debt and other revenue components (Adam and Bevan 2005). This comprehensive approach mitigates the risk of spurious results that could arise from omitted variable bias affecting the tax-toGDP ratio, and subsequently, economic growth. Second, we extend the dataset to 2021, providing a more contemporary analysis. While we exclude pre-1980 historical data, we posit that focusing on more recent data, with sufficient temporal coverage, is crucial to identify a relevant threshold reflective of the modern economic landscape's structural shifts. Third, our research probes the relevance of the identified threshold by examining its influence on alternative dependent variables, specifically the prosperity gap, thereby offering a more nuanced understanding of the tax-to-GDP ratio's impact on inclusive growth.

The econometric specification is as follows: = β0 + β1 > γ� + β2 > γ� ∗T + β3T + κ + κ + ε _(1)_ yyIn equation (1), the dependent variable represents the j-period ahead cumulative growth rate. Here, cc,tt+jj −yycc,tt II�T TTcc,tt cc,tt II�T TTcc,tt cc,tt TTcc,tt TTcc,tt + ββXXcc,tt cc tt cc,tt signifies the logarithm of real GDP per capita for country c in period t. The indicator function cc,tt > γ� represents the tax threshold dummy, which assumes a value of 1 if the tax-to-GDP yy II�Tratio exceeds the threshold TTcc,tt cc,tt γ , and 0 otherwise. T denotes the tax-to-GDP ratio for country _c_ in period _t_ . The vector includes covariates from growth theory and the government’s budget cc,tt TT constraint, including the investment-to-GDP ratio, debt-to-GDP ratio, per capita income, and the cc,tt XX revenue generated from other sources. We include the interaction between debt to GDP and tax threshold to allow for potential non-linearities in the impact of debt on growth. The terms κ and κ represent country and year fixed effects, respectively. We employ this model to analyze the j- cc period ahead prosperity gap as an alternative dependent variable. tt GJW methodology recommends choosing the threshold to maximize the R-squared of the model via a grid search across potential threshold values, but this method may present certain drawbacks. For example, the incremental increase in R-squared might lack statistical significance suggesting an inability to meaningfully choose across threshold outcomes. Moreover, the maximized R-squared does not necessarily mean that the coefficient of interest which reflects the magnitude of the discontinuous jump in the growth rate, will be at its optimal value at the same threshold. To address these concerns, our approach focuses solely on the coefficient of the tax dummy threshold, as it is more relevant for capturing any discontinuous jumps in the effect on growth or the dependent variable of interest.

# **4. Data**

The main variables used in this paper are described in table 1 and their descriptive statistics in table 2. The revenue-based indicators are from the International Monetary Fund’s Government Statistics Finance (GFS) database. We select the level of government for each country (that is, general, central, or budgetary central government) to maximize the number of non-missing observations in a set of spending and revenue categories over the sample. There is likely a vital role that fiscal decentralization can play in the relationship between tax and growth, however, given data limitations, we use aggregated data focusing on general where data is available or central government in the remaining countries. We also focus on tax revenue including social security contributions to GDP and use this definition of tax revenue throughout the paper. Since coverage of government debt data 7