Frédéric Frappart
email: frederic.frappart@cesbio.cnes.fr

Pierre Hiernaux

Françoise Guichard

Eric Mougin

Laurent Kergoat

Marc Arjounin

François Lavenu

Mohamed Koité

Jean-Emmanuel Paturel

Thierry Lebel

Rainfall regime over the Sahelian climate gradient in the Gourma region, Mali

Keywords: Precipitation, Gourma region, Sahel, interannual variability, diurnal cycle

The Sahelian zone is characterized by low and highly variable rainfall, which strongly affects the hydrology and the climate of the region and creates severe constraints for agriculture and water management.

This study provides the first characterization of the rainfall regime in a poorly described region of Central Sahel, Gourma region (14.5° to 17.5° N and 2° to 1° S) in Mali, as obtained from long-term daily precipitation time series covering the period 1950-2007 and from highfrequency dataset between 2005 and 2008. First, raingauge data collected since the middle of the 20 th century were analysed both in terms of interannual variability and spatial distribution.

Second, we investigate the diurnal cycle of precipitation and the nature of the rainfall using automatic raingauges during the period 2005-2008. This study also completes previous analyses conducted in Sahelian areas located further South, where the direct continental influence of the Saharan heat low is expected to be less pronounced in summer.

The Gourma region rainfall presents a succession of wet (1950)(1951)(1952)(1953)(1954)(1955)(1956)(1957)(1958)(1959)(1960)(1961)(1962)(1963)(1964)(1965)(1966)(1967)(1968)(1969) and dry decades . The decrease of the summer cumulative rainfall is explained by a reduction of the number of the rainy days in the southern Gourma region and by both a decrease of the number of rainy days and of the mean precipitation amount per rainy day in the North and the Centre. This difference may be related to their respective distances from the intertropical discontinuity, which is closer to northern stations. The length of the rainy season has varied since the 1950s with two episodes of shorter rainy seasons: during the drought of the 1980s and also, since 2000. However, this second episode is characterized by an increase of the mean rainfall per rainy day, which suggests an intensification of the rain events in the more recent years.

High-frequency data reveal that most of the rainfall is produced by intense rain events occurring mostly in the late evening and the early morning during the rainy season (July/August/September). Conversely, rainfall amounts are the weakest at noon, and this midday damping is more pronounced in the North. All these characteristics have strong implications for agriculture and water resources management.

Introduction

The arid and semi-arid regions of Africa are characterized by low and unreliable rainfall, which strongly affects the continental water cycle, water resources and food security [START_REF] Nicholson | Long term changes in African rainfall[END_REF]. The largest of these regions, the Sahel, extends across Africa along 6000 km from the Altlantic coasts of Senegal and Mauritania to the shores of the Red Sea in Sudan and from the Sudanian savannas in the South to the Sahara (14° N to 20° N in latitude). The Sahelian climate is characterized by a unimodal rainfall regime controlled by the West African Monsoon -WAM [START_REF] Nicholson | The Historical Climatology of Africa . Climate and History[END_REF][START_REF] Todorov | Sahel: the changing rainfall regime and the "Normals" used for its assessment[END_REF][START_REF] Morel | Atlas agroclimatique des pays de la zone du CILSS[END_REF][START_REF] Hiernaux | Les parcours du Sahel[END_REF]. During the 20 th century, the Sahel experienced a multidecennial drought that started at the end of 1960s with two sequences of extremely dry years in 1972-1974and 1983[START_REF] Todorov | Sahel: the changing rainfall regime and the "Normals" used for its assessment[END_REF][START_REF] Hulme | Rainfall changes in Africa: 1931-1960 to 1961-1990[END_REF]; Le [START_REF] Le Barbé | Rainfall climatology during the years 1950-90[END_REF][START_REF] D'amato | On the characteristics of the rainfall events in the Sahel with a view to the analysis of climatic variability[END_REF][START_REF] L'hôte | Analysis of a Sahelian annual rainfall index updated from 1896 to 2000 ; the drought still goes on[END_REF][START_REF] Lebel | Seasonal cycle and interannual variability of the Sahelian rainfall at hydrological scales[END_REF]. This is indeed the strongest measured climatic event of rainfall variability at these time and space scales [START_REF] Hulme | Climatic perspectives on Sahelian dessication: 1973-1998[END_REF]. Besides, the substantial changes in the climate conditions obliged Sahelian farmers and pastoralist communities to adapt in response to the decrease of the water resources [START_REF] Mortimore | Farmer adaptation, change and 'crisis' in the Sahel[END_REF][START_REF] Tarhule | Climate research and seasonal forecasting for West Africans: perceptions, dissemination, and use[END_REF]Pedersen and Benjamisen, 2008).

The few recent studies of Sahelian rainfall regimes using raingauge data were mainly carried out over large areas located further South (Le [START_REF] Barbé | Rainfall variability in West Africa during the years 1950-90[END_REF][START_REF] Lebel | Seasonal cycle and interannual variability of the Sahelian rainfall at hydrological scales[END_REF][START_REF] Bell | Integration of weather system variability to multidecadal regional climate change: The West African Sudan-Sahel zone, 1951-98[END_REF] whereas rainfall regimes of drier North, which experiences a strong climatologic influence of the Sahara heat low in summer, and Centre Sahel remain poorly described.

This study is the first to focus on the rainfall regime of the Northern AMMA-CATCH (African Monsoon Multidisciplinary Analysis -Couplage de l'Atmosphère Tropical et du Cycle Hydrologique) meso-scale site (14.5-17.5°N, 1-2°W), located in the Gourma region in Mali. It is based on ground-based measurements covering a range of complementary scales.

First, it aims to provide a climatology of rainfall over the Gourma region during the years 1950-2007 with time series of daily precipitation. Namely, the rainy season will be characterized in terms of length, distribution of precipitation, and number and intensity of rainy days. Historical trends which affected West Africa and the Sahel as a whole over the past century will be presented and discussed specifically for the Gourma region.

Second, smaller scale modes of rainfall variability will be investigated with high frequency measurements from the AMMA-CATCH raingauge network for the AMMA Enhanced Observations Period (EOP) [2005][2006][2007][2008]. Over the Sahel, and in the Gourma region as well, rainfall is of convective origin, and organized Mesoscale Convective Systems (MCS) account for most of it as shown by [START_REF] Mathon | Mesoscale convective system rainfall in the Sahel[END_REF]. Therefore, we explore how the distribution of rainfall rates associated to rainfall events over the Gourma region comply to existing studies.

The diurnal cycle is also a major feature of convection and rainfall over land [START_REF] Wallace | Diurnal Variations in Precipitation and Thunderstorm Frequency over the Conterminous United States[END_REF][START_REF] Yang | The Diurnal Cycle in the Tropics[END_REF] and is discussed here for the Gourma region.

Material and methods

The study region

The Gourma region, a region of 90,000 km 2 , belongs entirely to the Sahel and extends from South of the large loop made by the Niger River between Timbuktu and Gao -17°N -down to the border with Burkina-Faso -~ 14.5°N (Fig. 1). This region is mainly pastoral bracketed by the annual isohyets of 150 and 500 mm with an interannual coefficient of variation of the total annual rainfall ranging from 15 to 30% (Sivakummar, 1989). A comprehensive presentation of the Gourma AMMA meso-scale site can be found in Mougin et al. (this issue).

Dataset

Long daily rainfall series are available since 1897 for Timbuktu and since the 1920s or the 1950s for 25 stations over the Gourma region and the surroundings. The location of these stations is reported in Fig. 1b and Table 1, with corresponding data availability. To have a sufficient number of available data, with only use records from 1950 to 2007. Nine of all the historical stations are located in the Gourma region, 13 are located around the Gourma region and are helpful to characterize the rainfall regime of this region and 3 are located North, in the transition zone between Sahel and Sahara. Nevertheless, data are often for a few years. This long term precipitation dataset mostly originates from Direction Nationale de la Météorologie (DNM), the Malian meteorological service. In order to place our results in the broader context of the West African Monsoon, daily rainfall data from 35 additional stations, provided by DNM too, and located further South in Mali have also been used.

In order to characterize the diurnal cycle and the nature of precipitation over the Gourma region, the dataset used comes from the records of the 0.5 mm tipping bucket raingauges (AR for automatic raingauges) installed during the AMMA-EOP period 2005-2008 (see Fig. 1c and Table 1 for locations and period of data availability). In the following, we used the measurements recorded by the AR between June and September from 2005 to 2008.

Standardized rainfall anomaly index and standardized number days index

The standardized rainfall anomaly index was defined by [START_REF] Lamb | Persistence of subsaharan drought[END_REF] to study long term rainfall variations:

∑ = - = n i R i i R k i R n k I 1 ) ( ) ( ) , ( 1 ) ( σ (1)
where R(i,k) is the annual rainfall at station i for year k, ( ) R i and ) (i σ are the annual average and standard deviation over the reference period for station i, respectively, and n is the total number of stations used for the computation.

A similar standardized index can be defined for the number of rainy days as follows:

∑ = - = n i N i i N k i N n k I 1 ) ( ) ( ) , ( 1 ) ( σ (2)
where N(i,k) is the number of rainy days during the rainy season at station i for year k, ) (i N and ) (i σ the average and standard deviation over the reference period for station i respectively, and n is the total number of stations used for the computation.

Estimation of timing and length of the rainy season

Several criteria, known as agronomical criteria, exist to define the beginning and the end of the rainy season using thresholds on the amount of precipitation fallen during consecutive days [START_REF] Stern | The start of the rains in West Africa[END_REF][START_REF] Sivakumar | Agroclimatology of West Africa : Mali[END_REF]. These criteria are very similar and we decided to select the second one following [START_REF] Balme | Démarrage de la saison des pluies au Sahel: variabilité aux échelles hydrologique et agronomique, analysée à partir des données EPSAT-Niger[END_REF]. Following this criteria, for each raingauge and year, the beginning of the rainy season corresponds to an amount of rain of at least 20 mm in 3 days, after the 1 st of May not followed by a drought longer than 7 days in the 30 following days. The end of the rainy season corresponds to 20 successive days without rain, after the 1 st of September.

Dry spells length and probability of occurrence

The analysis of dry spells has been carried out using daily rainfall data between the 1 st of May and the 30 th of September. Years with missing days were excluded from the analysis. The length of dry spells (i.e., the number of days until the next day with rainfall greater than a given threshold value) and associated probability of dry spell occurrence have been estimated for different thresholds τ (1, 5, 10, 15, and 20 mm) at stations distributed along the Gourma region climatic gradient.

Following [START_REF] Sivakumar | Empirical analysis of dry spells for agricultural applications in West Africa[END_REF], the probability for a day i to be part of a dry spell occurrence with a threshold τ is defined as:

{ } N p N i i P N ∑ ⊂ < Ω = 2007 ;...; 1950 ) , , ( ) , ( τ τ (3)
where N is the number of year without missing days for a station, p is the rainfall and

{ N year for threshold of spell dry a of part is i if not if τ 1 0 = Ω .

Results and discussion

Major features of rainfall in the Gourma region

The mean annual rainfall over the Gourma region mesoscale site is characterized by a South to North decreasing gradient slightly larger than 1 mm km -1 (Fig. 2), a characteristic value found elsewhere in the Sahel [START_REF] Lebel | Seasonal cycle and interannual variability of the Sahelian rainfall at hydrological scales[END_REF]. It involves a gradation of bioclimatic zones distributed latitudinally with a slight southward inflexion from West to East [START_REF] Hiernaux | Les parcours du Sahel[END_REF]. It should be mentioned here, however, that isohyets positions have varied considerably over the past 60 years in the Gourma region (Fig. 3) and more generally in the Sahel, as shown in Lebel and Ali (this issue). The dry years isohyets (1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)) are shifted about 100-150 km to the South by comparison to those of the preceding wet period, corresponding to a decrease of 100 mm per year in the North of the study zone and of 150 mm per year in the South. For comparison, a shift to the South of the isohyets of around 200 km is observed for the whole Sahel (Lebel and Ali, this issue). Their curvature also changed during this period, showing that a East-West variability is also present along with the North to South climatological gradient. This observation is in accordance with results from previous studies [START_REF] Nicholson | The nature of rainfall fluctuations in subtropical West-Africa[END_REF][START_REF] Janicot | Spatio-temporal variability of West African rainfall. Part I: Regionalizations and typings[END_REF][START_REF] Nicholson | A re-evaluation of rainfall variability in the Sahel. Part I: Characteristics of rainfall fluctuations[END_REF][START_REF] Moron | Guinean and Sahelian rainfall anomaly indices at annual and monthly scales (1933-1990)[END_REF][START_REF] Nicholson | On the question of the "recovery" of the rains in the West African Sahel[END_REF]Lebel and Ali, this issue). Thus defining climatic zones remains a tricky task, especially in the northern part of the study domain where the mean rainfall over the wet period 1950-1969 was twice as high as during the dry period 1970-1989.

As in any other part of the Sahel [START_REF] Hiernaux | Les parcours du Sahel[END_REF], the seasonal distribution of the mean monthly precipitation over the Gourma region presents a single peak. The rainfall occurs during the Northern hemisphere summer, starting between May and July until September or October with a maximum in August (Fig. 4). Very few rainfall events are recorded before or after the rainy season.

For each station, the June July August September (JJAS) number of rainy days (noted Nd) has been plotted either versus the JJAS cumulative rainfall (noted R) or the mean rain per rainy day (noted Rd), averaged over the period 1950-2007 (Fig. 5). Only rainfall of 1 mm or more have been retained to avoid biases from possible mis-reporting of small rainfalls.

The number of rainy days (Nd) increases with the JJAS cumulative rainfall (R) (Fig. 5a).

Standard deviations of Nd slightly increase with R. On Fig. 5b, the mean rainfall per rainy day (Rd) exhibits a saturation at the value of 20 mm per day for JJAS cumulative rainfall greater than 400 mm (South Gourma region). It clearly decreases for rainfall between 200 and 400 mm (Central Gourma region), and further decreases for rainfall lower than 200 mm (North Sahel). The tiny variations of Rd over the 400-1000 mm range are consistent with the results from the previous studies of Le [START_REF] Le Barbé | Rainfall climatology during the years 1950-90[END_REF], and Le [START_REF] Barbé | Rainfall variability in West Africa during the years 1950-90[END_REF], for the same range of JJAS cumulative rainfall, even if daily rainfall were used here instead of rainfall per event. Correlations between R and Nd, and R and Rd, as a function of R are respectively presented on Fig. 5c and5d. R and Nd, and R and Rd are both strongly correlated with most of the correlation coefficients greater than 0.6. These results show the importance of both Nd and Rd on R.

Previous studies have highlighted the dominant role of Nd in the seasonal rainfall (Lebel et al., 1997;[START_REF] Le Barbé | Rainfall climatology during the years 1950-90[END_REF][START_REF] Barbé | Rainfall variability in West Africa during the years 1950-90[END_REF]. On the contrary, both the number of rainy days and the mean rainfall per rainy account for the annual rainfall in Gourma region.

Thus, our results depart from these studies which focused on more southern part of the Sahel.

A possible reason for that is that the local atmospheric environment, in which convection occurs, may play a stronger role in explaining the annual rainfall amount in Gourma region than further South. It would be useful to explore how this result relates to interannual changes of atmospheric humidity, as this parameter exerts a significant control on evaporation of cloud water and rainfall and may control the amount of rainfall reaching the surface. More broadly, the Gourma region may be more directly under the influence of West African Heat Low (WAHL) circulations (which correspond to continental depression associated with an increase of the potential temperature and are a main component of the West African Monsoon likely to play a significant role in the rainfall variability over Sahel, see [START_REF] Janicot | Spatio-temporal variability of West African rainfall. Part I: Regionalizations and typings[END_REF] for more details) than Southern part of the Sahel. In this respect, it would be useful to assess the interannual variations of the location of the InterTropical Discontinuity (ITD) with respect to the Gourma region. Indeed, from a wider perspective, in the past, changes in rainfall amounts have been perceived by the local population as associated with or even induced by changes in the winds (Ag [START_REF] Mahmoud | Le haut Gourma Central. 2nd[END_REF], a view which is not inconsistent with an ITD located close to the Gourma region.

Historical trend and variability of rainfall and number of rainy days over Gourma region

Fluctuations of the standardized rainfall anomaly index I R (Eq. 1) and of the standardized number of rainy days anomaly index I N (Eq. 2) have been calculated between 1950 and 2007 using daily rainfall collected at stations in and around Gourma region (Gourma region and surroundings in Table 1). The number of available stations is not constant for all years.

Ranging between 20 and 25 from 1950 to the end of the 1980s, it decreases down to 15 in the middle of the 1990s, then down to 10 in 2003 and down to 6 in 2006.

The variation of the standardized rainfall index over the Gourma region from 1950 to 2007 presents a positive anomaly from the 1950s to the end of 1960s and a long period of negative anomalies from the beginning of the 1970s until present (Fig. 6a). Three large negative precipitation anomalies can be observed : 1973-1974, 1982-1987 and 1995-1998. The two first correspond to the severe droughts occurred in 1972-1974 and 1983-1985 in the whole Sahel and the last one is in accordance with the study of L' Hôte et al. (2003) which considers that the 1990s were the driest decade since the beginning of the century in the Sahel. Even if the results for the Gourma region present a larger variability, the trends are consistent with those found during previous climatological studies on Sahel (Le [START_REF] Le Barbé | Rainfall climatology during the years 1950-90[END_REF][START_REF] Hulme | Climatic perspectives on Sahelian dessication: 1973-1998[END_REF][START_REF] Barbé | Rainfall variability in West Africa during the years 1950-90[END_REF][START_REF] L'hôte | Analysis of a Sahelian annual rainfall index updated from 1896 to 2000 ; the drought still goes on[END_REF]. Compared with the standard rainfall anomaly index for the whole Sahel between 1950 and 2002 [START_REF] Balme | Années sèches et années humides au Sahel: quo vadimus?[END_REF], the 1990s and the beginning of the 2000s appear to be a little bit wetter in the Gourma region (5 positive anomalies of I R ) than in whole Sahel, defined by the window 10°N-20°N, 20°W-20°E (2 positive anomalies). The last 20 years present a succession of wet and dry years with less dry conditions during the 1990s and drier from 2000 to 2007 as observed by [START_REF] Dai | The recent Sahel drought is real[END_REF].

Nevertheless, as the number of available station records declined from 24 to 15 by the end of the 1990s and down to 6 or 7 for the last 3 years, the results for the 2000s have to be considered with caution.

Figure 6b presents the time variations of the standardized number of rainy days anomaly index between 1950 and 2007. The standardized number of rainy days anomaly index (Fig. 6b) and the standardized rainfall anomaly index (Fig. 6a) present a similar pattern. This confirms the strong link between the number of rainy days and the annual rainfall observed in Niger (Le [START_REF] Le Barbé | Rainfall climatology during the years 1950-90[END_REF][START_REF] Barbé | Rainfall variability in West Africa during the years 1950-90[END_REF][START_REF] Balme | Années sèches et années humides au Sahel: quo vadimus?[END_REF]. Nevertheless, some differences appear. The major ones concern the drought of 1972-1974 where a positive anomaly is present in the standardized number of rainy days anomaly index (Fig. 6b) for 1974 and the drought of the 2000s where the standardized number of rainy days anomaly index is much more negative than the standardized rainfall anomaly index.

The decadal deviation to the mean annual rainfall (Fig. 7a) and to the mean number of rainy days (Fig. 7b) were computed for each climatic region (North, Centre, and South) to take into account the regional variability of the precipitation over Gourma region between 1950 and 2007.

Timing and length of the rainy season

Daily time series of rainfall have been analysed to estimate the length and the timing of the rainy season over Gourma region. The rainy season, defined with the criteria presented in section Estimation of timing and length of the rainy season, comprises 90% of the annual rainfall of Gourma region. This result is in good agreement with those obtained by [START_REF] Sivakumar | Agroclimatology of West Africa : Mali[END_REF] for the whole Mali and by [START_REF] Balme | Démarrage de la saison des pluies au Sahel: variabilité aux échelles hydrologique et agronomique, analysée à partir des données EPSAT-Niger[END_REF] for the Niger.

Figure 8a shows that the beginning of the rainy season ranges from the 5 th of June (day 155)

in the South to the 15 th of July in the North (day 195), i.e., a northward delay of five days per degree of latitude for the beginning of the rainy season with a maximum standard deviation of 15 days for each location during the considered period. The end of the rainy season ranges from the 10 th of September (day 253) in the North to the 1 st of October (day 274) in the South with a maximum standard deviation of 7 days for each location during the considered period.

Thus, the standard deviation is about twice smaller for the end date than for the start date. It means that there is more variability on the beginning of the rainy season than on the end.

Along the South-North climatic gradient, the length of the rainy season varies from 120 days in the South of Gourma region to 60 days in the North, that is to say a decrease of 20 days per degree of latitude (Fig. 8b).

Moreover, the length of the rainy season as well as its starting and ending dates have varied over the last 60 years. This variation is presented on Fig. 9 and Table 2 for 2). This decrease of length is caused by an earlier ending for the three zones and a later beginning (~10 days compared with the previous decade) in the South (Fig. 9).

Interannual variability of the rainfall

This sub-section deals with two aspects of the interannual variability of the rainfall in the Gourma region: the daily rainfall distribution and the occurrence of dry sequences and their frequency during the rainy season.

Previous studies in Niger showed that the diminishing of the number of events is the main factor explaining dry years in the Sahel (Le [START_REF] Le Barbé | Rainfall climatology during the years 1950-90[END_REF][START_REF] Barbé | Rainfall variability in West Africa during the years 1950-90[END_REF][START_REF] Balme | Années sèches et années humides au Sahel: quo vadimus?[END_REF]. In the Gourma region, at each station, the rainfall per rainy day is significantly larger during wet years compared with dry years, especially at the peak of the spell occurrence presents a similar but less smoothed evolution with respect to time. The length of dry spells (Fig. 11) and the rainfall per rainy day (Fig. 10) over the Gourma region varies the opposite way with respect to time. We also observe that even during the core of the rainy season (DOY 180 to 240), the probability of a dry spell greater than 5 days for a threshold of 10 mm is always greater than 50% in Bankass, 60% in Hombori, and 80% in Gourma-Rharous. This result stresses on the great variability of rainfall in Gourma region at intraseasonal scale.

Nature of rainfall

Most of the Sahelian rainfall is of convective origin [START_REF] Lebel | Seasonal cycle and interannual variability of the Sahelian rainfall at hydrological scales[END_REF]. Indeed, more than 90% of the Sahelian rainfall is produced by MCS [START_REF] Laurent | How important is the contribution of the Mesoscale Convective Complexes to the Sahelian rainfall ?[END_REF]. The more organized and efficient systems among the MCS, the Mesoscale Convective Complexes (MCC), account for more than 70% of this total [START_REF] D'amato | On the characteristics of the rainfall events in the Sahel with a view to the analysis of climatic variability[END_REF][START_REF] Laurent | How important is the contribution of the Mesoscale Convective Complexes to the Sahelian rainfall ?[END_REF]. Nevertheless, stratiform and convective precipitation both occur within the same complex of convectiongenerated cumulonimbus cloud. Convective rainfall refers to precipitation associated with young, active convection whereas stratiform rainfall refers to precipitation occurring in older, less active convection [START_REF] Houze | Stratiform precipitation in regions of convection: A meteorological paradox?[END_REF]. In terms of rain rates, stratiform precipitation is defined as rain rates lower than 12 mm h -1 associated with non convective structures [START_REF] Nachamkin | Observational analysis of a developing mesoscale convective complex[END_REF]. This threshold is selected to discriminate between stratiform (purely stratiform and stratiform precipitation of convective event) and purely convective rainfall. As the precipitation measured by the AR was summed by 10-min periods, the threshold of 12 mm h -1 becomes 2 mm per 10 min time period. As a consequence, rain rates lower than 2 mm per 10 minutes are considered stratiform, higher than 2 per 10 minutes purely convective.

Normalized histogramm of 10-min summed rainfall over Gourma region between 2005 and 2008 is presented on Fig. 12a. Low and moderate rain rates, which correspond to stratiform precipitation, represent almost 63 % of total rainfall, whereas purely convective rainfall represents only 37 %. Low rain rates (≤ 0.5 mm per 10 mn, i.e., ≤ 3 mm h -1 ) account for 43% of the total rainfall. Heavy rainfall (> 3 mm per 10 mn, i.e., > 18 mm h -1 ), which are associated with the active core MCCs (Namchamkin et al., 1994), represents 27 % of the total rainfall (and 73 % of the purely convective rainfall).

An important contrast can be observed in the stratiform/convective distribution of rainfall between day (9 a.m. to 9 p.m.) and night (9 p.m. to 9 a.m.). In Fig. 12b, the low rain rates, which are associated with stratiform, are ccount for relatively less rain during the day (33 % of the total rainfall for rain rates lower than 0.5 mm per 10 mn, i.e., 3 mm h -1 ) than during the night (50 %).

Diurnal cycle of rainfall

Three AR located in Kobou (1.502° W, 14.727° N), Bangui Mallamn (1.346° W, 15.398° N)

and Tintadeini (1.766° W, 16.409° N) have been selected from the denser network presented above because their location along the latitudinal gradient is representative of the three rainfall regimes identified in Gourma region.

The rainfall amounts recorded by the AR are accumulated every 2 hours in order to analyze the average diurnal cycle. Due to the relatively short length of the record, it is difficult to obtain very robust statistics. Nevertheless, the basic features, which emerge from the analysis, are consistent with the existing literature. The diurnal cycle of precipitation, composed for 2005-2008, is presented in Fig. 13 for Tintadeini (North), Bangui Mallamn (Center) and Kobou (South). Tintadeini (North) rainfall distribution presents two pronounced peaks -a first peak centred in the early evening (18:00-20:00 LST -Local Standard Time) and a second peak centred in the early morning (0:00-4:00 LST). Minimum rainfall occurs in the late morning (10:00-12:00 LST). The Bangui-Mallamn (Centre) diurnal cycle presents two maxima, one during late afternoon and one during night-time -early morning (22:00-6:00 LST) as well as a minimum between 10:00-12:00 LST. As for Tintadeini, the June distribution is unimodal and centred in the early afternoon. Rainfall was very scarce in September (less than 5 mm).

The diurnal rainfall variations at Kobou (South) show a broad peak centred on the late evening/early morning (22:00-6:00 LST). The maximum present between 10:00 and 12:00

LST is caused by one single event of 80 mm in August which is not smoothed because of the relatively short 3 years period. Nevertheless, all AR in the South Gourma region shows that rainfall around noon are more common than for North and Centre regions. Rain distribution presents large variations between months. Rainfall amounts are higher in August than in July with the same timing. Rain distribution in June is very similar to those observed in Tintadeini (North) and Bangui-Mallamn (Centre), but with higher amounts. Large precipitation was also recorded in September with two-hour sum greater than 10 to 15 mm. September diurnal cycle is bimodal with primary and secondary peak observed in the afternoon (12:00-20:00 LST) and late evening/early morning (22:00-4:00 LST) respectively.

Results from other AR (including 5 years record at Agoufou, in the central part of the Gourma region) strengthen both the bimodal distribution (late afternoon and early morning maxima)

and the seasonal pattern (more early afternoon rainfall in June).

Two-peaked rainfall distributions were also observed by [START_REF] Shinoda | Diurnal variations of rainfall over Niger in the West African Sahel : a comparison between wet and drought years[END_REF] and [START_REF] Mathon | Mesoscale convective system rainfall in the Sahel[END_REF] in Niger. They are typical of semi-desert and savanna regions of sub-Saharan Africa [START_REF] Mohr | Interannual, monthly, and regional variability in the wet season of the diurnal cycle of precipitation in Sub-Saharian Africa[END_REF]. Here, the recorded late afternoon and evening maxima are consistent with afternoon convective triggering while the morning maximum likely involves MCS life cycle. Differences in the diurnal cycle are noticed along the season. In July and August, the months with the largest amount of rainfall, precipitation mainly occurred either in the early morning or late in the afternoon. This is in accordance with [START_REF] Mohr | Interannual, monthly, and regional variability in the wet season of the diurnal cycle of precipitation in Sub-Saharian Africa[END_REF], who showed that August rainfall can be explained either by a greater concentration from shorter-lived organized convective systems and/or by convective systems originated in late afternoon. The September rainfall distribution is also bimodal whereas June distribution is unimodal and centred in the afternoon. Isolated convective cells can account for the low rainfall observed, as well as MCS passage when at their weakest in late morning.

CONCLUSION

This study provides the first characterization of the rainfall regime in Gourma region, Mali, since the 1950s. Common features and differences from studies undertaken in other Sahelian regions have also been underlined. The results of this study concerning the nature of rainfall, rainfall patterns and their changes during the last sixty years, are essential for policy makers and government agencies to manage water resources and to elaborate agricultural policies.

The rainfall regime in the Gourma region presented wet (1950)(1951)(1952)(1953)(1954)(1955)(1956)(1957)(1958)(1959)(1960)(1961)(1962)(1963)(1964)(1965)(1966)(1967)(1968)(1969) and dry decades as observed in other studies on Sahelian rainfall but differences were observed along the climatic gradient. Since 2000, the Gourma region still exhibits dry conditions. As observed for other Sahelian locations, the annual precipitation amount in Gourma region is mainly controlled by the number of rainy days but also by the rainfall per rainy day which accounts for part of the annual rainfall. In the northern and driest part of the Gourma region, the amount of rain per rainy day is also an important factor. This may also be true for the rain events since the occurrence of more than one rain events per day is very rare. The length of the rainy season has been varying since the 1950s. It shortened significantly during the 1980s due to a late start. However, it is associated with an increase of the mean rainfall per rainy day. This result suggests an intensification of the rain events in more recent years. Since 2000, a new decrease in the length of the rainy season was observed. In the South of the Gourma region, the rainy season is shorter now than in the 1980s and lost more than 20 days in comparison with the 1950s. The significance of these recent trends and how they may be related to changes in the hydrological cycle over land [START_REF] Lau | Detecting trends in tropical rainfall characteristics, 1979-2003[END_REF]Wu, 2007, Wild et al., 2008) needs to be investigated further.

Data acquired during the 4 years of the AMMA-EOP were used to characterize the diurnal cycle and the nature of precipitation at using data from AR installed in the Gourma region.

Despite the relatively short duration of the sampled period, coherent features of the diurnal cycle of rainfall emerge from this dataset. The diurnal cycles of rainfall amount in the North and the Centre display a well-defined bimodal distribution with maxima registered in the late afternoon and the early morning and minima around noon. The late afternoon maximum is consistent with the daytime growth of convection over land, while the early morning maximum likely involves the organization of convection in the form of long-lived MCSs. In the South, the diurnal cycle presents the same maxima in the late evening and the morning but not a sharp damping around noon. The factors modulating this damping are yet to be determined, but these rainfall data offer a way to explore this issue further, e.g.; in combination with colocated atmospheric analyses and MCS tracking products.

Thus, this study points to a significance of meridional gradients from the Central to Northern Sahel which are not only affecting the cumulative monsoon rainfall or the number of rainfall events, but also, the intensity of rainfall events and the diurnal cycle of rainfall amount.

Figures

  Figures 7a and 7b exhibit differences especially for the period 1970 -present. During the

  the 3 climatic zones of Gourma region. The average duration of the rainy season in the South of Gourma region (~110 days) and the beginning and ending days (10 th of June and 29 th of September respectively) are very similar to the results obtained by[START_REF] Balme | Démarrage de la saison des pluies au Sahel: variabilité aux échelles hydrologique et agronomique, analysée à partir des données EPSAT-Niger[END_REF] in Niger during the same period and for comparable latitudes (13° to 14° N), i.e., 105 days, 21 st of June and 4 th of October. We notice a decrease of the mean length of the rainy season for each zone (North, Centre and South) during the 1950-2007 period of 13, 12, and 22 days respectively due to both a delay of the starting date (from the 1 st of July to the 9 th of July in the North, from the 22 nd of June to the 27 th of June in the Centre, from the 8 th of June to the 21 st of June in the South) and earlier ending (from the 17th of September to the 12 th of September in the North, from the 24 th of September to the 17 th of September in the Centre, from the 5 th of October to the 26 th of September in the South). The decrease started in the middle of the 1960s with a minimal length reached during the 1980s associated with the severe drought of 1983-1985. The length of the rainy season increased to the levels of the 1960s during the 1990s, especially due to a later ending. During the last 10 years, we observe a new decrease of the duration of the rainy season towards the values observed during the drought of 1972-1974 in the North, and towards the values observed during the severe drought of 1983-1985 in the Centre. Further South, the current length of the rainy season is the smallest of our period of study (Table

  wet season. Three examples for the different climatic zones (North, Centre and South) of the Gourma region are presented in Fig.10. These results confirm that the rainfall distribution is governed by both the number of rainy days and the rainfall by rainy day.The rainy season in the Sahel is characterized by sequences of days without precipitation (or very low precipitation), known as dry spells. The length of a dry spell (i.e., the number of days until the next day with rainfall greater than a given threshold value) and associated probability of dry spell occurrence have been estimated for different thresholds τ (1, 5, 10, 15, and 20 mm) are presented in Fig.11for different rainfall thresholds at three different locations: Gourma-Rharous (North Gourma), Hombori (Centre Gourma) and Bankass (South Gourma). These thresholds were selected following the study of Sivakummar (1992) on the impact of the dry spells for agriculture in the Sahel. The dry spell length presents a similar time variation for all the locations and the thresholds. Nevertheless, the length of the dry spells increases both with the increase of thresholds and the decrease of the annual rainfall amount (Northward). The dry spell length drops down from day 120 (1 st of May) to day 160 (10 th of May) or 180 (1 st of July), depending on the threshold value. It remains low until day 220 (1 st of September) or 260 (20 th of September) and then increases. The probability of dry

FIGURESFig. 1

 1 FIGURES

Fig. 4 .

 4 Fig. 4. Distribution of the mean monthly precipitation over the period 1950-2007 for Gourma-Rharous (North), Hombori (Centre) and Bankass (South).

Fig. 5

 5 Fig.5. a) average number of rainy days (Nd) for each station versus average JJAS rainfall (R) for Gourma region (full circles, with standard deviation) and Malian stations south of Gourma region (dots). b) mean rainfall per rainy day (Rd) for each station versus average JJAS rainfall (R) for Gourma region (full circles, with standard deviation) and Malian stations south of Gourma region (dots). c) correlation coefficient (r) between R and Nd for each Gourma station, characterizing the inter-annual variability. d) correlation coefficient (r) between R and Nd for each Gourma station.

Fig. 9 .

 9 Fig. 9. Date of start (dashed line) and end (plain line), and length (dotted line) of the rainy season at three different locations in the Gourma region: a) North, b) Centre and c) South.

Fig. 10 .

 10 Fig. 10. Mean rainfall per rainy day on a 10-day period during the rainy season for dry (dashed line) and wet years (plain line) in Gourma-Rharous (North) (a), Hombori (Centre) (b), and Bankass (South) (c), over the period 1950-2007. Dry and wet years respectively correspond to negative and positive values of the standardized anomaly index estimated at the raingauge.

Fig. 11 .

 11 Fig. 11. Number of days until next rainfall greater than the threshold value (left column)grey dotted line: 1 mm, grey line: 5 mm, black line: 10 mm, grey dashed line 15 mm, black dashed line: 20 mm-and associated probability of dry spell occurence (right column) at Gourma-Rharous (North), Hombori (Centre) and Bankass (South).

Fig. 12

 12 Fig. 12. a) normalized histogramm of 10-min rainfall accumulation for the period 2005-2008 over Gourma region. b) normalized 10-min rainfall accumulation for the period 2005-2008 over Gourma region.In black, the total distribution, in dashed black, the distribution during the day (9 a.m. to 9 p.m.), in dotted black, the distribution during the night (9 p.m. to 9 a.m.).

Fig. 13 .

 13 Fig. 13. Rainfall distribution along the diurnal cycle by month from June to September for the 2005-2007 period in a) Tintadeini (North), b) Bangui Mallamn (Centre), and c) Kobou (South).

Table 1 .

 1 TABLES Overview of available historical raingauges (long term in the table) and automatic raingauges (short term) within and outside Gourma region.

Table 2 .

 2 Dates of start and end, and length of the rainy season per decades, between 1950 and 2007 for the 3 bioclimatic zones. The associated uncertainties represent the half of the standard deviation.

ACKNOWLEDGEMENTS

This work was performed within the framework of the AMMA project. Based on a French initiative, AMMA has been constructed by an international group and is currently funded by large number of agencies, especially from France, the UK, the US and Africa. It has been the beneficiary of a major financial contribution from the European Community's Sixth Framework Research Programme. Detailed information on the scientific coordination and funding is available on the AMMA international web site (https://www.amma-eu.org/). The authors want to thank Manuela Grippa for Fig. 1. FF is funded by a CNRS research grant.

Longterm rainfall data were kindly provided by the DNM of Bamako.


Mona Barake
email: mona.barake@psemail.eu

Tax Planning by European Banks

Keywords: Profit shifting, Tax planning, Banks, country-by-country reporting

This paper explores profit shifting behaviour by European banks through a newly available data source. Financial institutions as of 2014 started disclosing their activity on a country-by-country level following the CRDIV EU Directive. The country-by-country reporting (CbCR) requires European banks to file their revenues, profits, number of employees and taxes paid in all countries where they operate including tax haven countries. In this paper, I construct the database for bank CbCR from the banks filings and annual reports. The database includes 51 European banks headquartered in 18 different European countries between 2014 and 2020. I use the database to study profit shifting arising from international tax differences between countries. I find that the banks' profits are sensitive to the tax rate suggesting that banks lower their tax burden through their affiliates. The size of banks seems to have an effect, the larger the bank group, the more it might engage in tax planning. Profit shifting is estimated by using the tax differential methodology. The findings show that profit shifting by the top European banks is around 4-3% percent of the total profits booked abroad. This implies tax revenue losses of up to 3-2%. The introduction of a global minimum tax of 15% would generate between 300 to 2 billion euros depending on the final rules implemented.

Introduction

Tax havens are a well-known and ongoing concern of many governments. In the past ten years, many scandals have occurred shedding light on schemes related to tax avoidance and evasion. The Swiss bank UBS was accused of providing technical assistance to its US customers to hide $20 billion abroad; subsequently, Germany and France launched similar investigations. In 2015, the leaks exposed the HSBC banking group, suspected of tax evasion and money laundering. In 2016, the Panama papers revealed the activity of international banks in tax havens. The leaks showed that banks play a role in setting up front companies, foundations and trusts to facilitate tax evasion and money laundering for the benefit of their clients. However, little research focus on the profit shifting of the banks. That can be due to the fact that banks have a special business model. In a globalized world, opportunities for profit shifting may arise due to tax rates differences among countries. That may explain why the corporate tax rates are declining. The United States cut its tax rate from 35 percent to 21 percent in 2018 most probably to maintain its attractiveness in facing profit shifting. In this context, banks might be willing as other multinationals to benefit from low tax rates and engage in tax planning.

Corporate tax base erosion due to profit shifting is a large and consequential problem that may cause lower governments spending, budget deficits and new forms of indirect or direct taxes to compensate the diminishing corporate tax revenues. In this context, estimating the size of the profit shifting is of a great deal. Quantifying profit shifting gives an idea about the revenues escaping the tax system. It is as well of a great importance to check which countries are benefiting from profit shifting and which countries are suffering from its drawbacks. This can give more clarity on the direction of profit shifting among countries, which will help in the identification of the loopholes in the text laws. Big multinationals should also be assessed to check if some are more tax aggressive than some others in engaging in profit shifting. Most of prior studies focus on profit shifting by non-financial multinationals.

When it comes to estimating profit shifting, the main challenge is the access to data. Many researchers use the corporate financial and balance sheet micro-data from Orbis. It has been shown that these data suffer from many limitations because not all the profits are recorded (most of the profits in tax havens are missing). The data used in this paper do not suffer from these limitations, as the banks must report their activity in each country where they operate. As of 2014, the financial institutions in Europe started disclosing their activity on a country bycountry level, following an EU directive (Directive 2013/36/EU). The disclosures include the net banking income, the earnings before tax, the amount of taxes paid and the number of fulltime employees for each country were the bank has an affiliate. The data were hand collected and cover 51 European banking groups between 2014 and 2020 plus a number of foreign banks that operate in the EU. This novel data permits to answer some very important questions: Do banks choose to have affiliates in low tax locations? If all countries had the same corporate tax rate, which would gain or lose profits? What are the tax revenue losses? There are few studies that analyzed the Country-by-Country Reporting that was imposed on European banks. Jansky (2020) document misalignments of locations of profits and economic activity without estimating the profit shifting by the banks. The authors in Bouvatier et al. (2019) try to assess profit shifting by European banks by using a gravity model for the years 2015 and 2016 and base their estimations on the banks' net income. [START_REF] Fatica | How much profit shifting do European banks do?[END_REF] estimate profit shifting to be around EUR 9 billion with a sample of 27 European banks from 2014 to 2016.

In this paper, I implement the tax differential approach to estimate profit shifting. It is a widely used method in the literature [START_REF] Hines | Tax policy and the activities of multinational corporations[END_REF]Rice, 1994 andHuizinga &[START_REF] Huizinga | International profit shifting within multinationals: A multi-country perspective[END_REF]. This approach consists of estimating the tax semi-elasticity of profits. Then this elasticity is used to compute the true profits, in each country were the bank operates, absent tax differences between the foreign country and the home country of the bank. The findings suggest that banks engage in tax planning and that profit shifting by the top European banks is estimated to be around 5% percent of the profits booked abroad. The tax revenue losses are estimated to be around 5% percent of the total tax revenues from banks.

Literature Review

In many countries, policy makers have raised increasing concerns about profit shifting and the implied corporate tax base losses. The empirical identification of the existence and magnitude of profit shifting is not straightforward. Most existing studies are using an indirect identification method that measures the impact of changes in corporate tax rates on the profits of multinational subsidiaries. [START_REF] Huizinga | International profit shifting within multinationals: A multi-country perspective[END_REF] use the weighted tax rate differential with all other subsidiaries. They find significant evidence of profit shifting between subsidiaries and their parent firms as well as among the subsidiaries themselves. [START_REF] Johannesen | Are Less Developed Countries More Exposed to Multinational Tax Avoidance? Method and Evidence from Micro-Data[END_REF] implement the unweighted tax rate differential with other subsidiaries. They provide evidence that European MNEs shift profits to lower-tax rate countries and that large MNEs also exploit mismatches between tax systems and preferential tax treatments to reduce their tax burden. [START_REF] Lohse | Do Transfer Pricing Laws Limit International Income Shifting? Evidence from European Multinationals[END_REF] use the simple corporate tax rate. They test for profit shifting behavior by assessing the hypothesis that the host country's corporate tax rate exerts a negative impact on the affiliate's reported profits. They also reassess the profit shifting hypothesis by testing for a negative effect of the affliates' corporate tax rate difference with all other majority-owned entities within the multinational group (unweighted average) on reported operating profitability.

Using the BEA data, [START_REF] Clausing | The Effect of Profit Shifting on the Corporate Tax Base in the United States and Beyond[END_REF] finds that taxable income is very sensitive to corporate tax rates. Estimates of tax sensitivity are used together with data on reported foreign income to calculate how much "extra" income is booked in low-tax countries due to profit shifting. Then the author estimates what the tax base would be in the United States without profit shifting. According to her estimates, profit shifting cost the US between $77 billion to $111 billion in 2012. [START_REF] Dyreng | Exploring the role Delaware plays as a domestic tax haven[END_REF] focus on Delaware and find that US companies with Delaware subsidiaries reduce their tax burden by 15 to 24 percent. By using data about manufacturing plants in Europe, Egger et al. (2010) find that multinationals earn significantly higher profits than comparable domestic units in low-tax countries but significantly lower ones in high-tax countries. Using macro data on foreign affiliates, Tørsløv et al. (2018) estimate profit shifting by comparing the profitability of local companies to those of the foreign ones. They find that nearly 40 percent of the profits of multinationals are transferred each year to tax havens worldwide (more than 600 billion euros in 2015). Countries of the European Union seem to be the main losers of this evolution.

The literature dealing with profit shifting focus on multinational companies with no particular focus on financial firms. Little work sheds the light on the financial or banking sector. This might be due to the special business model of banks. However, Banks play a fundamental role in tax havens, which is hardly a surprise. Demirguc-Kunt and Huizinga (2001) check the determinants of bank profitability while distinguishing between local and foreign banks. Using data on the taxation of domestic and foreign banks over the period 1988-1995 in 80 countries, including several well-known tax havens (Hong Kong, Luxembourg, Panama, etc.); they find that taxes paid by foreign banks rise relatively little with the local statutory tax. This evidence supports the hypothesis that foreign banks engage in relatively extensive profit shifting. More recently, [START_REF] Merz | Profit shifting and tax response of multinational banks[END_REF] use the Bankscope database and show that banks practice profit shifting themselves, even more than non- financial multinational frms. While profit shifting activities are often associated with intangible assets and manipulation of transfer prices for firm specific goods, profit shifting activities of banks rely on manipulating intra-firm transactions (interest margin or services fees) and allocating certain functions and risks (credit management, investments analysis and the underwriting function). The authors find that reported earnings of multinational bank's subsidiaries significantly respond to host country tax incentives. Based on regulatory data from the German central bank, [START_REF] Langenmayr | Trading offshore: evidence on banks' tax avoidance[END_REF] confirm that banks that are present in OFCs can easily optimize their tax bills. [START_REF] Chernykh | Offshore schemes and tax evasion: The role of banks[END_REF] establish a strong link between the extraterritorial activities of banks and the tax evasion of companies that do business with these banks, based on a unique Russian data set. They find that offshore active banks facilitate the transfer of funds abroad for tax evading companies. Based on BIS locational database, [START_REF] Barake | Les banques et les paradis fiscaux[END_REF] show that banks have higher intra-group activity in tax havens than non-havens. This might suggest that banks shift some of their activities offshore.

There is as well a recent growing literature that deals with the Country-by-Country reporting (CbCR). [START_REF] Murphy | Country-by-country reporting[END_REF] argue that country-by-country reporting is needed so that tax authorities would be able to undertake risk assessments on the corporate tax returns they receive to determine which ones they wish to investigate. [START_REF] Overesch | Financial Transparency to the Rescue: Effects of Country-by-Country Reporting in the EU Banking Sector on Tax Avoidance[END_REF] find that European multinational banks increased their tax expenses relative to unaffected other banks after Country-by-Country Reporting became mandatory. In another work, Jansky (2020) explore the misalignment of location of profits and economic activity as well as the use of tax havens and present these findings as indirect evidence of profit shifting by European banks. Based on the individual country-by-country reporting published by the 37 largest European banks, Bouvatier et al.(2019) implement a gravity model to estimate profit shifting. They find that the tax savings for EU banks is estimated between 1 and 3.6 billion euros. The study by [START_REF] Fatica | How much profit shifting do European banks do?[END_REF] is also closely related to this work. The authors try to assess profit shifting by European banks. However, the sample used in their work comprise 27 banks headquartered in 8 different EU countries while the data constructed for this study incorporates 51 European banks headquartered in 18 different European countries. Adding to that, their sample is based on 2 years while this study time frame covers 7 years from 2014 to 2020. New research is being introduced thanks to the publication of the OECD macro CbCR data and micro CbCR data. [START_REF] Fuest | Corporate profit shifting and the role of tax havens: Evidence from German country-by-country reporting data[END_REF] estimate profit shifting by multinationals by using micro CbCR data for German multinationals. Their findings are similar to the ones of this paper regarding the magnitude of profit shifting. The OECD macro CbCR data is publicly available and has been recently used as well to determine profit shifting (Garcia-Bernardo & Jansky, 2022). This paper presents semi-tax elasticity coefficients, estimates profit shifting and tax revenue losses for European banks. Moreover, in this work, potential revenue gains from the introduction of a global minimum tax are computed.

Empirical Approach

The methodology employed in this paper follows a widely used approach in the literature to estimate profit shifting. The analysis is based on the theoretical model developed by [START_REF] Huizinga | International profit shifting within multinationals: A multi-country perspective[END_REF] which extends the work by Hines & Rice (1994). According to their model, the observed profits can be expressed as the sum of the true profits and profits shifted into the a jurisdiction i minus the cost of profit shifting. In order to estimate profit shifting, the authors compute the level of true profits by using the estimated tax elasticities in order to obtain profits without the effect of taxes. Once the level of true profits is determined, it would be possible to assess profit shifting. Most economists use an indirect method that is based on the tax differences among countries [START_REF] Clausing | The Effect of Profit Shifting on the Corporate Tax Base in the United States and Beyond[END_REF][START_REF] Johansson | Tax planning by multinational firms: Firmlevel evidence from a cross-country database[END_REF], Garcia-Bernardo & Jansky, 2022;[START_REF] Fuest | Corporate profit shifting and the role of tax havens: Evidence from German country-by-country reporting data[END_REF]. Therefore, the analysis consists first of regressions that relate affiliates' profits to tax rates which serve to obtain tax semi-elasticities of affiliates' profits. A negative relationship between profits and the level of taxation in a country would suggest profit shifting. The baseline specification is as follows:

Log(πikt) = b0 + b1Taxit + b2 Firmkt + b4 Countryit + φi + γk + qt + eikt. ( 1
)
where πikt are the profits before tax reported by each bank k in the jurisdictions i where it has an affiliate in year t. Firmikt includes two variables describing the bank activities. These variables are the number of employees reported by each bank k in the jurisdictions i where it has an affiliate in year t and the total assets for each bank in year t. Countryit includes the log of GDP, the log of GDP per capita, the log of distance between the affiliate and the headquarter country and an indicator for the governance constructed from the Worldwide Governance Indicator by the World Bank. The equation contains as well a set of fixed effects: country fixed effects φi that control for unobserved time-invariant differences among jurisdictions, group dummies γk to account for the structure of each bank and year dummies qt. Since the profits before tax variable comprise several observations with zero values, the value of one is added for every observation in order to use the log specification while preserving the sample size. The same is done for the variable of number of employees.

The variable of interest is Taxit which reflects the level of taxation in country i. In this work, several variables are used as a proxy for the level of taxation: the statutory tax rate, the effective tax rate and tax rate differentials. The statutory tax rate is obtained from KPMG. The effective tax rate is calculated based on the bank CbCR data as the sum of taxes paid by all foreign affiliates in country i divided by the sum of profits before tax of affiliates in that same country.

To circumvent endogeneity problems, effective average tax rates are computed only for countries in which at least two banks have affiliates in our sample. The statutory tax rate is used instead for countries where it is not possible to calculate an effective tax rate. Adding to that, two tax rate differentials are constructed. The first one consists of the unweighted average of the bilateral tax differentials between country i and all other affiliates of the bank group k using the effective tax rate. The second one is the same but using statutory tax rates instead of effective tax rates. The tax differential calculations incorporate the headquarter country's tax rate along with the bank group affiliates.

The different tax variables are all tested in the baseline regression. Equation 1 is then modified by adding the squared realization of the tax variable to check whether the tax-sensitivity of profits varies across high and low-tax countries. Different sub-samples are also used in order to check how profit shifting varies across the type of bank, the size and level of intangibles. For these specifications, the effective tax rate is used, which is the preferred measure in this study.

The disadvantage of this tax measure is that it could be affected by loss carry-forwards. However, the ETR is still a better proxy than the statutory tax rate as it reflects the true level of taxation as it is what the bank had to pay. Many countries have high statutory tax rates but introduces several exemptions which makes the real level of taxation disconnected from the official rate.

Data

Bank CbCR Data

The dataset in this study is based on different sources; the main one is the Country-by-Country Reporting data (CbCR). The CbCR reporting started recently in 2014 following the Article 89 of the CRD IV Directive 2013/36/EU. Banks that operate in the EU became obliged to disclose annually, for each country in which they have an establishment, the following items: turnover (net banking income), number of employees (on a full-time basis), profit or loss before tax, tax on profit or loss and the public subsidies received. I hand collected the data of CbCR from the banks' annual reports for the years 2014-2020. The empirical analysis includes all 37 of the largest and systemically relevant international banks based in Europe. The European Banking Authority (EBA) publish each year a list of systemic banks. I include the largest European banks that are listed as systemic by the EBA. The sample includes other non-systemic banks that are also headquartered in the EU.

Overall, I gather data on 37 multinationals systemic banks headquartered in 11 European countries (i.e. Austria, Belgium, Denmark, Finland, France, Germany, Italy, the Netherlands, Spain, Sweden and the United Kingdom), and operating in 90 jurisdictions worldwide. Adding to that, the sample also contains data on 14 European non-systemic banks headquartered in 10 European countries (i.e. Austria, Bulgaria, Luxembourg, Cyprus, Belgium, Denmark, Finland, Greece, Hungary and Ireland). The full list is reported in Table A1 in Appendix A. In the sample, some banks operate in as many as 79 countries while others have operations in only one country other than their domestic market. Some banks do not operate in countries listed as tax havens 2 , while some banks are implemented in 18 different tax haven countries. According to this sample, it can be said that 25 percent of the countries where the European banks locate themselves are tax haven countries. Among the top ten countries with the highest foreign profits, we can find three well documented tax havens: Hong Kong, Luxembourg and Belgium (Figure A1). The US and the UK are as well in the top locations with foreign profit. They are considered as possible tax havens according to the Tax Justice Network. On average, around 60% of profits of European banks are booked abroad, with 40% booked domestically. The profits in tax havens are around 16% (Figure A.2). For more details about the construction of the database, please see Appendix D.

The data on CbCR is obtained either from the bank's annual report or from a separate report filed by the bank under "capital requirements" or "country-by-country reporting". For each bank and for each year, the report must be found, and the following variables are retrieved: Net Banking Income, Earnings before Tax, Taxes Paid, and the Number of Full-Time Staff.

Generally, the reporting is homogeneous among the different banks. However, some banks report the business segments of their activities while others do not. A limited number of banks reports the assets by country. For the taxes paid, certain banks report total taxes paid, current taxes paid and deferred taxes. The current taxes paid are used in this work. As for the other variables, they are gathered from various sources. The GDP is taken from the World Bank. The GDP per capita is found in the CIA World Factbook. For the statutory tax rate, the KPMG database on corporate tax rate is used. The effective tax rate (ETR) is calculated by using the CbCR data.

Sample Analysis

Table 1. Descriptive Statistics

This table shows the descriptive statistics of the main variables in this study. The baseline is restricted to the positive sample without the headquarter country. The sample is split into tax havens and non-havens to highlight some of the differences between the two. The observations of all variables are similar except for the variable "income", for which we have 5221 total observations (3950 in non-havens and 1271 in tax havens). the 1,829 in non-havens. The data shows that the banks manage to have high profits in tax havens with a small number of employees. This highlights the differences in productivity levels of employees in tax havens and non-havens which can reflect profit shifting. Figure A.4 in the Appendix A shows that the productivity is much higher in tax havens (around 200 thousand euros per employee) than in non-havens and in the headquarter country (around 50 to 70 thousand euros per employee). Another interesting variable is the amount of taxes paid. In tax haven countries, the taxes paid are EUR 17.1 billion much lower than the taxes paid in nonhavens EUR 27.1 billion. The statutory tax rate is as expected lower in tax havens (18.1 percent) than in non-havens locations (24.4 percent). Similarly, the effective tax rate is 13.7 percent in tax havens against 21.5 percent in the other countries. A negative relation is expected between the level of profits and tax rates.

Results

Baseline analysis

The results of our baseline specification of equation ( 1) are presented in Table 1. Columns ( 1) and ( 2) show the results when using the tax variable effective tax rate or the statutory tax rate, Columns (3) and ( 4) when using tax rate differentials of country i with all other affiliates. The results show a negative and statistically significant association between profits and the level of taxation with the ETR specifications. With the statutory tax rate specifications, the coefficient is negative but not significant. Holding other factors fixed, an increase in the tax rate is associated with a decrease in profits. This suggests that tax differences among countries have an effect on the profit allocation and that banks reduce their tax burden by operating abroad.

The semi tax elasticity of -0.8 estimated in this paper with the ETR is in line with the literature. Beer et al. (2020) find an average tax semi-elasticity of corporate profits of -1, Heckemeyer and Overesch (2017) of -0.8. [START_REF] Fuest | Corporate profit shifting and the role of tax havens: Evidence from German country-by-country reporting data[END_REF] finds semi tax elasticity with German micro CbCR data of -0.5. Fatica & Gregori (2020) finds higher tax elasticities ranging from -2 to -5 using bank CbCR data, closer to the estimates of [START_REF] Clausing | The Effect of Profit Shifting on the Corporate Tax Base in the United States and Beyond[END_REF] with US MNEs data.

In another specification, the baseline equation is modified and the square of tax is added. The sign for the coefficient of ETR is negative and then changes sign with the ETR squared. This suggest that the lower the level of taxation, the more sensitive profits are to tax rate differences. However, the results obtained with the quadratic specification are not significant (see Table B.2 in appendix B).

Sub-sample analysis

The sample is first split into tax havens and non-havens. The semi-elasticity obtained with the tax havens sub-sample is negative and significant (-1.75). This semi-elasticity is much higher than the one obtained with the non-haven sub-sample of -0.5 which is also significant. This suggest that the profits recorded in low-tax jurisdictions are more sensitive to the local tax burden. The sample is also split by size to check whether it has an effect on profit shifting. It is possible that size could play a role since engaging in profit shifting might be costly. The sample is split into four sub-samples based on the quartiles of total assets of banks. The empirical model is reestimated for each sub-sample. The semi-tax elasticity is negative with all the specification but is only significant with the 4th quartile. It decreases with the 3 nd and 2 nd quartile then increases with the 1st but without being significant. These findings show that the size of the group might have a role and that profit shifting takes place within large firms. However, it is still an interesting result that the 1st quartile, reflecting the smallest firms in the sample, have the highest coefficient of semi-tax elasticity. This could be explained by the fact that regardless of the size of the firm, profit shifting could be an individual choice of the firm. Similarly, profit shifting could be affected by the level of intangibles of the firm. That is why, the sample is split into four quartiles based on the ratio of intangible assets to total assets. The variable of intangible assets is collected from the annual report for each bank for each year t. The 4 th quartile has the largest semi tax elasticity of -1.038. However, the 1 st and 3 rd quartile have semi-elasticities of -0.9 and -0.87 which are close to each other. The evidence of firms with high intangibles being more tax sensitive is not very strong with our sample.

Estimating profit shifting

In this section, profit shifting estimations are presented. Based on the regression analysis from the previous section, the semi-tax elasticity of -0.8 from the baseline regression results is used to compute the amount of true profits and the semi-tax elasticity of -1.7 is used if the country is a tax haven. The true profits are calculated by dividing the amount of reported profits by 1 plus the coefficient estimate of -0.8 (-1.7 if tax haven) multiplied by the tax differential of country i with the average effective tax rate of 20 percent3 . This yields the amount of profits we would expect to observe in each jurisdiction absent profit shifting. I assume there is no profit shifting occurring in the headquarter country and in jurisdictions with profitability ratio of employees less than twice the average profitability. The sum of profit shifting is set to be equal to zero among all countries for each banking group. The profit shifting estimates are then reallocated based on the share of employees among all countries that are not involved in shifting. The computations suggest that profit shifting is around EUR 3 billion or 3-4% of the total profits of banks abroad. Considering the statutory tax rate of countries with outward profit shifting, the findings suggest that the tax losses are around EUR 700 million. The results of profit shifting for selected countries are presented in Table 6. The profit shifting magnitude is similar to the one found by [START_REF] Fuest | Corporate profit shifting and the role of tax havens: Evidence from German country-by-country reporting data[END_REF]. Using micro CbCR data for German MNEs, they estimate that 18.3 billion is shifted which corresponds to 3.3% of the MNEs reported profits.

Comparing with Fatica and Gregori (2020) that use bank CbCR data, they find that profit shifting is around EUR 9.8 billion or 25%. This estimate is much higher than the one found in this paper. One explanation could be that the semi-tax elasticities estimated in this paper are much lower. It should be also noted that semi-elasticities estimated with micro data tend to be smaller than those estimated with macro data. This Table presents profit shifting estimations for selected countries in million of euros for the year 2019. True profits are estimated by using the tax differential method based on the semi-tax elasticities of -1.7 for tax haven countries and -0.8 for the rest of countries. The profit shifting estimation is compared with the total reported profits of banks abroad.

The introduction of the proposal of the global minimum tax would have an incidence on the incentives of profit shifting of banks. The proposal requires multinational companies with annual revenues above EUR 750 million to pay a top up tax in countries where their effective tax rate is less than 15%. Based on the CbCR data of the systemic banks, I estimate the revenues from a global minimum tax of 15% in two scenarios: 1) the country of headquarter collects the top up tax; 2) the host country applies the top-up tax. The methodology is taken from Barake et al (2022). In this paper, carve-outs4 are not accounted for, neither behavioral responses.

Conclusion

This paper undertakes a comprehensive analysis of tax planning by European banks. Using country-by-country data, we document negative semi-tax elasticities. This that banks engage in tax planning. Profit shifting is computed through the widely used tax differential methodology and is estimated to be around EUR 3 billion or 3-4 percent of the total profits abroad. I find that tax planning is likely costing the EU governments around 3 percent of the tax revenues paid by these banks. The tax losses are estimated to be around EUR700 million annually. These estimates have the advantage of using comprehensive data that include operations in many tax haven countries, unlike many studies that rely on financial data.

Even though the focus in the literature is mainly on profit shifting by multinational companies, however; there is a growing evidence that banks as well avoid taxes for their own account. The negative strong correlation between the profits abroad and the tax rates indicates that European banks can engage in tax planning by going into countries with lower tax rates. Moreover, the excessively high profitability ratios in tax havens are another indicator for the tax planning behaviour of European banks.

The country-by-country reporting is a first step into mapping the activity of banks. More reforms should bring more transparency on the activity of multinationals and financial institutions. Policy action that address problems associated with tax competition and corporate tax base erosion can be very tricky to implement. The OECD/G20 BEPS launched many actions to combat this issue. Still, these actions face many challenges due to the conflict of interest inside of the EU. Some EU countries are affected by profit shifting while others are benefiting. Profits are being redistributed in the EU from the countries with high tax rates to the ones with low tax rates. Reforms should consider worldwide consolidation in an era of globally integrated economies. A harmonization in the fiscal systems of EU countries is as well highly needed in order for tax rates to converge which will reduce the incentives to shift profits from an EU country to another. The global minimum tax initiative might also affect the profit shifting behaviour since it harmonizes the tax rates not only among European countries but with tax havens as well. The minimum tax would generate around 300million up to 2 billion euros for European countries from the sample of European banks depending on the design of rules of the proposal. Overall, the analysis spans all years of obligatory reporting, 2015 to 2020 plus one earlier year 2014. For each bank and each year, the report with country-by-country information should be extracted either directly in the annual report of the bank or in a separate document found on the website of the bank. Once the report is retrieved, the currency has to be checked as the banks report in different currencies: Euro, pound sterling, US Dollars, Danish krone, Swedish krone. Since most of the banks were reporting in Euro, the whole database was converted into Euro. Some banks report in thousands and some other in millions of euros, therefore the values were all converted to million of euros. Another aspect to look out for when retrieving the raw data is the sign of the variable "Taxes". Some banks report "taxes" as an expense in the CbCR data with a negative sign for taxes paid and positive sign for taxes received. Some other banks report taxes as an expense but with a positive sign for taxes paid and negative sign for taxes received.

Appendix A. Descriptive statistics

The data was harmonized in a way were there is no sign for taxes paid and a negative sign for taxes received.

It should be noted that reports were retrieved for all banks in our sample except for RBS 2014 and Standard Chartered 2014. Also, the LBBW 2014 report was incomplete, reporting only the net banking income and number of employees. In order to have a balanced database, the missing variables were imputed for these 3 banks in 2014. For RBS 2014, data were imputed by multiplying by a growth factor using 2015 data. This factor was calculated based on the growth rate for each variable. The pre-tax profits were calculated by multiplying by 1.45 and the other variables are calculated by multiplying by 1.1. For Standard Chartered 2014, data were imputed by multiplying by a growth factor using the 2013 data. A growth rate of 1.18 was used for the different variables. For LBBW 2014, profits before tax and taxes are the missing variables to be computed. The profits before tax were imputed from the ratio between the net banking income and pre-tax profits using reporting in other available years. Specifically, an average ratio of 0.4 was used for profits before tax. For the corporate taxes paid, the mean effective tax rates for each partner countries were calculated, then multiplied by the imputed profits before tax.

Limitations/ further remarks

The data are reported on a consolidated basis and present a satisfactory level of homogeneity across banks and years for the purpose of our analysis. However, some limitations stem from the primary data reporting process, which relies on the banks themselves.

• Depending on the banks' operational structure, some banks include intracompany dividends, especially for parent jurisdictions. • Some reporting excludes intracompany transactions, whilst other includes only cross

border and yet other excludes them entirely. • Some banks report income tax expense while others report taxes paid. The constructed database only reports one tax variable. Whenever the total tax expense is reported along with taxes paid, taxes paid were the variable selected. • Some banks were reporting two jurisdictions as one observation (e.g. China and Hong Kong). For these cases, the variables were divided equally by the number of jurisdictions reported by the bank.

• This database does not include the variable "subsidies received" that is required in the public country by country reporting of banks. • Null values in reports. For some affiliates variables were left blank or reported with null values. These have been replaced by zeros which should be carefully interpreted. • Some taxes and profits have time misalignment which can appear in the form of a tax deduction or tax credit in the following year of an exercise. For instance, Monte Paschi profits in Luxembourg were adjusted for 2017 and 2018 according to an explanatory footnote they provide in the CbCR report of 2018. The reason for this adjustment is that some profits appear in Luxembourg in 2018 even though they should have been offset by losses in 2017. This is what they refer to as timing misalignment. Another case about mismatches with taxes: the payment of taxes for HSBC in Hong Kong in 2019 was reported in 2020. Therefore, it would be best to readjust taxes paid in 2019 and 2020 to account for underreporting in the former and overreporting in the latter year. These can be readjusted by multiplying the earnings before tax by the average effective tax rate of 11% in 2019 and 2020.

Table 1

 1 presents the CbCR data used in this work. The Baseline sample is restricted to observations related to positive profits made abroad. The profits that are made by the banks in their headquarters countries are dropped to focus on the activity of banks abroad at first. The descriptive statistics show clear discrepancies between tax havens and non-havens. On average, the profits before tax in tax havens are EUR 118.5 billion slightly less than those generated in tax havens EUR 134.3 billion. The number of employees in tax havens is 661, much lower than

			Baseline			Non-havens			Tax havens
	Variable	Mean	Std.	min		Mean	Std.	Min	Max	Mean	Std.	Min	Max
			dev.				dev.				dev.
	Effective tax rate	19.6%	9.7%	0%	64.8% 21.5%	9.2%	0	40.8% 13.7%	8.5%	0	64.8%
	Statutory tax rate	22.9%	8.9%	0%	64.8% 24.4%	7.7%	0	55.0% 18.1%	10.5%	0	64.8%
	Income	343.9	1,064 -549 19,926	360.1 998.8	-13.1 14,145	293.6	1,246	-549	19,926
	Profits before tax	122.3	427	0 11,287	118.5 308.2	0	5,343	134.3	674.9	0	11,287
	Taxes	24.7	79.2 -463	1,321	27.1	78.5 -463.3	1,321	17.1	81.3	-36	1,252
	Staff	1,547	4,096	0 45,245	1,829 4,423	0 45,245	661	2,640	0	31,537
	Nb. of Obs.		5,271				4,000			1,271

isle of man, Jersey, Guernsey, Gibraltar, Grenada, Cyprus, Hong Kong, Ireland, Lebanon, Liechtsenstein, Luxembourg, Macao, Malta, Marshall islands, Monaco, Mauritius, Netherlands, Panama, Puerto rico, Seychelles, Singapore, St kits and Nevis, Sint Maarten, St Vincent and the grenadines, St lucia, Switzerlands and Turcs and Caicos.

Table 2 .

 2 Results for the baseline specificationThis table reports estimated coefficients from linear regressions, in which the dependent variable is the log of profits before tax. The main explanatory variable is the tax rate. The different specifications use two different measures for tax rates: the effective tax rate (ETR), the statutory tax rate and tax differences between the host and all bank locations.

	Variables	(1)	(2)	(3)	(4)
	Effective tax rate	-0.835***
		(0.263)
	Statutory tax rate		-0.024
			(0.666)
	Tax diff ETR			-0.861***
				(0.251)
	Tax diff Statutory				-0.689
					(0.576)
	Log (Staff)	0.604***	0.604***	0.604***	0.604***
		(0.010)	(0.010)	(0.010)	(0.010)

Table 3 .

 3 Semi-elasticities by Tax havens statusThis Table presents regression results with non-havens and tax havens sub-sample. The explanatory variable of interest is the Effective tax rate. *, **, *** indicates significance at the 10%, 5% and 1% level.

	Variables	Non-Havens	Tax Havens
	Dependent variable: log of profits before tax
	Effective tax rate	-0.524**	-1.747***
		(0.282)	(0.616)
	Log (staff)	0.615***	0.542***
		(0.011)	(0.024)
	Log (GDP)	1.073**	0.201
		(0.460)	(0.476)
	Log (GDP per capita)	-0.046	0.149
		(0.335)	(0.149)
	Governance Index	-0.179	0.740
		(0.251)	(0.589)
	Log (distance)	-0.203***	0.190**
		(0.052)	(0.093)
	Log (total assets)	0.823***	0.373
		(0.225)	(0.337)
	Country FE	yes	yes
	Bank FE	yes	yes
	Year FE	yes	yes
	Nb of Obs.	4,000	1,271
	R-sq	0.7494	0.6280

Table 4 .

 4 Semi-elasticities by firm sizeThis Table presents regression results by quartile. The sample is split into four quartiles based on the firm size proxied by total assets. The explanatory variable of interest is the Effective tax rate. *, **, *** indicates significance at the 10%, 5% and 1% level.

	Variables	1st quartile	2nd quartile	3rd quartile	4th quartile
	Dependent variable: log of profits before tax
	Effective tax rate	-1.159	-0.056	-0.622	-0.791***
		(1.318)	(1.093)	(0.409)	(0.334)
	Log(staff)	0.351***	0.569***	0.665***	0.568***
		(0.094)	(0.047)	(0.019)	(0.014)
	Log (GDP)	5.363**	-2.332	1.265***	0.482
		(2.244)	(2.036)	(0.470)	(0.381)
	Log (GDP/capita)	0.113	2.624	-0.787**	-0.012
		(0.149)	(1.653)	(0.357)	(0.180)
	Governance Index	0.745	0.427	0.283	0.154
		(1.410)	(0.792)	(0.406)	(0.307)
	Log(distance)	-0.065	0.050	0.045	-0.170**
		(0.459)	(0.126)	(0.069)	(0.077)
	Log (total assets)	0.690	2.368***	0.866*	1.133***
		(0.479)	(0.679)	(0.455)	(0.315)
	Country FE	yes	yes	yes	yes
	Bank FE	yes	yes	yes	yes
	Year FE	yes	yes	yes	yes
	Nb of Obs.	257	423	1,500	3,091
	R-sq	0.7923	0.8373	0.7633	0.7159

Table 5 .

 5 Semi-elasticities by intangible assets

	Variables	1st quartile	2nd quartile	3rd quartile	4th quartile
	Dependent variable: log of profits before tax
	Effective tax rate	-0.870*	0.226	-0.907**	-1.038**
		(0.494)	(0.803)	(0.386)	(0.438)
	Log (Staff)	0.525***	0.506***	0.614***	0.659***
		(0.028)	(0.029)	(0.019)	(0.019)
	Log (GDP)	-1.092	1.223	0.743	1.224**
		(0.731)	(0.938)	(0.486)	(0.486)
	Log (GDP/capita)	0.348**	0.556	-0.088	-0.502
		(0.158)	(0.426)	(0.212)	(0.372)
	Governance Index	-0.103	0.085	0.265	0.276
		(0.489)	(0.615)	(0.370)	(0.414)
	Log (Distance)	-0.047	-0.247**	-0.239***	-0.056
		(0.139)	(0.142)	(0.075)	(0.101)
	Log (Total assets)	0.941***	-0.307	1.345***	0.820**
		(0.365)	(0.769)	(0.318)	(0.416)
	Country FE	yes	yes	yes	yes
	Bank FE	yes	yes	yes	yes
	Year FE	yes	yes	yes	yes
	Nb of Obs.	1,029	728	1,866	1,648
	R-sq	0.7643	0.7665	0.7207	0.7643

Table 6 .

 6 Profit shifting by selected countries(EUR m)

	Country	Reported Profits	Profit shifting	share
	EU 27	34,697	-724	-2%
	European Tax havens	9,943	576	+6%
	Other Tax havens	16,483	1,721	+10%
	Rest of the world	48,736.3	-1,573	-3%

  TableC.1 and C.2 in Appendix C presents the potential revenues. Under the Headquarter scenario, the European countries would generate around 1-2 billion euros. These revenues would compensate for the profit shifting tax losses estimated in this work. Under the host country scenario, the revenues decrease to around EUR 300-600 million.

Table A .

 A 1. List of banks in the sample and their affiliates

	Figure A.1. Top 10 countries with profits (in 2019)
						Profits before tax
	HQ Bulgaria 10000 Belgium 12000 Austria 14000		Bank Name Raiffeisen bank international Belfius Central cooperative bank KBC Bank ERSTE	2014 21 5 1 26 8	2015 21 3 3 19 8	2016 20 3 2 18 8	2017 16 3 2 18 8	2018 16 3 2 19 8	2019 16 3 2 19 8	2020 2 3 19 16 8
	Cyprus 8000		Bank of Cyprus group RCB bank			8 2	7 2		7 2	6 2	6 2	5 2 .	5
				Danske Bank				15	15		15	15	16	16	16
	Denmark 6000		Jyske Nykredit Realkredit			6 6	5 3		5 3	5 3	4 2	3 2	3 1
	4000		Sydbank				3	3		3	3	2	2	2
	Finland		Aland bank				2	2		2	2	2	2	2
	2000		BNP Paribas BPCE				69 38	65 61		66 62	66 62	69 62	69 64	71 52
	France 0	HKG	Banque Postale Crédit Agricole Crédit Mutuel USA	MEX	BRA	GBR	2 47 22 DEU	2 44 18 LUX	2 43 21	2 48 23 CZE	2 48 23 ITA	2 47 23 BEL	11 49 22
	Société Générale Figures are in EUR m			80	80		81	80	79	80	73
				Bayern LB				8	6		6	6	5	5	7
				Commerzbank				12	12		12	12	12	12	12
				DZ Bank				19	21		20	23	23	19	18
	Germany		Deutsche Bank				32	58		61	60	60	51	48
				Helaba				7	7		7	9	10	9	9
				LBBW				12	9		8	9	10	10	10
	Nord LB Figure A.2. Profits by country type and year	6	6		6	6	6	6	6
	Greece		Alpha bank National bank of Greece			9 15	9 14		10 15	9 14	9 13	9 11	10 9
	Hungary		OTP bank		10 Profits before tax	10		10	10	10	12	12
	Ireland 80 000		AIB Bank of Ireland group			3 6	3 6		3 5	3 5	3 5	3 5	3 6
				Intesa Sanpaolo				30	30		31	29	30	31	31
	Italy 70 000		Monte dei Paschi			11	9		8	16	16	15	11
				Unicredit				33	33		34	36	32	35	30
	Luxembourg 60 000	Banque international du Luxembourg Abn Amro		8 17	8 17		6 18	6 18	5 18	6 15	6 15
	Netherlands		ING				37	37		39	39	40	40	40
	50 000		Rabobank				40	41		41	38	40	40	39
				BBVA				22	23		35	35	35	32	32
	Spain 40 000		Banco Sabadell Banco Santander			3 35	4 36		4 38	4 36	4 36	5 34	6 33
				Bankia BFA				4	4		4	4	2	2	2
	30 000		Handelsbanken				7	18		18	18	16	16	16
	Sweden 20 000		Nordea SEB Bank Swedbank				17 18 7	17 18 7		17 18 7	20 18 7	20 19 7	20 19 7	22 19 11
				Barclays				30	39		37	37	30	30	30
	10 000		HSBC				64	60		62	63	63	62	63
	UK	0	Lloyds Banking Group Nationwide RBS/NatWest Standard Chartered 2014 2015	2016	2017	9 4 48 59	8 4 48 62 2018		8 4 42 58	8 4 38 59 2019	9 2 28 57	2020	8 1 28 57	8 1 27 57
					non havens	tax havens	domestic
	Figures are in EUR m

Table A .

 A 3. The effective tax rate over time by country type

			Effective tax rate
	30%
	25%
	20%
	15%
	10%
	5%
	0%
	2014	2015	2016	2017	2018	2019	2020
			non-havens	tax havens	domestic
	Table A.4. Productivity by country type over years
			Profit per employee
	400 000
	350 000
	300 000
	250 000
	200 000
	150 000
	100 000
	50 000
	0
	2014	2015	2016	2017	2018	2019	2020
		non havens	tax havens	domestic	foreign banks
	Figures are in euros

  • The Royal bank of Scotland became NatWest bank starting from 2020 • Nordea changed its headquarter countru from Sweden to Finland in 2018 • Caixa and Bankia have merged in 2021

This work use the list of tax havens by Tørsløv et (2018) : Bahamas, Andora, Aruba, Anguilla, Antigua and barbuda, Bermuda, Bahrain , Barbados, Belgium, Belize, Antilles, british virgin islands, cayman islands, curacao,

This Table presents regression results by quartile. The sample is split into four quartiles based on the ratio of intangible assets. This ratio is calculated as intangible assets of bank k divided by the total assets of the bank k. The explanatory variable of interest is the Effective tax rate. *, **, *** indicates significance at the 10%, 5% and 1% level.

The average statutory tax rate is 20.87% in the EU, 19% in Europe and 23.7% globally (KPMG).

Substance-based carve-outs allow for a reduction in the tax base in which the top-up tax will apply. It will subtract 8% of the carrying value of tangible assets and 10% of payroll or employee compensation from profits.

Appendix B. Additional regressions results

Figures are for the year 2019 and in EUR m. The results from a minimum tax of15% are presented for selected European countries. In the headquarter scenario, the country where the bank is headquartered collects the top up tax from the minimum tax. In the Qualified Domestic Minimum Top-up Tax (QDMTT), it is the host country where the affiliate is located that collects the revenues from a minimum tax. In both scenarios, a minimum tax of 15% is assumed without accounting for carve-outs.

Appendix D. The construction of the bank Country-by-Country Database

EU Directive CRD IV article 89

Since 2015, country-by-country reporting became obligatory for financial institutions operating in EU countries under the Article 89 of the 2013/36/EU -Capital Requirements Directive IV. EU Member States require financial institutions to disclose publicly information on a consolidated basis for the financial year. This includes the activity of all their affiliates (subsidiaries and branches) on a country-by-country basis for the following items : turnover (net banking income), number of employees (on a full-time equivalent basis), profit or loss before tax, tax on profit or loss, and public subsidies received.

The constructed database uses information made available by this reporting obligation for banks in the EU. This includes reported information by 37 systemic banks headquartered in eleven European countries (i.e. Austria (1), Belgium (1), Denmark (1) France ( 6), Finland (1), Germany (7), Italy (3), Netherlands (3), Spain (4), Sweden (4), and the United Kingdom ( 6), and operating in up to 90 jurisdictions worldwide. The list of systemic banks is updated by the European Banking Authority (EBA) and can be found at the EBA's website: https://www.eba.europa.eu/risk-analysis-and-data/globalsystemically-important-institutions. La Caixa bank was not included in the systemic bank data due to missing values in profits before tax.

Other non-systemic european banks are considerd and added to the data used in this paper. This includes reported information by 14 non-systemic European banks that are headquartered in 10 European countries (i.e. Austria, Bulgaria, Luxembourg, Cyprus, Belgium, Denmark, Finland, Greece, Hungary and Ireland). This includes the following banks: AIB, Aland bank, Alpha bank, Bank of Cyprus group, Bank of ireland group, Banque internationale du Luxembourg, Belfius, Central cooperative bank, Jyske, National bank of greece, Raiffeisen bank international, OTP bank, RCB bank and Sydbank.

Methodology

The data have been collected manually either from annual reports or from country-by-country reports filed separately by banks each year. They were then compiled in a unified dataset.