_Table 1. Import share of total expenditure and protectionism in government procurement (cross-country)_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. variable:<br>Ln(Import share in<br>totalspending)|Discri|mination|Pref. t|reatment|Dom.|sourcing|Overall|indicator|
|Protectionism|0.031|-0.096|-0.185*|-0.227**|0.030|-0.153**|-0.039|-0.185*|
||(0.092)|(0.088)|(0.102)|(0.103)|(0.084)|(0.073)|(0.108)|(0.108)|
|EU||-0.151||-0.047||-0.124||-0.147|
|||(0.145)||(0.153)||(0.153)||(0.151)|
|WTO GPA||-0.507***||-0.551***||-0.546***||-0.524***|
|||(0.142)||(0.131)||(0.141)||(0.142)|
|Ln(GDP cap)||-0.070||-0.063||-0.060||-0.073|
|||(0.061)||(0.063)||(0.061)||(0.059)|
|Middle income||-0.235*||-0.223||-0.212||-0.202|
|||(0.141)||(0.135)||(0.134)||(0.138)|
|Low income||-0.460*||-0.405||-0.410||-0.422*|
|||(0.253)||(0.252)||(0.248)||(0.249)|
|Institutions WGI||0.001||0.001||0.001||0.002|
|||(0.003)||(0.003)||(0.003)||(0.003)|
|Obs|139|138|138|137|139|138|139|138|
|R-sq|0.00|0.23|0.02|0.24|0.00|0.25|0.00|0.25|

_Notes: The dependent variable is the import share in total spending averaged by country over the 2000-2019 period, in logs. The “protectionism” dummy equals the variables reported in Table A-1. EU and WTO GPA variables are indicators for being EU and WTO GPA members respectively as of 31_<sup>_st_</sup> _December 2018. The Ln(GDP cap) variable is the country’s GDP per capita, in logs, averaged across the years. Middle income and Low income are indicators for middle income and low income countries, repsectively. Institutions WGI is the average percentile rank across the six WGI indicators. All regressions include a constant term. Standard errors robust to heteroskedasticity are in parenthesis. * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level._ These results are indicative of a negative correlation between protectionist laws on government procurement and aggregate trade openness. In section A of the Appendix, we investigate how this correlation holds up for purchases by governments and those by private agents, measured from the ICIO tables of the TiVA database. We confirm a negative correlation that is not significantly different across the public and private sector. Overall, the relationship between protectionism and trade openness is weaker in the restricted sample of 66 countries of the TiVA data than in the full sample – a finding that is confirmed when we use the aggregate data from the ITPD-E database on the 66 countries. Under the assumption that the TiVA data can capture (at least partly) true differences in purchasing patterns between governments and the private sector, the lack of differences in the results between these two buyers suggest that our indicators of protectionism in government procurement identify restrictions that operate also on the sourcing decisions of firms.

To gain further insights into the relationship between trade openness and protectionism in government procurement, we leverage the industry dimension of the ITPD-E database. There are 170 industries in the data: 118 in manufacturing, 35 in the primary sector (agriculture, forestry, fishing and mining), and 17 in the service sector. Because of missing data on internal trade especially in primary and services industries, we estimate the cross-country regressions in Table 1 with controls by sector, and retrieve the coefficient on the protectionism indicator of interest. The results are displayed in Figure 3. The import shares in the manufacturing and in the primary sectors are lower in countries with laws in government procurement that provide preferential treatment to domestic firms and contain provisions for domestic sourcing, whereas there is no significant difference with respect to the inclusion of a clause on national treatment. Overall, the coefficients are often imprecisely estimated, especially for the service sector.

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