dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the large heterogeneity of banks, the main challenge of this paper is to determine a control group that satisfies the common trend assumption with the treatment group over the pre-treatment period. We extract a dataset of 14894 banks located in 129 countries over the period 2011-2018 from the Fitch Solutions database. By using this type of unconsolidated data, we classify banks at the entity level, according to where they operate. We split this dataset into two groups according to the NIRP, treated and control groups. Our treated group consists of 7964 banks operating in seven countries (Bulgaria, Denmark, Hungary, Japan, Norway, Sweden and Switzerland) and the Economic and Monetary Union (EMU) excluding Estonia. The initial control group consists of 6930 banks located in 104 low-, middle-, and high-income countries that have not adopted negative interest rates.
We use the information on mortgage loans (that represent a large share of household loans) and corporate loans to build a share of retail vs. corporate loans at the bank level. We do this with euro area data for data availability reasons. We then assume that banks granting relatively more credit to retail customers hold more retail deposits and vice versa. We define a dummy variable that equals one for high retail deposit banks with a share of mortgage loans relative to the sum of mortgage and corporate loans above the median of our sample. Based on this assumption, we are then able to compare the effects of NIRP on banks' profit and loss accounts using this proxy for high and low retail deposit banks augmenting Equation (  1 ) with an interaction term between NIRP and this dummy variable.
Exploring the determinants of banks' response to NIRP In this section, we assess which bank characteristics drive the impact of negative interest rates on banks' financial statements. Our database provides balance sheet information that allows us to explore which type of banks is most impacted by negative interest rates. We focus on three important characteristics of banks: the share of the deposits in their balance sheet, their size and their leverage ratio, and estimate the impact of negative interest rates on different subsamples according to these criteria. 17.
However, the effects 17 Previous studies have analyzed the effects of NIRP according to the competitive environment and the holding of liquid assets (see  Bottero et al. , 2019 , Boungou 2019 , Molyneux et al. , 2019). 18 We focus on customer deposits overall not on the distinction between retail and corporate deposits since we do not have this information in the Fitch database. See section 3. 3 for a discussion of the distinction. Note: DiD is the Difference-in-Differences parameter, estimated from Equation (1). High correspond to banks with a ratio of government securities to total assets greater than or equal to the median (2. 41%). Low refers to banks with a ratio of government securities to total assets less than 2. 41%. DiD is our Difference-in-Differences estimator. It refers to the interaction between the dummy Treated and dummy NIRP.
Conclusion This paper uses a large database of disaggregated profit and loss account data from 3637 banks operating in 59 countries over the period 2011 to 2018 to study the channels of banks' responses to negative interest rates. We find that the negative interest rate policy yields a decrease in net interest income that is only in part mitigated by an increase in net non-interest income. We provide evidence that this negative impact comes from the interest rate on deposits because of its downward rigidity due to the reluctance of banks to charge a negative interest rate on customer deposits. Banks respond to that shock by reducing the interest paid on non-customer deposit liabilities and their personnel expenses. Thus, NIRP could be seen as a trigger for accelerating a restructuring process with a view of lowering banks' costs.
Often specialized in assembly, these zones are the final stage in the Global Value Chain (GVC). Using industrial 1 The term 'SEZ' covers a wide range of more specific types of zones, such as Free Trade Zones (FTZs), Export Processing Zones (EPZs), Business Zones, Free Ports and others. For descriptions and classifications of different types of SEZs in the world, see  Farole (2011) ,  Farole and Akinci (2011) , and  Siroen and Yucer (2014). The International Labor Organization's (ILO) SEZs database reported 176 zones in 47 countries in 1986 and approximately 3,500 zones in 130 countries by 2006  (Boyenge, 2007). In addition,  Siroen et al. (2014)  find around 1,083 zones (excluding the Maquiladoras and US Foreign Trade Zones) in 2008 in their World FTZ database covering 158 countries. The differences in the number of SEZs found across databases are closely linked to the type of SEZ definition chosen, but there is clear evidence of growth in numbers  (Castilho et al.
, 2015). 2   Boyenge (2007)  present a new estimation in the ILO database on export processing zones. See also  Siroen et al. components from all over the world in their production processes they have consequently become prominent importers of intermediate goods. The authors point out that they have contributed to the deepening of the international production fragmentation, to the increase of trade and also to attract foreign direct investment  (FDI). Likewise, the predominant view of international organizations (The World Bank, WTO, UNC-TAD) is that although SEZs operate in rather singular ways, often disrespecting WTO rules -such as those in the WTO's Agreement on Subsidies and Countervailing Measures -, they do encourage foreign direct investment (FDI) spreading technology to the rest of the economy and creating positive externalities.
According to IBGE data the population of Manaus represented 52. 2% of the State in 2015 while in 1960 the percentage was only 24. 3%. According to PIA / IBGE data, in the state of Amazonas, manufacturing employment almost doubled between 2000 and 2010. The number of workers in manufacturing industries increased from 59 586 to 116 503 in the period -an increase of 96% above the average of 50%. According to SUFRAMA data, the number of employees increased from 50 005 in 2000 to 103 673 in 2010 (+ 107%) in the MFTZ. On an annual average basis, the number of companies increased from 307 to 431 at the same period, while the industrial structure in the Manaus Industrial Pole remained highly concentrated. This paper describes and evaluate some of the labor and social outputs of the various fiscal incentives applied in the Manaus Free Trade Zone, that allowed for a greater integration of the Brazilian economy in the international trade.
Methodology and Data In order to empirically estimate labor and social conditions in the MFTZ, this analysis is based on the study of the residuals -deviations between an observed value and the estimated value -, exploiting two different methods, as a measure of robustness check. The analysis uses crosssectional data, at the municipality level for the year 2010. Due to the lack of available data concerning only the perimeter of the free trade zone, the MFTZ is represented by the municipality of Manaus, which is compared to the other brazilian municipalities. The first model, Model A, estimates the expected Manaus' outputs by a residuals analysis, using OLS estimation. The second one, Model B, applies the stochastic frontier method of error term decomposition, which shows the level of labor standardsâ and socialâs efficiency generated by Manaus, using the Maximum Likelihood estimation.
Concepts and Econometric Specificities The methodology implemented for both residuals and stochastic frontier techniques will use linear regression with the Ordinary Least Squares (Model A) and the Maximum Likelihood estimations (Model B), respectively. In order to empirically estimate working and social conditions in the MFTZ, the main econometric specifications are as follows: ln y i = β 0 + n ∑ j=1 β j ln x j,i + γ iεUF + γ c + ε i (1) 8 We were unable to carry out analyzes from previous years to build a counterfactual of the situation without the MFTZ since the only Brazilian database that allows us to analyze at the municipality level is the census, which is carried out every ten years, and due to the unavailability of microdata for the census prior to the creation of the MFTZ (from 1920 to 1960, there are only tabulations at state and municipality level for the main variables, being published in printed format).
Data The usual data source in Brazilian social and labor studies is PNAD data, which is conducted annually, covering individual information on the main socio-economic variables. Nonetheless, a significant limitation made it unsuitable for our study: its representativeness and coverage do not extend to below state level. Hence, in order to focus on Manaus, a remaining representative at the municipality level, we turned to census data. To elaborate on our cross-sectional analysis, we used the individual/household level microdata from the Brazilian census of 2010. It is conducted every ten years by the Brazilian Census Bureau, the Instituto Brasileiro de Geografia e EstatÃstica (IBGE), the last one occurring in 2010. A detailed questionnaire (questionario da amostra)including individual and household's information on the main socio-economic variables, such as the general characteristics of population, education, labor, income, housing, migration, fertility, marriage, health, nutrition -is administered to a census sample of around 11% of the Brazilian population.
The survey samples about 70 million households and it is nationally representative, ensuring coverage of both rural and urban areas of all the 5. 565 municipalities of the federation. The data used to our control variables were obtained from IBGE. 10 As the random term can assume any value, it is common to assume that this term follows a normal distribution ν i ∼ N(0, σ ν 2 ). As the inefficiency term cannot be negative, there are several distributions that satisfy this restriction, among them truncated-normal, exponential and gamma. In this study, we use the truncated-normal distribution, since it allows the correction of any sources of bias in the model, bringing more consistent estimations. In our study, we use the municipality level, obtained by the aggregation of the individual-level information.
RPROD is a global database that complements EQCHANGE, by providing additional measures of the Balassa-Samuelson effect. RPROD includes five measures of this effect based on: the relative GDP per capita, the relative labor productivity, the relative consumer-price-to-producer-price ratio, and two relative sectoral value-added deflators. These measures are provided for a wide range of countries (up to 182 economies, depending on the considered indicator), spanning from 1973 to 2018 and using several weighting schemes associated with each country's main trading partners.
More recently, the BS hypothesis has also received renewed attention in developed economies (see, e. g. ,  Cardi and Restout, 2015; Hassan, 2016; Zhang, 2017; Berka and Steenkamp, 2018; Berka, Devereux, and Engel, 2018). To date, however, the issue of the BS eect has mostly concerned limited samples of countries. One reason for this is the absence of long series of internationally comparable productivity measures covering a wide geographical area. 3 The RPROD database provided by the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) aims at lling this gap by delivering a series of internationally comparable measures of the BS eect over an extended sample of countries. While initially the EQCHANGE database  (Couharde, Delatte, Grekou, Mignon, and Morvillier, 2018)  relied on the most widely used measure to capture the BS eectcross-country dierentials in GDP (Gross Domestic Product) per capitaRPROD adds to this proxy four additional ones.
Specically, the measures of the BS eect included in RPROD compare, for each country and its main trading partners, ve distinct indicators that proxy trends in relative productivity of the tradable to non-tradable sectors. The rst two indicators are respectively GDP per capita and labor productivity measured by GDP per worker, available for 182 countries over the 1973-2018 period. The third proxy, mainly available for advanced and some emerging countries, is the consumer-price index (CPI)-to-producer-price index (PPI) ratio. Finally, the last two indicators included in RPROD are based on three-and six-sectors' valueadded deators. Our ve measures of the BS eect use the weighting scheme of the EQCHANGE database, which takes into account dierent samples of trading partners and provides two alternatives to weight relative indicators: (i) time-invariant weights calculated respectively between  2008-2012 and 1973-2016, and  (ii) a time-varying scheme based on non-overlapping ve-year average weights.
By making publicly available a wide range of alternative measures of the BS eect, with global coverage, RPROD, as a complement to the EQCHANGE database, constitutes a useful tool to (i) document the presence of a catching-up process in developing/emerging countries and assess the robustness of the BS hypothesis, and (ii) contribute to the comparison of estimated equilibrium exchange rates and corresponding currency misalignments across alternative measures of the BS eect. It is important to have in mind that each proxy carries a dierent set of information. Therefore, we provide some empirical implications of.
CEPII Working Paper Measuring the Balassa-Samuelson eect: A guidance note on the RPROD database each measure documented in the literature in order to guide the users of the database in their choice of indicators. This guidance note outlines the construction and contents of RPROD by describing the proxies calculated and compiled by the CEPII to measure the BS eect. To this end, the rest of the paper is organized as follows. To set the stage, Section 2 proposes a simplied representation of the BS eect in order to identify its key underlying measures. Section 3 sets out the framework for calculating the ve measures of the BS eect included in RPROD. Finally, Section 4 provides some concluding remarks.
CEPII Working Paper Measuring the Balassa-Samuelson eect: A guidance note on the RPROD database PPP in the tradable goods sector ensures that the rate of return in tradables (R) is equal to its world value. Log-dierentiating the expressions for prices yields: p NT = A NT + NT w (9) p T = A T + T w = 0 (10) where variables in lowercase are expressed in logarithmic terms. Solving for the dierence, the increase in the relative price of non-tradable in terms of tradable goods can be written in the catching-up economy as: p NT p T = NT T A T A NT (11) Assuming for simplicity that N T T is the same in the two economies, we have: p NT £ p T £ = NT T A £ T A £ NT (12) Substituting (  11 ) and (  12 ) into Equation (4) yields the following expression of the appreciation of the real exchange rate: q = q T + NT T ( A T A £ T ) ( A NT A £ NT ) (13) Then, if PPP holds only for tradable goods, the appreciation of the real exchange rate in the catching-up economy will stem from faster relative productivity growth in the tradable goods sector compared to that of the advanced country.
The RPROD database This section describes the construction of the dierent measures included in RPROD to assess the BS hypothesis, as well as the sources of data used in the calculations. Table  A. 1 in the Appendix indicates the country coverage of RPROD for each measure. The simplied expressions of the BS eect described in Section 2 suggest that it can be measured by intercountry trend movements either in relative prices of non-tradable to tradable goods (Equation (  4 )) or in relative productivity of the tradable to non-tradable sectors (Equation (  13 )). However, in the absence of long series of internationally comparable total factor productivity measures and disaggregated prices, getting good proxies of the BS eect over an extended sample of countries is a challenge. It is also dicult to accurately separate the tradable and the non-tradable sectors as there is no direct measure of these sectors.
In this respect, RPROD provides series of relative labor productivity. We measure labor productivity as GDP per worker in constant 2011 PPP U. S. dollars. This indicator presents the main advantage of being available for a wide range of countries over a large time period. GDP per person employed, expressed in PPP, is calculated using PWT 9. 0 for the 1973-2014 period. 5 The 2015-2018 period is lled up using the International Labour Organization (ILOSTAT) database, which also provides the output per worker.
CEPII Working Paper Measuring the Balassa-Samuelson eect: A guidance note on the RPROD database  Ricci, Milesi-Ferretti, and Lee (2013)  and  Caputo (2018)  argue that using GDP per worker in assessing the BS hypothesis can be misleading. Indeed, if the catching-up country benets from equal productivity growth in both the tradable and non-tradable sectors, the resulting rise of the output per worker will have in the BS framework a neutral eect on the real exchange rate.
The CPI-to-PPI ratio A usual solution is to consider the ratio of the Consumer Price Index (CPI) to the Producer Price Index (PPI) for the home country relative to the CPI-to-PPI ratio for the country's major trading partners as in  Alberola, Cervero, Lopez, and Ubide (1999) ,  Alberola (2003) ,  Bénassy-Quéré, Béreau, and Mignon (2009), and Ricci, Milesi-Ferretti, and Lee (2013). Following this strand of the literature, the RPROD database provides a dataset on the relative CPI-to-PPI ratio for a sample of 72 advanced and emerging countries (  7 The time coverage of this indicator is constrained by PPI data availability. For this reason, the CPI-to-PPI ratio is mainly available for advanced economies over the 1973-2018 period, while the time coverage is more sparse for other countries. 8 For Argentina, for the years 2017 and 2018, we extract PPI from INDEC (Instituto Nacional de Estadisticida).
For the sake of completeness, see also  Couharde, Delatte, Grekou, Mignon, and Morvillier (2018)  for additional sources regarding CPI series over the 1973-2016 period. Measuring the Balassa-Samuelson eect: A guidance note on the RPROD database assuming all service industries as dealing in non-tradable goods has some drawbacks. Indeed, given that several services are increasingly traded internationally, this assumption can lead, in the case of most countries, to lower values of the tradable share in total output. The solution is to draw on a more disaggregated sectoral decomposition, as in some studies based on a six-sector disaggregation (MacDonald and  Ricci, 2005; Lee and Tang, 2007; Ricci, Milesi-Ferretti, and Lee, 2013; Caputo, 2018). Following this second approach, RPROD includes three-and six-sectors' value-added deators to track the evolution of the relative price of non-tradables to tradables.
This new database, which complements EQCHANGE, is the rst attempt to provide several measures of the BS eect for a wide range of countries, spanning up to more than 40 years. The RPROD database includes ve distinct proxies of the BS eect. Regarding the rst two measures, RPROD delivers consistent series of relative GDP per capita and relative labor productivity for 182 countries over the 1973-2018 period. The third measure relies on the relative CPI-to-PPI ratio which aims at comparing changes in the relative price of non-tradable goods of a country to those of its trading partners. However, due to the weak availability of PPI series for developing economies, this indicator is mainly available for advanced and emerging countries. Finally, accounting for the existing literature, the RPROD database encompasses two measures based on tradable and non-tradable sectors' value-added deators: the rst one relies on a broad decomposition of tradable and nontradable goods based on the three following sectors: agriculture, industry, and services; and the second one relies on a narrower six-sector decomposition.
measures are based on two sets of indicators: the rst indicator relies on labor productivity (Section 3. 1) and the three others, on relative pricesthe CPI-to-PPI ratio, three-and six-sectors' value-added deators (Section 3. 2). All these indicators are multilateral and involve comparisons of a country with its main trading partners. Data on weights associated with each country's trading partners come from the EQCHANGE database which (i) takes into account two main groups of trading partners (i. e. , 186 trading partners and the top 30 trading partners for each country), and (ii) includes two weighting schemes. The rst one is time-invariant and consists of two weight sets respectively representative of foreign trade between (i)2008-2012, and (ii) 1973-2016. The second weighting system is time-varying and is essentially based on non-overlapping ve-year average weights (for more details, see Couharde, Delatte, Grekou, Mignon, and Morvillier, 2018).
among others). 4It should be noticed that version 8. 0 of the Penn World Table introduces measures of capital stock and TFP since 1950 for a range of countries (https://www. rug. nl/ggdc/docs/capital_labor_and_tfp_in_ pwt80. pdf). RPROD reproduces time series on relative GDP per capita converted into dollars expressed in PPP terms already available in the EQCHANGE database. Data are collected from the Penn World Table (PWT) 9. 0. We update these data and extend them to a larger set of.
Data used to compute the pva series are extracted from the WDI database of the World Bank. The current and constant value added used to compute the six-sectors' value-added deators are taken from UNCTAD. The proxy BS_def 3 is available for the majority of countries until 2017, the starting date depending on the considered country. Turning to BS_def 6, it is available over the 1980-2017 period for the majority of countries (see TableA. 1).
use the Hodrick-Prescott lter in order to extract the CEPII Working Paper Measuring the Balassa-Samuelson eect: A guidance note on the RPROD database taxes (which are included in the calculation of CPI but not in that of PPI), the adjustment of regulated prices (which mostly concerns non-tradables) and quality changes in non-tradables and tradables. This means that the resulting changes in the ratio of two price series can studies that estimate the BS eect usually provide conicting evidence. As argued in the literature, these puzzling ndings can be explained by the invalidity of the several underlying assumptionssuch as law of one price in traded goods, intra-economy labor mobility, perfect capital markets and/or slow productivity growth in the non-tradable sectorwhich may undermine the prediction of the BS hypothesis.
among others). 4It should be noticed that version 8. 0 of the Penn World Table introduces measures of capital stock and TFP since 1950 for a range of countries (https://www. rug. nl/ggdc/docs/capital_labor_and_tfp_in_ pwt80. pdf). RPROD reproduces time series on relative GDP per capita converted into dollars expressed in PPP terms already available in the EQCHANGE database. Data are collected from the Penn World Table (PWT) 9. 0. We update these data and extend them to a larger set of countries (182) by using the World Development Indicators (WDI, World Bank) and the World Economic Outlook databases. Denoting BS_gdpc the BS measure based on this rst indicator, we get for a country i at time t: BS_gdpc i;t = GDP c i;t j=1 (GDP c j;t ) w i j;t N (14).