal of Economics, Vol. 117, No. 2. (May 2002), Pages 437-466. http://www.mitpressjournals.org/doi/abs/10.1162/003355302753650292

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the currency area will most likely be affected mutually; in turn, the needed response will be mutual.

Contrastingly, the Eurozone was, and still is, not mobile or homogeneous enough to share monetary

policy and restrict fiscal policy.

Of the four criteria, which justify the feasibility of a currency area (and all its implications), the

Eurozone meets three quite well. These criteria, established by economists Mundell, McKinnon and

Kenen, are product diversification, openness, restrictions on fiscal transfers, and labor and capital

mobility. The Eurozone is vastly diversified in its production, remarkably open to trade and – at

least on paper – restrictive of fiscal transfers. In regards to the fourth criterion, capital is extremely

mobile; however, its labor mobility is insufficient as to create an optimal currency area. This

infraction of the criteria means that the Eurozone (and even more so, the entire EU) is not an

optimal currency area. And in light of this labor mobility shortcoming, independent monetary and,

especially, fiscal capacity for each country is essential for the average citizen’s income to be

maximized; the euro prevents this.

Data (see Appendix for full data descriptions)

All but one of the variables was taken from the World Development Indicators, provided by

the World Bank3. The currency depreciation variable for all sixteen currencies came from Global

Financial Data4. The GDP per capita, measured in constant 2000 USD, was used as the dependent

variable to represent average income. Although there are many ways to gauge income, GDP per

capita is a recognized, standard measurement. Furthermore, changes in GDP growth, an

alternatively popular indicator of economic well-being, are too responsive to population growth for

the euro’s effect to be clearly viewed. The observed time span covers twenty-one years, from 1985

to 2005. The euro variable is a dummy variable, which means it “turns-on” (changes from 0 to 1) to

3 http://devdata.worldbank.org/dataonline/ 4 http://www.globalfinancialdata.com/index.php3

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denote the switch to the euro as a country’s currency. Eleven out of the twelve Eurozone countries

began using the euro in 1999. Greece took on the euro in 2001, which allows us to view the

currency’s effect from a somewhat staggered perspective. There are three countries, the UK,

Sweden and Denmark, that maintain their 0 values for the euro variable throughout the data. These

three countries are the “non-compliers” of the policy and, although eligible, they have chosen to

maintain their domestic currencies. Please note too, Slovenia (newest member of the Eurozone) is

excluded because it was not a EU member when the euro was first introduced and there are

insufficient data. Together, data pertaining to all fifteen countries are used to estimate effects.

I pursued controlling covariates that would potentially explain income as well as correlate

somewhat with a country’s use of the euro. Total trade, as a percentage of GDP, is a control in the

model because, as mentioned earlier, history tells us that increased trade results in increased income.

Additionally, as Eurozone countries traded more over time, the idea of a common currency became

more appealing. The increased trade generated by the euro potentially disallows the euro variable

from taking too much of the credit for an income increase since its introduction. As countries

increase average income, they generally become more industrialized, which leads to urbanization; the

urban population variable controls for this. Also, as an area becomes more urban, the more

internationally focused business becomes; in turn, a demand for a common currency results. The

growth in government expenditure variable comes from economic theory, which tells us that

increases in government spending lead to national income increases. As well, the fiscal restrictions

of the euro may hamper any growth in government expenditures. Although hyperinflation is never a

welcome sight, some inflation is an indication of a growing economy for which increasing average

income can be at least partly accredited. The inflation control variable could also explain some of

why a country, with historically volatile inflation, may desire the stability of a currency like the euro.

Either extreme exchange rate depreciation or appreciation may induce a country to look for a more

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internationally viable and stable currency, such as the euro. And a country’s exchange rate has many

potential implications for income levels throughout different sectors of an economy. This logic is

what led to the controls with which I analyze the hypothesis.

Empirical Methodology

To estimate the effect of the euro on income, a two-way fixed effects regression was used.

This type of regression uses fixed effects to predict the unobservable, but unchanging, attributes in

certain countries. Additionally, this regression technique observes shocks that occur over time.

Fixed effects is used mainly because it creates an unbiased estimation – the principal concern. A

random effects regression is another popular technique with panel data. Random effects considers

the individual differences as random disturbances drawn from some specified distribution. The

advantage of this approach is that fewer degrees of freedom are needed and individual differences in

the data are considered random, as opposed to fixed and capable of being estimated. However,

random effects requires that no correlation exist between the independent variables, which indicates

that it makes unbiased predictions on its own. However, after conducting a Hausman test (see

Appendix), the random effects regression proves biased; therefore, the fixed effects remains the

estimating baseline model. This model can be seen in the following equation (note: alpha represents

the fixed effects):

!

yit = "Xit + #dt + aii=1

314

$ + uit

Shown below is a more specific equation to the model used in this study, which indicates the

principal variables for the analysis:

!

gdp_capit = "0 + "1(euro)it + "2(trade)it + "3(urban)it + "4 (gov _expend _ grwth)it + "5(inf lat)it

+"6( fx _ depr)it + "7(year)t + aii=1

314

# + uit

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A detailed explanation of each control variable’s purpose is given in the previous section (“Data”).

Although unobservable characteristics are accounted for with fixed effects, this only applies to

unchanging characteristics within a country – a frequently unrealistic assumption. If landmass, for

instance, were unobservable then fixed effects would perform quite well in predicting the effect of

landmass on the dependent variable. But many unobservable covariates change with time. For

those changing, unobservable categories this model has its limitations. For example, an individual’s

confidence could have a lot to do with success in life. This success could very well prove lucrative,

increasing the individual’s income. The fixed effects will account for this unobservable estimator of

income; yet, as one’s confidence changes with time, no adjustments can be made. In turn, the effect

of confidence on income is underestimated and the euro’s impact is, at least slightly, overestimated.

Another concerning issue is endogeneity (also known as reverse causality), which occurs

when uncertainty exists as to what causes what – the main causal variable or the predicted outcome.

In econometrics, overcoming endogeneity can be laborious or even impossible. An instrumental

variable can be employed to combat this dilemma of cause and effect. There are three requirements

of an instrumental variable (IV): the IV must not directly explain the dependent; the IV must explain

the main estimator; and the main estimator must not explain the IV. Instrumental variables are not

easy to come by and are often more difficult to quantify. The model in this analysis may experience

slight reverse causality. It has been estimated that the use of the euro instead of the traditional

domestic currency impacts income. Yet, it is not implausible to say that income levels generated the

desire for the euro in the first place. As trade between European countries grew, so did incomes.

The idea of a common currency is quite appealing to those who benefit economically from trade,

leading to the advent of a new, shared currency. Unfortunately, an instrumental variable was not

located or created for this study.

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There is not a single missing value from 1985 to 2005 in any of the variables that comprise

the model, which is a rare occurrence in econometrics. However, in any research there will

inevitably be variables that could strengthen the model more, but such variables are not available or

cannot be quantified. The effect of these missing, pertinent variables is called omitted variable bias.

Omitted variables differ from instrumental variables in that they do not resolve issues of reverse

causality. Omitted variables are simply those variables that, when placed in the model, would affect

the estimated outcome. Without them, the estimators in the model are credited too much or too

little for their impact. For this analysis, the concern is whether omitted variables are either upwardly

or downwardly biasing the effect of the euro on income that is predicted by the model.

One variable that would benefit the model, had it been available, is a measurement of

economic losses generated by exporters who sell mainly to non-Eurozone countries. Acquiring such

data exporters as a whole is not difficult; in fact, the trade variable in the model absorbs some of this

effect. Yet, the impact that the highly appreciated euro had on people whose incomes are reliant on

exports to places other than Europe was not found after a thorough search. This omitted variable

would be quite telling considering the obstacles to extra-Eurozone exportation from the end of 2001

to the end of 2004 – a time period wherein the euro appreciated by over 52 percent against the US

dollar. This means that a good in 2004 cost 52 percent more for an American to buy than it did

three years prior, and that’s without the European vendor ever changing the price of that good.

Clearly, this had a negative impact on those trying to make money from exporting to non-

Eurozones. In the omission of this export variable, the euro is attributed too much (or upwardly

biased) in explaining the negative impact it had on income. Needless to say, this bias shouldn’t be

too great because it is the euro’s appreciation that theoretically damaged the well being of said

exporters. That is, if the euro variable takes credit from this omitted variable it is not completely

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undeserved, for without the euro it is very unlikely that such appreciation would have occurred

throughout all Eurozone countries so uniformly.

Homoskedasticity, or constant variance, of the residuals is a necessary assumption to make

in order to interpret the data legitimately. Heteroskedasticity (the contrary of homoskedasticity)

means that the difference between observed values and the model’s predicted values (variance in

residuals) are non-constant. The model, in such a case, does not estimate efficiently. So for some

levels of income, the model may appropriately fit, but it may fail to do so for others. The model

used in this analysis indeed has heteroskedasticity (see Appendix). Fortunately, Newey standard

errors can be used to rid the model of problems associated with non-constant variance;

correspondingly, such standard errors are utilized in this analysis. Serial correlation of the residuals

is another, even-more problematic concern, and, just like heteroskedasticity, the model in this

evaluation suffers from it (see Appendix). But, also like heteroskedasticity, it can be counteracted

with the use of Newey standard errors. If not countered, serial correlation could lead to spurious

correlation and false estimates of the impact and causality within the model.

Results

Overall, the analysis of the model generates a statistically significant and negative relationship

between joining the euro and average income; that is, the average incomes of people in the

Eurozone are adversely affected. Four different regressions were analyzed (Table 1 on following

page), all yielding results that at least partly condemn the euro for harming incomes. The first and

most simple regression shows the euro is significant at the 90 percent level. Ceteris paribus, the

average individual makes $635 less annually for merely earning his or her living in a country that

switched to the euro. If that exact country had simply not moved to the euro, this $635 penalty

would have been averted. When the average income across these twelve Eurozone nations is nearly

$24,000 (2005), this 2.6 percent loss may not appear too dire. But there were well over 300M people

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living in Eurozone countries in 2005, which means that in a single year, total economic losses

surpassed $190B. There are two control variables that prove to be strongly significant estimators of

income as well, trade and inflation.

In the three final regressions a series of lagged variables are used (Table 1). Each lag

represents the effect of the euro one year, two years and three years after its initial implementation.

Lags are not intended to reveal if the effects occur immediately, but rather, a year or so after the

initiation of a program – a common trend in the policymaking world. Although the euro variable

falls from significance once the lag(s) is/are added, the one-year lag of the euro remains significant

in all three regressions. This indicates that the euro had its most pronounced and telling effect on

income a year into its execution. The one-year lagged euro variable’s detraction from income ranges

from $743 to $1,006, depending on the number of additional lags. In any event, one year into

participation with the Eurozone, a country can expect unfavorable effects to the average income of

its citizens.

Some of the most revealing and interesting analytical outcomes are interaction terms, which

come from the mathematical product of two other variables. For this study, I have multiplied the

euro variable and a

derivative of the urban

variable to generate the

interaction term. The

derived urban variable is a

dummy variable (just like

the euro covariate) that

represents countries with

highly urban populations

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(greater than 85% of population in urban areas). The output of this regression is available in Table 2

and the graph above visually displays the interaction effect. In less urban countries, being on the

euro yields a higher income than that of their non-euro counterparts. However, living in a highly

urban country while on the euro severely penalizes incomes relative to non-euro countries that are

equally urbanized. These estimations are generated with all variables held at their means and the

year set to 2005. Perhaps highly urbanized countries that adopted the Euro are greatly dependent

on exporting to countries outside of the Eurozone. As a result, the high appreciation of the

currency hurt these countries’ incomes more significantly. It could also be due to relatively greater

volatility in urban, industrial nations during the first six years of the decade. Moreover, maybe more

rural, agriculturally based economies did not suffer the great economic shocks that would have

evoked a need for monetary and fiscal instruments – those revoked by the euro. To truly pinpoint

the cause of this noteworthy relationship, further research into these matters is necessary.

Robustness Checks

Although the model design has accounted for heteroskedasticity and serial correlation of the

error terms, unresolved issues remain. With the same falsifying effect as serial correlation,

autocorrelation of the dependent variable proves to be very much a problem in this model

(Appendix: Table 3). Unfortunately with the structure of the data, no preventive tool can be used to

generate stronger results. Had the model faired positively under a robustness check then issues of

autocorrelation could have been somewhat overlooked; this was not the case. By collapsing the

dataset into two periods, before and after the euro, the impact of the euro is isolated (Appendix:

Table 4). Now, the net changes from pre and post euro periods are averaged and they can be seen

even in the presence of autocorrelation. When a single change is used to calculate impacts, no errors

can occur in the standard errors, which is what is accomplished with the collapsed the dataset. The

model does not withstand the robustness check. Instead, the trade and urban variables stay

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significant estimators of income after averaging effects. This process, albeit decisive when affirming

robustness, requires extreme manipulation of the data, so it does not necessarily indicate that all

inferences derived from the model are incorrect. Nevertheless, the model is conclusively not robust

and decisions based on this model should be made prudently.

Conclusion

The final model comes from logically based estimators of average income on a

macroeconomic level; yet, these estimators still stay in some way connected to the advent of the

euro. Managing this while avoiding multicollinearity is not always a simple task, but it is something

that this model does quite well. Additionally, the model is adjusted to counter both problematic,

non-constant variance and biasing serial correlation. That said, the model is not robust and it suffers

from severe autocorrelation of the dependent variable, which are both quite concerning. Much of

this is due to a lack of observations (twelve countries to undergo treatment is not many) and time

(only seven years are in the data). For concrete analysis of something as slow moving as average

income, more time and observations will generate greater confidence and clarity. In the next decade

or so, eleven new EU members will provide much more determinable data to analyze once they

adopt the euro. Yet, this study’s findings, although imperfect, expose the potential consequences to

common currency policies inherent in a sub-optimal currency area. This study does not advocate a

retraction of the euro; rather, policies should be designed to enhance the four criteria for an optimal

currency area, especially in the genre of labor mobility. It would be far too costly and

counterproductive to ditch the euro at this stage. However, it is hypothesized in this study, and

agreed upon by others5, that labor mobility is a principal hindrance to the euro properly benefiting

the Eurozone as it was forecasted. The EU has laws and institutions capable of fostering a currency

5Dr. Willem F. Duisenberg, first President of the European Central Bank, at a breakfast meeting of the Council on Foreign Relations (New York, 19 April 2002), acknowledged this shortcoming of the euro common currency area. http://www.ecb.int/press/key/date/2002/html/sp020419.en.html

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area on par with the fifty United States. For instance, an Italian can already legally work in Finland –

just as a Floridian can be employed in North Dakota. Incentive programs should be set up to

encourage this already permitted labor mobility to move where it is economically most needed.

Until then, the full benefits of a common currency will not be enjoyed and consequences, such as

impaired income, will be suffered.

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Appendix

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (15) = 2727.28 Prob>chi2 = 0.0000 ---- Hausman test results ---- | (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fe . Difference S.E. -------------+--------------------------------------------------------------- euro | 1039.294 1543.407 -504.1125 99.12507 trade | 158.512 143.5148 14.99711 2.883547 urban | 378.9384 273.6135 105.3249 51.45107 gov_expend~h | 57.04399 49.06486 7.97913 . inflat | -52.21796 -61.06301 8.845047 . fx_depr | 16.77748 19.76911 -2.991626 . ----------------------------------------------------------------------------- b = consistent under Ho and Ha; obtained fromxtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 27.83 Prob>chi2 = 0.0001 (V_b-V_B is not positive definite) Wooldridge test for autocorrelation in panel data H0: no first order autocorrelation F( 1, 14) = 844.072 Prob > F = 0.0000

Variables Description

gdp_cap GDP per capita (constant 2000 US$); DEPENDENT VARIABLE

year Years 1985 - 2005

euro = 1 when countries use the euro; = 0 when they do not

trade Trade (% of GDP)

urban Urban population (% of total)

gov_expend_grwth General government final consumption expenditure (annual % growth)

inflat Inflation, consumer prices (annual %)

fx_depr Depreciation of currency against USD (annual %)

Description of Variables in Dataset Used for Analysis

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gdp_cap gdp_cap gdp_cap gdp_cap

euro -634.8** 33.69 80.98 101.1

(280) (209) (219) (232)

trade 110.4*** 116.4*** 113.4*** 109.5***

(14) (13) (15) (15)

urban 70.5 43.33 43.89 40.43

(63) (58) (69) (78)

gov_expend_grwth 69.32 38.46 44.23 54.65

(48) (42) (45) (41)

inflat 221.7*** 240.1*** 241.1*** 230.6***

(44) (49) (56) (52)

fx_depr 1.566 -10.39 -10.13 -9.472

(8) (7) (7) (7)

year 371.9*** 350.7*** 344.8*** 342.5***

(32) (31) (37) (40)

eurolag -742.6*** -1006*** -942.8***

(273) (277) (259)

eurolag2 378 151.8

(384) (195)

eurolag3 294.2

(466)

Constant -734199*** -690711*** -678842*** -673610***

(62822) (60968) (71761) (78123)

Observations 307 293 279 265

Countries 15 15 15 15

R-squared . . . .

Standard errors in parentheses

Explanatory

Variables

Dependent Variable

*** p<0.01, ** p<0.05, * p<0.1

Table 1: Impact of the euro

1985 - 2005 Dataset

Two-way fixed effects regression, using Newey standard errors

Dependent Variable

gdp_cap

year 358.4***

(26)

euro 275.7

(401)

hi_urban 86.19

(586)

euro*hi_urban -2749***

(603)

trade 117.6***

(7)

gov_expend_grwth 43.92

(33)

inflat 254.2***

(36)

fx_depr -9.651

(6)

eurolag -744.0*

(416)

Constant -703405***

(50685)

Observations 293

R-squared 0.98

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Explanatory Variables

Table 2: Interaction effect

1985 - 2005 Dataset

Two-way fixed effects regression

Dependent Variable

gdp_cap

ylag 1.028***

(0)

Constant -80.97

(78)

Observations 300

R-squared 1

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Explanatory

Variables

1985 - 2005 Dataset

Table 3: Autocorrelation of dependent

variable

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Dependent Variable

dify

europost -1734

(1290)

diftrade 241.9**

(89)

difurb 367.3**

-146

diffx 102.4

(74)

difinflat 4.662

(131)

difgov 354.2

(447)

Constant 2889

(1876)

Observations 14

R-squared 0.87

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Explanatory

Variables

Table 4: Robustness check

Ordinary least squares regression on

collapsed data