P Sans

World meat consumption patterns: An overview of the last fifty years (1961-2011)

Driven by economic development and urbanisation, protein consumption has surged world-wide over the last 50 years, rising from 61 g per person per day in 1961 to 80 g per person per day in 2011. This contribution analyses the apparent convergence of dietary models worldwide with respect to the proportion of ABP and especially meat in intake. By using FAO data for 183 countries over the period 1961-2011, the authors show the connection between annual per capita GDP and the level of ABP (R2 = 0.62) and meat consumption (R2 = 0.62). They emphasise the surge in ABP intake in emerging countries (China, Brazil) which has partly re-placed plant protein. However, for similar degrees of economic development, the composition of ABPs and the position of meat within this category vary significantly among countries, suggesting that historical, geographical, cultural and religious factors may be involved.

Introduction

Actual access to food determines the development of human societies and shapes dietary models (dietary quantities and patterns). Observations over long time spans make it possible to pinpoint the various stages of change in these models in most countries. After a subsistence economy stage, the dietary transition is characterised by quantitative growth in consumption of traditional foods, essentially of plant origin, through the combined effect of higher agricultural output and lower prices [START_REF] Combris | Consommation alimentaires: Tendances de long terme et questions sur leur durabilité[END_REF]Grigg, 1995a). Then came a nutritional transition characterised by a radical change in dietary structure when calorie saturation occurred: more expensive foods such as meat, fruit and vegetables superseded in part traditional foods as mean per capita income rose [START_REF] Popkin | Global nutrition dynamics: The world is shifting rapidly toward a diet linked with noncommunicable diseases[END_REF].

Indeed, over the last 50 years, meat consumption rose worldwide from 23.1 kg per person per year in 1961 to 42.20 kg per person per year in 2011. The same is true of proteins from dairy foods. The most developed countries have thus achieved on average levels of animal-based protein (ABP) consumption that exceed needs. Various authors have shown a degree of convergence in dietary models, especially as regards the boom in ABP consumption, first in groups of countries with high purchasing power [START_REF] Blandford | Changes in food consumption patterns in the OECD area[END_REF][START_REF] Gil | Food consumption and economic development in the European Union[END_REF][START_REF] Herrmann | Does food consumption converge internationally? Measurement, empirical tests and determinants[END_REF] then in countries with intermediate incomes (Regmi, Takeshima & Unnevehr, 2008): when incomes rise, the proportion of food-budget spending on proteins rises concomitantly with the share of ABPs in the diet and approaches the proportion in developed countries. Over the last 20 years, emerging countries have experienced a livestock revolution characterised by a surge in meat consumption especially meat from monogastric livestock (pork and poultry) [START_REF] Delgado | Food consumption patterns and economic growth. Increasing affluence and the use of natural resources[END_REF][START_REF] Speedy | Global production and consumption of animal source foods[END_REF]. This observation, which seems to accredit the claim that dietary models are converging or at least evolving along parallel courses, raises questions about the levels of ABP consumption such countries will reach. Could it be that they will approach the levels of the most developed countries or will they hit a consumption ceiling before that? This question relates to the determination of an inflection point in the consumption curve, as in the models described by [START_REF] Kuznets | Economic growth and income inequality[END_REF]. There would seem to be an income level beyond which ABP consumption falls off. By using data from 150 countries for the period 1980-2009[START_REF] Rivers Cole | Does global meat consumption follow an environmental Kuznets curve? Sustainability Science[END_REF] confirm that there is a turning point for meat (bovine meat, pig meat and poultry) consumption. However, that point is at a high per capita income level (estimated at US$36,400) that few countries in the world have reached. The authors therefore conclude that policy makers need to curb the increase in individual consumption. The inverted U-shaped relation between meat consumption and the level of income is confirmed by [START_REF] Vranken | Curbing global meat consumption: Emerging evidence of a second nutrition transition[END_REF] for data on 120 countries for the period 1970-2007: according to the specifications in the model chosen, the turning point lies between US$32,000 and US$55,000. For countries below the inflection point, a 1% rise in GDP expressed in constant 2005 international dollars would engender a 0.5% rise in meat consumption whereas a 1% rise in the same GDP for countries beyond the inflection point would generate a 1.2% reduction in consumption.

The potential consequences for the environment and the use of farmland of an individual increase in meat consumption combined with strong demographic growth on the scale of the planet (9 billion individuals in 2050) are incentives to change dietary practices in developed countries. Changes to nutritional recommendations [START_REF] Reynolds | Are the dietary guidelines for meat, Fat, fruit and vegetable consumption appropriate for environmental sustainability? A review of the literature[END_REF] and the promotion of individual behaviour intended to cut down on how often meat is consumed ('meatless days') or portion sizes are some of the levers recently proposed in these countries [START_REF] Dagevos | Sustainability and meat consumption: Is reduction realistic? Sustainability Science[END_REF][START_REF] De Boer | Meatless days" or "less but better"? Exploring strategies to adapt Western meat consumption to health and sustainability challenges[END_REF]. The search for new sources of both plant and animal protein is also recommended [START_REF] Boland | The future supply of animal-derived protein for human consumption[END_REF]. Recent studies have tried to measure the impact of such a reduction of meat and milk consumption in Europe [START_REF] Tukker | Environmental impacts of changes to healthier diets in Europe[END_REF][START_REF] Westhoek | Food choices, health and environment: Effects of cutting Europe's meat and dairy intake[END_REF] and the world [START_REF] Hedenus | The importance of reduced meat and dairy consumption for meeting stringent climate change targets[END_REF] on the environment, climate and land use. For example, [START_REF] Westhoek | Food choices, health and environment: Effects of cutting Europe's meat and dairy intake[END_REF] report that replacing 25 to 50% of current EU consumption of meat, eggs and dairy products would cut animal production by 50% and lead to reductions of 25 to 40% in greenhouse gases and about 40% in reactive nitrogen.

Given the scale of the challenge, it seems worth revisiting one of the assumptions underpinning much of this work, namely the increase in ABP consumption in conjunction with the growth in income in emerging or developing countries. This contribution proposes a detailed analysis for the period 1961-2011. We shall begin with a snapshot of world ABP consumption at the end of the period for the purpose of identifying the main trends. Then we shall analyse the evolution of ABP and meatbased protein (MBP) for the entire period.

Material and methods

We have used quantitative data from FAO food balance sheets (FAO, 2014) for the period currently available and for all countries individually. FAOSTAT supply data refer to national per capita supply at a retail level. This is calculated as (national production + import + stocks) -(exports + feed + non-food usage + usage input for food + wastage + closing stocks). Per capita availability is calculated by dividing total availability by the country's population size. Given the losses after the distribution stage, these are not quantities actually consumed by individuals. Besides, as availability is the outcome of a calculation, it depends on how well production is evaluated [START_REF] Hawkesworth | Feeding the world healthily: The challenge of measuring the effects of agriculture on healthVol[END_REF], even if the FAO corrects certain data [START_REF] Hallström | Meat-consumption statistics: Reliability and discrepancy[END_REF]. Despite these shortcomings, the source indicates intake in the form of time series and it is useful for measuring changes and for highlighting differences between countries or geographical regions.

The data are for daily per person calorie and protein intake and the proportions of plant and animal products in those intakes. Four groups are formed for animal products: (i) Meat (including offals), (ii) Milk products, (iii) Fish and Seafood products and (iv) Others (mainly eggs). This information has been enhanced by World Bank indicators about the geographical region to which the country belongs, annual per capita Gross Domestic Product (GDP), income group, population and urbanisation rate (for the period 2005-2011). The combination of the two data sets covers 183 countries (for 155 of which we have full data for the period 1961-2011).

First we analysed the variation of indicators extracted from the two bases and indicators calculated from each country's income group. This analysis was made for an average of the latest three years available (2009-2011) so as to smooth inter-annual variations. Simple regressions were calculated between the chosen indicator and per capita GDP (in natural log form) and the adjusted R-squared (R2) recorded. Then we analysed the change in calorie and protein intake and the contribution of the various groups (plant and animal) or sub-groups for the periods 1961-1963 (noted 1961 in what follows) and 2009-2011 (noted 2011). Only those countries for which complete data are available were included. Lastly, a closer analysis of ABP was conducted for six countries selected either because of their atypical consumption in their geographical or income-level sub-group (Argentina, India, Japan), or for the marked change in this aspect over the period (Spain, Brazil, China)

Results

Analysis by income group (2009-2011)

By using the World Bank's nomenclature of groups of countries by income, it can be observed that the number of kcal consumed by person per day on average over the period 2009-2011 tracks GDP (R2 = 0.92) and the urbanisation rate. The wealthiest groups consume 1.5 times more kcal per person per day than the poorest (Table 1). The number of grams of proteins consumed follows the same trend (R2 = 0.98), with a higher ratio between extreme groups (1.79). For the same period, the percentage of calories from ABP varies by a ratio of 3.4 between the extreme groups and is also closely related to GDP (R2 = 0.98). Expressed as percentage of total protein intake, ABPs make up between 21.9 and 59.5% for the poorest and richest countries, respectively (ratio 2.7, R2 = 0.99). Lastly the share of meat in ABP supply varies from 6 to 30 g per person per day for the extreme income groups (R2 = 0.98). This ratio of 1 to 5 between the intake levels of poorest and richest countries is the highest among the indicators studied.

Changes in protein consumption between 1961 and 2011

Proportion of protein in calorie intake

The proportion of protein in the calorie intake is stable between the two extremes for the period under study: 10.7% (min = 6.1, max = 16.1) in 1961 versus 11.2% in 2011 (min = 6.6, max = 15.7). This proportion is weakly correlated with per capita GDP even if it rose slightly over the period (R2 = 0.12 in 1961 versus 0.35 in 2011). Over the period, the share of plant protein in calorie intake falls (from 6.9% to 6.3%) to be replaced by ABP intake (which rises from 3.8% to 4.9%). The first exhibits a weak negative correlation with per capita GDP (R2 = 0.44 in 1961 versus 0.49 in 2011) in contradistinction to the second (R2 = 0.57 in 1961 versus 0.68 in 2011).

Protein intake

Protein consumption rose 31% over the period, from 61 g per person per day in 1961 to 80 g per person per day in 2011. Although the quantity of plant protein consumed rose (from 38 g to 44 g per person per day), its proportion in total intake fell (from 66% to 58%). This can be accounted for by the surge in ABP intake, the consumption of which rose from 23 to 36 g per person per day (+50%). Total protein intake levels remain highly variable depending on the countries and positively correlated with per capita GDP (R2 = 0.5 in 1961 and 0.62 in 2011) (Fig. 1)

Changes in ABP consumption and its proportion in protein intake

Much as is observed for total protein intake, consumption of meat protein rose worldwide from 9 to 15 g per person per day between 1961 and 2011. This phenomenon is observed in all geographical zones defined by the FAO, with the strongest rises in absolute value (N15 g/person/d) for Polynesia, Southern Europe and Eastern Asia. However, intake levels remain very different for geographical zones (min = 2.9 g/person/day for Southern Asia; max = 39.9 g/person/day for Oceania in 2011).

These differences are also observed between countries although a few major trends can be identified: consumption is positively correlated with annual per capita GDP (Fig. 2 and Table 1) and with urbanisation (R2 = 0.40 in 2011). The level of protein from meat in 1961 is a poor predictor of meat consumption in 2011 (R2 = 0.05).

Although variations in consumption over the study period are closely related to increases in GDP, the effects are not of the same magnitude for all countries, as illustrated for the six selected countries (Table 2). This situation can be explained by (i) very different initial intake levels, (ii) differentiated progression during the study period, and (iii) the relative place of meat in protein intake in each of these countries.

And beginning from very different levels of ABP consumption, meat protein consumption surged in Spain (+23 g/person/d over the period) as well as in Brazil and China. This increase is faster than ABP protein consumption, reinforcing the place of meat as the primary source. Japan has also seen a significant rise in meat consumption (+13 g/person/d), with meat now close behind fish as the primary source of ABP. Conversely, the increased ABP consumption in India has not challenged the hegemony of dairy proteins. Lastly, Argentina has seen a change in its dietary profile: over the period it is the only country of more than 10 million inhabitants where meat protein consumption has fallen significantly (-8 g/person/day), leading to a marked fall in its position as a source of ABP (-10% over the period) and a concomitant rise in dairy protein intake.

Moreover, the effect of the increased annual per capita GDP is variable across countries depending on the amplitude of the variation in meat protein consumption and its timing. Similar economic growth in Argentina and Brazil (+US$10,500) is reflected by a very different variation in meat protein consumption, which may be explained, among other things, by the already high level of such intake in Argentina. The same goes for China and Japan which had very different levels of development at the beginning of the period. Besides, by subdividing the period into three sub-periods of equal lengths (P1 to P3), it can be observed that the variation in meat protein consumption is not always highest in the period with the fastest growth in annual per capita GDP: in Spain, for example, meat protein consumption surged in P1 and P2 (which together made up only 43% of the total variation in GDP) and then fell during P3 when economic growth was strongest. The same goes for China whose upturn in meat protein consumption began in the period P2.

Variation in meat and fish protein consumption

For most countries of more than 10 million inhabitants in 2011, meat protein consumption rose faster over the period 1961-2011 than fish protein consumption (Fig. 3). Except for North Korea and South Africa, variation of fish protein consumption has been positive.

Discussion

Our aim is to offer a detailed analysis of animal-based protein intake and more especially of meat intake worldwide over the period 1961-2011 in order to highlight the dynamics of change and the main factors of variation including income.

Dynamics of ABP consumption and income

In 2011, the share of calories provided by ABP varied by a ratio of about 1 to 3 between the lowest-and highest-income countries. In addition, a close correlation (R2 = 0.91) was found between the calorie intake and annual per capita GDP in 2009-2011 on country group data (Table 1), confirming the findings by Gerbens-Leenes, Nonhebel, and Krol (2010) for a set of 57 countries for 2001 (R2 = 0.71) and by [START_REF] Popp | Food consumption, diet shifts and associated non-CO 2 greenhouse gases from agricultural production[END_REF] for 105 countries between 1990 and 2000. These differences may be explained by the fact that the analysis does not relate to the same number of countries but also by the use of three-yearly means in our case. Some 30 countries, mostly in sub-Saharan Africa have dietary consumption of less than 2200 kcal per person per day. By FAO estimates, by 2050 some 4.7 billion people (52% of the world population) may be living in countries with national averages of over 3000 kcal per person per day, up from 1.9 billion (28%) at the present time (OECD-FAO, 2014). In parallel, those living in countries with under 2500 kcal may fall from 2.3 billion (35% of present world population) to 240 million (2.6%) in 2050. P intake has risen by 10 g per person per day worldwide from 70 g per person per day in 1986in -1988in (Grigg, 1995b) ) t o 8 0 g p e r p e r s o n p e r day in 2009-2011 (2011). The proportion of ABP rose by 5% over the same period to 39.4% (Table 1). Our analyses confirm the connection be-tween the level of ABP consumption and annual per capita GDP: R2 values rose over the period under study (from 0.5 to 0.62) but are lower than those reported by Grigg (1995b) (R2 = 0.8). The situation observed in 2011 also reveals large variations in intake between high- and low-income countries: ABP represents just 21.9% of total protein intake (versus almost 60% for the richest countries) and meat protein just 6 g per person per day in the poorest countries (versus 30 g per person per day in rich countries). The increase in this intake is a major challenge for combatting malnutrition in these countries, especially in Africa because of the nutritional quality of ABP [START_REF] Schonfeldt | Dietary protein quality and malnutrition in Africa[END_REF].

Meat proteins are the major contributors worldwide to the increase in ABP. The growth in meat protein consumption has not occurred to the detriment of other protein sources (such as fish-Fig. 3) but contributes to a rise in overall ABP consumption. We confirm the positive correlation between meat protein consumption and per capita income (R2 = 0.62) and urbanisation (R2 = 0.40 in 2011), as in [START_REF] Kearney | Food consumption trends and drivers[END_REF]. Over the study period, the patterns of increase in consumption vary markedly from country to country. They are not related to the initial low consumption of meat proteins, which reflects the fact that the level of meat protein consumption in 1961 is a poor predictor of its 2011 level (R2 = 0.05). Growth is strong in a number of emerging countries (China, Brazil) which have seen both their mean income per inhabitant and their rate of urbanisation rise strongly over this period. So by using data from the China Health and Nutrition Survey for nine Chinese provinces and three megalopolises (Beijin, Shanghai and Chongqing), [START_REF] Zhai | Dynamics of the Chinese diet and the role of urbanicity, 1991-2011[END_REF] show how the change in food supply, related to new forms of distribution (supermarkets, catering outside the home) and urbanisation of population have contributed to the change in the Chinese diet. The share of cereals and legumes in the calorie intake declined for the period 1991-2011 in all categories of households while that of animal products and edible oils rose. This trend is more prominent, however, in urban areas and can be explained by the fall in the relative price of those products and the rise in the average household purchasing power [START_REF] Popkin | Dynamics of the nutrition transition toward the animal foods sector in China and its implications: A worried perspective[END_REF]. The same trends are observed in other SE Asian countries such as Vietnam [START_REF] Nguyen | Transition alimentaire et essor économique: Portrait en régions de la consommation de viandes au Vietnam[END_REF]. For Brazil, in their study of changes in diet of the urban population over the period 1962-1988[START_REF] Mondini | Changing diet patterns in Brazil (1962-1988)[END_REF] show that meat consumption rose essentially from the second half of the 1970s onwards. This was reflected by a 50% increase in the relative proportion of meat in the population's calorie intake between 1975and 2003[START_REF] Levy-Costa | Household food availability in Brazil: Distribution and trends (1974-2003)[END_REF]. This trend has been confirmed over the last decade, particularly with the effect of increased mean income per inhabitant and urbanisation, and has essentially involved chicken meat [START_REF] Schneider | Consumo de carnes por adultos do sul do Brasil: Um estudo de base populacional[END_REF].

Should the growth in the two drivers of meat protein consumption China and Brazil slow in the coming decades, it will be offset by upturns in consumption in other parts of the world such as the Middle East and North Africa where per capita meat consumption (in kg per person per year) should double by 2030 compared with its 2000 level according to the IFPRI's IMPACT model projection [START_REF] Msangi | Feeding the future's changing diets: Implications for agriculture markets, nutrition, and policy[END_REF].

The strong growth in meat protein consumption is not confined to emerging countries, though. Some OECD countries, especially in Europe (Spain, Italy, Netherlands) have seen their meat protein intakes rise at twice the world average. Thus, the Spanish diet has change radically since the 1960s shifting away from the Mediterranean diet [START_REF] Grigg | Food consumption in the Mediterranean region[END_REF]. As our analysis shows, it is the country that has experienced the greatest upturn in meat consumption worldwide, although ABP intakes were already quite high. However, this increase could not continue [START_REF] Chamorro | Innovations and trends in meat consumption: An application of the Delphi method in Spain[END_REF], in keeping with what is observed in many developed countries. Over the period 1991-2001, ABP intake has declined by 2.5 g per person per day for Western Europe (whereas it rose by 5 g per person per day for the period 1961-2011), under the impetus of a movement of decline in individual consumption observed in the main EU countries (France, Germany, Italy, etc.). Lastly and counter-intuitively, the pattern of meat protein consumption does not always follow the pattern of rising annual per capita GDP, as attested by the Spanish example (Table 2).

Dynamics of ABP consumption and country

Approaches by geographical region or by income group can identify the main trends in dietary patterns. However, they must be supplemented by studies at finer geographical levels. Our study shows, for example, that countries in the same sub-continent and the same income group (such as Argentina and Brazil) have very different meat protein intakes at the beginning of the period (1961) for similar annual per capita GDP levels. Likewise, Japan has a meat protein intake far below the average of developed countries. These examples reflect the fact that while the income approach reflects a large proportion of the variability in meat protein consumption it must be supplemented by other factors. Among these are cultural and religious differences (Grigg, 1995b) that affect dietary practices within a geographical area or within a country. [START_REF] Nam | Meat products and consumption culture in the East[END_REF] have illustrated the diversity of behaviour for meat consumption in various countries of Asia, including China, Vietnam and Japan. In Western Europe, de Boer, Helms, and Aiking ( 2006) compare spending on meat in predominantly Catholic countries (Italy, France, Spain, etc.) with predominantly Protestant countries (Netherlands, United Kingdom, Finland, Germany, etc.). Observing that levels are higher in the first group, they suggest that the difference might be explained in part by the relationship of Catholics with meat eating (pleasure and social status marker). It is also by mobilising cultural and religious factors that one can better understand the very low position of ABP other than milk in the nutritional intake of Indians. In India, animal and fish protein make up just 9 to 10% of daily calories and less than 30% of protein intake. Despite a significant rise in annual per capita GDP, essentially over the period 1993-2011 (Table 2), ABP intake has increased little, although starting from a very low level, and meat protein consumption has not moved. Lastly, as shown by the studies of [START_REF] Wang | Trends and correlates in meat consumption patterns in the US adult population[END_REF] and [START_REF] Daniel | Trends in meat consumption in the USA[END_REF] in the USA, meat protein consumption and composition vary from one ethnic group to another.

Conclusion

In the space of a few decades, that is, at a far faster rate than in developed countries in the twentieth century, dietary structure of many emerging and developing countries has changed radically [START_REF] Combris | Consommation et consommateurs[END_REF]. The increase in animal protein consumption is a marker of the nutritional transition affecting these countries. It is largely related to growing consumption of meat protein. The FAO (2014) expects this trend to continue for the next ten years at an average rate of 1.6% per year and essentially in developing countries because of increased per capita income (OECD-FAO, 2014), continuing urbanisation and demographic growth [START_REF] Msangi | Feeding the future's changing diets: Implications for agriculture markets, nutrition, and policy[END_REF]. While initially reserved to the wealthiest classes in a country's population, meat protein consumption trickles down to the entire population with changes in the dietary offer and distribution circuits and is promoted by the development of international trade. It thus contributes to improved cover of nutritional needs. This 'westernisation' of diet takes on various forms in different countries especially because of cultural and religious factors that affect the dissemination of the model. The prospect of generalisation of ABP consumption raises the question of its sustainability and invites us to explore the influence of new determinants of dietary behaviour (environmental awareness, human health, animal welfare, etc.).

Fig. 1 .

 1 Fig. 1. Change in total protein intake versus per capita Gross Domestic Product (GDP) a . A circle size proportional to country population.

Fig. 2 .

 2 Fig. 2. Changes in protein from meat versus per capita Gross Domestic Product (GDP) a . A circle size proportional to country population.

Fig. 3 .

 3 Fig. 3. Variation in meat protein and fish protein consumption over the period 1961-2011 in countries of more than 10 million inhabitants in 2011 a . a: from 1961-1963 consumption level (1961) to 2009-2011 one (2011).

Table 1

 1 Calorie and protein intake by income level.

	Income group a	Low Lower	Upper	High non	High	World
			middle	middle	OECD	OECD
	n	28	40	46	30	18	162
	Population (billions)	0.69 2.31	2.25	0.06	0.99	6.88
	GDP b (USD)	566	2025	6685	26,919	41,190 9430
	Urbanisation (% population) 30.1 45.4	61.5	68.7	77.8	52.0
	Total calories (kcal/pers/d)	2287 2597	2896	2987	3363	2847
	Protein (g/pers/d)	58	69	82	94	104	80
	Animal protein	2.2	3.7	5.2	7.4	7.4	4.4
	(% total calories)
	Animal protein	21.9 34.6	45.4	58.5	59.5	39.4
	(% total protein)
	Meat protein (g/pers/d)	6	12	19	30	30	15

a Country list is available in Supplementary data section. b GDP: Gross Domestic Product.

Table 2

 2 Changes in ABP (including meat) consumption and variation in per capita income in six countries.


Wahyoe Soedarmono
email: wahyoe.soedarmono@usbi.ac.id

Sigid Eko Pramono

Amine Tarazi
email: amine.tarazi@unilim.fr

The procyclicality of loan loss provisions in Islamic banks: Do managerial discretions matter?

Keywords: Islamic banks, loan loss provisions, procyclicality JEL Classification: G15, G21, M41

This paper is the first to examine whether the loan loss provisioning behavior of Islamic banks is procyclical. From a dynamic panel data methodology, the empirical results show that loan loss provisioning in Islamic banks is indeed procyclical, as higher economic growth leads to a decline in loan loss provisions. A closer investigation is also conducted to examine whether capital management, income smoothing, or signaling behavior can alter the procyclicality of loan loss provisions. Specifically, our results document that only capital management behavior can overcome the procyclicality of loan loss provisions. This paper therefore advocates the importance of strengthening discretionary behavior in Islamic banks in terms of capital management using loan loss provisions, particularly during economic boom.

Introduction

In the aftermath of the 2008 global financial crisis, the importance of strengthening prudential regulation in banking has acquired greater attention from academics and policy makers. This is because banks tend to behave imprudently during economic boom by lowering credit standards to boost loans, but strengthening credit standards during economic downturn when higher amount of loans is necessary to help economic recovery. Such bank behavior is often referred to as the procyclicality of bank credit risk management [START_REF] Altman | The link between default and recovery rates: Theory, empirical evidence, and implications[END_REF].

Prior literature on the procyclicality of bank credit management has been devoted to analyze the implication of loan loss provisioning system. [START_REF] Arpa | The influence of macroeconomic developments on Austrian banks: implications for banking supervision[END_REF] show that bank loan loss provisions are procyclical with business cycle in the case of Austrian commercial banks. [START_REF] Cavallo | Do banks provision for bad loans in good times? Empirical evidence and policy implications[END_REF] also show that higher economic growth is associated with a decline in loan loss provisions in banking. [START_REF] Laeven | Loan loss provisioning and economic slowdowns: too much, too late?[END_REF] subsequently report that loan loss provisions indeed decreases following higher earnings and loan activities due to economic boom.

For commercial banks operating in OECD countries, loan loss provisioning system is also procyclical as in [START_REF] Bikker | Bank provisioning behavior and procyclicality[END_REF]. [START_REF] Bouvatier | Banks' procyclical behavior: does provisioning matter?[END_REF] find the similar results for European banks, but they consider the impact of non-discretionary and discretionary provisions on loan growth 2 . Specifically, only discretionary provisions have a procyclical impact. [START_REF] Bouvatier | Effects of loan loss provisions on growth in bank lending: Some international comparisons[END_REF] extend their prior work by including bank sample from developing countries. They show that non-discretionary provisions still have a procyclical impact regardless of whether or not income smoothing is conducted. Their results also show that the procyclicality of loan loss provisions is more pronounced in developing countries.

Another strand of literature divides loan loss provisions into two components: general and specific provisions (Cortavaria et al., 2000). While general provisions aim to mitigate unexpected credit risk in the future, specific provisions are used to cover expected credit risk. Accordingly, general provisions can be considered a forwardlooking component, while specific provisions are a backward-looking component.

Because general provisions is a forward-looking component, whether or not general 2 Non-discretionary provisions are related to specific provisions that aim to cover expected credit risk, while discretionary provisions are related to general provisions that are also affected by managerial objective such as capital management, income smoothing or signaling.

provisions change is therefore affected by banks' managerial discetionary behavior [START_REF] Whalen | The nature of information in commercial bank loan loss disclosures[END_REF][START_REF] Beaver | Discretionary behavior with respect to allowances for loan losses and the behavior of security prices[END_REF]. Prior litetrature indicates that the discretionary behavior of bank managers in determining the amount of loan loss provisions consists of capital management, income smoothing and signaling behavior [START_REF] Bouvatier | Banks' procyclical behavior: does provisioning matter?[END_REF][START_REF] Ahmed | Bank loan loss provisions: A reexamination of capital management, earnings management and signaling effects[END_REF][START_REF] Lobo | Bank managers' heterogeneous decisions on discretionary loan loss provisions[END_REF]. This means that banks may increase loan loss provisions provisions to exercise different set of strategies; whether to enhance capital management, smooth earnings, signal its strength to market, or combination of these strategies.

In order to deal with the procyclicality of loan loss provisions, Fernandez de [START_REF] Fernandez De Lis | Credit growth, problem loans and credit risk provisioning in Spain[END_REF] shed light on the importance of dynamic provisioning system, especially when discretionary provisions cannot offset the procyclicality of nondiscretionary provisions. In this regard, the dynamic provisioning system requires additional type of provisions, which is statistical provisions. The creation of statistical provisions will smooth total provisions (general, specific and statistical provisions) during economic downturn, because banks already create statistical provisions as buffer during economic boom. This implies that loans disbursement during economic downturn can be maintained and hence, supporting economic recovery processes.

While the implementation of dynamic provisioning system for conventional banks has been widely assessed, there is no prior study that examines the similar issue for Islamic banks. For countries with dual banking system, understanding whether the current loan loss provisioning system in Islamic banks is procyclical provides important insights on the potential implication of dynamic provisioning system for Islamic banks. To the best of our knowledge, this paper is therefore the first attempt to investigate the procyclicality issues of loan loss provisions for Islamic banks. Our contribution in the present paper is twofold. First, we test how economic growth affects loan loss provisions in Islamic banks. Hence, we may characterize the presence of procyclicality, if higher economic growth is associated with lower loan loss provisions. Second, we augment the analysis by examining whether the procyclicality of loan loss provisions, if any, is conditional on banks' managerial discretionary behavior, which is related to capital management, income smoothing and signaling. As such, we may highlight whether or not the discretionary behavior of Islamic banks is sufficient to offset the procyclicality of loan loss provisions.

Eventually, this paper may provide policy implications whether the implementation of dynamic provisioning system is necessary for Islamic banks with different accounting standards compared to conventional banks.

As a matter of fact, the AAOIFI (Accounting and Auditing Organization for Islamic Financial Institutions)which has been established to harmonize regulatory frameworks that involve Islamic principles [START_REF] Grais | Corporate governance and Shari'ah compliance in institutions offering Islamic financial services[END_REF] also advocates the importance of implementing dynamic provisioning system for Islamic banks to cope with unexpected risks due to the difference in accounting treatment [START_REF] Quttainah | Do Islamic Banks Employ Less Earnings Management?[END_REF]. Unexpected risks of Islamic banks may come from PLS (profit-loss sharing) contracts that basically have two types of investment account:

Restricted Investment Account Holder (RIAH) and Unrestricted Investment Account Holder (UIAH). These contracts can be reported differently in bank income statement. [START_REF] Karim | International accounting harmonization, banking regulation, and Islamic banks[END_REF] points out that some Islamic banks treat RIAH as equity or liability, while others consider it as off-balance sheet item.

Ultimately, since Islamic banks' activities are based on PLS contracts, their credit risk management is also different than conventional banks. Understanding the procyclicality of loan loss provisions in Islamic banks is therefore crucial, as Islamic banks grow rapidly across countries from time to time. In 2016, Islamic banks around the world is predicted to grow by 90 percent with the total assets, reaching more than USD 1.8 trillion compared to only USD 939 billion in 2010 [START_REF] Cevik | The Behavior of Conventional and Islamic Bank Deposit Returns in Malaysia and Turkey[END_REF]. As the presence of Islamic banks becomes substantial, its role in economic development will be more pronounced than before. In this respect, addressing the procyclicality issues of loan loss provisions in Islamic banks should be more of a concern, particularly during economic downturn.

The remainder of this paper is organized as follows. Section 2 presents our data and hypotheses development. Section 3 presents our methodology and variables.

Section 4 describes empirical results, while Section 5 concludes the paper.

Data and hypothesis development

Data sources

For the purpose of this study, we retrieve a sample of 146 Islamic banks around the world from 1997 to 2012 covered by BankScope Fitch IBCA. Specifically, we retrieve balance sheet and income statement information of Islamic banks from the following countries: United Arab Emirates (10), Bangladesh (2), Bahrain (19), Brunei Darussalam (1), Egypt (2), UK (5), Gambia (1), Indonesia (3), Iraq (6), Iran (16), Jordan (3), Kuwait (9), Cayman Islands (1), Lebanon (3), Mauritania (2), Maldives

(1), Malaysia (17), Philippines (1), Pakistan (9), Palestinian Territory (2), Qatar (4), Russia (1), Saudi Arabia (4), Sudan (12), Singapore (1), Syiria (2), Tunisia (1), Turkey (4), and Yemen (4) 3 . We also include macroeconomic data such as real gross domestic product obtained from the World Bank.

Hypothesis development

The objective of this study is twofold. First, we aim to test whether the procyclicality of loan loss provisions occurs by running regressions of loan loss provisions on economic growth as explanatory variable of interest. Second, we test whether the procyclicality of loan loss provisions is conditional on Islamic banks' managerial discretionary behavior comprising capital management, income smoothing and signaling.

For such purposes, we specify Hypothesis 1 and Hypothesis 2 to tackle the first and second objective, respectively:

Hypothesis 1: Islamic banks build up higher loan loss provisions following economic boom Hypothesis 2 : Islamic banks' behavior in building up loan loss provisions in response to economic growth depends on the degree of banks' managerial discretionary behavior related to capital management, income smoothing or signaling.

Previous studies indeed document that bank capital management may affect loan loss provisioning behavior of banks [START_REF] Bouvatier | Banks' procyclical behavior: does provisioning matter?[END_REF][START_REF] Parker | Loan loss provisioning practices of Asian banks[END_REF]. Specifically, [START_REF] Bouvatier | Banks' procyclical behavior: does provisioning matter?[END_REF] report that banks with poor capitalization are less inclined to build up loan loss provisions, while [START_REF] Parker | Loan loss provisioning practices of Asian banks[END_REF] show that income smoothing strategies tend to be adopted in well-capitalized banks in Japan and poorly-capitalized banks in India. In this context, the role of bank capital management and income smoothing can not be separated in examining the issues of procyclicality in banking. Phrased differently, aside from capital 3 The number of banks are in parentheses. management, income smoothing using loan loss provisions should also be examined whether it affects the procyclicality of loan loss provisions. In parallel, signaling strategies using loan loss provisions are also somehow related to income smoothing strategies, as banks may signal its strength when they may generate higher profits and, thus, loan loss provisions. For such reasons, Hypothesis 2 considers not only the discretionary behavior of Islamic related capital management, but also income smoothing and signaling to examine whether the impact of economic growth on loan loss provisions depends on managerial discretions.

Methodology and variables

The methodology used in this paper comprises two stages. In the first stage, we test the occurrence of procyclical effect of loan loss provisions as stated in Hypothesis 1, while the second stage is to examine whether capital management, income smoothing or signaling using loan loss provisions during economic boom can offset the procyclicality of loan loss provisions as shown in Hypothesis 2.

Hypothesis 1 can be tested using the following equations in which we introduce the one-year-lagged value of dependent variable as control variable.

t i t i t i t i t i GROWTH NPL LTA LLPTA LLPTA , 3 , 2 , 1 1 , 0 ,            (1a) t i t i t i t i t i GROWTH NPL LTA LLRTA LLRTA , 3 , 2 , 1 1 , 0 ,            (1b)
In Eq. (1a) and Eq. (1b), we merely focus on the determinants of loan loss provisions that may reflect non-discretionary behavior of bank managers, because the procyclicality of loan loss provisions is mostly related to non-discretionary component of loan loss provisions (Bouvatier and[START_REF] Bouvatier | Banks' procyclical behavior: does provisioning matter?[END_REF]2012). On the other hand, adding more variables reflecting discretionary component of loan loss provisions into Eq. (1a) and Eq. (1b) will make the degree of freedom becomes smaller when we have only limited number of Islamic banks available in our sample.

From Eq. (1a), LLPTA is the ratio of loan loss provisions to total assets. For robustness consideration, we also use the ratio of loan loss reserves to total assets (LLRTA) as depenent variable in Eq. (1b). LTA is the ratio of total loans to total assets, while NPL is the ratio of non-performing loans to total loans and GROWTH is the real economic growth rate at the country level.

In the next turn, Hypothesis 2 can be tested using the following equations in which we add interaction terms between economic growth and three discretionary purposes of Islamic bank managers using loan loss provisions.

t i t i t i t i t i GROWTH CAP CAP GROWTH NPL LTA LLPTA LLPTA , 5 4 3 , 2 , 1 1 , 0 , *                 (2a) t i t i t i t i t i GROWTH CAP CAP GROWTH NPL LTA LLPTA LLRTA , 5 4 3 , 2 , 1 1 , 0 , *                 (2b) t i t i t i t i t i GROWTH EBTP EBTP GROWTH NPL LTA LLPTA LLPTA , 5 4 3 , 2 , 1 1 , 0 , *                 (2c) t i t i t i t i t i GROWTH EBTP EBTP GROWTH NPL LTA LLPTA LLRTA , 5 4 3 , 2 , 1 1 , 0 , *                 (2d) t i t i t i t i t i GROWTH SIGN SIGN GROWTH NPL LTA LLPTA LLPTA , 5 4 3 , 2 , 1 1 , 0 , *                 (2e) t i t i t i t i t i GROWTH SIGN SIGN GROWTH NPL LTA LLPTA LLRTA , 5 4 3 , 2 , 1 1 , 0 , *                 (2f)
Eq. (2a) and Eq. (2b) contain the interaction term between capitalization and economic growth (CAP*GROWTH) in order to examine the role of capital management in affecting the impact of economic growth on loan loss provisions. CAP is defined as the ratio of total equity to total assets. Meanwhile, the role of income smoothing in affecting the procyclicality of loan loss provisions due to economic growth is estimated using Eq. (2c) and Eq. ( 2d) by incorporating the interaction term between income smoothing and economic growth (EBTP*GROWTH). EBTP is the income smoothing variable defined as the ratio of earning before tax and provisions divided by total assets. Finally, Eq. (2e) and Eq. (2f) contain the interaction term between signaling and economic growth (SIGN*GROWTH) which aims to investigate how signaling behavior affects the link between economic growth and loan loss provisions. SIGN captures signaling behavior, which is measured by:

) ( 5 . 0 , 1 , , 1 , , t i t i t i t i t i TA TA ER ER SIGN     
ER is defined as earnings before tax and loan loss provisions, while TA is total assets. Overall, we estimate Eq. (1a) to Eq. (2f) using a dynamic panel data methodology for several reasons. First, the current loan loss provisioning behavior might be affected by the last year's provisioning behavior due to managerial learning and, thus, loan loss provisions are dynamic rather than static variable. Therefore, the use of dynamic panel data methodology is proper to control for dynamic movements of loan loss provisions. Second, loan loss provisions can be affected by explanatory variables introduced from Eq. (1a) to Eq. ( 2f), but those explanatory variables can also be affected by loan loss provisions. If this is the case, reverse causality problems might be an important drawback that acquires particular attention. The use of dynamic panel data methodology is also relevant, because it can avoid reverse causality problems between loan loss provisions and their determinants.

In terms of econometric procedure, we follow [START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF] to estimate dynamic panel data models as shown in Eq. (1a) -Eq.( 2f). This method is referred to as the two-step system GMM estimation or the system GMM. The system GMM is shown to be more efficient than the standard GMM [START_REF] Baltagi | Econometric Analysis of Panel Data[END_REF]. We consider orthogonal transformations of instruments to control for possible crosssectional fixed effects, while we also include [START_REF] Windmeijer | A finite sample correction for the variance of linear efficient two-step GMM estimators[END_REF] finite sample correction to ensure for the validity of the system GMM. Finally, the system GMM is valid when both the AR(2) and Hansen-J test are not significant, indicating that there is no second order autocorrelation among errors and overidentifying restrictions are valid, respectively.

Empirical results

Results

In Table 1, we present descriptive statistics of variables used in this study. No potential outliers can be detected, as the values of each variable seem economically plausible. We also eliminate all zero values in each variable to ensure that unavailable data is not treated as zero in our model estimations. Meanwhile, Table 2 presents the correlation structure of all variables. We can notice that all independent variables presented from Eq. (1a) to Eq. (2f) are not highly correlated and hence, potential multicollinearity problems are less likely to occur.

[Insert Table 1 andTable 2 here] In Table 3, we document that Islamic banks use loan loss provisions for nondiscretionary purposes in which higher loan-to-asset ratio (LTA) and non-performing loans (NPL) are associated with higher loan loss provisions. These results are consistent with prior literature on the procyclicality of loan loss provisions in conventional banks (e.g. Bouvatier and[START_REF] Bouvatier | Banks' procyclical behavior: does provisioning matter?[END_REF]2012;[START_REF] Bikker | Bank provisioning behavior and procyclicality[END_REF]. More importantly, loan loss provisions of Islamic banks are procyclical, as higher economic growth deteriorates loan loss provisions. Hypothesis 1 is therefore not rejected. Our dynamic panel data models to test Hypothesis 1 are also valid, because both the AR(2) and Hansen-J test are not significant. Such procyclical behavior of Islamic banks in building up loan loss provisions is indeed consistent with the results obtained for conventional banks as in prior literature presented earlier.

[Insert Table 3 here] Furthermore, we present our empirical results to test Hypothesis 2 in Table 4.

Model 1 is addressed to test whether capital management using loan loss provisions affects the procyclicality of loan loss provisions, while Model 2 and Model 3 are to test the impact of income smoothing and signaling behavior using loan loss provisions on the procyclicality of loan loss provisions, respectively.

[Insert Table 4 here] From Table 4, it is shown that higher loan loss provisions are still affected by the loan-to-assets ratio (LTA) and non-performing loans (NPL). In this context, the use of loan loss provisions for non-discetionary purposes as discussed earlier is not altered. Meanwhile, higher economic growth (GROWTH) still has a negative impact on loan loss provisions measured by either LLPTA or LLRTA. However, we find that only bank discretionary behavior related to capital management alters the procyclicality of loan loss provisions, as shown by the positive coefficients of interaction variable between capitalization (CAP) and economic growth (GROWTH) in Model 1 (Table 4). The positive coefficients of CAP*GROWTH is also higher than the negative coefficients of GROWTH.

For instance, if LLPTA is used as dependent variable, the negative impact of GROWTH on loan loss provisions indicating the procyclicality of loan loss provisions occurs when CAP is less than 24 percent. In other words, this may also indicate that the countercyclical effect of loan loss provisions is apparent if the equity-to-assets ratio exceeds 24 percent. The identical results occur when we use LLRTA as dependent variable, where the cut-off point of CAP is 3 percent in order to enable loan loss reserves become countercyclical.

All in all, loan loss provisions in Islamic banks with higher capitalization are countercyclical, because loan loss provisions increase in response to stronger economic growth. Islamic banks with higher capitalization are indeed more prudent by building up higher loan loss provisions during economic boom, which in turn enables them to decrease loan loss provisions in bad times. Consequently, banks with higher capitalization can boost loans disbursement during economic downturn. We therefore highlight that Islamic banks with higher capitalization may contribute well to economic recovery. Finally, our models estimated in Table 4 are valid, because the AR(2) test and the Hansen-J test are not significant at least at the 5 percent level.

Robustness checks

In order to ensure the robustness of our empirical models, we modify the specification of our dynamic panel data models. Specifically, we follow [START_REF] Bouvatier | Banks' procyclical behavior: does provisioning matter?[END_REF] by considering first-difference transformation instead of orthogonal deviation transformation of instruments. Table 5 and Table 6 present our results using this new specification. On the whole, the empirical results discussed earlier are not altered. All dynamic panel data models using first difference transformation of instruments are still valid, because the AR(2) test and the Hansen-J test are not rejected.

[Insert Table 5 andTable 6 here]

Conclusion

This paper is the first to examine whether the loan loss provisioning of Islamic banks is procyclical over business cycle. Our empirical results are supportive of the notion that loan loss provisions in Islamic banks are generally procyclical, because loan loss provisions decreases due to stronger economic growth. In other words, Islamic banks' provisioning behavior might deepen economic recessions, as loan loss provisions increases in response to a decline in economic growth.

However, a closer investigation reveals that the procyclicality of loan loss provisions can be offset by discretionary behavior of bank managers, particularly related to capital management behavior. In this sense, enhancing capital management using loan loss provisions as a discretionary behavior of Islamic bank managers during economic boom is more than sufficient to ensure that loan loss provisions increase in good times, but decrease in bad times. Accordingly, the use of dynamic provisioning system as recommended by the AAOIFI is not crucial for Islamic banks, because relying on the discretionary behavior of Islamic bank managers related to capital management using loan loss provisions can already offset the procyclicality of loan loss provisions.

Notes:

The definition of variables follows Table 1. Regressions are carried out using the system GMM following Blundell and Bond (1999) by taking orthogonal deviation of instruments into account and hence, we control for cross-sectional fixed effects. Models are valid if the AR(2) and Hansen-J test are not significant. ***, **, * indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.

Notes:

The definition of variables follows Table 1. Regressions are carried out using the system GMM following Blundell and Bond (1999) by taking orthogonal deviation of instruments into account and hence, we control for cross-sectional fixed effects. Models are valid if the AR(2) and Hansen-J test are not significant. ***, **, * indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.

Notes:

The definition of variables follows Table 1. Regressions are carried out using the system GMM following Blundell and Bond (1999) by taking firstdifference transformation of instruments into account and hence, we control for cross-sectional fixed effects. Models are valid if the AR(2) and Hansen-J test are not significant. ***, **, * indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.

Notes:

The definition of variables follows Table 1. Regressions are carried out using the system GMM following Blundell and Bond (1999) by taking firstdifference transformation of instruments into account and hence, we control for cross-sectional fixed effects. Models are valid if the AR(2) and Hansen-J test are not significant. ***, **, * indicate significance at the 1 percent, 5 percent, and 10 percent levels, respectively.

Table 1 .

 1 Descriptive statistics

	Variable	Definition

Table 2 .

 2 Correlation structure

	Variables	LLPTA	LLRTA	LTA	NPL	GROWTH	CAP	EBTP	SIGN
	LLPTA	1
	LLRTA	0.2766	1
	LTA	0.143	0.3512	1
	NPL	0.3699	0.7021	0.0009	1
	GROWTH	-0.0648	-0.002	0.0428	-0.0077	1
	CAP	-0.1161	0.1479	0.0044	0.1388	0.0135	1
	EBTP	-0.1322	0.0619	0.3473	-0.1635	0.1967	-0.0158	1
	SIGN	0.0123	-0.004	0.0188	-0.1135	-0.0321	-0.0018	-0.6156	1

Table 3 .

 3 The procyclicality of loan loss provisions

			Dependent variables
	Explanatory variables	LLPTA	LLRTA
	LLPTA(-1)	0.6973***
		(0.144)
	LLRTA(-1)		0.7768***
			(0.035)
	LTA	0.0042***	0.0091***
		(0.002)	(0.002)
	NPL	0.0130	0.0619***
		(0.022)	(0.013)
	GROWTH	-0.0245**	-0.0500***
		(0.009)	(0.017)
	Observations	311	310
	Number of index	73	72
	AR(2) test (p-value)	0.981	0.350
	Hansen-J test (p-value)	0.420	0.126

Table 4 .

 4 Do capital management, income smoothing and signaling alter the procyclicality of loan loss provisions?

			Model 1		Model 2		Model 3
	Explanatory variables	LLPTA	LLRTA	LLPTA	LLRTA	LLPTA	LLRTA
	LLPTA(-1)	0.6137***		0.6721***		0.5889***
		(0.105)		(0.121)		(0.162)
	LLRTA(-1)		0.8107***		0.7761***		0.8826***
			(0.051)		(0.051)		(0.088)
	LTA	0.0109***	0.0099***	0.0066***	0.0095***	0.0048*	0.0095**
		(0.003)	(0.003)	(0.002)	(0.003)	(0.003)	(0.004)
	NPL	0.0143	0.0587***	0.0081	0.0570**	0.0288	0.0315
		(0.019)	(0.015)	(0.022)	(0.022)	(0.030)	(0.037)
	GROWTH	-0.0814***	-0.0097	-0.0333	-0.0506***	-0.0204**	-0.0600**
		(0.030)	(0.042)	(0.025)	(0.017)	(0.010)	(0.023)
	CAP	-0.0233**	-0.0169*
		(0.013)	(0.010)
	CAP * GROWTH	0.3425***	0.2666**
		(0.189)	(0.342)
	EBTP			-0.0546	0.0052
				(0.039)	(0.045)
	EBTP *GROWTH			0.4644	-0.2104
				(0.746)	(0.609)
	SIGN					-0.0344	0.0177
						(0.063)	(0.059)
	SIGN*GROWTH					0.0099	-0.0764
						(0.440)	(0.737)
	Observations	311	310	311	300	256	249
	Number of index	73	72	73	68	68	64
	AR(2) test (p-value)	0.724	0.342	0.946	0.351	0.398	0.355
	Hansen-J test (p-value)	0.453	0.155	0.325	0.083	0.554	0.145

Table 5 .

 5 The procyclicality of loan loss provisions: First-difference transformation

			Dependent variables
	Explanatory wariables	LLPTA	LLRTA
	LLPTA(-1)	0.6474***
		(0.146)
	LLRTA(-1)		0.7247***
			(0.040)
	LTA	0.0045***	0.0112***
		(0.002)	(0.003)
	NPL	0.0147	0.0604***
		(0.023)	(0.018)
	GROWTH	-0.0202*	-0.0470**
		(0.013)	(0.020)
	Observations	311	310
	Number of index	73	72
	AR(2) test: p-Val	0.938	0.349
	Hansen-J test: p-Val	0.323	0.107

Table 6 .

 6 Capital management, income smoothing, signaling and the procyclicality of loan loss provision: First-difference transformation

			Model 1		Model 2		Model 3
	Explanatory variables	LLPTA	LLRTA	LLPTA	LLRTA	LLPTA	LLRTA
	LLPTA(-1)	0.5643***		0.6330***		0.5889***
		(0.101)		(0.128)		(0.162)
	LLRTA(-1)		0.7619***		0.7245***	0.0048*	0.8826***
			(0.045)		(0.046)	(0.003)	(0.088)
	LTA	0.0125***	0.0132***	0.0050	0.0124***	0.0288	0.0095**
		(0.004)	(0.004)	(0.004)	(0.004)	(0.030)	(0.004)
	NPL	0.0198	0.0578***	0.0113	0.0533**	-0.0204**	0.0315
		(0.021)	(0.016)	(0.024)	(0.024)	(0.010)	(0.037)
	GROWTH	-0.0941***	-0.0138	-0.0058	-0.0505***		-0.0600**
		(0.032)	(0.039)	(0.046)	(0.019)		(0.023)
	CAP	-0.0302**	-0.0120
		(0.015)	(0.012)
	CAP * GROWTH	0.4450**	-0.1952
		(0.217)	(0.302)
	EBTP			-0.0045	-0.0050
				(0.083)	(0.038)
	EBTP *GROWTH			-0.5048	-0.0052
				(1.713)	(0.346)
	SIGN					-0.0344	0.0177
						(0.063)	(0.059)
	SIGN*GROWTH					0.0099	-0.0764
						(0.440)	(0.737)
	Observations	311	310	311	300	256	249
	Number of index	73	72	73	68	68	64
	AR(2) test: p-Val	0.707	0.341	0.962	0.351	0.398	0.355
	Hansen-J test: p-Val	0.431	0.195	0.302	0.051	0.554	0.145