dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and descriptive analysis The specific database from the French Institute of Statistics (INSEE) used here provides information about the location of 12. 667 million people over 18 years  old, in 2003 and 2008. This database represents almost one third of the total French population. Information is available at the 364 zones d'emploi level, and also at the 36,570 French communes' (counties) level. This means that we can track important long or short migration movements both between and within the zones d'emploi. The only flows we are not able to measure are those regarding individuals that have moved within their own county -but this can hardly be considered as a migration movement. Calculations are based on INSEE's provided person weights per zone. In 2008, 30. 10% of the persons included in our dataset had moved out of the county they used to live in 2003, which corresponds to a rather high degree of mobility in Europe.
It provides some descriptive statistics both for the total population and for the retirees. AGE is defined by a person's age in years. The mean age for the total population of our database is 39. 29 years old, very close to the national mean age in France, which was of 39. 7 years old in 2008. The mean age for people over 18 is 49. 09, while the mean age for the retirees is 68. 38 years old. We also calculate the AGE-squared variable in order to model more accurately the effect of age, which may have a non-linear relationship with the migration decision. EDUC measures each person's level of educational capital. We consider two levels of educational attainment, which give, de facto, a proxy for each person's number of years of education: either less than the high school degree (baccalaureate) or with/more than the high school degree which also features all the different levels of the University degrees.
6% of our database and, more generally, of the French population. For retirees, we only consider two options, being alone (30. 33%) or otherwise (69. 67%) which basically corresponds to the second category (living as a couple). SPS defines a person's socio-professional status. The French National Statistical Institute defines 8 different types of status: (1) farmers, (2) individual entrepreneurs, which includes all individual type activities from merchants to artisans, (3) white collar workers, (4) blue collar workers, (5) employees, (6) intermediate professions, such as teachers, social workers or health services workers, (7) retirees and finally (8) the other non-working population. Unemployed people and job seekers do not appear separately but are classified according to their last professional experience.
Retirees represent 31. 11% of the dataset. Finally, OWN indicates whether a person is an owner of his/her house/apartment or a tenant (renter). As predicted, there is a higher proportion of retirees (76. 08%) who are real estate owners than the rest of the population (61. 12%). Table  2  features some basic statistics intra-zone and inter-zone migration with regards to personal attributes. Table  2  clearly shows that personal attributes influence the migration decision and the migration destination. People with a lower educational level, mono-parental families, farmers, small entrepreneurs and blue-collar workers move eagerly within a zone's limits but seem reluctant in quitting their initial zone d'emploi. On the contrary, people with high human capital, white collar jobs and low local family ties (living alone or in couple) are likely to move to a longer distance out of their initial zone d'emploi.
As expected, with regard to the working population, young, single and without family individuals are more willing to move than the workers who already have founded a family  (Courgeau and Meron, 1995). With regard to the retirees, being alone seems to be a more favorable condition for migrating than living as a couple. Börsch-Supan et al. (1990)  have argued that elderly migration and location is not independent from the children's choices and family arrangements. Bonnet and al. (2010)  study the effect of widowhood on location choices with similar results. Several studies suggest that the desire to live next to adult children is an important factor for elderly migration  (Silverstein, 1995; Rogerson and al. , 1997). However, it remains quite difficult to introduce the spatial convergence of parents' and children's location within a large dataset study.
Conclusion -discussion The aim of this paper is to study elderly migration in France between 2003 and 2008. This work had to manage an important amount of data. Its primal added-value relies on the fact that it uses several original datasets: first, a unique INSEE dataset with information on individual characteristics for 12. 67 million people over 18 years old, also indicating whether a person has moved or not between 2003 and 2008. In France, there is no available data on individual income which leads to collapsing individual heterogeneity mainly in human capital but also in age and family type. ; second, an original spatial dataset with locational characteristics for the zones d'emploi level, which represent the most accurate geographical level for regional economic analysis in France. These two databases had not been explored until now, and allow us to deliver a single and robust analysis of late-life migration in France.
Future work on elderly migration in France can follow different steps: first, a similar analysis at the 36,570 counties' level could allow the tracking of short migration trips due to reasons that have not necessarily been taken into account in the present study. Second, testing the retirees' decision to migrate by taking into account their children or their family's preferences can deliver further information on the elderly migration. However, such type of data is unavailable, especially for such a large database. Third, the economic impacts of elderly migration and the perspectives for growth of localized Silver Economies is a subject that both researchers and local economic and social policy workers should consider over the next years 6.
Agradecimientos Estoy en deuda con Lázaro Hotus Ika, Diego Pakarati Atamu, Valentín Riroroko Tuki y Alberto Hotus Chávez por haber compartido conmigo sus historias. También con los colegas Rolf Foerster y Sebastián Lorenzo por sus comentarios a una primera versión de este texto, así como a los evaluadores anónimos por sus consejos. Debo agradecer, asimismo, a Betty Haoa, documentalista del museo Fonck de Viña del Mar, por los documentos de archivo facilitados, y a Judith Hannoun, documentalista del Centre de Recherche et Documentation sur l'Océanie, Francia, por su preciada ayuda para ubicar los textos franceses.
Secondly, we test whether agglomeration patterns of CIs differ from those of noncreative industries (Non-CIs), and also if these patterns change across specific subgroups of CIs. Additionally, we examine whether different CIs coagglomerate. Then, in order to deal with previous methodological limitations, we use geo-referenced data from the SABI database (Bureau van Dijk) for the year 2012 at three digits NACE code for the MAB to calculate the relative distance-based M and m cumulative and density functions of agglomeration and coagglomeration  (Marcon and Puech 2010; Lang et al. 2015). Ours is the first paper to use these two complementary distance-based measures to analyse agglomeration patterns of creative firms. Both functions compare the location patterns of an economic sector to that of the aggregate economic activity.
[INSERT TABLE A HERE] [INSERT FIGURE 1 HERE] This study uses micro-geographic data from the SABI database (Bureau van Dijk) for the year 2012. SABI contains comprehensive information on firms in Spain, detailed by firms' geographical information (plain coordinates), employment, and among others characteristics at the 3-digit NACE level. 5 The SABI database provides information of 620,390 workers and 44,164 firms located in the MAB in 2012. 6 egarding the classification of CIs, we follow the proposal of UNCTAD (2010) as it is the broader in terms of industries considered. In addition, this classification is the most widely accepted among researchers (see  Boix and Lazzaretti 2012b ). UNCTAD's classification includes both manufacturing and service industries. Even so, the relevance of service creative firms is greater than manufacturing ones.
M functions We calculate the intra and inter-industry M-functions for every 1000 m between 0 and 20 km at the industry level for Non-CIs and CIs. We use the package dbmss for the calculations  (Marcon and Puech 2015). We use the plain coordinates (X-Y) for each firm located in the MAB. In the following, we explain a detailed definition of M functions of agglomeration and coagglomeration.
*This paper was partially funded by  Marie Curie Intra European Fellowship  ( PEIF-GA-2013-627114 ) within the  7th European Community Framework Programme ,  ECO2013-42310-R ,  ECO2014-55553-P , the " Xarxa de Referència d'R+D+I en Economia i Polítiques Públiques ", the  SGR  programme ( 2014 SGR 299 ) of the  Catalan Government , the  "Departament d'Universitats, Recerca i Societat de la Informació de la Generalitat de Catalunya" FI Fellowship  ( 2015 FI_B 00223 ) and the " Fundación SGAE ". We would like to acknowledge fruitful comments received on XREPP Doctoral Day 2015 and on Infer Annual Conference 2016, and also research assistance by  M. Lleixà. Any errors are, of course, our own.
Agradecimientos: En primer lugar deseamos agradecer la buena predisposición de todas y todos aquellos que nos recibieron durante nuestro trabajo etnográfico tanto en la provincia de Chaco Transformaciones socioeconómicas, migración y organización etnopolítica. [91] [92] [93] [94] [95] [96] [97] [98] [99] [100] [101] [102] [103] [104] [105] [106] [107]  como en la Región Metropolitana de Buenos Aires. Ello ha posibilitado una labor colaborativa, la cual enmarca el conjunto de las reflexiones presentes. En segundo lugar al equipo y a los directores del Programa de "Etnicidades y territorios en redefinición", quienes enriquecieron este manuscrito con sus aportes. Finalmente, no queremos dejar de mencionar a la Dra. Liliana Landaburu, que compartió con nosotros diferentes instancias de trabajo y de compañerismo, así como de docencia y amistad a Notas 1 Malena Castilla -en el marco de una beca doctoral otorgada por la Universidad de Buenos Aires-trabaja junto con las organizaciones étnicas del pueblo qom en la localidad de Pampa del Indio, provincia del Chaco, en relación con el análisis de la implementación de políticas de desarrollo.
Cette modalité constitue une tentative de questionner l'influence des différents cercles de socialisation politique en interrogeant un panel de citoyens. Ainsi, au sein d'un ou plusieurs quartiers urbains formant une entité spécifique, ont été menées une centaine d'enquêtes stratifiées par sexe et par âge. Ici, nous présenterons cinq observatoires dans cinq villes différentes (Rabat, Inezgane, Agadir, Marrakech, Tiznit). La sélection a été opérée selon les contingences et la disponibilité des enquêteurs. Nous ne souhaitions pas construire une base de données quantitatives généralisables mais davantage interroger la diversité des profils au sein du corps électoral marocain tout en pouvant le mettre en perspective avec le contexte local. (Desrues, 2016).
This is because otherwise if r is a frontier region, there would be perfect collinearity between the dependent variable and ∆ ln  / 0 ,* and the estimate of. obtained would be biased towards the share of frontier regions in our dataset (8%). By doing this, we obtain an estimate for. that is estimated only over the non-frontier regions. We expect. to be positive as the growth of the country frontier region has a positive effect on that of other regions in the same country. The productivity gap variable  (,*6. is defined as ln ?(@A B,CDE ?(@A F 0 ,CDE. The gap is equal to zero at the frontier and becomes increasingly negative for regions farther away from the frontier. 2 indicates the (hypothetical) effect of increasing the productivity gap by one unit when policy indicators are set to zero.
Nonetheless, in our sample, this endogeneity is anyway already minimised by the fact that structural policies are defined by central governments (or the EU) and the productivity growth is measured at the regional level. and the relatively small number of years in our dataset, we follow  Blundell and Bond (1998)  and also estimate our model using a system-GMM method. Noteworthy, the fact that we are using regional data enables to control for fixedeffects without creating the usual collinearity problems with the country-wide policy variables, which often do not display strong time variability. 8.
The data Our data consist of a panel of 265 regions from 24 OECD countries 9 defined at Territorial Level 2 (TL2), taken from the OECD Regional Database and covering the period 1997 to 2007. 10 We observe regional productivity since 1996, defined as GDP per worker, deflated with base year 2000 and PPP adjusted in US dollars. We use this measure to compute yearly regional productivity growth in percentages. Using the regional productivity data we are able to identify the regions which are at the productivity frontier in their country in each year. This allows us to compute the productivity growth of the frontier region (∆ ln  / 0 ) and the distance between a given region and the country frontier region (). 8 This is a recurrent problem in many cross-country econometric growth studies using structural policy variables that do not display a time dimension or have low time variability.
To circumvent this problem, researchers often have to revert to pooled or the GLS estimates that may generate bias on their own. Turning to our country-year level policy and macroeconomic variables, our measures of regulation are drawn from the OECD's Product-Market Regulation (PMR) Database. 11 The PMR indicators are a comprehensive and internationally comparable set of indicators that measure the degree to which policies promote or inhibit competition in areas of the product market where competition is viable. They measure the economy-wide regulatory and market environments in 34 OECD countries as well as in Brazil, China, India, Indonesia, Russia and South Africa. They are consistent across time and countries. In the original data, the values for each indicator vary between 1 and 6, with higher values indicating higher levels of restrictions.
EPL indicators are available for all 35 OECD countries as well as for 39 non-OECD countries and territories. Two EPL indicators were available to us: the first one, pertaining to regular contracts, was not retained in our analysis due to the lack of time and cross-country variation in this measure; the second one, pertaining to temporary contracts, presents considerable variation as this form of employment contracts has been subject to reform in many countries in the period we consider. In terms of macroeconomic variables, we consider trade openness, captured by total trade flows as a percentage of GDP, government debt to GDP ratio, and inflation (measured as the rise in the consumer price index). All macroeconomic data are drawn from OECD sources. Table  1  provides summary statistics of the variables.
Robustness checks Our results may be sensitive to specificities of our data or to the way in which we have defined a single frontier region for each country. We first consider the possibility that the presence of frontier regions in our dataset for analysis may influence our results. We remove from our data all the observations that correspond to a country's frontier region in any given year. As a result, we are left with 2,281 observations for which the productivity gap is not zero. Results are presented in Table  3. The marginal effects computed from the results of the OLS and system GMM specifications of columns (  6 ) and (  7 ) respectively are shown graphically in Figures  3  and 4  below. Results from the OLS with fixed effects specification are similar to those obtained previously.
Marginal effects on productivity growth Another possible important bias may come from our definition of the frontier regions. Instead of using the region with the highest productivity in region r's country, we propose an alternative definition of the frontier by creating a synthetic frontier where the productivity is equal to the average productivity in the three most productive regions in the country in a particular year. We also compute an alternative measure of the distance to the frontier, which is now equal to the difference between the log of productivity of region r and the log of the average productivity of the three most productive regions. We then remove from our dataset the three regions that were used in order to create the synthetic frontier region. This is to avoid having a positive productivity gap between the highest productivity region and the synthetic average and to capture the effect of this synthetic frontier and of the policies on those regions that are not part of this frontier.
64 point increase in productivity growth. Although the effects estimated on lagging regions are very large, they remain of a more realistic magnitude over the main range of regions in our dataset. Finally, given the time period of our analysis it could be argued that Poland, Ireland and Spain could have an overly important weight on the results. During the pre-crisis period, these three countries experienced particularly rapid growth and undertook important structural reforms. Accordingly, we replicated our base estimates after removing Polish, Irish and Spanish regions from our sample (see Table  6 ). Marginal coefficients represented in Figures 9-11 show that our results are robust to the exclusion of these countries. Marginal effects on productivity growth.
Impact of macroeconomic factors We now turn to the analysis of the macroeconomic variables. Table  7  reports our base results using the full dataset and the single frontier region. The first three robustness checks, including the alternative definition of the frontier, are offered in Table  8. Table  9  shows the results obtained once we exclude Polish, Spanish and Irish regions. Again, the coefficient on frontier region growth is highly significant and robust across specifications. We show below in Figures 12-18 the marginal coefficients obtained from our preferred GMM estimations. Figures from the OLS with region fixed effects estimations are included in the appendix. Figure  12  corresponds to the results from column (7) in Table  7. As expected, increasing trade exposure has a positive and significant effect on regions with large productivity gaps (values less than -1.
Summary statistics Mean Std. dev. Min Max Productivity levels (US$ constant prices) 60730. 7 36522. 7 14169. 69 609012. 8 Productivity growth 1. 69 3. 04 -7. 84 13. 9 Frontier region growth 3. 66 6. 42 -10. 57 64. 68 Productivity gap -0. 43 0. 43 -3. 7 0 EPL temporary contracts 4. 47 1. 21 1. 25 5. 75 PMR state control 3. 52 0. 79 1. 76 4. 85 PMR barriers to entrepreneurship 3. 71 0. 54 2. 55 4. 56 PMR barriers to trade and investment 5. 14 0. 53 2. 85 5. 77 Trade openness (X+M)/GDP 31. 17 16. 05 9. 48 92. 37 Debt as % of GDP 66. 77 28. 13 12. 6 175. 27 Inflation rate (in %) 2. 36 1. 65 -0. 9 14. 15 Source: Own calculations using data from OECD Regional Database, OECD PMR Database, OECD EPL Database and OECD Economic Outlook Database.
Fuente: Comisión Económica para América Latina y el Caribe (CEPAL), sobre la base del Fondo Monetario Internacional (FMI), World Economic Outlook Database, abril de 2018, para el PIB; Naciones Unidas, National Accounts Main Aggregates Database, para el valor agregado manufacturero; Organización Mundial del Comercio (OMC), Statistics Database, para el comercio de bienes y servicios; y Conferencia de las Naciones Unidas sobre Comercio y Desarrollo (UNCTAD), base de datos UNCTADSTAT, para la inversión extranjera directa (IED). a Se excluyen el transporte y los viajes. b Los acervos de IED se calcularon a partir de los acervos informados en la base de datos UNCTADStat, ajustándolos por la depreciación del capital y por el índice de precios al productor del sector manufacturero de los Estados Unidos.
Capítulo IPerspectivas del Comercio Internacional de América Latina y el Caribe • 2018 Estados Unidos y Europa: participación del 1% de hogares de mayores ingresos y del 50% de hogares de menores ingresos en el ingreso nacional, 1980-2016 Comisión Económica para América Latina y el Caribe (CEPAL), sobre la base de World Inequality Database [en línea] https://wid. world/data/.
Gráfico I. 16 Fuente:Fuente: 16 Fuente: Comisión Económica para América Latina y el Caribe (CEPAL), sobre la base del Fondo Monetario Internacional (FMI), World Economic Outlook Database, abril de 2018, para el PIB; Naciones Unidas, National Accounts Main Aggregates Database, para el valor agregado manufacturero; Organización Mundial del Comercio (OMC), Statistics Database, para el comercio de bienes y servicios; y Conferencia de las Naciones Unidas sobre Comercio y Desarrollo (UNCTAD), base de datos UNCTADSTAT, para la inversión extranjera directa (IED). a Se excluyen el transporte y los viajes. b Los acervos de IED se calcularon a partir de los acervos informados en la base de datos UNCTADStat, ajustándolos por la depreciación del capital y por el índice de precios al productor del sector manufacturero de los Estados Unidos.
: Capítulo IPerspectivas del Comercio Internacional de América Latina y el Caribe • 2018 Estados Unidos y Europa: participación del 1% de hogares de mayores ingresos y del 50% de hogares de menores ingresos en el ingreso nacional, 1980-2016 Comisión Económica para América Latina y el Caribe (CEPAL), sobre la base de World Inequality Database [en línea] https://wid. world/data/. Gráfico I. 5 (En porcentajes) A. Estados Unidos B. Europa 25 25 20 20 15 15 10 10 5 5 0 0 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 50% de menores ingresos 1% de mayores ingresos Fuente.
Fuente:Comisión Económica para América Latina y el Caribe (CEPAL), sobre la base de Panel Internacional de Recursos, Global Material Flows Database [base de datos en línea] http://www. resourcepanel. org/global-material-flows-database. El déficit físico de América Latina y el Caribe en el comercio de minerales (equivalente a sus exportaciones netas) alcanzó 459 millones de toneladas en 2017, su máximo histórico. Casi la totalidad de ese déficit se originó en el sector de los minerales metálicos. En efecto, mientras las importaciones regionales de minerales metálicos y no metálicos han tenido una cuantía similar en términos físicos durante la década actual, las exportaciones de minerales metálicos como el hierro y el cobre históricamente han excedido con creces las de minerales no metálicos.
Los amplificados se corrieron por electroforesis capilar en un equipo ABI-3500, 1 uL de muestra más 9 uL de formamida HiDi y 0,2 uL de Liz 500. Se usó un capilar de 50 cm y POP 7, y las condiciones fueron de 1,6 KV de voltaje de inyección y 15 s de tiempo de inyección. Siguiendo lo recomendado en el inserto de los mixes de sondas P034 y P035 se procedieron a confirmar las deleciones de un solo exón, mediante una reacción en cadena de polimerasa (PCR) de dos exones a la vez (el exón que tiene supuestamente la deleción y un exón control adicional). Los datos extraídos de la electroforesis capilar se analizaron mediante el software libre Coffalyser. Net v. 140721. 1958 (www. mlpa. com), también recomendado por la MRC-Holland  (11). Este análisis se realizó sobre el radio del DQ (dosaje de la fluorescencia captada por cada fragmento).