3.1. Investing in infrastructure is important, but what matters is to spend it well Data on infrastructure investment and maintenance are difficult to obtain. Few fiscal authorities disclose granular expenditure data and if so, they often lack common or harmonized public accounting standards, such that specific infrastructure expenditure items can be mixed with other expenditure types. Especially in developing countries the level of transparency varies, and more generally the definition of – for instance – “infrastructure maintenance” spending differs. Moreover, the sectoral disaggregation of public spending data is difficult due to the organization of public budgets – including distribution across federal and regional entities -- thus making it difficult to distinguish public spending on water, electricity or transport infrastructure respectively. Rather than relying on nationally published budget figures, we tap two international sources of investment data in order to compile a consistent panel of public transport expenditure.<sup>2</sup> From the OECD International Transport Forum database we obtain transport infrastructure investment spending for 57 middle and high income countries, covering the time from 1995 to 2016 (OECD, 2018). This is complemented by public infrastructure investment data from the World Bank’s BOOST initiative, available through the Open Budgets Portal (World Bank, 2018b). Together, these yield a panel of 603 individual country-year observations from 85 countries, covering all income groups.<sup>3</sup> In addition, information on the reliability of the transport infrastructure is proxied by a measure of the quality of logistic services provided by the Logistics Performance Index (LPI), a benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics (World Bank, 2018a). The LPI 2018 allows for comparisons across 160 countries, and offers country specific scores along six dimensions: (i) customs, (ii) infrastructure, (iii) international shipments, (iv) logistics competence, (v) tracking and tracing, and (vi) timeliness. The infrastructure sub-indicator aggregates a quality scoring of ports, railroads, roads, and information technology. The timeliness subindicator measures reliability rather than quality per se. By scoring timeliness of shipments in reaching destination within the scheduled delivery time, this sub-indicator is a measure of unexpected transport disruptions – rather than the average performance. All LPI indicators are scored on a scale from 1 (worst) to 5 (best).

The resulting country-year panel allows us to exploit dynamic and cross-sectional variation to relate infrastructure spending to the quality of transport infrastructure, while controlling for all major confounders. Specifically, we conduct pooled Ordinary Least Squares regression analysis, including country-level fixed effects and clustering standard errors at the country level. In our main specification, the LPI Timeliness sub-indicator is regressed on the logarithm of per capita public road spending (including both maintenance and investment) and a series of control variables.<sup>4</sup> Control variables include population density, urbanization, GDP growth and governance indicators.

Annex A.1 presents estimates for the main model and variants thereof. The spending coefficient turns out significant and – like other controls -- shows the expected signs and a reasonable magnitude; the goodness of fit measure obtains as R<sup>2</sup> = 71%.<sup>5</sup> However, the key result emerges by toggling the governance control: Increasing public road spending improves transport reliability significantly –only if governance standards are strengthened at the same time. That is, fixing governance by introducing the corresponding control

> 2 Unfortunately, data on other infrastructure types are too sparse for systematic empirical analysis.

> 3 The sample is reduced to 313 country-year observations, when controlling for disaster events.

> 4 The results are robust to using alternative dependent variables, including the LPI:Overall or LPI:Infrastructure indicators.

> 5 When introducing additional controls for disaster events, the overall fit as well as point and error estimates remain largely unaffected.

6