Defense Spending and Unemployment. Evidence from Southern European Countries
-
Chletsos Michael
und
Roupakias Stelios
Abstract
This paper applies the ARDL approach to cointegration in order to address the defense unemployment nexus. We use information on Portugal, Italy, Greece and Spain for the period 1960–2015. Our main results suggest that: (i) there is a stable long-run relationship between the variables under consideration for Portugal, Greece and Spain. (ii) Defense spending decreases (increases) unemployment for the case of Portugal and Greece (Spain), (iii) the impact of non-defense spending is weaker than that of defense spending and (iv) Okun’s law is validated for Portugal, Greece and Spain. (v) These results are robust to the use of heterogeneous panel cointegration and causality analysis.
Acknowledgments
We are very grateful to two anonymous referees for a number of very useful suggestions and comments. All remaining errors are ours.
Appendix A
Multiple breakpoint tests
Table A1 reports the results of the Bai and Perron (1998, 2003) procedure. The first two rows show the double maximum tests, UD-max and WD-max, which reject the null hypothesis of zero breaks at the 5 percent significant level. Similarly, the SupF statistics confirm that there is a structural change in the data. In the following two rows of Table A1 we present the number of breaks detected by the SBC and the LWZ. In the case of Portugal and Italy where the criteria detect different number of breaks, we rely on the information about mean unemployment before the first break date and after each subsequent break, shown at the lower part of Table A1. For example, since mean unemployment is very dissimilar before 1976 for the case of Portugal, and after 1976, 1988 and 2008, we conclude that Portugal has three significant breaks. Similarly, we find that Italy has two changing means. In the case of Greece and Spain two and three breaks respectively are identified by both criteria.
Structural break tests of Bai-Perron (1998, 2003).
| Test | Portugal | Italy | Greece | Spain |
|---|---|---|---|---|
| UDmax | 71.27* | 15.75* | 43.22* | 65.65* |
| WDmax | 102.61* | 34.56* | 74.32* | 144.06* |
| SupF(1) | 1.87 | 1.87 | 0.95 | 5.89 |
| SupF(2) | 30.93* | 8.43* | 3.77 | 19.54* |
| SupF(3) | 71.27* | 11.48* | 5.88 | 30.84* |
| SupF(4) | 56.79* | 6.35* | 43.22* | 44.96* |
| SupF(5) | 45.29* | 15.75* | 33.73* | 65.65* |
| SBC | 3 | 2 | 2 | 3 |
| LWZ | 2 | 1 | 2 | 3 |
| Mean unemployment before break | 2.56 | 5.11 | 3.77 | 3.45 |
| Break dates | 1976 | 1977 | 1983 | 1980 |
| [8.51] | [9.03] | [11.34] | [16.62] | |
| 1988 | 1986 | 2008 | 1999 | |
| [8.6] | [9.46] | [20.51] | [15.68] | |
| 2008 | 2008 | |||
| [13.5] | [22.38] |
The 5% critical values for the supF(l) test in the case of non-stationary variables are 8.58, 7.22, 5.96, 4.99 and 3.91 for l=1, 2, 3, 4, 5 respectively. Critical value for UDmax test is 8.88. Critical value for WDmax test is 9.91. Mean unemployment rates following the break are reported in brackets below the estimated break dates.
*Denotes statistical significance at 5% level.
Appendix B
CUSUM and CUSUM of squares tests
The figure reports the CUSUM (Column 1) and the CUSUM of squares (Column 2). The countries from first row to the last are Portugal, Italy, Greece, Spain.
Appendix C
Panel unit root and cointegration tests
To investigate the unit root properties of the data, we employ two first generation unit root tests, namely the Im, Pesaran, & Shin (2003) (IPS) test and the Breitung (2000) test. The IPS test is a heterogeneous panel unit root test based on individual ADF tests. On the other hand, the Breitung tests assume a common unit root across the countries. [28] Both tests, however, do not account for cross-section dependence in the data, and hence, they are valid for inference only if cross sectional independence occurs. Hence, in further sensitivity analysis, we employ the second generation panel unit root test proposed by Pesaran (2007) (CIPS), which allows for cross-sectional dependence. The results are reported in Table C1. Taken together, both the first generation and the second generation tests suggest that all variables, except for GDP growth, have a unit root problem and hence we need to proceed by testing for panel cointegration, in order to exclude the possibility of spurious correlation.
Results for panel unit root tests.
| First generation | Second generation | |||||
|---|---|---|---|---|---|---|
| IPS t-stat | Breitung t-stat | Pesaran CIPS | ||||
| Level | 1st difference | Level | 1st difference | Level | 1st difference | |
| U | 0.51 [0.697] | –6.34 [0.000] | –1.15 [0.125] | –1.88 [0.029] | 1.05 [0.853] | –4.79 [0.000] |
| M | 1.29 [0.902] | –11.07 [0.000] | –1.76 [0.039] | –4.25 [0.000] | 0.56 [0.715] | –4.86 [0.000] |
| NM | 0.78 [0.784] | –11.69 [0.000] | 0.80 [0.789] | –9.46 [0.000] | –0.97 [0.000] | –4.03 [0.000] |
| G | –5.45 [0.000] | –13.10 [0.000] | –3.38 [0.000] | –4.79 [0.000] | –4.12 [0.000] | –7.856 [0.000] |
The null hypothesis is that the variable follows a unit root process. Numbers in brackets are p-values. Schwarz Bayesian Criterion was used to determine the optimal lag length for First Generation tests. Two lags are introduced to adjust for serial correlation in the errors for the Second Generation test. The estimations are based on 216 observations for the period 1960–2015.
To determine whether a long-run relationship exists between the variables under consideration, the Pedroni (1999) panel and group ADF statistics, the Kao (1999) ADF statistic and the Westerlund (2007) cointegration tests are employed. The first two tests are residual based tests, which are robust only when cross sectional independence is assumed. The third test has higher power than the residual based tests and yields robust inference, even in the case of cross sectional dependence, provided that bootstrap p-values are used (Herzer 2016).
Results of the cointegration analysis are reported in Table C2. Pedroni’s statistics yield conflicting evidence. More precisely, panel ADF statistic rejects the null hypothesis of no cointegration at ten percent significant level. However, group ADF statistic cannot reject the null hypothesis. On the other hand, Kao’s ADF statistic implies that the variables are cointegrated at the one percent significant level. The last four rows of the table report the group-mean statistics, Gτ and Gα, which test the null hypothesis of no cointegration against the alternative that there is cointegration for at least one cross sectional-unit, and the panel statistics, Pτ and Pα, which test the null of no cointegration against the alternative that the panel is cointegrated. As can be verified, the four statistics proposed by Westerlund (2007) indicate that there is a stable long-run relationship between the variables.
Panel cointegration tests.
| Pedroni (1999) | ||
| Panel ADF statistic | –1.687* | |
| Group ADF statistic | –1.218 | |
| Kao (1999) | ||
| ADF statistic | –2.420*** | |
| Westerlund (2007) | ||
| Gτ | – 3.959*** | (0.000) |
| Gα | – 20.763*** | (0.000) |
| Pτ | – 8.012** | (0.000) |
| Pα | – 23.702** | (0.000) |
Robust p-values obtained through bootstrapping with 500 replications are shown in parenthesis. *** (**) Indicate rejection of the null hypothesis of no cointegration at the 1% (5%) level.
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Artikel in diesem Heft
- Frontmatter
- Can News Draw Blood? The Impact of Media Coverage on the Number and Severity of Terror Attacks
- International Cooperation: Testing Evolution of Cooperation Theories
- Potential uses of Numerical Simulation for the Modelling of Civil Conflict
- Defense Spending and Unemployment. Evidence from Southern European Countries
Artikel in diesem Heft
- Frontmatter
- Can News Draw Blood? The Impact of Media Coverage on the Number and Severity of Terror Attacks
- International Cooperation: Testing Evolution of Cooperation Theories
- Potential uses of Numerical Simulation for the Modelling of Civil Conflict
- Defense Spending and Unemployment. Evidence from Southern European Countries
