ࡱ>  +-"#$%&'()*Boqsu w K M O Q[ (bjbj 8+!FE"4y"y"y""""8"4$"L%q((L(()) )LLLLLLLNaQL9y"=))==Ly"y"((;L:FFF=by"(y"(LF=LFFF(^"v?FKuLHLF+RB+RFF+Ry"F) /PFD4t7\)))LL D)))L====+R))))))))) : Are Workers Remittances Relevant for Credit Rating Agencies? Rolando Avendao OECD Development Centre Norbert Gaillard Bank for International Settlements Sebastin Nieto Parra OECD Development Centre Abstract Remittances flows are an important source of financing for developing countries. In addition to the microeconomic impact at the household level, remittances have grown into an important pillar of macroeconomic stability, acting to reduce financial vulnerabilities, lessen probability of current account reversals and ultimately reduce credit risk. This paper, assessing the country risk models for the three main Credit Rating Agencies (CRAs), focuses on the role of workers remittances in the estimation of the sovereign ratings of 83 emerging countries over four dimensions. First, it explores the role that workers remittances play in country risk models. Second, it assesses the extent to which CRAs take remittances into account in determining ratings. Third, it seeks to capture the potential effect that remittances have for sovereign ratings when considered in rating models. Fourth, it attempts to assign sovereign ratings to unrated Latin American countries. We conclude that while CRAs take remittances flows nominally into account as a determinant of ratings, remittances have a real and significant impact on a certain type of sovereign rating, through the reduction of the volatility of external flows as well as the improvement of the solvency ratio. Such effects are concentrated on a set of low and middle income countries, for which the dependence on remittances is high. JEL Classification: O11, F24, F3, G24 Keywords: credit rating agencies, remittances, sovereign ratings, emerging and developing capital markets. THIS DRAFT 09 MAY 2009 I. Introduction Workers remittances can have an effect in the way that country risk is assessed. Previous research on the access of sovereigns to international capital markets (see Ratha (2006) and World Bank (2006)) suggests that sovereign creditworthiness could be improved by including remittances flows in key indebtedness indicators, such as debt-to-exports and debt service to current account ratios. These have been identified in the literature as common determinants of country ratings. It is worth noting two surveys at the crossroads of the literature on sovereign ratings and remittances flows. First, Ratha et al. (2007) define a standard ratings model and find that a number of unrated countries would be likely to have higher ratings than expected, notably on account of foreign currency inflows such as remittances. According to Ratha (2006), country credit ratings by major international rating agencies often fail to account for remittances. Second, rating agencies note in their country studies that remittances matter to determine ratings for countries in which this flow is considerable. Fitch (2008) underlines that remittance flows may positively impact ratings (e.g. El Salvador). In its outlook for Mexico, Standard and Poors (2005) stresses remittances importance as an income source for the balance of payments, and their impact on other determinants of sovereign ratings, such as public finances. More recently, Moodys highlights that, for a country like the Philippines, a slower economic growth for 2009 would also be explained by a decline in remittances, which account for more than 10% of domestic output and are a major driver of consumption. Despite these stylized facts, little research has been devoted to analyse the impact that remittances have on sovereign ratings assigned by CRAs. Our paper attempts to address this question by building a rating model over a long time span (1993-2006), and estimating ratings for a sample of 83 emerging countries and the three main CRAs. Concretely, this paper seeks to answer three key questions: Do rating agencies really take remittances fully into account in their analyses? How can we capture the effect of remittances on ratings? And finally, what is the potential effect of remittances when included in market variable estimations? With this purpose, it is crucial to understand why CRAs should take remittances into consideration when assigning ratings. Although the effects of workers remittances at the macro and the micro level have been largely studied (see World Bank, 2006 for a review of the literature), the evidence on the implications for ratings is still scarce (World Bank 2006, Ratha 2007). The question is crucial given the importance of remittances flows towards the developing world, with Latin America being no exception. Over the last twenty years remittances have increased considerably, even by comparison to other capital flows to developing economies. Currently, remittances account for around one third of total financial flows to the developing world. With foreign direct investment (FDI), remittance flows to developing countries overtook Official Development Assistance (ODA) in the nineties and for some developing countries reached comparable and even higher levels than FDI flows. In that context, Ratha (2004) and Ratha, Mohapatra and Xu (2008) provide an in-depth analysis of the importance of remittances since the late 1990s. In Latin American and Caribbean countries the increase in remittances flows is also considerable and their proportion is high with respect to other external flows. During the period 1970-2006, remittances accounted for about 50 percent of FDI and were twice as large as ODA flows. In 2006, remittances represented more than 80 percent of FDI and more than eight times ODA flows. This is particularly clear in some Central American countries. During the same year, the volume of remittances towards developing countries reached about 70 percent and 85 percent of FDI and ODA flows respectively. Figure 1. Net flows to developing countries  Source: Authors, based on World Bank and OECD data, 2009. At the macro-economic level, an important characteristic concerns remittances impact on the balance of payments, in particular when compared with other capital flows. Figure 2 exhibits the volatility of the major capital flows to emerging countries over the period 1990-2006. Figure 2. Volatility of flows by period 1990-2006  Source: Authors based on World Bank and OECD data Note: the volatility of a quantity is defined as its coefficient of variation (the standard deviation of the quantity divided by the mean of its absolute value). The normalisation avoids finding larger volatilities for larger flows. It is calculated for each recipient and then averaged over all developing countries in the sample. By comparing the volatility of remittances with respect to other flows we notice they have a lesser volatility with respect to portfolio flows (equity and bond flows) but also with respect to FDI flows. Even regarding ODA flows, remittances have a lower volatility than other capital flows. This effect is clearer in the later years of the sample. Importantly, the correlation between remittances and other flows is small, contributing to the stability of the total external flows. The correlation of remittances with FDI and ODA are close to 0.20 and 0.0 respectively. Migrants remittances constitute a large source of foreign capital in many Latin American countries and can be considered as a stable source of financing compared with other financial flows. Remittances, in the same way as foreign investment or exports, are important items in the balance of payments, contributing to mitigate credit risk at the country level. More precisely, remittances strengthen financial stability by reducing the probability of current account reversals (Bugamelli and Paterno, 2005). This, in turn, can be related to the probability of default studied in country risk models. In the same way, remittances can have a countercyclical effect in most countries of the region, thus significantly reducing growth volatility (Fajnzylber and Lopez 2007). The overall conclusion of Close to Home, the comprehensive World Bank study on Latin America, is that remittances are an engine for development, but that they are neither manna from heaven nor a substitute for sound development policies. Empirical evidence shows that remittances can have a positive impact on economic growth and poverty reduction in developing countries by allowing capital accumulation at the household level (Giuliano and Marta Ruiz-Arranz , 2005; Osili, 2006; Dustmann and Kirchamp (2001). For the case of Latin American countries, we also find a positive effect of remittances at the microeconomic level (see Massey and Parrado , 1998 for the case of Mexico and Cardona Sosa and Medina, 2006 for Colombia). However, as pointed out elsewhere, migrant-based income can become costly to emerging countries when resources are mismanaged. Remittances may reduce the governments incentive to maintain fiscal policy discipline (Chami et al., 2008). Moreover, this dependence raises a moral hazard problem by reducing the political will to implement reforms and pushing real exchange appreciation. These findings are consistent with Amuedo-Dorantes and Pozo (2004) who relate higher remittances flows to the reduction of the receiving countrys competitiveness. Remittances should matter when country risk is assessed in developing countries given the direct effect that they have on the balance of payments (e.g., stability in the capital flows, exchange rates) and more generally on the real economy (e.g., economic growth, output volatility, poverty reduction). Indeed, remittances can play a positive role in the credit risk of a country and this impact can be observed directly (e.g., stability of balance of payments) or indirectly (e.g. reduction of income inequality, creation of new firms). For Latin American countries, the central empirical analysis of this paper, remittances are crucial, given their high level with respect to the size of the economies (e.g., Guyana, Honduras, Haiti, Jamaica, El Salvador) or given their high level in absolute values (e.g., Mexico, Brazil, Colombia, Guatemala, El Salvador, Ecuador). Differences among countries in the region remain important. Figure 3 shows that remittances are particularly relevant for Central American and Caribbean countries. Figure 3. Remittances in Latin America 2006  Source: Authors calculation, based on Global Development Finance, 2009. Workers remittances can therefore have both direct and indirect impacts on national economies. In this paper we analyse the relationship between remittances and country credit risk through the balance of payment channel. More precisely, we study the impact of remittances on countrys creditworthiness via two dimensions. First, we analyse a common channel to measure remittances on country risk (Ratha 2005, World Bank 2006). By including remittances in a traditional solvency ratio studied in the ratings literature (i.e., the ratio of debt to exports of goods and services), remittances can improve sovereign ratings. Second, we introduce volatility of external flows as an additional variable explaining sovereign ratings. This variable is related to the variability of trend of major inward external flows (FDI flows, Portfolio flows, ODA, Bank loans, Exports and Remittances.). These flows are particularly important for Latin America, where saving rates are low and dependence on external financing high. Our results show that remittances can reduce volatility of external flows given their stability (when compared to other flows) and low correlation with other external flows. The remainder of this article is organised as follows. In section II, we provide a review of the literature on sovereign ratings and in particular on the relevance of sovereign ratings for emerging economies as well as the determinants of these ratings. Section III presents the most important stylised facts and analyses the results of the econometric model. In particular, this section analyses the impact of remittances flows on four representative models of the literature in ratings and our own model, where a counterfactual analysis is presented. We also provide an empirical analysis for countries with a high share of remittances (as a percentage of GDP). Finally, section IV provides concluding remarks and sketches the major policy implications that follow from this research. 2. Review of the literature In this section we provide first some empirical evidence on the relevance of CRAs for capital markets and more especially for emerging economies. Second, we present the main results of the seminal papers related to the determinants of ratings. It has been pointed out that the recent financial market turbulence has brought credit rating agencies under fire and academia as well as policy-makers argue for a reform of the business model of CRAs (Portes 2008). Rating agencies are faced with a serious conflict of interest, to the extent that their remuneration comes from rated issuers (for a theoretical analysis see Mathis, McAndrews and Rochet, 2008), both in the context of public of private borrowers. This is a crucial issue, given CRAs considerable and increasing role on international capital markets. In this context, there is a large and useful literature studying the impact of ratings on market prices and bond spreads. Focusing on market prices, Kaminsky and Schmukler (2001) find that downgrades and upgrades have an impact on country risk and stock returns: these rating changes are transmitted across countries, with neighbour-country effects being more significant. They conclude that rating agencies may contribute to heighten financial instability. The study of sovereign risk assessment has focused on comparing ratings to market spreads. For the period 1987-1994, Cantor and Packer (1996) find a greater impact on spreads from a rating change in the case of Moodys or if it is related to speculative-grade countries. Reisen and Von Maltzan (1999) show that, during the period 1989-1997, Fitch, Moodys and S&Ps downgrades have a significant impact on spreads, contrary to upgrades, which were anticipated by the market. For them, sovereign ratings have the potential to moderate euphoria among investors on emerging markets but rating agencies failed to exploit that potential in the 1990s. Sy (2001) highlights the strong negative relationship between ratings and EMBI+ spreads declines during periods of high risk aversion (e.g., 1997-1998). Mora (2006) examines Moodys and S&Ps ratings and concludes that the procyclicality of ratings is not ascertained when considering the post Asian crisis years. Analyzing sovereign ratings issued by the three agencies for 1993-2007, Gaillard (2009) finds that the procyclicality of ratings was much sharper during periods of high risk aversion (1997-1998 in particular) than periods of low risk aversion (2005-2007). He also highlights the greater stability of Moodys ratings. In a different way, Cavallo et al (2008) develop a simple Hausman specification test and find that there is some informational content in sovereign ratings that is not completely captured by market spreads. Additional tests reinforce their conclusion that ratings matter. Lastly, going beyond the traditional ratings vs. spreads view, Roubini and Manasse (2005) present an original sovereign risk assessment methodology by using a binary recursive tree. This enables them to better discuss appropriate policy options to prevent crises. The literature focusing on sovereign ratings methodology has expanded since the mid 1990s. Cantor and Packer (1996) identify five variables that may explain S&P and Moodys sovereign ratings: per capita income, inflation, external debt, level of economic development and default history. Jttner and McCarthy (2000) show that Cantor and Packers model becomes less accurate after the Asian crisis. They suggest that the determinants of 1998 ratings are the current account balance, the indicators for economic development and default history, the interest rate differential vis--vis the USD, and the range of problematic assets. Nevertheless, several follow-up studies corroborate Cantor and Packers results. For Afonso (2003), most significant variables for 2002 ratings (per capita income, inflation, indicators for economic development and default history) are already determinants for Cantor and Packer. Moodys own study (Moodys 2004) produces a similar finding: two of their four explanatory variables (per capita GDP and external debt) are the same as Cantor and Packers. The main finding of Moodys is the incorporation of a political variable that significantly improves the model. For Rowland (2005), the level of international reserves, as a share of GDP, and the openness of the economy are additional relevant determinants. Sutton (2005)s findings are consistent with previous papers. He also argues that the maturity structure of structure of international banking claims against both private and public sector entities in the country is a significant variable. 3. Empirical model 3.1.1. Data Description As noted in the previous section, the literature on sovereign ratings is extensive. We have tried to focus on the most representative work to identify the variables considered by agencies when assigning a rating to public borrowers. The traditional approach in the literature has been to regress the dependent variable (sovereign rating) on a series of macroeconomic and institutional indicators. Table 1 summarizes the period and variables used by Cantor and Packer (1996), Rowland and Torres (2004), Sutton (2005) and Mora (2006) to analyze the determinants of sovereign ratings. Whereas Cantor and Packer and Sutton base their analysis on a cross-country study, Rowland and Torres and Mora use panel data to estimate rating determinants. Most of these studies use one or more of the available ratings published by the three main rating agencies, Standard and Poors, Moodys and Fitch. Table 1 contains also our preferred model, that summarizes our analysis of previous rating models. Table 1. Summary of models and variables  Source: the authors based on Cantor and Packer (1996), Rowland and Torres (2004), Sutton (2005) and Mora (2006) The results presented in the table above are straightforward. Sovereign ratings are associated to a countrys fundamentals and, in contrast with sovereign spreads (e.g., Eichengreen and Mody, 2006), endogenous factors are only analysed. More precisely, macroeconomic conditions (e.g., inflation rate, GDP growth), solvency ratios (e.g., external debt over exports, external debt service over GDP) and structural aspects (e.g., GDP per capita, economic development) are employed as determinants of sovereign ratings. In this paper we use data on annual ratings from the three main rating agencies: Standard and Poors, Moodys and Fitch. The covered period is 1993-2006, the frequency is annual and the initial sample includes 83 rated countries (excluding High Income countries according to World Banks definition). Data on ratings has been transformed linearly according to Table 2. Table 2. Rating Linear Transformation  Source: Authors transformation, based on Rating agencies methodologies and Gaillard (2009).. Data for macroeconomic variables comes from the World Development Indicators and data on national debt from the Global Development Finance (World Bank). Table 3 provides a rsum of the main macroeconomic variables used across the different rating models. Table 3. Descriptive Statistics for Variables  Sources: Global Development Finance, World Development Indicators, International Financial Statistics, 2009. 3.1.2. Testing Previous Models for Sovereign Ratings: The Effect of Remittances We start by testing the four representative models proposed in the literature, for the period 1993-2006. We expect to identify the most relevant determinants for ratings in our sample. Regarding previous studies, the sample includes a larger number of countries and a 14-year time span. We run regressions on OLS and fixed-effect panel data, using the sovereign rating of the three rating agencies as the dependent variable. Moreover, we are interested in analysing the impact that remittances could have on the behaviour of rating agencies. As presented, remittances flows can be shock absorbers for the economy, and they play a role in reducing the countrys vulnerability by providing resources that can have a positive impact on the balance of payments and on the appreciation of the exchange rate. More generally, remittances can improve a countrys creditworthiness and thereby facilitate its access to international capital markets. One significant and key solvency ratio used in the literature to explain the behaviour of sovereign ratings is the debt-to-exports ratio. We start by including this variable in the standard rating models introducing remittances in the ratios denominator to capture the entire effect of the current account incomes. At this point we only introduce remittances through the solvency ratio as our second core variable (i.e., volatility of external flows) is not studied in the literature. An increase in the remittances (as well as for the case of the exports of goods and services) can have a positive effect on the current account and can induce appreciations on the real exchange rate. By the same, these revenues in the balance of payments can serve as a cushion against external shocks and then reduce the risk of default of the external debt. In fact, since we are interested in the countrys capacity to pay the entire total external debt (private and public), it is reasonable to include remittances in this ratio, to capture total incomes received by nationals in the balance of payments. To quantify the impact of remittances on sovereign ratings, we test the standard models for ratings by excluding/including the flow of remittances in the external debt to exports ratio. Data on exports comes from the Global Development Finance (GDF) and data on workers remittances from the International Financial Statistics (IFS). Figure 4 exhibits the evolution of our solvency ratio (debt over exports) for Latin American and Caribbean countries, where the relative impact of remittances in indebtedness indicators remains heterogeneous. In general, the effect of remittances is higher in Central American and Caribbean countries (e.g., El Salvador, Jamaica, Guatemala, Dominican Republic) than in other countries of the region (e.g., Argentina, Brazil, Chile, Mexico, Peru, Venezuela). Figure 4. Ratio of External debt over Exports (tdoverx_wr) and over Exports and Remittances (tdoverx) Latin American and Caribbean countries  Source: Global Development Finance and International Financial Statistics, 2009. Figure 4. Ratio of External debt over Exports (tdoverx_wr) and over Exports and Remittances (tdoverx) Latin American and Caribbean countries (cont.)  Source: Global Development Finance and International Financial Statistics, 2009. Following the literature review, we opt for testing our hypothesis on a group of models on sovereign ratings. Table 4 summarizes results of four representative models (Cantor and Packer (1996), Rowland and Torres (2004), Sutton (2005) and Mora (2006)), for the three main agencies over the period 1993-2006, using both ratios, total debt over exports of goods and services, and workers remittances ( EMBED Equation.3 ) and total debt over exports of goods and services ( EMBED Equation.3 ). Table 4a. Determinants of Sovereign Ratings (1993-2006) Cantor and Packer (1996), Rowland and Torres (2004)  Table 4b. Determinants of Sovereign Ratings (1993-2006) Sutton (2005)  Table 4c. Determinants of Sovereign Ratings (1993-2006) Mora (2006)  Results in Table 1 show that for most models the ratio debt over exports (with or without remittances) is negative and significant for the three major rating agencies. Indeed, it is a key and relevant variable explaining sovereign ratings. For instance, taking Cantor and Packer (1996) model, columns 1 to 6 in Table 4a show that the solvency ratio (i.e. Debt over Exports) is statistically significant at 1 per cent and negatively correlated with sovereign ratings. In addition to this ratio, other variables are crucial to explain ratings assigned by agencies: GDP per capita, GDP growth, inflation rate, current account over GDP, historical default and international reserves over GNP. Finally, when comparing the impact of including and excluding remittances on the ratio debt over exports, the value of the coefficient is almost the same for both cases (in absolute terms), for all rating models studied. Results suggest that rating agencies are not sensitive to workers remittances when they rate sovereigns. Indeed, an inclusion of remittances supposes a reduction of the solvency ratio and consequently a higher coefficient (in absolute value) can compensate for the remittances effect in the sovereign rating. This finding is explained empirically in detail for our general model. 3.1.3. Proposing a General Model and Testing Effect from Remittances Traditional models on the determinants of ratings include a solvency indicator, such as the ratio debt to exports. By introducing remittances (as suggested by Ratha, 2005), we have tested if remittances flows play a role in reducing external vulnerabilities. In addition, we introduce a consistent explanatory variable for sovereign ratings in which remittances can play a crucial role: the volatility of external flows. As specified above, when compared to other external flows (i.e. exports, portfolio flows, FDI flows, ODA), remittances display a much lower volatility and lower correlation to these flows; they can act as a cushion vis--vis capital flights. We assess the volatility of external flows as a second channel through which remittances flows are likely to affect sovereign ratings. Our hypothesis is that remittances can reduce the total volatility of inward external flows, which is itself a powerful explanatory variable for sovereign ratings. We use the variance as a measure of volatility of external flows. We decompose the variance of inward external flows as follows:  EMBED Equation.3  where  EMBED Equation.3 corresponds to the variance of inward external flows of country  EMBED Equation.3 at time t,  EMBED Equation.3 is the weight of the external flow i with respect to the total external flows in country  EMBED Equation.3 ,  EMBED Equation.3 is the variance of the external flow i as a share of GDP between t-5 and t,  EMBED Equation.3  is the covariance between the external flows over GDP i and j and from t-5 to t. Figure 5 presents the volatility of external flows by including and excluding remittances. There is a considerable reduction of the external flows volatility for some Central American countries (e.g., Honduras, Guatemala, Nicaragua) and high-remittance receptors (e.g. Ecuador, Colombia, El Salvador). Figure 5a. Volatility of Inward External Flows with and without Workers Remittances  Source: Global Development Finance and International Financial Statistics, 2009. Figure 5b. Average Volatility of External Flows with and without Workers Remittances  Source: Authors calculation, based on Global Development Finance and International Financial Statistics, 2009. Considering the results from the four standard models and our new volatility indicator presented above, we propose the following model for our analysis (we name it General Model):  EMBED Equation.3  where  EMBED Equation.3 corresponds to the transformed rating of country i at time t,  EMBED Equation.3 is the per capita GDP in current international dollars,  EMBED Equation.3  is the products growth,  EMBED Equation.3 corresponds to annual inflation,  EMBED Equation.3  is the annual balance budget as a share of GDP,  EMBED Equation.3  is the current account position (as a share of GDP),  EMBED Equation.3  is the ratio of total debt to exports,  EMBED Equation.3  is a dummy variable for countries taking value 1 for countries having experienced a default during the previous 20 years,  EMBED Equation.3 is the ratio of reserves to GNP,  EMBED Equation.3  is the volatility of external of flows,  EMBED Equation.3 is a dummy variable for those countries covered by the Bond Index calculated by JPMorgan,  EMBED Equation.3 is a year fixed effect,  EMBED Equation.3  is the country-individual effect and  EMBED Equation.3  is an error term. Within this setup, EMBED Equation.3 measures the elasticity of sovereign ratings with respect to the ratio debt-to-exports, after controlling for all the other factors (GDP, inflation, volatility, etc.). The term  EMBED Equation.3 is capturing differences in sovereign rating across time not explained by the other determinants. In this model, we are mainly interested in the variables that can be affected by the flow of remittances, particularly the volatility indicator (i.e., the volatility of inward external flows) and the solvency ratio (i.e., debt-to-exports).  Table 5 shows the results for our general model and the three rating agencies. We report results using the two variables of interest, the volatility indicator and the solvency ratio, including and excluding remittances. Table 5. General Model All Sample  Source: Authors calculation. First, we analyze results for regressions including remittances in the volatility of capital flows as well as in the debt ratio. We focus on regressions (i), (iii) and (v) for Table 5. Not surprisingly, GDP per capita is positive and significant at the 1 per cent level. GDP growth, on the contrary, is not significant for our sample and it is negatively correlated with ratings (the exception being for Moodys). An increase of inflation supposes a decrease in the rating, and this result is significant for Standard and Poors. The balance budget is negative and significant for the case of Fitch. Unexpectedly, the current account balance is negatively associated to the ratings, indicating that countries running current account deficits may be better rated; there are multiple explanations for this result, one of them being that an important number of commodity exporters, with fragile macroeconomic performance in the past, experienced significant surpluses. Both variables of interest, the debt-to-exports ratio and the volatility of external flows, are consistently negative and significant for all rating agencies. Indeed, additionally to the standard variable used to explain the impact of remittances on ratings (i.e., the solvency ratio), the new variable introduced (i.e., the volatility indicator) is an important one when explaining ratings. The variable default is negatively correlated to the sovereign rating, as expected, and is significant for Standard and Poors. The reserves-to-national product ratio is positively related to the rating in the case of S&P and Moodys, highlighting the increasing role of precautionary reserves for impeding defaults. Now we focus on regressions (ii), (iv) and (vi), that considers the new variables excluding remittances from the debt/exports ratio and the volatility of external flows. The results do not change significantly, and are indeed very similar to the ones using the original indicators. 3.3. Counterfactual analysis for Latin America General Model To assess the potential effect that the modified solvency ratio as well as the volatility indicator could have on the potential rating for some countries on Latin America, we construct a simple counterfactual scenario, looking at the rating evolution when remittances flows are taken into account. We use the observed ratio debt-to-exports and the counterfactual ratio debt-to-exports that we estimated for the previous regressions, this is, excluding remittances flows (TDX_wr). By the same, we use the observed volatility indicator and the counterfactual volatility indicator that we estimated for the previous regressions, this is, excluding remittances flows (volat_indicator_wr). We estimate our initial model with the counterfactual variable:  EMBED Equation.3  and we obtain the vector EMBED Equation.3 as the fixed-effect estimator. Then, we use the observed ratio debt-to-exports (TDX) as well as the volatility of external flows and calculate the change in the observed rating using this ratio and the  EMBED Equation.3 coefficients. We obtain the potential improvement in the sovereign ratings for the Latin American countries included in the sample. Figure 6a depicts the observed rating for Standard and Poors (Observed  EMBED Equation.3 in the figure), the predicted model (Predicted  EMBED Equation.3 in the figure) that estimates ratings depending on the observed TDX_wr ratio (debt-to-exports excluding remittances) and on the observed volat_indicator_wr ratio (volatility of external flows excluding remittances) as well as the potential rating in the scenario including workers remittances in our variable of interests, debt/exports and volatility of external flows (Counterfactual  EMBED Equation.3 in the figure). Figure 6a, 6b, and 6c also provide shadow and predicted ratings for Latin American countries (in the figures, both categories are included under the name predicted). The naming shadow ratings concerns unrated countries for a given year and a specific agency, while predicted ratings refer to countries that are already assigned a rating (see Table 6 for the two series of periods covering shadow and predicted ratings respectively for each country and agency). Table 6. Shadow and Predicted Ratings by CRA and by Country  Sources: Fitch (2008a), Moodys (2008), S&P (2007). Results are very similar from one agency to the other. The main findings are the following. First, ratings yielded by the predicted and the counterfactual models are, on average, lower than observed ratings for investment grade issuers (Chile, Mexico) and Peru. By contrast, predicted and counterfactual ratings are higher for defaulting countries (Argentina, Dominican Republic, Ecuador). This may prove that the model tends to attenuate the boom-bust debt cycle. Second, when they are compared for a given country, predicted and counterfactual ratings are very close, except for smaller countries (i.e. El Salvador, Honduras, and Nicaragua): the latter being higher than the former. This gap could suggest that ratings of small economies are more likely to be enhanced by the inclusion of remittances. Figure 6a. Counterfactual Analysis for Latin America S&P  Source: Authors calculation. Figure 6b. Counterfactual Analysis for Latin America Moodys  Source: Authors calculation. Figure 6c. Counterfactual Analysis for Latin America Fitch  Source: Authors calculation. Including the debt-to-exports ratio and the volatility of external flows in the estimation does not substantially alter the estimation. We infer that including remittances in the rating agencies model does not have an effect on improving the rating for most Latin American countries. To check the robustness of this result, we test the opposite estimation, using the variables TDX and Volat_indicator as follows:  EMBED Equation.3  and doing the counterfactual with the variable excluding remittances (TDX_wr). The results are very similar to those presented in Figures 6a, 6b, and 6c. 3.3. Model for High-Remittance Receptors The wide range of countries in the sample makes it difficult to identify if there is a clear impact of remittances flows in the regressions. Initially, we would expect that for those countries where remittances have a non-negligible weight (as a share of GDP) in the economy, the change in our two benchmark variables (i.e., solvency ratio and volatility indicator) including and excluding remittances would be significant. For this reason, we decide to calculate a threshold variable (for each country and year) taking the value 1 when the ratio Remittances/GDP was higher than a given percentage and zero otherwise. The objective is to identify those countries and years where remittances are more important. Note that this dummy is non constant over time, and therefore can be included in the fixed-effect panel. Then, we calculate a crossed term with the non-constant dummy and the variables TDX and Volat_indicator, that will detect the interaction effect between countries with a high share of remittances and our variable of interest. Thus, we test the following model for the whole sample:  EMBED Equation.3  where  EMBED Equation.3 takes the value 1 when the ratio Remittances/GDP was higher than a given percentage and zero otherwise.  EMBED Equation.3  and  EMBED Equation.3 are the interaction effects between countries with a high share of remittances and the ratio debt over exports and the volatility of external flows respectively. Table 7 summarizes the result for the sample, using two different thresholds: 3.5 and 5.0 per cent, respectively. ` Table 7. Regression with Threshold Model  Source: Authors calculation. Regressions in Table 7 allow isolating the effect that remittances can have for those countries where they are more important. With the 3.5 per cent threshold, the dummy variable is significant for two of the agencies. The interactive term for the volatility of external flows, also, is positive and significant. Increasing the threshold to 5 per cent does have an effect on the dummy variable and it does also affect the interactive variable, with a positive and significant effect on the sovereign rating. This result suggests that for countries where remittances are more important, high remittances have a direct effect on ratings (the dummy variable remittances over GDP is significant) but above all, and more relevant, they could have a premium in their ratings, meaning that there is an indirect effect of remittances on ratings (the interactive variable for the volatility of external flows is significant). Indeed, there is an insight. The indirect impact of remittances on ratings goes mainly through the volatility of external flows and not through the solvency ratio as argued in previous research. For countries where the ratio remittances-to-GDP is higher than 3.5 per cent the elasticity of the rating with respect to the variable volatility of external flows is  EMBED Equation.3 . Since  EMBED Equation.3  is positive, the weight of the variable volatility of external flows is reduced. We find that including an interaction term between the ratio remittances/GDP and the volatility of flows variable denotes a more inelastic rating for those countries where precisely remittances are more important. In other words, for countries in which the ratio of remittances over GDP is high there is an indirect effect of remittances. Indeed, there is a premium for these economies. More precisely, for countries in which remittances are relatively high, the negative impact of the volatility of external flows on the rating can be attenuated. In any case, as it is depicted in Figures 7a, 7b, and 7c, the effect is somehow limited. We estimate the predicted rating for Latin American countries, for the model including the threshold for remittances/GDP. The predicted ratings are presented in Figure 7a, 7b, and 7c. The main finding is that predicted ratings are higher than observed ratings for all speculative grade countries (except Bolivia). Figure 7a. Predicted vs. observed analysis for Latin America S&P All  Source: Authors calculation. Figure 7b. Counterfactual Analysis for Latin America Moodys All  Source: Authors calculation. Figure 7c. Predicted vs observed analysis for Latin America Fitch All  Source: Authors calculation. 4. Conclusion This paper analyzes the impact of remittance flows on sovereign ratings for developing and emerging countries over the period 1993-2006. Remittances are an important source of external financing for developing countries. They may also have served to reducing country default risk most significantly in the smaller economies. In order to capture the impact of remittances on sovereign risk, we analyzed two core variables. First, we used a traditional solvency ratio employed in the literature and second we introduce a second determinant, the volatility of external flows. Using a model of the determinants of sovereign ratings and then a counterfactual estimation, we find that the impact of including remittances in both the ratio debt-to-exports and the volatility of external flows is weak. This suggests that CRAs are either not sensitive remittance flows, or that the inclusion of remittances in these two variables does not improve ratings. When specifying a second model for identifying highly remittance-dependent economies, we find that, for this set of countries, remittances have an indirect, yet positive impact on ratings through a premium (captured with the interactive dummy variable remittances over GDP and the volatility of external flows). The results presented above are consistent with previous research (e.g. Roubini and Manasse, 2005) showing that there is not a single model to rate countries and that not all variables have the same impact on a sovereign rating. For the remittances variable, the countries most affected are those where remittances account for a higher share of GDP. Indeed, the impact depends more on the volatility of external flows than on the solvency ratio (debt over exports). This paper also provides shadow ratings for countries which are not rated by the three main CRAs, in particular some Central American and Caribbean countries, where relative remittances flows are higher. Our analysis provides useful information on the potential ratings for some of these countries, a crucial step for accessing international capital markets. This approach implies that remittance flows cannot, as a whole, enable low and medium income countries to improve their creditworthiness significantly. However, remittances may prove very useful for some small Central American and Caribbean countries. Rating agencies, and other financial institutions, should take remittances into consideration to rate countries with idiosyncratic profiles. 5. References AFONSO A. (2003), Understanding the Determinants of Sovereign Debt Ratings: Evidence for the Two Leading Agencies, Journal of Finance and Economics, Vol. 27, No.1. ALTMAN E. (1989), Measuring Corporate Bond Mortality and Performance, Journal of Finance, Vol.XLIV, No.4. AMUEDO-DORANTES C. and S. POZO (2004), Workers Remittances and the Real Exchange Rate: A Paradox of Gifts, World Development Vol. 32, No. 8. CANTOR R. and C. MANN (2003), Measuring the Performance of Corporate Bond Ratings. CANTOR R. and F. PACKER (1994), The Credit Rating Industry, Federal Reserve Bank of New York, Quarterly Review, Summer/Fall, New York. CANTOR R. and F. PACKER (1996), Determinants and Impact of Sovereign Credit Ratings, Federal Reserve Bank of New York, Economic Policy Review, October, New York. CAVALLO E., A. POWELL and R. RIGOBON (2008), Do Credit Rating Agencies Add Value? Evidence from the Sovereign Rating Business Institutions, Inter-American Development Bank, Working Paper No.647, Washington, DC. CHAMI R., A. BARAJAS, T. COSIMANO, C. FULLENKAMP, M. GAPEN, and P. MONTIEL (2008), Macroeconomic Consequences of Remittances, International Monetary Fund, IMF Occasional Paper No.259, Washington, DC. DUSTMANN C. and O. KIRCHAMP, The Optimal Migration Duration and Activity Choice After Re-Migration, IZA Discussion Paper 299, Institute for the Study of Labor, Bonn, Germany. FAJNZYLBER P. and J. H. LOPEZ (2007), Close to Home. The Development Impact of Remittances in Latin America, World Bank, Washington, DC. FITCH RATINGS (2008a), Complete Sovereign Rating History, January 30. FITCH RATINGS (2008b), Republic of El Salvador, February 28. FITCH RATINGS (2009), fitchratings.com. GAILLARD N. (2009), Fitch, Moodys and S&Ps Sovereign Ratings and EMBI Global Spreads: Lessons from 1993-2007, International Research Journal of Finance and Economics, April. GIULIANO P. and M. RUIZ-ARRANZ (2005), Remittances, Financial Development, and Growth, International Monetary Fund, IMF Working Paper 05/234. Washington, DC. JUTTNER J.D. and J. MC CARTHY (2000), Modelling a Rating Crisis, Macquarie University, Unpublished, Sydney. KAMINSKY G. and S. SCHMUKLER (2001), Emerging Markets Instability: Do Sovereign Ratings Affect Country Risk and Stock Returns?, World Bank Working Paper 2678, Washington, DC. LEVICH R., G. MAJNONI and C. REINHART (2003), Ratings, Rating Agencies and the Global Financial System, Kluwer Academic Publishers. MASSEY D. and E. PARRADO (1998). International Migration and Business Formation in Mexico. Social Science Quarterly 79(1): 120. MATHIS, J., J MC ANDREWS, and J-C ROCHET (2008) Rating the Raters, Toulouse Working Paper. MOODYS INVESTORS SERVICE (2004), A Quantitative Model for Foreign Currency Government Bond Ratings, February. MOODYS INVESTORS SERVICE (2008), Sovereign Default and Recovery Rates, 1983-2007, March. MOODYS INVESTORS SERVICE (2009), moodys.com. MORA N. (2006), Sovereign credit ratings: guilty beyond reasonable doubt?, Journal of Banking and Finance Vol.30. OSILI U. (2006), Remittances and savings from international migration: Theory and evidence using a matched sample, Journal of Development Economics, Vol 83, Issue 2, August. PORTES R. (2008), Ratings Agency Reform, VOX, 22. RATHA D. (2004), Understanding the Importance of Remittance Flows. Migration Policy Institute, Washington, DC. RATHA D., P. DE and S. MOHAPATRA (2007), Shadow Sovereign Ratings For Unrated Developing Countries, Development Prospects Group, World Bank, World Bank Policy Research Working Paper 4269, June, Washington, DC. RATHA D., S. MOHAPATRA and Z. XU (2008), Outlook for Remittance Flows 2008 2010: Growth expected to moderate significantly, but flows to remain resilient , Migration and Remittances Team, Development Prospects Group, The World Bank, Migration and Development Brief No.8, November 11, Washington, DC. REISEN H. and J. VON MALTZAN (1999), Boom and Bust and Sovereign Ratings, OECD Development Centre, Working Paper, No. 148, Paris. ROUBINI N. and MANASSE P. (2005), Rules of Thumb for Sovereign Debt Crises, International Monetary Fund, IMF Working Paper 05/42, Washington, DC. ROWLAND P. and J. L. TORRES (2004),  HYPERLINK "http://ideas.repec.org/p/col/000094/002337.html" Determinants of Spread and Creditworthiness for Emerging Market Sovereign Debt: A Panel Data Study, Banco de la Repblica de Colombia, Borradores de Economa, Bogota. STANDARD AND POORS (2005), Sovereign Report Card: EMBIG, Commentary Report, September 19. STANDARD AND POORS (2007), Sovereign Ratings History Since 1975, January 3. STANDARD AND POORS (2009), standardandpoors.com. SUTTON G. (2005), Potentially endogenous borrowing and developing country sovereign credit ratings, Bank for International Settlements, BIS Occasional Paper No.5, Basel. SY A. (2001), Emerging Market Bond Spreads and Sovereign Credit Ratings: Reconciling Market Views with Economic Fundamentals, International Monetary Fund, IMF Working Paper 01/165, Washington, DC. TOXOPEUS H. and R. LENSINK (2007), Remittances and Financial Inclusion in Development, UNU-WIDER, Research Paper No. 2007/49, Groningen. WORLD BANK (2006), Global Economic Prospects: Economic Implications of Remittances and Migration. Washington, D.C.  The authors are indebted to Sara Bertin, Thomas Dickinson, Kiichiro Fukasaku, Juan de Laiglesia, Javier Santiso and the LEO (Latin American Economic Outlook) team of the OECD Development Centre for helpful comments and discussions. The views expressed in this paper do not necessarily reflect those of the OECD. Mailing address: OECD Development Centre, 2, rue Andr Pascal, 75775 Paris Cedex 16, France.  See for instance Standard and Poor's (2005) and Fitch (2008).  Moody's: Slowing remittances hurt RP, Manila Bulletin, February 14, 2009 (online article).  OLS estimations are not reported and we can provide them under request.  However as noted beforehand, remittances may increase moral hazard problems for government public finances and reduce the competitiveness for other sectors of the economy (i.e. Dutch disease effect).  Data on exports from the Global Development Finance also include total workers remittances registered in the Balance of Payments. The GDF defines Exports of Goods, Services and Income (XGS) as the total value of goods and services exported, and receipts of compensation of employees, and investment income. In order to calculate our solvency ratio we first exclude workers remittances and compensation of employees from the XGS variable (solvency ratio without remittances) and then we include workers remittances (from the IFS) in the denominator of the solvency ratio (solvency ratio with remittances). Workers remittances, a transfer and not an income entry in the balance of payments, are treated as compensation of employees in Global Development Finance because they are often uneasy to distinguish from compensation of non-resident workers and migrants. We therefore have usually workers remittances and compensation of employees contained in the Export series. Workers remittances and compensation of employees comprise current transfers by migrant workers, wages and salaries earned by non-resident workers. In addition, migrants transfers, a part of capital transfers, are treated as workers remittances in Global Development Finance. We therefore restrict our analysis to the series of workers remittances, and exclude compensation of employees and migrants tranfsfers (as estimated by GDF database). See GDP Volume 1 for more details.  As for the case of the models presented above, we build an artificial ratio by subtracting the total amount of Workers Remittances from the variable Total Exports, and we name it  EMBED Equation.3 , this is, the ratio debt-to-exports excluding workers remittances. Again, when comparing the coefficient for the variable Total Debt/Exports with and without remittances, these are very similar. A coefficient test shows that they are not significantly different from the previous regression. By the same, in order to analyse the impact of remittances through the volatility of capital flows, we subtract Workers Remittances to the calculation of the volatility of external flows, and we name it  EMBED Equation.3 , this is, the volatility of external flows by excluding workers remittances.  A complementary approach consisted of defining a variable taking the difference between the two ratios and volatilities, this is:  EMBED Equation.3 and  EMBED Equation.3 , and test for the significance of these variables in the general model. For the first difference, when including them in the model together with the ratio debt over exports it was significant at 1%, but it becomes non-significant when excluding the ratio. The second difference is not significant in the model at the 5% level (except for Fitch).  These results are not reported but they can be provided under request.  We tested other configurations to take into account the importance of isolating those ratings most likely to be affected by the remittances flows. We included the ratio remittances-to-GDP as an explanatory variable in equation (1), but this was not significant for the sample. Also, we separated the sample in income groups, following the World Bank classification (lower income/middle income/higher income, etc.) and performed regressions on each group. Finally, we opted for the non-constant dummy variable.  2>?IQjk0 ; < p { | ʿʴʩʚ{k_WLAWLWhfMhOJQJhfMh!OJQJh!OJQJhfMh)6OJQJh{EhO6OJQJmH sH h{Eh)6OJQJmH sH h{Eh)OJQJmH sH h{Eh1OJQJmH sH hfMhCk3OJQJhfMh}OJQJhfMh9]FOJQJhfMh)OJQJhfMh*5CJ OJQJhfMhTd5CJ OJQJhfMh)5CJ OJQJ?@ASlmPQwxgd)$a$gd)  . ; ? @ E F G P    ( + 2 3 _ l u | ~ {{{q{q{qh!OJQJ^JhfMh1OJQJ^JhmQOJQJ^JhfMh>`OJQJ^JhfMhOJQJ^JhfMh)OJQJ^JhcZOJQJhfMh*iOJQJhfMh!OJQJh!h!OJQJhfMhDjOJQJh!OJQJhfMh)OJQJ, R S U V [ ] _ g h l r s   % ) 9 : A h p ϸܫ󡔊}pfpp}\hDOJQJ^JhOJQJ^JhfMh"OJQJ^JhfMh 8OJQJ^Jh&;OJQJ^JhfMhCK3OJQJ^Jh_OJQJ^JhfMhDjOJQJ^JhCK3OJQJ^JhfMh5OJQJ^JhfMh*iOJQJ^JhfMhOJQJ^Jh OJQJ^JhfMh)OJQJ^J$  OPپ|n`|`U`|hmQ5OJQJ^JhfMh5OJQJ^JhfMh85OJQJ^JhfMh)5OJQJ^Jh@DOJQJhfMh)OJQJhfMh:|(OJQJhfMh1OJQJhfMh)OJQJ!hfMh)B*OJQJ^JphhDOJQJ^JhfMh"OJQJ^JhfMh 8OJQJ^JhfMh)OJQJ^JIQYac DQ]`˹ݬ~t~g~~t~Z~MCMh#FOJQJ^JhfMhPOJQJ^JhfMh1OJQJ^JhfMh6OJQJ^Jh_OJQJ^JhfMhsxOJQJ^JhfMh*OJQJ^JhcOJQJ^JhPCvOJQJ^JhfMh?OJQJ^J#hfMh)5CJOJQJ^JaJ#hfMh?5CJOJQJ^JaJhfMh)5OJQJ^J(jhfMhWf 0J,5OJQJU^JCDE  K$a$gdUtj$a$gd)gd)gd|0 7$8$H$gd~gd~ 2$xa$gd~ R7$8$H$gdsxgdsx`hw׳ל׏|obUHUUצ>h5/OJQJ^JhfMhjNOJQJ^JhfMh7LOJQJ^JhfMh(ZOJQJ^JhfMhrN:OJQJ^J%jhfMhsx0J,OJQJU^JhfMh:OJQJ^Jh:OJQJ^JhfMh@OJQJ^JhfMhDjOJQJ^JhfMh_OJQJ^Jh_OJQJ^JhfMhsxOJQJ^JhfMhPOJQJ^JhfMhP6OJQJ^J +BDWkw%<\ĹĮĹġĐp[p(hfMhfMCJOJQJ^JaJmH sH (hfMhtCJOJQJ^JaJmH sH hfMhrN:OJQJ!jhfMh(Z0J,OJQJUhfMh|OJQJ^JhfMh(ZOJQJhfMh7LOJQJhfMh|OJQJhfMh) OJQJ^JhfMh7LOJQJ^Jh5/OJQJ^JhfMh6OJQJ^J!"$+/NWZcelrwąąsssĚ^s^D3h5/B*CJOJQJ^JaJmH nHphsH tH(hfMhbOmCJOJQJ^JaJmH sH "h<8CJOJQJ^JaJmH sH (hfMh6CJOJQJ^JaJmH sH (hfMhKCJOJQJ^JaJmH sH (hfMhsxCJOJQJ^JaJmH sH (hfMhtCJOJQJ^JaJmH sH (hfMh"CJOJQJ^JaJmH sH "hfMCJOJQJ^JaJmH sH :EQvxy"*-3GTo➑zm`mSmIh5/OJQJ^JhNthNtOJQJ^JhNthY]OJQJ^JhNthCk3OJQJ^JhY]OJQJ^JhfMhCk3OJQJ^JhfMhzoOJQJ^JhfMh~OJQJ^J3h5/B*CJOJQJ^JaJmH nHphsH tH9hfMhzoB*CJOJQJ^JaJmH nHphsH tH9hfMh~B*CJOJQJ^JaJmH nHphsH tH[\erU])TbŸҸҸūҸҸҸrrrehfMh>`OJQJ^JhfMh@OJQJ^JhfMh) OJQJ^J#hfMh|0OJQJ\^JnH tH hfMh|0OJQJ^JhfMhn[COJQJ^JhfMh)OJQJ^JhfMh5/OJQJ^Jh5/OJQJ^JhfMhXMOJQJ^JhfMhCk3OJQJ^JhNtOJQJ^J'!&7@E[ipsu CJKҾ|qbVbh cCJOJQJ^JhfMh)CJOJQJ^JjhUtjhv_UhfMh)5OJQJ^JhfMhTEOJQJ^JhfMhn[COJQJ^JhfMh:[OJQJ^JhfMh sOJQJ^Jh cOJQJ^Jh1OJQJ^Jh OJQJ^JhfMh) OJQJ^JhfMh)OJQJ^JhTEOJQJ^J!KLc d """X$Y$M(N(;*<*`,a,b, R7$8$H$gdy R7$8$H$gdV$a$gd)gd)Kiklc d ")"-"z"""###%#>#y######ǹ󮧘ҋ~t~tҋgZghfMh@OJQJ^JhfMh:[OJQJ^JhhOJQJ^Jhhh)OJQJ^JhfMh9POJQJ^JhfMh)CJOJQJ^J hfMh)jxch#Fh#FUhfMh)5OJQJ^Jhh5OJQJ^Jh9POJQJ^JhfMh5/OJQJ^Jh5/OJQJ^JhfMh)OJQJ^J#$$$#$W$$$$%q%s%}%~%%%%%%&&&&&&>'Z'w'''''''M(N((̸̞̑y̸̸l_hfMhVOJQJ^JhfMhOJQJ^JhfMh)6OJQJ^Jh'OOJQJ^JhfMh9]FOJQJ^JhfMhx|OJQJ^JhfMh9POJQJ^Jh9POJQJ^JhTEOJQJ^JhfMh)OJQJ^JhfMhjiOJQJ^JhfMh@OJQJ^JhfMh OJQJ^J$(((()) **:*<*E*O*]*_*t***(+B+H+I+++`,a,b,c,,,,,j-q-{-----ܵ樛wmwh&OJQJ^JhfMh}@ OJQJ^Jh'OOJQJ^JhfMh)OJQJ^JhfMhB POJQJ^JhfMhOJQJ^JhfMh9POJQJ^JhfMhDOJQJ^JhfMhyOJQJ^Jh9POJQJ^JhfMhVOJQJ^JhfMh.NOJQJ^J%----0.C.X.b.....d/f///////000(050A0B0k0l0m0n0o000000000µٵµµٵٚzkhfMh)CJOJQJ^Jj|hv_hv_U hv_hv_hfMhf5OJQJ^JhfMh)5OJQJ^JhfMh1OJQJ^JhfMhB POJQJ^JhfMhDOJQJ^Jh OJQJ^JhfMh}@ OJQJ^JhfMh)OJQJ^JhfMhXOJQJ^J'b,l0m0n0o000000055xssnngdh gd)$ R7$8$H$a$gdD$ R7$8$H$a$gd) R7$8$H$gd) 7$8$H$gd)J7$8$EƀLH$gd) 000111161I11222-2k222222222222+3,3.3:3W3k3n33ŸŸܸyyyyl^h h6OJQJ^JhfMh&OJQJ^JhfMh:jOJQJ^JhfMh6OJQJ^Jh>(OJQJ^JhfMhOJQJ^JhfMhh OJQJ^JhfMh DOJQJ^JhfMhPOJQJ^Jh9POJQJ^JhfMhOJQJ^JhfMhDOJQJ^JhFOJQJ^J!333333333444455*50565?5@555557777*848;8<8888׽ʽʽʽ佦佰rerhfMh?5OJQJ^JhfMh1OJQJ^JhfMhfOJQJ^JhfMh)OJQJ^JhfMhPOJQJ^Jh9POJQJ^JhfMh DOJQJ^JhfMhh OJQJ^JhfMh&OJQJ^JhfMhDOJQJ^JhfMhOJQJ^Jh h D6OJQJ^J"558888899==DDKKKK3K4KLLOOO>OAOBO$a$gd{Egd{Egdgdfgd)88884959?999999-:.:/:<:G:U:^::::::::::::༯|o|e|XXeKXeXhfMhOJQJ^JhfMh[/OJQJ^JhOJQJ^Jhch[/OJQJ^JhchKOJQJ^Jhch2t|OJQJ^Jhc6OJQJ^JhfMhK6OJQJ^JhfMhKOJQJ^JhfMh2t|OJQJ^JhFOJQJ^JhfMhEOJQJ^JhfMhfOJQJ^J#hfMhf5CJOJQJ^JaJ:::::;;;;V;;;;;;;;;\<`<7=@===>)>.>R>\>d>u>>>> ?\?|?ABKܸŸwjhfMhPOJQJ^Jhv_hHOJQJ^Jhv_h)OJQJ^Jhv_hfOJQJ^JhfMhcOJQJ^JhfMh7OJQJ^JhfMhfOJQJ^Jh OJQJ^JhfMhOJQJ^JhfMh2t|OJQJ^JhfMh[/OJQJ^JhOJQJ^J(KKKKKKK3K4KO>O?O@OAOBOOOOQQ"S#S'S+S.SUSveQe&h!h{Eh!CJOJQJ^JaJ h!h{ECJOJQJ^JaJh{ECJOJQJ^JaJjh7h{EUh7h{EOJQJ^Jh1h{EOJQJ^Jh{EOJQJ^Jh1h{E5OJQJ^Jh{E5OJQJ^Jh{E5CJOJQJ^Jh h{E5CJOJQJ^Jhv_OJQJ^JhfMh)OJQJ^JBOOOQQ,S-S.STSUSWSXSSSTTTTTTTTTTTTTTT$a$gd{Egd{EUSVSXSSSSSWTXTTTTTTTTTTTTTTTU*OJQJ^Jh sh{E5OJQJ^J)`a-a.a:aEa^a_a`aaabacaeaaaaaaaaabbb)bJbKbLbMbNbPbbbbbbbbcüʢʼ|ʢobh^Xh{EOJQJ^Jh h{EOJQJ^Jj%h[h[Uh7h#FCJOJQJ^Jh#FCJOJQJ^Jh7h{ECJOJQJ^Jj8%h[h[U h[h[ h7h{Eh{EOJQJ^Jh[Oh{E5OJQJ^Jh{E5OJQJ^J#h{Eh{E5OJQJ^JmH sH &ObPbbbbbbbdddd e eeeeJeXeYe[e\e]e^e_e`eae$R^R`a$gd{E Rgd{Egd{Ec_cfczc|cccccccc d5d7d8dKdLdMdNdOdPddddddddddddd e}ododoh{E5OJQJ^Jh h{E5OJQJ^J%j@h h{EEHOJQJU^J'jhsM h h{EOJQJUV^J%jsh h{EEHOJQJU^J'jdsM h h{EOJQJUV^J!jh h{EOJQJU^Jh^Xh{EOJQJ^Jh{EOJQJ^Jh h{EOJQJ^J" eeeeeGeHeYeZe\eueeeeeeefffffggggg%gSg\ggghiiiiij8jjjjjjjjj kkkkkջլ㕢㕢Շh^Xh{E5OJQJ^Jh^Xh{EOJQJ^Jh{EOJQJ^JjHh h{EOJQJUjxh h{EOJQJUh{E5OJQJ^Jh h{E5OJQJ^Jh h{EOJQJ^Jj\h h{EOJQJU4aebecedeeefegeheiejekelemeneeeeeeeggFiGijjjk$R^R`a$gd{Egd{EkkllnnVoWopoqoGqHqvrrrr svswsyszssstttt$a$gdUtj$a$gd{Egd{Ekkkkkll!msmmmmmmmnWoXokolomonoooqoxoyoooo˾˾s\I%j<h\gh{EEHOJQJU^J-jM h{ECJUVaJmH nH sH tH !jh^Xh{EOJQJU^Jh^Xh{EOJQJ^J%j8h-Mh'3EHOJQJU^J3jMթM h-Mh{ECJUVaJmH nH sH tH h-Mh{EOJQJ^J!jh-Mh{EOJQJU^Jh =h{EOJQJ^Jh{EOJQJ^Jh2h{EOJQJ^Jooooooooooooopppp+p-papbpupvp䟒{h]F-jޥM h{ECJUVaJmH nH sH tH h{E6OJQJ^J%j`@h\gh{EEHOJQJU^J-jǥM h{ECJUVaJmH nH sH tH h\gh{EOJQJ^Jh\gh{E6OJQJ^J%jo>h\gh{EEHOJQJU^J-jM h{ECJUVaJmH nH sH tH h^Xh{EOJQJ^Jh{EOJQJ^J!jh^Xh{EOJQJU^Jvpwpxpzp{pppppppppppppppp,q-q2q3q=qAqDqEq1rvrĭьubььььQ h{Eh{EOJQJ^JmH sH %jFh*Hzh{EEHOJQJU^J-jЩM h{ECJUVaJmH nH sH tH h*Hzh{E6OJQJ^J%jiDh\gh{EEHOJQJU^J-jM h{ECJUVaJmH nH sH tH h^Xh{EOJQJ^Jh{EOJQJ^J!jh^Xh{EOJQJU^J%jyBh\gh{EEHOJQJU^Jvrwrrrrrrrrs s)s+susvswsxszsssssss tQttttttʿԔxkk^kM!jh h{EOJQJU^Jh =h{EOJQJ^Jh h{EOJQJ^Jh{ECJOJQJ *h{Eja *hUtjhUtjU *h{EOJQJ^Jhjh{ECJOJQJ^Jhjh{ECJOJQJh{EjIhjh{EUh{EOJQJ^Jh[Oh{E5OJQJ^Jh{E5OJQJ^J#h{Eh{E5OJQJ^JmH sH tttttttttttttu uuuu)u*uɸ󨛊vcUUA'j7dsM h^Xh{EOJQJUV^Jh^Xh{E6OJQJ^J%jU h^Xh{EEHOJQJU^J'jdsM h^Xh{EOJQJUV^J!jh^Xh{EOJQJU^Jh^Xh{EOJQJ^Jh h{E5>*OJQJ^J!jh h{EOJQJU^J%j h h{EEHOJQJU^J-jhM h{ECJUVaJmH nH sH tH h h{EOJQJ^J*u+u,ufuguzu{u|u}uuuuuuuuuuuuuvv,v-vκΓlYE'jdsM h^Xh{EOJQJUV^J%j h^Xh{EEHOJQJU^J'jdsM h^Xh{EOJQJUV^J%j= h^Xh{EEHOJQJU^J'jodsM h^Xh{EOJQJUV^J%jI h^Xh{EEHOJQJU^J'jZdsM h^Xh{EOJQJUV^Jh^Xh{EOJQJ^J!jh^Xh{EOJQJU^J%jY h^Xh{EEHOJQJU^J-v.v/vevfvyvzv{v|vvvvvvv6w7wJwKwLwMwnwowwwκΓlYB-juM h{ECJUVaJmH nH sH tH %j] h^Xh{EEHOJQJU^J'jsM h^Xh{EOJQJUV^J%j h^Xh{EEHOJQJU^J'jsM h^Xh{EOJQJUV^J%jÙ h^Xh{EEHOJQJU^J'jdsM h^Xh{EOJQJUV^Jh^Xh{EOJQJ^J!jh^Xh{EOJQJU^J%j h^Xh{EEHOJQJU^Jwwwwwwwwwwwwx x3x4x5x6xOxPxcxdxexfxxxxİĉvbO%j h^Xh{EEHOJQJU^J'j9|tM h^Xh{EOJQJUV^J%j, h^Xh{EEHOJQJU^J'j stM h^Xh{EOJQJUV^J%j` h^Xh{EEHOJQJU^J'jsM h^Xh{EOJQJUV^Jh^Xh{EOJQJ^Jh{EOJQJ^J!jh^Xh{EOJQJU^J%jE hX!h{EEHOJQJU^Jxxxxxxxxxxyyyyyyz z z zzzzǺǺǺǺlǺ_QG_G:h =h{EOJQJ^Jh{EOJQJ^Jh h{E5OJQJ^Jh h{EOJQJ^J%jF h^Xh{EEHOJQJU^J'j stM h^Xh{EOJQJUV^J%j h^Xh{EEHOJQJU^J'jstM h^Xh{EOJQJUV^Jh^Xh{EOJQJ^J!jh^Xh{EOJQJU^J%j h^Xh{EEHOJQJU^J'jdsM h^Xh{EOJQJUV^Jt z z zz{{|||$|%|̓΃σЃ $ Ra$gd{E$a$gd{E$a$gd{Egd{E Rgd{Ezzzzzzzzz{ {({-{1{h{l{u{v{{{{{{{{{{{||||||#|$|l||||}ܾ{lhfh{ECJOJQJ^Jhfh{ECJOJQJh{Ej9 h[h[U h[h[ hfh{Eh^Xh{E5OJQJ^Jh{E5OJQJ^Jh h{E5OJQJ^Jjh{E0J,OJQJU^Jh h{EOJQJ^Jh =h{EOJQJ^Jh{EOJQJ^J(}O}P}Q}r}y}}8~ EGJV_rށU\/Zʃ̓σЃփAt˄Ż旋xxxh h{EOJQJh{EOJQJh h{E5OJQJh{E5OJQJh h#FOJQJ^Jh h{E6OJQJ^Jh1OJQJ^JhWOJQJ^Jh{EOJQJ^Jh2Xh{EOJQJ^Jh h{EOJQJ^Jh^Xh{EOJQJ^J.˄9;%=R܆/0CDEFƇ$%&'꼫Ꙉwlah'3h{EOJQJhN>h{EOJQJ!j h h{EEHOJQJU!j h h{EEHOJQJU#jwM h h{EOJQJUV!jo h h{EEHOJQJU-j M h{ECJUVaJmH nH sH tH jh h{EOJQJUh{EOJQJh h{EOJQJh^Xh{EOJQJ%Ɉ 2;=>QRSTȉԉ݉*+emƊȊ֊؊ي߼߼tߴhߴߴߴߴ߼h7h{E6OJQJ!j hBh{EEHOJQJUjM h{EOJQJUV!j hBh{EEHOJQJUjM h{EOJQJUVh{EOJQJjh{EOJQJUhN>h{E6OJQJhN>h#FOJQJhN>h{EOJQJh'3h{EOJQJh'3h]OJQJ' #+9@]Ë̋͋ȼuiYIYi=h'3OJQJmH sH h'3h;S6OJQJmH sH h'3hoA6OJQJmH sH hoAOJQJmH sH hROJQJmH sH h;SOJQJmH sH h{EOJQJmH sH h^Xh{EOJQJmH sH  *h{EOJQJhOh{EOJQJ *hUJph{EOJQJhN>h{EOJQJjh{EOJQJU!j& hBh{EEHOJQJUjM h{EOJQJUVӌԌՌHIrА2pr/0HIgdA$a$gdA$a$gdb$a$gdqgd{E͋UЌьҌԌՌ܌ތߌ ´{m{b{b{WKhbOJQJmH sH j8 hbh*OJQJjh{E0J,OJQJUh6OJQJhN>h{EOJQJ!j-2h h{EEHOJQJU-jDM h{ECJUVaJmH nH sH tH jh h{EOJQJUh(h{E6OJQJh{E6OJQJh{EOJQJh h{EOJQJh h{E6OJQJ&'_`xyԙՙ֙IJKMvwz{$a$gd{Egd{E Rgd{E4"#`atuvwyö{hWC'jHvM h^Xh{EOJQJUV^J!jh^Xh{EOJQJU^J%j6h#Fh#FEHOJQJU^J-jM h#FCJUVaJmH nH sH tH !jh h{EOJQJU^J%jh h{E0J,OJQJU^Jhrh{EOJQJ^Jh{E6OJQJ^Jh h{E6OJQJ^Jh^Xh{EOJQJ^Jh{EOJQJ^Jh h{EOJQJ^J./014KLљ֙ܙݙ IhUKh6OJQJ^J%j?hp&h{EEHOJQJU^J-jM h{ECJUVaJmH nH sH tH h{EOJQJ^J%j=h h{EEHOJQJU^J'jHvM h h{EOJQJUV^J!jh h{EOJQJU^Jh h{EOJQJ^Jh^Xh{EOJQJ^J!jh^Xh{EOJQJU^J%j;h^Xh{EEHOJQJU^JIKSTVuvwxyz{#$ahӛԛ$׻񞔊}}s}sis}\R}R}R}h2OJQJ^Jh^Xh{EOJQJ^Jh1OJQJ^Jh OJQJ^Jh h{EOJQJ^Jh6OJQJ^Jh{EOJQJ^Jh h{E5>*OJQJ^JhUh{ECJOJQJh{ECJOJQJjAhbhbU hbhbh{Eh05OJQJ^Jh{E5OJQJ^Jh h{E5OJQJ^J $)167Sqv08N{|w`-j[M h2CJUVaJmH nH sH tH !jh h{EOJQJU^Jh]h26OJQJ^Jh h{E5>*OJQJ^JhA+OJQJ^Jh1OJQJ^JhbxOJQJ^Jh:h{E6OJQJ^Jh:h{EOJQJ^Jh{EOJQJ^Jh h{EOJQJ^Jh2OJQJ^J!ܣݣߣ&'*+I$a$gd $a$gd{Egd{EFEƀLgd{EϟПџҟ֟ޟy &FTjpqsηΚΚΚ΃yl_U_U_Kh#FOJQJ^Jh;OJQJ^Jh^Xh{EOJQJ^JhBh{EOJQJ^JhbxOJQJ^Jh{EOJQJ^Jh h2OJQJ^Jh2OJQJ^J%jkh2h2EHOJQJU^J-j[M h2CJUVaJmH nH sH tH h h{EOJQJ^J!jh h{EOJQJU^J%jih2h2EHOJQJU^JKXYZۣߣ&'()*+IQRƶtj]THh.h{E5OJQJh;5OJQJhUh{ECJOJQJh{ECJOJQJjmh h U h h h h{EOJQJh h{E5OJQJh{E5OJQJhUdh;5OJQJmH sH hUdh{E5OJQJmH sH hUdh{EOJQJmH sH hWOJQJ^Jh{EOJQJ^Jh;OJQJ^Jh.h{EOJQJ^Jڤ -.ȽܴܗՌ|iO61h#h{EB*CJOJQJ^JaJmH phsH 3h#h{E5B*CJOJQJ\aJmH phsH %hUdh h 5OJQJmH sH hUdh{E5OJQJmH sH jMh h U'h&Nh{EOJQJmHnHsH tH uh;5OJQJh{E5OJQJh h{EOJQJhUh{ECJOJQJ h h h h{E5OJQJj4$h h Uh h 5OJQJ.0mo^_5$[$\$a$gd{Egd{E$a$gd{E./01:;BCR 3;su7Klmnoاϻ|hQ|||||-h>h{EB*CJOJQJaJmH phsH 'hJ`B*CJOJQJaJmH phsH 'h{EB*CJOJQJaJmH phsH 'h>B*CJOJQJaJmH phsH -hh{EB*CJOJQJaJmH phsH 'h{EB*CJOJQJaJmH phsH 1hh{EB*CJOJQJ^JaJmH phsH -hh{EB*CJOJQJaJmH phsH  "DK !"ejĪɪ#%(3KԬԓ}ԬiԬԬԬ'hJ`B*CJOJQJaJmH phsH *h6B*CJOJQJaJmH phsH 1hh{EB*CJOJQJ^JaJmH phsH 'h{EB*CJOJQJaJmH phsH 'h*DB*CJOJQJaJmH phsH -hh{EB*CJOJQJaJmH phsH 'hB*CJOJQJaJmH phsH &Khikë뽩~g~g~N50hh{E6B*CJOJQJaJmH phsH 1hh{EB*CJOJQJ^JaJmH phsH -hh.tqB*CJOJQJaJmH phsH -hh{EB*CJOJQJaJmH phsH 'h.tqB*CJOJQJaJmH phsH 'hB*CJOJQJaJmH phsH -hh{EB*CJOJQJaJmH phsH -hhG GB*CJOJQJaJmH phsH 'hG GB*CJOJQJaJmH phsH :=\_"&1YԬԘԘ~o~]L hfh{ECJOJQJ^JaJ#hn,h{E5CJOJQJ^JaJhJ`5CJOJQJ^JaJh{E5CJOJQJ^JaJ h h{Eh{E'hJ`B*CJOJQJaJmH phsH 'hB*CJOJQJaJmH phsH 'hB*CJOJQJaJmH phsH -hh{EB*CJOJQJaJmH phsH 'h{EB*CJOJQJaJmH phsH v=\l*Y󬖬oZ(hh{EOJQJ^JmH nH sH tH +hh{E6OJQJ^JmH nH sH tH  hh{EOJQJ^JmH sH +hh{E6OJQJ^JmH nH sH tH (hh{EOJQJ^JmH nH sH tH hh{E6OJQJ^J$hh{E6B*OJQJ^Jph!hh{EB*OJQJ^Jphhh{EOJQJ^J*+˳̳}~ RSngd+2$a$gd+2$a$gd{Egd{E R7$8$H$gd{Eʳ˳̳}~ 龨m[mE/%h{EOJQJ^J+hh{ECJOJQJ\^JaJmH sH +hh#FCJOJQJ\^JaJmH sH "h#FCJOJQJ^JaJmH sH (hh#FCJOJQJ^JaJmH sH hh{EOJQJ^J1hAh{EB*OJQJ^JmH nH phsH tH +h;h{EOJQJ\^JmH nH sH tH %h{EOJQJ\^JmH nH sH tH .h.h{E6OJQJ\^JmH nH sH tH +h.h{EOJQJ\^JmH nH sH tH  !"CRSfg.enoȶ󺮟ydIy8 h.h{EOJQJ^JmH sH 4h.h{E6B*OJQJ^JmH nH ph# sH tH (h.h{EOJQJ^JmH nH sH tH 1h.h{EB*OJQJ^JmH nH ph# sH tH h.h{EOJQJ^Jh{E6B*OJQJ]phh{EB*OJQJphhh{EOJQJ^J(hh+CJOJQJ^JaJmH sH h+h+OJQJ^Jh+OJQJ^Jhh+OJQJ^Jno~01;< hi $ R7$8$H$a$gdK[gd{E R7$8$H$gd{Egd{Eit}÷1.:<LθΪΘcN51ha;h{EB*OJQJ^JmH nH phsH tH (h.h{EOJQJ^JmH nH sH tH 7h.h{E6B*OJQJ]^JmH nH ph# sH tH 1h.h{EB*OJQJ^JmH nH ph# sH tH #h.h{E5OJQJ^JmH sH h.h{E6OJQJ^J+h.h{EOJQJ\^JmH nH sH tH h.h{EOJQJ^J$h.h{E6B*OJQJ^Jph!h.h{EB*OJQJ^JphLM *+/]ghкvhvM5$ hK[hK[OJQJ^JmH sH .hK[B* OJQJ\^JmH nH phYsH tH 4hK[hK[B* OJQJ\^JmH nH phYsH tH hK[B*OJQJ^Jph!hK[hK[B*OJQJ^JphhK[h{EOJQJ^J!hK[h{EB*OJQJ^Jph)hK[h{EB*OJQJ^JmH phsH +h{EB*OJQJ^JmH nH phsH tH 1ha;h{EB*OJQJ^JmH nH phsH tH +hK[B*OJQJ^JmH nH phsH tH  hi ⭚|cHc/1hfh{EB*OJQJ^JmH nH phsH tH 4h.h{E6B*OJQJ^JmH nH phsH tH 1h.h{EB*OJQJ^JmH nH phsH tH h.h{EOJQJ^J!h.h{EB*OJQJ^Jph$h.h{E6B*OJQJ^Jph$hK[h{E6B*OJQJ^Jph(hK[h{E0J35B*OJQJ^JphhK[h{EOJQJ^J!hK[h{EB*OJQJ^JphhK[hK[OJQJ^Jfghlͼ#ö靐wfwP;%+hph{E6OJQJ^JmH nH sH tH (hph{EOJQJ^JmH nH sH tH +hph{EOJQJ\^JmH nH sH tH  hph{EOJQJ^JmH sH 1hph{EB*OJQJ^JmH nH phsH tH hh{EOJQJ^J1hfh{EB*OJQJ^JmH nH phsH tH hK[hK[OJQJ^JhK[h{EOJQJ^J1hK[h{EB*OJQJ^JmH nH phsH tH +h{EB*OJQJ^JmH nH phsH tH fg;<̿οklnofggdK[2$a$gd{Egd{E R7$8$H$gd{E#:;<fFʿ̿ο3Y͸wjU?U?U. hBh{EOJQJ^JmH sH +hh{E6OJQJ^JmH nH sH tH (hh{EOJQJ^JmH nH sH tH hh{EOJQJ^J(hh{ECJOJQJ^JaJmH sH +hph{E6CJOJQJ^JaJmH sH +hph{ECJOJQJ\^JaJmH sH (hph{ECJOJQJ^JaJmH sH hph{EOJQJ^J hph{EOJQJ^JmH sH (hph{EOJQJ^JmH nH sH tH ()fg,M^mnoҬxbxP;(hK[hK[OJQJ^JmH nH sH tH "h{EOJQJ^JmH nH sH tH +hBh{E6OJQJ^JmH nH sH tH (hBh{EOJQJ^JmH nH sH tH  hBh{EOJQJ^JmH sH hBh{E6OJQJ^JhBh{E6OJQJ\^J+hBh{E0J4>*B*OJQJ\^JphhBh{EOJQJ\^J$jhBh{EOJQJU\^JhBh{EOJQJ^JoU|<N˸淪tfQ;+hh{E6OJQJ^JmH nH sH tH (hh{EOJQJ^JmH nH sH tH hBh{E6OJQJ^J(hBh{EOJQJ^JmH nH sH tH hBh{EOJQJ^J hBh{EOJQJ^JmH sH  hK[h{EOJQJ^JmH sH %hK[OJQJ\^JmH nH sH tH +hK[hK[OJQJ\^JmH nH sH tH hK[OJQJ^JmH sH  hK[hK[OJQJ^JmH sH NTegijkmopqrsӰӈvaVGVQRSTUVZw}UǺ{h[[[[QMCjhA0J,UhRhh~CJOJQJhh~hh~CJOJQJ%jhh~hh~CJEHOJQJU'jfM hh~hh~CJOJQJUVhh~h jDCJOJQJ!jhh~h jDCJOJQJUhh~h jDCJOJQJhh~hACJOJQJ!jhh~hACJOJQJU%jېhh~hh~CJEHOJQJU'jXM hh~hh~CJOJQJUV$%&'((($a$*gd{E*  !"#&'(hfMhg6FOJQJhumHnHujhAUUjhnUhnhshACJ hACJjhA0J,U h/%hA hn hA hhACJOJQJ^JaJhACJOJQJ^JaJhA#    PAGE \* MERGEFORMAT 1 6&P 1h:pR. A!"#$% xcDd #0  # A"b>J%8 (]bD@=b>J%8 (]p_X';bx} |EvOH$tWܙᎈ(@4$% H@WQW1"\.oB93xT0&}{oO]Uq4iys nyfq tuun #:HX! |ϯy㎗SiUWï[eCx#^ nJζU%ȶ|k>v iD#GN#hn6gycVOЪ B~=TO߭}'pß]CϱJ3ۿgm1=Orfz^_ɁߺP !,z!CغXr;(fǒٿkS | ԯ.nW1`Lw-ˋO!|'f[c67fgb6aOHS$fCn7<%1ny.1ܾ.fb6]y{c6P!#Q[ aOsj<27j+!O{6C^X:J!FVBN,<}e,<}MVB!C5CiVB#CnG[n xmk1{vm@)q r]kU+88PVA.e,Gp*f[0U\K@|Y =w>iVbn~X|R?vR?IOE+.>SOI}?=1F LOsM]{kE}?'>;[>=r/P>߯tlQ7[>WLx!{٦>2| `QO">а[x7u; GiIk~Gh[hK蘴N(J(@Ss_G\> sQAEލ&rM'WïWF 36{E]>v1,@sN) | ɍM_,R C>-tc.I^64d1@rz&_CӍ$!O+mtQ.\d鬤UMji x0= WJ^=i^wxVBZz$?:ZK?ZzZ+,#UxUωt7BhG;6Gl߱"M}P8NS9q%Whկu:GαbCW_Qp_oT]e  *WQn;cEtۆϝ.rˀ=.M 8Ż^GyS-n4~96@OV#o&_&қfYk{:7ĵ gmEb@W.v,yҼ ss !fҪPi(t$fRv>n:ru_fZѴ(0 T 6wܮ. ;j=aX/J-Wb+`ZS޳}Hr9o9q& L^.J;|7{.ﺋ5Vb,F>>M[22%¼e׺5Z[r']cFFWԹ?_s_q,ZX,.@-c|{1biq>u&rux_0[\c-^ \Svvb~a)?(CdzbbJf]?S=N+K;r}#}Mq-۳.ӜV{>A<,$Jk.# K"7w5To҅gd\ra6d)fsW T"|+jOf*ݔdr/.q/5\ieinLYe_?טQ]u+üSթ#+RxFq\|,5-N7Gkov>ө}+\YQL^8VnFE5ĵ6B#"ȟi:*?0(@ʴiMC/VIAeddͧ#Qq-}Sw$ ߞ4?<($j4;H}8ti{9ӽ]Ns׌Ȍ 9~ zֺծb.ke"d-o9J+)%y^mϢUS ͹+r%ɺ]Sk{qZ-͵By $DbS,5zՏ:/6iˊ#4>!Cik\7~RN=Mu;1}> xA o&lvMgkܿuq_%h:Ѽ_SCt 0\"pZZ ҍ@%xNquG^xRs^{q|௘L~Ujk/׻DG"encQA[J<"QcL2:,LBXb:,uX0~&X: a!X5:L6 MXmǺm7~K}n8$b&y $iRmh蘔f(J `>uø?#0?G{!L\Œ:Gr:ď_W:ao:ԃ=fgOb3MjiFۢcsX9^l*,F5. NgWO8]؋H9MCR-G)sE9;V?8'FW+ a Fz)[̜<t#5_8Z~CmÊŽΩ$7FpIt5.MGҩRcԂ.cUR*.q}sqǓRo6YGI;.A,@}cBcb!;IS7T6`beD2Y1ɊnEmۦjIrX 9okS_t`(BZdҘ/cP=&$0mV;ȷt'1ZA~ߨ)p0}{P6M$gdr2̭?EķI? ڗ~z==L#q}PYIgnz|7w_7 <jy~Ig& 4PxpOѝ~F: aMF;} g2+bMX>k,].Ar#Z;J]QʎW Mfk /r#^2f֒mb*]ÖF<l(kd%,Fc8&2<tXd.\ Njq^:~~bYڤfjŞd%|޳4bk-Yl|,'qnoa󀹀1(7_miφZڱ--|Ԭ>pIn |3VB֭u%̺ͬqp!7- XWr՟o >9a o=OXiV#+&w8p^iw:zߺjXK'jj 7kSf5/鶖Lc&U,jka\I|WN9n ˭F:`o[l&kZ-P⃰~vl`#mw]:-m}Y'kn+`-mY;b Kշv+C줭;kk~g?q[Q\Jppi [l^žc/z%hmw$A_'?b]s5jZ[PΠi=e7;6Vb ci3̳;YO57!{k沷`6{&#-mjw>tv ߁=}o Wt8aYl:̈́v{-}6so|uQnq\9>ᬇ$_$p h8Lݟg7%E`f}(dkǍlc ʶC{;z)]W_\9ƲrSQ:}q/v) GZgGrG`u 2 f y%*Oa1P?}!.4taAώ G.KvQpUAmh>wg[yls\лߏr#Q>GK+%VZ$]M5K5JeΕzeެzh ;\N:[_MX+V=A=:¯Z]W!{Ս: l+>uc[\翕 aE|walBǡ@FwBHW{>}ձV}.ۉuQhV?ij6<4#Ȑ~|YW?=]ho\Wwj+G7{:+鐌r}T+שL&c ̨Shڧ#32uU>?nhiݴs0Zut5c.{  BQU*}|9=3==.}4.'K{]gt9^ܯK=+#A`96r{;sWߑp47݊n]-hw3jG{Zɻŵr &W˻Yu?et+gIr95Vį#C>MrC}L73;(סFۈ^]Ih;Ij-x|ْzqF#rjo{)Qڦ,Kw#:w{OKwz2ěLxzoCr\\_686pFߖyJWy_ oHݼj{ \>cflIg\操ޞdYH&K7QB.eNosr7DFy=d7<՚N}M}hpg6H+<"^&q}{9Ncw, %d/w<ή3/A3~Y[M6}tߧq}7cGwrR{tCݮ=C}s`Էfpio|[.\IпGHq;]U1Y…/}U s@sPn\]F>ַ}F6Ag ҷV}{q/814M$S#qMsQD\]L'9t:Oh)?׾z߀Ni9M_#'G{jwt`_8/Уzs^GBȟE_;~}ZG0\Z+s`"XNkhf lߵ).g6V exd+WW[sI4%06 Om;0V}R_9:k tm(]8C B_ Tou8v!/h |r6*>}apU}rPpgWM'm ; _t8/8Ew݁==/KL Njgӗt:?8')|׋1uZ\/7Vw@p>^,]Kip|V}WopvN`3K-="o 8p|AWoW޵Cp 󠪯%lumSZ>"١MC6Xs^hHzޡ>:j9wd%@ZDҤЯ:Uk>K:CSГdChz+dUx~֪=/5dxdThI-}kJ>[Xp[x1+IJx75TIC}Y:;C$Car84AKhJ-}s.A_Wz.yp/z Vxn@-៉;|'H s Ȅ6k:܏ߡ«::"Exkxe\_8&p\C wў;hqx(%< 5ޞߋvݗ°=vYa7{O>hxEg!|:| ]wD~f {9ZLƒ aP&LB?07G$$#/FqD=C &g2888 8 8 8g2q6!g8/&dl )8`لF*S8<琖?&q6Aل$M83?wFq@թJ&M>~Wyu +|7'r؅sLֺoL>R/>R=cu $a]]C?`ai瞆in0 8c0D2D1h2fk՝?}`lO%4zF 0aα@M_C]>>a} 8_(WOpjꫬ/WIaؿ^}I*f?ΩS}[o } mѷ"GVm-}ko m-зV$z"b] Z @M߷y9_L"I/<@#ӗMQ)8+@M}.<$//@sXSM{-f.@9OC4p71[Ka~a_7a~a_qN=}e?&Cd+9^'OM7&CD zF 0aα@M/n6\//p[płAs;} cv"*czTnҷBA;}{2A)oA-OY __wEҷ֊Ŋ n++@# Q ξ@M}اSVm-}mm{V;0?z)6GSpe3oE }.~ 11młv/s[ }o1-ж"BO$m1-E~9 6 P 1fVjǙIدϞwds[vO76? o<>R|q:q;Ks bFGDG>5WbSm-}3:#aK(p$!Idn 1]nTPT@TT<AxzzE% '( o&SrBuW{&GuuW~ޫ V<_ao~sc !17i]Q^Gc=ܛ54}*󩥥JCN7:) EXi{Pi[Ҝ&];pdU`>ѳeok}6jei(?j=0_FﶽRZ~/g.mU+;nw>VeK'ʖiYmy ڲØ{:G=Xo[o~޲ e봅s=-){ alξPew_Mv|ރa~ICG}]\N{0OZV;}k_V)6ѻT=wg{RܻR̻&2\z:xauw:ƻV=Z] u8g͉A\{u=zI3Xg]_xݻg޶+yS}Q\̏z;Bճ~o-Vo=^snޤ>cאַU=CU1_Lm`~;͹gCհRv^Z2i}Q@[77}﫯>V_mV}>:o&t6q9MWŜ9 E0GnZJqm[O+-iGksgߐ 1c{iyhZIyUw,[*UQ[V2dM,7<9ܯUznxzŗ/WWՠߩC},Yߥ.U&U_w6tM{ t/W>@]H]_]+cIK1?Nl̫9L;qhk5Qjda1h>WAgI|:g_@gA{@% Dܞ7=}طqWZC3<ľabzxV5R#k݁I-΍ |]ؤѦ.]x}}|ݧ'kg/ ^NYotc}ׁ[/7iľ7aϝ}wKi>.˒C2 >#x"x|pjVM;$9RuM-gņCC_hsC?~;)}W3}K!T˃pDn+uj<S DkQعOcW#M t}|fuxlmԶvb.HkުHc7t?3.K>-|O0W%Ba7cmL6>\. w0@G3G^ ӴP&]48Ke'!F(tqkջ!/ϫsO4XاGjr"jHzD@=2 Fʓש#"\ [sD$1$<=;SϗÓog '_mߓz892uFd<הȭ鑻]FB_SOőG]"OEsyȽ(h2Fr=+N}ODV9WDV:WGwy.9BGTGu-u95+h>QId`#D;hؤ}BX>9ӹ)212adTyjo95wprGTǐt1;FF%񥁎OtC~(~G*^.'gwFhcjh㚨13Zo&t7#v\8'qF;;EI`̃x +!k!Cs0V4ދUK敐AvK[{^ n⿝!:ȯk7ŗ}Q/Lx**"aJ ms&ƶRO29BiC|P(uG%DyzzEQ;O=]2u wvzvz6ONVɡjó=_ayl\e˯\/h\jj>uݦmw-h"ia إg533,=|d8Q|=M[>>W߮mV>SuXi=B^+7 Poפ1&/OAu<c̈=ɾ t w߇5KԾ[J AijEMzK/UUyU*IN,v*c&&힋sML-s&7An(:#y8nA'QO.VTN(^XtZWԫI_,n7c`Ӷ u}6 l:ɝtzvq)QGv:[Wŵ8WѺ~xx(ui k% ;q%g#J&@?]r!2G}dYk/ѐaqNg{JԝPt Xrڹ&#`C52ԥvVW8N;Ѿ2rDu+DKumꑝ/S|:XvѤzp;ι˳g_T=g|u@SRZ/YKWzH'x?w;B/ϩOzg:sS>Ѥ†IJ]Ǖ:'גּe \3ʖ-{ueӮKy&7v*{ufe/% Fn7J7^.}bkXw[ht1_ޅz@C^~SZG'  ttUv뗲QeGr.}Gyw|/zB&;:Vb8WݢxLup]MwpyNg;ǗcSˏj_6Gczr{`O`ݎ#pC/ڿ1c:BlmW3odyO 9 9vu\t9No#t^b4s&:Nr x%0}_Þe.:&D1Txbh@АC# /Rxnߧ>96C0a7^nG͹}?HsDtfGވܜ&r] }*r~^}dB^j?NI64;7fFsF.>u> /l8@ǵF̝==wlttѹG2_p.j6ڰj P`şQ 6\ww\Ɲq*%^i5"L}cƿcyA'&WƔ6>B&O|th]ߜ37>6*oPsoݼ=2e:E?eʿ} ė~_pw_ȌL]1AJy#!1H`W BITndBldXd%ĵp-@cb{A~Sd&(;p`Kait(*3Ӕ ypb)QF+3ҩݻW<()tܶTOM+ɟ̛:6~wե6c*u 퇱GcЪ1D8Bch1q0 /ZF`A@s ĥ J;G/@GX=f!zDucI\Vp<_2ȴi#G36+X}N?uMpeѽ7h6q~$azB"!71–Lpz'{#rzd|d/Wӵ7|ϙ# ~r~({eAGz`?t8VlXtsm;^_2WC Y}xi?_ބ/dr%8ģk_%rn#MVܒE!lqGs idC8#;ZF8D-p ȷz+b6i{øn28#5Hv,6o29<NJW]X-F4.0j[Gx۟-xDݜPqܳQGligtgcB_<"m](Fz(sWG2\as0ks*SE| {|b;!h6qiV|'h(8 oOO o0Nι}>At03%~|I%S϶3_uɚ{sѸ̦-'(Fz|gO,o5X >q!$>Q>qlam0gb)ޑ ;X"}ofˏʞ54֋c@/@{H轞W3z%?ʺ2e/bmU>HPPUfvJ9TB9c}5K{xP|O ~FzwOqԹqϟ̷wFʍahQd =|?7/Y#(, e$߉r*\0¼_yؘGcw}m q)qng܋jro|!C"#\"mL= spSd_s%zd>&M(E:y;+>>5bm,J'/OG$Z`{"sHDBT[V~ixh688q PYS2`yf88 Z;aL+lgmp9a}mv98ie܌Ȍw:#SoFSWGI<."g~98B68}>)7Bi*gc7<3c owk<ʴ7Bf8YnYc\498ӥ/VF8e,g:y;g+8`qQO88볈.#Bi*gcG*cmdpppf%ݭx_b(Ɗ8Yn\29$>"<^Ur9sQVZN}͙tmdbet=2Yx&NPL ~82S"q$"þlE`s\QNA(qrpVVKx[;aL7ne98!EX!C"#UeZN}fLXF&VF8e/M*N32}H'o'i`q$ZqpGgn mh[԰w25r8(pN&Epppf+%ݭx_pX  O72v%>Vw2B&DG?<^UsntmdbeSf88?|ۤ4)7Qtvy8[a߄8xj]E[W Ҳr8(q쾓r*\0<3c owk<"i#Fq6Ӎ6@a9|f3E`qԺ3?88۟-l98]wŒqp } f8xUdh.al屄c ˴8Yn\29$>"<^UC3"͘.=|Lp g_|Tf:e&CNN>3g+8aSO88󣈃lB1NA|;,#3E|8xj]E{tA3)7φ}'U`yf88֎y/D8,FlgmNHhspV䐈txU&߹e98)9܌pp|EIiSo<0Sq/"~8xj]Eu! ~/ w2Y.Z Fg3[y,nB2md(f8xq6aLH'WirmOl3"͘.=|Lp g_|Tf:e&CNN>3g+8SO88󣈃U;a88qsY밾Ac}N>CT~SVuqpjI|6M>qM]w--r8sXFF8e>&(&2}lUS"b#l8 =)BA܅2]^1\K^ Y*8N\~}ӼsQ2mdppp6nl̛ h[ G"C6V gine98KsQB&DGĹ<Թc紷3".>XF&VF#37Q>mR98)7Qtvy8[K@twEI<.̏"go4N&Epppf+%ݭx_pX  O72v"!2M>zwG98)9܌pp|EIiSo<0Sq&8xj]E_麓INA4;,#3/cFQ2 o*ĢNET7|AA{(rFq Ǡ4"2M~q~LȾ~\AE +mdbeSf883?|ۤ4)7Qtvy8[U$`zȧRaug~-'9dnK. =Zxqw1Sab]k[ۄ ` Dh(@膆b= /4qxr2P *eIܜ\зr TC(wxm$ ?r*1H3cowkH"io!zx_CXF&p9]g|Do#)78F6Dʃҗ2i B$DEY(8BHjqvQA"Ox aKse(g?1>K==7?'?I9ADSi'쎷#_Br]=atlHM%ק~ːtBf>6?BNPVd'~2_ &ot8}8p$NQ.Z F.֎y/Di#Fqn8RQ6k*?]b ŸU u(tq|ƔlQtI^M=[ArP(kc߈u1\1t#&~SUmNT$R%qۢX')HtPI +w$z3 ; GϘ<f?k"A Qi~@AD-ע–'SwcҳS!P婀0@Ү߶=!GiAYyC%@H:<5(YԄbO"e|S!ߐpb:Kw ~D8xPl)7dx 1Z7g̍ ʛ4TܽOOZDX*cx{d˴u6!ӯJ5n܍߫:g-}&tف9ϑLF&VF3=(685bAya/Q?e IdepJ/OpT4'[*DDȮto=T=>SL$N9iHdnZ<!N7>}]6̸K)Gtq'i;י\±3P^2z&2,Qg㊣a8~2BJ8O~ӊlC:_msXWC<kf0<a ֎w/D1icut,cwoybf&⫩/8oUrAW=U3fhr?˸{$r@8Ml3'UZXÎvqqI kvvLS=e޿^!laxˎҿ\T@zzTy }ON{5ʓR z| u̓(3qO Cصg{Ug]c)cJC1D)jcD:y gy.aֿ|!7\c?)?yŢFLLj#3{g|D=ǹ0aga/Q7 tZpT$~l;l!*DtMí|\}<LGu{Tq'TXsBQAo(Aȹa>O )\SPA#Ӟ;Bkf0<+`HA{db(Ɗ렙gYn-67x{uѽGy3 jp濖7,1L^h3%#֩8g& r` v{Ej]mKI$m6Ц=-6@ &-IH0`Ą!= D/^KXrln{2qmnisrBsKqF[Y12 tLte^ҭjOcĵhm•Zf8݊[[yVkJm:nUv]R},fޗV>!ԭvy [mq^6*'ػJ@J(ޓ1A i+@J V |{{oTK9j(XIGqsWl\hWW\sۂ2IS\2~N{q=u[vw.7~GYۗ R{C\_̆l&'{ djdO? L0}1ۇW$;"^zj Lw[:M\o=_|GgH,~O(dz 3@soHwkGxuYٌ/~Ώ$fkntwMIZף_X}sejBf3Mf> -O }@rt~Qbt~au^}vn8f8[z/yJ7+"Ұ=G[nЉLkW_ԉ·otNKkCg,s~ٚ+:࠲^8M6 aο g1\h }/fMa/Cb?zIzchl,7㣤m=~{Q iF$Xa\]~q)M4sER?ףo̷.V琸.nsgQe}AG_OJoǯ"~ z-o8Kվ@! PRVDd 0  # A"߆桽-_qdc@=߆桽-_qd3SBy,x] př6pɖ$aDTD#cl38l36K*G* R ԕ*pRE0g*!%D fݝݕŴ3=3_ׯ{{!!&@H2LEV."$>b~X, (8Vi*1tZTl !Q/x:, ,{]%fvd<|F{yvtUmPт3}#H˾?ϳEFcuw{yPIqP<n 𯼏L8wJd_lSWpLhJ[Ri֍܋ mlt}Ec[h/hrV=K?:slEHD $ЕV"n H/d_AB?FPt[d0IZS B(0PhO~.&Lzh[kcv(ks\"Cb%oPaLBN^:`:Phv(eqQm4#~ ivjWm{ʹBpbyy 6w)y Q/ڢVHZmQŬ "4zg;H"M=q"iH܍#_ Vr.Z֑>@֝Ǒ4p#@2cf+B79bNc#ZCe ܺ']{VB5&:#C¸%nNUˬjz gz=g~sEg:?'Y3?ٱS y~v)y$'fV@rtNHuPgd !ر,̋pve]"})b+__~-˜x,N 9GɔLSgp@Ӊ_R!F >Zh99 ʄ:z|:1kTb,ъG5&6g#0qg O@`hf6`8#1i6 ef`*!VA.Qꀝx 3,e㷏X@#ܝڙ uV{ji2&zB)$}zB zB)$=?8" d1,<#Г@O8/b0Kdxld6ӄ*2_T[0I [8IgXx`.%R [CÒ[M8 r}0}/G>t_#L}_&ȧ䝘3:WVK&:z? :Xi<"CX$FCְLwf;sZ~k"2V!k31d}p ɗ桍Xo@`9"PV"2q$&Mʨ,|e\\C{s=u$nbm w6,%-s2?e])sLm0чQa- #^h1Ո`>]=z^X7bWԈ?}='j?zyH 1).sώڭ55@;-y`Gqh;/:EH<Q&}bZH<1= [(+<M:㳿3=*>!D5⍁C!qv0O:VC\-8nwxzˑx#/|h!\>GE3\SyyWk&>`!"ɗf8,Vjio̸&ՌOΡZeW ~qM>x!>rW'@UK@sF.K,@cV)|mt2zu6vcΔ2u[2沕n^[T.ٮJs ?rqW[!}/.yr_pV\_ nQϘ ^ kW0A9nQ75LtTe+[srJe9UinAg<_.n[))v1bA-%x([,`+v Ěi1c.7)cbڕ-NMsrJѵ_cKYb7%rq*-[p6\_ nQM[/_ѵd |ȮT-|%>[T.ٮJr )-|bߢ-&Ezr>XlPks{Fİ3-}Wr|ms2xugv]1/Vl{W&s]Ε&חc'd({ ?VtN)Z?KlB'rN@ osQpW&¯}Pf[L}?HNnQ2:sے[|Ś(-FK^,.%X-Zq}%~ )&¯p ('B-&wCNnQ2:sے[|E[$xys/[,q}%E!7l~E׮[@9n15EpNnQ2:sے[|EwIn!(廧_Knm/n1V-ě@ofwBo2z)]r"bqfE6q]{ԽҫOhe5^2&ّh3wCOV;T56i*?Oet}bҶ~/jCB-b~ӛ N:'u7Py7Pq:^>oBNF=#fe /'}8Z nl7zqp(ދzCiq炾'&N z?$it?B?T~6Z}n%\- $?S\"|?{w"__xwW..eʘU.۬OmN2^)i6SRs} @L9b> 9#8܄3<9h2W]cg(aO|mfC7oy*ruRh0b/_'W':z-'6 O܂cS_#o'.8S.}Bd'e=T_UY|I?^h?;p(G?鸑*Q G_E~{׿I]rrtyl2&}k<*SۯO{M{TTG G^1bz:.؄Lȷ>!GK{JyqtG͛rtG_ G?9'$G'q58T, b˗K='woYWՏ;U:cR,{bmCbwlRá o^{[S e~Ჭ b[+񻌉SoU~j =Mfʿ=zߧO_.ȿymoylO;;aNiſf͛o>"3߫;Qv:sQJ-P^&˘\-ޟwBׇ&ebwlRé {4H^eL=xT Q姶HӤm[Sg9\oo^ǿeu[/~0񞅝0pi!΋l?:ۛ7^M%l6B@yo'ѭ_saGzO6]xpJ)yO՟x9,c+rbx_z* ]񣟋I^㋗_qť]x5yO՟&^O{."Ϳ]Dƣbxl*?G&m/OBkFx7Qd8yb:>;~>y9顴}{vnw=t߹ĩw=o:^6dL=ms䉛pb3e܋.y`Geb6ֈ߼b\g߬w̻=@zc(ww9ٶFn =B xy :{cN_n_ 8^w?Ƿ>- 9<x`9r}yـyc^=C~0"Ci~^M/q ;ZltY,a= 7Q<@#𻩎L/TI񣟋&nU>tw11.^ k  ?ou`B[Mdyk0j:U ̰E:B_> >M4#M1PS2:1i.tTE.jOFu q O @ȅzdFDZ@fe ;}Gq%%3Wx>w1).D5O]yiΆ}Dz){ʾy=|h~}ézF}8 W&waX"qĝ93fz\sjGLB`S5bO_jOaǦ;\'s -˰.oآ:kek\)#N@?i>qOf|{˲g˫V6wr=%r>VMQ6wh;Ѧ8C)pe"@6ꐶyB;)rKF:tΆpmÆv๎ eYiPZ 4(Q^jqqq 9qɄŬJR9:~޹* 9wŬ=w&n'ON>jzI+KΚY <[8&#Z|Xy,Y/G\GcskuV}.e1:gb [W- Xg_3n'~*doq%y7}_g=U/.ˤ SmvOU:WL<-Ș>yu֋Q泭RMӍgڏuVʚ?Y\g%*d5?p ɑ0'6޻ԇ{&YS_:kʱ)Y{5+sD:kD=æWZgMqkS\#©I: )~vlu?wmc#y8uņ8p6uV 5:kf\:1S!R-b\uVڿ:jk&mQuTP9`(S_n7Ůw&n6~昪~؁Z~ m|cu͡s@4,Yxg GՓGu<8F.E\Xr؞8V 0X/h_Dd '0  # A"u_nlWyqxQ_@}@=I_nlWyqxTfb$3_xܝp}ߗ$A # whomV9g\A )SFlŤj QM5KO09a2iq*c ƭ&hG#uԠ%Ÿu Dz}u)Υ~-VKR8;C[88SfBs@L}}N?,؟]&}h :6;^TΔbyC~{?Շ ೳ厠܋lN8ՇU /lL=k8kЮ1o.;N@+O!dú7a1}8zβ{~}l}) 3N`UM:o9*Lۨ_}'ƈYwrj7z|Mv#kmVC{ޙ7sm!J)XމW?p;󷊇,;,0) #ɑ]b;?7(.Wޱv}8ԝ`NԮ7+?^_>|ak/F` ~_G?va1~r 2 ]ɏeN\ ~1oe{}_#Z rӟ5E0@Ez|^oKY:vm6њfj3,vyZVn>^KpmU|qK+SU/ipR}˴/'vp(Շ̟V3C6ӟ5rҿUl8@6^%&6>Xi[Rbsd_㼢>*(fYqmkcd{HS BVFBX~`':G;g:x 7LmLDgɝ",==rc]=Z]ӓw;<ܩ"sscb} 40[7N hʱPl o姳@ڟ5"Pb'a#Pdlͥ5,vVU!g"{򑔟?@uٯ|5?GbO`|#1ܨ"m?/6," Qz3wWW*V'WYkHp> k|_S~TVwO ]]^Wu,#Vr/0~ٕb bxrk}.[OǯO!꺙_sXs]Υfnl0[mWÎǠ<7LjOS,ȯ;jDUƯ?C1`صep7U(`䁊LHʯ]/?Wgxyu+Hom 7H{?9  _mX.ҭv+,GdKU}@6V;ʯ?*|+a~tu]3 )ίO+./598~RT[OǯWKn{̯Sǝ_'=\ƻ :WǛOc_[lC¯䫦Zq?ʻ_)?U~ɧh:vk ~>*"y"= έ=|m5=>|X$M_?-Fik98F}DjنeIJlmWW*V'WY(T> Q~]B A<`%̯K ϧe`|7<^@1A._gA5_%ΛP( Vgǚ|?p3s{%U~o-`o±fmLVMgEmy \kίo= ]kJٳ&@Hϔq;ڮZk>2㳝WFz lcQEڊT/6,_wSV_;U󕀊IUr#J> )?*|+a~:\40+28`o\ 8~-%l=k>~84nίӇ<\=9iƯayfp]릝$/"$UNƯo_Ac~cͫY쨭WOk ~>*gLL~,2~*Hom 7H[{[Nlr u8n _5_ X_%g9Sl,{?S V80[Yw4{K=!Pf[5_٣7HX5un=w&]jzG{zMY#0[7XLƯ_/l_ łŎB%ʯWWеz ~>*"y"=! ~qYk._jM%sO>,3~ծK j't? ߵ_5_ X_%g9ST_s~T+a~egJnJeאeʁ\ 8~e]du`)^=9dsm@>㱦!ƞ3Ќ_ײ|HT<`%̯k/ea41޹769ڱ:wx{ 5__5 !_í{"pTٴ7\)ȁ^ԧ\yH-9Zf*c( \X|>е:Jve9 TWڮ |5COɫe+5Gw—f&q}m jP: Jro(XNEϯ>Wȍ GE`|\~udGvv,֏ 9Rr7x ̯%V5=|=oI٦Z׷Pol-c]8|S'ק׉,=M^{xbGm(rmG7Zg3nQ=`䁊LH@W2>3_2fϵ>"7jUIG9)T/6,_wSVTN¯QDU}@6V;ʯ|8ϯ}CJk|=z=:ʁ(rqtuS+{tg:|Zǚ[:Zt(:Mg1%-jiؚ iV-n_0Q"~- ~}~cłgJ_o9#\9ޤkͅK JLL3_2f:F ?_fW|ݫ]×fϯMT~|%bu~rO%ʯb#ʯk~TVvz:0?1̱Zzu- ǯ gy`)^%x n^D8|íЌ_80V:wP{Jyp?`<^VrsL%7ʁ\k%7"[Oǯѝg̯ ǝ_XsGsEPΌFy4pO)ΐp_HEm!UnIB.w"~]#}#d0 U`sioT7 _QoR۵mQ=`䁊LH¯0Vg֬_5~ִ6j0l`Ic*Hom 7H[{eGw—f&q* @kjP: JrDW=pgZJzw'{D%t 䖐ץs'J"qz>5ΛP( ܺxsNdf:Oeɦ!U--j|5kjK"_? }|9Uϰ7=Mj֙ W") *OG~eekoӚf_f|M~,2h77[U~ծK6X-qk8n _5_ X_%g9B7J_,:A1^cuәsq`|/6b\&ui0ȥԩAl l=k,'ZA>_Ozu'ɎS 輐6<)`.qI-j5k߉%l_=YmU~+\@Imך ~>*"y"=S>a~ڮ|Uk/?Wg8_%?? ͯn_{vlM)V@{ܕ_ĩt+J@$*9J_d;:A<`%̯s^<}j%3g@LokcT`5B._K$q4|<7ϝ_yus+E_C©զDOBEm!UW[1{WMϯ:=ͯƚY쨭W` jxh3ty"y"=S>Y^Uk._s~՚K5j4bzͯpT/6,_wSV_;U󕀊IUr#q(Ʋ@nnAT<`%̯s2COKrp˯+yi _qz>uA m-4Ky6{^>rl0ߐ?_'|/SoQpYvUs&-_*cŅJ_?fm{7U몈c`䁊$t~EFm\5͗k>"jͯn_{vlM)V@{{JTN¯Q\DU}r*ʯ?*0Vnd 3.{&_grgn~˯33%7 l=k^<_{f~B>:9౦C;N\dW171Zvǚo_D]#W,ړжQ[%ʯ7~?uؤkGE[ TgG~ee k9jM%j0,Yuyn~opݫ]×fmJZ$N[\_A{J_5~dtVrOSLdsf<_;_rxgy~_Eu:Sy͞9tιKg|C [I->8;CUWŝ"@&;q`Y!ܱgJ_?>k֙G UE0@Ez|$Ìïל_~5{}n_ȴ_1pU/6,ϯMTa+_%g9S_,(*0 no!f2rJ!ȭ ǯ+(qm@` %r0P <r%7hcڷï3_?0}~ ;C(r ~ok\W") *S~Y]ǫk9jM%sj0L2|Yn~ _{vlIJq~|%bu~b\%ʯSQ~ 1zϢ:w9rwQQ7l=hܕn죠)tς)n+~<[bMqx s'J}zl-cڷï,Q&:_s^(wgB3h_|͘7/^TNPI@h[|n?Xv,p==w|Op|xL K7,Яۨ_vڲ{4j3j k\& `#qS<ީv5YŇZ }[9ZLLUh{ۮ K@$<(9A[nsJGbG3qh9rgέtsfsfh_Uw|`]ޣmŃG{hYmΙ?M7A<9~'_ ;aRiXc>x/m؜-WSLd9;I [j'Uw~jgL|yu/ ~ےA X$g%g9+DsV}|;36u]wy ~1E=drq9=l?s. YcM9 \x$oq85[447|ޏ+gtoߗE9*9ClSh؜xhΪtKrv d l{9S]tRj/q6׽~o[rV6(,gOhΪHhD17|%䞔_L-eRr B..gSSwK:r>rI;~gvy,ZUVy߯;)׉-'+Yrg7?Su8nKjD9[H{;kݭT4gտ[2_d zj2NNɃjOmζн~l`juY[i|4gPb2&RW2/*ػA1rd^rq9;;dwȮϸ}_#׈1֐uuwV {<8Sqf aC #xWPewkmY/YΞ#g|eأ{t+9*ќ -2K@P{VE؀ګi 4؅ҿǮgjs w 4}^Z6(,gOhΪhz!lгW,"#㗐]O,XB..gxsct1^"rq9{a9f9[mu-NE{^YC5NLN_(N& 63ğ5g}9Gl,֖Dc*cA[k62xawme{ls %geUlؾJ4goqbyl/g]<˽,];[z@.\\x1ޫ5OYu9˻.琏^]Yǖ"R9[Gzf83/s+3@qj-i%gd9rogY~c+- 9&j/}Yr$9+('FsV~8-gkb}lz^,=^HO2q9;Ks63.g5FAN Uaϓ%t^J׉3/:_k@qjދ՜rgs5/|^grf#]}i 9\RLl,[ZZ$g%g9+DsVGl{yTa Rzz(S;,gdW rq9{^zKȖ˱SrX)B~=/g\OҶ9&뜕ϒzfrv癥[YXvHu󮓙^gmK%buY9sV9;H?r>|7iǻgKx'gul>{x.y[@^\*gy[>_9ϦT٦g:2l-X\lXs&e~MbVrb*7Ru}Cm4r#[v joo2~Br-9+IrVrJ4goq?<`%:,g3h%aϝ9|m rÏOǣz-ukGAly*g7-5ŞV_8kvMcW1 -j5A\lKrٛҞn%g5^%a݈;|-UjB9;~d#3λVP{Zn{߶lP*V'YYʟ*ќUn9;z$;#3cGj{;Uj11~ 5dwi~ sZ&7\)FnȎcMV9;~cM]A~=s=SmEp+{w-rV;Y/TeٹYv} nþ-}kc!Tmgu/9 [^zdP:IJrVT[>ɅgE`9cqh= h{4>