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Abstract

China faces the challenge of maintaining fiscal sustainability amid volatile oil prices (OPs). Fluctuating prices affect revenues and expenditures, complicating consolidation. Understanding how revenue- and expenditure-based policies respond to such shocks is key, yet few studies examine their interaction over time with nonlinear, time-varying effects. This study analyzes the relationship between OP shocks, fiscal consolidation, and fiscal policy composition in China from 1990 to 2025. Using time-varying parameter local projection (TVP-LP), time-varying parameter vector autoregression (TVP-VAR), and structural vector autoregression (SVAR) models, it captures the time-varying and nonlinear responses of the cyclically adjusted primary balance (CAPB) to domestic fiscal shocks and external OP changes. Results show that consolidation is highly sensitive to OP volatility, debt levels, and the choice of revenue- or expenditure-based measures. Expenditure-based consolidation yields lower output costs and more sustainable debt paths, while revenue-based measures improve fiscal balances but can worsen short-term contractionary effects. Policymakers need to enhance expenditure efficiency, mobilize revenue strategically, build fiscal buffers, and strengthen medium-term debt management. The study adds to the literature by demonstrating the relevance of time-varying fiscal dynamics and the impact of institutional and external influences. Findings guide emerging economies facing commodity price shocks and rising debt, stressing the importance of adaptive and well-sequenced consolidation strategies.

JEL Classification E62, H60, Q43, C32, F41

Keywords

Fiscal consolidation oil price shocks cyclically adjusted primary balance time-varying parameter models fiscal policy composition

Introduction

The link between oil price (OP) shocks and fiscal consolidation is central to fiscal policy, especially in emerging, resource-dependent economies like China (Lawal et al., 2024; Slimane et al., 2022; Sohag et al., 2022). As a major oil importer, China’s fiscal position is highly sensitive to energy price fluctuations, which affect revenues, expenditures, and fiscal discipline (Guo et al., 2024; Sohag et al., 2022). Fiscal consolidation, reducing deficits and debt through higher taxes or spending cuts, aims to ensure fiscal stability. Empirical findings are mixed: some studies (Afonso & Leal, 2022; Agnello et al., 2013; Ando et al., 2025; David et al., 2022; Heylen et al., 2013) link consolidation to durable debt reduction, while others (Buthelezi, 2024; Buthelezi & Nyatanga, 2023a; Deskar-Škrbić & Milutinović, 2021; Georgantas et al., 2023) find adverse effects. A key gap is the role of external factors, like OP volatility, in shaping consolidation outcomes, particularly in emerging economies. Given oil’s importance to production and energy, price swings can strain fiscal policy. This article analyzes China’s fiscal consolidation over time, emphasizing how OP shocks influence policy effectiveness and offers insights for resilient, adaptive fiscal frameworks.

This study uses the cyclically adjusted primary balance (CAPB) (Afonso & Leal, 2022; Ando et al., 2025; Buthelezi & Nyatanga, 2023a, 2023b, 2023c; Deskar-Škrbić & Milutinović, 2021; Heylen et al., 2013) as a proxy for China’s fiscal consolidation. The CAPB adjusts the primary balance (PB) for cyclical effects, isolating structural fiscal efforts and showing how OP shocks affect fiscal positions. As a major oil importer, China is highly exposed to oil volatility. While research focuses on oil-exporting economies (Sohag et al., 2024), it does not explicitly account for fiscal consolidation. Given China’s growing oil dependence and evolving policies (Rioux et al., 2019), understanding the impacts on budgetary responses, debt management, and deficit control is crucial for fiscal stability (Thi Huong Vuong et al., 2024).

Most studies on OP volatility focus on oil-exporting countries and the global economy (Sohag et al., 2024), with little attention to China’s fiscal response, particularly in fiscal consolidation (Rioux et al., 2019). Existing research often relies on static models that overlook fiscal policy dynamics (Zhang, 2022). This study fills that gap by using a time-varying model to examine how China’s fiscal consolidation adjusts to OP shocks, offering new insights into the behavior of a major oil-importing economy. The study’s significance lies in advancing the understanding of how OP fluctuations influence China’s fiscal policy and consolidation. As a key player in global oil markets, China’s fiscal response has broad economic implications. The findings will help policymakers design more resilient fiscal frameworks to manage external shocks (Guo et al., 2024) and will contribute to global debates on fiscal consolidation in oil-importing economies (Sohag et al., 2022). This study seeks to answer the following key economic question: What is the impact of OP fluctuations on fiscal consolidation efforts in China? Specifically, the study explores how fluctuations in global OPs influence China’s fiscal stance, including its ability to reduce fiscal deficits, manage public debt, and sustain fiscal consolidation initiatives over time. The study employs a time-varying model to examine the temporal dynamics of this relationship, allowing for a more nuanced understanding of how the fiscal response to OP shocks has evolved in China’s changing economic context.

The rest of the article highlights the following. The next section outlines the literature review. Subsequently, the methodology is discussed, followed by the empirical results. Finally, the conclusion and policy implications of the article are presented.

Literature Review

Research on OP volatility and its fiscal and macroeconomic effects has expanded, emphasizing the energy markets’ role in global growth and the vulnerability of oil-dependent economies. Despite advances in understanding fiscal impacts, contradictions remain on procyclicality, structural reforms, and how uncertainty spreads across economies. OP volatility affects macroeconomic outcomes largely through corporate behavior. In China, energy market reforms reshaped this transmission. Thi Huong Vuong et al. (2024) show that after the 2013 oil pricing reform, firms became more exposed to oil shocks, with profitability declining under greater uncertainty. The reform reduced firms’ fiscal space by removing state protections that stabilized input costs. Zhang et al. (2020) find an inverted U-shaped link between OP uncertainty and cash holdings, influenced by capital intensity and state ownership. Large, capital-intensive state-owned enterprises (SOEs) maintain stronger buffers, while smaller firms remain vulnerable, reflecting China’s structural imbalance, where SOEs act as quasi-fiscal agents but contribute to resource misallocation.

Rioux et al. (2019) find that China’s efforts to boost domestic oil production improve supply security but do not shield the economy from global oil shocks. Zhang (2022) shows that China’s fiscal stance influences global inflation through an OP channel, creating international feedback loops that amplify uncertainty shocks. These studies reveal strong links between firm- and macro-level exposure to oil volatility. However, the literature overlooks long-term effects on productivity, investment efficiency, and capital allocation, while relying too heavily on firm size and ownership as proxies for resilience, neglecting technological and sectoral differences.

Fiscal responses to fluctuations in OPs are well-documented. Sohag et al. (2024) find that in the Organization of the Petroleum Exporting Countries (OPEC+), fiscal balances improve following positive oil shocks but weaken over time, showing persistent procyclicality. Although not a member of OPEC+, China’s reliance on imported oil and its fiscal adjustments to external shocks display similarities. Yu and Zhang (2019) observe that China’s fiscal policies both respond to and influence global OPs. Comparative studies emphasize institutional factors: in Russia, fiscal outcomes remain tied to hydrocarbon prices (Sohag et al., 2022), while Oxford Analytica (2015) shows that oil-dependent governments often avoid severe austerity to maintain social stability. This underscores the trade-off between fiscal consolidation and welfare. Conversely, Mexico demonstrated resilience to falling OPs (Oxford Analytica, 2015), though the reasons behind this resilience, whether from reforms, institutions, or politics, are still unclear. Buthelezi (2025) showed that fiscal consolidation lowers the PB by 1.366% and slightly reduces gross domestic product (GDP) per capita. A debt threshold of 31.4% of GDP marks the point where debt harms growth. Above this level, consolidation further amplifies the negative effects of the debt.

Overall, cross-country evidence indicates diverse fiscal responses, but explanations often lack integration with institutional- and political-economy frameworks.

A central debate in the literature concerns countercyclical versus procyclical fiscal policy amid OP volatility. Pieschacón (2012) finds that countercyclical rules enhance welfare by stabilizing output but are constrained by political pressures to increase spending during booms. El Mahmah and Kandil (2019) stress fiscal reforms that strengthen the PB and reduce oil dependence, aligning with diversification goals, though their analysis overlooks the differences in institutional maturity. Adedoyin et al. (2017) show that strong fiscal rules buffer oil rent shocks and sustain fiscal space, but they focus mainly on short-term effects, neglecting long-term sustainability under global energy transitions. Adeniyi and Fagbemi (2019) reveal that negative oil shocks sharply increase debt burdens, exposing asymmetric fiscal risks; however, they overlook potential mitigating responses such as diversification or debt restructuring.

Recent studies highlight the asymmetric and nonlinear effects of OP shocks. Hu et al. (2018) find no asymmetry in China, citing policy buffers and energy diversification, though they overlook indirect channels like trade and financial spillovers. In contrast, Borozan and Cipcic (2022) show that in small oil-importing economies, growth falls more from price hikes than it rises from declines, reflecting structural vulnerabilities. Slimane et al. (2022) find stronger fiscal multipliers during oil downturns, suggesting temporary cushioning but raising sustainability concerns amid high deficits. Abubakar et al. (2023) observe short-run asymmetry with long-run convergence, though this may ignore the lasting effects from structural reforms and fiscal diversification.

The literature shows that fiscal vulnerability to oil shocks depends on institutional quality, negative shocks have stronger effects, and sustainability relies on countercyclical capacity and political economy factors. However, most studies focus on short-term responses, overlook long-term trade-offs, and rarely consider structural factors like diversification or capital intensity. The link between oil volatility and fiscal consolidation is underexplored. Oil-dependent economies react with surpluses in booms and deficits in downturns, but sustaining consolidation through deficit reduction, debt management, and reduced oil reliance remains insufficiently studied, limiting understanding of long-term fiscal sustainability and policy credibility.

Methodology

This study examines the dynamic relationship between OP shocks, OP uncertainty, and fiscal consolidation in China, using the CAPB as a proxy for discretionary fiscal policy. The PB is defined and cyclically adjusted using the output gap and a country-specific fiscal semi-elasticity. The framework is extended to include government debt and OPs, reflecting fiscal constraints and exposure to external shocks. Dynamic effects are estimated using the time-varying parameter local projection (TVP-LP) model, which generates time-varying impulse responses across horizons and captures structural shifts associated with economic diversification and policy reforms. Robustness is assessed using a time-varying parameter vector autoregression (TVP-VAR), allowing for time variation in coefficients and shock covariances, and a structural vector autoregression (SVAR) as a baseline for comparison. This multi-model approach isolates structural from cyclical effects, captures short- and medium-term dynamics, and provides a robust assessment of how OP shocks influence fiscal consolidation over time.

Theoretical Framework

The PB represents the government’s fiscal balance excluding interest payments on its debt (Chrysanthakopoulos & Tagkalakis, 2024), as shown in Equation (1).

p b_{t} = g r_{t} - g e_{t}

(1)

where gr_t denotes government revenues at time t, and ge_t represents government expenditures (Chrysanthakopoulos & Tagkalakis, 2024). The CAPB adjusts the PB to account for economic fluctuations, specifically by estimating what the PB would be if the economy were operating at its potential output y* rather than its actual output y (Alesina & Ardagna, 2010). This relationship is depicted in Equation (2).

c a p b_{t} = p b_{t} - ϵ \cdot \frac{(y_{t} - y^{*})}{y^{*}}

(2)

where $ϵ$ captures the budget’s sensitivity to the output gap, y_t denotes the actual output, while y* represents the economy’s potential output (Alesina & Ardagna, 2010; Guajardo et al., 2014). To estimate the potential output for the variable y*, we use the Hodrick–Prescott (HP) filter, the trend component T_t is extracted as a proxy for potential output, and the cyclical component C_t (Kaiser & Maravall, 1999). The HP filter minimizes the following objective in Equation (3).

\min_{T_{t}} \sum_{t = 2} {(y_{t} - T_{t})}^{2} + λ \sum_{t = 2} {[(T_{t} + 1 - T_{t}) - (T_{t} - T_{t} - 1)]}^{2}

(3)

where, λ = 1,000 determines the smoothness (De Jong & Sakarya, 2016). The cyclical component C_t reflects deviations of actual output y_t from potential output T_t, capturing the state of the economy relative to its long-term trend. The output gap, og_t_, is defined in Equation (4).

{o g}_{t} = \frac{(y_{t} - y^{*})}{y^{*}}

(4)

The adjustment factor for the PB is then represented in Equation (5).

{a_o g}_{t} = ϵ \cdot {o g}_{t}

(5)

To extend the theoretical framework, we incorporate additional variables of interest, specifically government debt gd_t and OP op_t, as shown in the extended theoretical framework in Equation (1).

\underset{Extended therical framework}{\underset{⏟}{\underset{Theoretical framework}{\underset{⏟}{{c a p b}_{t} = {p b}_{t} - {o g}_{t}}} + {g d}_{t} + {o p}_{t}}}

(6)

In this framework, Equation (6), the CAPB is influenced not only by the output gap but also by government debt and OP fluctuations (Alesina & Ardagna, 2010; Guajardo et al., 2014). Debt reflects fiscal constraints, while OPs act as external shocks affecting fiscal balances, especially in oil-dependent economies.

Identification of Fiscal Consolidation Episodes

The empirical identification of fiscal consolidation episodes is grounded in the premise that discretionary fiscal policy actions can be inferred from systematic changes in cyclically adjusted budgetary aggregates. In line with the structural balance literature (Alesina & Ardagna, 2010; Guajardo et al., 2014), we employed the CAPB as a proxy for the discretionary fiscal stance, thus isolating policy-induced variations from those arising endogenously through the operation of automatic stabilizers. This approach rests on the assumption that the fiscal semi-elasticity of the budget balance to the output gap sufficiently captures cyclical influences, thus enabling the extraction of the structural fiscal position. The construction of fiscal consolidation episodes used starts from using Equation (1), which is the PB. To remove cyclical components, we adjusted the PB using the output gap, og_t, and a country-specific overall fiscal semi-elasticity, ε, as shown in Equation (7).

c a p b_{t} = p b_{t} - ε \cdot o g_{t}

(7)

In the baseline specification, ε = 0.5, consistent with the prevailing estimates for advanced and emerging economies, while robustness checks explore the interval ε ∈ [0.2, 0.5]. The discretionary fiscal tightening in a given period is explained by the first difference in the CAPB reflected in Equation (8).

∆ c a p b_{t} = c a p b_{t} - c a p b_{t - 1}

(8)

Following established practice in Equation (9), a fiscal consolidation episode is defined as any year in which:

∆ {c a p b}_{t} \geq τ,

(9)

where τ represents a minimum improvement threshold, expressed in percentage points of GDP. The baseline adopts τ = 1.5 p.p., reflecting a balance between sensitivity and specificity: the threshold is stringent enough to avoid classifying minor cyclical corrections as policy-driven consolidations, yet sufficiently inclusive to capture meaningful discretionary efforts. Sensitivity analysis were performed for τ ∈ 1.0, 1.25, 1.75, 2.0 (Alesina & Ardagna, 2010; Guajardo et al., 2014). For each identified consolidation episode, we decomposed the CAPB change into its revenue and expenditure components in Equation (10).

∆ e^{c u t_{t}} = - ∆ e_{t},

(10)

We attribute the episode to one of two mutually exclusive categories in Equation (12).

T y p e = \{\begin{matrix} E x p e n d i t u r e - b a s e d, i f ∆ e^{c u t_{t}} > r_{t} \\ R e v e n u e - b a s e d, i f ∆ r_{t} > {r_{t}}^{c u t_{t}} \end{matrix}

(12)

This classification reflects the dominant instrument approach, assigning the consolidation to the fiscal lever exerting the larger contribution to the CAPB improvement.

Model Specification of TVP-LP

The TVP with local projections model is designed to estimate the dynamic responses of a dependent variable to an explanatory variable, accounting for changes in the relationship over time (Inoue et al., 2024). This model combines TVPs with the local projection method, which allows for the estimation of impulse response functions (IRFs) over different horizons. At each horizon h, the local projection model is specified in Equation (13).

y_{t + h} = α_{h (t)} + β_{h (t) X_{t}} + ϵ_{t + h}

(13)

where y_t₊ _h is the dependent variable at horizon h. The X_t is a vector of explanatory variables. The α_h₍ _t ₎ is the time-varying intercept. The β_h₍ _t ₎ is the time-varying coefficient of X_t, capturing how the explanatory variables affect the dependent variable at different points in time. While ϵ_t₊ _h is the error term (assumed to be white noise), which captures the residuals of the model (Rios-Avila, 2020). The key feature of the TVP-LP model is the time-varying coefficients, α_h₍ _t ₎ and β_h₍ _t ₎, which evolve to reflect structural changes (Inoue et al., 2024; Müller & Petalas, 2010). These parameters follow random walk dynamics, as shown in Equation (14).

β_{h (t)} = β_{h (t - 1)} + η_{h (t)}

(14)

where η_h₍ _t ₎ ~ N(0,Q_h) is the innovation term, representing the random shock or change in the coefficient at time t, with variance Q_h. Similarly, the time-varying intercept α_h₍ _t ₎ evolves as in Equation (15).

a_{h (t)} = a_{h (t - 1)} + h_{(t)}

(15)

where ν_h₍ _t ₎ ~ N(0,Σ _h ) is the innovation in the intercept at time t, with variance Σ _h . The error term ϵ_t₊ _h is assumed to be normally distributed with zero mean and constant variance (Rios-Avila, 2020) in Equation (16).

ϵ_{t + h} \sim N (0, σ^{2})

(16)

The IRF at horizon h measures the dynamic response of y_t₊ _h to a shock in X_t at time t. In the TVP-LP model, the IRF equals the time-varying coefficient β_h₍ _t ₎, estimated for each t. For China, analyzing the link between OP shocks and fiscal consolidation requires capturing evolving dynamics shaped by economic diversification and policy reforms, something static models cannot do (Inoue et al., 2024; Müller & Petalas, 2010). The TVP-LP model estimates time-varying impulse responses, showing how OP shocks affect fiscal outcomes like revenues and the CAPB across horizons. Its flexibility accounts for structural shifts, asymmetric effects, and endogeneity, yielding robust insights into how OPs influence fiscal consolidation over time.

Robustness Check of the SVAR Model Specification

To complement the baseline TVP models, we estimate a conventional SVAR with constant parameters. This serves as a robustness check and facilitates interpretation under the assumption of stable contemporaneous relationships (Sims & Zha, 2006). The SVAR system is specified as follows:

A y_{t} = \sum_{i = 1}^{p} B_{i} y_{t - i} + u_{t},

(17)

where A denotes the contemporaneous impact matrix, B_i are the lagged coefficient matrices, and u_t represents orthogonal structural shocks. The specification of the model is outlined in matrix (18).

\underset{C o n t e m p o r a n e o u s i m p a c t m a t r i x}{\underset{⏟}{[\begin{matrix} 1 & a_{12} & a_{13} & 0 & 0 \\ 0 & 1 & a_{23} & 0 & 0 \\ 0 & 0 & 1 & 0 & a_{35} \\ a_{41} & a_{42} & a_{43} & 1 & 0 \\ 0 & 0 & 0 & 0 & 1 \end{matrix}]},} y_{t} = \underset{E n d o g e n o u s v a r i a b l e s}{\underset{⏟}{[\begin{matrix} {c a p b}_{t} \\ {p b}_{t} \\ {o g}_{t} \\ {g d}_{t} \\ {o p}_{t} \end{matrix}]}}, ε_{t} \underset{S t r u c t u r a l s h o c k s}{\underset{⏟}{[\begin{matrix} ε_{t}^{c a p b} \\ ε_{t}^{p b} \\ ε_{t}^{o g} \\ ε_{t}^{g d} \\ ε_{t}^{o p} \end{matrix}]}}

(18)

The vector y_t contains the endogenous variables, with A capturing their contemporaneous interdependencies; diagonal elements are normalized to one, zeros reflect theory-based exclusion restrictions, and free parameters a_ij represent contemporaneous causal effects. Following the literature, the capb responds contemporaneously to the pb and og, reflecting immediate discretionary and cyclical fiscal adjustments (Auerbach & Gorodnichenko, 2012). PB reacts within-period to ogv but not capb, consistent with institutional constraints and fiscal accounting (Blanchard & Perotti, 2002). The og adjusts instantly to op, capturing rapid transmission of global energy shocks (Hamilton, 1983; Kilian, 2009). The gd contemporaneously depends on capb, pb, and og, reflecting immediate debt accumulation dynamics (Debrun & Kapoor, 2010). OP is treated as contemporaneously exogenous, as global OPs are driven by international markets and unaffected by domestic conditions within-period (Kilian, 2009). To capture broader fiscal macroeconomic interactions, the SVAR framework is augmented to incorporate government revenue gr and government expenditure ge, enabling a more comprehensive representation of the fiscal transmission mechanism and its contemporaneous linkages with macroeconomic aggregates. This is reflected in matrix (19).

\underset{C o n t e m p o r a n e o u s i m p a c t m a t r i x}{\underset{⏟}{[\begin{matrix} 1 & a_{12} & a_{13} & 0 & a_{15} & 0 & 0 \\ 0 & 1 & a_{13} & 0 & a_{25} & 0 & 0 \\ 0 & 1 & 1 & 0 & a_{35} & 0 & 0 \\ 0 & 0 & 1 & 1 & 0 & a_{46} & 0 \\ a_{41} & a_{42} & a_{43} & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 1 & a_{67} \\ 0 & 0 & 0 & 0 & 0 & a_{76} & 1 \end{matrix}]}}, y_{t} = \underset{E n d o g e n o u s v a r i a b l e s}{\underset{⏟}{[\begin{matrix} {c a p b}_{t} \\ {p b}_{t} \\ {o g}_{t} \\ {g d}_{t} \\ {o p}_{t} \\ {g r}_{t} \\ {g e}_{t} \end{matrix}]}}, ε_{t} \underset{S t r u c t u r a l s h o c k s}{\underset{⏟}{[\begin{matrix} ε_{t}^{c a p b} \\ ε_{t}^{p b} \\ ε_{t}^{o g} \\ ε_{t}^{g d} \\ ε_{t}^{o p} \\ ε_{t}^{g r} \\ ε_{t}^{g e} \end{matrix}]}}

(19)

In the extended specification, the CAPB responds contemporaneously to the pb, og, and op, reflecting discretionary fiscal adjustments to fiscal balances, cyclical conditions, and external price shocks (Auerbach & Gorodnichenko, 2012). The pb reacts within-period to og and op, capturing the immediate effect of economic activity and commodity prices on revenues and expenditures, but it excludes same-period feedback from capb due to legislative and institutional lags (Blanchard & Perotti, 2002; Ilzetzki et al., 2013). The og adjusts instantly to op through cost-push and income effects (Hamilton, 1983; Kilian, 2009). Government debt gd depends contemporaneously on capb, pb, og, and gr, consistent with debt accumulation identities and empirical evidence (Debrun & Kapoor, 2010). The op is treated as contemporaneously exogenous, reflecting global market determinants (Baumeister & Hamilton, 2015; Kilian, 2009). The gr and government expenditure ge respond contemporaneously to domestic fiscal and macro conditions but not to op, as revenue and expenditure adjust faster to domestic than external shocks (Perotti, 2007). This constant-parameter SVAR framework allows us to compare the estimated impulse responses and transmission mechanisms with those from the TVP-VAR and TVP-LP models, thereby strengthening the robustness and credibility of the empirical findings.

Data Used

To analyze the impact of OP shocks and the CAPB as a proxy for fiscal consolidation, this study employs annual data from 1990 to 2025. The data sources include the International Monetary Fund (IMF) database. The HP filter is applied to derive the potential output shown in Figure 1.

Figure 1.

Source: IMF (2025), and the estimation of the HP-F is the author’s calculations.

The HP is necessary for constructing the CAPB, while all monetary values are adjusted for inflation to ensure consistency across the analysis period.

Appendix Table 1 in Supplemental Material shows that the variables used in this study are guided by the theoretical framework. The CAPB, adjusted for business cycles, isolates structural fiscal efforts, while the PB captures immediate fiscal outcomes. The output gap, reflecting deviations from potential output, highlights the influence of economic conditions on fiscal adjustments. Government debt measures fiscal sustainability and the constraints on countercyclical policies. Lastly, OPs, the primary external factor, assess the impact of price volatility on fiscal revenues and stability. These variables together provide a robust framework for analyzing the relationship between OP shocks, fiscal consolidation, and macroeconomic stability in oil-dependent economies.

Results

The results of the Dickey–Fuller and Phillips–Perron unit root tests presented in Appendix (Table 2 in Supplemental Material) reveal mixed integration properties among the variables. For the CAPB d.capb, PB d.pb, output gap d.og, and OPs d.op, the test statistics are significantly more negative than the critical values, allowing for rejection of the null hypothesis of a unit root.

These variables are stationary after first differencing, classifying them as I(1). In contrast, government debt d.gd, revenue d.gr, and expenditure d.ge remain non-stationary, suggesting higher-order integration (potentially I(2)) or sensitivity to the lag structure and deterministic components. Their persistence indicates long-run stochastic trends, which are important for modeling fiscal sustainability. While most macro-fiscal indicators follow an I(1) process, these fiscal aggregates require careful treatment in cointegration and error-correction models. Proper handling of non-stationarity is essential to avoid spurious regressions and to ensure reliable impulse responses in TVP-LP and TVP-VAR models.

Appendix Table 3 in Supplemental Material shows descriptive statistics. The CAPB and pb have mean deficits of −1.601 and −2.642, respectively, with high variability indicated by standard deviations (SDs) of 2.271 and 2.597, and slight skewness of 0.004. The output gap og averages −0.052 but exhibits skewness and kurtosis of 0.01, reflecting cyclical shocks. The gd averages 37.345 with high variability and significant skewness of 0.004, while the kurtosis of 0.390 is close to normal. OPs op are highly volatile, with an SD of 32.208 and a significant kurtosis of 0.018, indicating the presence of extremes. Revenue gr and expenditure ge strongly reject normality (p = .0000). Appendix Table 4 in Supplemental Material shows correlations: CAPB is positively correlated with pb 0.3599 and og 0.6967, suggesting fiscal balances improve in expansions. CAPB is negatively correlated with gd −0.8323, gr −0.3808, and ge −0.6120, indicating deficits align with higher debt and expenditure.

The output gap also exhibits negative correlations with debt −0.5889, revenue −0.3638, and expenditure −0.5083, reflecting the procyclical deterioration of fiscal aggregates in periods of weak economic performance. Notably, government debt is strongly and positively correlated with both revenue 0.7267 and expenditure 0.8677, while the revenue–expenditure correlation is extremely high 0.9610, underscoring the structural linkage between fiscal inflows and outflows. OPs display weaker correlations overall, though they are moderately associated with government debt 0.5240, revenue 0.7892, and expenditure 0.6947, highlighting the role of commodity price fluctuations in shaping fiscal dynamics. Taken together, these correlations emphasize the interdependence between fiscal balances, debt dynamics, and macroeconomic conditions, and they point to the potential for multicollinearity in econometric estimation if not properly addressed.

Fiscal Consolidation Episodes in China

Using CAPB, three key fiscal consolidation episodes in China (2010–2023) exceed 1.5% of GDP. In 2010, CAPB rose 2.17 points via revenue-based consolidation, driven by higher revenues and reduced off-budget spending post-2008 stimulus, stabilizing finances with minimal expenditure shocks (Alesina & Ardagna, 2010; Guajardo et al., 2014). In 2021, a 3.83-point CAPB increase was expenditure-based, reducing public investment and controlling spending to address local government debt. Expenditure-led measures are less contractionary, as they preserve productive spending and lower output costs (Alesina et al., 2019; Lawal et al., 2024). In 2023, CAPB rose 1.62 points via revenue-led measures, with enhanced tax collection and reduced off-budget financing to counter property-sector and subnational debt pressures. Revenue-led consolidations improve fiscal balances but may amplify short-term contraction and distributional burdens (Guajardo et al., 2014). Spending-based consolidations generally produce smaller output losses and better debt dynamics, while revenue-based measures can dampen private activity. China’s experience shows that expenditure-based consolidations had lower growth costs than revenue-led ones. These episodes highlight the importance of policy composition, institutional context, and country-specific factors in balancing fiscal discipline, short-term growth, and long-term sustainability.

Time-varying Shock of OP on the Fiscal Consolidation Response Over Time

Figure 2 presents the TVP-VAR estimation. Graph (d) shows a decline in China’s fiscal consolidation from 1990 to 1994, coinciding with high OP volatility. Rising oil import costs have constrained fiscal space, widened deficits, and delayed consolidation, highlighting the vulnerability to external shocks due to limited hedging and energy diversification. These findings align with other studies: Sohag et al. (2022) show Russia’s fiscal policy reacts to OP fluctuations, and El Mahmah and Kandil (2019) find that declining OPs prompted fiscal consolidation to manage deficits. From 1995 to 1999, fiscal consolidation gradually recovered, supported by macroeconomic reforms and tax modernization (Zhang et al., 2025). However, persistent OP fluctuations continued to pressure fiscal discipline, indicating policy adjustments were only partially effective in mitigating external shocks.

Figure 2.

Notes: Variables: gd: Government debt; og: Output gap; op: Oil prices; pb: Primary balance. CAPB proxies fiscal consolidation.

During 2008–2009, fiscal consolidation deteriorated sharply amid the global financial crisis (Ren et al., 2024; You et al., 2023). OP shocks and countercyclical spending pushed fiscal metrics below equilibrium. Stimulus packages delayed consolidation but were necessary to support growth. From 2016 to 2025, consolidation recovered through deficit reduction and improved expenditure efficiency. However, persistent oil shocks and rising public debt have limited fiscal gains toward the end of this period.

TVP-LP Shock of OP on Fiscal Consolidation

Figure 3 shows the TVP-LP for CAPB responses. Graph (a) indicates that a PB shock initially reduces fiscal consolidation. Between years 6 and 8, consolidation temporarily rises above equilibrium, suggesting policy overcompensation and highlighting the evolving nature of fiscal responses to changing macroeconomic conditions.

Figure 3.

Notes: Variables: gd: Government debt; og: Output gap; op: Oil prices; pb: Primary balance. X-axis: yearly horizons. CAPB proxies fiscal consolidation.

Under the constant-parameter model, a PB shock causes a steady decline in fiscal consolidation for four years, stabilizing around year 6 before a gradual recovery. This static response limits capturing the dynamic, nonlinear effects seen in the TVP model. Graph (b) shows the TVP model diverging between years 5 and 10, with volatility in years 7–8 indicating heightened sensitivity of the output gap to structural or external shocks. The constant-parameter model shows a smoother, predictable cyclical path but understates actual fluctuations and vulnerabilities. The comparison highlights the importance of incorporating time-varying dynamics in fiscal analysis, as static models may misjudge consolidation effects and lead to suboptimal policy.

Graph (c) shows that following a government debt shock, the TVP model records a steady decline in fiscal consolidation, stabilizing below equilibrium and recovering moderately from year 6. The constant-parameter model shows smoother, predictable cyclical adjustments. TVP results indicate that China may struggle to restore consolidation after debt shocks, raising financial risks from high local debt and Local Government Financing Vehicle (LGFV) borrowing. Sharp rebounds after year 9 highlight the risks of policy overcorrection, emphasizing the need to balance consolidation and growth with built-in fiscal buffers. Graph (d) shows that after an OP shock, the TVP model records an initial consolidation rise in years 1–6, a sharp decline in years 7–9, and a strong rebound above equilibrium. The constant-parameter model shows gradual early gains, then a decline, stabilizing below equilibrium by year 10. TVP dynamics adjustment, downturn, and rebound align with prior studies (El Mahmah & Kandil, 2019; Pieschacón, 2012; Sohag et al., 2024), underscoring the importance of modeling time-varying fiscal responses in oil-exposed economies.

TVP-VAR and SVAR Robust Check Shock of OP on Fiscal Consolidation

Figure 4 presents the model estimation results of the TVP-VAR model for the CAPB responses to shocks in key macroeconomic variables. The analysis considers both time-varying and constant parameter specifications to elucidate the dynamics of fiscal consolidation in response to these shocks. In Graph (a), the TVP model indicates that an OP shock leads to an initial increase in fiscal consolidation over the first 2 years, accompanied by cyclical fluctuations in response to PB shocks. Subsequently, the fiscal consolidation stabilizes and begins to increase at an accelerating rate, highlighting the persistence of OP shocks in shaping fiscal policy. The constant-parameter model shows similar dynamics to the TVP model in the first 2 years but stabilizes thereafter without sustained growth. Graph (b) (output gap shock) shows the TVP model with a V-shaped reaction in years 1–2, followed by cyclical downward fluctuations, while the constant model stabilizes below equilibrium. Graph (c) (debt shock) shows the TVP model with a steady decline in consolidation from years 2 to 10, whereas the constant model rises briefly in year 1, then declines. Graph (d) (OP shock) shows the TVP model declining over 10 years, while the constant model rises initially in years 1–3, then gradually falls, highlighting differing adjustment paths.

Figure 4.

Notes: Variables: gd: Government debt; og: Output gap; op: Oil prices; pb: Primary balance.

Figure 5 provides a robustness check of the SVAR model for the response of the CAPB to the structural shocks. Graph (d), which examines OP shocks, shows that an exogenous rise in OPs triggers cyclical fluctuations in the CAPB during the first 4 years, underscoring fiscal vulnerability to commodity price volatility. Fiscal consolidation then strengthens until about year 5, before gradually stabilizing and converging to a long-run equilibrium by year 15. The short-run cycles reflect the initial inflationary and expenditure pressures of oil shocks, especially in economies with energy subsidies or high import dependence, which disrupts fiscal balances. The subsequent rise in consolidation suggests corrective measures, such as expenditure rationalization and revenue mobilization, once shock effects subside. Long-run stabilization indicates gradual fiscal adjustment consistent with sustainability under resource-price volatility. Two policy insights emerge: (a) the short-run fragility of fiscal balance highlights the need for sufficient buffers and countercyclical tools in commodity-dependent economies; and (b) although the balance is eventually restored, the adjustment is nonlinear and slow.

Figure 5.

Notes: Variables: gd: Government debt; ge: Government expenditure; gr: Government revenue; og: Output gap; op: Oil prices; pb: Primary balance. X-axis: yearly horizons. CAPB proxies fiscal consolidation.

This outlines the importance of institutional mechanisms such as sovereign wealth or stabilization funds and credible fiscal rules to smooth adjustments and reduce the destabilizing effects of commodity price shocks. The results show that the impulse responses closely mirror those from the TVP-VAR model, reinforcing confidence in the baseline findings. This alignment is notable given the CAPB’s sensitivity to fiscal shocks and specification uncertainty, suggesting that the core fiscal transmission mechanisms reflect structural features of the data rather than model-specific effects. However, comparing the SVAR and TVP-VAR results with the TVP-LP estimates reveals some differences in magnitude, despite the directional consistency. This is expected, as local projections estimate responses horizon-by-horizon, which can introduce greater variability and bias-variance trade-offs relative to system-based approaches. Thus, the magnitude differences in TVP-LP responses should be viewed as methodological sensitivities rather than contradictions. The convergence across models indicates stability in the underlying fiscal dynamics, while the variation in magnitudes highlights nonlinear and time-varying elements in consolidation. This suggests that although the direction of fiscal adjustment is predictable, its strength and persistence depend on economic conditions and the modeling approach used.

Figure 6 shows the variance decomposition of the CAPB, highlighting the relative importance of structural shocks. Several key patterns emerge. First, own CAPB shocks explain between 1.44% and 50.9% of its variance, confirming strong persistence in fiscal dynamics, though their declining share over time points to the rising influence of external factors. Second, PB shocks account for up to 50.8% of the variance, underscoring the central role of discretionary fiscal policy in shaping consolidation, beyond cyclical effects. Third, output gap shocks explain as much as 61.6% of CAPB fluctuations at certain horizons, reflecting fiscal sensitivity to business-cycle conditions and reinforcing the importance of countercyclical policy for debt stability. Fourth, government debt shocks contribute up to 43.2%, indicating a feedback loop between debt accumulation and fiscal adjustment, with higher debt tightening, fiscal constraints, and prompting corrective measures. Finally, OP shocks explain 5.2%–25.1% of the variance, emphasizing the exposure of fiscal balances to global commodity price volatility, especially in energy-dependent or resource-linked economies.

Figure 6.

Notes: Variables: gd: Government debt; og: Output gap; op: Oil prices; pb: Primary balance. X-axis: yearly horizons. CAPB proxies fiscal consolidation.

The decomposition shows that CAPB dynamics are driven by a combination of discretionary fiscal actions, business-cycle conditions, debt pressures, and external price shocks rather than a single dominant source. The prominence of output gap and debt shocks at different horizons highlights the dual challenge of managing short-run cyclical volatility, while ensuring long-term debt sustainability. The influence of OP shocks further indicates the need for fiscal buffers to avoid procyclical responses. The policy implications are clear: stable fiscal consolidation requires (a) stronger institutions to limit volatility from discretionary PB shocks, (b) countercyclical fiscal rules to smooth business-cycle effects, and (c) economic diversification to reduce exposure to commodity price swings. The sizable impact of debt shocks also underscores the importance of credible medium-term debt management to avoid destabilizing feedback loops.

TVP-LP Fiscal Shocks

Figure 7 presents TVP-LP estimates of the CAPB in response to fiscal shocks. Graph (e) shows that government revenue shocks initially reduce fiscal consolidation over the first 5 years, reflecting a “fiscal illusion” in China: revenue windfalls ease budget constraints, leading local governments to expand spending or delay consolidation. China’s fiscal federalism, where the central government controls major taxes but local governments handle most expenditures, amplifies this effect, as local authorities prioritize immediate developmental goals, especially infrastructure, over debt reduction. From year 6 onward, revenue gains begin to support fiscal consolidation, though fluctuations remain. This delayed adjustment indicates that once short-term priorities are addressed, authorities gradually strengthen the CAPB. The volatility highlights the instability of revenue-driven consolidation, driven by both cyclical revenues and the lack of a binding medium-term fiscal framework. Graph (f) shows that government expenditure shocks initially improve fiscal consolidation over the first 5 years. This counterintuitive effect arises because much of China’s spending is on productive investment, infrastructure, technology, and industrial policy, which boosts growth, expands the tax base, and strengthens the structural fiscal position. After this period, the effect becomes cyclical, with alternating improvements and deteriorations. Rising recurrent and social expenditures, particularly in health, education, and social security, eventually strain fiscal sustainability. The asymmetric CAPB responses to revenue and expenditure shocks reflect China’s fiscal institutions: revenue shocks are initially absorbed by local spending, while expenditure shocks support short-term consolidation but generate cyclical stress over time. This underscores the challenge of sustaining fiscal consolidation amid central-local imbalances, weak stabilizers, and procyclical expenditure dynamics.

Figure 7.

Conclusion

This study examines the dynamic interactions between OP shocks, fiscal consolidation, and policy composition in China (1990–2025) using TVP-LP, TVP-VAR, and SVAR models. Fiscal consolidation is influenced by OP volatility, government debt, and the choice of expenditure- versus revenue-based measures. Expenditure-based consolidations incur smaller output costs and support sustainable debt, while revenue-based adjustments, though improving balances, can cause short-term contraction and distributional pressures. Oil shocks initially disrupt consolidation but can be mitigated by rationalizing spending and gradual revenue enhancement. Policy recommendations include improving expenditure efficiency, protecting growth-enhancing investments, strengthening revenue collection, and maintaining fiscal buffers through stabilization or sovereign wealth funds. Fiscal strategies should account for nonlinear, time-varying dynamics to ensure adaptive responses under changing economic conditions. Limitations include the focus on China, the exclusion of other external shocks, reliance on historical data, and potential measurement errors. Future research could explore other commodity-dependent economies, macro-financial linkages, climate risks, and institutional reforms. Overall, China’s fiscal consolidation reflects the interplay of policy composition, external shocks, and institutions. Expenditure-based measures are generally less contractionary, while revenue-based measures require careful sequencing. Oil volatility and high debt further constrain consolidation, highlighting the need for dynamic, adaptive, and well-buffered fiscal frameworks.

Footnotes

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest regarding the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Supplementary Material

ORCID iD

Eugene Msizi Buthelezi

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Supplementary Material

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When the Barrel Hits the Budget: Oil Prices and Fiscal Consolidation in China

Abstract

Keywords

Introduction

Literature Review

Methodology

Theoretical Framework

Identification of Fiscal Consolidation Episodes

Model Specification of TVP-LP

Robustness Check of the SVAR Model Specification

Data Used

Results

Fiscal Consolidation Episodes in China

Time-varying Shock of OP on the Fiscal Consolidation Response Over Time

TVP-LP Shock of OP on Fiscal Consolidation

TVP-VAR and SVAR Robust Check Shock of OP on Fiscal Consolidation

TVP-LP Fiscal Shocks

Conclusion

Footnotes

Data Availability Statement

Declaration of Conflicting Interests

Funding

Supplementary Material

ORCID iD

References

Supplementary Material