Middle East, Asia and Pacific, Latin America, and the Caribbean) and across 165 countries for the period 2000 – 2017.

In addition, the BOOST database does not capture domestic spending through extrabudgetary vehicles, including State-Owned Enterprises (SOEs) and Road Funds. The research drew upon financial statements of these entities from the World Bank’s Power and Transport State-Owned Enterprises (SOE) Database, comprising a panel of systematic standardized data drawn from company financial statements, which is consistent at the observation level and comparable across SOEs and years. The database covers 135 SOEs in the power and transport sectors (including road, rail, and air) across 19 countries. In the case of the road sector, it is known that an important subset of countries has implemented off-budget Road Funds, which capture earmarked revenues from transportation fuel levies and channel them towards road maintenance activities. Expenditure from Road Funds must be considered alongside recurrent budgetary expenditure in order to allow for cross-country comparisons on the adequacy of road maintenance allocations. It was established that 25 countries from the BOOST database have implemented Road Funds, and financial statements were collected for 20 of these, while the remainder of countries with Road Funds were dropped from the more detailed road sector analysis.

Cross-country comparisons of road maintenance expenditure also entail some normalization against the extent of the road network. For this purpose, the International Road Federation database was used, which provides information on road network characteristics across 205 countries. In particular, the total length of the primary and secondary network is used for normalization purposes since tertiary roads are typically a municipal responsibility.<sup>5</sup> The reported share of paved and unpaved roads was also used to adjust maintenance expenditure benchmarks for comparison purposes. The World Bank’s ROCKS database provides such regionally differentiated unit cost data for road maintenance activity.

Finally, to get a complete picture of infrastructure expenditure sources, the research also draws upon the World Bank’s Private Participation of Infrastructure (PPI) database that covers over 6,400 infrastructure projects in 137 middle- and low-income countries with private sector participation.

One big distinction between both the PPI database and the AidData’s Global Chinese Development Finance Dataset versus the others used in the analysis is that the latter provide actual executed expenditure, while the former only record financial commitments at the year of project closing, without recording how this is spent over time. Based on the assumption that infrastructure projects typically take five years from financial closing to complete, commitment data are converted to simulated disbursement data for greater compatibility with other sources. Similarly, AidData is also smoothened by taking five-year moving averages.

Exploratory data analysis is complemented with selective use of econometric techniques. To explore the relationship between infrastructure spending and GDP over time, with a view to assessing the extent of cyclicality, a fixed effects panel data model is estimated and further complemented with a panel vector autoregression (PVAR) analysis. In addition, the efficiency of infrastructure spending is assessed using Data Envelopment Analysis. The details of these techniques are discussed in the relevant sections.

> 5 Care was taken to establish whether the institutional responsibility for maintaining primary and secondary road networks in any given country was consistent with the corresponding level of the budget. Road network lengths were averaged over time to address some problematic fluctuations in the data over time.

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