Abstract. We analyse the determinants of firm entry in developing countries using Argentina as an illustrative case. Our main finding is that although most of the regional determinants used in previous studies analysing developed countries are also relevant here, there is a need for additional explanatory variables that proxy for the specificities of developing economies (e.g., poverty, informal economy and idle capacity). We also find evidence of a core-periphery pattern in the spatial structure of entry that seems to be mostly driven by differences in agglomeration economies. Since regional policies aiming to attract new firms are largely based on evidence from developed countries, our results raise doubts about the usefulness of such policies when applied to developing economies.
JEL classification: R12, R30, C33
This study analyses the determinants of firm dynamics in developing countries, using Argentina as an illustrative case. It explains firm entry and exit at the regional level, distinguishing three groups of manufacturing activities: low-, medium-and high-tech. The study finds that both region-and sector-specific determinants explain firm dynamics, but the impact is not homogeneous across sectors. In particular, for low-tech industries, there is a need for explanatory variables as a proxy for the specificities of developing economies (poverty, informal economy and idle capacity). There is also evidence of a core-periphery pattern according to which agglomeration economies and previous entries/exits have different effects in core and peripheral regions. These results are relevant for policymakers in developing countries, who should take into account not only the specificities of such economies, but also the regional heterogeneity both in terms of the level of development and industrial composition within the country.
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En este artículo se analizan los determinantes de la dinámica empresarial en los países en desarrollo, tomándose como ejemplo la Argentina. Se explica la entrada y salida de las empresas a nivel regional y distinguen actividades manufactureras de tecnología baja, media y alta. Se concluye que hay factores regionales y sectoriales que explican esa dinámica, aunque el efecto no es homogéneo en todos los sectores. En los sectores de tecnología baja son particularmente relevantes las variables explicativas características de las economías en desarrollo (pobreza, economía informal y capacidad ociosa). Existe un patrón centro-periferia por el que las economías de aglomeración y las entradas y salidas anteriores tienen efectos diferentes en la región central o periférica. Los encargados de formular políticas en los países en desarrollo deberían tener en cuenta no solo las particularidades de esas economías, sino también la heterogeneidad regional en materia de desarrollo y composición industrial del país. Palabras clave Empresas industriales, empresas manufactureras, países en desarrollo, estudios de casos, desarrollo de empresas, datos estadísticos, modelos econométricos, Argentina Clasificación JEL R12, R30, C33
El estudio de la especialización productiva regional es clave para diseñar políticas de desarrollo territorial. Sin embargo, las medidas usualmente utilizadas no tienen en cuenta la interdependencia entre actividades y presentan otros problemas relacionados con el nivel de desagregación sectorial empleado. Para superar estas limitaciones, proponemos una nueva forma de definir la especialización regional a partir de técnicas de análisis multivariado, que son aplicadas a datos del total de empleo asalariado registrado en el sector privado de Argentina. Primero, conformamos un conjunto de perfiles sectoriales de coaglomeración territorial y, a partir de ellos, definimos una tipología empírica de Áreas Económicas Locales en función de sus patrones productivos. Los resultados muestran que la metodología propuesta ayuda a capturar interdependencias entre actividades, distinguir dentro de una categoría especializaciones cualitativamente diferentes y dar cuenta tanto del tipo de especialización como del grado de diversidad productiva regional.
Purpose
During recent years, the long-run relationship between the unemployment rate (UR) and the labor force participation (LFP) rate has been examined in-depth in developed and developing economies. This paper aims to explore this relationship for Iranian women in 31 provinces from 2005Q2 to 2019Q1.
Design/methodology/approach
To examine the existence of a long-run relationship between female LFP and UR, the time-series cointegration approach has been used. Furthermore, regarding the low power of the univariate cointegration approach, the authors consider a panel version of the cointegration tests developed by Westerlund.
Findings
Both time-series cointegration tests and panel cointegration test support the unemployment invariance hypothesis for most Iranian provinces, especially the most religious ones. As it implies an invariance to supply side policies, it seems that reducing legal and cultural barriers could be more relevant to decrease female UR and increase LFP than training programs or R&D policies. The present results also suggest that, for this group of regions, a more centralized policy design could be appropriate, instead of a regional one.
Originality/value
This study investigates whether the unemployment invariance hypothesis holds for Iran, which has not been analyzed before for the Iranian labor market. Moreover, the study adopts a regional approach, which takes into account the huge regional differences in Iran.
Este artículo tuvo como objetivo construir un índice de afectación económica por COVID-19 para analizar el impacto potencial de la pandemia y las medidas de aislamiento entre sectores y regiones de Argentina, durante el trimestre de mayor recesión en el país, abril-junio de 2020. Teniendo en cuenta la estructura productiva sectorial de las regiones, el grado de operatividad de cada sector, la movilidad laboral a nivel regional y la potencialidad del teletrabajo en los distintos sectores, se muestra que la pandemia ha tenido un impacto desigual entre regiones y que el perfil de especialización sectorial es un factor explicativo relevante. Los perfiles turísticos, extractivos y de industrias pesadas se han visto mucho más afectados que los agro-industriales y de manufacturas livianas.
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