- Develop a robust empirical method to identify the subset of indicators (from a large set) which are significantly associated with measures of trade and investment outcomes at the sector level (trade and investment sector “success”).

- Use the model estimates to construct robust benchmark scores of country/sector performance against the rest of the world for the identified subset of indicators.

In this way, econometric analysis complements the existing ordinal-ranking based CPSD methodology by: i) providing statistical assessment of the strength of association between the individual indicators and the sector specific performance and by ii) explicitly taking into account indicator’s forecasting performance as well as the effects of other indicators and potential confounding factors in the process of constraints benchmarking. The produced benchmarking heatmap empowered by the empirical model estimates provides actionable and refreshable signals of the identified sector level constraints which can be used to facilitate efficient data-driven economic policy decision making.

In the remaining parts of this section we first discuss data which is used in the empirical analysis before discussing in detail the empirical design and econometric approach. We then present the baseline results together with conducted specification checks.

# Data

We use three general types of data in the analysis:

## I. <u>Foreign direct investment data</u>

We use the Financial Times’s _fDi Markets_ database for FDI data. The database provides a comprehensive source of information on the firm-level greenfield investment, containing reported deals/projects worldwide over the 2003-2019 period for 206 countries. The data set contains information on the recipient and the source country, principal industry sector to which the foreign direct investment type can be mapped, the year/month of the transaction, the name of the investing firm as well as various other information. The _fDi Markets_ database focuses on the _new_ FDI projects and provides cleaner information on the FDI sector level patterns relative to the balance of payments (BOP) based FDI data. The latter contains sector level information on FDI flows only for a limited number of countries, does not distinguish between existing (reinvested earnings) and new FDI activities and may also be distorted by “round-tripping” behavior of firms. A potential disadvantage of the _fDi Markets_ database is that the reported values of FDI projects are estimates which may differ from the actual recorded values.<sup>16</sup> Moreover, while the data set coverage is extensive after 2010, in earlier years the coverage may not be fully comprehensive. We circumvent these disadvantages by focusing the analysis on the last decade (2010-19 period) and using the available information to construct binary FDI success variable – which takes value 1 if the country/sector pair received FDI (from any country) over the specified period and zero otherwise. The use of a binary indicator is imperfect as it does not distinguish by size and number of investments, and thus the relative share/frequency of inward FDI that a country may command relative to the rest of the world and to the size of its economy. However, a discrete indicator is possibly less inadequate than potential alternatives. The project value in _fDi Markets_ database is a rough

> 16 The fDi Markets database uses public information sources such as media sources to inform its database. See: https://www.fdimarkets.com/faqs/

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