dataesr/scientific-paragraphs-categorization huggingface dataset:
Improving judicial performance in order to enhance the business environment has been a policy goal for many governments in the last decades. Following the suggestions of several international organizations, most countries have tried to speed up their case resolution systems by streamlining judicial procedure. However, not as much attention has been devoted to test the potential drawbacks of similar reforms in terms of supplying a quicker but yet qualitatively inferior justice, thus contradicting the well-known legal maxim justice delayed is justice denied. The present work wishes to contribute to the empirical literature on the topic by proposing two alternative ways to further disentangle the relationship between judicial performance and judicial quality. Exploiting a dataset of 171 countries for the 2003-2016 time period, we find statistically significant evidence of a strong and negative relationship between courts' delay and countries' quality of the justice.
Data Table  1  presents the key descriptive statistics for the data employed. Our sample consists of 171 countries observed for the period 2003-2016. Panel A reports the means and standard deviations for the judicial quality variables. In order to guarantee the 1 The panel vector autoregression approach can be implemented in a straightforward way in Stata by using "pvar" command. See  Abrigo and Love (2016)  for further clarification of the software code. Sigmund and Ferstl (2019)  provide a user-friendly introduction to the implementation of PVAR approach in the R package. 2016) , and the more canonical Rule of Law Index (RoL) provided by the World Bank's Worldwide Governance Indicators project  (Kaufmann et al. , 2011). The lowest level of the JQI is observed in Iraq while the countries with the highest level are Australia, United Kingdom and Singapore.
This article analyzes the impact of firms' workforce composition on cultural distance and export performance. On the one hand, empirical literature has shown that there are dark trade costs that hinder trade performance, among which is cultural distance; on the other hand, the literature has shown that firms' export performance is positively correlated with employees' qualifications. Using a unique database of 59,606 French firms, we reconcile these two streams of literature by estimating a structural gravity model. We demonstrate that hiring more executives decreases the negative impact of cultural distance and that firms with a higher share of executives have a higher probability of exporting and a higher level of recorded exports. As exporting involves practicing foreign languages or managing intercultural differences, firms with a greater reliance on skilled workers who exhibit this "export culture" have a higher probability of exporting, export more products and benefit from higher export values.
Indeed, most recent studies have compared the use of skilled and nonskilled workers in exporting and domestic firms  (Brambilla et al. , 2012 (Brambilla et al. , , 2016) )  or linked workforce composition with trade only through the high quality products channel  (Kugler and Verhoogen, 2012 and Bastos et al. , 2018). Moreover, the current study is the only one to consider the case of French firms, using a unique employer-employee database from the French D'eclaration annuelle des donn'ees sociales (DADS), which is an annual census covered by statistical confidentiality that details information of French firm demand for employment categories such as blue collar workers, employees, executives, and other intellectual professions. Approximately 59,606 firms and 72 destination markets over the period 2000-2015 are considered in our article.
The structural gravity model we use to analyze the triptych "skilled labor, cultural distance and export performance" remains conventional but undisputed. Based on the previous works of  Anderson and Van Wincoop (2003)  and  Chaney (2008) , multilateral resistance, as well as firm-level productivity, are taken into account. The recent measure of cultural distance that we use is derived from the World Value Survey (WVS), which was developed by  Spolaore and Wacziarg (2016). This dataset and that of the DADS are combined with international trade data from French Customs and FICUS and FARES Databases on the performance of French firms, which are provided by the Institut National de la Statistique et des Etudes Economiques (INSEE), the French National Statistical Institute. We obtain several results that are a useful contribution to the theory of international trade.
Spolaore and Wacziarg (2016)  retain 98 questions for 74 countries 2 See  Anderson and Van Wincoop (2003) ,  Baldwin and Taglioni (2006)  and  Yotov et al. (2016)  for an in-depth discussion on the importance of controlling for multilateral resistance. 3 We use a simple linear probability model rather than nonlinear models such as Probit or Logit models in this case because it avoids the incidental parameter problem due to the sizable set of fixed effects we include in all regressions and provides simple direct estimates of average marginal effects. We follow guidelines of previous papers estimating gravity models on a similar dataset such as  Fontagné and Orefice (2018)  or  Martin and Mayneris (2015). to compute the measure of cultural distance between countries. Questions retained from the WVS are classified into 7 different categories: perceptions of life (A), environment (B), work (C), family (D), politics and society (E), religion and morale (F), and national identity (G).
Then, all of the indexes are summed across all 98 questions, and we obtain a measure of cultural distance between countries. We use this measure to obtain the cultural distance between France and 72 other countries 4. For instance, according to the measure, Belgium and Luxembourg are the closest countries to France in terms of culture, while Jordan and Egypt are the most distant countries from France. As a robustness check, we use a second measure of cultural differences based on the genetic distance metric developed by  Cavalli-Sforza et al. (1994). Their method allows identification of relatedness in populations based on classic genetic markets. Spolaore and Wacziarg (2016)  match populations to countries and provide a comprehensive dataset for genetic distance at the country level.
Data Our empirical analysis relies on six different databases. First, we use French Customs data that provide individual information regarding the exports of French firms for the period 2000-2015. This dataset includes export records at the firm, product and destination level for all exporting firms located in France. All product-level transactions are classified at the 8-digit level of the combined nomenclature (CN). Therefore, the potential number of observations could be very large. To obtain a relevant dataset, we sum all exports by destination market over all firms. We restrict our sample to 72 destination countries for which we have information on cultural distance in relation to France. We also restrict our analysis to firms that have a median size (total employment) of at least 10 over the period 1995-2015.
Second, we use the FICUS and FARE databases provided by the French National Institute of Statistics (INSEE), which gathers accounting and performance variables at the firm level and covers all French firms, with the exception of firms with no employees or in the agricultural and financing sectors. Specifically, the database contains information about firm value added, nominal gross output, number of employees, intermediate inputs, tangible and intangible capital, investment goods and date of creation. These variables are used to compute the total factor productivity (TFP), size and age of French firms. Third, we use employer-employee data from the Declarations of Social Data (DADS) to compute the share of executives and intellectual professionals (cs3), the share of employees (cs5) and the share of blue-collar workers (cs6) in total employment for each firm.
The category "employees" includes administrative employees in firms, for example secretaries. In contrast, the category of executives and intellectual professionals includes engineers or white collar workers in firms. These three categories of workers are mutually exclusive. Fourth, we rely on the World Development Indicators (WDIs) provided by the World Bank to obtain information on country population and GDP. Fifth, all time-invariant gravity variables, such as geographical distance and dummy variables for colonial links, common border, common language and European Union membership, are obtained from the CEPII GeoDist and Gravity databases. Finally, for the aforementioned measure of cultural distance, based on the World Values Survey, we rely on the database provided by  Spolaore and Wacziarg (2016).
To illustrate how the correlation between complexity and tolerance can be evaluated in macaque social organization, we calculated the complexity indices of the social system in two species of contrasting social relationships: tolerant Tonkean macaques (Macaca tonkeana) and intolerant rhesus macaques (Macaca mulatta). It should be noted that the data needed to calculate the indices are still scarce, so the variables selected were those for which the necessary information was available.
Complexity By summing (2. 9) the three indices D, F and C measured in each species, we obtained the following values for the complexity index K: 1. 76 for rhesus macaques and 2. 54 for Tonkean macaques. This result is consistent with the hypothesis that system complexity increases with social tolerance among macaques. However, the bulk of the effect comes from the difference in the combinability index (figure  1 ). In addition, any conclusion on overall complexity would be affected by the relative weights that could be assigned to each of the three indices. royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 8: 200895 It should be noted that we have used the example of macaque social systems to describe the calculation of the complexity index, but data from a higher number of species would be necessary to test whether the indices measured in different kinds of species are statistically different.
Long-Term Trends in Income and Living Standards We start with a review of quantitative reconstructions of Russian and Soviet economic development. Figure  2  combines available reconstructions of Russian and Soviet GDP per capita from 1860 through 2018. We use the 2020 version of the Maddison Project Dataset, to which we add Russian GDP per capita between 1860 and 1884 as reconstructed by  Kuboniwa et al. (2019). 5 For the purpose of this comparison, we plot the Russian series against the per capita GDP series of a group of selected comparator countries, all estimates are in 2011 international dollars. 2. 1 Per Capita GDP in the Russian Empire the late 17th century. Broadberry and Korchmina (2021)  calculate Russian GDP per capita in the 18th century at a decadal frequency. They find that Russia was a part of "little divergence" in income between Northern and Western Europe and the rest of the continent, which went along with the great divergence between continents  (Allen, 2001;  de  Pleijt and van Zanden, 2016 ;  Broadberry and Malinowski, 2021).
In the 18th century, Russian national income per capita was about 40% to 70% of British per capita income, but it was higher than in Poland, the only other Eastern European country for which data are available in this period. According to  Broadberry and Korchmina (2021) , Russian national income per capita grew between the 1690s and the 1740s, when Russia was closing the gap with the most developed economies of  Northern and Western  Europe, but afterward it stagnated and then declined. As a result, GDP per capita was only 16% higher at the beginning of the 19th century than at the beginning of 5 The Maddison Project Dataset is one of the most comprehensive datasets with reconstructions of historical GDP series widely used in economic history literature. Its aim is to produce standardized historical national accounts comparable across countries and over-time.
The dataset is available here: https://www. rug. nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020?lang=en, accessed October 26, 2021. Bolt and van Zanden (2020)  discuss potential biases in the reconstruction of income series for a large set of countries in the distant past. Because data at the regional level are not available for most of the period, we use the national series of per capita GDP in historical national boundaries. Namely, before 1913, we use GDP per capita in the Russian Empire outside Finland. For the period between  1913 and 1941 , we use figures f or t he t erritory o f t he U SSR i n i ts i nterwar b orders. F or the years between  1945 and 1991 , we use the figures f or t he U SSR i n i ts p ostwar b orders. A nd, f or 1 992 forward, we use data for post-Soviet Russia.
On the one hand, serfdom deincentivized serfs. On the other hand, being residual claimants, landlords were directly interested in the efficient organization of labor. Early literature argued that the new, postemancipation institutional environment was introduced too slowly to change much, preserving the legacy of serfdom  (Zaionchkovskii, 1968). Nafziger (2012)  uses new village-level data from Moscow Province to demonstrate that former serf communities continued to have smaller land endowments and bear higher obligations after emancipation than former state peasants. There are, however, no data for the pre-emancipation period, and therefore, one cannot estimate effects of the reform using this dataset. The peasant commune was another institution that dominated the countryside in the Russian Empire.
They also show that former serfs responded more strongly to changes in grain prices and weather shocks adjusting the composition of their crops accordingly. In addition,  Markevich and Zhuravskaya (2018)  provide evidence that the land reform, which introduced communal land tenure on peasant lands after emancipation, undermined agricultural productivity growth. This effect was more pronounced in provinces with repartition communes. Using a province-level panel dataset for the early 20th century, Castaneda  Dower and Markevich (2019)  analyze the impact of the 1906 Stolypin reform. The reform granted peas-ants in repartition communes the right to privatize land strips they cultivated. On top of that, peasants in both repartition communes and hereditary communes got the right to consolidate separated land strips into a single privatized allotment.
What Did Stalin Maximize? An important source of complexity in measuring the economic effectiveness of coercion is that Soviet repressions were likely driven by both the political and economic reasoning. Markevich (2016)  uses panel data on annual plan fulfillment by the Soviet industrial ministries during the postwar years and finds t hat t he n umber o f p enalties i mposed o n e mployees w as nega-tively correlated with their production achievements. This is in line with a view that economic considerations drove punishment in the Soviet system. The author also shows that data are consistent with the political explanation of Stalin's repression. Using a unique individual-level dataset on the Great Terror in the Soviet ministry in charge of metallurgy, he shows that individual characteristics related to politics, such as party membership or a high position in the Soviet hierarchy, predict the probability of arrests.
The government also used food shortages in the early 1930s to sell grain to Soviet citizens for gold, which it used to pay for imports  Osokina (2019). First, the recent literature sheds light on the historical debate about the extent to which the Great Famine was caused by the Soviet industrialization strategy. 47  In particular, historians long debated whether the 1932-1933 famine deaths could be attributed to the victims of Stalin's Big Push policies, or whether they were driven by negative weather shocks and resulting poor harvests  (Conquest, 1986; Tauger, 2001 ;  Davies and Wheatcroft, 2004; Graziosi, 2015). Naumenko (2021) resolves this debate with newly available data. Using district-level crosssectional and regional-level panel datasets on mortality in Ukraine, she finds t hat b oth faster collectivization and excess grain procurement are positively associated with mortality for the Holodomor famine years.
Controlling for weather indicators and prefamine local characteristics, collectivization explains up to 52% of excess mortality. Another important famine-mortality correlate is the local economy structure: regions with industries prioritized by the Soviet government had lower excess mortality. In contrast, weather changes explain no more than 8% of excess mortality. Moreover, when the author estimates the grain production function using pre-1917 data and predicts the 1933 harvest, she finds t hat t he p redicted h arvest i s c lose to officially reported figures, which are no worse than an average harvest f rom 1924 to 1929. Second,  Markevich et al. (2021)  show that the 1932-1933 famine disproportionally hit areas populated by ethnic Ukrainians. The authors construct new regional-level and districtlevel panel datasets that cover the bulk of the territory of interwar Soviet Union to explore whether ethnic Ukrainians experienced higher mortality during the famine.
The recent papers on railroad development demonstrate their importance for the integration of factor and product markets in such large economies as the United  States and India (Donaldson and Hornbeck, 2016; Donaldson, 2018). There are no such studies on either Imperial or Soviet Russia. In an addition to these five known unknowns, there must be many more unknown un-knowns. New insights could be drawn from the unexplored data that the Russian archives must contain. The Russian Empire kept excellent statistical records at the national level and at the lower levels (province, district, city, etc. ), in the second half of the 19th century and the early 20th century. During that period, Russian statisticians followed the Western European norms of bookkeeping. This makes the Russian Empire a unique example of developing country with a developed-country quality of statistics, a trove that is waiting to be studied by a new generation of historians and social scientists.
We derive these figures f rom t he o fficial w ebpages o f n ational s tatistical o ffices. A rmenia: Statistical Committee of the Republic of Armenia, http://www. armstat. am/en/; Azerbaijan: The State Statistical Committee of the Republic of Azerbaijan, https://www. stat. gov. az/?lang=en, Artsakh Republic National Statistical Service, http://stat-nkr. am/en; Belarus: National Statistical portal of the Republic of Belarus, http://www. belstat. gov. by/en/; Estonia, Finland, Latvia, Lithuania, Poland, Russia: OECD Regional Statistics and Indicators database, https://stats. oecd. org/Index. aspx?DataSetCode=REGION_ECONOM; Georgia:.
Data Our dataset consists of weekly data for different indices classified into three categories of government bond markets: developed, emerging, and frontier. We use total return indices, meaning that they incorporate both bond price changes and coupon payments. The sample period spans from 10 May 2002 to 29 December 2017. The category of emerging markets encompasses three different subcategories: (i) USD-denominated government bonds represented by the JP Morgan Emerging Markets Bond Index Plus (EMBI+), (ii) local currency government bonds with returns hedged to the USD using currency derivatives, and (iii) local currency government bonds converted to USD without currency hedging. 4 The EMBI+ is a commonly used sovereign bond emerging market benchmark, which tracks the performance of USD-denominated debt instruments issued by governments of emerging markets, and it includes Brady bonds, loans, and Eurobonds.
For example, the 'iShares J. P. Morgan USD Emerging Markets Bond ETF' (Ticker: EMB) can be used to access USD-denominated emerging markets, 'SPDR Barclays Capital Emerging Markets Local Bond ETF' (Ticker: EBND) for local-currency emerging markets bonds, and the 'NN Frontier Markets Debt (Hard Currency) Fund' (ISIN: LU0990547431) for frontier bond markets. These are illustrations of funds, but investors have more options, depending on their geographical location. Our insights can also benefit portfolio managers of fixed income funds who manage portfolios by allocating to individual bonds and can choose between different types of bonds that we analyze in this paper. Our dataset also includes a global bond market portfolio, which is proxied by the JP Morgan Global Aggregate Bond Index (GABI).
Acknowledgements We would like to thank the anonymous referee, the editor ( Jonathan Batten ),  Jyri Kinnunen ,  Dolly King ,  Ansgar Belke ,  Matthias Walting , and conference participants of the 17 th  INFINITI Conference on International Finance , the 7 th  Paris Financial Management Conference , the 59 th Annual Meeting of the  Southern Finance Association (SFA), Research Seminar in Economics and Finance at the University of Jyväskylä , and the  Summer Seminar of Finnish Economists  for valuable comments. Vanja Piljak  would like to thank the  Waldemar von Frenckells Foundation from Finland  for financial support.
Extant literature has examined intangible assets at firm and macro-levels but lacks their comprehensive analysis at territorial level. Yet, large discrepancies exist between regions, which cannot be attributed to micro-level variations alone, or observed from a higher level of aggregation to countries. We examine regional economic implications of intangible capital focusing on different sets of regional intangible assets. We build a combined dataset at the European regional level and perform spatial regression analysis accounting for cross-region interdependence. Results indicate significant effects of intangibles and the importance of their complementary relationships for regional development. Implications for research and policy making are derived.
The lack of homogenous, comprehensive and comparative data on intangible assets collected and available for regions is also among the reasons of a low share of regional studies relative to crosscountry research. According to  Suriñach and Moreno (2011) , there is a need to develop more and better regional databases in order to improve the analysis of effects of intangible capital on regional economic performance. Moreover, a better understanding of the role of various dimensions of intangible capital for regional development is required by adopting an integrative approach to regional intangibles that build upon and extend existing studies conducted on regional as well as firm/industry and country levels. In this paper, we move intangible capital research towards greater consideration of regional level of analysis to examine intangible assets-performance linkage.
This is in line with the necessity of extending the research agenda, beyond the microeconomic economic dimensions of economic performance  (Dumay, 2013; Lin & Edvinsson, 2021). This paper investigates one of the close environments for organisations taking a regional focus. We consider intangible assets holistically and analyse how different sets of intangible assets are simultaneously related to regional economic development in terms of GDP, taking into account spatial interactions. For that, we build a dataset of European regions applying NUTS classification (The Nomenclature des Unités Territoriales Statistiques) and collecting the data on assets (tangible and intangible) and other regional characteristics from official statistical databases as well as constructing a composite indicator of regional intangible capital.