dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper is part of a broader project, namely the World Inequality Database (WID. world), that attempts to produce annual distributional statistics -and possibly micro data on income and wealth distributions -that are comparable across countries  (Alvaredo et al. 2016). For this, we follow a common methodology that involves the combination of national accounts, surveys, and fiscal data in a consistent manner to produce distributional national accounts. The methodology was already applied for the United States  (Saez and Zucman, 2016; Piketty, Saez and Zucman, 2016) , France  (Garbinti, Goupille-Lebret and Piketty, 2016, 2017) , China  (Piketty, Yang and Zucman, 2017)  and Russia  (Novokmet, Piketty and Zucman, 2017). Although there are similarities across countries regarding methods, lessons can be drawn from country-specific cases to help produce new databases for future works, in a context of scarcity of data.
Section 3. Data sources, Concepts and Methodology This paper relies on four types of data sources: household surveys, income tax data, wealth rankings and national accounts. We define the Middle East as the region going from Egypt to Iran, and from Turkey to the Gulf countries. We start by putting together a macroeconomic database including annual series on population and national income between 1990 and 2016. Basic descriptive statistics for 2016 are reported in Table  1. The region is characterized by very large between-country inequality (we further discuss this issue in section 4). All details about the data sources and methods used to construct homogenous national accounts are described in the online appendix. In order to estimate the distribution of income in the Middle East, our general methodology follows three steps.
We begin with the Middle East household income surveys data series (step 1), which we correct using (i) generalized Pareto interpolation techniques (see  Blanchet, Fournier and Piketty, 2017  for the description of the interpolation method) and (ii) personal income tax micro-data available for Lebanon (see Assouad 2017 for a description of these fiscal data and their main limitations) (step 2). We then use national accounts and rich lists in order to impute tax-exempt capital income (step 3). Our concepts and methods generally follow those described in the Distributional National Accounts guidelines used for the World Inequality Database  (Alvaredo et al. , 2016). In particular, the methodology in three steps is very similar to that used for China in  Piketty, Yang, Zucman (2017) , and for Russia in  Novokmet, Piketty, Zucman (2017) , with some differences highlighted in the following sections.
Section 3. 1. Step 1: constructing a household income database for the Middle East Income and inequality data are scarce in the Middle East, notably in the poorest and the richest countries. Although many national statistics offices undertake household surveys on income or expenditure, access to the data is very limited. Until recently, it was almost impossible to obtain micro-data 2. Finally, when they exist, the databases are often of poor quality (see  Bibi and Nabli, 2010  for a review of existing data, and an assessment of their access and quality). The first part of our work consists in gathering available sources to create a Middle East income database and generate raw survey-based inequality series at the national and then the regional levels. Table  2  summarizes the years on which household survey data were available: there is for each country between 1 and 16 years with data.
Regarding the format, there are four cases: (1) 8 countries with survey micro-data; (2) 6 countries with tabulated information on the distribution of income, extracted from household surveys reports and/or statistics offices publications; (3) 3 countries with data on expenditure and consumption (tables or micro-data); (4) 1 country, Saudi Arabia, with no detailed published data. In the online appendix we provide a thorough description of all data sources country by country, the information available, and the methodology used to combine them to produce income distribution series over the 1990-2016 period (Appendix A). We briefly summarize three main issues regarding the data construction process in the following. A first issue concerns the definition of income. Unfortunately, the data quality makes it impossible to harmonize the series in a completely satisfactory manner.
Only the micro-data for Turkey contain relatively detailed information on income categories (wages, pension and other replacement income, business, and capital income) enabling to distinguish between different income concepts. Other micro-databases only provide total disposable income, with however some additional information on imputed rental income and/or the amount of taxes on consumption and durable goods, property taxes etc. paid for some years and countries. Tabulated data usually contain limited information on the definition of income. Whenever possible, the survey income concept that we use attempts to approach pre-tax, post-replacement income (see DINA guidelines,  Alvaredo et al 2016). More precisely, pension income (and other replacement income such as unemployment insurance) is included, while pension contributions (and other social contributions financing replacement income flows) are deducted.
Therefore, in the trade-off between harmonizing our database (between years and/or countries) and approaching the pre-tax income concept we choose the latter. This is a substantial limitation that needs to be corrected in the future. The second issue concerns the unit of observation. We take the adult individual as the basic unit, and we assume that income is equally split between adult household members (see  Alvaredo et al, 2016). We are therefore interested in the distribution of per-adult equal-split income 3. We normalize our series to the adult population (i. e. aged 20 and more). Using the generalized Pareto interpolation techniques developed in  Blanchet, Fournier and Piketty (2017)  and the gpinter web interface (www. wid. world/gpinter), we estimate the full distribution of raw survey income separately for all countries and for the region as a whole 4.
Additionally, in order to ensure maximal comparability across countries and time, we choose to anchor all country-year-level income distributions to the relevant per adult national income. That is, for every country-year pair, we proportionally upgrade all income levels for all percentiles so that per adult average income coincides always with per adult average national income observed in our macroeconomic database (therefore keeping the income distribution and shares constant). By doing so, we certainly do not pretend that available national income series are perfectly comparable. We simply assume that these are the most comparable income series we have: national accounts at least attempt to apply the same definition of national income in all countries (as defined by the SNA Guidelines developed under the auspices of the UN and other international organizations), which is not the case with survey income.
To correct for this, we proceed as follows: we impute a fraction of the missing income (the gap between national income and total survey income) to nationals only, so that the ratio between survey income (augmented by the imputation) and national income reaches 30, 50, 70, or 100%. We take as benchmark survey distributions for Gulf countries the series where this ratio equals 50%, except in Qatar where we take the series where the ratio is 30%. 7   Section 3. 2. Step 2: Fiscal data correction Self-reported survey data is well-known to underestimate incomes at the top (say, within the top decile, and particularly within the top percentile). Generally speaking, the strategy followed in the World Inequality Database (WID. world) in order to correct for this is to use income tax micro-files (together with national accounts and wealth data in order to cover tax-exempt income).
In case income tax data do not exist or is limited in scope (e. g. in case one can only access income tax tabulations rather than micro-files), the DINA Guidelines recommend to supplement existing data with generalized Pareto interpolation techniques (see  Alvaredo et al. , 2016, and Blanchet, Fournier and Piketty 2017 ). In the case of Middle East countries, income tax data are unfortunately extremely limited. Lebanon is the only country for which we were able to access income tax micro-files. These data are relatively detailed, consisting on yearly quasi-exhaustive micro-files over the 2005-2014 period (see Assouad 2017 for a detailed analysis of 7 In Qatar, given that foreigners represent a large share of the total population (90%), and that the ratio between survey and national income is particularly low (22%), top income shares are very sensitive to the operation that reattributes part of the missing income to the nationals only (see Figure  9a  -Qatar).
We therefore only attribute a share of missing income so that the ratio survey/national income equals 30% and not 50% as in other countries. this data source). However, for other countries, despite our best efforts, we do not have any income tax data (not even income tax tabulations). This is unfortunate, because household surveys in the Middle East appear to underestimate top incomes at least as much as in the rest of the world, and possibly more. In particular, survey-based inverted Pareto coefficient b(p) are implausibly low for top incomes, generally around 1. 5-1. 7 (and sometime even less than 1. 5) at the level of the top 10% (i. e. p=0. 9). 8 In contrast, in all countries in the world with reliable income tax data, inverted Pareto coefficients b(p) are typically between 2 and 3 (or even more in high inequality countries), and tend to follow a U-shaped generalized Pareto curve, with a rising part within the top decile (see  Blanchet, Fournier and Piketty, 2017).
The Lebanese income tax micro-files confirm this general finding: top income levels reported in tax data are much higher than in household surveys (top 1% incomes are typically 2-3 times higher, with large variations across income levels and over years), and the tax-corrected inverted Pareto coefficients within the top decile are around 3 or higher (see  Assouad 2017). 9 The reasons why household surveys almost systematically lead to excessively low b coefficients typically come from the fact that surveys suffer from various under-reporting, truncations and top coding problems (with top coding, or self-censored top incomes, b naturally becomes very close to 1 at the very top). 10 Naturally, surveys have other merits, and include detailed socio-demographic information that one could never obtain using tax data.
However, for the study of the top decile -and also for the study of the total inequality level of a country, given the importance of the income share going to the top decileit is necessary to supplement surveys with other sources and methods. In order to construct our benchmark series, we choose to adopt correction factors that are based upon the income tax data from Lebanon. More precisely, the income tax micro-files enable us to compute correction coefficients for thresholds and upper 8 See on-line technical appendix, Table  A3. 9 See also section 4 and Table  3  below for corrected b(p) coefficients. 10 Hasly and Verme (2013, Figure  10 , p. 28) use household income surveys for Egypt between 1999 and 2010 and argue that top-decile inverted Pareto coefficients around 1. 5-1. 7 are not unusual by international standards.
However this conclusion comes entirely from the fact that they compare with coefficients coming from household surveys (which are artificially low). average income by g-percentiles. 11 We apply no correction below p=0. 8, i. e. we assume correction factors exactly equal to 1 below the top 20%, which is approximately the case in the Lebanese data (see  Assouad, 2017  on the choice of profiles). These coefficients do not depend on the Lebanese income level, but only on the percentile. We apply the average correction coefficient per percentile over the 2005-2014 period in Lebanon to all other countries. We have also computed a large number of variant series based upon alternative assumptions (see on-line appendix). 12 The impact on the overall inequality level in the Middle East and the comparison with other world regions is relatively limited (as a first approximation).
In order to derive more precise estimates, we would need to have access to income tax data (at least in the form of income tax tabulations, and ideally in the form of micro files) for all Middle East countries. Section 3. 3. Step 3: Missing capital income and wealth correction Finally, we correct our fiscal income series to take into account non-reported and taxexempt capital income. Important components of capital income are missing from fiscal income data, even in the absence of any tax evasion (see the discussion in  Alvaredo et al 2016 and Piketty, Yang and Zucman, 2017). They typically include corporate retained earnings and imputed housing rental income. We assume these "non-fiscal" income y nf is equal to 10% of national income in each country, a reasonable figure given our findings in other countries.
We should stress that this wealth-based correction has a relatively limited impact on our final income inequality estimates (and in particular a much smaller impact than the fiscal data correction), so that the uncertainty that we are facing here is unimportant for our main findings (see section 4, Figures  10a-10c ). In order to estimate wealth inequality, we proceed as follows. Most observers tend to assume -and probably rightly so -that the level of wealth inequality in the region is high by both international and historical standards. However, there is substantial uncertainty about the exact level of wealth concentration, due to the almost complete lack of proper statistical evidence. Here we follow a simple methodology similar to that applied in Novokmet,  Piketty and Zucman (2017)  and use rich lists to produce estimates of top wealth shares for Middle Eastern countries in 2016, which we then use to allocate tax-exempt capital income.
These estimates are obviously far from perfect, but they probably tend to minimize the gap with the Middle East (in particular, we have access to more extensive income tax data for China and India than the for the Middle East). In brief: according to our estimates, the Middle East appears to be the most unequal region in the world. This is true for the top decile income share, as well as for other inequality indicators; e. g. the top percentile income share is about 30% in the Middle East, vs. 12% in Western Europe, 20% in the USA, 28% in Brazil, 18% in South Africa, 14% in China and 21% in India (see Figure  6 ). This is also true for synthetic indicators such as the Gini coefficient (see on-line series at WID. world). We find it particularly informative to compare the overall levels of the income shares going to the bottom 50%, the middle 40% and the top 10% and 1% in the Middle East and other countries (see Figures  7a-7b ).
For instance, according to our benchmark estimates, the bottom 50% of the population receives about 9% of total income in the Middle East (vs. 18% in Europe), as compared to 64% for the top 10% (vs. 37% in Western Europe). This clearly illustrates that differences in distributions can make an enormous difference when comparing income and welfare levels across countries. Section 4. 3. Analyzing the evolution of income inequality in the Middle East and other robustness checks We now turn to our results regarding the evolution of income inequality in the Middle East over the 1990-2016 period. We should stress again that the data sources at our disposal are insufficient to properly analyze trends in inequality. In our benchmark estimates, we find a declining inequality trend at the regional level between 1990 and 2010, followed by a rising trend between 2010 and 2016.
20 Unfortunately available series for the top 10% and top 1% share in South Africa do not cover all years, so it is difficult to make a complete comparison with the Middle East at this stage. series from Eastern and Western European countries for instance, inequality increases (relative to Western Europe) but to a much lesser extent than in the Middle East. In order to better understand the origins of our high inequality estimates, we compare on Figures  11a-11b -11c the results obtained with our benchmark national income series (combining survey data, national accounts, income tax and wealth data), the results obtained with the fiscal income series (ignoring the wealth correction, which as one can see plays a relatively minor role) and the results obtained with the survey data alone. Here we distinguish between the survey income series obtained with different country-level average incomes (anchored on observed country-level per adult national incomes) and the survey income series simulated by assuming fixed country-level average incomes (thereby neutralizing the impact of between-country inequality).
As one see, both the within-country inequality effect (fiscal data correction) and the between-country inequality effect play an important role in accounting for the very high inequality estimates. Finally, we have also simulated what would have been the evolution of income inequality in the Middle East over the 1990-2016 if within-country inequality had remained fixed at the observed 1990 level. As one can see from Figures  12a-12b , the evolution of total inequality at the level of the Middle East taken as a whole would have been virtually the same, with declining inequality between 1990 and 2010 and rising inequality since 2010. This shows that our estimates are mostly driven by the evolution of between-country inequality. This is partly due to the fact that we do not have survey data for all years (see Table  2 ), so that for some countries our inequality estimates display very little time variations (and in some cases no time variation at all).
This is also due to the fact that even in countries with several surveys over the period, we observe limited variations in income inequality, and these variations tend   3 ). Most of these variations are relatively modest in magnitude, so it is not surprising that most of the evolution of Middle East inequality is driven by the evolution of betweencountry inequality and the fact that the gap in average income between oil-rich countries and other countries has been trending downwards (but is still very large in level). If we had access to adequate income tax data throughout the 1990-2016 period, we might reach different conclusions and find a strong within-country rising inequality trend (such as the one found in a large number of very different countries across the world, e. g. in the USA, Europe, India, China, South Africa, Russia, with varying magnitudes).
Section 5. Concluding comments In this paper we have combined household surveys, national accounts, income tax data and wealth data in order to estimate the level and evolution of income concentration in the Middle East for the period 1990-2016. According to our benchmark series, the Middle East appears to be the most unequal region in the world, with a top decile income share as high as 64%, as compared to 37% in Western Europe, 47% in the USA, and 55% in Brazil. This is due both to enormous inequality between countries (particularly between oil-rich and population-rich countries) and to large inequality within countries (which we probably under-estimate, given the limited access to proper fiscal data). To our knowledge, this is the first attempt to measure income inequality at the level of the Middle East taken as a whole, and also the first attempt to use income tax data and generalized Pareto interpolation techniques to correct household survey data in the Middle East.
The data at our disposal is highly imperfect, and we still face considerable limitations in our ability to measure inequality in the Middle East. In particular, there is much uncertainty about inequality trends in the period under study. However the general conclusion that the overall inequality level is one of the highest in the world -if not the highest in the world, with the possible exception of South Africa -appears to be very robust. In conclusion, we would like to stress the importance of increasing transparency on income and wealth in the Middle East. In particular, it is critical that Middle East countries provide access to household surveys micro-files, and even more importantly that they provide access to income tax data (at least in the form of income tax tabulations). It is very difficult to have an informed public debate about inequality trends -and also about a large number of substantial policy issues such as taxation and public spending -without proper access to such data.
In particular, the subjective and objective conditions of recovery were identified. During the last, Principal Analysis phase, several types of data analysis were undertaken by researchers from two sites, a sociologist and a public health researcher/psychiatrist. Recurrent themes were extracted from a subset of 13 memos (from the Instrumentation Phase) and a coding system of progression along factors of recovery was developed. The memos were again re-examined, with these factors in mind, to develop typology of trajectories.
Sampling Procedures The qualitative sample was selected from the RCT overall sample (n = 703) in four cities and hence met inclusion and exclusion criteria of the RCT study (see Table  2 ). Thirty-six participants were recruited for the qualitative study from the experimental group (n = 24) and the control group (n = 12), using random selection when possible and respecting the predetermined RCT breakdown of 70% (experimental) and 30% (control group). Randomization was halted to increase the recruitment of women (every nth female rather than every nth participant). Fifteen percent (n = 5) of the final sample were women. All participants provided consent for participating in the qualitative study and chose where to be interviewed. The total inclusion period was 8 months, and data were collected over 36 months, from September 2011 to September 2014.
The Hypothesis Reformulation section describes how the factors of recovery were identified in terms of the early data. This reformulation rejected the notion of recovery as a monolithic positive phenomenon. The Principal Analysis section which includes the most in-depth analysis of this qualitative study, describes the development of a scale from the data already collected in the first phase. The scale enables recovery to be measured as a progression along two dimensions, Objective Factors and Subjective Factors of recovery. This scale was further applied to a subset (n = 13) of the memos from the first phase and a three-fold typology of recovery trajectories was extracted. The results of this in-depth re-analysis of the data are illustrated with cases based on the study participants' own words.
After all interviews were completed, data collected in the four cities were pooled. The qualitative research team developed a common grid to organize the interview material, based on four themes: housing, psychiatric disorders, addictions, and relationships. Researchers also composed a series of memos that summarized the experience of each participant as indicated in their interviews. Four broad domains areas were covered: (1) sociodemographic characteristics and history: age, family, salient events in the life course, illnesses and health, experience on the streets, residential trajectory; (2) abilities, challenges; (3) the program's place within the person's personal development (for the experimental group participants); (4) more general information concerning the recovery process. This preliminary phase of the study confirmed the irreducibility of different recovery experiences to a list of objective elements of "good recovery", leading to the rejection of such a list.