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on Economic Geography |
| By: | Orhun Sevinc |
| Abstract: | Using administrative data covering the economic geography of Turkish manufacturing firms I show that density increases a location’s productivity through both typical firm productivity and stronger association of firm size and productivity—a measure of within-sector allocative efficiency. IV estimates suggest a density elasticity of allocative efficiency that accounts for about one third of the overall impact of density on productivity. A model with decreasing returns to scale and convex cost of avoidance from the burden of regulations can explain the estimated density-allocative efficiency relationship on the grounds that denser locations provide lower degree of internal diseconomies. |
| Keywords: | Density, Allocative efficiency, Cities in developing economies |
| JEL: | R10 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:tcb:wpaper:2125&r= |
| By: | Reuben Ellul |
| Abstract: | Employment in Gozo is characterised by disadvantages due to the island’s geographical and transportation constraints. It also benefits from advantages linked with its geography – in terms of physical and environmental endowment - as well as the abilities of its workers. This study attempts to measure these effects on employment data, both at regional and sectoral levels. A shift-share analysis is carried out using data from 2000 to 2019, estimating a deficit in regional employment numbers with respect to the whole of Malta of between 1271 and 1948 jobs, with a further shift share regression indicating that employment in Gozo grew by 1.5% less than the national average. The performance of eleven sectors is assessed, with three sectors, (i) Professional, scientific and technical activities; administrative and support service activities, (ii) Real estate activities and (ii) Information and communication, appearing to have some innate advantages. |
| JEL: | R11 R15 |
| URL: | http://d.repec.org/n?u=RePEc:mlt:ppaper:0521&r= |
| By: | BAH, Mamadou Mouminy |
| Abstract: | This paper analyses the effects of agglomeration economies on firm labour misallocation, using the Ivorian firm data from 2013-2016. After measuring the degree of firm labour misallocation in the first step, we assess the level of labour misallocation in denser regions in the second step. The results show on the one hand that the average labour misallocation (labour gap) at the firm level is 2,825,887 FCFA ($5,137.97 ) over the period 2013-2016 and this gap has significantly decreased over years. On the other hand, firms located in denser regions exhibit lower labour misallocation. In terms of the magnitude, both localisation and urbanisation economies are large and statistically significant. A 10% increase in the degree of localisation in a region reduces the labour misallocation by 7.41% on average, while a 10% increase in the degree of urbanisation alters the labour misallocation by 4.26%. These findings confirm that labour misallocation has a geographical dimension, in addition to the firm characteristics. A sound policy needs to accounts for the spatial distribution of firms and the creation of active poles of development in major Ivorian regions. |
| Keywords: | Localisation, Urbanisation, Misallocation, Total factor productivity, firm-level data |
| JEL: | D24 L25 O4 R3 |
| Date: | 2021–08–15 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:109314&r= |
| By: | Samarth Gupta (National Council of Applied Economic Research) |
| Abstract: | Do agglomeration-based spillovers impact firms more than the technical know-how obtained through inter-firm collaboration? Quantifying the effect of these treatments on firm performance can be valuable for policy-makers as well as managers/entrepreneurs. I observe the universe of Indian MSMEs inside an industrial cluster but with no collaboration (Treatment Group 1), those in collaboration with other firms for technical know-how but outside a cluster (Treatment Group 2) and those outside cluster with no collaboration (Control Group). Selection of firms into these treatments and sub-sequent performance of the firm may be simultaneously driven by observable factors. To address selection bias and overcome model mis-specifcation, I use two data-driven, model-selection methods, developed in Belloni et al. (2013) and Chernozhukov et al.(2015), to estimate causal impact of the treatments on GVA of ?rms. The results suggest that ATE of cluster and collaboration is nearly equal at 30%. I conclude by offering policy implications of the results. |
| Keywords: | Entrepreneurship, Firm Performance, SME |
| JEL: | L25 L26 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:nca:ncaerw:129&r= |