Table 5: Workers employed at ICT firms commute more than other workers

||ICT|ICT<sup>_∗_</sup>|non-ICT|Manufacturing|Retail|Other<br>Industries|
|---|---|---|---|---|---|---|
|Workers average|18.6|20.6|13.2|22.8|11.7|15|
|commute|||||||
|Observations|126|60|407|87|202|118|
|_H_0 : ICT = non-ICT|0.001|0.0005|||||

_Notes:_ ICT<sup>_<u>∗</u>_</sup> excludes retailers and repairers. Time expressed in minutes. P-values reported for Wald tests. _Sources:_ Authors’ survey.

observed data and are exogenous and constant throughout the exercise. We rationalize the observed concentration of the ICT sector in the model by setting the productivity for ICT in location in which there are no ICT firms to zero. This however implies that we are not able to study the formation of new ICT clusters. Nevertheless, we do not expect this to have a strong impact on the analysis of the paper.

Moreover, in the model there are not systematic differences between ICT workers and other workers. Yet, ICT workers may have preferences for larger cities, and thus driving the observed clustering of ICT firms. In our data, we observe that workers in ICT firms travel on average 18 minutes, compared to 13 minutes for workers in the other sectors (table 5).<sup>26</sup> In the paper, we abstract from the role of commuting. We refer to Fratto (2019) for a discussion of their role.<sup>27</sup> Spillovers via input-output linkages describe another channel of the impact of a change in the road infrastructure on the industrial composition. In the model, we assume that the cost of transporting goods in industry _j_ for consumption is the same as the cost of transporting those goods for use as material inputs.

We abstract from the role of externalities. If we decompose productivity into an exogenous and an endogenous component, ln _A_<sup>_j_</sup> _i_<sup>=ln</sup><sup>_aj_</sup> _i_<sup>+</sup><sup>_ψj_</sup> � _n_<sup>ln</sup><sup>_exp{κ_</sup> _ni_<sup>_jL_</sup> _n_<sup>_j}_,where</sup><sup>_κj_</sup> _ni_<sup>isa</sup> function of travel time between locations _n_ and _i_ , this is equivalent to assume that _ψj_ = 0. Based on this specification, the level of productivity in location _i_ industry _j_ depends on some underlying exogenous component and it is increasing in the number of workers employed in that industry weighted by some function of travel time. While we consider this latter channel valid, it would simply strengthen the quantitative results while not altering the implications.

Often the ICT sector is deemed promising because of its ability to tap into the international markets. Yet, we do not find clear evidence of its greater ability to access international markets (table 6): ICT firms are as likely as others to import inputs from and to sell output abroad, even when excluding retailers and repairers from the sample. The high percentage of inputs and output exchanged on the domestic market paints a picture of an ICT sector

> 26The figure includes only workers employed in Palestinian private firms. Most notably, it excludes workers employed in foreign firms either in Israeli settlements or in Israel.

> 27An additional element worth investigating is the role of the access to the Israeli labor market. See for instance Etkes (2012).

20