Julien Papaix

Luke Barrett

Jeremy Burdon

Peter Thrall

Julien Papaïx

Metapopulation dynamics in

come

Introduction

Spatial structure is an important determinant of epidemiological dynamics in host-pathogen systems: epidemic threshold, disease dynamics and severity.

However, the characteristics of individual populations can counterbalance metapopulation processes but their effects in interaction with habitat spatial configuration and dispersal have been disregarded.

Introduction

Spatial structure is an important determinant of epidemiological dynamics in host-pathogen systems: epidemic threshold, disease dynamics and severity.

However, the characteristics of individual populations can counterbalance metapopulation processes but their effects in interaction with habitat spatial configuration and dispersal have been disregarded.

Quantify the role of local habitat characteristics on the host-pathogen dynamics while considering the metapopulation spatial structure.

INRA-CSIRO

Host-pathogen metapopulation dynamics 01/07/2014 2 / 15

The pathosystem

Results

INRA-CSIRO

Host-pathogen metapopulation dynamics 01/07/2014

Conclusions

Conclusions:

-Modelling of the spatio-temporal dynamics of two interacting species. -Deciphering the effects of spatial structure vs. habitat characteristics.

Perspectives:

-How to consider genetic data? -Effect of the metapopulation dynamics on patterns of resistance / infectivity.

Conclusions

Conclusions:

-Modelling of the spatio-temporal dynamics of two interacting species. -Deciphering the effects of spatial structure vs. habitat characteristics.

Perspectives:

-How to consider genetic data? -Effect of the metapopulation dynamics on patterns of resistance / infectivity.


Frédéric

Modeling of connectivity: What for? How do we do it? Where to go?

Darboux Modeling of connectivity: What for?

Where are the links/connections in the watershed? Are they active/How long do they last? How do they behave as a whole?

Frédéric Darboux

Modeling of connectivity: What for?

Where are the links/connections in the watershed?

Are they active/How long do they last?

How do they behave as a whole?

Frédéric Darboux Modeling of connectivity: What for?

Where are the links/connections in the watershed?

Are they active/How long do they last?

How do they behave as a whole?

Frédéric Darboux Modeling of connectivity: What for?

Where are the links/connections in the watershed?

Are they active/How long do they last?

How do they behave as a whole?

Frédéric Darboux Modeling of connectivity: What for?

How to go farther than case studies? generate your data! Don't be shy... generate your data!

Frédéric Darboux Modeling of connectivity: What for?

Conclusions

Connectivity is about space and time patterns.

Justify that accounting for connectivity is valuable (and not just state it).

Justify your algorithms.

How to account for details.

Combining numerical modeling and theory application.

From case study to proven understanding (using data generation).

From spatially-distributed modeling to a lumped index (ultimate goal).

Frédéric Darboux Modeling of connectivity: What for?

Foreword

  Surface hydrology and sediment transport Not a review A few insights about how numerical models are used for connectivity Questions addressed using models Foreword Surface hydrology and sediment transport Not a review A few insights about how numerical models are used for connectivity Questions addressed using models Foreword Surface hydrology and sediment transport Not a review A few insights about how numerical models are used for connectivity Questions addressed using models Foreword Surface hydrology and sediment transport Not a review A few insights about how numerical models are used for connectivity Questions addressed using models Foreword Surface hydrology and sediment transport Not a review A few insights about how numerical models are used for connectivity Questions addressed using models Where are the links/connections in the watershed? Are they active/How long do they last? How do they behave as a whole? Frédéric Darboux Modeling of connectivity: What for? López-Vicente et al., 2013, Catena Cavalli et al., 2013, Geomorphology Water connectivity vs Sediment connectivity In hydrology, you need water to carry the sediments Sediment connection ⇒ Water connection Water connection Sediment connection More work on sediment connectivity needed, and models can help on this. López-Vicente et al., 2013, Catena Cavalli et al., 2013, Geomorphology Water connectivity vs Sediment connectivity In hydrology, you need water to carry the sediments Sediment connection ⇒ Water connection Water connection Sediment connection More work on sediment connectivity needed, and models can help on this. López-Vicente et al., 2013, Catena Cavalli et al., 2013, Geomorphology Water connectivity vs Sediment connectivity In hydrology, you need water to carry the sediments Sediment connection ⇒ Water connection Water connection Sediment connection More work on sediment connectivity needed, and models can help on this.

Frédéric DarbouxModeling of connectivity: What for?

Theoretical inputs

Percolation theory [Darboux et al., 2002, Earth Surf. Proc. Landf.] [Lehmann et al., 2007, Hydrol. Earth Syst. Sci.] [Harel et al., 2013, Water Res. Research] Graph theory [Heckmann et al., 2013, Geomorphology] Queuing theory [Harel et al., 2013, Water Res. Research] Frédéric Darboux

Modeling of connectivity: What for? [Peñuela et al., 2013, Hydrol. Earth Syst. Sci.] What are the important features? generate your data! How to make things more general? generate your data!


domain: spi phys

text:
Marine Ruffenach
email: marine.ruffenach@onera.fr

Sebastien Bourdarie

Julien Mekki

Didier Falguère

Jean-Roch R Vaillé

Jérôme Carron

A proton detector for energies from 2 MeV to 20 MeV

Abstract-CNES and ONERA have developed a radiation monitor ICARE-NG to measure protons and electrons in radiation belts. This instrument is able to measure a wide range of energy for protons and electrons, but no measurements of few MeV protons are performed. The objective of this study is to extend capabilities of this radiation monitor by adding a low energy proton sensor. In this article the design of a detector for low energy protons compatible with the ICARE-NG instrument is presented. Response functions for protons and electrons of the low energy proton detector are calculated using Monte-Carlo simulations. The calculation of predicted count rates of particles using the AP-8 and AE-8 models will be performed. Index Terms-Geant4, Monte-Carlo simulations, proton, radiation belts, radiation monitor, response function, space environment.

I. INTRODUCTION

UE to the magnetic field of the Earth, protons and electrons are trapped in a restricted region of the magnetosphere and constitute radiation belts [START_REF] Bourdarie | Space radiation environment[END_REF]. A good knowledge of these regions is mandatory to prevent damages on satellites or even their loss [START_REF] Baker | The occurrence of operational anomalies in spacecraft and their relationship to space weather[END_REF]. While a bunch of measurements of proton flux with energies greater than 10 MeV are available, in-situ measurements of 1-10 MeV protons are not common. The challenge to have good measurements of low energy protons is to accurately discriminate protons with energy of a few MeV to protons with energy of several tens of MeV, and to energetic electrons. Such monitors have already been developed: MEPED [3] [4], LPT [START_REF] Sasaki | Technical data acquisition equipment for GOSAT[END_REF], SST [START_REF] Burch | The THEMIS Mission[END_REF] and RBSPICE [START_REF] Mitchell | Radiation Belt Storm Probes Ion Composition Experiment (RBSPICE)[END_REF]. The aim of this study is to design a sensor of low energy protons compatible with the existing ICARE-NG instrument which flew over several missions as JASON-2 [START_REF] Boscher | In Flight Measurements of Radiation Environment on Board the French Satellite JASON-2[END_REF] and SAC-D [START_REF] Boscher | In-Flight Measurements of Radiation Environment on Board the Argentinean Satellite SAC-D[END_REF]. Valid proton energy range measured by ICARE-NG on JASON-2 and SAC-D is from 12.8 MeV to 190 MeV. Such a new sensor will extend this energy range down to 2 MeV protons. energy protons are given. In section III the modelling of the instrument is described. In particular, Monte-Carlo simulations are outlined. Section IV focuses on numerical results of response functions of the detector and the modelling of predicted count rates of particles. Finally, conclusions are given in section V.

II. THE INSTRUMENT'S GEOMETRY

The geometry of the low energy proton detector has to satisfy several objectives: (1) an accurate discrimination of ionizing particles in a mixed field must be guaranteed, (2) it must be compatible with the existing ICARE-NG electronic design, and (3) it must be compatible with the actual mechanical design of ICARE-NG. Satisfying the second constraint imposes to implement a sensor based on solid state detectors with coincidence and anti-coincidence modes and to measure energy deposition in the 0-10 MeV range approximatively. The third objective means that the size of the sensor is limited in term of dimension and weight. The first objective is much more challenging: for example, in a 700 µm thick silicon diode a 1-MeV proton can deposit the same amount of energy than a 100-MeV proton or a 1-MeV electron. So, the first challenge to face is to reduce the contribution of electrons and high energy protons in order to get accurate measurements of low energy protons. To reduce the contribution of electrons and high energy protons the shielding is made of tungsten. However, the Bremsstrahlung depends on the square of the atomic number of the element so the tungsten fosters this phenomenon. The Bremsstrahlung depends also on the energy of the incident electron so to reduce this effect aluminum is added on both sides of the tungsten to reduce the energy of electrons before they reach the tungsten. At the main entrance of the detector, a samarium-cobalt magnet is used to deflect the major part of electrons before they reach the first silicon diode. Because protons are heavier, they are not affected in the energy range of interest (few MeV). The magnet is described in section III. Aluminum baffles are used to attenuate the scattering of particles in the collimator. To improve measurement quality, two silicon diodes are used to collimate particles. Moreover, diameters of diodes are chosen so as to reduce the contribution of particles coming from the sides of the instrument. In particular, the diameter of the first diode is lower than the one of the second diode. Last but not least, the instrument has to be compatible with the ICARE-NG instrument for which the complete description can be found in [START_REF] Boscher | In Flight Measurements of Radiation Environment on Board the French Satellite JASON-2[END_REF]. The ICARE-NG A proton detector for energies from 2MeV to 20MeV M. Ruffenach, S. Bourdarie, J. Mekki, D. Falguère, J. R. Vaillé, and J. Carron D instrument consists of three detector heads (A, B, and C). Both sensors A and C are set to measure protons while the Head-B measures electrons. Particle fluxes for electrons from 250keV to 3.2MeV and for protons from 12.8MeV to 190MeV are obtained. The goal now is to be able to implement either the actual Head-C of ICARE-NG or the new low energy proton sensor with no impact on electronic or mechanical design. This leads to other constraints for the geometry of the low energy proton detector. The positions of diodes in the new detector's head have to be roughly the same than in the Head-C. Also, the volume of the new head is limited by other heads so the thickness of the shielding must be the same as for the Head-C. As the Head-B detects electrons, the low energy proton head has to not interact with other heads, especially due to the magnetic field produced by the magnet. A steel strapping is used to confine the magnetic field inside the head. An illustration of the geometry of the low energy proton head taking into accounts all the constraints discussed above is shown in Fig. 1. The front silicon diode of 700µm thick has a sensitive area of 50mm² while the back silicon diode of 300µm thick has a sensitive area of 113mm².

III. THE MODELING OF THE INSTRUMENT

A. Magnetic field calculation

The magnet is composed of two samarium-cobalt pieces so that the magnetic field is oriented diametrically. The height of the samarium-cobalt magnet is a compromise between the total volume of the detector and the deflection of electrons.

The magnetic field of the samarium-cobalt magnet is calculated using PS-PERMAG software [START_REF]PS-PERMAG Software for Calculating Permanent Magnetic Fields[END_REF]. The vertical cross-section of this calculation is presented in Fig. 2.

Between the two pieces, the magnetic field is approximately uniform. At the center the magnetic field is equal to 0.36T. These calculations do not take into account the steel strapping which will increase the intensity of the magnetic field between the two pieces and confine the magnetic field inside the head. Results of this magnetic field calculation are used in Monte-Carlo simulations to calculate the response of the low energy proton detector.

B. Monte-Carlo simulations

Response functions of protons and electrons in anticoincidence and coincidence modes of the low energy proton head are computing from Monte-Carlo simulations based on the GEANT-4 ("GEometry ANd tracking") toolkit [START_REF] Agostinelli | Geant4-a simulation toolkit[END_REF] [START_REF] Allison | Geant4 developments and applications[END_REF]. The geometry of the low energy proton detector is drawn and translated into GEANT-4 using FASTRAD Software [START_REF]FASTRAD Software -The 3D CAD Tool For Radiation Shielding Analysis[END_REF]. In these simulations a spherical source with a radius equals to 4.5𝑐𝑐𝑐𝑐 is assumed with a cosine-law angular distribution for velocity vector directions [START_REF] Santin | Normalisation Modelling Sources. Geant4[END_REF]. Five million of tries are performed per incident energy. The efficiency of the detector is simulated by calculating the geometric factor, GEF, which is obtained from the ratio between the number of particles detected per channel over the total number of particles launched per incident energy. Its expression is given in (1) and calculated using [START_REF] Santin | Normalisation Modelling Sources. Geant4[END_REF] and [START_REF] Sullivan | Geometric factor and directional response of single and multi-element particle telescopes[END_REF].

𝐺𝐺𝐺𝐺𝐺𝐺 = 4𝜋𝜋 2 𝑅𝑅 2 𝑁𝑁 𝑑𝑑𝑑𝑑𝑑𝑑 𝑁𝑁 𝑇𝑇𝑇𝑇𝑑𝑑𝑇𝑇𝑇𝑇 ( 1
)
where 𝑅𝑅 is the radius of the source. At the entrance of the detector, an aluminum foil of 50µm is added for Monte-Carlo simulations to take into account the MLI (Multi-Layer Insulation) of the satellite which will cover the instrument.

IV. NUMERICAL RESULTS

A. Response functions

Proton response functions in anti-coincidence and coincidence modes are shown in Fig. 3 and in Fig. 4.

In Fig. 3 two regions can be highlighted: protons with an incident energy lower than 10MeV and protons with an incident energy higher than 65MeV. The first part is attributed to protons which depose all their energy in the front diode of the detector. The second part is attributed to protons which cross the shielding before reaching the front diode.

In Fig. 4 the response function indicates two regimes: protons with energies lower than 65MeV which come from the aperture of the detector and depose energy in the two diodes, and protons with incident energies higher than 65MeV and cross the shielding before reaching the two diodes.

With Fig. 3 and Fig. 4, 2-20MeV protons are measured thanks to anti-coincidence and coincidence modes. In the two cases, high energy protons cross the shielding and are detected with a higher geometric factor than the one of 2-20MeV energy protons. Simulations were performed using the same number of particles for all incident energies. Nevertheless, in space fluxes of particles depend on the energy. Flux models will be taken into account in the subsection IV. B. to investigate the relative contribution of 2-20 MeV protons and higher energies.

Electron response functions in anti-coincidence and coincidence modes are shown in Fig. 5 and Fig. 6.

In Fig. 5 some electrons are detected in anti-coincidence mode. More precisely, they are mostly photons produced by the Bremsstrahlung. If we compare with the geometric factor of protons in anti-coincidence, the one in the case of electrons is much lower. This is due to the magnet which deflects the major part of electrons. In addition, electrons deposit energies less than 2MeV so some electrons will be detected only in the first channels.

In Fig. 6 few electrons are detected in coincidence mode but with a very low geometric factor. Furthermore, for all these simulations (protons and electrons) no threshold has been taken into consideration to filter out low energy depositions (and electronic noise by the way).

With a threshold set at 0.5MeV for example, which is the case for the Head-A of ICARE-NG, few electrons would be detectable only in anti-coincidence mode. These response functions characterize the efficiency of the sensor to measure protons and electrons, but do not take into consideration particles distribution. In subsection IV. B, response functions and fluxes of particles are used to simulate particles' counts that will be returned by the instrument.

B. The modeling of calculated counts of particles

Fluxes of particles depend strongly on the satellite's location in the radiation belts. To take into account the response functions of the detector and particle distributions, expected count rates for each channel are computed. The expression of count rates for a given deposited energy channel is provided in (2) and extracted from [START_REF] Ruffenach | Proton radiation belts anisotropy as seen by ICARE-NG Head-A[END_REF].

𝐶𝐶 = � � 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓(𝐺𝐺, 𝛼𝛼 𝑇𝑇𝑇𝑇𝑙𝑙𝑇𝑇𝑇𝑇 ) × 𝑀𝑀𝑀𝑀𝑀𝑀(𝐺𝐺, 𝛼𝛼 𝑇𝑇𝑇𝑇𝑙𝑙𝑇𝑇𝑇𝑇 ) × 𝑑𝑑𝐺𝐺𝑑𝑑𝛼𝛼 𝑇𝑇𝑇𝑇𝑙𝑙𝑇𝑇𝑇𝑇 𝐸𝐸 𝛼𝛼 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 (2)
where 𝑀𝑀𝑀𝑀𝑀𝑀(𝐺𝐺, 𝛼𝛼 𝑇𝑇𝑇𝑇𝑙𝑙𝑇𝑇𝑇𝑇 ) and 𝐺𝐺𝑓𝑓𝑓𝑓𝑓𝑓(𝐺𝐺, 𝛼𝛼 𝑇𝑇𝑇𝑇𝑙𝑙𝑇𝑇𝑇𝑇 ) are respectively the geometric factor and the unidirectional differential flux at a given incident energy and local pitch-angle. In a first step, particle fluxes will be considered isotropic at spacecraft locations so response functions computed from an isotropic source will be adopted. After [START_REF] Boscher | High-Energy Electrons in the Inner Zone[END_REF] [18] and [START_REF] Sicard | GREEN: the new Global Radiation Earth ENvironment model (beta version)[END_REF] a correction factor is applied to electron fluxes with energies greater than 800 keV at L<2.5 returned by AE-8 model [START_REF] Sawyer | AP-8 Trapped Proton Environment for Solar Maximum and Solar Minimum[END_REF] [21]: AE-8 fluxes are divided by a factor 100 for 𝐿𝐿 < 2.5 and for energies greater than 800 keV. Count rates are calculated for all deposited energy channels along the magnetic equator. Results obtained for the anti-coincidence mode are shown in Fig. 7. Count rates obtained in Fig. 7 exhibit three regions. The first one with a red contour indicates count rates dominated by protons with energies greater than 65 MeV. The second one represented in green points out count rates dominated by energetic electrons. For the third one (with no specific contour) 2-20MeV protons dominate count rates. Clearly, the adopted sensor geometry allows discriminating 2-20 MeV protons accurately in a large radiation belt domain, and particularly allows sampling the pic flux. For 𝐿𝐿 < 1.7 high energy protons dominate count rates but their contribution cannot be easily removed since these protons cross the aluminum-tungsten shielding. To limit even more their contribution the shielding has to be thicker. But design constrains to fit with the actual ICARE-NG radiation monitor, make it difficult to increase again mass and volume of the low energy proton sensor.

In the final version of the paper, results for the coincidence mode will be discussed. Also, count rates of low-energy protons will be calculated by taking into account the pointing direction of the monitor according to the magnetic field.

V. CONCLUSION

A new sensor of low energy protons has been designed. This sensor is compatible with the ICARE-NG instrument and can replace the Head-C according to the needs. Monte-Carlo simulations have been performed to calculate response functions of the sensor. By combining response functions calculated with GEANT4 and particle fluxes given by AP-8 and AE-8 models, predicted count rates have been calculated.

Sensor performances indicate that the detector is sensitive to 2-20MeV protons for 1.7 < 𝐿𝐿 < 3.5.

Fig. 1 .

 1 Fig. 1. Cross-section of the geometry of the low energy proton detector. Aluminum is represented in grey, tungsten in blue, diodes in pink, steel strapping in green, and pieces of samarium-cobalt in hatched areas.

Fig. 2 .

 2 Fig. 2. Vertical cross-section of the magnetic field produced by the magnet.

Fig. 3 .

 3 Fig. 3. Proton response function in anti-coincidence mode. Geometric factor (cm².sr) in incident energy (x-axis)-deposited energy (y-axis) map.

Fig. 4 .

 4 Fig. 4. Proton response function in coincidence mode. Geometric factor (cm².sr) in incident energy (x-axis)-deposited energy (y-axis) map.

Fig. 5 .

 5 Fig. 5. Electron response function in anti-coincidence mode. Geometric factor (cm².sr) in incident energy (x-axis)-deposited energy (y-axis) map.

Fig. 6 .

 6 Fig. 6. Electron response function in coincidence mode. Geometric factor (cm².sr) in incident energy (x-axis)-deposited energy (y-axis) map.

Fig. 7 .

 7 Fig. 7. Count rates of particles in McIlwain L-parameter (x-axis)-deposited energy (y-axis) map for the low energy proton detector in anti-coincidence mode.

This work was conducted as part of a co-financing of doctoral thesis research CNES N° 17054 -ONERA N° 9218.


Measuring Top Incomes in the Middle

In order to estimate the entire income distribution of the Middle East, we first gather available income data to estimate the raw survey distribution for each country, over the period. Table A1 summarizes the survey sources used in this paper. When we could not access micro-data, we use survey income tabulations extracted from survey reports published online by the national statistical offices. Raw statistical data are given the directory "HouseholdSurveyData". The Stata format do-files used to generate the raw survey distributions can be found in Gpinter/Do/IncomeSurveySeries (do_"countryname"). For countries with tabulated data only, the excel files "Input/`countryname'IncomeDistribution.xlsx" include the computations to derive input files to be used with the "Gpinter" (Generalized Pareto interpolation) web interface (http://wid.world/gpinter). In what follows, we briedly present the databases and methodology used.

1. Egypt, Iraq, Jordan and Palestine

We have access to survey micro-data for seven countries. For Egypt, Iraq, Jordan, and Palestine we use the harmonized databases created by the Economic Research Forum (OAMDI initiative) and take the households total disposable income as income concept. For some years and countries, we have additional variables (as the expenditure on taxes and levies, or imputed rental income) that we include to approach the pre-tax, post-replacement income concept defined in [START_REF] Alvaredo | Distributional National Accounts (DINA) Guidelines: Concepts and Methods used in the World Wealth and Income Database[END_REF] 1 . All computations can be found in each country specific do file, in Gpinter/Do/IncomeSurveySeries. The variables available are presented in the sheet "ERFHarmonization", in the excel file "CleaningMicroData.xlsx".

1 Pension income (and other replacement income such as unemployment insurance) is included in income, while pension contributions (and other social contributions financing

Turkey

For Turkey, we use the Household Budget Survey micro-data for the years 1994, 2002 and 2013. As explained in the main paper, the Turkish micro-data are more detailed so that we can adjust the income variable to approach the pre-tax, postreplacement income concept. The sheet "TurkeyHBSIndivHarmonization", in "CleaningMicroData" presents how we computed the income variable.

For the years 2014-2016, we use tabulations on per capita income computed with the Income and Living Conditions Survey and available online on the Turkish Statistical Institute website. The raw data and computations can be found in the directory "HouseholdSurveyData/Turkey".

Iran

For Iran, we used micro-data publicly available online on the website of the Statistical Center of Iran, for the years 2010 and 2013. The micro data distinguish between Rural and Urban areas. We therefore used the Gpinter "merge option" to infer the national income distribution per adult. The raw data and computations can be found in the directory "HouseholdSurveyData/Iran".

Gulf-countries

For Gulf countries, only tabulations published by national statistical offices are available. They systematically distinguish between "National" and "Foreigner" populations. Using the Gpinter interface, we derive the per adult income distribution for each sub-group of the population over the period. The merge option enables us to correct for sampling issues (noted in the Kuwaiti data by El [START_REF] El-Katiri | Anatomy of an oil-based welfare state: Rent distribution in Kuwait. Kuwait Programme on Development, Governance and Globalisation in the Gulf States[END_REF], but that we find in other Gulf countries) and the fact that foreigners are systematically under sampled in survey data. We directly weight the distribution by the adult population of each sub-group. Details about the estimation of the share of foreigners in the total population are presented in the excel file "GulfCountries.xlsx" (directory HouseholdSurveyData). Table A2 displays inequality statistics derived from survey data for foreigners, nationals and the entire population of Gulf countries. Taking into account foreigners increases inequality in all countries and for all years. Note however that the distinction between the two populations can only be done at the survey distribution level, as we afterwards normalize the series to the national average income per adult and loose track of the differential in average income between the two populations. We therefore still highly underestimate income inequality within these countries.

Saudi Arabia

Three household surveys have been conducted in Saudi Arabia, in 1999Arabia, in , 2007Arabia, in and 2013, but the reports published by the statistical office do not contain exploitable information on the income or expenditure distribution. We therefore proceed as follow. First, we take the average standardized distributions of income computed with available survey data in other Gulf countries, that is we divide all thresholds and bracket averages for all 127 generalized percentiles by average wealth, and we compute the arithmetic average for the five countries, over the period. It seems unjustified to adjust this average Bahrain-Kuwait-Oman-Qatar-UAE normalized distribution to Saudi Arabia directly as it would imply that Saudi Arabia inequality levels follow the same trends as the other countries. Given its weight in the Middle East in terms of population and income, we attribute the most equal Bahrain-Kuwait-Oman-Qatar-UAE normalized distribution (the year 2008) that we adjust to the Saudi per adult national income over the period. Note however that this procedure is rather conservative, given that published Gini coefficients (51.3 and 45.9 in 2007 and 2013 for the Saudi population) are greater than what is observed in other countries (see Table A2).

6. Countries with information on expenditure only: Syria, Yemen, and Kuwait

Finally, for Syria Yemen and Kuwait, only information on expenditure is available. We therefore proceed as follow. First, we use the Palestinian micro-data, for which both household income and expenditure are available, to compute the residual saving rates by g-percentile, that is, for each g-percentile, we compute the ratio of income over expenditure and we apply these ratio to the Syrian and Yemeni expenditure distributions. For Kuwait, we first derive the ratios between income and expenditure by g-percentile using Bahraini and Emirati tabulations, for which tabulations are available for both the income and expenditure distributions. We compute these correction coefficients for the foreigner and national populations and apply them to the expenditure distribution of each sub-group of the Kuwaiti population (see do_CoefficientExpGulfCountries).

7. Other issues: unit of observation and cleaning procedure Two additional remarks on the procedure are in order. First, as explained in the main paper, we take as unit of observation the adult individual, aged 20 years and more. For all countries with micro-data, with the exception of Iran, we estimate the number of adults in each household, using information on the household type or the age of household members in the case of Turkey. We then normalize the total adult population in the survey data to the demographic figures gathered in WID.world and deduce the income per adult. For Iran, we divide household income by the household size multiplied by an estimate of the adult population in each area (Urban and Rural, see do_Iran for more details).

Finally, for countries with tabulations only, the number of adults by income bracket is rarely available. We therefore divide household income in each group by the average household size multiplied by the share of adults in the entire economy, assuming that the income distribution per adult is the same as the household distribution. We do the same when the average household size per bracket is available2 .

The second issue concerns the cleaning of the survey micro-data. All hypotheses made and comparison between variants can be found in "CleaningMicroData.xlsx", in the directory "HouseholdSurveyData". For each country, we dismiss the observations for which the ratio between the income and the average income is greater than what is observed on average for other years and countries.

Appendix B. Income and wealth distribution series

Our detailed income and wealth distribution series are given in the directory AAP2017DistributionSeries, within the zipped directory AAP2017.zip. This directory includes our benchmark distribution series (GpinterIncome/Output) as well as alternative series, the complete computer codes and raw material publicly available (household survey tabulations and billionaire data) that we used to construct these series. For more details on the organization of these files, see ReadMeAAP2017DistributionSeries.pdf and other "ReadMe" files within each directory. The main robustness checks and variant series are presented in AAP2017Appendix.xlsx and are summarized on Figures B5-B17, which we briefly describe in the following.

Appendix B.1 Income distribution series

The general methodology to construct our income distribution series is summarized in the main paper (Section 3) and consists of three steps: in step 1 we use survey data and generalized Pareto interpolation techniques [START_REF] Blanchet | Generalized Pareto Curves: Theory and Applications to Income and Wealth Tax Data for France and the United States, 1800-2014[END_REF] to estimate survey income distributions for each country; in step 2 we use the Lebanese fiscal data to correct the top of these distributions and obtain corrected estimates of the distribution of fiscal income, by g-percentile; in step 3 we use national accounts and wealth data in order to include tax-exempt capital income (such as undistributed profits, imputed rent and other "non-fiscal income") and to obtain corrected estimates of the distribution of pre-tax national income by gpercentile. We then aggregate each country specific distribution using the merge option in the Gpinter interface.

In the following, we discuss a number of additional issues about variant series and robustness checks. We focus on the regional distribution. The effect of each correction on within-country inequalities is broadly the same as in Lebanon, to a large extent by construction and details about variants on national distributions can be found in [START_REF] Assouad | Rethinking the Lebanese Economic Miracle: The Extreme Concentration of Income and Wealth in Lebanon, 2005-2014[END_REF].

Figures B1-B4 display the main results of the papers and the impact of the two corrections. Two facts stand out. First, the impact of the fiscal correction is larger than the one of the wealth correction. Second, both corrections do not change the trends. Furthermore, as one can see by comparing Tables A1,B3 and B4, the difference in magnitude of the fiscal correction for each country comes, first, from the initial survey inequality levels and second, from the ratio between total survey income and total national income, which can be very low by international standards for some countries (Table A1). Given that, we focus on variants on the fiscal correction when we aggregate the series.

Appendix B.1.1 Fiscal Data Correction

Regarding the fiscal-data correction, we choose in our benchmark series to apply the same average upgrade factors by g-percentile (estimated using the Lebanese fiscal data over the 2005-2014 period) to the entire 1990-2016 period. In the absence of alternative data to correct the top of the survey distributions, it seems reasonable to apply the Lebanese correction coefficients to other countries3 . Nevertheless, it would clearly be unjustified to assume that the correction follows the same trends as Lebanon4 .

Details about the Lebanese fiscal micro-files, their limits and how to combine them with survey data are provided by [START_REF] Assouad | Rethinking the Lebanese Economic Miracle: The Extreme Concentration of Income and Wealth in Lebanon, 2005-2014[END_REF]. In particular, the fiscal micro files are based upon a "taxable income" concept (i.e. income subject to income tax, after a number of deductions allowed by tax legislation) that may be significantly smaller than "fiscal income" (defined as the sum of all income items legally subject to taxation, before any deduction) due to large deductions and exemptions. We therefore compute the fiscal correction coefficients by assuming that the ratio between taxable income and fiscal income is equal to r=80%. Figures B5-B8 display variants with r=60% and r=90% for the Middle East series and for Lebanon. The impact at the country level is greater than at the regional level 5 , or in other words, the effect of the correction and the hypotheses made does not cumulate when we aggregate countries together. data may correspond to a variation in tax evasion behaviors rather than real changes in top income shares. 5 Although it is higher in Lebanon than in other countries due to its smaller share of total survey income over national income.

Appendix B.1. 2 Missing capital income correction

As explained in the main paper (section 2.2), we proceed to a second correction accounting for the missing capital incomes. There are two steps. The first one is to estimate the amount of capital income missing. For Lebanon, they are estimated using national accounts and public finance reports and represent 20% of national income. For other countries, in absence of better data, we assume that they represent 10% of national Income, a rather conservative amount given the low ratios between total fiscal income and total national income observed (see excel file ShareNationalIncome.xlsx). The second step consists in re-allocating these amounts to estimate the distribution of total personal income. To do so, we first assume that they follow the same distribution as wealth, that we estimate using billionaires data (see Appendix B.2 below). Second, we use the family of Gumbel copulas to estimate the joint distribution of fiscal income and missing capital income.

The effect of each hypothesis made during the last step on national distributions is explained in detail for the Lebanese case in [START_REF] Assouad | Rethinking the Lebanese Economic Miracle: The Extreme Concentration of Income and Wealth in Lebanon, 2005-2014[END_REF]. We nevertheless provide robustness check on hypothesis on the size of the billionaires' family to estimate the wealth distribution that is used to reallocate the missing capital income (see section B.2 below). As shown in Figures B14 andB15, this has a small impact on the series, smaller than the effect on each national distribution.

Finally, Figures B16 andB17 show the series according to alternative definition for the Middle East (without Turkey, without Iran or without Gulf countries). For the two first, this has no effect on our main conclusion. As the main driving force behind the extreme concentration of income in the Middle East is the differential in per adult income between oil-rich countries and other population-rich countries, our series without Gulf countries are way below our benchmark estimates. Note that the level of inequality stays nevertheless very high with the top 1% and top 10% of the population accounting for almost 25 and 56% of total income respectively.

Appendix B.2 Wealth distribution series

Regarding the distribution of missing capital income, we assume it follows the same distribution as wealth. Wealth data are very limited in the Middle East. Unlike other countries, where we can use a combination of sources and methods, all we have in the region at this stage is billionaire data. The estimation procedure is the same for all countries and described in detail in [START_REF] Assouad | Rethinking the Lebanese Economic Miracle: The Extreme Concentration of Income and Wealth in Lebanon, 2005-2014[END_REF]. We discuss here some issues concerning rich list data.

To construct our billionaires' data, we combine two main sources: Forbes and Arabian Business Rich Lists. With the exception of Lebanon, billionaires' wealth does not seem particularly high in the Middle East by international standards. Rich Lists nevertheless do not take into account the wealth of state rulers, which may create a bias particularly significant in the region 6 . We include additional sources on leaders' wealth (see excel file "BillionairesData.xlsx, sheets "ForbesRoyalsDictators" and "OtherSources"). The data remain however limited, as state leaders' wealth included do not cover all ruling families and are often available for limited time periods (for example, we have information on the Assad family's wealth for two years only). This leads to very variable estimates of the share of billionaires' wealth to national income, which do not display any trends. We therefore use the average share of billionaires' wealth in national income over the period to estimate the 2016 wealth distributions, 6 Politicians' wealth is included only if the wealth comes from activities realized before coming to power.

that we adjust to the average wealth per adult in the period 1990-2016 period (see excel file BillionairesData.xlsx, sheets "Stata" and the do file do_gpinter_MEWealth).

For countries without billionaires' data -Iran Jordan Palestine Yemen -we simply upgrade the average standardized US-France-China distribution of wealth to each country specific average wealth, estimated as described in [START_REF] Assouad | Rethinking the Lebanese Economic Miracle: The Extreme Concentration of Income and Wealth in Lebanon, 2005-2014[END_REF] 1992,2002,2006,2008,2010,2013 70% 1996-1998, 2004-2008, 2010 Top 1% (Definition 0.9)

Distribution of fiscal income estimates (fiscal series combine survey and income tax data but do not use wealth data to allocate tax-exempt capital income), series in market exchange rate. The variants is on the definition of fiscal income (assuming that the taxable income the micro-files equals 60, 80 and 90% of fiscal income -80% is the benchmark assumption).

  School of Economics, INET at Oxford, and Conicet) Lydia Assouad (Paris School of Economics, ENS Paris-Saclay) Thomas Piketty (Paris School of Economics) This version: September 27 th 2017 This appendix supplements our paper and describes the full set of data files and computer codes (AAP2017.zip) that were used to construct the series. The zip file AAP2017.zip includes the following files (in addition to the pdf files of the main paper and present appendix): -AAP2017MainFiguresTables.xlsx : figures and tables presented in the main paper -AAP2017MainFiguresTables_NoLinks.xlsx : figures and tables presented in the main paper without outside links to other files -AAP2017Appendix.xlsx : figures and tables presented in the appendix -AAP2017Appendix_NoLinks.xlsx.xlsx : figures and tables presented in the appendix without outside links to other files Appendix A. Data construction

Figures

  Figures B9-B10 show the effect of the correction profile chosen to compute the

Figure

  Figure B5 . Top 10% income in the Middle East, 1990-2016: impact of the tax correction (1)

  Figure B7 . Top 10% income share in Lebanon: impact of the tax correction (1)

Figure

  Figure B8 . Top 1% income share in Lebanon: impact of the tax correction (1)

  Figure B11 . Pareto coefficients curves in the Middle East, 1990: impact of tax correction (2)

  Figure B12 . Pareto coefficients curves in the Middle East, 2016: impact of tax correction (2)

  Figure B13. Inverted Pareto curves in the Middle East in selected years, final benchmark distribution

  by multiplying the national income by the average wealth/income ratio for countries with available data reported in WID.world. OAMDI, 2014. Harmonized Household Income and Expenditure Surveys (HHIES),http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; HIECS 1999/2000 -Central Agency for Public Mobilization and Statistics (CAPMAS). Egypt:

	Economic Research Forum (ERF).
	OAMDI, 2017. Harmonized Household Income and Expenditure Surveys
	(HHIES), http://www.erf.org.eg/cms.php?id=erfdataportal. Version 2.0 of Licensed
	Data Files; HIECS 2015 -Central Agency for Public Mobilization and Statistics
	(CAPMAS). Egypt: Economic Research Forum (ERF).
	OAMDI, 2014. Harmonized Household Income and Expenditure Surveys Average ratio (total survey
	income) (HHIES),http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; HEIS 2010 Survey years income)/(total national Format
	-Department of Statistics (DOS), The Hashemite Kingdom of Jordan. Egypt: Turkey 1994, 2002-2016 micro-data and 43% tabulation Economic Research Forum (ERF). Iran 2010, 2013 49% micro-data
	Egypt	1999, 2004, 2008, 2010, 2012, 2015	40%	micro-data
	Iraq-Syria-Other (non-Gulf)	1992-2013	53%
	Iraq	2007	60%	micro-data
	Syria	2004	56%	micro-data
	Jordan
	Economic Research Forum (ERF).
	OAMDI, 2017. Harmonized Household Income and Expenditure Surveys
	(HHIES), http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; HEIS 2013
	-Department of Statistics (DOS), The Hashemite Kingdom of Jordan. Egypt:
	Economic Research Forum (ERF).

OAMDI, 2014. Harmonized Household Income and Expenditure Surveys (HHIES),http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; HEIS 2008 -Department of Statistics (DOS), The Hashemite Kingdom of Jordan. Egypt: Economic Research Forum (ERF). OAMDI, 2014. Harmonized Household Income and Expenditure Surveys (HHIES),http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; HEIS 2006 -Department of Statistics (DOS), The Hashemite Kingdom of Jordan. Egypt: Economic Research Forum (ERF). OAMDI, 2014. Harmonized Household Income and Expenditure Surveys (HHIES),http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; HEIS 2002 -Department of Statistics (DOS), The Hashemite Kingdom of Jordan. Egypt: OAMDI, 2016. Harmonized Household Income and Expenditure Surveys (HHIES), http://erf.org.eg/data-portal/. Version 2.0 of Licensed Data Files; IHSES

Table A1 . Household surveys used in this paper (1990-2016)

 A1

	Country	Year	Group	Bottom 50%	Middle 40%	Top 10% Top 1%	Gini	P10/ average	P50/ average	P90/ average	P99/ average	Pareto b(10%)	Pareto b(50%)	Pareto b(90%)	Pareto b(99%)
			Nationals	24%	45%	30%	7%	39%	34%	75%	168%	487%	3.2	2.0	1.7	1.3
			Foreigners	21%	41%	38%	8%	46%	28%	70%	197%	535%	3.9	2.3	1.8	1.6
			Total	23%	43%	34%	7%	42%	29%	72%	179%	511%	3.7	2.2	1.8	1.5
			Nationals	30%	44%	26%	6%	31%	47%	88%	159%	398%	2.3	1.6	1.6	1.5
			Foreigners	20%	44%	37%	7%	46%	34%	60%	197%	585%	3.2	2.7	1.8	1.3
			Total	23%	45%	32%	7%	40%	33%	71%	178%	503%	3.3	2.2	1.7	1.4
			Nationals	30%	44%	26%	6%	31%	47%	88%	159%	398%	2.3	1.6	1.6	1.5
			Foreigners	20%	44%	37%	7%	46%	34%	60%	197%	585%	3.2	2.7	1.8	1.3
			Total	23%	45%	32%	7%	41%	33%	71%	180%	515%	3.3	2.2	1.7	1.3
			Nationals	31%	47%	23%	4%	28%	51%	83%	158%	318%	2.1	1.7	1.4	1.4
			Foreigners	31%	44%	25%	6%	29%	50%	88%	154%	387%	2.1	1.6	1.6	1.6
			Total	22%	44%	34%	7%	43%	36%	63%	202%	487%	3.0	2.5	1.6	1.4
			Nationals	34%	45%	21%	5%	24%	58%	89%	145%	293%	1.8	1.5	1.4	1.6
			Foreigners	36%	41%	23%	5%	23%	54%	94%	134%	349%	2.0	1.4	1.6	1.5
			Total	22%	43%	35%	7%	44%	33%	56%	227%	468%	3.3	2.8	1.5	1.5
			Nationals	20%	49%	31%	6%	43%	26%	77%	199%	443%	4.2	2.1	1.5	1.3
			Foreigners	13%	44%	42%	10%	56%	16%	55%	219%	677%	7.0	3.1	1.9	1.6
			Total	17%	48%	35%	7%	50%	12%	64%	213%	519%	9.3	2.6	1.6	1.4
			Nationals	28%	47%	25%	5%	33%	34%	79%	172%	353%	3.2	1.8	1.4	1.3
			Foreigners	22%	50%	28%	5%	41%	18%	81%	196%	374%	6.1	1.9	1.4	1.3
			Total	20%	48%	31%	6%	45%	25%	71%	198%	460%	4.4	2.2	1.5	1.4
			Nationals	28%	47%	24%	4%	32%	40%	86%	170%	327%	2.7	1.7	1.4	1.2
			Foreigners	27%	47%	26%	4%	34%	36%	82%	175%	338%	3.0	1.8	1.4	1.3
			Total	23%	44%	33%	7%	41%	-2011 31%	73%	181%	65% 522%	3.5	2.1	micro-data 1.7 1.4
		Yemen					2006					33%			micro-data
		Gulf Countries Nationals Saudi Arabia Foreigners Total	27% 20% 19%	47% 43% 46%	26% 37% 35%	1995-2013 5% 34% N/A 8% 46% 7% 46%	37% 28% 26%	86% 64% 63%	164% 199% 209%	376% 30% 30% 554% 521%	2.9 3.9 4.2	1.7 2.5 2.5	1.5 1.8 N/A 1.6	1.4 1.4 1.4
		Oman Bahrain	Nationals Foreigners Total	20% 18% 17%	52% 45% 45%	27% 1995, 2005, 2015 4% 42% 2010 37% 5% 48% 38% 6% 50%	28% 34% 30%	68% 55% 53%	215% 234% 233%	29% 332% 37% 493% 497%	3.9 3.2 3.6	2.3 3.0 3.1	tabulation 1.3 1.1 1.5 1.1 tabulation 1.6 1.3
		UAE					1998, 2009				39%			tabulation
		Kuwait					2007, 2013				21%			tabulation
		Qatar					2007, 2012				23%			tabulation

Table A2 . Gulf-Countries Income Inequality Series: nationals vs foreigners in the survey data

 A2

	70%	Figure B1 .
	65%
		1995
	Bahrain 60%	2005
	55%	2015
	50%	2007
	Kuwait
	45%
	Oman 40%	2010
	35%
		2007
	Qatar
	30%	2012
	1990	1995	2000	2005	2010	2015
		1998
	UAE
		2009
	Source: Authors' computations using survey data.

Decomposing the level of Middle East top 10% income share

	35%	Figure B2 .
	30%
	25%
	20%
		Top 10% (final series)
		Top 10% (fiscal series)
	15%
		Top 10% (survey series)
	10%
	1990	1995	2000	2005	2010	2015
	Distribution of income (before taxes and transfers, except pensions and unempl. insurance) among equals-plit adults aged 20 and more (income of
	households divided equally among adult members). National income estimates combine survey, fiscal, wealth and national accounts data. Fiscal income
	estimates combine survey and income tax data (but do not use wealth data to allocate tax-exempt capital income). Survey income series solely use self-
	reported survey data (but anchors national distributions to per adult national income). Series in market exchange rate.

Decomposing the level of Middle East top 1% income share

	45%	Figure B3 .
	40%
	35%
	30%
	25%
	20%
	15%
	10%
	1990	1995	2000	2005	2010	2015

Top 1% (final series) Top 1% (fiscal series) Top 1% (survey income) Distribution of income (before taxes and transfers, except pensions and unempl. insurance) among equals-plit adults (income of households divided equally among adult members). National income estimates combine survey, fiscal, wealth and national accounts data. Fiscal income estimates combine survey and income tax data (but do not use wealth data to allocate tax-exempt capital income). Survey income series solely use self-reported survey data (but anchors national distributions to per adult national income). Series in market exchange rate.

Decomposing the level of Middle East Middle 40 % income share

  Distribution of income (before taxes and transfers, except pensions and unempl. insurance) among equals-plit adults (income of households divided equally among adult members). National income estimates combine survey, fiscal, wealth and national accounts data. Fiscal income estimates combine survey and income tax data (but do not use wealth data to allocate tax-exempt capital income). Survey income series solely use self-reported survey data (but anchors national distributions to per adult national income).Series in market exchange rate.

	20%	Figure B4 .
	15%
	10%
		Middle 40% (final series)
	5%	Middle 40% (fiscal series)
		Middle 40% (survey series)
	0%
	1990	1995	2000	2005	2010	2015

Decomposing the level of Middle East Bottom 50% income share

  Distribution of income (before taxes and transfers, except pensions and unempl. insurance) among equals-plit adults (income of households divided equally among adult members). National income estimates combine survey, fiscal, wealth and national accounts data. Fiscal income estimates combine survey and income tax data (but do not use wealth data to allocate tax-exempt capital income). Survey income series solely use self-reported survey data (but anchors national distributions to per adult national income).Series in market exchange rate.

	Country	Year	Bottom 50% Middle 40%	Top 10%	Top 1%	Gini	P10/ average P50/ average P90/ average P99/ average	Pareto b(10%)	Pareto b(50%)	Pareto b(90%)	Pareto b(99%)
		1995	23%	43%	34%	7%	42%	29%	72%	179%	511%	3.7	2.2	1.8	1.5
		2005	23%	45%	32%	7%	40%	33%	71%	178%	503%	3.3	2.2	1.7	1.4
		2015	23%	45%	32%	7%	41%	33%	71%	180%	515%	3.3	2.2	1.7	1.3
		1999	26%	43%	30%	8%	36%	41%	76%	163%	481%	2.6	1.9	1.8	1.6
		2004	27%	44%	28%	7%	35%	41%	78%	164%	448%	2.6	1.9	1.7	1.5
		2008	28%	44%	27%	7%	33%	44%	80%	162%	425%	2.4	1.8	1.6	1.6
		2010	29%	45%	26%	6%	31%	45%	83%	161%	400%	2.4	1.7	1.5	1.5
		2012	29%	45%	25%	6%	31%	45%	83%	160%	373%	2.4	1.7	1.5	1.5
		2015	29%	44%	27%	7%	33%	44%	81%	157%	432%	2.4	1.8	1.7	1.7
		2010	22%	47%	31%	7%	41%	27%	76%	183%	458%	4.0	2.0	1.6	1.5
		2013	25%	46%	29%	6%	37%	34%	79%	176%	431%	3.2	1.9	1.6	1.4
	Iraq	2007	24%	44%	32%	10%	40%	32%	76%	168%	511%	3.3	2.0	1.8	1.9
		1992	24%	45%	31%	9%	40%	32%	75%	172%	478%	3.4	2.0	1.7	1.8
		2002	26%	47%	27%	6%	35%	37%	80%	174%	378%	2.9	1.9	1.5	1.5
		2006	26%	47%	28%	6%	36%	35%	81%	180%	392%	3.1	1.8	1.5	1.4
		2008	27%	46%	28%	6%	35%	39%	81%	172%	396%	2.8	1.8	1.6	1.4
		2010	26%	43%	31%	9%	37%	36%	78%	158%	498%	3.0	1.9	1.9	1.9
		2013	26%	46%	28%	6%	36%	35%	81%	175%	401%	3.1	1.8	1.5	1.5
		2007	22%	44%	34%	7%	43%	36%	63%	202%	487%	3.0	2.5	1.6	1.4
		2013	22%	43%	35%	7%	44%	32%	55%	226%	467%	3.3	2.8	1.5	1.5
		2005 2007	20% 20%	46% 47%	33% 33%	8% 8%	45% 46%	27% 23%	72% 70%	196% 203%	486% 499%	4.1 4.7	2.2 2.3	1.6 Bottom 50% (final series) 1.7 1.6 1.6
	Oman	2010	17%	48%	35%	7%	50%	12%	64%	213%	519%	9.3	2.6	1.6	1.4
		1996 1997	22% 22%	49% 49%	29% 29%	5% 5%	41% 41%	26% 26%	77% 77%	197% 197%	400% 400%	4.2 4.2	2.0 2.0	1.4 Bottom 50% (fiscal series) 1.3 1.4 1.3
		1998	22%	49%	29%	5%	41%	26%	77%	197%	400%	4.2	2.0	1.4	1.3
		2004	21%	48%	30%	6%	42%	24%	77%	188%	439%	4.5	2.0	1.5	1.3
		2005 2006 2007	20% 21% 19%	49% 48% 48%	31% 31% 33%	6% 6% 7%	44% 43% 46%	23% 24% 21%	74% 74% 70%	204% 192% 203%	438% 444% 479%	4.8 4.6 5.2	2.1 2.2 2.3	1.5 1.6 Bottom 50% (survey series) 1.4 1.3 1.6 1.4
		2008	20%	49%	31%	6%	44%	24%	72%	198%	434%	4.6	2.2	1.5	1.3
		2010	20%	48%	33%	7%	45%	22%	71%	200%	475%	4.9	2.3	1.6	1.4
		2011	20%	48%	31%	6%	44%	23%	73%	199%	439%	4.7	2.2	1.5	1.4
		2007	20%	48%	31%	6%	45%	25%	71%	198%	460%	4.4	2.2	1.5	1.4
		2012	23%	44%	33%	7%	41%	31%	73%	181%	522%	3.5	2.1	1.7	1.4
	SaudiArabia	2008	20%	46%	34%	7%	45%	27%	65%	205%	490%	4.1	2.5	1.6	1.4
	Syria	2004	21%	49%	30%	5%	42%	25%	75%	200%	343%	4.3	2.1	1.4	1.5
		1994	21%	44%	34%	9%	44%	28%	70%	180%	559%	3.9	2.2	1.8	1.6
		2002	22%	43%	35%	10%	43%	29%	70%	177%	584%	3.7	2.2	1.9	1.6
		2003	23%	44%	34%	9%	42%	30%	71%	174%	576%	3.6	2.2	1.9	1.6
		2004	23%	45%	32%	9%	41%	29%	74%	177%	496%	3.7	2.1	1.7	1.8
		2005	24%	46%	30%	7%	40%	30%	78%	177%	467%	3.6	2.0	1.6	1.5
		2006	25%	46%	29%	7%	38%	33%	78%	174%	458%	3.3	1.9	1.6	1.5
		2007	25%	46%	29%	6%	37%	33%	80%	173%	425%	3.3	1.9	1.6	1.5
		2008	25%	46%	30%	7%	38%	33%	79%	171%	434%	3.3	1.9	1.7	1.6
		2009	24%	45%	31%	8%	40%	31%	76%	172%	523%	3.5	2.0	1.7	1.5
		2010	25%	45%	30%	8%	39%	33%	77%	172%	472%	3.3	2.0	1.7	1.7
		2011	24%	45%	31%	8%	39%	33%	76%	174%	486%	3.3	2.0	1.7	1.6
		2012	25%	45%	31%	8%	39%	34%	76%	173%	459%	3.2	2.0	1.7	1.7
		2013	25%	44%	30%	7%	38%	36%	77%	168%	446%	3.0	2.0	1.7	1.7
		2014	24%	45%	31%	9%	40%	34%	77%	168%	478%	3.3	2.0	1.8	1.8
		2015	23%	45%	32%	9%	40%	33%	76%	168%	489%	3.3	2.0	1.8	1.8
		2016	23%	44%	33%	10%	41%	33%	75%	164%	507%	3.3	2.1	1.9	2.0
		1998	19%	46%	35%	7%	46%	26%	63%	209%	521%	4.2	2.5	1.6	1.4
		2009	17%	45%	38%	6%	50%	30%	53%	233%	497%	3.6	3.1	1.6	1.3
	Yemen	2006	21%	49%	30%	5%	42%	25%	76%	203%	349%	4.4	2.1	1.4	1.5

Table B3 . Country-Level Income Inequality Series: Income Shares and Other Indicators for the Survey Distribution Distribution

 B3 of survey income (before taxes and transfers, except pensions and unempl. insurance) among adults, combining only survey and national income data.

	Country	Year	Bottom 50% Middle 40%	Top 10%	Top 1%	Gini	P10/ average P50/ average P90/ average P99/ average	Pareto b(10%)	Pareto b(50%)	Pareto b(90%)	Pareto b(99%)
		1995	15%	32%	53%	17%	59%	20%	49%	176%	807%	5.5	3.5	2.8	2.1
	Bahrain	2005 2015	16% 16%	34% 33%	50% 51%	16% 16%	56% 57%	23% 23%	50% 49%	178% 180%	813% 827%	4.7 4.8	3.4 3.4	2.7 2.7	1.9 1.9
		1999	19%	32%	49%	18%	53%	28%	53%	165%	782%	3.8	3.1	2.8	2.3
		2004	19%	34%	47%	16%	51%	29%	56%	169%	743%	3.7	2.9	2.6	2.2
		2008	20%	34%	46%	16%	50%	32%	57%	167%	710%	3.4	2.8	2.6	2.3
	Egypt	2010 2012	21% 22%	35% 36%	43% 43%	14% 14%	48% 47%	33% 33%	61% 61%	170% 171%	676% 639%	3.3 3.3	2.6 2.5	2.4 2.4	2.1 2.2
		2015	21%	34%	46%	17%	50%	31%	58%	162%	719%	3.4	2.7	2.7	2.4
		2010	16%	35%	49%	16%	57%	19%	53%	185%	744%	5.8	3.2	2.5	2.2
	Iran	2013	18%	35%	47%	15%	53%	24%	56%	181%	716%	4.5	2.9	2.4	2.1
	Iraq	2007	16%	32%	52%	21%	58%	22%	51%	164%	804%	5.0	3.3	3.0	2.7
		1992	16%	33%	51%	20%	57%	22%	52%	171%	762%	5.0	3.3	2.8	2.6
		2002										4.0	2.8	2.3	2.1
		2006	19%	36%	45%	14%	52%	25%	59%	188%	661%	4.3	2.8	2.3	2.0
	Jordan	2008 2010	19% 18%	35% 32%	45% 50%	14% 21%	51% 55%	28% 24%	58% 54%	180% 156%	667% 795%	3.9 4.5	2.8 3.1	2.4 3.0	2.1 2.7
		2013	19%	36%	46%	14%	52%	25%	59%	182%	673%	4.3	2.8	2.4	2.1
		2007	15%	32%	52%	16%	59%	25%	43%	200%	775%	4.4	3.9	2.5	2.0
	Kuwait	2013	15%	32%	53%	16%	60%	22%	38%	221%	739%	4.9	4.5	2.3	2.1
		2005	16%	37%	47%	16%	56%	21%	55%	192%	676%	5.3	3.0	2.3	2.4
	Lebanon	2006 2007	16% 13%	38% 34%	46% 52%	16% 18%	55% 61%	21% 16%	56% 48%	192% 198%	662% 785%	5.2 7.0	3.0 3.7	2.3 2.5	2.4 2.3
		2008	13%	34%	53%	19%	62%	16%	47%	198%	801%	7.1	3.7	2.5	2.3
		2009	13%	34%	52%	18%	61%	16%	48%	199%	799%	7.0	3.7	2.5	2.2
		2010	13%	34%	53%	18%	62%	16%	47%	200%	814%	7.1	3.7	2.5	2.2
		2011	13%	34%	53%	18%	62%	15%	46%	199%	817%	7.2	3.8	2.5	2.2
		2012	13%	33%	54%	18%	62%	15%	45%	200%	831%	7.3	3.8	2.6	2.2
		2013	13%	33%	54%	18%	62%	15%	45%	200%	832%	7.3	3.9	2.6	2.2
		2014	13%	34%	53%	18%	62%	15%	46%	199%	813%	7.2	3.8	2.5	2.3
	Palestine Oman	2010	11%	35%	53%	16%	64%	8%	43%	208%	815%	13.8	4.1	2.4	2.0
		1996	16%	38%	47%	12%	55%	19%	56%	205%	669%	5.8	3.0	2.2	1.8
		1997	16%	38%	47%	12%	55%	19%	56%	205%	669%	5.8	3.0	2.2	1.8
		1998	16%	38%	47%	12%	55%	19%	56%	205%	669%	5.8	3.0	2.2	1.8
		2004	15%	37%	48%	13%	56%	17%	55%	193%	726%	6.4	3.1	2.3	1.8
	Qatar	2005 2006	14% 15%	37% 37%	49% 49%	14% 13%	58% 58%	16% 17%	52% 52%	207% 195%	717% 728%	6.9 6.5	3.3 3.3	2.2 2.4	2.0 1.8
		2007	13%	35%	51%	15%	60%	15%	49%	201%	767%	7.5	3.6	2.4	2.0
		2008	14%	37%	49%	13%	58%	17%	51%	201%	713%	6.6	3.4	2.3	1.8
		2010	14%	35%	51%	16%	60%	15%	49%	199%	761%	7.1	3.5	2.4	2.1
		2011	14%	36%	49%	14%	58%	16%	51%	201%	716%	6.8	3.4	2.3	2.0
		2007	14%	36%	49%	15%	59%	18%	50%	200%	749%	6.2	3.4	2.3	2.0
		2012	16%	33%	51%	17%	57%	21%	50%	180%	835%	5.1	3.4	2.7	2.0
	SaudiArabia	2008	14%	34%	52%	15%	60%	18%	45%	202%	781%	5.9	3.9	2.4	2.0
	Syria	2004	15%	38%	47%	12%	56%	18%	54%	207%	573%	6.0	3.1	2.1	2.1
	Turkey	1994 2002	14% 15%	32% 31%	54% 54%	20% 21%	60% 60%	19% 19%	47% 47%	173% 170%	869% 902%	5.9 5.7	3.7 3.6	2.9 3.0	2.4 2.4
		2003	15%	31%	54%	21%	59%	20%	48%	168%	897%	5.4	3.5	3.0	2.3
		2004	16%	33%	52%	20%	58%	20%	50%	174%	785%	5.5	3.3	2.8	2.5
		2005	17%	35%	49%	17%	56%	21%	55%	179%	760%	5.2	3.1	2.6	2.2
		2006	18%	35%	48%	16%	54%	23%	56%	178%	756%	4.7	3.0	2.5	2.1
		2007	18%	35%	47%	15%	53%	24%	57%	179%	707%	4.6	2.9	2.5	2.1
		2008	18%	34%	48%	16%	54%	23%	56%	174%	712%	4.8	3.0	2.6	2.3
		2009	17%	34%	50%	18%	56%	22%	53%	172%	842%	5.0	3.2	2.7	2.1
		2010	17%	33%	49%	18%	55%	23%	54%	173%	763%	4.7	3.1	2.7	2.4
	UAE	2011 2012	17% 17%	33% 33%	50% 50%	18% 18%	56% 56%	23% 24%	53% 53%	174% 174%	785% 741%	4.8 4.6	3.2 3.1	2.7 2.7	2.2 2.5
		2013	18%	33%	49%	17%	55%	25%	54%	170%	725%	4.4	3.1	2.7	2.4
		2014	16%	33%	50%	20%	56%	23%	53%	167%	764%	4.7	3.2	2.8	2.6
		2015	16%	33%	51%	20%	57%	23%	52%	165%	776%	4.8	3.2	2.9	2.6
		2016	16%	32%	53%	23%	58%	22%	50%	159%	792%	4.9	3.4	3.1	2.8
		1998	13%	34%	53%	17%	61%	17%	43%	204%	818%	6.3	4.0	2.5	2.0
		2009	11%	33%	56%	14%	64%	20%	35%	224%	773%	5.5	5.0	2.3	1.9
	Yemen	2006	15%	38%	47%	13%	56%	18%	54%	210%	581%	6.2	3.1	2.1	2.2

Table B4 . Country-Level Income Inequality Series: Income Shares and Other Indicators for the Fiscal Distribution Distribution

 B4 of fiscal income (before taxes and transfers, except pensions and unempl. insurance) among adults. Fiscal series combine survey, fiscal and national income data.

	Bahrain 70%
	68%
	Egypt
	66%
	Iran
	64%
	Jordan 62%
	60%
	Kuwait
	58%
	Lebanon 56%
	54%
	52%
	Palestine 50%
	1990	1995	2000	2005	2010	2015
	Qatar
	Turkey
	UAE

Top 1% income share in the Middle East, 1990-2016

  Distribution of income (before taxes and transfers, except pensions and unempl. insurance) among equals-plit adults (income of households divided equally among adult members). National income estimates combine survey, fiscal, wealth and national accounts data. Fiscal income estimates combine survey and income tax data (but do not use wealth data to allocate tax-exempt capital income). Survey income series solely use self-reported survey data (but anchors national distributions to per adult national income). Series in market exchange rate.Distribution of income (before taxes and transfers, except pensions and unempl. insurance) among equals-plit adults (income of households divided equally among adult members). Pretax national income estimates combine survey, fiscal, wealth and national accounts data. Fiscal income estimates combine survey and income tax data (but do not use wealth data to allocate tax-exempt capital income). Survey income series solely use self-reported survey data (but anchors national distributions to per adult national income). Series in market exchange rate.

	15% 20% 25% 30% 35%	Figure B17 . Top 1% (benchmark)
	10%	Top 1% (without Turkey) Top 1% (without Iran)
		Top 1% (without Gulf countries)
	5%	Top 1% (full ME, fiscal income)
		Top 1% (full ME, survey income)
	0%
	1990	1995	2000	2005	2010	2015

Note however that dividing by the average household size could change the order of the group, or merge some group, resulting in a loss of information. In this case, we divide each income group by the overall average household size.

An alternative way to correct the top of the survey distribution is to apply upgrades factors from other countries for which such series exist, as China or Russia. We rather assume that the countries of the region are more comparable to Lebanon (institutions in place, quality of survey data etc.). Additionally, the correction concerns country-specific survey distributions with their own trends, which make the assumption more reasonable.

As discussed in Assouad, 2017, Lebanon has undergone major political instability during the 2005-2014 period, culminating in 2006 with the Israeli July war. The trends in the fiscal

Appendix B. Income and wealth distribution series

References


Stéphanie Jacquet

Karine Huber

Jérémy Bouyer

Christine Chevillon

Thomas Balenghien

Claire Garros

Culicoides imicola, biting midge species: a recent invader species in the Mediterranean basin ?

come