Jinzhao Chen
email: jichen@pse.ens.fr

Thérèse Quang
email: therese.quang@u-paris10.fr.

Vinh Q T Dang

Nicolas Fleury

Valérie Mignon

J Chen Thanks Pierre-Cyrille

International Financial Integration and Economic Growth: New Evidence on Threshold Effects

Keywords: International financial integration, Economic growth, Panel threshold regression model International financial integration, Economic growth, Panel threshold regression model JEL classification: F3, F4, O4

Recent research highlights that countries differ with respect to their experience with capital flows and do not systematically gain from capital account liberalization. This paper is related to the empirical literature that investigates the particular conditions under which international financial integration (IFI) is growth-enhancing. Relying on non-linear panel techniques, we find that countries that are able to reap the benefits of IFI satisfy certain threshold conditions regarding the level of economic, institutional and financial development, and the inflation rate. Our results also reveal a differentiated behaviour of foreign direct investment and portfolio liabilities compared to debt liabilities.

International Financial Integration and Economic

Growth: New Evidence on Threshold Effects *

Introduction

The last three decades have witnessed a massive increase in cross-border capital flows, following the rapid decline in capital controls that occurred in industrial countries in the 1980s and in developing countries since the early 1990s. During that period, international capital transactions between countries rose from about 5% of world GDP to over 20%. Intensification of the financial globalization (FG) process has inevitably drawn the attention of economists and policymakers on the actual macroeconomic implications of unfettered capital flows. In particular, their real benefits for long-term economic growth remain highly controversial (Obstfeld, 2009). Although capital flows are presumably beneficial for receiving countries as they gain access to cheaper financing, the history of international financial integration (IFI) has not been inherently smooth nor risk-free. In the context of developing countries, surges in capital inflows can pose critical challenges such as strong currency appreciation pressures, asset price bubbles or rapid credit growth that induce fragilities in the financial sector. The financial crises of the 1980s and 1990s in Latin America and Southeast Asian also sorely illustrated the disrupting effects of large swings in capital flows. More recently, the financial turmoil undergone by developed countries has led economists to adopt an even more wary stance towards financial globalization. The difference, this time, was that the crisis originated in the mature financial systems of rich economies, considered before as highly resilient, and emphasized the vulnerabilities of the international monetary and financial system as a whole. True also, the phenomenon of global current account imbalanceswith capital flowing "uphill" from emerging markets and oil-exporting countries to the developed world, especially the US -played a major role in laying the conditions for the crisis. [START_REF] Rodrik | We must curb international flows of capital[END_REF] observe that: "it does not seem to matter how capital flows. That it flows in sufficiently large quantities across borders -the celebrated phenomenon of financial globalization -seems to spell trouble", and recommend that international flows of capital should be curbed. Against this backdrop, even the International Monetary Fund has recently endorsed the legitimacy of capital controls as a part of a policy toolkit to help countries manage large inflows of capital (see [START_REF] Ostry | Managing Capital Inflows: What Tools to Use?[END_REF][START_REF] Moghadam | Recent Experiences in Managing Capital InflowsCross-Cutting Themes and Possible Policy Framework[END_REF]. Although the institution remains a fervent advocate of the positive role of international finance in promoting global economic efficiency, it increasingly recognizes that the process of financial globalization bears potential adverse effects if let go adrift. On research ground, neither the theoretical nor the empirical studies could provide a consensus to reconcile the controversy over the desirability of liberalizing capital flows. On one hand, standard neoclassical theory asserts that gains from IFI are straightforward and work mainly through three channels to stimulate growth. First, IFI improves global allocative efficiency as it allows financial resources to flow to their most productive uses. Specifically, it can provide capital-scarce countries with the resources they need to grow faster. Second, IFI facilitates international risk-sharing and risk-diversification across countries, leading to a re-composition of portfolio in favor of riskier assets offering higher returns [START_REF] Obstfeld | Risk-Taking, Global Diversification, and Growth[END_REF]. Moreover, it helps promoting domestic financial development (Levine, 2001): for instance, IFI enhances stock market liquidity and the presence of foreign banks tends to improve the functioning of the domestic banking system and the provision of financial services (see also [START_REF] Levine | Foreign banks, financial development, and economic growth[END_REF][START_REF] Klein | Capital account liberalization, financial depth, and economic growth[END_REF].

On the other hand, second-best theories predict that eliminating the distorting effects of capital controls may actually amplify the negative effects of other pre-existing distortions, resulting in welfare loss. [START_REF] Martin | Globalization and Emerging Markets: With or Without Crash?[END_REF] show that the propensity of financial crash is higher in economies which liberalize trade in financial assets before trade in goods. [START_REF] Stiglitz | Capital Market Liberalization, Economic Growth, and Instability[END_REF] argues that information asymmetries are intrinsic to financial transactions, and views IFI as hazardous and potentially exacerbating problems of bad resource allocations and excessive borrowings. [START_REF] Ju | Domestic Institutions and the Bypass Effect of Financial Globalization[END_REF] find that institutions affect the patterns of international capital flows: weak domestic institutions, especially weak property rights, can reduce the profit opportunities of both domestic and foreign firms. Thus, some theories suggest the existence of pre-requisites conditioning the gains from IFI and the need to sequence reforms when devising growth strategies [START_REF] Mckinnon | The order of economic liberalization : Fiancial Control in the Transition to a Market Economy[END_REF].

Like theoretical studies, the vast empirical literature fails to provides robust evidence of a positive link between IFI and economic development either.1 Moreover, recent studies tend to emphasize the negative correlation between capital flows and economic growth, either by showing that countries grow more rapidly when they rely less on external financing [START_REF] Prasad | Foreign Capital and Economic Growth[END_REF], or by observing that foreign capital tend to flow, paradoxically, to countries with low productivity growth [START_REF] Gourinchas | Capital Flows to Developing Countries: the Allocation Puzzle[END_REF]. Concurrently, there has been a revival of the threshold research agenda, with the aim to rationalize the potential benefits of IFI. [START_REF] Kose | Financial Globalization and Economic Policies[END_REF], for instance, maintain that "various threshold effects play an important role in shaping the macroeconomic outcomes of financial globalization". However, empirical evidence have so far yielded conflicting conclusions. [START_REF] Edison | International financial integration and economic growth[END_REF] find some evidence that IFI interact with institutional factors, but not with the degree of financial development or macroeconomic policies. In contrast, [START_REF] Kose | Thresholds in the process of international financial integration[END_REF] stress the key role of domestic financial development in improving the cost-benefits trade-off from capital flows.

This paper re-examines the IFI-growth nexus, focusing on the presence of contingency effects in the relationship. The difference from previous financial globalization literature lies in the use of non-linear panel estimation techniques. To our best knowledge, this paper is the first to apply panel threshold regression (PTR) model, developed by Hansen (1999), to assess the relationship between IFI and economic growth, using various and complementary indicators of financial integration. This empirical strategy allows us to investigate the threshold effects of the IFI-growth link in a more adequate and flexible way than previous studies. First, the PTR methodology provides endogenous identification of threshold levels, which we believe may bring new insights into the issue compared to ad hoc methods of sample-splitting or linear modeling with interaction terms. Second, it allows to determine whether the threshold effect is statistically significant and the non-linear specification validated. The alternative variables selected as threshold are country characteristics deemed to be pre-requisites allowing IFI to be growth-enhancing.

We find evidence that countries capable of reaping the benefits of IFI satisfy certain threshold conditions in these variables. In particular, the level of institutional and financial development, and the pace of inflation appear as significant thresholds. Our results also reveal a differentiated behavior of foreign direct investment and portfolio liabilities compared to debt liabilities.

The paper proceeds as follows. In the next section, we describe the dataset and detail (i) how we measure IFI, and (ii) the choice of threshold variables. In section 3, the empirical methodology, consisting in a panel threshold regression (PTR) model, is presented. Results are discussed in section 4; and the final section concludes.

Data and measurement issues

Our empirical study is based on a standard growth regression model, as traditionally implemented in the literature (e.g. [START_REF] Barro | Economic Growth[END_REF]. We build an annual panel dataset consisting of 80 countries observed over the 1984-2007 period. The dependent variable is the real per capita GDP growth.2 Control variables are selected in accordance with robustness results highlighted in influential past studies (e.g., [START_REF] Levine | A Sensitivity Analysis of Cross-Country Growth Regressions[END_REF][START_REF] Sala-I-Martin | I Just Ran Two Million Regressions[END_REF][START_REF] Sala-I-Martin | Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach[END_REF]. They include the initial income -to control for conditional convergence -, a proxy for the initial stock of human capital, the investment rate, and the growth rate of population. We also test an alternative specification where, besides the initial conditions, we control for macroeconomic policies by including government spending (expressed as a ratio of GDP) and inflation. The underlying motivation is derived from endogenous growth theory, which enables economic policies to have permanent effects on economic growth [START_REF] Easterly | National Policies and Economic Growth: A Reappraisal[END_REF].

Starting with one of these baseline models, we adopt a sequential strategy and run successive regressions, using both different IFI indicators and alternative threshold variables (one equation is estimated for each pair of IFI indicator and threshold). In what follows, we justify our approach to select IFI and threshold variables, and discuss some key conceptual and measurement issues.

Data on international financial integration

Measuring IFI

The empirical literature has been inexorably confronted with the difficulty of identifying and measuring financial integration in a consistent manner, both over time and across countries [START_REF] Edison | Capital Account Liberalization and Economic Performance: Survey and Synthesis[END_REF]. The different approaches considered so far have brought about two main types of indicators with different informational contents.

De jure indicators place an emphasis on government policies and identify the presence of regulatory measures restricting capital account transactions. They are designed using official information reported in the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER), or, in some cases, the data contained in the OECD's Code of Liberalization of Capital Movement. The basic de jure measure is a simple binary variable indicating the years where some kind of capital controls were active. [START_REF] Quinn | The Correlates of Change in International Financial Regulation[END_REF], [START_REF] Montiel | Do capital controls and macroeconomic policies influence the volume and composition of capital flows? Evidence from the 1990s[END_REF], [START_REF] Miniane | A New Set of Measures on Capital Account Restrictions[END_REF], [START_REF] Magud | Capital Controls: an Evaluation[END_REF]Reinhart (2007), Chinn andIto (2008) and [START_REF] Schindler | Measuring Financial Integration: A New Data Set[END_REF] developed methodologies beyond this on/off indicator and proposed more informative and disaggregated measures.

De facto indicators differ from the latter in that they intend to assess a country's actual exposure to international capital flows. These quantitative indicators, based on the direct observation of existing types of cross-border transactions, are therefore likely to be more objective and less prone to measurement errors than rule-based indicators. Empirical applications examining the macroeconomic implications of financial integration have commonly used either gross measures of capital inflows and outflows, or stock measures of foreign assets and liabilities accumulated over time.

In this paper, we chose to rely on the second aspect of financial openness because de jure indicators have several well-known drawbacks that make them unsuitable for our analysis. First of all, these indicators tend to be highly persistent, while our panel threshold approach rules out the possibility of using variables that are time invariant. Secondly, measures of legal restrictions may not reliably reflect actual financial openness. Some authors have, for instance, highlighted the fact that de jure indicators are poorly correlated to the amount of external financing received in developing countries (e.g. [START_REF] Prasad | Effects on Financial Globalization on Developing Countries: Some Empirical Evidence[END_REF][START_REF] Edison | Capital Account Liberalization and Economic Performance: Survey and Synthesis[END_REF]Edwards, 2007); this reveals the usual limited capacity of governments to enforce capital controls effectively. Certain types of restrictions may influence the composition of capital flows [START_REF] Edwards | How Effective Are Capital Controls?[END_REF][START_REF] Montiel | Do capital controls and macroeconomic policies influence the volume and composition of capital flows? Evidence from the 1990s[END_REF][START_REF] Magud | Capital Controls: an Evaluation[END_REF][START_REF] Binici | Controlling capital? Legal restrictions and the asset composition of international financial flows[END_REF]), yet their effect on volumes remains debatable. In any case, de jure indicators fail to capture accurately the intensity and efficacy of government restrictions.

Hence, for the purpose of our analysis, it seems more relevant to focus on de facto financial openness; arguably, the theoretical growth-benefits will hinge on the amount of capital flows experienced by a country. One major shortcoming, however, is that de facto measures may be affected by the same range of factors influencing economic growth -such as macroeconomic policies or political circumstances -, while growth outcomes may, in turn, drive capital flows [START_REF] Eichengreen | Capital Account Liberalization: What Do Cross-Country Studies Tell Us?[END_REF]. Therefore, we will have to account explicitly for this potential endogeneity bias in assessing the growth-IFI relationship. 3We examine four measures of financial integration drawn from [START_REF] Lane | The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 1970-2004[END_REF]' database. These authors computed the accumulated stocks of foreign assets and liabilities for an extensive sample of countries. Their data provide more accurate IFI indicators than capital flow measures at least for two reasons: (i) stock measures are generally less sensitive to short-run factors and, thus, less volatile than flow measures; (ii) [START_REF] Lane | The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 1970-2004[END_REF] also carefully corrected the gross stocks for price and ex-change rate fluctuations. We consider both the sum of total stocks of external assets and liabilities (as a share of GDP) and the total stocks of liabilities. In order to account for a potential composition effect, we also break down the accumulated stocks of liabilities into FDI plus portfolio equity liabilities, and debt liabilities.

Trends in IFI and stylized facts

Even though financial globalization appears as one of the most irresistible phenomena of our days, it remains unevenly spread among countries and its benefits for growth are hard to detect in the data. Figure 1 pictures the evolution of the different measures of IFI in countries grouped into industrial (23), emerging (20) and other developing countries (37). 4Figure 2 presents the simple correlations between economic growth and IFI. The period covered is 1984-2007. Several key points emerge from these figures.

First, industrial countries are, by all measures, the most financially opened and receive the lion's share of cross-border capital flows. As shown in Figure 1, Panel (a), their average total stocks of external assets and liabilities rose dramatically from 80% of GDP in 1984 to over 400% in 2007. Panel (b) also indicates that these countries are by far the most important recipient of external financing. Comparing Panel (c) and Panel (d) reveals, however, that the composition of inward capital flows has been strongly biased towards debt liabilities, which represent on average 130% of industrial countries' GDP in 2007, while FDI and portfolio equity liabilities add up to only 65% of GDP.

Second, the progression of de facto financial integration has been more contained in emerging and other developing countries. These economies also exhibit a very different pattern of capital flows compared to rich economies. Indeed, Figure 1, Panel (b) and Panel (c) show that they have relied relatively more on FDI and portfolio equity flows and reduced gradually their amount of debt liabilities in recent years. This suggests the importance of examining various components of capital flows when assessing the growth-impact of IFI. Finally, emerging economies have attracted more FDI and portfolio equity investments, on average, than other developing economies. However, with a ratio of FDI and portfolio equity inflows to GDP of 40%, they still fall far behind industrial countries.

Third, in Figure 2, Panel (a) and Panel (b), where IFI is captured by the two more aggregated indicators -namely, total stocks and total liabilities -, its correlation with real per capita GDP growth turns out to be rather weak. However, there is a more apparent

Data on threshold variables

This paper examines the existence of contingency effects in the IFI-growth relationship and assesses whether it is potentially intermediated by some third factors. These factors are country characteristics that, as suggested by some economic theories and emerging evidence, work as pre-requisites for IFI to be growth-promoting. 5 In particular, we look at the level of income, the degree of trade openness, the quality of the institutional setting, the soundness of macroeconomic policies and the level of financial development. [START_REF] Kose | Thresholds in the process of international financial integration[END_REF] summarize these variables influencing the IFI-growth nexus as a set of "threshold" conditions.

We begin by investigating the IFI-growth relationship in different country income groups. The succession of crises experienced by emerging countries in the 1990s has given reasons to think that the effects of capital account liberalization may vary with domestic structural conditions, such as the level of financial and institutional development. While these characteristics are often difficult to capture and disentangle, they are generally considered better developed in high-income countries. Therefore, some authors have proposed to assess how a country's overall level of economic development interacts with its degree of financial openness. Their results provide mixed evidence.

On the one hand, [START_REF] Alesina | The Political Economy of Capital Controls[END_REF] find evidence of a small positive association between capital account liberalization and growth with a sample of 20 high-income countries, while [START_REF] Grilli | Economic Effects and Structural Determinants of Capital Controls[END_REF] find a negative link in a sample dominated by developing countries. Considering a broader cross-country dataset including six regions and more than 400 countries in the world, Edwards (2001) finds opposite and significant effects of capital account liberalization on growth in high and low-income countries, although the significance of his results are sensitive to the choice of indicator for capital account openness.

On the other hand, [START_REF] Arteta | When Does Capital Account Liberalization Help More than It Hurts?[END_REF] only report scant evidence of a stronger growth-effect in high-income countries. Relying on the results of a counter-factual study assuming that the Great Depression did not take place, [START_REF] Bordo | chap. Implications of the Great Depression for the Development of the International Monetary System[END_REF] infer that freer capital mobility in the wake of World War II would have had little effect on economic growth in advanced industrial countries, but that it would have permitted a more efficient allocation of resources in the developing world, accelerating at least slightly the process of economic growth and development there. Here, rather than dividing our sample into high-income and low-income countries or including a dummy variable interacted with IFI, we use the initial income, measured by the lagged value of logarithm of real per capita GDP, as a threshold variable.

The degree of financial openness as well as the effect of IFI on growth may also depend on a country's degree of trade openness. On the one hand, [START_REF] Aizenman | On the hidden links between financial and trade opening[END_REF] argue that trade opening will inevitably lead to financial opening due, notably, to a public finance linkage between them. [START_REF] Aizenman | Endogenous Financial and Trade Openness[END_REF] investigate the relative magnitudes in the directions of causality between trade and financial openness and conclude that in an era of rapidly growing trade integration, countries cannot choose financial openness independently of their degree of trade integration. One the other hand, financial integration without trade openness could lead to a misallocation of resources when foreign capital flows into noncompetitive domestic industries [START_REF] Brecher | Tariffs, foreign capital and immiserizing growth[END_REF]. Here we use trade openness as one of the pre-conditions and rely on a conventional indicator, which equals the sum of exports and imports of goods and services divided by GDP. As in the case of financial openness, de jure indicators have also been developed to measure trade openness policy [START_REF] Wacziarg | Trade Liberalization and Growth: New Evidence[END_REF], but, once again, they are less informative and time-variant than indicators of de facto trade openness. 6Not only have institutions been argued as a fundamental source of long run growth [START_REF] Acemoglu | Institutional causes, macroeconomic symptoms: volatility, crises and growth[END_REF], they are also assumed to influence economic outcomes by providing the right incentives for a country to allocate resources efficiently. Moreover, economists have especially emphasized the importance of institutional arrangements that establish the security of private property, the enforceability of contracts and an effective legal system [START_REF] Mishkin | The Next Great Globalization: How Disadvantaged Nations Can Harness Their Financial Systems to Get Rich, Introductory Chapters[END_REF], all of which could affect the role of IFI on economic growth. However, institutional features (or quality) are the most complex variables to quantify. In this paper we rely on the International Country Risk Guide (ICRG) database to derive a proxy for the quality of institutions because it provides the most complete database available with the longest time span. 7 The political risk rating monitored in ICRG covers fundamental aspects of institutional quality such as law and order, corruption, the quality of the bureaucracy, government stability, democratic accountability, investment conditions and socio-economic context. We construct a composite index (a simple annual average) of five political risk components: corruption, democratic accountability, investment conditions, law and order, and socio-economic context. This index ranges from 0 to 58, with higher values indicating better institutional quality. These data are available on an annual basis starting from 1984. 8Literature on reform sequencing underscores the importance of achieving macroeconomic stability and strengthening domestic financial institutions before liberalizing capital markets [START_REF] Echeverria | Sequencing Capital Account Liberalization -Lessons from the Experiences in Chile, Indonesia, Korea, and Thailand[END_REF]. Macroeconomic policies aimed at preserving sustainable macroeconomic balance may be necessary to avoid sharp reversals in capital inflows (sudden stops) which increase the risk of crisis. Using the black market premium as an indicator of macroeconomic imbalances, [START_REF] Arteta | When Does Capital Account Liberalization Help More than It Hurts?[END_REF] report evidence of threshold effects of financial openness on the growth, which depend on macroeconomic policies. However, few empirical studies (see, e.g., [START_REF] Edison | International financial integration and economic growth[END_REF][START_REF] Kose | Thresholds in the process of international financial integration[END_REF] look at the role of monetary and fiscal policies in generating the contingency effects of IFI on the growth. In order to asses the relevance of macroeconomic policies, we introduce as threshold variables the ratio of government expenditure to GDP (proxy for fiscal policy) and the inflation rate calculated as the logarithmic first difference of the consumer price index (proxy for monetary policy). 9Some authors maintain that capital flows are determined by the efficiency of a country's financial sector. Potential benefits derived from financial integration, in the context of emerging and developing countries, will thus depend on the advancement of their financial systems. Using a model with domestic and international borrowing constraints, [START_REF] Aoki | Adjusting to capital liberalization[END_REF] show that capital account liberalization is not necessarily beneficial when the domes-tic financial system is underdeveloped.10 In the empirical literature, one common practice for measuring financial development is to focus on financial depth and compute the size of the formal intermediary sector relative to the size of the economy. Our proxy for financial depth equals the ratio of private credit (claims on the non financial private sector) to GDP. We are aware that this is a narrow measure which only partially reflects the level of financial development in an economy as it does not account for financial services provided outside the banking system, i.e. on financial markets. There are two reasons for focusing on the banking sector to investigate the role of financial development [START_REF] Azman-Saini | Foreign direct investment, economic freedom and economic growth: International evidence[END_REF]: first, bank credits are the only feasible sources of financing for the majority of developing countries in our sample; second, the number of available observations for equity market indicators are insufficient to conduct panel threshold regression.

Panel threshold regression (PTR) model

This study is based on the assumption that IFI will impact growth in a nonlinear way. We follow the methodology developed by Hansen (1999) to test for the existence of threshold effects in the growth-IFI relationship. First considering one potential threshold, the PTR model takes the form:

y it = µ i + β ′ 1 x it I(q it ≤ γ) + β ′ 2 x it I(q it > γ) + e it ( 1
)
where the subscript i indexes the individual and the subscript t indexes the time period.

The dependent variable y it is regressed on an individual-specific fixed effect µ i , and a vector of controls x it , which slope coefficients switch between regimes depending on the value of an observable variable q it . The indicator function, I(.), which equals 0 when q it is below the threshold parameter γ and 1 in the other case, generates an abrupt transition mechanism between two extreme regimes. An alternative way to present this mechanism is to write:

y it = { µ i + β ′ 1 x it + e it q it ≤ γ, µ i + β ′ 2 x it + e it q it > γ.
(2)

In this specification, observations fall into two discrete classes and respond to distinct regression functions, where the vector of slope parameters is either β 1 or β 2 . The PTR model provides an homogeneous transition mechanism for all the individuals in the sample but still allows each of them to switch between regimes, depending on the threshold condition that is satisfied at a given date. In this way, the PTR approach solves the heterogeneity and time variability issues by allowing the model parameters to vary both across individuals and over time.

The main appeal of the PTR methodology lies, however, in the endogenous determination of the threshold value. Hansen (1999) proposed a sequential procedure for estimating the threshold parameter. After a first preliminary step consisting in removing the individualspecific effects, equation ( 1) is successively estimated by ordinary least squares (OLS) with all possible values of γ. 11 The least square estimate of the threshold parameter γ is then selected as to minimize the sum of squared residuals obtained previously. Once γ is identified, the slope coefficient estimates are simply β1 (γ) and β2 (γ), yielded from OLS estimations in each regime separated by γ. In other words, the PTR model is a piece-wise linear one.

The final issue to be addressed is the statistical significance of the estimated threshold parameter. Since γ is not identified under the null hypothesis of no threshold, Hansen (1999) suggests a non-standard inference strategy based on bootstrap simulations of the asymptotic distribution of the likelihood ratio test. 12The methodology can be generalized to account for multiple thresholds. In our analysis, we start by considering the following double-threshold regression model with three potential regimes:

GROW T H it = µ i + X ′ it θ + β 1 IF I i,t-1 I(q it ≤ γ 1 ) + β 2 IF I i,t-1 I(γ 1 < q it ≤ γ 2 ) (3) +β 3 IF I i,t-1 I(γ 2 < q it ) + e it
where the dependent variable, GROW T H, is the change in real per capital GDP (in log).

We use the lag of the IFI indicator to circumvent the potential reverse causality problem posed by this variable. 13 In equation (3), only the slope coefficient on IF I i,t-1 switches between regimes depending on the value of an observable threshold variable q it , while the slope coefficients on the other controls, X it , are constrained to remain invariant. 14 In this way, we isolate the variable of interest and concentrate the analysis on the contingency effects in the IFI-growth relationship. The growth-enhancing -or debasing -effects of IFI will vary with the threshold condition.

Empirical results

Equation ( 3) is estimated for each selected threshold variable and measure of IFI. As we focus on long term growth, a timespan of twenty-four years seems sufficiently large to characterize the long period, taking into account the data availability. Empirical studies dealing with growth regressions have traditionally averaged observations over fixed-length intervals (typically, 3 or 5-years intervals) to eliminate business-cycle fluctuations. However, while averaging clearly induces a loss of information, it is not obvious that ad hoc methods of period averaging effectively remove cyclical effects. In order to save useful information to implement a more flexible model that allows for some parameter heterogeneity, we estimate the PTR with annual data. 15The regressions results are shown in Table 1 to Table 6. For each selected threshold variable, we report in columns [1] the estimation results associated with the different measures of IFI. 16 The upper panels in Table 1 to Table 6 show the coefficients and test statistics for the growth determinants, and the inferior panels display the threshold estimates along with their confidence intervals, test statistics and bootstrap pvalues. For robustness checks,17 we also run regressions where we include the threshold variable among the initial explanatory variables and report the results in columns [2] of Tables 2 to 6. The underlying motivation is that the variable used as threshold may, in fact, also have a effect on growth -besides the threshold effect -, and not controlling explicitly for it may affect the results. 18First, consistent with the conditional convergence hypothesis, we find a negative coefficient for the initial income. The other explanatory variables also have the expected signs. As predicted by theory, human capital and investment facilitate growth, whereas population growth has the opposite -although not significant -effect.

Second, in every estimated regressions, we find at most a single threshold in the conditioning variables -i.e. the null hypothesis of a single threshold cannot be rejected at 5% significance level. 19 Interestingly, the results highlight the existence of a composition effect in capital flows: the threshold estimates vary significantly across the four measures of IFI, irrespective of the conditioning variable considered. Like in similar previous studies (e.g. Edwards, 2001;[START_REF] Edison | International financial integration and economic growth[END_REF][START_REF] Kose | Thresholds in the process of international financial integration[END_REF], the estimated coefficients on IFI tend to be smaller (in absolute terms) than the ones on core growth determinants. As capital accumulation is a major channel through which IFI increases growth, controlling directly for the volume of domestic investment in the regressions should logically result in lowering the estimated impact of IFI. Moreover, the fact that FDI and portfolio liabilities are found to have larger growth-enhancing effects suggests, indirectly, that the channel of influence for FDI effect on growth may also be through transfers of technology and knowledge, and efficiency gains [START_REF] Borensztein | How does foreign direct investment affect economic growth?[END_REF]. This also seems to support the hypothesis that capital flows exhibit different properties.

In Table 1, the results for the tests of threshold indicate that the income level influences the relationship between IFI and growth in half the cases. The threshold values obtained endogenously with the PTR modeling approach are 8.2 (i.e. about 3600 US dollars) when we measure IFI as the stock of total liabilities, and 10.2 (26,000 US dollar) when we use FDI plus portfolio equity liabilities. The associated bootstrap p-values are respectively 0.06 and 0.04. By contrast, non-linearity is rejected for total stocks and debt liabilities. The PTR model implies asymmetric responses of output growth to IFI when it takes the form of total liabilities. In the lower regime, where income level is below the estimated threshold, IFI has a negative and significant effect on growth; and in the higher regime, its effect becomes significantly positive. The results obtained with FDI and portfolio equity liabilities are markedly different. While the estimated threshold of income level is much higher than in the precedent case, the slope coefficient on IFI, measured by FDI plus portfolio equity liabilities, is always positive and significant. However, it decreases when we move from the low to the high regime. From the viewpoint of the receiving economy, this means that external liabilities are all the more beneficial than they are composed of FDI and portfolio equities, although the marginal gains in terms of output growth will decrease as the economy catches up with rich countries.

In Table 2, two specifications are tested for each IFI indicator. In model [1], where trade openness is introduced only as the threshold variable, the results show the existence of two differentiated regimes for all measures of IFI, except for FDI and portfolio liabilities. The threshold estimates indicate a trade openness ratio of 67 to 69% of GDP. Below this threshold value, IFI has a negative impact on growth, although it is significant only for debt liabilities. Once the trade openness ratio rises above the estimated threshold, the coefficient on IFI becomes positive and significant. In model [2], we include trade openness in the set of explanatory variables and observe that, in most cases, this change of specification does not modify the threshold estimates and only marginally alters the slope coefficients in the different regimes. However, the results for the tests of threshold are severely affected as none of the threshold estimates is now found significant. Thus, we do not find evidence that trade integration is a pre-requisite for financial integration.

Evidence from Table 3 strongly points to a sample-split based on the quality of institutions. Interestingly, the institutional quality threshold interacts homogeneously with the stock measures of total assets plus liabilities, total liabilities and debt liabilities. For these three IFI indicators, the results show a positive and significant effect of IFI conditioned by a threshold value of 18.4 in the institutional quality index. This seem to confirm that good institutions are fundamental to provide the right incentives so that external financial resources can generate social value and be earmarked to high-profitability projects. However, when moving to specification [2], the significance of the threshold estimate only holds for the measure of debt liabilities. Finally, worth noticing is the differentiated behavior of FDI and portfolio equity liabilities compared to the others IFI indicators. Indeed, FDI & portfolio investments brings higher growth-benefits to more institutionally challenged countries, and overall, their positive effects are larger than for the other types of capital flows.

In Table 4, we test the relevance of financial development as a threshold in the IFI-growth relationship. Threshold estimates are found significant in regressions using the stock of total assets and liabilities, and the stock of FDI plus portfolio equity liabilities; these values equal 45.7% and 47.2%, respectively, and remain invariant when private credit enters the set of controls. The results also suggest that, while IFI is positively related to growth, an increase in IFI leads to higher additional output growth in financially under-developed economies relative to financially developed ones. Thus, contrary to some theories and past empirical studies, we do not find evidence that a well-functioning financial system is crucial in the IFI-growth nexus. The 2007-2009 financial turmoil shows that the level of financial depth seems more immaterial than complementary to the financial globalization process.

Finally, we find that sound macroeconomic policies also play a role in conditioning the direct benefits of IFI. However, maintaining a low inflation appears more important than containing government spending. In Table 5, practically all the tests of threshold conclude in favor of a non-linear relationship between IFI and growth, although the confidence intervals are sometimes too large for policy relevance. This time, FDI and portfolio equity liabilities exhibit the same asymmetric dynamics as the other IFI measures; in low inflation regime, IFI has a positive growth effect, whereas in high inflation regime, this effect becomes negativealthough not significant in most cases. By contrast, non-linearity is strongly rejected when the level of government spending is used as a threshold variable (Table 6).

Concluding Remarks

Relying on non-linear panel techniques, we estimate the relationship between IFI and economic growth and provide new evidence that financial integration could facilitate economic growth in countries satisfying certain threshold conditions, regarding institutional quality, private credit, inflation, and to a lesser extent trade openness. Moreover, we were able to determine these threshold levels endogenously by taking advantage of the PTR modeling approach and test the validity of the non-linear structure of the IFI-growth relationship. Previous studies do not permit to establish such conclusions. In sum, the PTR methodology offers a strategy to deal with the cross-country heterogeneity regarding their experience with capital flows and allows to isolate the particular conditions under which IFI is statistically related to economic growth. However, one limit we see in the PTR approach is that it does not permit to investigate how one threshold condition can substitute to another. So far, we do not have knowledge of studies examining this question. Finally, our results also point to the existence of a composition effect in capital flows: (i) threshold values vary across different measures of IFI; (ii) debt inflows induce an asymmetric response of output growth compared to FDI inflows which impact remains positive but decreases from low regimes to high regimes.

Since the beneficial effects of financial integration for a country depend on the prior development of third economic and social characteristics, a haste financial liberalization without considering these pre-requisites will bring more harms than benefits. On the other hand, delaying financial openness until the country meets these conditions could lead it to miss some opportunities to develop. An oriented, selective and progressive capital account opening, with priority given to help the country satisfy the aforementioned economic and social conditions, would help it to find the path to growth. However, as highlighted by many authors, the conditions for IFI to be successful are fundamentally the same as the ones needed to promote growth in general. [18.083, [13.500, [17.958, [17.458, [33.167, [33.167, [17.583, [13.500, 20.917

Table 1: Threshold regression using initial income

[1] [2] [1] [2] [1] [2] [1] [2] Initial Income -0.

A Data appendix

Notes:

The sample consists of 80 countries. We restricted the emerging group to economies included in JP Morgan EMBI Global Index. The motivation is that developing countries considered in the EMBI Global Portfolio are financially more developed and integrated with international markets than the other developing countries (see [START_REF] Cavanagh | Current account balances, financial development and institutions: Assaying the world "saving glut&quot[END_REF].

  Stock of external debt liabilities

Figure 1 :

 1 Figure 1: Stock measures of financial integration (by group)

Figure 2 :

 2 Figure 2: IFI-growth correlations

  Figure A.1: Percentage of observations above threshold by year

Table 2 :

 2 Threshold regression using trade openness

	Threshold estimate 9.213 8.176 10.197 9.213	95% confidence interval [7.715, 10.122] [7.802, 9.789] [10.094, 10.382] [7.802, 10.352]	Test of Threshold 14.104 18.558 17.687 11.338	Boostrap p-values 0.117 0.058* 0.042** 0.230	Number of countries 80 80 80 80	Number of observations 1760 1760 1760 1760	Notes: t-statistic derived using robust standard errors are in parentheses. P-values are bootstrapped with	1000 replications and 10% trimming percentage; significance at 10%, 5% and 1% are denoted by *, **, and	***, respectively.
	20

Table 3 :

 3 Threshold regression using institutional quality

	Debt liabilities	[1] [2]	-0.048 -0.055	(-5.434) (-6.242)	0.047 0.042	(4.670) (4.089)	0.220 0.201	(4.907) (4.004)	-0.082 -0.082	(-0.699) (-0.694)	0.001	(2.844)	-0.002 -0.001	(-1.751) (-1.358)	0.010 0.006	(3.483) (1.903)	18.417 18.417
	FDI + PF equity	[1] [2]	-0.058 -0.068	(-6.103) (-6.932)	0.034 0.024	(3.350) (2.317)	0.240 0.212	(5.725) (4.378)	-0.078 -0.078	(-0.665) (-0.658)	0.002	(4.045)	0.053 0.053	(7.346) (7.476)	0.019 0.015	(5.189) (3.891)	33.875 33.833
	Total liabilities	[1] [2]	-0.055 -0.060	(-5.776) (-6.328)	0.046 0.042	(4.628) (4.115)	0.222 0.205	(5.018) (4.137)	-0.082 -0.082	(-0.699) (-0.698)	0.001	(2.506)	-0.002 -0.001	(-1.609) (-0.908)	0.009 0.006	(4.584) (3.020)	18.417 18.417
	Total stocks	[1] [2]	-0.057 -0.062	(-5.933) (-6.394)	0.048 0.043	(4.670) (4.145)	0.226 0.208	(4.783) (4.064)	-0.067 -0.072	(-0.594) (-0.622)	0.001	(3.218)	-0.002 -0.001	(-1.024) (-0.365)	0.005 0.004	(4.452) (3.301)	18.417 18.417
	Variable		Initial Income		Schooling		Investment rate		Population growth		Institutions	IFI slope parameters	Low regime (INSTI≤ γ)		High regime (INSTI> γ)		Threshold estimate	95% confidence interval

Table 4 :

 4 Threshold regression using private credit

	Variable Total stocks Total liabilities FDI + PF equity Debt liabilities	[1] [2] [1] [2] [1] [2] [1] [2]	Initial Income -0.051 -0.047 -0.047 -0.048 -0.061 -0.055 -0.041 -0.036	(-4.682) (-4.073) (-4.572) (-4.283) (-5.887) (-4.983) (-4.131) (-3.389)	Schooling 0.044 0.045 0.046 0.052 0.032 0.032 0.047 0.047	(4.153) (4.165) (4.221) (4.706) (2.849) (2.923) (4.317) (4.296)	Investment rate 0.241 0.242 0.235 0.230 0.221 0.223 0.228 0.230	(5.246) (5.273) (5.060) (4.905) (4.931) (4.959) (4.795) (4.823)	Population growth -0.014 -0.020 -0.066 -0.068 -0.060 -0.060 -0.066 -0.067	(-0.131) (-0.184) (-0.548) (-0.582) (-0.486) (-0.499) (-0.544) (-0.565)	Private Credit -0.015 -0.027 -0.014 -0.015	(-2.240) (-3.618) (-1.858) (-1.933)	IFI slope parameters	Low regime (PRIVY≤ γ) 0.013 0.012 0.014 -0.0001 0.078 0.073 0.009 0.009	(3.818) (3.488) (3.911) (-0.055) (5.511) (5.060) (2.693) (2.674)	High regime (PRIVY> γ) 0.003 0.003 0.003 0.009 0.018 0.020 -0.002 0.001	(2.462) (2.943) (1.782) (4.454) (4.304) (4.600) (-0.740) (0.222)	Threshold estimate 0.457 0.457 0.459 0.204 0.472 0.472 0.472 0.472	95% confidence interval [0.380, [0.365, [0.380, [0.117, [0.444, [0.431, [0.300, [0.117,	0.472] 0.767] 1.086] 1.147] 0.489] 0.489] 0.535] 1.147]	Test of Threshold 25.249 15.724 12.412 6.209 33.404 24.685 9.418 4.017	Bootstrap p-value 0.019** 0.064* 0.122 0.463 0.002*** 0.009*** 0.219 0.707	Number of countries 75 75 75 75 75 75 75 75	Number of observations 1650 1650 1650 1650 1650 1650 1650 1650	Notes: t-statistic derived using robust standard errors are in parentheses. P-values are bootstrapped with 1000 replications	and 10% trimming percentage; significance at 10%, 5% and 1% are denoted by *, **, and ***, respectively.

Table 5 :

 5 Threshold regression using inflation

	Variable Total stocks Total liabilities FDI + PF equity Debt liabilities	[1] [2] [1] [2] [1] [2] [1] [2]	Initial Income -0.057 -0.053 -0.056 -0.051 -0.058 -0.054 -0.050 -0.045	(-5.878) (-5.497) (-5.928) (-5.528) (-6.082) (-5.799) (-5.592) (-5.197)	Schooling 0.047 0.034 0.044 0.033 0.043 0.030 0.046 0.034	(4.611) (3.489) (4.45) (3.352) (4.291) (3.13) (4.601) (3.461)	Investment rate 0.219 0.207 0.216 0.206 0.220 0.204 0.216 0.205	(5.119) (4.74) (4.894) (4.599) (5.121) (4.647) (4.825) (4.534)	Population growth -0.068 -0.072 -0.084 -0.086 -0.082 -0.083 -0.085 -0.086	(-0.593) (-0.628) (-0.717) (-0.737) (-0.704) (-0.712) (-0.722) (-0.739)	Inflation -0.021 -0.021 -0.020 -0.021	(-4.033) (-4.090) (-4.123) (-4.158)	IFI slope parameters	Low regime (INFL≤ γ) 0.005 0.004 0.010 0.008 0.022 0.020 0.012 0.009	(4.166) (3.863) (5.001) (4.441) (4.994) (4.738) (4.089) (3.304)	High regime (INFL> γ) -0.004 -0.001 -0.003 0.0004 -0.092 -0.051 -0.002 0.0004	(-1.854) (-0.58) (-1.957) (0.335) (-3.247) (-1.648) (-1.893) (0.458)	Threshold estimate 0.093 0.093 0.129 0.129 0.202 0.202 0.129 0.129	95% confidence interval [0.088, [0.087, [0.096, [0.090, [0.198, [0.008, [0.096, [0.034,	0.129] 0.129] 0.225] 0.225] 0.225] 0.231] 0.225] 0.225]	Test of Threshold 39.435 15.363 44.038 15.918 23.505 8.754 34.200 11.331	Boostrap p-values 0.000*** 0.041** 0.000*** 0.033** 0.004*** 0.198 0.001*** 0.090*	Number of countries 80 80 80 80 80 80 80 80	Number of observations 1760 1760 1760 1760 1760 1760 1760 1760	Notes: t-statistic derived using robust standard errors are in parentheses. P-values are bootstrapped with 1000 replications	and 10% trimming percentage; significance at 10%, 5% and 1% are denoted by *, **, and ***, respectively.

Table 6 :

 6 Threshold regression using government spending

	Variable Total stocks Total liabilities FDI + PF equity Debt liabilities	[1] [2] [1] [2] [1] [2] [1] [2]	Initial Income -0.051 -0.048 -0.051 -0.050 -0.060 -0.059 -0.047 -0.045	(-5.438) (-4.774) (-5.506) (-4.831) (-6.257) (-5.581) (-5.317) (-4.9)	Schooling 0.049 0.048 0.050 0.048 0.048 0.045 0.050 0.049	(4.767) (3.872) (4.933) (3.886) (4.766) (3.779) (4.895) (3.892)	Investment rate 0.237 0.244 0.231 0.231 0.239 0.238 0.229 0.233	(5.256) (5.189) (5.288) (5.082) (5.684) (5.337) (5.183) (4.971)	Population growth -0.100 -0.092 -0.083 -0.089 -0.081 -0.089 -0.083 -0.092	(-0.818) (-0.723) (-0.704) (-0.71) (-0.688) (-0.71) (-0.696) (-0.723)	Government exp. -0.066 -0.036 -0.050 -0.056	(-0.661) (-0.333) (-0.502) (-0.545)	IFI slope parameters	Low regime (GOV≤ γ) 0.007 0.006 0.006 0.006 0.045 0.044 0.007 0.009	(4.219) (3.765) (3.717) (2.663) (5.497) (5.014) (2.764) (2.53)	High regime (GOV> γ) 0.001 0.001 -0.001 -0.001 0.019 0.018 -0.002 -0.002	(1.033) (1.149) (-1.501) (-1.047) (4.859) (4.153) (-2.284) (-1.804)	Threshold estimate 0.103 0.103 0.196 0.196 0.101 0.101 0.193 0.116	95% confidence interval [0.098, [0.094, [0.096, [0.095, [0.094, [0.094, [0.096, [0.095,	0.249] 0.287] 0.287] 0.287] 0.266] 0.269] 0.287] 0.287]	Test of Threshold 12.042 10.083 16.156 12.139 11.246 11.084 13.299 10.258	Boostrap p-values 0.114 0.142 0.046** 0.117 0.161 0.180 0.077* 0.135	Number of countries 80 80 80 80 80 80 80 80	Number of observations 1760 1760 1760 1760 1760 1760 1760 1760	Notes: t-statistic derived using robust standard errors are in parentheses. P-values are bootstrapped with 1000 replications	and 10% trimming percentage; significance at 10%, 5% and 1% are denoted by *, **, and ***, respectively.

  Table A.2: Variables descriptions and sources

	Variable Growth Initial Income Human capital Investment rate Population growth Government expenditure Inflation Trade openness Institutions Private credit Total stocks Total liabilities FDI and portfolio liabilities Stock of external FDI and port-Definition Growth rate of PPP real GDP per capita (calculated in loga-rithmic terms) Logarithm of real per capita GDP lagged one period Logarithm of average years of secondary in the population over age 15, lagged one period Investment to GDP ratio Annual growth rate of popula-tion calculated in logarithmic terms Government spending as a share of GDP Logarithmic first difference of Consumer Price Index Sum of exports and imports to GDP Composite index of the five po-litical risk components: cor-ruption, law & order, qual-ity of the bureaucracy, demo-cratic accountability, socioeco-nomic conditions. Domestic credit to private sec-tor to GDP Total stocks of external assets and liabilities, divided by GDP Stock of external liabilities, di-vided by GDP folio equity liabilities, divided by GDP Debt liabilities Stock of external debt liabili-ties, divided by GDP Table A.3: Summary statistics Source Penn World Table Version 6.3 PWT Barro and Lee (2010) PWT World Development Indicators (WDI) PWT International Financial Statis-tics (IFS) PWT International Country Risk Guide WDI Lane and Milesi-Ferretti (2007) idem idem idem Variable Mean Std. Dev. Min. Max. Number Total stocks Whole sample 1.763 1.439 0.403 8.718 80 Industrial countries 2.869 1.984 1.076 8.718 23 Emerging countries 1.265 0.936 0.555 4.947 20 Other developing countries 1.345 0.757 0.403 4.11 37 Total liabilities Whole sample 1.085 0.752 0.265 4.507 80 Industrial countries 1.482 0.91 0.429 4.507 23 Emerging countries 0.855 0.566 0.265 2.923 20 Other developing countries 0.964 0.648 0.301 3.765 37 FDI+PF equity Whole sample 0.305 0.299 0.008 1.986 80 Industrial countries 0.506 0.446 0.106 1.986 23 Emerging countries 0.25 0.145 0.088 0.557 20 Other developing countries 0.209 0.164 0.008 0.803 37 Debt liabilities Whole sample 0.776 0.566 0.125 3.528 80 Industrial countries 0.962 0.523 0.322 2.51 23 Emerging countries 0.604 0.489 0.125 2.391 20 Other developing countries 0.754 0.608 0.142 3.528 37 Table A.4: Cross-correlation matrix a Variables Growth Initial Human Invest Pop. Gov. liab. -ness -tions credit -tion spending 1 0.174* 1 0.019 0.192* 1 0.165* 0.059* 0.581* 1 0.265* -0.167*-0.232* -0.082* 1 0.104* -0.075*-0.221* -0.152* 0.159* 1 Total Total FDI+PF Debt Open Institu Private Infla income capital -ment growth stocks liab. equity Growth 1 Initial Income 0.134* 1 Schooling 0.121* 0.763* 1 Investment 0.243* 0.655* 0.565* 1 Pop. Growth -0.223* -0.294* -0.244* -0.311* 1 Total Stocks 0.001 0.313* 0.204* 0.225* -0.089* 1 Total liab. -0.050** 0.117* 0.0787* 0.121* -0.012 0.917* 1 FDI+PF liab 0.103* 0.336* 0.293* 0.276* -0.089* 0.796* 0.669* 1 Debt liab -0.109* -0.009 -0.037 0.025 0.027 0.780* 0.941* 0.381* Openness 0.108* 0.232* 0.199* 0.324* -0.055** 0.442* 0.326* 0.518* Institutions 0.179* 0.773* 0.618* 0.576* -0.229* 0.298* 0.148* 0.357* 0.597* 0.479* 0.483* -0.256* 0.332* 0.235* 0.274* 0.161* -0.142* -0.052**0.013 -0.194 -0.220* -0.132 -0.093* -0.109* 0.045* 0.031 -0.118* -0.277* -0.225* -0.254* 0.049** Private credit 0.060* Inflation Government spending

a

Significant coefficients, at 5% significant level, are denoted by a *.

[START_REF] Prasad | Effects on Financial Globalization on Developing Countries: Some Empirical Evidence[END_REF];[START_REF] Rogoff | Effects on Financial Globalization on Developing Countries: Some Empirical Evidence[END_REF];[START_REF] Kose | Financial Globalization and Economic Policies[END_REF]

2009) provide detailed reviews of the empirical literature.

See Appendix Table A.2 for data description and sources.

De jure measures are not completely exogenous either as capital controls tend to be imposed by weak government. See[START_REF] Eichengreen | Capital Account Liberalization: What Do Cross-Country Studies Tell Us?[END_REF] for a discussion.

Appendix Table A.1 details the country sample.

Some authors focus on the absorptive capacity of host countries, which could be affected by these pre-conditions, to explain the weak or scant evidence of FDI-growth relationship (see[START_REF] Crespo | Determinant Factors of FDI Spillovers -What Do We Really Know?[END_REF] for a survey of this branch of literature.).

See, e.g.,Barro and Lee (1994),[START_REF] Sachs | Economic Convergence and Economic Policies[END_REF] and[START_REF] Hall | Why Do Some Countries Produce So Much More Output Per Worker Than Others?[END_REF] for this kind of binary index. To measure the trade integration,[START_REF] Bachellerie | L'intégration commerciale est-elle une condition préalable à l'intégration financière ?[END_REF] group 58 countries using the major existing regional trade agreements (RTAs), which cover various degrees ("steps") of the[START_REF] Balassa | The Theory of Economic Integration[END_REF]s classification of economic integration.

Other existing indicators of institutional quality include Hall-Jones index[START_REF] Hall | Why Do Some Countries Produce So Much More Output Per Worker Than Others?[END_REF], the index of economic freedom of Fraser Institute[START_REF] Gwartney | Economic Freedom of the World: 2006 Annual Report[END_REF] and the World Bank Governance Indicators (WBGI).

[START_REF] Arteta | When Does Capital Account Liberalization Help More than It Hurts?[END_REF] took the index of law and order of this database as the proxy of institutional strength; Chinn and Ito (2007) use this database to assess the effect of legal and institutional environment development.

We follow[START_REF] Edison | International financial integration and economic growth[END_REF] and calculate inflation using consumer price index, while[START_REF] Kose | Thresholds in the process of international financial integration[END_REF] use the first difference of inflation as the measure of monetary policy.

Because total factor productivity stagnates in the long-run or employment decreases in the short-run.

In practice, the search over values of γ may be limited to specific quantiles. It is also recommended to eliminate a minimum percentage of extreme values to ensure that the results are not driven by potential outliers.

The properties and validity of this bootstrap procedure are also discussed in[START_REF] Hansen | Inference When a Nuisance Parameter Is Not Identified Under the Null Hypothesis[END_REF] andHansen (2000).

Besides, some authors argue that non-linear modeling strategies can mitigate endogeneity issues[START_REF] Bereau | Currency Misalignments and Growth: a New Look Using Nonlinear Panel Data Methods[END_REF][START_REF] Omay | Re-examining the threshold effects in the inflationgrowth nexus with cross-sectionally dependent non-linear panel: Evidence from six industrialized economies[END_REF][START_REF] Fouqau | Modles changements de rgimes et donnes de panel: de la non-linarit l'htrognit[END_REF]. Arguably, our threshold model captures the contrasting growth effects of IFI at different levels of the threshold variable and reduces the potential endogeneity bias in the same way as the presence of interaction terms in a linear model (see[START_REF] Aghion | Exchange rate volatility and productivity growth: The role of financial development[END_REF].

Restricting the coefficients of some variables to be constant has no effect on the asymptotic distribution theory for threshold parameters and regression slopes(Hansen, 1999).

For instance,[START_REF] Khan | Threshold Effects in the Relationship Between Inflation and Growth[END_REF]; Omay and znur Kan (2010); Lopez-Villavicencio and Mignon (2011) use annual data to study the non-linearities in the relationship between growth and inflation.[START_REF] Bereau | Currency Misalignments and Growth: a New Look Using Nonlinear Panel Data Methods[END_REF] assess the non-linear growth effects of currency misalignments.

We did not report the results for the alternative regression model where we controlled directly for government spending and inflation as the estimated coefficients were unstable across different IFI indicators.

Another robustness check consisted in changing the trimming parameters from 10% to 5 or 15%. Only the threshold estimate for institutions, in the regression using the measure of total stocks, was slightly affected. Since we obtained the same estimates for the slope parameters and threshold variable in all the remaining cases, we did not report the results.

In various studies, threshold values tend to decrease significantly as new explanatory variables are introduces[START_REF] Omay | Re-examining the threshold effects in the inflationgrowth nexus with cross-sectionally dependent non-linear panel: Evidence from six industrialized economies[END_REF]

In Appendix figure A.1, we report how the panel observation are distributed relative to the estimated thresholds


Marion Le Texier
email: marion.le.texier@parisgeo.cnrs.fr.

TOWARDS AN AGENT-BASED MODEL OF EURO COINS' DIFFUSION

Keywords: Agent-Based Modeling, spatial interaction, coins transactions, multilevel, international mobility

This article is expected to provide an improved methodology for the construction of a macro-meso-micro European mobility model within a multidisciplinary framework. Such a research is supposed to lead to a better understanding of the spatial and social structure of the relations between European countries and citizens that we grasp by studying the diffusion of foreign Euro coins. An Agent-Based Model prototype is developed in this sense to bring to light multiple components of diffusion acting on different spaces and temporalities. Assumptions on macroscopic mobility are taken into account by spatial interaction models and agent-based models, with sociological parameters of individual behavior being mostly related to the social network analysis and the time geography in order to model mobility decisions (scheduling of activities and destination choices).

INTRODUCTION 1.1 General Objective

The general objective of the author PhD thesis is to reveal the spatial and social structures of the relationships between European countries and citizens from Euro coins dissemination processes. A substantial part of our research is based on the analysis of aggregated patterns of coins diffusion throughout European countries using the ESDO (Euro Spatial Diffusion Observatory) database. Those aggregated patterns however result from multiple interactions between individuals through time and space. The very essence of coins diffusion is indeed a monetary transaction between two people sharing the same coin machine or the same cash drawer in a short period of time. It is a fairly simple process but repetitions and the mobility of people lead to complex structures over space. Such a process leans itself into diffusion and spatial interaction theories [START_REF] Wilson | Complex spatial systems: Challenges for modelers[END_REF]. In this particular paper however, it is argued that Agent-Based Model (ABM) can help in revealing how aggregated patterns emerge over time from individual monetary interactions. In recent years, spatial ABM's have demonstrated their capacity to help in identifying the source of complex spatial patterns (e.g. [START_REF] Parker | Agent-based modeling of complex spatial systems[END_REF] and [START_REF] Banos | La simulation à base d'agents en sciences sociales : une béquille pour l'esprit humain ?[END_REF]).

In this paper we sketch-up an ABM prototype that simulates coins exchange in a simplified space. It is assumed that coins movements act at multiple levels and that they have a stochastic component. Moreover, the model embeds the mobilities and exchange processes portrayed from empirical analysis by [START_REF] Grasland | Euroluca: a simulation model of euro coins diffusion[END_REF] and [START_REF] Nuno | How many coins are you carrying in your pocket?[END_REF]. In fact, as a Euro coin is conveyed across space by persons, it offers a good representation of both social and spatial networks, as well as it allows the study of both territorial inequalities with regards to international accessibility and social inequalities, when looking at international contacts. Behind those questions lurk important issues both in terms of planning (identifying places that are most open to international and are forming peaks of potential innovation) and political construction of the EU. What are the social categories that are the most connected to neighboring peoples and which are therefore potential vectors for establishing a common European identity? What places / people are the most likely to be points of entry or points of international dissemination of an epidemic affecting European territory?

The main purpose of this paper is to build up a theoretical simulation model on simplified space to reveal patterns of diffusion in an international settlement system. More precisely, the paper aims at investigating the flows and movements of persons within the network of cities in Europe. This article attempts to answer the methodological question of measuring the intensity of social and spatial networks that are established on both sides of borders in Europe, since the introduction of the common currency on 1 January 2002. As money is largely spatially redistributed by individuals, examining the distribution of euro coins seems to be an original way to access and measure the importance of relationships between people located in different European countries and to determine the degree of integration of regions and cities within the regional neighborhood [START_REF] Berroir | La diffusion spatiale des pièces euro en Belgique et en France[END_REF]. Indeed, as objects carried by individuals, a significant amount of foreign coins concentrated in one point is considered to be a reflection of the existence of multiple paths between the concerned place and the rest of the Eurozone. It is by then considered an indirect territorial marker of international openness, when that kind of data is currently missing (Grasland andGuérin-Pace, 2003, 2004).

A conception at the disciplinary crossroads

If money circulation has been mainly described as a random walk -Brownian movement [START_REF] Blokland (van) P | The Euro diffusion project[END_REF]Stoyan, 2002;Stoyan et al., 2004 in their prior work on euro coins circulation) and Lévy flights (Brockmann and Theis, 2008;Brockmann, 2010), we argue that, as money doesn't move by itself, only destination's choices of the individuals matter.

Considering the individual in its decision leads us to the decision trees used in disaggregated land use and transport models since travel demand is derived from the demand for activities [START_REF] Bowman | The day activity schedule approach to travel demand analysis[END_REF][START_REF] Ben-Akiva | Integration of an activity-based model system and a residential location model[END_REF], meaning that travel is not only constrained by space but also by time [START_REF] Hägerstrand | What about people in regional science?[END_REF]. All these assumptions are summarized in Roy (2004:5), explaining that: "travel is a demand derived from the need to carry out activities at various times at other points in space". In the model, the rules constraining the mobility of the agents transporting the coins are based on previous methodologies which are linked to mobility decisions and spatial interaction models, implemented here within the frame of a probabilistic simulation of discrete destination choices. [START_REF] Thill | Toward conceptualizing trip-chaining behavior: A review[END_REF] claim that theoretical studies of trip-chaining behavior were developed mainly in connection with central place theory and the previous standard formulation of consumer demand in space by [START_REF] Lösch | Die Räumliche Ordnung der Wirtschaft[END_REF]. If new geographical economics has made criticisms about the old models in location theory, and thus on the prior works of [START_REF] Christaller | Die Zentralen Orte in Süddeutschland[END_REF] and [START_REF] Lösch | Die Räumliche Ordnung der Wirtschaft[END_REF], it has nevertheless be considered that they are still relevant when it comes to modeling the evolution of urban systems (Fujita and Krugman, 2004) (p.147). As the interest of the model is not market areas but cities' locations, a Löschian environment appears to be a relevant way to differentiate spaces through cities' size and location.

MODEL

The objective pursued here is to model a certain number of exchanges and coins brewing within a fictive isolated continent made from people living in cities located in different countries. The model will be split into five components: the environment, the money, the agents, the timeline and activity scheduling, the destination choice and finally the exchange of coins.

Environment

Following [START_REF] Christaller | Die Zentralen Orte in Süddeutschland[END_REF] and [START_REF] Lösch | Die Räumliche Ordnung der Wirtschaft[END_REF], the environment of the model has been built using a network of cities, hierarchically defined according to three different administrative levels and localized within three different countries. To fit as much as possible with the European Union geography [START_REF] Cattan | Le Système des Villes européennes[END_REF][START_REF] Cicille | Les villes européennes, Analyse Comparative[END_REF], the will was to find a standard exogenous situation in which the cities system would follow a well-defined hierarchical structure.

The structure follows an administrative principle (i.e. the scale parameter k is equal to 7) in order to prevent cities to be located on borders (which would have implied that they would share a common population and that money in cities from the beginning of the model could have had different origins) and to keep the exact same internal structure in the different countries. The total system is thus made of 3 large, 18 middle-sized and 90 small cities.

As the objective is similar to the transportation network inherent to the minisum location problem [START_REF] Ottaviano | New economic geography: what about the n?[END_REF], the network of cities is assumed to be as close as possible to a planar straight-line graph. Hence, it has been built following a Delaunay triangulation (i.e. the sum of vertices from origin i to destination node j is minimal and within the network, every city can be either an origin i and a destination j).

Moreover, as tourism is a great source of international mobility and thus of inequality within territories, two touristic landscapes (sea and mountain) have been added to the environment. According to [START_REF] Christaller | Die Zentralen Orte in Süddeutschland[END_REF], touristic sites are located in peripheral areas, which is the reason why they have been localized at the external borders of the continent. In order to be able to distinguish the influence of such migrations on the local brewing of coins, we have kept a country free of these particular external amenities. Transport is considered as homogeneous and isotropic, which supposes that the cost is the same in every direction. The transport cost is normalized by a vertice, and is the shortest path along the Delaunay graph, as shown in Figure 1. Besides, is chosen to be a quarter, which is taken as the differential ratio between time cost for people commuting within a functional urban area and people commuting toward an urban center).

The environment described above and the populations allocated to each city are the two determinants in agents' location. Indeed, every place is exogenously endowed with a population , following a Zipf law [START_REF] Sanders | Dynamics of the European urban network. Technical report, Final report in the framework of the European program "Time-Geographical approaches to Emergence and Sustainable Societies[END_REF].

is the total population of a nation and P the total population of the system. Moreover, so as every nation is made of similar structures and population.

Such as in the EMU, every nation n in the model produces its own coins. The total number of coins C is:

It has been shown in (Le Texier 2011) that euro coins don't behave similarly according to their values. Small and high coins are differentiated in this sense in the model: v = 1; 2; 5; 10; 20 cents et V = 50; 100; 200 cents. Still from empirical evidences (the euro coins production within the EMU) we know that the proportion of the small values represent two thirds of the total. Each agent has got a money-bag of small coins and of big coins. At the beginning of the simulation, the size of the money bag is identical among the population. This implies that: where

If populations are chosen to be different between nations, then these differences will be observed between coins production as well, since importance is given to the constancy of the number of coins distributed among agents. The primary size of the money-bag is thus dependent of neither individual nor national richness.

Agents

One of the hypotheses of the model is that mobile people are the only ones who allow the brewing of the coins. The mobile agents are considered to be working people. As students and young retired are considered in this model as sharing the same activities than working people, the main criterion is age: people from 15 to 64 are considered as mobile agents. The immobile agents are defined as a mass of inertia attributed to every city under the form of a certain rate: they don't move but interact with the mobile agents. The respective importance of the both subpopulation is fixed according to the characteristics of European population. We assume that mobile agents have four types of mobility which will be described in 2.3.

According to [START_REF] Beaverstock | Managing across borders: knowledge management and expatriation in professional service legal firms[END_REF] analysis of the intercity career trajectories of managerial elites and [START_REF] Taylor | Measurement of the world city network[END_REF] creation of intercity matrices from the location strategy of producer service firms, white collars, through their business trips, are one of the main driving forces of transnational flows between international cities. Their proportion in the mobile population will be fixed to 10%, which corresponds at the weight of managerial elite and academic professional within the French population (National French Survey 2006).

Thus, in this model skill is assumed to be the main driver of work and leisure mobilities (through business trip and income). As it is often considered in literature when it comes to differentiate labor, two types of skills are taken into account in the model: low and high, giving birth to a second categorization of mobile agents: w (white collars) and b (blue collars). This hypothesis is mostly based on works from the geographical economics literature, which often observe workers interregional mobility through the prism of high and low skills (see for instance [START_REF] Pas | The effect of selected sociodemographic characteristics on daily travel-activity behavior[END_REF]; [START_REF] Pissarides | Unemployment and the inter-regional mobility of labour[END_REF]; [START_REF] Korsu | Job accessibility, residential segregation and risk of long-term unemployment in the Paris region[END_REF]).

The model proposes four types of mobility, whose appearance depends on a given timeline: business (I), commuting (II), multipurpose activities (III), and tourism (IV). The agent-based model is thus discrete in time. Unlike the use in urban mobility and transport models, the scheduling of activities is not integrated in a single Nested Logit Model or discrete choice models like in [START_REF] Adler | A theoretical and empirical model of trip chaining behavior[END_REF] or in [START_REF] Kitamura | A model of daily time allocation to discretionary out-of-home activities and trips[END_REF], for instance. The choice of an activity is here defined by a timeline whose steps are days. The timeline constrains the type of mobility of agents by defining a main activity in the day. As it has been depicted by many authors [START_REF] Stock | A practice-based approach to the conceptualization of geographical mobility[END_REF][START_REF] Bell | Comparing temporary mobility with permanent migration[END_REF], for instance, touristic flows are mostly happening according to seasonality. Thus, months are defined according to work and holidays periods. During two weeks in winter and two months in summer, mobile agents are allowed to go in holidays, depending of an annual budget of holidays. Weeks are split into two categories of days: the week days (going from Monday to Friday) during when agents work (they may commute or go in business trips) and the week-end during when agents are making local multi-purposes trips (shopping, leisure, etc). Figure 2 represents the successive behavioral choices potentially made by an agent during one iteration of the model.

Trips and destination choices

Once the activities have been scheduled, the second step is to choose a destination. The decision process related to mobility follows a simple gravitational model which is expressed in a probabilistic manner as in Huff (1964) (i.e. it determines the probability for each place to be frequented). The general formulation of a destination is thus defined in the model as:

where:  is the probability for people living in i to go in j  is a topological distance between i and j (as discussed in 2.1)  is a distance decay parameter  is the population of city j  is a population weighting parameter  A is a specific attribute of places, independent of population, e.g. touristic attributes  is the weighted parameter of attribute A Business trips concern white collars only. It is assumed that the restraint due to distance is much lower than the one of commuting trips or multi-purposes trips. Besides, the attraction played by the size of cities is high: = 0

Activity types 2 & 3 can be either realized within the settlement of residence or the Functional Urban Area (FUA) than we consider as an agglomeration of settlements organized around a main centre. In the first case, only the distance to be reached matters and this last one is constrained to a certain neighborhood:

Trips realized within the FUA (Functional Urban Area) are somehow polarized by the main urban pole of the neighborhood such as both the size of cities and the distance to be reached constrain the destination choice:

Holidays trips are always made toward touristic destinations which are unequally distributed among places. High skill workers are less handicapped by distance in their choices for holidays than low skill ones:

Exchange

Once people have moved to a particular place, they meet the immobile people of this city as well as the other migrants. To keep the model as simple as possible, it is considered that agents may only take part in an exchange once per time step. Pairs of agents are randomly made within cities. Pairs of agents are randomly selected from the population at destination within each city. If total population is uneven, one agent is left aside from the exchanges. Every agent is included in a pair and participates to one exchange per day. This is a high simplification to say that there is only an exchange per day and that everybody participates.

Further, we assume that there is equal probability to interact with someone from another social group through shops. In fact, we think that this is reasonable to think that most of the coins exchanged are independent of social networks: people exchange coins with shops' keepers, with taxi drivers, through coffee machines, etc and rarely through a direct contact. Eventually only mobilities' types and geographical environment differentiate money bags.

An exchange is made of a pair of agents and we decide that only one of them receives. We then define a receiver and a giver. The exchange being directional, the money bag of the receiver does not impact on the exchange process. Besides, the system has no memory, which implies that the giver/receiver probabilities are independent from each other and along time. The number of type of coins exchanged is constrained by the giver's money bag. Denote the number of coins exchange by: where:  is the number of small value coins exchanged  is the number of high value coins exchanged

The amount of money exchanged is defined to be smaller than the amount of money contained in the money bag, thus no debt is allowed:

are taken randomly from a normal distribution with , the mean of the distributions, and the respective variances. These differences between average probabilities come from empirical observations. Indeed, it has been demonstrated that small coins diffuse less than high ones [START_REF] Grasland | La diffusion spatiale, sociale et temporelle des pièces euros étrangères : un problème complexe, 9 th Journées de Méthodologie Statistique[END_REF][START_REF] Texier | La pièce euro comme marqueur de mobilités transnationales[END_REF]. [START_REF] Nuno | How many coins are you carrying in your pocket?[END_REF] and primary empirical results have also shown that the probability to choose more or less coins within an exchange is independent of individuals characteristics such age, genre or salary.

CONCLUSION & PERSPECTIVES

The model presented in this article will be the subject of an implementation in Scilab. The model is constructed in a learning perspective, meaning that its evaluation will be a continuous process. As the diffusion of euro coins is related to the idea of innovation, the focus will be shifted on the presence of foreign coins in individual moneybags. The final stage of the diffusion process is theoretically obtained when all coins are perfectly mixed and their proportion remain stable across borders: the simulation will end when the statistical distributions of foreign coins among agents and cities will have reached their maximal entropy. The main aim of this study being to isolate the key paths to transnational mobility and individuals mixing within the European Monetary Union, the results of the simulation will be compared with data on pursues' contents collected by the ESDO and CEPS-Instead projects in France (15 surveys from March 2002 to October 2008), Belgium (December 2003), Germany (December 2004) and Luxembourg (June 2006) on an average of 2000 respondents per survey. Finally, the spatial structure of the model will be transformed to better fit to the French-German-Belgium-Luxembourgish area (addition of a language barrier within a country, monocentric versus polycentric national cities system, etc).

Figure 1 :

 1 Figure 1: The Isolated Continent.

Figure 2 :

 2 Figure 2: Activity diagram.