Keywords: Capital inflows, Real exchange rate dynamics, Economic growth. JEL Codes F3, F4, O4

This paper assesses the impact of capital inflows and their composition on the real exchange rate and economic growth in developing countries. Capital inflows can directly support economic growth by relaxing constraints on domestic resources, but can also indirectly weaken growth through the appreciation of the real exchange rate. We employ the Generalized Method of Moments (GMM) for dynamic panel data to deal with the endogeneity bias. Using a large sample of 77 low-and middle-income countries over the period 1980-2012, the results clearly show that capital inflows affect directly and indirectly economic growth. Our main findings are as follows: (i) a 1 percent increase in total net capital inflows appreciates the real exchange rate by 0.5 percent; (ii) the real exchange rate appreciation effect of remittances is twice as big as the effect of aid, and ten times bigger than the effect of FDI; (iii) overall, capital inflows are associated with higher economic growth after netting out the negative impact of real exchange rate appreciation. Doubling capital inflows per capita would increase growth by about 50 percent, resulting in a gain of roughly 2 additional percentage points on top of the 3.7 percent annual growth rate observed within the sample over the period 1980-2012.

Introduction

The growth impact of external capital inflows remains a matter of debate, especially for developing economies [START_REF] Kose | Financial Globalization: A Reappraisal[END_REF]. Assuming free capital markets and diminishing returns, the standard neoclassical theory predicts a positive relationship, with external resources flowing from capital-abundant developed countries to capital-scarce developing economies. But the "Lucas paradox" shows that the neoclassical prediction is weakly supported by the facts, and that when flows move in this way they are not necessarily associated with those economies which have highest growth levels, illustrating the so-called "allocation puzzle" [START_REF] Gourinchas | Capital Flows to Developing Countries: The Allocation Puzzle[END_REF]. The debate on growth and external capital is as old as development economics. Critical views on the external financing have been initially analyzed in relation with both the indebtedness, let say the sustainability problem of debtors, and the "crowding out" phenomenon. For example, in their influential model, [START_REF] Chenery | Foreign Assistance and Economic Development[END_REF] do not care about the way the saving-investment gap is covered.

Neither the return on investments, nor the repayment terms of external resources are considered. In doing so, the borrower potentially undermines the long run economic growth.

The "crowding out" argument suggests that external resources can reduce the domestic effort to save and substitute for domestic financing in the most profitable projects. Although adverse effects may exist, the conventional thinking is that positive impacts on GDP growth clearly outweigh the negative ones [START_REF] Weisskopf | The impact of Foreign Capital Inflow on Domestic Savings in Underdeveloped Countries[END_REF].

The objective of this paper is to revisit the relation between economic growth and external financial resources by focusing recent literature and proposing empirical analysis on a large sample of low-and middle-income countries.

First, we hypothesize that not only do net capital inflows matter, but also that their composition and fluctuations are critical. For example, while short-term flows such as portfolio investments can be procyclical, private transfers can help smooth adverse economic shocks. Private transfers can, for instance, protect the standard of living of households when credit and insurance markets are nonexistent or not available for all (see [START_REF] Thorbecke | The interrelationship linking growth, inequality and poverty in sub-Saharan Africa[END_REF].

Second, beyond their expected direct positive impact, we hypothesize that capital inflows may cause some indirect negative effects by their action on the real exchange rate.

The literature on the long-term determinants of the real exchange rate identifies capital inflows as one of the most robust determinants. According to this literature, capital inflows increase the level of domestic expenditure in a similar way to the effect of windfalls from natural resources (Corden and Neary, 1982). While the price of tradable goods is exogenous, the price of non-tradables is endogenous to the dynamics of the domestic economy. Excess demand pressures raise the relative price of non-tradable goods, and weaken the competitiveness of the tradable sector. Just as capital inflows can be spent differently depending on their nature (equity or debt, short-term or long-term), their impact on the real exchange rate can also vary according to their composition.

Third, we hypothesize that the effects of capital flows on economic growth depend on the level of development and the exchange rate regime. At first glance, the amount and composition of capital inflows would be expected to vary with the recipient country's level of development. For instance, low-income countries receive more per capita Official Development Assistance (ODA) and fewer portfolio investments due to their small domestic financial markets. While a fixed exchange rate can bring stability, and thus long-term returns, it lacks the flexibility and ability to smooth out shocks that a floating or intermediate regime may provide.

Our empirical analysis is based on the dynamic panel GMM technique to deal with endogeneity issues. As expected, our main results show that capital inflows affect economic growth through two different channels: a direct conventional positive one reflected in the investment/savings gap, or the benefits resulting from international transfers of know-how; and an indirect and negative one through the relative price (the real exchange rate). In other words, while foreign capital has a positive impact on growth, it can also affect growth prospects by appreciating the real exchange rate and weakening the recipient country's competitiveness. Accounting for the real effective exchange rate effect, a doubling of per capita net inflows increases the annual growth rate by about 50 percent, which means a gain of roughly 2 additional percentage points on top of the 3.7 percent annual growth rate observed within the sample over the whole period . The real appreciation stemming from remittances is twice as big as the effect of aid, and ten times bigger than the effect of FDI.

The rest of the paper is organized as follows: Section 2 briefly reviews the existing literature covering both the direct impact on growth (higher savings/investment balance) and the indirect impact through the real exchange rate. Section 3 analyzes descriptive statistics and defines our empirical strategy, including the estimation methodology and the treatment of endogeneity. Section 4 discusses the main results, and Section 5 offers concluding remarks and draws policy implications.

Capital inflows and their components: what are the expected direct and indirect effects?

The difficulty in reaching an unambiguous conclusion on the way external financial resources and their components affect economic growth is related to the different channels of influence.

Direct implications on economic growth

Private transfers have become the second largest type of financial flow to developing countries, just behind FDI. The cost/benefit analysis of these transfers which occur alongside migrants' remittances delivers mixed conclusions. The positive impact on the GDP growth generally results from a higher level of permanent domestic consumption (see [START_REF] Aggarwal | Do Remittances Promote Financial Developement ?[END_REF], and more rarely from "building booms". As [START_REF] Giuliano | Remmittances, Financial Development, and Growth[END_REF] established, remittances boost economic growth by providing appropriate alternative means to release financial constraints on domestic investments. Together, these effects can be conditional on the quality of the recipient country's economic policies and institutions [START_REF] Catrinescu | Remittances, institutions, and Economic Growth[END_REF]. These positive effects can be partially offset by a "brain drain", that is the loss of productive capacity due to the loss of skilled workers. The magnitude of the loss is, however, difficult to assess. It depends on both the opportunity cost of migrants working abroad and the domestic unemployment rate. In addition, while migration can be a source of new opportunities for the country of origin to export, an excessive reliance on remittances can lead to the problem of the "Samaritan Dilemma" [START_REF] Buchanan | The Samaritan's Dilemma[END_REF], where the recipients overconsume, and more generally generate "perverse incentives".

The financial flows related to ODA have been discussed at length in prominent works dealing with the principles that govern aid allocation rules. Burnside and Dollar's 2000 paper has been very influential in academic and policy spheres. By using standard regression techniques from the economic growth literature, the authors explore the effect of foreign aid on domestic growth. They find a strong positive effect for low-income countries pursuing good policies, but no tangible impact for countries with severely distorted policy regimes. Accordingly, aid effectiveness is conditional on resources flowing to the most efficient countries. Overall, previous empirical studies do not provide clear conclusions. The results vary depending on the sample, the specification of the econometric model, and how the endogeneity biases are treated (see [START_REF] Doucouliagos | Aid effectiveness and growth: A Meta study[END_REF][START_REF] Murinde | Financial Development and Economic Growth: Global and African Evidence[END_REF].

Aid flows tend to be associated with human capital and infrastructure expenditures.

While these expenditures, which correspond to what Hirschman (1958) called "Social Overhead Capital" (SOC), are essential for the development process, their benefits may only appear in the long term through enhanced productive capacity. Some recent works have questioned the growth impact of aid, generating controversial debates. [START_REF] Rajan | Aid and Growth: What Does the Cross-Country Evidence Really Show?[END_REF] use different estimators on cross-section and panel datasets covering a large sample of developing countries. The authors do not find clear evidence to support a positive and robust impact of ODA. Using the same approach and similar data, Arndt, Jones and Tarp (2010) reach an opposite conclusion, especially when the effect of aid is extended to social welfare variables other than economic growth, such as poverty alleviation, the provision of basic health care, and primary education. This impact has been in close relation with the original Millennium Development Goals (MDGs), and now with the Sustainable Development Goals (SDGs) (see [START_REF] Arndt | Assessing Foreign Aid's Long-Run Contribution to Growth and Development[END_REF]. The role of ODA is, therefore, ambiguous and difficult to clarify beyond the current expenditure focused on the building of human capital and infrastructure services (see Guillaumont, McGillivray and Wagner, 2013;[START_REF] Guillaumont | M. Boussichas and P. Guillaumont, Financing Sustainable Development[END_REF].

The impact of foreign direct investments (FDIs) mainly depends on what kinds of activities are financially supported. The impact can be limited if FDIs consist of "pure" transfers of assets from the public sector to the international private sector during privatization. This is the case when the government uses the cash to reduce international debt.

The only predictable effect would then be the potential long-term improvement of firm If the empirical literature on the impact of FDI does not give unambiguous results, the use of meta-regression techniques has recently provided some useful insights on this issue.

From 103 micro and macro studies, [START_REF] Bruno | Reexamining the Conditional Effect of Foreign Direct Investment[END_REF] show that the number of studies where FDI is found to support growth is four or five times the number of studies where the coefficient is negative. In addition, the authors find that the FDI effect is larger than commonly suggested, for example in De [START_REF] Vita | Growth Effect of FDI and Portfolio Investment Flows to Developing Countries: A disaggregated Analysis by Income levels[END_REF] for countries below some critical human capital and financial development level. Although FDI can be beneficial in triggering economic growth and development of African economies, also using a metaregression analysis from 32 studies, [START_REF] Wooster | Productivity Spillovers from Foreign Direct Investment in Developing Countries: A Meta-Regression Analysis[END_REF] show a higher statistical significance of documented spillover effects in Asian countries.

The openness of the capital account to short-term flows has been undoubtedly one of the most controversial subjects in recent decades. To a large extent, the pros and cons are reflected in [START_REF] Stiglitz | Capital market Liberalization, globalization and the IMF[END_REF] changes in the external environment and to sudden slowdowns in private capital inflows [START_REF] Calvo | Capital Flows and Capital-Market Crises: The Simple Economics of Sudden Stops[END_REF]. Therefore, via a contagion effect, countries can be made more vulnerable to capital outflows, giving rise to economic crises unrelated to the domestic management of the affected countries.

Indirect implications on the real exchange rate

Beginning with the influential works of Edwards (1989) and [START_REF] Williamson | The exchange rate system[END_REF], there is now a very extensive strand of literature that deals with the specific relationship between capital inflows and the equilibrium real exchange rate, which is determined by factors that affect both a country's internal and sustainable external situation. Net capital inflows are seen as one of the determinants that increase the demand and price of non-tradable goods. The relative price of non-tradables goes up and modifies the initial macroeconomic equilibrium.

The question that arises here is whether the real exchange rate can be affected differently depending on the composition of capital inflows.

As mentioned earlier, remittances can act as a buffer to smooth consumption, for example when the recipient economy is suffering from an economic downturn [START_REF] Lueth | Are Workers' Remittances a Hedge Against Macroeconomic Shocks? The Case of Sri Lanka[END_REF][START_REF] Chami | Macroeconomic Consequences of Remittances[END_REF]. In this case, remittances help to maintain stability by compensating for the effect of macroeconomic shocks, and present only limited risk of a significant real exchange rate appreciation. Conversely, remittances can be connected with various kinds of investment projects. The risk of real exchange rate appreciation is particularly strong if resources are channeled to real estate (construction booms), while it seems negligible if they are spent on imported durable goods. The empirical results are mixed.

The studies of [START_REF] Chami | Macroeconomic Consequences of Remittances[END_REF]; Izquierdo and [START_REF] Montiel | Remittances and Equilibrium Real Exchange Rates in Six Central American Countries[END_REF]; and [START_REF] Rajan | What Undermines Aid's Impact on Growth?[END_REF] are not conclusive. [START_REF] Rodrik | Industrial Policy: don't ask why, ask how[END_REF] finds that competitive exchange rate policies were difficult to promote in Jordan and Egypt because of the loss of competitiveness due to transfers from migrant workers in the Gulf countries. In a different context, [START_REF] Naceur | Disaggregated Capital Flows and Developing Countries' Competitiveness[END_REF] share the same view, arguing that a long-term increase in remittances, and aid devoted to poverty alleviation generate increased spending on non-tradables.

The impact of ODA mainly depends on how resources are used. Assuming that a significant part of official flows is targeted to enlarge a country's basic infrastructure, the relative contribution of domestic consumption to global expenditure should be considered as an important factor in analyzing the evolution of the exchange rate. When the recipient country suffers from supply constraints, capital inflows associated with consumption put more

Études et Documents n° 2, CERDI, 2017

pressure on the relative price of domestic goods than the capital inflows channeled to those investments which have a significant proportion of imported goods. [START_REF] Cerra | Foreign Aid and Real Exchange Rate Adjustments in a Financially Constrained Dependent Economy[END_REF], highlight the complexity of this issue. Foreign aid is expected to appreciate the real exchange rate if it stimulates productivity within the tradable sector, while depreciation is likely to occur if aid is channeled to improve productive capacity in the nontradable sector.

The impact of FDI on non-tradable prices varies greatly according to the specific type of operation. When FDI is for imported machinery and equipment, beyond a potential transitory effect, there is little risk of a sustained appreciation leading to exchange rate disequilibrium. A positive effect of FDIs is also expected on the use of productive resources through transfers of technology, managerial know-how, and other intangible assets [START_REF] Agénor | Capital Inflows, External Shocks, and the Real Exchange Rate[END_REF][START_REF] Javorcik | Does Foreign Direct Investment Increase the Productivity of Domestic Firms? In Search of Spillovers Through Backward Linkages[END_REF][START_REF] Kinda | Investment Climate and FDI in Developing Countries: Firm-Level Evidence[END_REF][START_REF] Kinda | Foreign ownership, sales to multinationals and firm efficiency: the case of Brazil, Morocco, Pakistan, South Africa and Vietnam[END_REF]. However, as mentioned earlier, FDIs may also consist of "pure" transfers of domestic assets between residents and non-residents, somewhat counterbalancing the argument on relative prices. The once and for all revenues or bonanzas resulting from selling public enterprises can be channeled to permanent current expenditures, increasing the price of non-tradables. The number of studies that deal with the impact of private flows on the real exchange rate is limited and results are mixed, as shown by [START_REF] Athukorala | Capital Inflows and the Real Exchange Rate: Comparative Study of Asia and Latin America[END_REF]. [START_REF] Lartey | Capital Inflows, Dutch Disease Effects and Monetary Policy in a Small Open Economy[END_REF] and [START_REF] Saborowski | Capital Inflows and the Real Exchange Rate: Can Financial Development Cure the Dutch Disease?[END_REF] find that FDIs cause a real appreciation.

The role of short-term capital transactions remains a matter of concern for developing countries. In low-income economies, commercial bank loans and international portfolio investments can be seen as temporary transactions. This is consistent with unit root tests, suggesting that short-term capital inflows are stationary [START_REF] Elbadawi | Capital Flows and Long Run Exchange Rate Equilibrium in Chile[END_REF].

However, for middle-income countries that have liberalized their capital account, these variables may have a stochastic trend or be part of a long-term cycle, which leads to the appreciation or depreciation of the real exchange rate. This effect potentially extends to all kind of short-term inflows because domestic banks play a bigger role in these transactions than in FDI. In a recent paper dealing with transition in Central and Eastern Europe economies, Boero, Marvomatis and Taylor (2015) have reconsidered the question. They provide evidence that these economies have known two sources of long run appreciation of their currencies. The first source is due to the Balassa-Samuelson effect. It relates to the relative importance of FDIs which positively affect both the internal productivity level and the international convergence of the costs in the standard of living. The second source relates to the other components of the capital account; they do not necessarily affect the productivity level and can be a driving force for a prolonged real appreciation.

Empirical methodology and descriptive statistics

The specification of the models

We first estimate the effect of net capital inflows on the real effective exchange rate, and then on the economic growth. We use a dynamic specification given the potential inertia of both REER and GDP growth, the former variable being expressed in log in the econometric regressions. Note that we estimate separately the following equations 1 : where 𝑅𝐸𝐸𝑅 𝑖,𝑡 and 𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ 𝑖,𝑡 stand for the real effective exchange rate and the economic growth for country 𝑖 in non-overlapping 5-year periods t. For a country i the REER is defined as follows, where CPI j is the Consumer Price Index of the country's partner j; e j and w j are the nominal bilateral exchange rate and the weight of the j-th partner in the total bilateral non-oil imports and exports of the country (i). The weighting pattern refers to the 10 largest trading partners over the period of 2000-2005.

𝑅𝐸𝐸𝑅 𝑖,𝑡 = 𝛾 + 𝜔𝑅𝐸𝐸𝑅
𝐿𝑜𝑔(𝑅𝐸𝐸𝑅) 𝑖 = ∑ [𝑤 𝑗 * 𝐿𝑜𝑔(𝑒 𝑗 𝑗=10 𝑗=1 * ( 𝐶𝑃𝐼 𝑖 𝐶𝑃𝐼 𝑗 ⁄ ))] (5)
An increase in REER indicates a real appreciation in the exchange rate (i.e. a loss of price competitiveness). For each of the 77 low-and middle-income countries (see Appendix 1 1

The simultaneous estimation of the two equations is theoretically more efficient than separate estimates provided that two conditions are met:-First, error terms should be cross correlated. Second, the simultaneous equations model should be well specified. Misspecification potentially affects the entire system. These reasons underline why a by-equation estimation strategy has been preferred.

for the list of countries), 7 observations are available for the periods of 1980-2012; averaged periods are considered to minimise short-term fluctuations 2 .

𝑇𝑜𝑡𝑎𝑙 𝐹𝑙𝑜𝑤𝑠 𝑖,𝑡 is a per capita net inflow which consolidates Foreign Direct Investment (FDI), Foreign aid (Aid), Remittances, Portfolio investment, and Other Flows. All capital inflows are expressed as a proportion of the population, to control for the heterogeneity of country size. There is no obvious variable for expressing capital flows as ratios. The empirical works assessing the impact of ODA on economic growth tend to report capital flows as a ratio of GDP level, while a number of recent papers focusing on private flows, such as FDI or portfolio investments, report these flows in per capita terms (as a share of population) (see [START_REF] Arndt | Assessing Foreign Aid's Long-Run Contribution to Growth and Development[END_REF]. Following [START_REF] Alfaro | Why doesn't Capital Flow from Rich to Poor Countries? An Empirical Investigation[END_REF], we express capital flows as a ratio to the population, because this variable is more normally distributed, and less subject to both non-stationarity and endogeneity issues. In addition, because population is more stable, expressing capital inflows in per capita terms allows us to capture their real dynamic, rather than GDP fluctuations.

In an alternative specification, we estimate the impact of Total Flows instability. To identify this phenomenon, we first regress net capital inflows on their value lagged by one period, and a deterministic trend to estimate shocks whose occurrence is predictable. Second, for each of the non-overlapping 5 year sub-periods, the standard deviation of the regression residuals is used to capture the instability (i.e. occurrence of non-predictable shocks). See

Appendix 2Table 1 for the definition of the variables and data sources, and Table 2 for descriptive statistics. 𝑌′ 𝑖,𝑡 in equations ( 1) and (2), represents a vector of control variables to explain REER including: trade openness (Trade) as defined by the ratio of imports plus exports over GDP, the standard Terms of Trade, the ratio of Government Consumption over GDP, the Balassa Index defined as the ratio between the country's real per capita GDP, and the weighted mean of the same variable for the 10 major trading partners considered for the REER. This last variable is used to capture the impact of the increasing price of non-tradable goods over the development process within a sample for which per capita GDP levels are quite heterogeneous. Hence, we control for most of the usual determinants of the real exchange rate (e.g. [START_REF] Devarajan | Real Exchange Rate Misalignment in the CFA Zone[END_REF][START_REF] Combes | Capital Flows, Exchange Rate Flexibility and the real Exchange Rate[END_REF]. Trade openness mitigates the rise of domestic prices and real appreciation. The impact of terms of trade is a priori ambiguous:

when the terms of trade increase, REER can appreciate if the income effect dominates the 2 1980-1984; 1985-1989; 1990-1994; 1995-1999; 2000-2004; 2005-2009; 2010-2012 substitution effect. The Balassa Index is expected to be positively correlated with the real exchange rate. Finally, government consumption is assumed to fuel REER when the majority of public spending is oriented toward non-tradable goods and services.

In equations ( 3) and (4) 𝑋 ′ 𝑖,𝑡 stands for the vector of control variables to explain GDP growth. This vector includes the initial level of GDP per capita (GDPPC), Polity2 to capture the degree of democracy, Natural Rents, trade openness, and real effective exchange rate (REER). Trade openness [START_REF] Wacziarg | Trade Liberalization and Growth: New Evidence[END_REF] and democracy [START_REF] Acemoglu | Democracy Does Cause Growth[END_REF] are expected to promote economic growth. According to the convergence hypothesis, the higher is the level of development; the lowe is the expected economic growth rate. The impact of natural rents is ambiguous (see Sach and Warner, 1995;[START_REF] Brunnschweiler | The Resource Curse Revisited and Revised: A Tale of Paradoxes and Red Herrings[END_REF].

On the one hand, a "Dutch disease" phenomenon can impede growth, but on the other hand, the discovery and exploitation of raw materials can extend country endowment, and the resulting additional income can contribute to an increase in domestic investments.

We include 𝜗 𝑖 and 𝑣 𝑖 to control for unobserved time-invariant country-level characteristics that are potentially correlated with government revenue, and 𝜑 𝑡 and 𝜛 𝑡 for common time-variant shocks that affect all developing countries. 𝜎 𝑖,𝑡 ,∅ 𝑖,𝑡 , 𝜀 𝑖,𝑡 and 𝜍 𝑖,𝑡 are idiosyncratic error terms3 .

Blundell and Bond's (1998) system-GMM estimator for dynamic panels is implemented for two reasons. First, the OLS estimator is inconsistent since the lagged dependent variable is introduced with country fixed-effects [START_REF] Nickell | Biases in Dynamic Models with Fixed Effects[END_REF]. Second, the GMM estimator controls for the potential endogeneity of the explanatory variables due to measurement errors, reverse causality, or omission of pertinent variables. In fact, both GDP growth and net capital inflows can be affected by common shocks. For instance, a discovery of natural resources may attract foreign direct investment while affecting economic growth patterns. Furthermore, deteriorating economic and financial conditions could significantly reduce investor incomes and capital inflows, weakening the economic growth. With regard to reverse causality, high capital inflows may increase investment and boost economic growth, but sustained growth in a recipient country can send out positive signals about the country's prosperity and attract more capital inflows.

Equations are taken in first difference to remove country fixed effects. Equations in levels and first differences are combined in a system and estimated with lagged differences and lagged levels of the explanatory variables as instruments. Therefore, the system-GMM estimator helps reduce the endogeneity issues given that the lagged values used as instruments for capital flows are not affected by the contemporaneous levels of economic growth. The validity of the instruments is tested by the Sargan-Hansen over-identification test and by the second order serial correlation test AR(2); the null hypothesis indicates that the error term does not exhibit auto-correlation. Finally, to deal with the problem of instrument proliferation, the matrix of instruments is collapsed to ensure that the number of instruments does not exceed the number of countries [START_REF] Roodman | A note on the theme of too many instruments[END_REF]). To deal with the problem of "weak instruments", and to augment the precision of the estimation, an external instrument capturing economic growth in developed countries is added: the 10 largest bilateral donors of each country are considered and we generate an average donor growth weighted by the amount of aid that a country receives from those particular donors [START_REF] Tavares | Does Foreign Aid Corrupt?[END_REF]. This external instrument may affect the allocation of foreign aid and the other net capital inflows including FDI and remittances, but does not directly affect economic growth in recipient countries.

Net capital inflow statistics

The aggregated net total of external financing is broken down into 5 broad categories: private unilateral transfers or remittances; official development assistance (ODA); foreign direct investments (FDI); portfolio investments (corporate bonds and other private debt securities); other inflows including liabilities to foreign banks. This sub-section presents an overview of the long-term evolution of the volume as well as the composition of net capital inflows over the period 1980-2012. Statistics are provided separately for low-and middleincome countries, LIC and MIC, respectively, for the whole period and for sub-periods. 1980-1989 1990-1999 2000-2012 creation of manufacturing firms, with some expected positive upstream and downstream effects on economic growth. Accordingly, the size and the composition of financial inflows matter. Different kinds of external resources are likely to induce different impacts depending on the per capita income level. The complexity of the potential relations between growth and external capital inflows is reflected by the correlation and distribution of the dots in Figure 3.

Regression results

Table 1 shows the effect of capital inflows on the real exchange rate (REER). The statistical tests do not invalidate the econometric method. In other words, the null hypothesis of the Sargan and the AR (2) tests are not rejected. Moreover, the positive coefficient of the lagged dependent variable highlights an inertia effect that legitimates the dynamic panel specification. Total capital inflows is associated with a real appreciation of the exchange rate (equation 1). A 10 percent increase in capital inflows appreciates the REER by roughly 5 percent. Although the coefficient decreases somewhat, this result is robust to the introduction of government consumption and the instability of net total capital inflows: the first variable is significant (equations 3 and 4) while the second is not (equation 5). Equations 2 and 4 break down total capital inflows into their different components. In equation 2, FDI (0.03) and Aid (0.14) elasticities, appreciate moderately the real exchange rate, while Portfolio investments have a strong impact. Bear in mind that this component is marginal in total inflows (see Figures 1 and2). In other words, portfolio investments are closely related to a limited number of sub-periods in a narrow range of specific countries such as Brazil (2010Brazil ( -2014) ) or Malaysia (1995)(1996)(1997)(1998)(1999). The effect of Remittances is only significant in equation 4 where we control for government consumption. The positive effect of this variable is approximately twice the effect of Aid and ten times larger than the impact of FDI. With respect to the vector of control variables, coefficients of the terms of trade, the Balassa Index, and government consumption are all statistically significant with positive signs, while an increase of trade openness is found to reduce the real appreciation. In Table 2, the specificity of low-income countries (LICs) is captured through a multiplicative dummy variable associated with total capital inflows or capital components (equations 1 to 4). The impact of total inflows on the REER is higher in LICs (equation 1)

where the demand side outperforms the supply response, generating a real appreciation of the external value of the domestic currency. The elasticity of the REER is about 1 percent for LICs compared to 0.4 percent for MICs. When the different categories of capital inflows are considered (equation 2), this appreciation effect of remittances proves to be greater in LICs.

Alternative specifications (equations 3 and 4) do not invalidate these regression results.

Table 2 also displays the specific effect resulting from the exchange rate system. A dummy variable is introduced for countries with a "peg" regime. This variable, which is considered in a multiplicative way with the total capital inflows (equation 5) is obtained from Ilzetzki, Reinhart and Rogoff (2010)'s classification, and combines no separate legal tender, pre-announced peg (or currency board arrangement), pre-announced horizontal band (which is narrower than or equal to +/-2 percent), or de facto peg. This binary variable does not necessarily coincide with the bipolar view of the two corner regimes. We do not hypothesize that only hard pegs or floating exchange rate systems are viable (see [START_REF] Summers | International Financial Crises: Causes, Prevention, and Cures[END_REF][START_REF] Fischer | Exchange Rate Regimes: Is the Bipolar View Correct?[END_REF]. We only suggest that, on average, arrangements where intentions of a limited flexibility of the exchange rate are clearly expressed produce some comparable outcomes. The regression coefficient of this variable is negative and significant, reflecting that "peg regimes" mitigate the appreciation effect that stems from capital inflows. One explanation is that peg regimes go hand in hand with appropriate controls by monetary authorities who regulate domestic credit and prevent inflation pressures. In developing economies, a good example of this effect can be found in the Franc Zone, where the fixed parity of the CFA franc vis-à-vis the Euro has been maintained for several decades, implying strong and efficient regulatory control of the money supply. In Table 3, capital inflows are considered in relation to the impact of their instability.

Compared to Table 1 where inflows proved non-statistically significant when all were considered together, the breaking down of the variable provides additional information and does not reject the hypothesis that instability matters for FDI and remittances.

In Table 4 attention is drawn to the analysis of the effect of external capital flows on GDP growth. The negative sign of the lagged endogenous variable could be interpreted as the consequence of a stop-and-go growth cycle. Trade openness and natural rents promote economic growth while the Polity2 variable is not significant. It is worth mentioning that the positive impact of natural rents does not indicate the absence of negative long-run effects that we poorly capture with the model. Indeed, the presence of natural rents can be a source of the "Dutch disease" phenomenon with the accompanying perverse consequences for the implementation of the diversification process. Total capital inflows positively affect growth (equations 1, 3, 5, and 6), but their instability does not prove to be a relevant regressor (equations 5 and 6). Coefficients are semi-elasticities which can be interpreted as follows: -a doubling of per capita total capital inflows leads to an increase in average annual growth of about 50 percent (equation 1). The transmission channel can be more deeply explored by controlling for the real exchange rate impact (equations 3, 4 and 6), using the REER to distinguish the direct positive impact of inflows from the negative indirect impact through relative prices. Through this distinction we expect the coefficient of total capital inflows to be higher when the REER is incorporated in the econometric specification. The results confirm our expectation: a 100 percent appreciation of the REER is associated with a 25 percent reduction in annual GDP growth (a reduction of about 1% of growth rate). Moreover, it is worth noting that the coefficient of total inflows varies significantly depending on the introduction (or not) of the REER: equation (1) versus equation (3). In equation ( 3), the coefficient of total capital inflows is roughly twice the coefficient of this variable in equation

(1). Consequently the direct effect represents a doubling of the average GDP growth rate over the whole period (3.8 percent).4

Excluding aid, the different components of capital inflows promote economic growth.

It is worth noting that the coefficient associated with the instability of total net capital inflows is not statistically different from zero. At least two reasons may explain these results. First, uncertainty may play a key role in economic decisions, so it can be expected to have an impact on growth. Therefore, uncertainty should ideally be measured ex ante. It should either emanate from an expectations model, or from a firm survey. Because both measures are difficult to implement, ex post measures have generally been used. A variance calculated over the economic cycle is an example of such an approach. Other ex post measures are derived from GARCH models, but they rely on high frequency data which are not available for this empirical work. Second, the idiosyncratic variability is not big enough once period-and country-fixed effects are introduced. Put differently, instability measures are mainly time-or country-invariant. Table 5 presents the results of our tests for the presence of specificities for low-income countries (LICs) in the GDP growth model. It is worth noting that unlike for MICs, the impact of remittances proves negative for LICs (equation 4). Among the different potential explanations, is the fact that the increase in income might be used for imported consumer goods rather than to stimulate investments [START_REF] Chami | Do Worker Remittances Reduce Output Volatility in Developing Countries?[END_REF]. Furthermore, a high ratio of remittances could lead to reductions in institutional quality, leading to, for instance, higher corruption which reduces government effectiveness [START_REF] Abdih | Remittances and Institutions: Are Remittances a Curse?[END_REF]. A pessimistic view of the macroeconomic impact of remittances in poor countries has been reported by [START_REF] Chami | Are Immigrant Remittance Flows a Source of Capital for Development?[END_REF] who emphasize the potential disincentives effects of remittances on labor market participation. [START_REF] Clemens | Why Don't Remittances Appear To Affect Growth?[END_REF] also point out the significant noise in the data about remittances, as well as the difficulty in disentangling the impact of remittances to, and migration from, origin countries.

In Table 6 we revisit the role of the instability of the different components of capital inflows with respect to GDP growth rate. Overall, the results show that the instability of the different capital flows is not statistically significant, in accordance with the results we found in Table 4. The ability of a country to dampen the potential negative macroeconomic consequences of instability is likely to depend on its institutional factors and macroeconomic policies. [START_REF] Alguacil | Inward FDI and Growth: the Role of Macroeconomic and Institutional Environment[END_REF] showed a strong heterogeneity in the absorptive capacity of FDI inflows in host countries. Furthermore, the most volatile capital inflows identified in the literature, namely portfolio investments, are almost non-existent for most of the low-and middle-income countries we investigated (Figures 1 and2). Accordingly, country case studies are likely to be the best way to learn about the management of volatile inflows of short term capital when the capital account liberalisation is targeted.

Table 7, presents the results for the hypothesis that the impact of total inflows on economic growth could be conditional on the exchange rate regime. No statistical difference is found across the different specifications, with or without the REER.

Conclusion and policy implications

After a sharp decline during the 1980s, net capital inflows to developing countries have significantly increased in the 2000s. This phenomenon is in line with the acceleration of globalization via the traditional channel of international trade, but also through an increasing financial openness in developing economies. Globalization has also been accompanied by a pronounced modification in the composition of capital inflows. While foreign aid was initially the prevailing source of finance, and still remains so for low income countries, the role of ODA is now much smaller for middle income economies, which now depend mainly on FDIs and to a lesser extent on remittances. We find that the contribution of net capital inflows to the variation in the real effective exchange rate (REER) is significant. Not only do capital inflows tend to reduce competitiveness, but their instability also strengthens this impact, especially through the effect of FDIs and remittances. Overall, the appreciation effect on the REER proved more pronounced for LICs. This effect may be related to low supply-side capacity and the implication of net capital inflows in the increase of the non-tradable to tradable price ratio.

We also found a strongly positive and significant impact of net capital inflows on GDP growth, in accordance with the expected contribution of external resources to filling the saving/investment gap. However, we did not detect a difference with respect to the level of development. On average, doubling net capital inflows would lead to a net increase in average growth of about 2 percentage points over the whole sample, including for the LICs. Adjusting this increase for the indirect impact of the external financial capital inflows due to real exchange rate appreciation, we also found that greater inflows would lead to a growth rate of 7.4 percent compared to the 3.7 percent observed over the period 1980-2012. While the direct impact on growth does not differ across the two per capita GDP levels, it is worth noting that the indirect impact is significantly higher and proved to be not negligible for LICs. The elasticity of the REER to the total capital inflows is about 1 percent for LICs, against less than 0.4 percent for MICs. The results for economic growth hold when we control for other determinants of growth such as the presence of natural rents, trade openness, an institutional variable, and the long run convergence effect. A more extended econometric specification rejected the hypothesis that instability of net capital inflows, or their respective components impacted on the REER or the GDP growth rate.

Although the influence of ODA did not prove statistically significant for explaining GDP growth, including for LICs, it is likely to affect the long run well-being of populations through different indirect channels. Official aid potentially contributes to an increase in human capabilities and infrastructure, and helps the promotion of public goods which condition the long run development process and integration into the world economy. The impact of FDIs on growth is a more direct one. It has been found to be stronger in LICs than in MICs, conflicting with some views that attractiveness of a country is conditional on the quality of its institutions, the availability of a high level of the human capital, or the quality of the financial market. This result probably needs to be qualified. Indeed, most often, LICs benefit from FDI oriented to the exploitation of natural resources with few backward and forward effects. FDIs in MICs are likely to have stronger structural influences through horizontal and vertical relations within the domestic economy. Therefore, the challenge for LICs is to use FDIs as a lever to promote both raw material processing and a larger participation in global value chains. This paper has also shed light on the effect of Portfolio investments, which remain limited in LICs. If the regression coefficients have shown significant impacts on GDP growth, they have also displayed some risks of currency overvaluation. The same problem arose with the remittances that support domestic consumption and housing investments in a context where the relative price of the non-tradable goods rises. Developing countries should fully take account of the fact that capital inflows, while critical to finance development needs and spurring economic growth, can also lead to significant REER appreciation and loss of competitiveness, thereby complicating macroeconomic management. Together, the complex nature of inter-relations between variables calls for the State to play an active role between excessive regulation and unbridled liberalization of external capital flows and their components.

Figure 1 :Figure2:

 1 Figure 1: Per capita total net capital inflows and their structure (current U.S. dollars)

Figure 3 :

 3 Figure 3: Net capital inflows and economic growth (total sample)

  Standard errors in brackets: *** p<0.01, ** p<0.05, * p<0.1.

  𝑖,𝑡-1 + 𝜏𝑇𝑜𝑡𝑎𝑙𝐹𝑙𝑜𝑤𝑠 𝑖,𝑡 + 𝜋𝑌′ 𝑖,𝑡 + 𝜗 𝑖 + 𝜑 𝑡 + 𝜎 𝑖,𝑡 (1) 𝑅𝐸𝐸𝑅 𝑖,𝑡 = 𝑎 + 𝑏𝑅𝐸𝐸𝑅 𝑖,𝑡-1 + 𝑐𝐴𝑖𝑑 𝑖,𝑡 + 𝑑𝐹𝐷𝐼 𝑖,𝑡 + 𝑒𝑅𝑒𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒𝑠 𝑖,𝑡 + 𝑓𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝑖,𝑡 + 𝑔𝑂𝑡ℎ𝑒𝑟𝑓𝑙𝑜𝑤𝑠 𝑖,𝑡 + ℎ𝑌′ 𝑖,𝑡 + 𝜗

𝑖 + ∅ 𝑖,𝑡 (2) 𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ 𝑖,𝑡 = 𝛼 + 𝛿𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ 𝑖,𝑡-1 + 𝛽𝑇𝑜𝑡𝑎𝑙𝐹𝑙𝑜𝑤𝑠 𝑖,𝑡 + 𝜃𝑋 ′ 𝑖,𝑡 + 𝑣 𝑖 + 𝜛 𝑡 + 𝜀 𝑖,𝑡

(3)

𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ 𝑖,𝑡 = 𝑘 + 𝑚𝐺𝐷𝑃𝐺𝑟𝑜𝑤𝑡ℎ 𝑖,𝑡-1 + 𝜂𝐴𝑖𝑑 𝑖,𝑡 + 𝑝𝐹𝐷𝐼 𝑖,𝑡 + 𝜆𝑅𝑒𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒𝑠 𝑖,𝑡 + 𝜚𝑃𝑜𝑟𝑡𝑓𝑜𝑙𝑖𝑜 𝑖,𝑡 + 𝜉𝑂𝑡ℎ𝑒𝑟𝑓𝑙𝑜𝑤𝑠 𝑖,𝑡 + 𝜌𝑋 ′ 𝑖,𝑡 + 𝑣 𝑖 + 𝜍 𝑖,𝑡 (4)

Table 1 : Net capital inflows and the real effective exchange rate (REER)

 1

		(1)	(2)	(3)	(4)	(5)
	Log(REER) (-1)	0.332***	0.321***	0.261***	0.291***	0.291***
		(0.0289)	(0.0381)	(0.0452)	(0.0412)	(0.0443)
	Log(FDI)		0.0267***		0.0236***
			(0.00731)		(0.00745)
	Log(Remittances)		0.171		0.232**
			(0.115)		(0.114)
	Log(Aid)		0.141**		0.115**
			(0.0574)		(0.0504)
	Log(Other flows)		0.00104		0.0108
			(0.0118)		(0.00929)
	Log(Portfolio)		1.494***		2.036***
			(0.391)		(0.316)
	Log(Total flows)	0.468***		0.344***		0.63***
		(0.124)		(0.120)		(0.149)
	Total(Total flow instability)					0.0111
						(0.0122)
	Trade openness	-0.00448*** -0.00379*** -0.00454*** -0.00387*** -0.0045***
		(0.000261) (0.000891) (0.000861) (0.00110)	(0.0009)
	Terms of trade	0.000384** 0.000353	0.000605** 0.000214	0.00066**
		(0.000162) (0.000307) (0.000276) (0.000262) (0.0003)
	Balassa index	0.00151*** 0.00135** 0.00122** 0.00153*** 0.002**
		(0.000461) (0.000524) (0.000491) (0.000471) (0.0005)
	Government consumption			0.0110*** 0.0137***
				(0.00349)	(0.00369)
	Constant	0.174	-9.139***	1.986**	-13.09***	-0.768
		(0.795)	(2.777)	(0.820)	(2.164)	(0.91)
	Observations	273	271	255	257	251
	Number of countries	64	63	62	62	61
	Number of instruments	26	35	27	36	27
	AR(1)	0.027	0.0307	0.0523	0.0262	0.0317
	AR(2)	0.8957	0.5722	0.9479	0.5845	0.8228
	Sargan	0.1012	0.1459	0.1864	0.1635	0.1806
	Standard errors in brackets: *** p<0.01, ** p<0.05, * p<0.1.

Table 2 : Real exchange rate, net capital inflows in low income countries (LICs): variation according to the exchange rate system

 2

		(1)	(2)	(3)	(4)	(5)
	Log(REER) (-1)	0.314***	0.322***	0.311***	0.304***	0.310***
		(0.0242)	(0.0381)	(0.0368)	(0.0356)	(0.0328)
	Log(FDI)		0.0249***		0.0233**
			(0.00729)		(0.00914)
	Log(Remittances)		0.0970		0.0715
			(0.101)		(0.105)
	Log(Aid)		0.118*		0.129*
			(0.0622)		(0.0695)
	Log(Other flows)		-0.000192		0.0101
			(0.0109)		(0.00900)
	Log(Portfolio)		1.253***		1.592***
			(0.403)		(0.300)
	Log(FDI)*LIC		-0.0176		-0.114
			(0.269)		(0.216)
	Log(Other flows)*LIC		0.180		-0.451
			(0.582)		(0.886)
	Log(Portfolio)*LIC		-3.489		1.241
			(3.166)		(4.793)
	Log(Remittances)*LIC		1.264***		1.061**
			(0.482)		(0.488)
	Log(Aid)*LIC		-0.122		-0.0869
			(0.113)		(0.139)
	Log(Total flows)	0.345**		0.155		0.413***
		(0.138)		(0.159)		(0.122)
	Log(Total flows)*LIC	1.001***		1.230***
		(0.254)		(0.294)
	Log(Total flows)*peg regime					-0.0162***
						(0.00620)
	Trade openness	-0.00444*** -0.00397*** -0.00411*** -0.00348*** -0.00398***
		(0.000680) (0.000887) (0.000689) (0.000918) (0.000805)
	Terms of trade	0.000441*	0.000477*	0.000247	0.000219	0.000336
		(0.000268) (0.000279) (0.000279) (0.000261) (0.000282)
	Balassa index	0.00155*** 0.00148*** 0.00134**	0.00192*** 0.00130**
		(0.000586) (0.000469) (0.000549) (0.000399) (0.000522)
	Government consumption			0.0144***	0.0191***
				(0.00417)	(0.00355)
	Constant	-1.166	-0.685	0.242	-13.15	0.691
		(0.764)	(8.385)	(1.239)	(12.19)	(0.741)
	Observations	273	271	255	257	243
	Number of countries	64	63	62	62	62
	Number of instruments	30	44	31	45	30
	AR(1)	0.028	0.0297	0.0478	0.0242	0.0465
	AR(2)	0.8049	0.6502	0.8796	0.6233	0.5733
	Sargan	0.1566	0.1259	0.197	0.1926	0.279

Table 3 : REER: Impact of specific capital inflows and their instability

 3

Table 4 : Effect of capital inflows on the GDP growth rate

 4

		(1)	(2)	(3)	(4)	(5)	(6)
	GDP Growth (-1)	-0.0532** -0.0706**	-0.0440* -0.0760**	-0.0459*	-0.0406
		(0.0246)	(0.0339)	(0.0242)	(0.0351)	(0.0251)	(0.0255)
	Log(FDI)		0.00534***		0.00465***
			(0.00136)		(0.00117)
	Log(Remittances)		0.0715**		0.0871***
			(0.0293)		(0.0302)
	Log(Aid)		-0.0105		-0.00610
			(0.00965)		(0.00850)
	Log(Other flows)		0.00413***		0.00331***
			(0.000924)		(0.000768)
	Log(Portfolio)		0.165***		0.195***
			(0.0448)		(0.0482)
	Log(Total flows)	0.0193***		0.0365***		0.0361**	0.0476**
		(0.00727)		(0.00854)		(0.0153)	(0.0193)
	Log(REER)			-0.0108* -0.0164***		-0.0131**
				(0.00642) (0.00401)		(0.00635)
	Total(Total flow inst)					6.51e-05	4.77e-05
						(7.77e-05) (9.06e-05)
	Log(GDPPC)	-0.00303	-0.00866	-0.00745 -0.00599	-0.00666	-0.0103
		(0.00591) (0.00569)	(0.00658) (0.00543)	(0.00589) (0.00703)
	Trade openness	0.0409*** 0.0318***	0.0418*** 0.0266**	0.0446*** 0.0433***
		(0.00647) (0.0116)	(0.00685) (0.0114)	(0.00481) (0.00624)
	Polity2	0.000989** 0.000514	0.000689 0.000279	0.000963** 0.000713
		(0.000479) (0.000418) (0.000484) (0.000410) (0.000484) (0.000484)
	Natural rents	0.000407* 0.000843*** 0.000294 0.000598*** 0.000180	0.000147
		(0.000209) (0.000223) (0.000218) (0.000212) (0.000212) (0.000241)
	Constant	-0.0824	-1.539***	-0.109	-1.789***	-0.163*	-0.147
		(0.0642)	(0.360)	(0.0694)	(0.408)	(0.0940)	(0.105)
	Observations	310	311	309	310	310	309
	Number of countries	69	70	69	70	69	69
	Number of instruments 24	40	25	41	27	28
	AR(1)	0.0221	0.0224	0.0197	0.0204	0.0224	0.02
	AR(2)	0.2482	0.2727	0.2119	0.263	0.2094	0.1938
	Sargan	0.3474	0.1414	0.3259	0.114	0.449	0.3765
	Standard errors in brackets: *** p<0.01, ** p<0.05, * p<0.1

Table 5 : Effect of capital inflows on LICs' economic growth

 5

		(1)	(2)	(3)	(4)
	GDP Growth (-1)	-0.0692** -0.0956**	-0.0713** -0.102**
		(0.0350)	(0.0427)	(0.0328)	(0.0408)
	Log(FDI)		0.00500***		0.00430***
			(0.00139)		(0.00114)
	Log(Remittances)		0.0636*		0.0791**
			(0.0326)		(0.0326)
	Log(Aid)		-0.0133		-0.0181
			(0.0167)		(0.0149)
	Log(Other flows)		0.00409***		0.00328***
			(0.00109)		(0.000972)
	Log(Portfolio)		0.152***		0.186***
			(0.0551)		(0.0525)
	Log(FDI)*LIC		0.0321		0.0349
			(0.0384)		(0.0353)
	Log(Other flows)*LIC		-0.0662		0.0164
			(0.186)		(0.172)
	Log(Portfolio)*LIC		0.221		0.148
			(0.485)		(0.427)
	Log(Remittances)*LIC		-0.104		-0.134*
			(0.0665)		(0.0689)
	Log(Aid)*LIC		0.00875		0.0166
			(0.0212)		(0.0198)
	Log(Total flows)	0.0415***		0.0601***
		(0.00839)		(0.0110)
	Log(Total flows)*LIC -0.0346		-0.0451
		(0.0330)		(0.0326)
	Log(REER)			-0.0117*	-0.0136***
				(0.00633) (0.00388)
	Log(GDPPC)	-0.00859	-0.00183	-0.0120** -0.00119
		(0.00548)	(0.00549)	(0.00604) (0.00534)
	Trade openness	0.0446*** 0.0270**	0.0431*** 0.0267**
		(0.00506)	(0.0111)	(0.00623) (0.0113)
	Polity2	0.000987** 0.000746*	0.000722 0.000527
		(0.000484) (0.000411)	(0.000479) (0.000387)
	Natural rents	0.000273	0.000907*** 0.000205 0.000711***
		(0.000196) (0.000247)	(0.000218) (0.000242)
	Constant	-0.121*	-1.705	-0.134*	-1.939*
		(0.0695)	(1.056)	(0.0692)	(1.020)
	Observations	310	311	309	310
	Number of countries	69	70	69	70
	Number of instruments 27	41	28	42
	AR(1)	0.0247	0.0212	0.0244	0.022
	AR(2)	0.2779	0.4055	0.2791	0.4356
	Sargan	0.4661	0.0758	0.4608	0.0805
	Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1

Table 6 : Capital inflows and their instability, an investigation of the impact on the GDP growth rate

 6

		(1)	(2)	(3)	(4)	(5)
	GDP growth (-1)	-0.0273	-0.0155	-0.0652	-0.0182	-0.0179
		(0.0469)	(0.0333)	(0.0415)	(0.0365)	(0.0387)
	Log(REER)	-0.0343***	-0.0196***	-0.0154**	-0.0248***	-0.0346***
		(0.0082)	(0.00624)	(0.00624)	(0.00541)	(0.00639)
	Log(GDPPC)	-0.0139	-0.0119*	-0.000523	-0.00360	-0.0086
		(0.0102)	(0.00715)	(0.00830)	(0.00713)	(0.0080)
	Trade	0.0143	0.0420***	0.0512***	0.0624***	0.0157
		(0.0185)	(0.0104)	(0.0113)	(0.0124)	(0.01511)
	Polity2	0.0011**	0.00105**	0.000696	0.00154***	0.0014**
		(0.00046)	(0.000498)	(0.000501)	(0.000433)	(0.0006)
	Natural rents	0.00089*	0.000581**	0.000323	0.000176	0.0012***
		(0.00046)	(0.000283)	(0.000284)	(0.000265)	(0.00037)
	Log(FDI)	0.1168***
		(0.0412)
	Log(FDI instability)	0.00031
		(0.00122)
	Log(Total-FDI instability)	0.00005
		(0.00006)
	Log(Total-FDI)	0.114***
		(0.0397)
	Log(Other flows)		0.0639**
			(0.0292)
	Log(other flows instability)		0.00139
			(0.000877)
	Log (Total-other flows instability)		-0.000135
			(0.000178)
	Log(Total -other flows)		0.0614**
			(0.0297)
	Log(Portfolio)			0.112
				(0.0899)
	Log(Portfolio instability)			-0.000886
				(0.00150)
	Log(Total-portfolio instability)			-6.62e-05***
				(1.97e-05)
	Log(Total-portfolio)			-0.0100
				(0.0351)
	Log(remittances)				-0.00654
					(0.0280)
	Log(remittances instability)				0.00169
					(0.00109)
	Log(Total-remittances instability)				-4.75e-05
					(6.57e-05)
	Log(Total-remittances)				0.0603**
					(0.0278)
	Log(aid)					0.0728***
						(0.0259)
	Log(aid instability)					-0.00009
						(0.00139)
	Log(Total-aid instability)					-0.00005
						(0.00007)
	Log(Total-aid)					0.0717***
						(0.0263)
	Cons tant	-0.4528*	-0.222	-0.703	0.181	-0.2146
		(0.2363)	(0.152)	(0.573)	(0.149)	(0.1554)
	Obs ervations	213	283	298	279	221
	Number of countries	64	68	69	68	64
	Number of instruments	30	30	30	30	30
	AR(1)	0.0101	0.0004	0.0274	0.0431	0.0134
	AR(2)	0.2760	0.7883	0.3994	0.2854	0.2932
	Sargan	0.1475	0.5696	0.2117	0.3425	0.0781

Standard errors in parenthes es ; *** p<0.01, ** p<0.05, * p<0.1

Table 6 : Capital inflows and their instability, an investigation of the impact on the GDP growth rate

 6

Table 7 : Effect on GDP growth and the peg regime economies

 7

		(1)	(2)	(3)
	GDP Growth (-1)	-0.0683*	-0.0658	-0.0837*
		(0.0412)	(0.0419)	(0.0442)
	Log(Total flows)	0.0334***	0.0390***	0.0452***
		(0.0109)	(0.0129)	(0.0133)
	Log(Total flows)*peg regime	0.000559	0.000608	0.000937
		(0.000675) (0.000703) (0.000810)
	Log(Total flows)*LIC			-0.0581
				(0.0391)
	Log(Total flows)*LIC*peg regime			-0.00113
				(0.00144)
	Log(REER)		-0.00891	-0.00719
			(0.00723)	(0.00723)
	Log(GDPPC)	-0.00303	-0.00350	-0.00385
		(0.00561)	(0.00633)	(0.00641)
	Trade openness	0.0406***	0.0362**	0.0366**
		(0.0139)	(0.0146)	(0.0142)
	Polity2	0.000583	0.000430	0.000559
		(0.000483) (0.000475) (0.000465)
	Natural rents	0.000802*** 0.000841*** 0.000905***
		(0.000299) (0.000283) (0.000284)
	Constant	-0.175**	-0.164	-0.104
		(0.0817)	(0.101)	(0.103)
	Observations	278	278	278
	Number of countries	66	66	66
	Number of instruments	27	28	30
	AR(1)	0.0001	0.0001	0.0001
	AR(2)	0.4399	0.4888	0.3706
	Sargan	0.4895	0.4436	0.4907
	Standard errors in brackets: *** p<0.01, ** p<0.05, * p<0.1

Table 2 : Descriptive statistics

 2

	Variables	Obs Mean	Std. Dev.	Min	Max
	Log(FDI)	549 5.591211 0.4215709 -1.839352 7.249561
	Log(remittances)	550 6.191947 0.3486743 -0.8348733 7.161528
	Log(aid)	598 4.292293 0.4454119 2.899052 6.153531
	Log(other flows)	570 7.372105 0.358694 -0.8100761 7.780177
	Log(portfolio)	558 6.993121 0.3951974 -2.199669 7.237709
	flows)	567 6.27252 0.3398315 -0.3081539 7.184275
	Log(REER)	552 4.787605 0.5994099 3.524452 11.83555
	Total flows instability	557 9.326372 14.93246 0.0303224 123.5963
	Trade	578 56.96595 38.99618 9.436654 510.855
	Terms of trade	529 114.9497 49.55432 22.14194 488.4496
	Balassa index	451 119.532 37.7183	72.18857 326.8341
	Log(GDPPC)	570 6.918076 1.054014 4.450716 9.037553
	Polity2	562 0.727847 6.130204	-9.8	10
	Natural rents	580 11.10079 12.54401 0.000499 69.99982
	Government consumption 556 14.14722 5.87593	2.80376 40.65649
	GDP growth	574 3.7714	4.16064	-42.4511	33.347

In order to reduce the number of instruments, time fixed effects have been omitted in equations where categories of capital inflows appear.

When the breakdown of capital inflows is considered (equations 2 and 4), it proves impossible to distinguish direct and indirect effects

Financial support from the DFID-ESRC Growth Research Programme, under Grant No. ES/L012022/1, is gratefully acknowledged. The previous version of the paper has benefited from the comments by Pierre-Richard Agénor and Kyriakos Neanidis, University of Manchester, and Centre for Growth and Business Cycle Research.

Total flows instability

Calculated from the regression residual of total flows on the lagged variable and a deterministic trend. The same definition is adopted for the different components of flows Authors'calculation


Maria-Magdalena Titirici

Markus Antonietti

Niki Baccile

Hydrothermal carbon from biomass: a comparison of the local structure from poly-to monosaccharides and pentoses/hexoses

Carbon particles are synthesized under hydrothermal conditions using different biomass (glucose, xylose, maltose, sucrose, amylopectin, starch) and biomass derivates (5 -hydroxymethyl-furfural-1aldehyde -HMF -and furfural) as carbon sources. Carbons obtained from mono and polysaccharides, hexose and pentose sugars, and from the biomass derivatives, HMF and furfural, are compared from the particle morphology, chemical composition and structural point of view. A clear structural and morphological difference can be observed in carbons from pentoses and hexoses but in the last case, no matter the nature of the hexose sugar, all carbon materials showed astonishing similarities opening the way to the use of renewable biomass in the synthesis of such carbon materials.

Introduction

Research on materials usually gives the priority to increase the performance-to-cost ratio, disregarding the sustainability of the methods, techniques and processes involved in the conception and synthesis of the material itself. In the field of carbon-based materials activated charcoals are usually made under high-energy conditions. Recently developed routes to obtain a periodic porous carbon network 1,2,3,4 were successful but again did not take into account any criteria of sustainability. In this case, it involves: indirect impregnation techniques (mesoporous silica is generally used as template), hydrogen fluoride etching 1,2 , and finally high carbonization temperatures 1 .

Even direct templating of resins (phenol/formaldehyde) 4 are still far from following sustainable principles such as energy and atom economy, low toxicological impact of materials and processes and use of renewable resources.

The problem of carbon synthesis under sustainable conditions was recently revisited and implemented by several research teams 5,6,7,8 , where hydrothermal treatment of biomass in water under relatively mild conditions provided bulk, mesoporous, or nanostructured carbon materials. This technique was already known for longer times 9 , but the need of exploring cheap and sustainble ways to obtain chemicals 10 and carbons from raw materials other than crude oil or natural gas (for soot generation) lead to a re-exploration of this field. In addition, the implementation of a low-cost pathway to recycle byproducts of farmed biomass would additionally represent a way to sequester significant amounts of CO2 5 , creating a materials benefit at the same time. 50 The use of hydrothermal synthesis between 180°C and 220°C allowed to obtain carbon-based powders, nanofibers 11,12 , or sponge-like mesoporous carbons being potentially useful as soil conditioner, ion exchange resins or sorption coals 7 . The synthesis proved to be feasible when 55 glucose 6 is used or even when side products from raw biomass materials like oak leafs and orange peals are taken 7 . In the first case, surface chemistry could also be modified by mean of hydrophilic or hydrophobic coupling agents 13 . Along similar lines, the group of Clark showed that a slightly 60 different approach using expanded starch treated with sulfuric acid instead of pure hydrothermal conditions could provide functional materials with disordered mesoporosity which proved to be satisfactory as catalysts in the esterification of succinic acid 14,15 . In spite of the undoubted usefulness of the 65 recent re-discovery of the hydrothermal process to obtain carbonaceous material, some basic work is still lacking as far as process of formation and final structure are concerned. Some groups tried to investigate, directly or indirectly, the reaction mechanisms which transforms glucose first into 5-70 hydroxymethyl-furfural-1-aldehyde (HMF), the dehydrated intermediate 16,17 , and then from here to the carbonaceous structure 18,19 , but a clear reaction path is still missing and chemistry of furans and furan-derivated compounds is far too large to easily forecast any possible result 20 . Even less is 75 known about the final material structure mainly because of its intrinsic complexity and lack of technique allowing discrimination among all carbon sites with satisfactory resolution. In general, FT-IR, and in some cases FT-Raman, is the main, easily accessible technique used to discriminate between various functional groups (C=O, C=C, aliphatic carbons) 7,18,19 . XPS was also used to identify the main carbon sites but resolution here is worse than in vibrational-based spectroscopies. 13 C solid state NMR has also been already tempted 18,8 but its use was a complement to other techniques. Even if exploitation of solid state NMR data on these materials was in its very beginnings, the authors have found that hydrothermal carbon materials hand out very nicely 10 resolved 13 C spectra which have deserved recent deeper investigations 21 . So far, several studies 7,8,22,23 have proved that different types of biomass could be used to obtain carbon under hydrothermal conditions but in no case a clear comparison analyzing the local structure has been made.

In this study we will attribute the chemical and structural fingerprint of hydrothermal carbons obtained from hexose and pentose sugars as well as from their corresponding main dehydration intermediates, HMF and furfural reactions 24,25 . The aim of this work is to lead a comparative structural study 20 of hydrothermally synthesized carbon materials obtained from different saccharides classified according to their number of carbons (pentoses vs. hexoses) and growing complexity (mono-vs. di-vs. polysaccharides). We will show that all materials obtained from hexoses-based mono (glucose, HMF), di-(maltose, sucrose) and polysaccharides (amylopectin, starch) have the same chemical nature in terms of atom percentage and functional groups, as verified by 13 C solid-state Cross Polarization (CP) NMR analysis and Scanning Electron Microscopy (SEM) images.

On the other hand, pentose-based (xylose, furfural) carbonaceous materials clearly showed interesting morphological and chemical differences with respect to hexose-based ones. This work also clearly underlines that no substantial difference exist between monosaccharide-and polysaccharidederived carbons, suggesting that the complexity of sugarcontraining biomass hardly has any influence on the materialforming mechanisms. This is important as it indicates that basic studies performed on simple test molecules, like 40 glucose 21 , have broader validity and that no matter the complexity of the saccharide source, the final material have very similar local functionalities and connection patterns.

Experimental Materials

45

For all samples, about 15 ml of a deionized water solution containing 10 wt% in mass of carbohydrate biomass was used. D(+)-glucose, D(+)-xylose, D(+)-maltose monohydrate, sucrose, amylopectin from potato starch, starch from potatos or carbohydrate-dehydrated derivatives, HMF and furfural, down in a water bath at room temperature. The obtained black solid powder is then separated from the remaining aqueous solution by centrifugation (7000 rpm for 20 minutes) and put into an oven at 80°C under vacuum overnight for drying. Sample notation for carbon material introduces an italic-styled 60 capital "C" before each carbonized material; e.g., if glucose is the starting product, then C-glucose is its corresponding carbon sample obtained from hydrothermal treatment. In some cases, the terms C-hexose or C-pentose are used referring to all carbons from hexose and pentose sugars. Solid-state NMR: 1 H and 13 C solid-state Magic Angle Spinning (MAS) NMR experiments have been acquired on Bruker Avance 300 MHz (7 T) spectrometer using the 4 mm zirconia rotors as sample holders spinning at MAS rate MAS= 75 14 kHz. The chemical shift reference was tetramethylsilane (TMS; = 0 ppm). Proton-to-carbon CP MAS was used to enhance carbon sensitivity: recycle delay for all CP experiments is 3 s and TPPM decoupling is applied during signal acquisition. Cross-polarization transfers were 80 performed under adiabatic tangential ramps 26 , 27 to enhance the signal with respect to other known methods 28 and CP time tCP= 3 ms was found to be a good compromise in order to have a good overview on all carbon species. Number of transients is 1840 (C-glucose, C-xylose) and 1200 for all other carbon 85 samples. Peak attribution was done after references 29, 30, 31, 32 and 33.

Elemental chemical analysis was performed on a (C, N, O, S, H) Elementar Vario Micro Cube. SEM images were acquired on a LEO 1550/LEO GmbH Oberkochen provided 90 with a Everhard Thornley secondary electron and In-lens detectors. N2 adsorption and desorption isotherms were performed at 77 K with a Quadrachrome Adsorption Instrument and BET method was used for specific surface area determination.

95

Results and discussion

Monosaccharide-derived carbons

Particle dispersions of carbonaceous materials were prepared from biomass at 180°C in water in a closed autoclave. The decomposition of xylose leads to separated carbon spheres with diameter between 100 and 500 nm (Table 1) while glucose-based carbon is characterized by a mixture of spheres whose size varies between 500 nm and 1 m, randomly 110 dispersed inside an interconnected matrix of smaller particles (< 200 nm). 13 C NMR spectra present some common peaks at 208 ppm (though a difference in 4 ppm at higher fields is observed for C-xylose), 175 ppm, 150 ppm and 40 ppm. On the other side, significant differences occur in the regions between 130 and 110 ppm, at 75 ppm and between 40 and 20 ppm. These observations show that similarities can be related to a comparable amount of carbonyl groups (aldehydes, ketones and carboxylic acids at chemical shifts between 210 and 170 ppm) as well as to the presence of oxygen-substituted protonated and non-protonated C=C bonds resonating at 150 ppm. On the contrary, the peak at 129 ppm, which is generally attributed to aromatic carbons, as it is typical for graphitic structures or long-range conjugated double bonds, indicates the higher aromatic character of C-xylose carbon with respect to C-glucose. The peak at 75 ppm is indicative for the presence of hydroxylated methylene groups, which constitute an important part of C-glucose, while almost no hint of such groups is observed in C-xylose. Finally, at low chemical shifts, C-xylose seems to be dominated by ether groups resonating in the 40-50 ppm region while C-glucose shows an additional contribution of methylene groups, as it has been already discussed.

Overall, it seems that carbon material obtained from xylose has a higher aromatic character than C-glucose, and its higher carbon content (68.5%) supports this view (Table 2).

Nonetheless, the oxygen level keeps quite high even in C-xylose (27.3%) meaning that the conjugated C=C network is always accompanied by larger quantities of functional groups (CHO, CC=CO, COOH), furan rings and ethers.

To reveal further details of this difference, we also tried to carbonize the known intermediates of dehydration, HMF and furfural under the same carbonization conditions. SEM images and 13 C NMR spectra of carbons obtained from HMF and furfural are also presented in Figure 1 and Figure 2. The C-HMF microstructure is composed of small particles (< 200 nm) which forms an interconnected network, and the similarities to C-glucose are obvious. Interestingly, furfural produces larger polydispersed spherical carbon particles whose size ranges between 500 nm and 3 m (Table 1). The analysis of local chemical environments around carbon atoms probed by 13 C NMR also reveals some astonishing similarities between C-glucose and C-HMF and between C-xylose and C-furfural, respectively. The characteristic aromatic peak at 129 ppm is clearly prominent only in the pentose-derived carbon samples. Elemental analysis ( 4), it is self-evident the destructuring process which took place during carbonization and transformed the original desordered 90 bulky materials into micrometer-sized particles and/or spheres. Chemical composition largely changed during hydrothermal process, as well: saccharides turn into dark brown or black powders with a carbon content increasing from original 40w% to 64-70w% (Table 2); meanwhile, 95 oxygen presence is reduced from 53w%, as found in, e.g., glucose, to 27-30w% for carbon powders.

C-Sucrose

Interconncted spheres 700-2000

C-Amylopectin

Interconncted spheres 1000-2000

C-Starch

Interconncted spheres 300-1000

Pentoses

C-Furfural

Dispersed spheres 500-3000

C-Xylose

Dispersed spheres 100-1000 13 C solid-state NMR (Figure 2) spectra from C-maltose, C-sucrose, C-amylopectin and C-starch show exactly the same characteristics already observed for C-glucose and C-HMF. The materials can be considered as chemically equivalent, in good agreement with the elemental analysis shown in Table 2, where the carbon content for all hexose-based carbons is 64% ± 1%.

Mechanistic considerations

As we pointed out earlier in the discussion, it is generally assumed that dehydration of pentoses and hexoses leads to the 20 formation of furfural and HMF as a first and main dehydration product. Our experiments strongly suggest that these furans are also the reacting species for carbon material. In fact, the morphologies and chemical structures of carbons obtained from saccharides are directly related to those of carbons C-furfural. These side-product molecules mainly come either from the dehydration of carbohydrates or from the rehydration of the furans, plus the hydrolytic splitting of those intermediates.

Table 3 shows the normalized integrated intensity of HMF, furfural and 4-oxo-pentanoic acid detected 50 by GC in the final liquors of the indicated final materials as well as for pure furfural and HMF solutions at three different concentrations, used as reference for quantification. Errors are estimated to 10% of the indicated values and account for possible discrepancies in manual simulation of each GC peak.

55

When only monosaccharides and furans values are compared, one observes that: 1) carbonization process from glucose seems slightly more efficient if compared to xylose one, since HMF concentration in solution for C-glucose after reaction is less than half with respect to remaining furfural concentration 60 from C-xylose; additionally, the amount of final carbon powder from glucose is about 1.5 times higher than carbon obtained from xylose. This is probably not a big surprise as it is known from furan chemistry that furfural, the dehydration product of xylose, is a low reactive compound due to the joint 65 stabilizing effects of furan aromatic ring and carbonyl function 20 with respect to polycondensation. On the other hand, even if general knowledge about the reactivity of HMF itself is smaller, its molecular similarity to furfuryl alcohol, a widely studied and highly reactive modified furan 20 , may 70 justify its higher reactivity. 2) The reaction seems to be less efficient when starting from pure furans rather than from carbohydrates, especially when pure furfural is employed (concentration in solution after reaction is around 5 w%). As pointedc out earlier, reactivity of furfural is low and 75 extremely condition dependent. In general, addition of proper co-monomers, like furfuryl alcohol, increase its reactivity 20 . Consequently, the heterogeneous medium composed of furans and dehydrated forms of saccharides (when sugars are used instead of pure HMF and furfural) may be highly favourable 80 to the overall efficiency of the carbon-formation process. Reaction between HMF and de-hydrated glucose was recently used to obtain liquid alkanes [START_REF] Huber | [END_REF] . 3) The higher the HMF content, the lower the 4-oxo-pentanoic acid content (also known as levulinic acid, it is a common side product of 85 hexose dehydration, and it is expected to increase with decreasing hexose concentration 25 ), indicating the competition of monomolecular decomposition reactions and the (at least) bimolecular carbonization reaction which is obviously promoted at higher concentrations. 90 Chemical reactions in the presence of a di-or polysaccharide do follow the same path, that is, dehydration of hexose units and formation of HMF, which then turns into hydrothermal carbon. As shown in Table 3, HMF residues do not exceed the 0.4 w%, and the actual values for C-maltose (0.14 w%) and 95 C-sucrose (0.19 w%) are almost as low as those registered for glucose (0.12 w%). In the case of C-amylopectin and C-starch, residual HMF increases, respectively, to 0.26 w% and 0.37 w%. The slightly higher values are attributed to a delayed dehydration kinetics due to the required hydrolysis of the macromolecular structure towards the monosaccharides. An additional aspect which constitutes a difference between hexoses and pentoses is the colloidal structure of the carbon powders: nicely dispersed, separated spherical particles are always found for C-xylose and C-furfural. Explanation for this may probably come from the limited water solubility of furfural (< 8.5 m/v%), which tends to emulsify in solution, and carbonization may only take place inside droplets. The intense peak at 129 ppm observed for these materials, typical 10 for aromatic C=C sites, is most presumably indicative for a higher degree of self reaction between furfural molecules within the droplets via the unprotected, highly reactive, 5position 20 . On the contrary, 13 C solid state NMR spectra and elemental analysis data for C-hexoses and C-HMF are remarkably 20 similar, thus indicating that the system most presumably has to pass the same reaction pathway via the hydroxymethylfurfural stage and before carbonization can take place. In addition, interconnected spherical particle networks are generally observed, especially for low-weight sugars-derived carbons. These results may appear in contrast with previous experiments, where relatively well separated particles from mono-and polysaccharides (glucose, starch or rice) under similar conditions were obtained 35,36, . Nevertheless, those experiments were lead in presence of metal salts (Fe(II), Co(II), Cu(II), etc..) or metal nanoparticles 1 Experiments were performed on remaining liquor solutions of indicated samples after 24h of hydrothermal treatment synthesis of indicated carbon materials. On the contrary, pure HMF and furfural solution were freshly prepared for comparison purposes. Residual solution w% values for HMF and furfural are calculated after a linear fit of recorded values for pure solutions. Errors, coming from integration procedure, are estimated to be ± 10% of indicated concentration values.

(Fe2O3), which can act as external nucleators and stabilizers [START_REF] Particles | Synthesis, Characterization and Mechanisms of growth[END_REF] . Since our experiments used pure sugar solutions, emulsion polymerization occurs in absence of a stabilizer; hence, the dispersed spherical particles formed from pentoses should 35 depend only on the hydrophobic character of furfural. An important question may arise. Is hydrothermal carbon just a polymerization product of HMF and furfural? At the moment, a clear-cut answer cannot be provided but an insight on polyfuranes generally reveal the existance of cross-linked 40 polymers which may appear as black and glassy materials 20 according to the polymerization mechanism and type of added co-monomers. Unfortunately, the number of examples provided in ref. 20 and references therein show that the chemistry of furans is very large and many possible reaction 45 ways can simultaneously occur especially in an heterogenous system like the one where hydrothermal carbon is obtained. Hydrothermal carbon is on the contrary a rather low-dense, dark brown powder which is not soluble in common solvents. So, even if during hydrothermal carbonization furfural and 50 HMF are most likely the main reactive species, we cannot depict neither the exact reaction mechanisms nor the clear final structure, yet. This last point will be dealt with in a further communication by mean of highly advanced solid state NMR techniques 21 . 55 Finally, the fact that no substantial difference exist between all hydrothermal hexose carbons also shows that glucose can be safely used as a model molecule for the understanding of the formation of these materials 21 . Scheme 5 makes a summary of the main results of this communication, 60 indicating that all hexoses (including their dehydration product, HMF) lead to the same type of material, called for convenience carbon-, while pentoses lead to a different type of carbon, called arbitrairily carbon-.

Conclusions

In this work, we compared hydrothermal carbons synthesized from diverse biomass (glucose, xylose, maltose, sucrose, amylopectin, starch) and biomass derivatives (HMF and furfural) under hydrothermal conditions at 180°C with respect 75 to their chemical and morphological structures. SEM, 13 C solid state NMR and elemental analysis on final powders combined to GC-MS experiments on residual liquor solutions were the main tools which allowed us demonstrate that all sugars in their hexose form, no matter their complexity, 80 degradate into hydroxymethyl furfural, which finally condenses to a carbon-like material having morphological similarities and the same chemical and structural composition. On the contrary, all sugars in their pentose form dehydrate into furfural, which in turn react to provide very similar 85 carbon materials as obtained from pure furfural. 13 C solid state NMR show that the local structure of these two families of carbons, from hexoses and pentoses, are relatively different. Contrary to simple expectations, starting from more complex biomass instead of clean sugars does not harm the outcome of the hydrothermal carbonization reaction, and remarkable similarities between the products of homologous series do occur, both with respect to morphology and local structural connectivity. This is a positive outcome concerning the green chemistry aspects of this process, as even complex waste biomass can be used without too much influence on the final carbonized structure: biological diversity is simply reduced by the elemental steps of the carbonization reaction. This study paves the way to the use of complex biomass as renewable source for carbon materials.

Dr. F. Babonneau and G. Laurent (Laboratoire de Chimie de la Matière Condensée de Paris, CNRS-Univ. Pierre et Marie Curie, Paris, France) for providing acces to solid-state NMR facilities.

Notes and References

65 Characterization

 65 Gas Chromatography (GC) coupled to Mass Spectroscopy (MS) was used to separate and identify the main molecular species by mean of the NIST database included in the spectrometer software package. The instrument used is an 70 Agilent Technologies (GC= 6890N; MS= 5975) apparatus.

  100

Figure 1 and

 1 Figure 1 and Figure 2 show, respectively, SEM pictures and solid state 13 C CP-MAS NMR spectra of C-glucose and Cxylose (highlighted in gray for convenience) carbon materials. Despite the similar chemical nature of the employed sugars, the first being a hexose and the second a pentose, the final 105

Figure 1

 1 Figure 1 SEM images of samples C-glucose, C-HMF, C-xylose and C-furfural

Figure 2 13 C

 13 Figure 2 13 C solid-state CP-MAS NMR spectra (tCP= 3 ms) of C-starch, 60

Figure 3 Figure 4

 34 Figure 3 SEM images of samples C-sucrose, C-maltose, C-starch and 10

  obtained from pure furans according to the following parallelism: C-hexoses ~ C-HMF and C-pentoses ~ C-furfural. Additionally, intermediate molecules derived from saccharide dehydration (levulinic acid, dihydroxycetone, formic acid, acetic acid, formaldehyde, pyruvaldehyde, etc…)25 cohexist with furans they can be probably be responsible for particle size, powder texture and aggregation discrepancies. The striking similarities of NMR fingerprint spectra between all hexose-based carbons and C-HMF, or between C-xylose and C-furfural clearly prove that chemical complexity, the usual 35 problem of raw biomass as an educt for materials chemistry, is indeed essentially resolved throughout hydrothermal carbonization by driving all saccharides only through two main reaction pathways: from sugar to furan-based (furfural or HMF) intermediate and from the furan to carbon. This is 40 supported by GC-MS analysis (figure1, supplementary material information) of the side products in the remaining product waters, where in all cases the nature and amount of the unreacted by-products is very similar for the hexoses, e.g. C-glucose and C-HMF, and pentoses, C-xylose and 45

  65

Figure 5 -

 5 Figure 5 -Reaction pathways encountered in the formation of hydrothermal carbons from hexose and pentose sources and leading to the differently structured and shaped materials.

Table 2

 2

	) reveals, first of
	all, a high oxygen content, which undoubtely makes these
	materials different from coal; secondly, carbon content in
	C-HMF (65.6%) is lower than in C-furfural (68.6%), and

those values are coherent with values detected for C-glucose and C-xylose, discussed above. Mechanistic implications will be discussed later. Isothermal nitrogen adsorption/desorption experiments (results not shown) indicate them as non-porous materials (BET specific surface areas <10 m 2. g -1 ).

Table 1

 1 Morphological and dimensional aspect of carbon powders, as observed from SEM images.

	80
		shape. Polysaccharides including amylopectin and starch,
		whose SEM images (Figure 1) show interconnected spherical
		particles ranging from 700 nm to 2m, provide very similar
		dispersion patterns despite their low solubility in water at
	85	room emperature.
		When one compares SEM structures of final carbons to
		original saccharides (examples from pure glucose, xylose,
		amylopectin and starch powders are provided in Figure

65

Polysaccharide-derived carbons SEM micrographs of carbon materials from disaccharides (maltose, sucrose) and polysaccharides (amylopectin, starch) are depicted in Figure

3

. C-maltose and C-sucrose are 70 composed of interconnected particles, in coexistance with domains (larger in C-maltose) where aggregation occurs. Differences in morphology and size are summarized in Table

1

: C-maltose is composed of a larger number of small (200-500 nm) particles in coexistance with larger spheres (2 m).

75

On the contrary, C-sucrose is composed of spherical particles whose size vary between 700 nm and 2 m. Strong similarities are found for C-maltose and C-glucose materials, both caracterized by isolated large spherical particles within a large matrix of interconnected small particles with ill-defined

Table 2

 2 Elemental analysis of carbon materials

	Sample	C w%	Elemental analysis H w%	O w%*
		Hexoses
	C-Glucose	64.47	4.69	30.85
	C-HMF	65.63	4.15	30.22
	C-Maltose	64.70	4.54	30.76
	C-Sucrose	64.15	4.77	31.09
	C-Amylopectin	65.76	4.56	29.69
	C-Starch	64.47	4.57	30.97
		Pentoses
	C-Xylose	68.58	4.11	27.31
	C-Furfural	68.60	3.90	27.50

Table 3

 3 Relative integrated peak areas from GC-MS experiments and w% concentration for residual HMF and furfural compounds.1

	Sample (solution)	+ Oxopentanoic acid	HMF	HMF w%	Furfural	Furfural w%
		Hexoses
			0.20	0.50
	Pure HMF solution		0.50	1.00
			1.00	2.00
	C-Glucose	0.12	0.01	0.12
	C-HMF	0.05	0.43	0.91
	C-Maltose	0.11	0.02	0.14
	C-Sucrose	1.00	0.05	0.19
	C-Amylopectin	0.66	0.09	0.26
	C-Starch	0.54	0.15	0.37
		Pentoses
					0.31	0.50
	Pure furfural solution				0.52	1.00
					1.00	2.00
	C-Furfural				2.42	5.08
	C-Xylose				0.21	0.29

were used as received (Sigma Aldrich). In order to prevent any contamination from multiple experiments, the mixture was sealed into a glass vial inside a typical PTFE-lined autoclave system and hydrothermally reacted in a pre-heated oven at 180°C for 24h. After reaction, the autoclave is cooled 55

Acknowledgements