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In here, you can find the latest info about my current research as well as some of my codes and data.
Post #3: The spatial extent of MNE spillovers in Indonesia
Post #3: The spatial extent of MNE spillovers in Indonesia
Spillovers from multinationals have received significant attention as a possible source of productivity-enhancing effects. In my work with Zhiling Zhang (EUR) and Hengky Kurniawan (VUA), we study MNE spillovers across different channels (within industry, through vertical linkages and through relatedness). While the paper show that relatedness-mediated spillovers are crucial for domestic firms' productivity, we also show different spillovers channels have different spatial reaches. The graph on left reports one of our findings: the correlation between MNE spillovers (measures through MNE employment) and domestic TFP decays over space, especially for relatedness-mediated spillovers and less so for spillovers through backward linkages.
Post #2: Spillover from exporters in Colombia?
Post #2: Spillover from exporters in Colombia?
I am currently working on a paper with Jacopo Timini on “learning from exporters” effects. Like for multinational enterprises, we hypothesize that exporters generate productivity-enhancing externalities to domestic-oriented firms. We test our idea in the case of Colombian manufacturing plants over the period 2001-2011, and our baseline and 2SLS regressions provide substantial evidence of spillover effects through backward linkages (à la Javorcik). We just finished drafting the paper so, soon enough, you can read it in some working paper series (or you can contact me if you want to have a look at the draft)!
In the graphs on the left, you can see the "exposure" to export activities across different industris through backward (top graph) and forward linkages (bottom graph). In terms of backward linkages, the most exposed sectors are rather variegated: Clothing (IOT 20 and 21), Furniture (IOT 35), Machinery and Equipment (IOT 33), Basic Metal products (IOT 31) and Chemicals (IOT 27-28). Interestingly, with the exception of sector 33, 20 and 21, the most exposed industries via forward linkages are not same as for backward linkages: Other Machinery and appliances (IOT 32), Paper (IOT 21) and Rubber and plastics (IOT29).
Average related variety based on patent subclasses between 2005-2015.
Post #1: How to compute related and unrelated variety?
Post #1: How to compute related and unrelated variety?
Since the paper by Frenken et al. (Regional Studies, 2007), entropy-based measures of technological and industrial variety have become popular. In my first paper (Annals of Regional Science, 2015) I used those same measures to study their effect on the economic performance of European regions. Back then I spent quite some time figuring out how to compute related and unrelated variety. Having gained some experience on that, I prepared an easy step-by-step example (in R and Stata) for those who may need it. You can find it here.
More recently, I have been working on the role related and unrelated variety in patents in driving innovation performance in Europe. The map on the left shows the spatial distribution of patent-based related variety across European regions. If you are interested in the topic, keep an eye on my website: there may be a working paper version coming soon!
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