where ln is the natural logarithm; Δ represents the difference over time; N measures population, E employment, and WP working-age population; α is the output elasticity of capital and equals a standard value of 0.33; z is the structural variables listed in Table 2. Constant return to scale and a constant WP ratio are assumed.

The estimation period spans from 1996 to 2016, although it shortens when some structural indicators are added to the model specification, in particular the indicators from the World Economic Forum (WEF) database which are available only since 2006 – see Table 2.

As shown in equation 1, all components are separable and independent of each other. It is therefore possible to compute the effect on output growth of structural reforms, namely changes in structural variables, z. Each dependent variable is in fact regressed on all the competitiveness indicators (Table 2) to identify those structural variables whose explanatory power remains robust and statistically significant across different model specifications.<sup>6</sup> Table 3 lists dependent variables, their description, and source. Cross-country panel data regressions are used to estimate both the capital-output ratio and employment rate equations. The Hausman test is used for fixed and random effects.<sup>7</sup>

**Table 3. Dependent Variables**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Real output|Real GDP in billions of constant 2011 international dollars|WEO|
|Total stock of<br>capital|Capital stock, billions of constant 2011 US dollars, extended with<br>depreciation and investment series from the WEO|Penn World Table, WEO|
|Employment|Employees and self-employed in thousands, extended with employment<br>growth rate from the WEO|Penn World Table, WEO|

The methodology is subject to several caveats, the most important being the endogeneity of the regressors. First, the Hausman test is performed to verify whether the random-effect specification may be appropriate for cross-country regressions. The Pesaran (2004) test is used to control for the presence of cross-sectional dependence in panels with many cross-sectional units and few time-series observations. The robustness of the estimates is also verified by employing the Arellano-Bond General Method of Moments (GMM) estimator, although that is also subject to caveats. For example, it may underestimate the impact of several common determinants of the steady-state level of income, such as human capital, as discussed in Hauk and Wacziarg (2004). The adoption of the GMM estimator requires losing at least two periods of data, which may alter the results when the starting sample is small. Finally, it may also be subject to the problem of weak instruments: the first-stage relationship between differenced independent variables and lagged dependent ones may be weak. The problems are addressed by using the Blundell and Bond System GMM (see Section 4).

To better control for potential endogeneity, the ideal would be to cross-check the estimates based on macroeconomic time series with those obtained from using sector- and firm-level data (see also the discussion in Egert and Gal 2016).

> 6 The baseline model does not control for the lagged dependent variable, but the robustness of the results is tested under this specification (see Section 4).

> 7 As discussed in Lusinyan (2018), the random effect specification may allow to capture both between and withincountry information, the latter being relevant especially for factor utilization.

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