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References

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Annex 1. List of Developing Countries in the Sample by Income Level Group

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Note: The year count reflects number of observations of regressions for total expenditure as the dependent variable in sample used in system GMM. Countries are divided according to 2008 GNI per capita, calculated using the World Bank Atlas method. The groups are: low income, $975 or less; lower middle income, $976 - $3,855; upper middle income, $3,856 - $11,905.

Annex 2. Raw Correlations

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Annex 3. Econometric Approach: Additional Notes

Instrumental Variables Fixed Effects

We instrument the domestic output growth rate with the real output growth of trading partners, weighted by their share of exports, and with the lagged real domestic output growth. This instrument passes the Craigg-Donald Wald instruments F-test relative to the critical values suggested by Stock and Yogo (2002) for both developed and developing countries. However, IV-FE estimation fails to reject the null of the Durbin-Wu-Hausman (DWH) test in health expenditures specification. This suggests that the instrument used is not valid for these specifications. This is one of the reasons why we turned to S-GMM. Results for the DWH test do not improve when the lagged real domestic output growth is excluded from the set of instruments.

System-GMM

We determine the number of lags used in each particular specification (i.e., one for each of the three dependent variables studied) based on the degree of exogeneity of the explanatory variables used with respect to the dependent variable (i.e., whether they are a priori assumed to be predetermined or endogenous), and on whether this lag level passes the tests for validity of the instruments (Hansen-statistic) as well as of serial correlation of the disturbance term (evidence of an AR2 process in first differences indicates that the tested lag structure is invalid). In most cases, we used the second lag to instrument real GDP growth (second lag in the transformed equations; and first lag first differences in the levels equation, and the first lag to instrument the lag of fiscal balance as percent of GDP (first lag in the transformed equations; and contemporaneous first differences in the levels equation).

A large instrument count in system GMM models can overfit endogenous variables in finite samples and weaken the Hansen test used to check the validity of the instruments. Roodman (2008) illustrates this point. We address this problem—as suggested by him—by restricting the lag range used in the instrument matrix to only one lag as opposed to all available lag periods and by collapsing the instrument matrix so that there is only one instrument for each variable and lag distance.

Annex 4. Cyclicality of Total, Education, and Health Public Expenditures: Fixed Effects and IV-Fixed effects

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Notes: **, *, and + denote statistical sigificance at the 1, 5, and 10 percent respectively.t-statistics reported in parenthesesAll regressions include year-fixed effects.

Annex 5. Cyclicality of Recurrent Expenditures

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Notes: **, *, and + denote statistical sigificance at the 1, 5, and 10 percent respectively.t-statistics reported in parenthesesAll regressions include year-fixed effects and time period dummies.
1

The authors are grateful for helpful comments and suggestions received from Emanuele Baldacci, Ugo Panizza, Fidel Jaramillo, Abdoul Wane, and Benedict J. Clements. They are also grateful to Ezequiel R. Cabezon and John Piotrowski for excellent assistance in the collection of data. Responsibility for remaining errors and omission lies with the authors.

4

For further discussion of this point see IDB (2009) and references therein.

5

For example, see Gavin and Perotti (2007), Riascos and Végh (2003), Caballero and Krishnamurthy (2004), and Susuki (2006).

6

Buti and Sapir (1998) and Balassone, Francese and Zotteri (2008) find similar evidence in EU countries.

7

Alternatively, the output gap could be used. We explored this option; however, this specification did not pass the Hansen tests for S-GMM nor the Durbin-Wu-Hausman tests of exogeneity for an Instrumental Variables Fixed Effects model (IV-FE). Both, the fiscal variables and GDP growth could also be expressed as deviations from a long-run trend by using the Hodrik-Prescott filter. Yet there are well-known problems associated with detrending series in developing countries which could add substantial measurement error to our estimation. Both of the econometric methodologies employed in this paper control for country-specific effects, either by time-demeaning the variables or by first differencing, which helps to overcome this problem.

8

Education and health spending were converted into constant prices in domestic currency using the GDP deflator. The conclusions of the paper do not change if CPI is used instead of GDP deflator. In any case, the GDP deflator is preferable since it also captures changes in prices of intermediate inputs.

9

Data classified along the UN’s COFOG functional classification of expenditure are also available in the Government Financial Statistics (GFS) database. However, country coverage therein is too spotty, and not suitable for the econometric analysis performed in this study.

10

See Annex 2 for correlations among all variables used.

11

Potential output for each country is computed with a Hodrick-Prescott filter.

12

Countries are divided according to 2008 GNI per capita, calculated using the World Bank Atlas method. The groups are: low-income, $975 or less; lower-middle income, $976 - $3,855; upper-middle income, $3,856-$11,905. See Annex 1 for a list of the countries included in each group.

13

All models discussed in this paper included time period dummies to control for global shocks.

14

Following Lane (2003) and Jaimovich and Panizza (2007), we instrumented the domestic output growth rate with two variables: one measuring external shocks equal to the real output growth of trading countries, weighted by their share of exports, and the other by the lagged real domestic output growth.

15

See Annex 3 for additional notes on the econometric approach.

16

The sample size corresponds to the number of countries for which data on total expenditures are available for univariate regressions. The sample size varies for multivariate as well as for education and health regressions.

17

Hallerberg and Strauch (2002) find primary expenditures in EU member states to be countercyclical while Lane (2003) finds different degrees of cyclicality in OECD countries based on country-specific estimates of fiscal cyclicality.

18

It should be noted that Table 4 reports results from S-GMM, whereas Jaimovich and Panizza use instrumental variables. This, together with our larger sample size, could explain the difference in results.

19

Table 5 reports results from S-GMM. We also explored the IV-FE methodology on two sub-samples: one for good times and another for bad times (following Jaimovich and Panizza, 2007). We find procyclicality for total expenditures in both good and bad times, but these specifications fail to reject the Durbin-Wu-Hausman test null when the dependent variables are education and health expenditures suggesting that the instrument is not valid.

20

Using the output gap, Clements Faircloth and Verhoeven (2007) also find that primary expenditures are procyclical only during bad times in Latin America.

21

This result is broadly robust to the use of an alternative definition of good and bad times. The latter are defined as periods of positive and negative real GDP growth, respectively. These results are available from the authors upon request.

22

Time dummies are not included in these specifications to preserve degrees of freedom because the number of observations is substantially lower when the sample is divided in groups by income level.