## 5. Determinants of Fiscal Cyclicality

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### I. Introduction and methodology
- Sample and period:
  - 51 developing countries, period 1970–2002.
  - Cross-sectional regressions: final N = 35 (16 countries dropped due to missing regressors).
- Two-stage empirical strategy:
  - Stage 1: country-by-country time-series regressions to estimate short-run elasticities β0 using an error-correction model when cointegration present; otherwise estimate β0 from Δ log G = β0 Δ log Y + μ.
  - Stage 2: cross-sectional regressions explaining variation in estimated β0 using country characteristics Z (equation (7)).
- Error-correction framework:
  - Long-run steady-state elasticity δ from G = A Y^δ.
  - Error-correction representation captures short-term impact (β0 on Δ log Y) and adjustment via error-correction term [log Gt-1 − δ log Yt-1] with speed γ.
  - If γ insignificant, short-run elasticity estimated from first-difference regression.

### II. Main empirical findings — stationarity, cointegration, and long-term elasticities
- Stationarity and cointegration:
  - Error-correction term (γ) significant in about 35 percent of countries for each spending aggregate.
  - 80 percent of sample countries have a significant error-correction term for at least one spending aggregate.
  - Adjustment coefficients γ are negative (dynamic stability) when significant.
- Long-term elasticity δ (Table 3 averages):
  - Total Spending average = 1.26
  - Current Spending average = 1.32
  - Spending on Goods and Services average = 0.97
  - Wages and Salaries average = 0.93
  - Other Goods and Services average = 1.21
  - Capital Spending average = 1.19
  - Non-Interest Current Spending average = 1.35
  - Non-Interest Total Spending average = 1.33
  - Interpretation: long-term elasticity δ is on average greater than 1 (consistent with Wagner’s law).
- Average adjusted R2 in Table 3 range: 0.29 to 0.66 depending on category.

### III. Short-term elasticities (β0) — magnitudes and cross-country variation
- Summary statistics (Table 4 averages):
  - Total = 0.72
  - Current = 0.41
  - Goods and Services = 0.77
  - Wages = 0.37
  - Other Goods = 0.97
  - Capital = 1.98
  - Non-Interest Current = 0.70
  - Non-Interest Total = 1.01
- Frequency and magnitude:
  - Short-run elasticity positive in about half the countries; mean coefficients positive for all spending categories.
  - For total spending: about 50 percent of countries show positive and significant short-run elasticity with mean coefficient above unity (1.24 reported as average of significant coefficients).
  - Non-interest total spending: 53 percent of countries show large short-run elasticity with mean coefficient close to 2 (1.78 reported).
  - Capital spending: highest short-run elasticity — mean coefficient 1.98 for full sample and 3.26 for 49 percent of cases where coefficient significant.
  - On average across countries where cointegration found, the long-term elasticity is 75 percent larger than the short-term elasticity.
  - For capital spending, the short-term coefficient exceeds the long-term coefficient in 18 percent of sample countries.
- Notable country-level negatives:
  - Non-wage current spending strongly negative in Kenya and Barbados.
  - Capital spending negative in Egypt and Venezuela.
  - Non-interest current spending slightly negative in India and Thailand.

### IV. Cross-country determinants of fiscal cyclicality (Table 5, weighted least squares, N = 35)
- Power dispersion (Henisz index, average index (0,1)):
  - Negative and statistically significant for most spending categories except other goods and services.
  - Example coefficient estimates (Table 5):
    - Total Spending = -1.98 (0.785) **
    - Current Spending = -1.41 (0.783) *
    - Spending on Goods and Services = -1.99 (0.930) **
    - Wages and Salaries = -2.53 (1.201) **
    - Capital Spending = -4.11 (2.250) *
    - Non-Interest Total Spending = -2.47 (1.027) **
  - Interpretation: greater dispersion of political power tends to dampen procyclical short-term elasticity.
- Government size (average government spending as percent of GDP):
  - Negative and significant for most categories (exceptions: other goods and services and capital spending).
  - Example coefficients:
    - Total Spending = -0.03 (0.017) *
    - Spending on Goods and Services = -0.06 (0.020) **
    - Non-Interest Current Spending = -0.04 (0.021) *
    - Non-Interest Total Spending = -0.05 (0.023) **
  - Interpretation: larger government size tends to stabilize spending (smaller short-term elasticity).
- Financial risk (ICRG financial risk rating; higher points → higher risk):
  - Positive and significant in several specifications.
  - Example coefficients:
    - Total Spending = 0.08 (0.025) ***
    - Current Spending = 0.05 (0.025) *
    - Spending on Goods and Services = 0.06 (0.028) **
  - Interpretation: higher financial risk is associated with greater procyclicality.
- Output volatility (standard deviation of GDP growth):
  - Positive effect for spending on goods and services: coefficient 0.69 (0.29) **.
  - Other categories: no robust significant effect.
- Average ODA as share of GNI:
  - No significant impact on short-term elasticity across spending categories.
  - Interpretation: aid dependence does not contribute to cyclicality in this sample.
- Standard deviation of terms of trade:
  - No significant effect in Table 5 estimates.
- Per capita GDP (PPP):
  - Coefficients presented as 0.00 with occasional significance at *; overall not an important influence across most specifications.
- Goodness-of-fit (Table 5 adjusted R2 by category):
  - Total Spending = 0.26
  - Current Spending = 0.17
  - Spending on Goods and Services = 0.36
  - Wages and Salaries = 0.01
  - Other Goods and Services = 0.06
  - Capital Spending = 0.00
  - Non-Interest Current Spending = 0.08
  - Non-Interest Total Spending = 0.19
  - Observations = 35 for all regressions; root mean square errors range 0.59 to 1.74.

### V. Conclusions and policy implications
- Main empirical conclusions:
  - Main components of government spending are procyclical in about half of all countries; degree varies by spending category.
  - Output and government spending are cointegrated in a majority of countries for at least one spending aggregate — long-term relationship consistent with Wagner’s law.
  - Cross-country determinants: power dispersion and government size typically dampen short-run procyclical spending; financial risk and output volatility contribute to procyclicality.
- Policy implications (authors’ three principal recommendations):
  1. Fiscal rules or fiscal responsibility laws may help limit discretion for procyclical fiscal policy during upswings.
  2. Strengthening checks and balances (greater dispersion of power) is compatible with good policymaking and helps reduce cyclicality of government outlays.
  3. In countries with a long-term spending–output relationship, short-run spending cuts or surges will tend to revert to the long-term spending/GDP ratio; durable savings require accompanying longer-term structural reforms.
- Suggestions for future research:
  - Distinguish between good times and bad in empirical analysis.
  - Assess how IMF-supported economic reforms and fiscal reforms have affected procyclical behavior of spending.
  - Extend analysis to short- and long-term movements of revenues and fiscal balances.

*Source: IMF Working Paper section "5. Determinants of Fiscal Cyclicality" (content unit: _wp04202 - 5. Determinants of Fiscal Cyclicality).*

### References..............................................................................................................

### _wp04202 - References..............................................................................................................

### References
- References................................................................................................................................21

### Tables
- 1. Augmented Dickey Fuller Tests ..........................................................................................12
- 2. Error-Correction Model: Estimates of Gamma—The Adjustment Coefficient...................13
- 3. Error-Correction Model: Estimates of Delta—The Long-Term Coefficient .......................14
- 4. Error-Correction Model: Estimates of Beta—The Short-Term Coefficient ........................15

*https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2004/_wp04202.pdf*

### 5. Determinants of Fiscal Cyclicality...................................................................................

### 5. Determinants of Fiscal Cyclicality

### I. Introduction
- Study documents co-movements of government spending and output for 51 developing countries (period 1970–2002).
- Methodological contributions:
  - Documents spending–output co-movements across 51 developing countries.
  - Introduces an error-correction approach to separate short-term variations from longer-term relationships.
  - Explains cross-country variation in spending–output relationships by focusing on political and economic determinants.

### II. Literature review (key points)
- Theoretical expectations:
  - Neoclassical "tax smoothing" (Barro, 1979) implies procyclical budget surplus but typically assumes exogenous government spending.
  - Keynesian view: fiscal policy should accommodate automatic stabilizers and use discretionary policy in booms/recessions.
- Empirical evidence in OECD:
  - Mixed: countercyclical for some components (Galí, 1994); no discernible pattern for government consumption in G-7 (Fiorito, 1997); current spending mildly countercyclical but investment procyclical (Lane, 2003).
  - Transfers and subsidies have become more countercyclical over time (Arreaza, Sørensen, and Yosha, 1999).
  - Evidence of "cyclical ratcheting" (rise in government spending/GDP ratio through asymmetric adjustments).
- Evidence in developing countries:
  - Predominantly procyclical government spending (Kaminsky, Reinhart, and Végh, 2004; Braun, 2001; Gavin and Perotti, 1997; Talvi and Végh, 2000).
  - Political/institutional factors (voracity effect, fractioned power) can drive overspending during transitory revenue increases.
- Gap addressed by paper:
  - Joint assessment of short- and long-run relationships between government spending and output using an error-correction framework.

### III. Framework for analyzing co-movements of government spending and output
- Conceptual setup:
  - Long-run steady-state elasticity: δ defined by G = A Y^δ (equations (1) and (2)).
  - Autoregressive distributed lag for gradual adjustment (equation (3)).
  - Error-correction representation (equation (5)): captures short-term impact (Δ log Y term with short-run elasticity β0) and deviation from steady-state via error-correction term [log Gt-1 − δ log Yt-1] with speed γ.
  - If γ insignificant, short-run elasticity β0 estimated from Δlog G = β0 Δlog Y + μ (equation (6)).
- Two-stage empirical strategy:
  1. Country-by-country time-series regressions to estimate short-run elasticities β0 (from error-correction model where cointegration exists, otherwise from equation (6)).
  2. Cross-sectional regressions explaining variation in estimated β0 using country characteristics Z (equation (7)).
- Explanatory variables in cross-section (Z) include:
  - Output volatility (standard deviation of GDP growth).
  - Power dispersion index (average index (0,1) from Henisz (2000)).
  - GDP per capita (PPP).
  - Standard deviation of terms of trade.
  - Measures of financing availability: average share of ODA as share of GNI; ICRG financial risk rating.

- Expected effects (stated hypotheses):
  - Output volatility → increase short-term elasticity (procyclicality).
  - Power dispersion → reduce short-term elasticity (dilution of power expected to reduce procyclicality).
  - Higher GDP per capita → negative effect on short-term elasticity (poorer countries rely more on discretionary policy).
  - Greater external vulnerability (terms of trade volatility) → decrease short-term elasticity (governments use spending as insurance).
  - Higher financial risk → increase short-term elasticity (limits countercyclical scope).

### IV. Empirical results
- Data and coverage:
  - Sample: 51 developing countries, period 1970–2002.
  - Cross-sectional regressions drop 16 countries due to missing regressors (final cross-section N = 35 for regressions reported in Table 5).
  - Spending categories examined: total spending; current spending; government consumption (goods and services); wages and nonwage government consumption; capital spending; non-interest total spending; non-interest current spending.

- Stationarity and cointegration:
  - Table 1: Augmented Dickey-Fuller tests for log variables across countries and spending categories (summary counts provided).
  - Error-correction term (γ) significant in about 35 percent of countries for each spending aggregate.
  - 80 percent of sample countries have a significant error-correction term for at least one spending aggregate.
  - Adjustment coefficients γ are negative (dynamic stability) when significant.

- Long-term elasticity (δ) — Table 3 highlights:
  - The long-term elasticity δ is on average greater than 1 (consistent with Wagner’s law).
  - δ is highest for non-interest current and total spending.
  - Reported average δ across categories (Table 3 averages): Total Spending average = 1.26; Current Spending average = 1.32; Spending on Goods and Services average = 0.97; Wages and Salaries average = 0.93; Other Goods and Services average = 1.21; Capital Spending average = 1.19; Non-Interest Current Spending average = 1.35; Non-Interest Total Spending average = 1.33.
  - Average adjusted R2 in Table 3 range: 0.29 to 0.66 depending on category.

- Short-term elasticity (β0) — Table 4 highlights:
  - Short-run elasticity positive in about half the countries; mean coefficients positive for all spending categories.
  - For total spending: about 50 percent of countries show positive and significant short-run elasticity with mean coefficient above unity (1.24 reported as average of significant coefficients).
  - Excluding interest payments (non-interest total spending): 53 percent of countries show large short-run elasticity with mean coefficient close to 2 (1.78 reported).
  - Capital spending exhibits the highest short-run elasticity: mean coefficient 1.98 for full sample and 3.26 for 49 percent of cases where coefficient significant.
  - Current spending has the smallest short-run elasticity but increases when interest payments excluded.
  - On average across countries where cointegration found, the long-term elasticity is 75 percent larger than the short-term elasticity. For capital spending, short-term coefficient exceeds long-term coefficient in 18 percent of sample countries.
  - Notable country-level negatives: non-wage current spending strongly negative in Kenya and Barbados; capital spending negative in Egypt and Venezuela; non-interest current spending slightly negative in India and Thailand.
  - Table 4 average short-run (overall average) by category: Total = 0.72; Current = 0.41; Goods and Services = 0.77; Wages = 0.37; Other Goods = 0.97; Capital = 1.98; Non-Interest Current = 0.70; Non-Interest Total = 1.01. (Table 4 also reports averages of significant coefficients separately in column “Average 2/”.)

- Cross-country determinants (Table 5 — weighted least squares, N = 35 observations per regression):
  - Power dispersion:
    - Negative and statistically significant for all spending categories except other goods and services.
    - Coefficient estimates (Table 5): e.g., Total Spending = -1.98 (standard error 0.785) **; Current Spending = -1.41 (0.783) *; Spending on Goods and Services = -1.99 (0.930) **; Wages and Salaries = -2.53 (1.201) **; Capital Spending = -4.11 (2.250) *; Non-Interest Total Spending = -2.47 (1.027) **.
    - Interpretation: greater dispersion of political power tends to dampen procyclical short-term elasticity.
  - Government size:
    - Negative and significant for most categories (except other goods and services and capital spending).
    - Coefficients (Table 5): e.g., Total Spending = -0.03 (0.017) *; Spending on Goods and Services = -0.06 (0.020) **; Non-Interest Current Spending = -0.04 (0.021) *; Non-Interest Total Spending = -0.05 (0.023) **.
    - Interpretation: larger government size tends to stabilize spending (smaller short-term elasticity).
  - Financial risk:
    - Positive and significant in several specifications, indicating higher short-term elasticity where financial risk is higher.
    - Example coefficients (Table 5): Total Spending = 0.08 (0.025) ***; Current Spending = 0.05 (0.025) *; Spending on Goods and Services = 0.06 (0.028) **.
    - Interpretation: higher financial risk reduces scope for countercyclical policy and is associated with greater procyclicality.
  - Output volatility:
    - Positive effect on short-term elasticity for spending on goods and services: coefficient 0.69 (0.29) **.
    - Other categories show no robust significant effect.
  - Average ODA as share of GNI:
    - No significant impact on short-term elasticity across spending categories.
    - Interpretation: aid dependence does not contribute to cyclicality of spending in this sample.
  - Standard deviation of terms of trade:
    - No significant effect in Table 5 estimates.
  - Per capita GDP (PPP):
    - Coefficients presented as 0.00 with occasional significance at *; overall not an important influence across most specifications.

- Goodness-of-fit / summary statistics (Table 5):
  - Adjusted R2 by spending category: Total Spending = 0.26; Current Spending = 0.17; Spending on Goods and Services = 0.36; Wages and Salaries = 0.01; Other Goods and Services = 0.06; Capital Spending = 0.00; Non-Interest Current Spending = 0.08; Non-Interest Total Spending = 0.19.
  - Observations = 35 for all reported regressions.
  - Root mean square errors vary by category (range 0.59 to 1.74).

### V. Conclusions and policy implications
- Main empirical conclusions:
  - Main components of government spending are procyclical in about half of all countries; degree varies by spending category.
  - Output and government spending are cointegrated in a majority of countries for at least one spending aggregate — long-term relationship consistent with Wagner’s law.
  - Cross-country determinants: power dispersion and government size typically dampen short-run procyclical spending; financial risk and output volatility contribute to procyclicality.
- Policy implications (three drawn by authors):
  1. Fiscal rules or fiscal responsibility laws may help limit discretion for procyclical fiscal policy during upswings.
  2. Strengthening checks and balances (greater dispersion of power) is compatible with good policymaking and helps reduce cyclicality of government outlays.
  3. In countries with a long-term spending–output relationship, short-run spending cuts or surges will tend to revert to the long-term spending/GDP ratio; durable savings require accompanying longer-term structural reforms.
- Suggestions for future research:
  - Distinguish between good times and bad in empirical analysis.
  - Assess how IMF-supported economic reforms and fiscal reforms have affected procyclical behavior of spending.
  - Extend analysis to short- and long-term movements of revenues and fiscal balances.

### Appendix — Definitions and data sources (summary)
- Fiscal data source: IMF’s Government Financial Statistics.
- GDP, GDP deflators, exchange rates: IMF’s International Financial Statistics.
- CPI deflators and spending in constant prices: World Economic Outlook (WEO) database.
- Explanatory variables:
  - Output volatility: standard deviation of GDP growth (average).
  - Power dispersion index: Henisz (2000), average index (0,1).
  - Output per capita: WEO data.
  - Government size: average government spending (consumption plus investment) as percent of GDP.
  - Terms of trade volatility: standard deviation from WEO data.
  - Average ODA as share of GNI: WEO data.
  - Financial risk rating: ICRG (higher risk points → higher risk).

*Source: IMF Working Paper section "5. Determinants of Fiscal Cyclicality" (content unit: _wp04202 - 5. Determinants of Fiscal Cyclicality).*

### References

### References

### Books and Monographs
- Agénor, P.R., and Montiel, P.J., 1996, 
Development Macroeconomics (New Jersey: Princeton 
University Press).
- Blanchard, O., and Fischer, S., 1989, 
Lectures in Macroeconomics (Cambridge: MIT Press).
- Taylor, J.B., and M. Woodford (eds.), 1999, 
Handbook of Macroeconomics (Amsterdam: 
North-Holland).
- Peacock, Alan, and Alex Scott, 2000, “The Curious Attraction of Wagner’s Law,” 
Public 
Choice, Vol. 102, No. 1–2, pp. 1–17.
- Wagner, A., 1911, “Staat in nationalökonomischer Hinsicht,” Handwörterbuch der 
Staatswissenschaften, pp. 743–745, third edition, Book VII (Jena: Lexis).

### Working Papers and Reports
- Agénor, P.R., McDermott, J., and Prasad, E.S., 1999, “Macroeconomic Fluctuations in 
Developing Countries: Some Stylized Facts,” IMF Working Paper 99/35 (Washington: 
International Monetary Fund).
- Fiorito, Riccardo, 1997, “Stylized Facts of Government Finance in the G-7,” IMF Working 
Paper 97/142 (Washington: International Monetary Fund).
- Forbes, Kristin, and Roberto Rigobon, 1998, “No Contagion, only Interdependence: Measuring 
Stock Market Co-movements,” NBER Working Paper No. 7267 (Cambridge, 
Massachusetts: National Bureau of Economic Research).
- Kaminsky, Graciela L., Carmen M. Reinhart, and Carlos Végh, 2004, “When it Rains, it Pours: 
Procyclical Capital Flows and Macroeconomic Policies,” NBER Working Paper 
(forthcoming).
- Talvi, Ernesto, and Carlos A. Végh, 2000, “Tax Base Variability and Procyclical Fiscal Policy,” 
NBER Working Paper 7499 (Cambridge, Massachusetts: National Bureau of Economic 
Research).
- Velasco, Andres, 1993, “A Model of Endogenous Fiscal Deficits and Delayed Fiscal 
Adjustment,” C.V. Starr Center for Applied Economics Working Paper No. 93–04 (New 
York University).
- Braun, M., 2001, “Why Is Fiscal Policy Procyclical in Developing Countries” (Harvard 
University).

### Journal Articles
- Arellano, M., and Bond, S., 1991, “Some Tests of Specification for Panel Data: Monte Carlo 
Evidence and an Application to Employment Equations,” 
Review of Economic Studies, 
Vol. 58, Iss. 194, pp. 277–97.
- Barro, R., 1979, “On the Determination of the Public Debt,” 
Journal of Political Economy,      
Vol. 87, No. 5, pp. 93–110.
- Bohl, M.T., 1996, “Some International Evidence on Wagner’s Law,” 
Public Finance, Vol. 51, 
pp.185–200.
- Chang, T., 2002, “An Econometric Test of Wagner’s Law for Six Countries Based on 
Cointegration and Error-correction Modeling Techniques,” 
Applied Economics, Vol. 34, 
pp. 1157–69.
- Ericsson, N., McKinnon, J., 2002, “Finite Sample Properties of Error Correction Tests for 
Cointegration,” 
Econometrics Journal, Vol. 5, No. 2, pp. 285–318.
- Fatás, Antonio, and Mihov, Ilian, 2003, “The Case for Restricting Fiscal Policy Discretion,” 
The 
Quarterly Journal of Economics, Vol. 118, No. 4, pp 1419–48.
- ———, and Tryphon Kollintzas, 1994, “Stylized Facts of Business Cycles in the G7 from a 
Real Business Cycles Perspective,” 
European Economic Review, Vol. 38, pp. 235–69.
- Galí, Jordi, 1994, “Government Size and Macroeconomic Stability,” 
European Economic 
Review, Vol. 38, No. 1, pp. 117–32.
- Hercowitz, Zvi, and Michael Strawczynski, 2004, “Cyclical Ratcheting in Government 
Spending: Evidence from the OECD,”
 Review of Economics and Statistics, Vol. 86, 
No. 1, pp. 353–61.
- Lane, Philip R., 1998, “On the Cyclicality of Irish Fiscal Policy,” 
Economic and Social Review, 
Vol. 29, No. 1, pp. 1–17.
- ———, 2003, “The Cyclical Behavior of Fiscal Policy: Evidence from the OECD,” 
Journal of 
Public Economics, Vol. 87, pp. 2661–75.
- Lin, C., 1995, “More Evidence on Wagner’s Law for Mexico,” 
Public Finance, Vol. 2,         
pp. 267–77.
- Lucas, R.E., and N.L. Stokey, 1983, “Optimal Fiscal and Monetary Policy in an Economy 
Without Capital,” 
Journal of Monetary Economics, Vol. 12, pp. 55–94.
- Murthy, N.R.V., 1993, Further Evidence on Wagner’s Law for Mexico: An Application of 
Cointegration Analysis,” 
Public Finance, Vol. 48, pp.77–85.
- Payne, James. E., and Bradley T. Ewing, 1996, “International Evidence on Wagner’s 
Hypothesis: A Cointegration Analysis,” 
Public Finance, Vol. 51, No. 2, pp. 258–74.
- Perotti, Roberto,1996, “Redistribution and Non-Consumption Smoothing in an Open 
Economy,” 
Review of Economic Studies, Vol. 63, pp. 411–33.
- Rodrik, Dani, 1998, “Why Do More Open Economies Have Bigger Governments?” 
Journal of 
Political Economy, Vol. 106, No. 5, pp. 997–1032.
- Sørensen, B.E., Wu, L., and Yosha, O., 2001, “Output Fluctuations and Fiscal Policy: US State 
and Local Governments, 1978–1994,” 
European Economic Review, Vol. 45, pp. 1271–
1310.
- Tornell, Aaron, and Philip R. Lane, 1998, “Are Windfalls a Curse? A Non-Representative 
Agent Model of the Current Account,” 
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pp. 83–112.
- ———, 1999, “The Voracity Effect,” 
American Economic Review, Vol. 89, pp. 22–46.

### Chapters and Contributions in Edited Volumes
- Arreaza, A., Sørensen, B.E., and Yosha, O., 1999, “Consumption Smoothing Through Fiscal 
Policy in OECD and EU Countries.” In: Poterba, J.R., Von Hagen, J., (Eds.), 
Fiscal 
Institutions and Fiscal Performance (Chicago: University of Chicago Press).
- Gavin, Michael, and Roberto Perotti, 1997, “Fiscal Policy in Latin America,” NBER 
Macroeconomic Annual, pp.11–71 (Cambridge, Massachusetts: MIT Press).
- Stein, Ernesto, Ernesto Talvi, and Alejandro Grisanti, 1999, “Institutional Arrangements and 
Fiscal Performance: The Latin American Experience,” Chapter 5 in James R. Poterba 
and Jürgen von Hagen (eds.), 
Fiscal Institutions and Fiscal Performance (Chicago: 
University of Chicago Press).

### Data and Online Source
- The PRS Group, International Country Risk Guide (ICRG), http://www.icrgonline.com.

*Source: _wp04202 - References*

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