Professor, Jean-Louis Combes
email: l.combes@udamail.fr

Rasmané Ouedraogo
email: rasmane.ouedraogo@etu.udamail.fr

Catherine Araujo Bonjean

Keywords: Inclusive growth, Taxes revenue, Inequality, Employment. JEL codes F43, D63, O11, H20

Despite high economic growth in the last decades, many developing countries remain into poverty, income inequality and unemployment of young people, which have led some countries to adopt inclusive growth strategies. In this paper, we show how inclusive growth can boost tax revenue mobilization in developing countries. Effective public policies, improving social cohesion are fundamental to meet citizens' aspirations and therefore incite them to comply with taxes. To this end, this study uses GMM techniques to deal with endogeneity issue and spans 55 developing countries over the period of 1995-2010. We find that inclusive growth has positive effect on tax revenue mobilization. This finding is robust to various aspects of inclusive growth measurements and additional controls.

Introduction

Many countries are recognizing the urgency of enhancing tax resource mobilisation (TRM) in a global climate of economic uncertainty and competition. The global crisis convinced policymakers in developing countries that the time had come to reduce their dependence on external resources and their vulnerability to external financial shocks by addressing the potential of TRM. TRM at a significant level is essential to solidify ownership over development strategy, and to strengthen the bonds of accountability between governments and their citizens. In effect, DRM provides "policy space" to developing countries which is often constrained under the terms and conditions of external resource providers. According to [START_REF] Mascagni | Tax revenue mobilisation in developing Countries: issues and challenges[END_REF], the governments in developing countries need additional financial resources to address the huge development challenges they face. While great progress was made in recent years towards achieving the Millennium Development Goals, a large proportion of people in low-income countries still face poverty, inequality, unemployment of young people, malnutrition, vulnerability to natural disasters and preventable diseases, amongst others. Aid has certainly contributed to alleviating some of these issues, but it is becoming increasingly clear that the development challenge requires increasing tax revenue.

Developing countries across the world always suffer from insufficient supply of internal resources. Very low tax to gross domestic product (GDP) ratio is a common characteristic of most of the developing countries. This situation remains contrasted with the high economic growth rates that developing countries have experienced in recent decades. Economic growth is expected to raise people's income, create job and opportunities and therefore increase the tax base. However, this expectation is sometimes hampered by the absence of straight mathematical relationship between economic growth and the increase in the tax base. Indeed, it must that the benefits of growth to be shared to a large part of the population in order to meet citizens' aspirations (availability of public goods, reduction of poverty and inequality) and therefore incite them to comply with taxes. Previous literature focused on economic structural characteristics and institutional quality as the main determinants of tax mobilisation and failed to integrate tax payer expectations of government policies and their willingness to pay taxes (see for a review [START_REF] Chelliah | Trends in Taxation in Developing Countries[END_REF][START_REF] Stotsky | Tax Effort in SubSaharan Africa[END_REF], Bird, Martinez-Vazquez and Torgler 2008;[START_REF] Haque | Determinants of low tax efforts of developing countries[END_REF][START_REF] Crivelli | Resource Blessing, Revenue Curse? Domestic Revenue Effort in Resource-Rich Countries[END_REF]. This papers aims to fill the gap in previous literature by focusing on the effect of inclusive growth on the mobilization of tax revenue. According to [START_REF] Alm | Why do people pay taxes[END_REF], tax compliance increases with (perceptions of) the availability of public goods and services. They suggest that governments can increase compliance by providing goods that citizens prefer in a more efficient and accessible manner, or by more effectively emphasizing that taxes are necessary for the receipt of government services. Accordingly, the main concern of taxpayers is what they get directly in return for their tax payments in the form of public services. Therefore, the implementation of an effective and fair taxation system is essential in order to finance public policies given the fact that there is strong relationship between citizens and the state. According to the concept of resource bargain, the state negotiates resources to finance public policies and the citizens expect in return for their tax payments in the form of public services (Fjeldstad and Semboja 2001;[START_REF] Moore | Revenues, state formation, and the quality of governance in developing countries[END_REF]). Then, individuals may pay taxes because they value the goods provided by the government, recognizing that their payments are necessary both to help finance the goods and services and to get others to contribute (Fjeldstad and Semboja 2001). Regarding this theory of fiscal exchange, tax compliance depends on the respect of this contractual relationship and the policies of the government. If the fiscal exchange is enforced, that implies that populations will benefit from growth though government policies. That is what expected with inclusive growth policy. Inclusive growth ensures that the economic opportunities created by growth are available to all-particularly the poor-to the maximum possible extent.

The growth process creates new economic opportunities that are unevenly distributed.

Inclusive growth means the participation of all in the tangible benefits of economic growth, made possible mainly by job creation that can lead to inequality and poverty reduction.

Taxation plays a vital role in promoting citizenship and reciprocal relations between the taxpayer and government. It is about encouraging people to make a contribution for which they receive something in return. In Sierra Leone, for instance, public resistance to the introduction of the Goods and Services Tax in 2009 (which is another name for value added taxes (VAT)) forced the government and its key donors to link the new tax to improved provision of services.

These discussions aforementioned reveal that inclusive growth can boost tax revenue mobilization or tax compliance. This paper aims to study the relationship between inclusive growth and tax revenue mobilization. While a very limited works studied the determinants of inclusive growth (See [START_REF] Anand | Inclusive Growth: Measurement and determinants[END_REF], there is no empirical study that tackles the consequences or the benefits of inclusive growth for developing countries. This paper is attempting to come to grips with the link between inclusive growth and tax revenue mobilization and therefore it seeks to understand how to mobilize resource in order to finance development. No doubts that effective public policies, improving social cohesion and the nexus between revenues and public goods are fundamental to meet the aspirations of the citizens and therefore encourage them to comply with taxes.

Using GMM estimators to deal with endogeneity issue, our results show that inclusive growth has positive impact on tax revenue mobilization. This finding is robust to various controls and alternative measurements of inclusive growth index and the dependent variable.

The rest of the paper is organized as follow. Section 2 presents the measure of the inclusive growth index. Section 3 specifies the empirical strategy and the explanatory variables, while Section 4 presents the results obtained from the estimates. In section 5, we analyze transmission channels through which inclusive growth could affect tax revenue mobilization.

Section 6 concludes and draws up some policy recommendations.

Measurement of Inclusive growth

The notion of inclusive growth has captured a growing importance in the literature, reflecting the desire to define and measure an index that includes most prominent indicators of long lasting development policies. However, it is noteworthy that there is no consensus on how to define inclusive growth and how to measure it. The definition of inclusive growth originates from the notion of pro-poor growth. The term "inclusive growth" was coined by Kakwani and Pernia 2000) and refers to the notion of participation in and benefitting from growth processes. Therefore, it goes beyond the reduced concept of pro-poor growth that focused on the level and distributions of income outcomes and takes into account non-income outcomes.

Since then, there is a battery of works which tried to capture the dimensions of an inclusive growth process and conceptualize the patterns of inclusive growth (See for review [START_REF] Ali | Measuring Inclusive Growth[END_REF][START_REF] Bhalla | Incluisve Growth? Focus on Employment[END_REF][START_REF] Ianchovichina | Inclusive Growth Analytics: Framework and Application[END_REF][START_REF] Klasen | Measuring and Monitoring Inclusive Growth: Multiple Definitions, Open Questions, and Some Constructive Proposals[END_REF][START_REF] Mckinley | Inclusive Growth Criteria and Indicators: An Inclusive Growth Index for Diagnosis of Country Progress[END_REF][START_REF] Rauniyar | Inclusive Development: Two Papers on Conceptualization, Application, and the ADB Perspective[END_REF], Blanke and others 2015). The most common of all these works is that economic growth does not automatically translate into widely shared gains and inclusive growth is mostly referred to poverty alleviation, inequality reduction and employment. In a report on inclusive growth, the Organization for Economic Cooperation and Development (OECD) (2012) identifies three problems that even the record levels of growth of the 1990s and decade of 2000s failed to tackle: poverty, unemployment and inequality. Therefore, inclusive growth has two dimensions: benefit-sharing (inequality and poverty) and participation (employment). The benefit-sharing dimension looks into whether the process led to a decrease in poverty and in income inequality. This segment of the analysis is aligned with the concept of "relative pro-poor growth", which differs from "absolute pro-poor growth" due to the understanding that, for growth to be pro-poor, poor people's income must grow faster than that of wealthier people, resulting in a decrease in inequality [START_REF] Grosse | Measuring Pro-Poor Growth in Non-Income Dimensions[END_REF]. The participation dimension is the second significant dimension in conceptualizing inclusive growth and in differentiating it from pro-poor growth. The participation dimension looks into how the society is involved in the process, given that such involvement is essential for promoting social coherence and for capacity-building, which are crucial for the sustainability of an inclusive growth process. Analyzed in the economic sphere, a participatory process can be thought of as characterized by generating employment for a significant part of a country's population. Such conceptualization of inclusive growth based on three dimensions is in line with Ramos, Ali and Son (2007), [START_REF] Ramos | Mapping Inclusive Growth in Developing Countries[END_REF] who attempted to define and measure inclusive growth. Notwithstanding the unconcluded concept of inclusive growth, some authors have tried to measure it. However, likewise the absence of definition, the measurement of inclusive growth is still not settled. [START_REF] Ramos | Mapping Inclusive Growth in Developing Countries[END_REF]. However, all of these attempts suffer from serious shortcomings. First, an update to any attribute of a composite index causes the index to be modified and the weights are highly arbitrary and sensitive to the choices of the author. Second, a merely analysis of trends does not give us a sense of how much is growth inclusiveness and then does not give us neither a clear picture nor very much of an idea on how to proceed. In this paper, we follow closely [START_REF] Ali | Measuring Inclusive Growth[END_REF] who attempted to construct inclusive growth index using a macro social mobility function, following the micro literature on income distribution. This measure of inclusive growth is based on a utilitarian social welfare function drawn from consumer choice literature, where inclusive growth depends on two factors: (i) income growth; and (ii) income distribution or employment. More specifically, [START_REF] Ali | Measuring Inclusive Growth[END_REF] propose an income equity index as:

𝑤 = 𝑦 * ̅̅̅̅ 𝑦 ̅ (1)
Where 𝑤 is the equity index, 𝑦 ̅ is the average of income or employment rate and 𝑦 * ̅̅̅ represents inclusive growth.

Equation (1) implies that income or opportunities are equitably (inequitably) distributed if 𝑤 is greater (less) than 1. For a completely equitable society, 𝑤 = 1. Rearranging, 𝑦 ̅ * = 𝑤 * 𝑦 ̅.

Inclusive growth requires increasing 𝑦 ̅ * , which could be achieved by: (i) increasing 𝑦 ̅ , i.e increasing average income through growth or the average level of opportunities; (ii) increasing the equity index of income or the index of opportunities, 𝑤; or (iii) a combination of (i) and (ii). Differentiating the above equation:

𝑑𝑦 ̅ * = 𝑤 * 𝑑𝑦 ̅ + 𝑑𝑤 * 𝑦 ̅ (2)
Where 𝑑𝑦 ̅ * measures the change in the degree of inclusive growth. If 𝑑𝑦 ̅ * > 0, growth is more inclusive. Equation (2) allows us to decompose inclusive growth into income growth and change in equity or opportunities in the society. The first term in the right side of equation ( 2) is the contribution of increase in average income (keeping income distribution or average opportunities constant) while the second term is the contribution of changes in the income distribution or job opportunities (keeping the average income or employment rate unchanged).

Therefore, inclusive growth depends on the sign and the magnitude of the two terms. It is noteworthy that the two terms can be as well as all positive (growth is inclusive) or negative (growth is not inclusive) or a combination of different signs of the two terms. For this latter case, the inclusiveness of growth will depend on which contribution outweighs the other. To illustrate the evolution of inclusive growth, we rearrange equation (2) as:

𝑑𝑦 ̅ * 𝑦 ̅ * = 𝑑𝑦 ̅ 𝑦 ̅ + 𝑑𝑤 𝑤 (3)
This transformation decomposes inclusive growth into growth (𝑦 ̅) and percentage change in equity or opportunities (𝑤).

Data and stylized facts

Our dataset includes 55 developing countries, only data availability restricted our sample (see the appendix for a list of countries). As pointed out above, we focus on income inequality and employment. We collect annual data for the period from 1995 to 2010. We define three-year panel data : 1995-1997; 1998-2000; 2001-2003; 2004-2006; 2007-2010. This type of data is preferable to pure time-series or cross-sectional data, as it marries possible inter-temporal dynamics and important cross-country variation. Three-year averaging is, moreover, likely to minimize non-systematic errors in the data. GDP per capita and tax revenue over GDP are from IMF datasets (IFS and World Economic Outlook). Income inequality data refers to Gini index extracted from Standardizing the World Income Inequality Database (SWIID). We take the advantage of a recently-compiled cross-country dataset that distinguishes market (before taxes and transfers) inequality from net (after taxes and transfers) inequality. In this study, we consider the net income inequality index. Gini index is ranged between 0 and 100, with higher values representing high unequal income distribution. To build the indicators of income distribution used in the econometric estimations, we reverse the original indicator of Gini Index by the following formula: G i,t = (100 -Gini index )/100. This transformation ranges G between 0 and 1. On this basis, G increases with the improvement of the distribution of income. As for employment, we extract data from International Labor Organization datasets.

We choose as indicator employment to population ratio which is the proportion of a country's population that is employed. Ages 15 and older are generally considered the working-age population. The series is harmonized to account for differences in national data and scope of coverage, collection and tabulation methodologies as well as for other country-specific factors such as military service requirements. The remaining variables include trade openness (import plus exports in percentage of GDP), the share of agriculture value added in percentage of GDP, natural resource rents in percentage of GDP, foreign aid per capita and public expenditures on education over GDP extracted from World Development Indicators database, the World Bank.

We generate inclusive growth index for each 3-year window. In figure 1, we present the relationship between government revenue over GDP (excluded grants) and inclusive growth defined with respect to income inequality and employment rate. As we can observe, there is strong positive relationship between inclusive growth and government revenue. We will come back with further econometric analysis.

Figure 1: Inclusive growth and government revenue, 1995-2010.

Econometric model

Given the strong inertia of government revenue, we use a dynamic specification. More specifically, we estimate the following equation:

𝑅𝑒𝑣 𝑖,𝑡 =∝ +𝛿𝑅𝑒𝑣 𝑖,𝑡-1 + 𝛽𝐼𝑛𝑐𝑙𝑢 𝑖,𝑡 + 𝜃𝑋′ 𝑖,𝑡 + 𝑣 𝑖 + 𝜑 𝑡 + 𝜀 𝑖,𝑡 (4)
Where 𝑅𝑒𝑣 𝑖,𝑡 represents government revenue for country 𝑖 in time 𝑡, 𝐼𝑛𝑐𝑙𝑢 𝑖,𝑡 is inclusive growth, 𝑋′ 𝑖,𝑡 stands for other explanatory variables. We include 𝑣 𝑖 to control for unobserved time-invariant country-level characteristics that are potentially correlated with government revenue and 𝜑 𝑡 to control for time-varying shocks that affect all developing countries.

Inclusive growth is suspected of endogeneity because of omitted variables bias and reverse causality. Indeed, some variables could affect both revenue and inclusive growth. For instance, given the high dependence of developing countries to external conditions, an international crisis may reduce government revenue as well as its ability to achieve its objectives, namely inclusive growth. Furthermore, endogeneity of revenue may be due to reserve causality. For example, a country that experiences high inclusive growth may mobilize taxes, while at the same time government uses mobilized resources to finance its policies and distribute between citizens. To deal with endogeneity issues arising from simultaneity bias and reverse causality, we use GMM estimators which are more suited for dynamic panel data. Apart from endogeneity of government revenue, GMM estimators allow us to correct for endogeneity of all right-hand side variables. There are two GMM estimators commonly used: the difference-GMM estimator [START_REF] Arellano | Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations[END_REF] and the system- GMM estimator [START_REF] Arellano | Another look at the instrumental variable estimation of error component models[END_REF][START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF]. For the difference-GMM estimator, equation ( 4) is differenced in first order in order to remove country fixed effects, and the first differentiated variables are instrumented by the lagged values of the variables in level. As for the system-GMM estimator, both equations in levels and in first differences are used in a system that allows the use of lagged differences and lagged levels of the explanatory variables as instruments. Therefore, system-GMM estimator is an extension of the difference-GMM estimator. In this paper, our preferred estimator is the system-GMM

because it has been highlighted that the lagged values of variables in level as it is done with the difference-GMM estimator are sometimes poor instruments for variables in first differences. To check the validity of the instruments, we use the Hansen test for overidentifying restrictions and Arellano and Bond's test that investigates that there is no second-order serial correlation in the first-differenced residuals.

Explanatory variables

As for explanatory variables, we estimate our model using factors driving government revenues. In selecting all of these variables, we follow closely the previous literature. More specifically, beyond inclusive growth we include the following variables:

The level of development defined as the log of real GDP per capita "Log(GDPPC". Indeed, a higher level of development goes together with a higher capacity to pay and collect taxes, as well as a higher relative demand for public goods and services (see [START_REF] Chelliah | Trends in Taxation in Developing Countries[END_REF][START_REF] Bahl | A Regression Approach to Tax Effort and Tax Ratio Analysis[END_REF][START_REF] Tanzi | Quantitative Characteristics of the Tax Systems of Developing Countries[END_REF][START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF][START_REF] Pessino | Determining Countries' Tax Effort[END_REF].

The share of agriculture value added in percentage of GDP "Agriculture". It is expected to be negatively associated with tax revenues because the sector is harder to tax (Stosky and WoldeMariam 1997, [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF], Botlole 2010). Agriculture sector in developing sector is characterized by a large number of small producers who sell their output in informal markets, either to exchange for other goods, or for self-consumption. This coupled with poor or a nonexistent book-keeping record makes it notoriously difficult to tax.

Trade openness expressed as imports plus exports in percentage of GDP. Contrary to agriculture sector, trade taxes are easier to collect, especially in developing countries.

Therefore, a high degree of openness is expected to generate a higher tax ratio (See Chelliah  The total public expenditure on education as percent of GDP and represents the level of education "Education". Indeed, more educated people can understand better how and why it is necessary to pay taxes. With a higher level of education compliance will be higher. Therefore, it is expected a positive relationship between this variable and the level of tax effort [START_REF] Pessino | Determining Countries' Tax Effort[END_REF]Fenochietto 2010, Keen andPery 2013).

 Total aid per capita "Aid". Another concern is the potential tax displacement effect of aid.

It is often argued that an increase in aid inflows will lower the government's incentives to increase its tax effort, or even that tax revenues can be reduced due to policy reforms linked to aid flows (McGillivray and Morrissey, 2001). We therefore include foreign aid per capita to control for this potential tax displacement effect of aid.

Natural resource rents in percentage of GDP "natural_rents". The effect of natural resources on government revenue is ambiguous. Indeed, on the one hand a resource-rich country can generate large taxable surplus [START_REF] Gupta | Determinants of Tax Revenue Efforts in Developing Countries[END_REF], while on the other hand natural resources might reduce the incentives of the governments for collecting taxes [START_REF] Lim | Tax Effort and Expenditure Policy in Resource-Rich Countries, Economic development policies in resource rich countries[END_REF][START_REF] Martinez-Vazquez | Mexico: An Evaluation of the Main Features of the Tax System[END_REF].

Quality of governance: We include "polity2" index that measures the degree of democracy in a country. It is widespread that quality of governance is a key factor driving tax mobilization (Gupta 2007, Le, Moreno-Dodson and[START_REF] Le | Tax capacity and tax effort. Extended Cross-Country Analysis from 1994-2009[END_REF].

Results

Baseline results

We present here baseline results obtained by using OLS with fixed effects, difference GMM and system GMM. To check the importance of inclusive growth, we report on the one hand the results for the effects of economic growth, income inequality and employment on government revenue and on the other hand the effect of inclusive growth on government revenue. This allows us to compare the results of the two estimates and therefore show how inclusive growth more matters for tax mobilization. Table 1 reports results for the effects of GDP growth, income inequality and employment on government revenue, while Table 2 reports results for inclusive growth. We observe that GDP growth is positively associated with government revenue, while income inequality (except column 8) and employment (except column 12) are not different from zero. However, Table 2 sheds light that the coefficient associated with inclusive growth is strongly positive and significant in all columns (except column 4). Furthermore, this coefficient is high than the one associated to GDP growth in Table 1. Then inclusive growth more matters. In other words, an increase of 1 percent in inclusive growth results in a rise of government revenue ranging between 0.12 and 0.31 percent over GDP. When citizens benefit from growth, they are more likely to comply with tax authorities and this results in an increase of revenue collected by the government.

This finding is consistent with the theory of fiscal exchange according to which compliance increases with (perceptions of) the availability of public goods and services. Governments may increase compliance by providing goods that citizens prefer in a more efficient and accessible manner [START_REF] Alm | Why do people pay taxes[END_REF]. By offering new opportunities to all and reducing income inequality, government improves its accountability, increases tax bases and incites citizens to comply with taxes. Furthermore, we observe that the coefficient associated with inclusive growth measured with respect to income inequality is higher than when it is measured with respect to employment. Therefore, it might be thought that inequality reduction brings in more resources to the government than employment promotion.

As for the remaining variables, we observe that the level of education, aid and natural resource rents are positively associated with government revenue, while the agriculture share in percentage of GDP affects negatively government revenue. These findings are consistent with previous works (Stosky andWoldeMariam 1997, Gupta 2007;Bird, Martinez-Vazquez and Torgler 2008).

Fixed effects

Difference-GMM System-GMM

Sensitivity analysis

In this section, we analyze some conditional factors that can affect the positive relationship between inclusive growth and revenue mobilization. To this end, we consider some indicators of policies that a country can pursue to achieve inclusive development. For instance, to improve inclusive growth, developing countries can adopt policies such as building schools and hospitals, training teachers and doctors, providing access to water, sanitation and transportation. Therefore, we interact inclusive growth index with the following variables: social protection expenditures; primary school pupil-teacher ratio (the number of pupils enrolled in primary school divided by the number of primary school teachers); the number of hospital beds per 1,000 people; infant mortality rate; and paved roads in percentage of total roads. Results are reported in Table 3.

In the last decade, social protection has emerged as a policy framework employed to address poverty and vulnerability in developing countries. Therefore, social protection has a strong focus on poverty reduction and on providing support to the poorest (de Haan 2000; Barrientos and Hulme 2005). Such policy may reduce inequality and increases tax bases. Table 3 shows that the interaction term with social protection is positive and significant (see column 1). This finding implies that inclusive growth is likely to be most helpful to government revenue when it is combined with social protection.

Developing countries are characterized by low levels of human development (education and health) and their abilities to achieve the Millennium Development Goals (MDGs) require enhanced actions. Education and health have to contend with the lack of teachers, nurses or doctors, with an absence or too little material. To benefit from growth, no doubts that people need access to education and health in order to become effective participants. Investments in the two sectors are visible and citizens can easily assess government actions and then decide their attitudes toward taxation. Results reported in Table 3 highlight that inclusive growth does not contribute to resources mobilization in countries experiencing an increase in pupilteacher ratio and mortality rate. The interaction terms with the two variables are negative and significant (see columns 2 and 4). However, when governments provide hospital beds, they are likely to incite citizens to pay taxes (see column 3).

At last, we consider transportation issue. Developing countries suffer from a severe lack of infrastructure. It is widely acknowledged that the infrastructure deficit is one of the key factors preventing developing countries from realizing its full potential for economic growth and competitiveness in global markets. Infrastructure can affect not only the quantum, but also the spatial distribution of economic activity. For instance, investments in transportation infrastructure may cause some economic activity to shift from the roads areas, as a result of lower transportation. Such investments promote surely inclusive growth by allowing many people to benefit from these infrastructures. Table 3 shows that the interaction term with the percentage of paved roads is not different from zero.

Robustness checks

To check the validity of our findings we undertake a number of robustness exercises. Note that we reported here results for inclusive growth measured with respect to income inequality.

Results for inclusive growth measured with respect to employment are similar and they are reported in appendix.

(i) Testing for additional controls on baseline specification

We add further controls in the baseline specification in order to take into account other variables likely to affect government revenue. These additional controls are terms of trade "TOT", remittances in percentage of GDP "remittances", consumer inflation rate "inflation", public debt in percentage of GDP "debt", conflict, and natural disaster "disaster". Results are reported in Table 4.

In first column we control for terms of trade shock 1 . Given the fact that developing countries are the subject of external shocks, controlling for terms of trade shocks aims to test whether our findings are robust to economic fluctuations. Several studies have found that changes in the terms of trade-the price of exports relative to the price of imports-can account for half of the output volatility in developing countries [START_REF] Mendoza | The Terms of Trade, the Real Exchange Rate, and Economic Fluctuations[END_REF][START_REF] Kose | Explaining Business Cycles in Small Open Economies: How Much Do World Prices Matter[END_REF][START_REF] Broda | Terms of Trade and Exchange Rate Regimes in Developing Countries[END_REF].

Even controlling for these fluctuations, we observe that inclusive growth still affects positively government revenue. Furthermore, the coefficient associated with terms of trade shock is not different from zero.

In column (2), we include migrants' remittances. Beyond the private use of remittances, they can play a role at macroeconomic level. Indeed, remittances increase recipient endowments and therefore their capacity to pay taxes. [START_REF] Ebeke | Remittances, value added tax and tax revenue in developing countries[END_REF] shows that remittances significantly increase both the level and the stability of government tax revenue ratio in receiving 1 To measure the shock, we resort to the Hodrick-Prescott (H-P) filter, which generates a smooth trend and stationary deviations. The deviation of terms of trade from its H-P-filtered trend will then capture the shock. We set a smoothing parameter of 6.25. countries. Our results reported in column (2) are consistent with this finding. Moreover, our hypothesis about the effect of inclusive growth on government revenue remains in force.

To ensure that our finding is robust to the Olivera-Tanzi effect, we include inflation rate in column (3). According to Olivera-Tanzi theory, an economic situation involving a period of high inflation in a country results in a decline in the volume of tax collection and a slow deterioration of real tax proceeds being collected by the government of that country. Table 4 shows that we do not find Olivera-Tanzi effect, while inclusive growth remains important driver of government revenue.

In columns (4) and ( 5), we include conflict and natural disaster. Indeed, both conflicts and natural disasters destroy economies and dislocate societies and therefore make tax collection difficult. Even controlling for the two types of difficult situations, inclusive growth still affects positively government revenue.

In last columns ( 6) and ( 7), we control for GDP growth and income inequality index in order to ensure that our results are not sensitive to these two variables. We find that not only the two variables are not significantly different from zero but also inclusive growth remains strongly significant and positive.

Overall, we conclude that our finding is robust to many additional controls. Then, inclusive growth matters for resource mobilization in developing world through the increase in tax base and the incentive to comply with taxes.

(ii) Testing for alternative main dependent variable

Up to now, we use total government revenue excluded grants as main dependent variable.

Government Revenues encompass social contributions (e.g. contributions for pensions, health and social security), taxes other than social contributions (e.g. taxes on consumption, income, wealth, property and capital), and other revenues. Here, we exclusively consider tax revenues in percentage of GDP in order to take into account the real capacity to increase revenue through taxation. Results are reported in Table 5. As we can observe, the coefficients associated with inclusive growth are strongly significant and positive in all columns except columns 2. However, these coefficients are smaller than those of table 2 and 4. This can be due to the exclusion of other sources of revenues. As for the other independent variables, we find an Oliveria-Tanzi effect (see column 4-8), while conflict and natural disasters are negatively associated with tax revenue.

(1)

(2)

(3) (4) (5) (6) (7)

(iii) Testing for alternative measure of inclusive growth

We now change our measure of inclusive growth. So far, we employed [START_REF] Ali | Measuring Inclusive Growth[END_REF]'s methodology to construct inclusive growth index. We test whether our results are robust to an alternative measure of inclusive growth. We follow [START_REF] Ramos | Mapping Inclusive Growth in Developing Countries[END_REF] by constructing a composite index combining the data on income inequality and employment rate. We first compute the simple average of the Gini index and employment rate and then we compare to GDP growth within each three-year window. Results reported in Table 6 do not alter our previous findings. In other words, inclusive growth policies have positive effects on tax revenue mobilization.

(1)

(

Conclusion

In this paper, we estimate the effect of inclusive growth on tax revenue mobilization.

Effective public policies, improving social cohesion and the nexus between revenues and public goods are fundamental to meet the aspirations of the citizens and therefore encourage them to comply with taxes. This paper uses GMM techniques to deal with endogeneity issue and covers 55 developing countries over the period of 1995-2010. We found that inclusive growth has a positive effect on tax revenue mobilization. This finding is robust both for inclusive growth measured with respect to income inequality and employment; various additional controls, alternative measure of inclusive growth and the dependent variable.

Furthermore, this positive effect works in countries where inclusive growth is associated with social protection and an increase in the number of hospital beds. However, infant mortality rate dampens the gains of inclusive growth. To mobilize sufficient tax revenue for the post-2015 International Development Agenda, our findings are important in terms of policy recommendations. First, we call for the adoption of inclusive development strategies in developing countries that meet citizen needs. Developing country incumbents should improve public services through reforms and promote efficiency, transparency and accountability in the use of public resources in order to ensure social inclusion of people. Employment and combating income inequality should be promoted to allow everyone to participate in society and the economy. Education and health are major sectors which require important investments. Second, concerted efforts should be made to increase tax compliance and therefore mobilize broad mix of resources.

Appendix

  Many researchers merely compare trends in economic growth, poverty line, income inequality and social indicators (See Habito 2009) or construct a simple average of indicators (See McKinley 2010,

  1971,[START_REF] Botlhole | Tax effort and the determinants of tax ratio in Sub-Sahara Africa[END_REF][START_REF] Pessino | Determining Countries' Tax Effort[END_REF], Keen and Pery 2013).

Table 1 : Testing for effects of gdp growth, income inequality and employment

 1

	Lag dep. variable	0.106	0.0821	0.0989	0.0946	-0.138	0.327*** 0.350*** -0.107	0.701*** 0.726*** 0.656***	0.717***
		(0.136)	(0.131)	(0.137)	(0.135)	(0.0883) (0.0867) (0.0898) (0.0727)	(0.0840)	(0.0854) (0.0846)	(0.0707)
	GDP growth	0.0512*			0.0455	0.1011***			0.0694**	0.0523*			0.0508**
		(0.0277)			(0.0278)	(0.0296)			(0.0272)	(0.0273)			(0.0224)
	Gini_net		-0.0726		-0.0555		-0.0122		-0.0995*		0.0184		0.0501
			(0.0637)		(0.0599)		(0.0602)		(0.0594)		(0.0516)		(0.0536)
	Employment			0.0398	0.0251			0.508**	-0.103			0.0763	0.197***
				(0.137)	(0.129)			(0.201)	(0.147)			(0.0915)	(0.0753)
	Log(gdppc)	0.564	1.039	0.859	0.441	7.004*** 15.14*** 16.92*** 7.649***	-0.759	1.138	-1.843	-1.837**
		(2.158)	(2.248)	(2.362)	(2.285)	(1.600)	(3.187)	(3.118)	(1.368)	(1.337)	(1.353)	(1.328)	(0.934)
	Agriculture	-0.0264	-0.0163	-0.0152	-0.0316	0.0417	-0.107** -0.186*** 0.0539	-0.0493	0.0416	-0.142*** -0.178***
		(0.0555)	(0.0566)	(0.0588) (0.0565)	(0.0746) (0.0501) (0.0682) (0.0628)	(0.0664)	(0.0660) (0.0348)	(0.0384)
	Trade	0.0304	0.0362*	0.0347	0.0298	0.0214	0.0355	0.0444*	0.0132	0.0167	0.00826	0.0431*** 0.0301***
		(0.0207)	(0.0214)	(0.0225) (0.0214)	(0.0299) (0.0246) (0.0260) (0.0220)	(0.0106)	(0.0123) (0.0147)	(0.00963)
	Education	1.628*** 1.549*** 1.533*** 1.608***	1.209*** 1.458*** 2.008*** 1.398***	1.319*** 1.407*** 1.952***	2.037***
		(0.424)	(0.407)	(0.440)	(0.438)	(0.392)	(0.362)	(0.440)	(0.288)	(0.254)	(0.256)	(0.338)	(0.238)
	Aid	0.0263** 0.0273** 0.0235*	0.0290**	0.0220	-0.0120	-0.0287	0.00524	0.0414*** 0.0312** 0.0575*** 0.0505***
		(0.0126)	(0.0125)	(0.0129) (0.0123)	(0.0207) (0.0161) (0.0223) (0.0151)	(0.0126)	(0.0129) (0.0178)	(0.0115)
	Natural_rents	0.381*** 0.380*** 0.377*** 0.383***	0.497*** 0.549*** 0.568*** 0.510***	0.120*** 0.106*** 0.118***	0.130***
		(0.0583)	(0.0596)	(0.0587) (0.0593)	(0.0583) (0.0532) (0.0719) (0.0364)	(0.0269)	(0.0274) (0.0287)	(0.0278)
	Polity2	-0.0891	-0.113	-0.0975	-0.0961	0.148	0.0576	-0.149	0.124	0.225**	0.165**	0.176*	0.204***
		(0.104)	(0.105)	(0.115)	(0.103)	(0.125)	(0.133)	(0.148)	(0.110)	(0.107)	(0.0711) (0.0988)	(0.0632)
	Constant	3.979	4.113	-0.297	6.383					5.153	-12.68	7.131	-2.220
		(16.15)	(17.40)	(17.33)	(16.23)					(13.21)	(14.69)	(15.18)	(9.310)
	Observations	151	151	151	151	91	91	91	91	151	151	151	151
	Countries	54	54	54	54	38	38	38	38	54	54	54	54
	R-squared	0.697	0.694	0.691	0.699
	AR(1): p-value					0,028	0,472	0,031	0,136	0,01	0,01	0,016	0,003
	AR(2): p-value					0,518	0,772	0,429	0,259	0,799	0,609	0,944	0,685
	Hansen test, p-value					0,37	0,125	0,291	0,55	0,603	0,648	0,914	0,72
	Nb instruments					8	8	8	8	12	12	12	12
	Note: Time effects are included in all the regressions. Robust standard errors in parentheses.
	The dependent variable is government revenue in percentage of GDP.
	***p<0.01, significant at 1% ; **p<0.05, significant at 5%; *p<0.10, significant at 10%

Table 2 : Baseline results: effect of inclusive growth

 2

		Measurement with respect to inequality	Measurement with respect to employment
		Fixed effects Diff-GMM System-GMM Fixed effects Diff-GMM System-GMM
	Lag dep. variable	0.122	-0.0249	0.689***	0.121	-0.0234	0.732***
		(0.137)	(0.101)	(0.0735)	(0.142)	(0.0927)	(0.0766)
	Inclusive_Growth 0.1235*** 0.3105*** 0.1424***	0.1043	0.2738*** 0.1074*
		(0.0391)	(0.0574)	(0.0489)	(0.0684)	(0.0757)	(0.0602)
	Log(gdppc)	2.022	12.97*** -1.321	1.089	11.51*** -0.856
		(2.112)	(3.068)	(1.168)	(2.408)	(1.940)	(0.953)
	Agriculture	-0.0333	-0.0355	-0.0834**	-0.0252	0.0475	-0.0228
		(0.0566)	(0.0527)	(0.0373)	(0.0541)	(0.0642)	(0.0428)
	Trade	0.0261	-0.0259	0.00543	0.0295	-0.0305	0.000797
		(0.0187)	(0.0212)	(0.00965)	(0.0217)	(0.0345)	(0.0106)
	Education	1.562***	1.062*** 1.197***	1.605***	1.686*** 1.280***
		(0.410)	(0.323)	(0.204)	(0.429)	(0.340)	(0.210)
	Aid	0.0223*	-0.00210 0.0404***	0.0241*	-0.00292 0.0317***
		(0.0125)	(0.0206)	(0.0136)	(0.0126)	(0.0203)	(0.0116)
	Natural_rents	0.359***	0.393*** 0.101***	0.361***	0.445*** 0.106***
		(0.0539)	(0.0664)	(0.0235)	(0.0567)	(0.0677)	(0.0292)
	Polity2	-0.0653	0.145	0.0732	-0.0991	0.190	0.145*
		(0.110)	(0.122)	(0.0814)	(0.107)	(0.133)	(0.0746)
	Constant	-6.092		12.67	0.266		6.243
		(16.01)		(10.00)	(18.18)		(9.085)
	Observations	151	91	151	151	91	151
	Countries	54	38	54	54	38	54
	R-squared	0.705			0.697
	AR(1): p-value		0,321	0,014		0,197	0,009
	AR(2): p-value		0,44	0,826		0,292	0,759
	Hansen test, p-value		0,422	0,824		0,186	0,57
	Nb instruments		8	12		8	12
	Note: Time effects are included in all the regressions. Robust standard errors in parentheses.
	The dependent variable is government revenue in percentage of GDP.
	***p<0.01, significant at 1% ; **p<0.05, significant at 5%; *p<0.10, significant at 10%

Table 3 : Testing for sensitivity analysis-with inclusive growth measured with respect to income inequality

 3

Table 4 : Testing for additional controls-with inclusive growth measured with respect to income inequality

 4

Table 5 : Testing for alternative dependent variable-with inclusive growth measured with respect to income inequality

 5

Table 6 : Testing for alternative measure of inclusive growth

 6

Table A1 : Testing for additional controls-with inclusive growth measured with respect to employment

 A1 Time effects are included in all the regressions. Robust standard errors in parentheses. The dependent variable is tax revenue in percentage of GDP. ***p<0.01, significant at 1% ; **p<0.05, significant at 5%; *p<0.10, significant at 10%

		(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	Lag dep. variable	0.708***	0.782***	0.745***	0.752***	0.776***	0.814***	0.771***	0.785***
		(0.0692)	(0.0464)	(0.0435)	(0.0294)	(0.0169)	(0.0250)	(0.0417)	(0.0407)
	Inclusive_Growth	0.1644*** 0.1571*** 0.1514*** 0.1812*** 0.1799*** 0.2110*** 0.1317*** 0.1394***
		(0.0415)	(0.0370)	(0.0313)	(0.0224)	(0.0214)	(0.0155)	(0.0263)	(0.0265)
	Log(gdppc)	-1.006	0.190	0.383	-0.218	0.152	-0.310	-0.415	-0.161
		(0.665)	(0.527)	(0.338)	(0.283)	(0.362)	(0.274)	(0.311)	(0.260)
	Agriculture	-0.0694*** -0.0571*** -0.0595*** -0.0164	0.00533	-0.0152	-0.0176	-0.0179
		(0.0220)	(0.0180)	(0.0189)	(0.0157)	(0.0168)	(0.0118)	(0.0136)	(0.0172)
	Trade	0.0130	0.0260**	0.0285**	0.00435	0.0134*	-0.00457	0.0191**	0.0216**
		(0.0122)	(0.0110)	(0.0127)	(0.00599) (0.00777) (0.00562)	(0.00834) (0.00908)
	Education	0.193	0.187	0.270**	0.354***	0.482***	0.376***	0.320***	0.353***
		(0.165)	(0.142)	(0.108)	(0.0977)	(0.117)	(0.0848)	(0.124)	(0.116)
	Aid	0.0470*** 0.0476*** 0.0472*** 0.0224*** 0.0241*** 0.0193*** 0.0201*** 0.0127**
		(0.0106)	(0.0108)	(0.00996)	(0.00617) (0.00671) (0.00483)	(0.00405) (0.00537)
	Natural_rents	0.0904*** 0.0819*** 0.0887*** 0.0163	0.00890	-0.00535	0.0199	0.0128
		(0.0233)	(0.0180)	(0.0185)	(0.0172)	(0.0106)	(0.0193)	(0.0176)	(0.0243)
	Polity2	0.169***	0.228***	0.212***	-0.0313	-0.00796	-0.0341	0.0650	0.0563
		(0.0653)	(0.0487)	(0.0532)	(0.0519)	(0.0286)	(0.0348)	(0.0448)	(0.0436)
	ToT		0.0171	0.00662	0.0718*** 0.0810*** -0.00282	0.0335*	0.0616**
			(0.0450)	(0.0439)	(0.0233)	(0.0182)	(0.0180)	(0.0186)	(0.0263)
	Remittances			-0.0439	-0.00395	0.0161	0.00620	0.0196	0.0547*
				(0.0487)	(0.0305)	(0.0247)	(0.0258)	(0.0320)	(0.0306)
	Inflation				-0.113*** -0.108*** -0.122*** -0.0991*** -0.102***
					(0.00821) (0.00772) (0.00703)	(0.00950) (0.00907)
	Conflict					-1.399*** -0.854*** -0.777*** -1.172***
						(0.282)	(0.291)	(0.215)	(0.370)
	Disaster						-0.0333*** -0.0215** 0.0158
							(0.00957)	(0.0108)	(0.0158)
	GDP_growth							0.0568	0.0615
								(0.0598)	(0.0614)
	Employment								-0.0214
									(0.0214)
	Constant	9.780*	-2.867	-2.863	1.972	-1.687	1.700	3.773	2.740
		(5.015)	(4.412)	(2.316)	(2.234)	(2.718)	(1.983)	(2.324)	(2.630)
	Observations	147	145	144	144	144	144	144	144
	Countries	55	54	53	53	53	53	53	53
	AR(1): p-value	0.041	0.031	0.049	0.035	0.034	0.057	0.053	0.037
	AR(2): p-value	0.25	0.256	0.239	0.31	0.246	0.243	0.195	0.202
	Hansen test, p-value 0.444	0.52	0.649	0.354	0.559	0.444	0.922	0.98
	Nb instruments	12	12	12	13	13	13	14	14
	Note:

Table A2 : Testing for alternative dependent variable-with inclusive growth measured with respect to employment

 A2 Time effects are included in all the regressions. Robust standard errors in parentheses. The dependent variable is government revenue in percentage of GDP. ***p<0.01, significant at 1% ; **p<0.05, significant at 5%; *p<0.10, significant at 10%

		(1)	(2)	(3)	(4)	(5)
	Lag dep. variable	0.764***	0.889***	0.836***	0.689***	0.725***
		(0.0526)	(0.0432)	(0.0615)	(0.0575)	(0.0614)
	Inclusive_Growth	0.0893	0.1966***	0.2415***	0.05600	0.1064**
		(0.0782)	(0.0741)	(0.0935)	(0.0452)	(0.0414)
	Inclusive*Social protection	0.0743***
		(0.0208)
	inclusive*Pupil-teacher ratio		-0.0737***
			(0.0175)
	Inclusive*Bed			0.0193**
				(0.0095)
	Inclusive*Mortality				-0.0516***
					(0.0149)
	Inclusive*Road					0.1317
						(0.3356)
	Log(gdppc)	3.318***	2.224***	-0.872	-1.214	-0.584
		(0.999)	(0.568)	(0.745)	(0.833)	(0.727)
	Agriculture	-0.133***	-0.0534*** 0.0213	-0.106***	-0.0356
		(0.0303)	(0.0178)	(0.0380)	(0.0353)	(0.0354)
	Trade	0.0206***	0.0141*	0.0235***	-0.000354	-0.000892
		(0.00701)	(0.00742)	(0.00791)	(0.00768)	(0.0103)
	Education	0.0408	0.823***	1.450***	0.878***	1.291***
		(0.244)	(0.193)	(0.212)	(0.173)	(0.158)
	Aid	0.0334***	0.0288***	0.0117	0.0470***	0.0391***
		(0.0120)	(0.00807)	(0.00925)	(0.00701)	(0.00847)
	Natural_rents	0.0756***	0.0400*	0.0313	0.104***	0.0659**
		(0.0207)	(0.0211)	(0.0209)	(0.0231)	(0.0264)
	Polity2	0.0633	0.0855*	0.115	0.136**	0.00642
		(0.0530)	(0.0505)	(0.0706)	(0.0688)	(0.0667)
	Constant	35.64***	17.27***	4.738	13.18*	5.113
		(9.257)	(4.568)	(6.710)	(7.796)	(6.200)
	Observations	120	134	116	151	103
	Countries	43	49	49	54	43
	AR(1): p-value	0.028	0.044	0.042	0.015	0.086
	AR(2): p-value	0.906	0.664	0.792	0.932	0.589
	Hansen test, p-value	0.588	0.47	0.704	0.664	0.753
	Nb instruments	12	12	12	12	12
	Note:

Table A3 : Testing for sensitivity analysis-with inclusive growth measured with respest to employment Table A4: Data sources

 A3

	code	Variable	Data sources
	Inclusive	Inclusive growth measured with
	growth	respect to income inequality
		and employment

This work was supported by the LABEX IDGM+ (ANR-10-LABX-14-01) within the program "Investissements d'Avenir" operated by the French National Research Agency (ANR). http://www.cerdi.org/

Author's calculations. Income inequality data are from SWIID (2013)


Didier Faivre
email: didier.faivre@malix.univ-paris1.fr

Jean-David Fermanian

Gael Giraud

Peter Martey Addo

Florent Mc

Credit Aggregates, Countercyclical Buffer: stylised facts *

Keywords: JEL-Classification: E32, E51 Keywords: Private Credit, Credit Cycle, Cointegration, Error Correction Model

The relationship between Credit to private sector, Growth and investment is in a first step evaluated empirically through Error Correction models (ECM), using Credit Level for various national economies. The more important results are the following: the quality of estimation results for the relationship between Investment (with a separate analysis for Business and Households) and Credit is much better than for the relationship between GDP and Credit and in most cases it's the Investment cycle that explains the Credit cycle. In addition, specific results for United States are given, replacing Credit Level data by Credit Flow data. In this case, both cycles drive each other for Business, whereas for Households, it's the investment cycle that drives the Credit cycle.

1 Basel III Private Credit Framework

Context

The Basel III countercyclical buffer is based on two macroeconomic variables, the deviation of the credit-to-GDP ratio with respect to its trend and the credit-to-GDP gap. The variables are described in details in Section IV of the Basel III document [BCBS, 2010a] and in the Guidance document [BCBS, 2010b]. The goal of the buffer is to lower the procyclicality of Private Credit.

Credit definition

In order to measure the private credit, the Basel Committee on Banking Supervision (BCBS) in its guidance guide document [BCBS, 2010b] recommends using a broad definition of credit that will encompass all sources of debt funds for the private sector, including funds raised abroad. This document underlines that ideally the definition of credit should include all credit extended to households and other non-financial private entities in an economy independent of its form and the identity of the supplier of the funds. The choice of a broad definition for the credit is aimed at limiting the incentive for banks to transfer some credit to the shadow banking system, as the total amount of credit and the associated regulatory constraints are then independent of which entities supply the funds to the private sector.

All securities held by banks and other financial institutions in trading and banking books should be part of the the credit aggregate, as well as securities held by other residents and non-residents. In order to avoid overlapping, the BCBS advises that credit flows between financial institutions should not be taken into account.

Public debt is also excluded from the aggregate as BCBS analysis showed its inclusion would dilute the cyclical properties.

Data

Several sources are available for private credit statistics: Central Banks' series, a World Bank database1 , IMF International Finance Statistics (IMF-IFS)2 and a new database created by the Bank for International Settlements (BIS) that provides series with on average forty five years of quarterly data for forty advanced and emerging countries. This new database is described in a BIS paper [START_REF] Dembiermont | How much does the private sector really borrow -a new database for total credit to the private non-financial sector[END_REF]]. It's first reminded that these credit series need to be defined by the borrower, the lender and the financial instruments. The borrowers can be Non-Financial Corporations (NFC)3 , Households and Non-Profit Institutions Serving Households (NPISHs). These three sectors make up the "non-financial private sector". The database managers intend to take into account all sources of credit, whatever the country or category of the lender. It means especially that these data aim to encompass securitised credits held by the shadow banking sector or crossborder lending, in addition to credit by domestic commercial and saving banks or credit unions.

The paper described in details the series that have been selected, how they have been adjusted for breaks due to borrower, lender or instruments coverage changes. The series are available on a quarterly basis. In case they were initially compiled at an annual frequency quarterly data have been estimated using a Chow-Lin method for data completion. The National Financial Accounts 4 are the main data source, despite the fact that in most countries their new presentation 5 started only in the 1990s or later. The financial accounts encompass domestic and foreign loans to NFC, Households and NPISHs, as well as debt securities issued by non-financial corporations. The BIS database contains four series, for which the following notations are used : Credit t = total credit to private sector, Credit banks t = total credit to private sector by domestic banks, Credit H t = total credit to households and NPISHs, Credit N F C t = total to NFC.

For countries or periods for which financial accounts were not available, total credit to the private non-financial sector was estimated by using statistics about domestic bank credit (and possibly other domestic financial institutions) and statistics about cross-border bank credit provided by the BCBS as an approximation of total cross-border credit. In addition, during the subprime crisis many banks had to provide securitisation structures 6 with credit lines and guarantees [Acharya10 and al., 2010], this show how the ratio between domestic banks credit to private sector and total credit to private sector can be difficult to estimate.

The following other methodological issues are underlined:

• As the database aims at capture all lending sources, the credit volume due to loans within a private non-financial sector -especially the corporate sector -is included in the series and the data are not consolidated 7 . It has been decided that the series wouldn't be consolidated, because the credit's source (bank vs. corporate) doesn't affect the debt sustainability. However, consolidation may be relevant for loans between a parent company and its subsidiaries, as the only motivation for these loans is often tax optimisation. In practice, it has not been possible to remove those loans from the new credit series but trade credit are excluded from them.

fact the the sum of the credit data for Non-Financial Corporations (NFC) and Non-financial non-corporate business (NFNCB). The two sectors makes up the Non Financial Business, NFB. In the paper, the notation NFC is retained 4 Available as claims to private sector in the IMF-IFS database for most countries.

5 Currently under OECD System of National Account, 1993 (SNA93) except for Australia which follows SNA08

6 Known as conduits, Special Purpose Vehicle (SPV) or Special Purpose Vehicle (SIV) 7 Consolidation would imply netting out credits between entities of a given private sector

• Before the implementation of the International Financial Reporting Standards (IFRS)8 , derecognised securitised loans were off-bank's balance sheets and it may have an important impact on bank credit series. Acharya andal (2010) [Acharya10 andal., 2010], Khasawneh (2007) [Khasawneh, 2007] showed that during the subprime crisis many banks registered heavy losses triggered by liquidity or credit guarantees they were committed to provide as sponsor to some Securitisation structures9 . However, if financial account (in their new presentation) are used to compile the total credit to non financial private sector, securitisation is not an issue credit from all sectors is taken into account, including all kind of conduits to which banks sell loans portfolios.

• If financial accounts are not available for a given country or period, the cross-border component of credit to private sector has to be estimated using the BIS International Banking Statistics (IBS). Several caveats need to be made regarding the IBS regarding cross-border lending. First, while these series take into account cross-border lending by foreign banks, they don't include loans provided by non-bank entities and in addition they are not always available on the same period than the domestic bank credit.

• Under IMF or UN rules, the nominal amounts plus accrued interest is used for loans'valuation while debt securities are supposed to be valued at market prices.

In practice, debt securities are often reported at nominal values for various reasons which can alter international comparability.

• The series are reported in national currencies, thus currencies fluctuations can impact these statistics through cross-border credit and even domestic loans. Emerging economies have shown in the past that they are especially vulnerable to credit crisis associated to important exchange rates swings (Mexico, 1994, Argentina 2001, Thailand-Indonesia, Philippines and even Korea 1997-1998, Turkey (1994, 1999, 2001) Hungary (2009[START_REF] Mgi | [END_REF]).

Comparison between Bis New database and other sources

The Credit-to-GDP ratio at t is defined for a given country as:

Credit t -to-GDP t -ratio = Credit t GDP t × 100% (1.1)
The McKinsey Global Institute (MGI) published three reports about the credit-fueled growth period that experienced many mature and emerging economies between the late nineties and 2007 [START_REF] Mgi | [END_REF] and the following ongoing deleveraging process [START_REF] Mgi | [END_REF], [START_REF] Mgi | [END_REF]. The Credit-to-GDP-ratio for year 2008 among several sources are displayed for selected countries and three aggregates in tables 1 to 3 and figure 1 shows the historical evolution. In Appendix A the same tables are available for year 2011 and several historical figures). Overall, results between MGI are very similar to BIS results. The purpose of this estimation is to determine if GDP is the most relevant flow variable to link to Credit when defining the countercyclical buffer, whatever may be its mathematical definition. It's very important to remind at this stage that Credit as previously defined is a stock variable. Credit vs. GDP and GDP vs. Credit elasticities are measured by using Error Correction Model (ECM) in their simplest way:

Credit t -to-GDP t -
Credit t = aGDP t + b + t ∆Credit t = α∆GDP t + λ t-1 + η t GDP t = a Credit t + b + t ∆GDP t = α ∆Credit t + λ t-1 + η t (2.1)
The following methodology is used: as ECM models are not estimated in order to find a formal alternative to the current definition of the countercyclical buffer or for forecast purpose, instead of trying to Estimate Vector Error Correction Model with lags and using step-wise methods, a simple ECM at annual frequency is estimated using end of the year data or quarterly data 10 . In addition elasticities are then measured through unique parameters, thus easier to interpret. All data are divided by GDP-deflator, i.e real, then logged. This enables to estimate the elasticities between Credit and GDP. The procedure is done for thirteen countries, using all data available.

Other data Sources, Seasonally Adjustment

The Bis data source for Credit Level in the case of USA is the Flow of funds database 11 . This database contains Level and Flow series, that can both be Seasonally Adjusted (SA) or not (NSA). The BIS data for Credit Level are the NSA Level data. The other economic series come from the data provider Macrobond. It has been tested in the case of USA that the following estimation results are very close when using SA series. As a consequence, it is chosen to use for all countries Bis series as they are, without making any seasonal adjustment. On the reverse, for GDP series, when SA series are not available in Macrobond 12 , GDP series have been seasonally adjusted using the X-13ARIMA-SEATS seasonal adjustment method 13 . 10 The results presented in this first version of the paper are obtained from annual data. In addition, the Granger causality tests are provided both for the annual and quarterly estimation. The estimation and tests tables for annual data will be replaced by the ones for quarterly data in the next version of the paper

Statistical tests : individual series and cointegration tests

Before estimating the ECM models, several tests are done in four steps (Appendix B).

•

Step 1: The Data Generation Process (DGP) of GDP and Credit series are analyzed through autocorrelations and partial autocorrelations (upon one time differentiated series ) and Augmented Dickey-Fuller (ADF) tests upon series and upon one time differentiated series (Tables 17 to 20, pages 28 to 30). Credit and GDP series are found to be both I(1) in all countries. The number of lags, k in ADF tests is selected using in a first step Akaike criteria, with a maximum for k set at = 3. Then, the residuals of ADF tests are also tested through serial correlation and Ljung-Box Portmanteau (LB) test, which give very similar conclusion. If the t-statistic associated with the t th lag is well below 2 and (LB) test show that the residuals of ADF tests are uncorrelated, then the final number of lags is set at k-1. In practice, the number of lags choosen by Akaike criteria is 1 for all countries, both for GDP and Credit, so that it has to be chosen between 1 and 0 through the LB test. The methodology is explained in pages 60 to 62 of [Pfaff, 2008].

• Step 2: A preliminary step before doing the ADF cointegration tests is to choose between the three possible DGP for the residuals of the basic regression of GDP vs. Credit and Credit vs. GDP: trend, drift, none (equations B.1a, B.1b, B.1c page 28).

The none DGP is chosen in all cases according to the tests (Table 21, page 32) and it can be seen as a preliminary indicator on charts (examples 9, 10, 11, 12, pages 30 and 31) that these residuals do not exhibit significant trend or drift.

• Step 3: Then using Mac-kinnon tables, cointegration ADF tests between are done for all countries using the residuals whose DGP has been chosen in the previous step (Table 22, page 33).

• Step 4: Finally, for each ECM, a choice is made between a version with drift and and a version without drift, using Fischer test (Table 23, page 34).

The main results are the following: among the thirteen countries studied, it is possible to estimate both a Credit vs. GDP ECM and GDP vs Credit for only five countries (Australia, China, South Korea, Spain, United States, Tables 4 and5). For Sweden and Canada it's only possible to estimate a Credit vs. GDP ECM. For Japan and Germany no estimation is possible for any sub-period. For Japan, as the real estate/equity bubble is generally seen as exploding in 1992, it was tried to estimate a model from 1966 to 1992 but results were not better. For Germany may be problem is the reunification in 1990. For Spain, even if data were available from 1970, the model was estimated on a shorter period . That may be due to the fact that Spain became a democracy in 1976 and entered in European Union in 1986. For France, possible explanations may be that many "market-friendly" reforms occurred in France from 1976 to 1986, including the end of price and strong credit management by the government.

For all countries except China, data are available on a quarterly basis, so the estimation can also be done using Q1, Q2, Q3 data (results available on demand). Instead of using Q4 data for GDP, an alternative approach using all information available is to use the sum of Q1, Q2, Q3, Q4 real GDP, which is exactly the annual real GDP. The estimation was done for all countries and gave similar results (results are very similar for all countries and available on demand). Using quarterly data it is only possible to get ECM estimation for Australia, Canada, United States with similar elasticities for the three countries.

The Granger causality tests (Table 24 page 35) indicate that it's possible to say that GDP Granger-cause Credit for four Countries (Australia, Canada, Sweden, United States) using annual data, when using quarterly data it's only possible to say that for Canada and United States.

Overall for the five countries for which both the estimation of Credit vs. GDP and GDP vs. Credit ECM are possible, for most countries Credit vs. GDP elasticity is above 1, whereas the GDP vs. Credit Elasticity (which has no reason to be exactly the inverse of the previous one) is well below one and well below the previous elasticity. This results shouldn't be surprising regarding the growing Credit to GDP ratios that can be observed in all economies. Nevertheless, even for those countries both the significance of ECM parameters and so the model quality cannot be considered as especially good and in addition when using quarterly data the estimation is only possible for three countries. It is an incentive to find another variable that can be better explained by credit variations.

Credit/investment ECM

Using the same methodology than in the previous section, ECM models are now estimated to study the relationship between private credit and investment. The study is restricted to the six countries for which both the investments series and Credit series are available for at least thirty years (Australia, Canada, France, Japan, United Kingdom, United States).

• The first ECM links NFC's investment at t (Invest N F C t ) and Credit N F C t .

When not defined as itself, private NFC investment can be calculated as fixed private capital formation minus residential investment (e.g. USA).

• The second ECM links Investment from households and NPISHs' (Invest H t ) and Credit H t and non profit organizations Investments from households are calculated as investments in residential sector plus durable goods expenditure. The investments from NPISHs cannot be identified separately except for Canada, it is assumed anyway their amount is weak compared to households investments14 .

The aim of the study is to determine whether it's better to establish a link between Investment and credit or between GDP and Credit, nor to break down relationship between investment in a given quarter and Credit during for example the last four quarters.

The following results show that the link between investment and credit is easier to identify than the link between GDP and credit: parameters are quite significant for almost all ECM models, with only one exception for which the t -statistic coefficients linked to coefficient λ doesn't enable to validate an ECM model15 . In this estimation, end of the year series are used for both investment and credit, using real data. The same ECM models has been estimated by replacing the Q4 series for investment by (Q1+Q2+Q3+Q4), as described in section 2.3. The results are quite similar and available on demand.

From the Granger causality tests results (Table 36 page 44 and Table 37 page 44), we can get the following results :

• For Corporates it's possible to say that Investment Granger-cause Credit in Australia, Canada, France, Japan, United Kingdom, United States. For Japan its not possible to have a conclusion.

• With respect to Households, its only possible to conclude that, for the periods studied, Investment Granger-cause Credit in Canada and United States and that Credit Granger-cause Investment in Australia, with no clear conclusion for other Countries.

These results strongly advocate to study two alternatives the private Credit to GDP ratio : Credit N F C to private investment ratio and credit H to households investments ratio. [START_REF] Kydland | Business Cycles: Real Facts and a Monetary Myth[END_REF] [START_REF] Kydland | Business Cycles: Real Facts and a Monetary Myth[END_REF]] :

7: Invest N F C t = f (Credit N F C t ), ECM model
• Let's Y be the GDP. A
= i with i negative, it is said that X leads Y , if i positive, it is said that X laggs Y . If i = 0, it is said that X coincides with Y .
All series have been divided by the deflator of the USA GDP and logged. The calculations have been done using both real and nominal series. The results are not very different using nominal series but generally leads to slightly lower correlations and for this reason the detailed results are reported here on real series. As the monetary policy changed in the mid 80's in USA, the analysis is done on three periods : 1959-2014, 1959-1986, 1986-2014 17 .

Several charts (2 page 18, 17 page 45, 19 page 46) show that on the three periods of analysis Credit to private sector is procyclical but laggs the Cycle. The "shift" with GDP is two quarters between 1959 and 1986 whereas it is four quarters between 1986 and 2014. The same conclusion can be inferred from other charts (3 page 18, 18 page 45, 20 page 46) between Credit to private sector and three variables that coincides with GDP : private consumption, private investment and importations.

The main difference between the two periods lies in the correlations between Monetary Aggregates and GDP or Credit to private sector.

Between 1959 and 1986, M0, M1, M2 and M2-M1 were procyclical and leading the cycle (2 or 3 quarters), with also a strong correlation with Credit to private sector and again a negative phase shift of two or three quarters. Between 1986 and 2014, there is no link between the last three monetary aggregates and GDP, whereas M0 is countercyclical . On the same period M2-M1 is positively correlated with credit to private sector but with a lag of 3 quarters, whereas we see negative correlations between Credit and M0 (negative shift of two quarters) and M1 (positive shift of three quarters) (charts 21 page 47 and again 3 page 18, 18 page 45, 20 page 46).

In summary: Credit lags GDP and Investment for both sub-period by three or four quarters. In the first sub-period monetary aggregates M0, M2-M1 and M2 lead GDP and Credit by three or four quarters, whereas in the second sub-period its only possible to say that M2-M1 lags the GDP by three quarters. All the previous studies has been done using credit stock series. In the flow of funds series 18 , flow series are also available at quarterly and annual frequencies. After 1970, the flows data are not always equal to the first order difference of the stock series and sometimes the difference can be important. According to the federal reserve, the difference can be explained by interpolation and extrapolation methods. In addition as flow series are the sum of new credits, amortizing and early redemption of past credit, flow data can sometimes be negative. The following methodology is used. X t being a series of real credit flows (X) t that are sometimes negative, let's define

dLN (X t ) approx = X t+1 -Xt |Xt| if |X t+1 -Xt| |Xt| < 1 X t+1 -Xt max(|Xt|,|X t+1 | ) else (4.1)
Then, starting the approximate series LN (X t ) approx is built as:

LN (X t ) approx = LN (X 1 ) approx + t-1 i=1 dLN (X i ) approx if t > 1 LN (X 1 ) approx = LN (X 1 ) (4.2)
There is obviously no guarantee to get a positive series this way, but in practice this approximation enables to get a positive a series on all countries (USA, Canada, Australia and even Japan and whatever the series frequency), conditional to the fact that (X) 1 > 1 . ECM results for USA are displayed in Tables 10 to 13 19 . The fact that all parameters of the ECM models are very significant and so that the results are even better the ones from subsection 2.4 is a topic for further research.

For Corporates, Credit Granger-cause investment and also Investment Granger-cause Credit, whereas for Households it's only investment that Granger-cause Credit(

Conclusions

In the first two parts of this paper the link between Private Credit Stock and GDP or Investment is studied. Among thirteen important national economies 20 , a long term significant relationship between Private Credit Stock and GDP can only be found for three countries: Australia, Canada and United States. For the two North America countries, its the GDP that explains the Private Stock Level so that more GDP means more Credit, whereas for Australia its on the reverse the Private Stock Level that explains the GDP. For the six national Economies 21 for which the same study is done between Investment and Private Credit Stock, both for Corporates and Households, a long term very significant relationship can be found in all cases but one22 . The Investment cycle drives the Credit Cycle for Corporates for five of these six countries, the exception being Japan for which its not possible to have a conclusion. With respect to Households, its only possible to conclude that for the periods studied, Investment cycle drives the Credit Cycle in Canada and United States, that Credit Cycle drives the Investment cycles in Australia, with no clear conclusion for other Countries. Then two results specific to the United States are found for two sub-periods, 1959-1986 and 1986-2014 : Credit lags GDP and Investment for both sub-period by three or four quarters. In the first sub-period monetary aggregates M0, M2-M1 and M2 lead GDP and Credit by three or four quarters, whereas in the second sub-period its only possible to say that M2-M1 lags the GDP by three quarters.

Finally, a very significant statistical relationship is found between Corporate Credit Flows (resp. Households) and Corporates Investment (resp. Households Investment). In this case, both cycles drive each other for Business, whereas for Households, it's the investment cycle that drives the Credit cycle. It remains an open question to see if the fact that some results that can be found for United States but not for most other countries can be explained but better statistical data or by fundamental economic dynamic difference.

A Appendix 1, Credit ratios

Figure 1 :

 1 Figure 1: Credit-to-GDP-ratio, Historical charts, selected countries

Figure 2 :

 2 Figure 2: H.P. Correlations GDP , USA, 1959-2014

Figure 4 :

 4 Figure 4: HP Correlations between Investment and private Credit, USA, 1959-2014

Figure 6 :

 6 Figure 6: HP Correlations between Investment and private Credit, USA, 1986-2013

Figure 13 :

 13 Figure 13: Residuals of linear regressions Credit vs. Investment and Investment vs. Credit, NFC, USA, 1952-2014

Table 1 :

 1 Credit t -to-GDP t -ratio, 2008

	ratio, 2008

Table 2 :

 2 Credit H

	Country	BIS	MGI
	Australia	82%	n/a
	Canada	98%	54%
	China	98%	96%
	France	110%	110%
	Germany	58%	66%
	Italy	79%	81%
	Japan	109%	96%
	South Korea	105%	115%
	Spain	138%	136%
	Sweden	165%	n/a
	Switzerland	74%	75%
	United Kingdom 100%	114%
	United States	73%	78%

t -to-GDP t -ratio, 2008 Credit N F C t -to-GDP t -ratio, 2008

Table 3 :

 3 Credit N F C t -to-GDP t -ratio, 2008

Table 4 :

 4 Credit t =f (GDP t ), ECM model

Table 5 :

 5 GDP

t = f (Credit t ), ECM model 11 Documents de travail du Centre d'Economie de la Sorbonne -2016.14

Table 6 :

 6 Credit N F C

t = f (Invest N F C t ), ECM model 13 Documents de travail du Centre d'Economie de la Sorbonne -2016.14

Table

Table 8 :

 8 Credit H t = f (Invest H t ), ECM model

Table 9 :

 9 Invest H t = f (Credit H t ), ECM model

	3 Cyclical correlations GDP Monetary Aggregates Credit
	USA
	3.1 H.P. GDP/credit analysis
	Cyclical correlations between GDP, Credit to private sector and various economic variables
	and Monetary Agregates (M0, M1, M2, M2-M1) are calculated for United States 16 using
	the methodology developped by

  series X is said to be procyclical if Corr(X t , Y t ) is positive and close to 1, countercyclical if Corr(X t , Y t ) is negative and close to -1 and acyclical if Corr(X t , Y t ) is small.

• Regarding the phase shift , if a series is procyclical but with Corr(X t+h , Y t ) peaking at h

  Table 54 page 61). 18 Financial Accounts of the United States http://www.federalreserve.gov/releases/z1/ 19 All tests with same procedure as in subsections 2.1 and 2.4 in Appendix E pages 53 to 59. Documents de travail du Centre d'Economie de la Sorbonne -2016.14

	21

Table 10 :

 10 ECM model Credit Flow NFC vs. INVEST NFC, United States

Table 11 :

 11 ECM model INVEST NFC vs. Creditflow NFC, United States 22 Documents de travail du Centre d'Economie de la Sorbonne -2016.14

Table 12 :

 12 ECM model Credit Flow NFC vs. INVEST H, United States

Table 13 :

 13 ECM model INVEST NFC vs. ECM Creditflow H, United States

Table 14 :

 14 Credit t -to-GDP t -ratio, 2011, Q2

	Credit H t -to-GDP t -ratio, 2011, Q2
	Country	BIS	MGI
	Australia	108%	105%
	Canada	89%	91%
	China	28%	n/a
	France	54%	48%
	Germany	57%	60%
	Italy	43%	45%
	Japan	67%	67%
	South Korea	77%	81%
	Spain	85%	82%
	Sweden	76%	n/a
	Switzerland	112%	n/a
	United Kingdom 92%	98%
	United States	84%	87%

Table 15 :

 15 Credit H t -to-GDP t -ratio, 2011, Q2Credit N F C t -to-GDP t -ratio, 2011, Q2

	Country	BIS	MGI
	Australia	66%	59%
	Canada	94%	53%
	China	124%	n/a
	France	113%	111%
	Germany	52%	49%
	Italy	80%	82%
	Japan	107%	99%
	South Korea	101%	107%
	Spain	133%	134%
	Sweden	161%	n/a
	Switzerland	83%	n/a
	United Kingdom 87%	109%
	United States	65%	72%

Table 16 :

 16 Credit N F C t -to-GDP t -ratio, 2011, Q2 Figure 7: Credit banks t /Credit tFigure 8: Credit H t /Credit t

Table 18 :

 18 ADF tests on ∆Credit t

Table 19 :

 19 ADF tests on GDP

t 29 Documents de travail du Centre d'Economie de la Sorbonne -2016.14

Table 20 :

 20 ADF tests on ∆GDP t B.2 Step 2, DGP of residuals of linear regressionsFigure 9: Residuals of linear regressions Credit t vs. GDP t and GDP t vs. Credit t , USA, 1952-2014 30 Documents de travail du Centre d'Economie de la Sorbonne -2016.14Figure 10: Residuals of linear regressions Credit t vs. GDP t and GDP t vs. Credit

t , Australia, 1960-2014 Figure 11: Residuals of linear regressions Credit t vs. GDP t and GDP t vs. Credit t , United Kingdom, 1968-2014 Figure 12: Residuals of linear regressions Credit t vs. GDP t and GDP t vs. Credit t , China, 1986-2014 Table 27: ADF test on ∆Credit N F C t Table 28: ADF test on Credit H t 37 Documents de travail du Centre d'Economie de la Sorbonne -2016.14

Table 29 :

 29 ADF test on ∆Credit H

t C.2 Step 2, DGP of residuals of linear regressions

Table 38 :

 38 HP Correlations "GDP" Monetary Aggregates Credit,USA, 1959USA, -2014

http://data.worldbank.org/indicator/FS.AST.PRVT.GD.ZS, annual frequency

These series start in 2001 for most countries)

At least in the case the USA, it's easy to check using the flow of funds database (http://www.federalreserve.gov/releases/z1/) that the credit data for Non-Financial Corporations are in

Documents de travail du Centre d'Economie de laSorbonne -2016.14

The International Accounting Standards Board's (IASB) mandate is to develop, and to promote the use of IFRS. Its equivalent for the United States is the Financial Accounting Standards Board (FASB).

known as conduits, Special Purpose Vehicle (SPV) or Special Purpose Vehicle (SIV).4Documents de travail du Centre d'Economie de laSorbonne -2016.14

Documents de travail du Centre d'Economie de laSorbonne -2016.14

The series used for each country are displayed in Table25pages 36 at Appendix C which contains the same tests procedure than the one explained in subsection 2.3.

Households investment as a function of Credit H t for France. The explanation may be the strong governmental intervention through tax subsidies in France. On the reverse, the ECM model between Credit H t and Households investment for France can be validated.12 Documents de travail du Centre d'Economie de laSorbonne -2016.14

The results of the same analysis for Euro Area will be provided in the next version of the paper. No interesting result was found for Japan and United Kingdom

In addition to several charts, all correlations are displayed in Tables38 to 43, pages 48 to 52.

Australia, Canada, China, France, Germany, Italy, Japan, South Korea, Spain, Sweden, Switzerland, United Kingdom, United States

Australia, Canada, France, Japan, United Kingdom, United States 23 Documents de travail du Centre d'Economie de la Sorbonne -2016.14

Credit and Investment of Households in France

Documents de travail du Centre d'Economie de la Sorbonne -2016.14Table 21: Testing the type of process for ECM residuals

Documents de travail du Centre d'Economie de la Sorbonne -2016.14Table 35: Fischer tests ECM 1 vs. ECM 2, variables Invest H t and Credit H t

Appendix 2, ADF tests credit and GDP Dickey-Fuller Test three different combinations: