ࡱ>  bjbj Sz:  Y"{8KtCC"eee   IIIIIII$LMOpI"" ""IeeKr-r-r-" eeIr-"Ir-r-/70eL9 <*0IK0K 0,^PV,^P7070,^Pc0< r-.    II-^   K""""^P          : The determinants of non-performing loans: an econometric case study of Guyana Tarron Khemraj Assistant Professor of Economics New College of Florida Sukrishnalall Pasha Lecturer University of Guyana ABSTRACT The study attempts to ascertain the determinants of non-performing loans in the Guyanese banking sector using a panel dataset and a fixed effect model similar to Jimenez and Saurina (2005). Consistent with international evidence we find that the real effective exchange rate has a significant positive impact on non-performing loans. This indicates that whenever there is an appreciation in the local currency the non-performing loan portfolios of commercial banks are likely to be higher. Our empirical results show that GDP growth is inversely related to non-performing loans, suggesting that an improvement in the real economy translates into lower non-performing loans. We also find that banks which charge relatively higher interest rates and lend excessively are likely to incur higher levels of non-performing loans. However, contrary to previous studies, our evidence does not support the view that large banks are more effective in screening loan customers when compared to their smaller counterparts. Key Words: Non-performing loans, Panel regression model, Guyana. I. INTRODUCTION It is widely accepted that the quantity or percentage of non-performing loans (NPLs) is often associated with bank failures and financial crises in both developing and developed countries. In fact, there is abundant evidence that the financial/banking crises in East Asia and Sub-Saharan African countries were preceded by high non-performing loans. The current global financial crisis, which originated in the US, was also attributed to the rapid default of sub-prime loans/mortgages. In view of this reality it is therefore understandable why much emphasis is placed on non-performing loans when examining financial vulnerabilities. The aim of this study is to analyse the sensitivity of non-performing loans to macroeconomic and bank specific factors in Guyana. In particular, it employs regression analysis and a panel dataset covering 10 years (1994 to 2004) to examine the relationship between non-performing loans and several key macroeconomic and bank specific variables. To the authors knowledge this is the first study to examine the determinants of non-performing loans in Guyana. As such, it will contribute to the existing literature by providing evidence on the causes of impaired loans in a small developing country. The study employs firm-level data which are rarely used by analysts who study non-performing loans. Therefore, the paper extends the literature on non-performing loans and utilizes both macroeconomic and bank specific variables. Apart from contributing to the literature, the paper may also have important practical implications for commercial bankers and bank regulators/supervisors in the Guyanese banking system. For instance, the findings may be used to develop a framework for measuring and assessing credit risk an important element of study for the financial stability unit of a central bank. The article finds that both bank specific and macroeconomic factors impacts on the loan portfolios of commercial banks in Guyana. In particular, we find a significant positive relationship between non-performing loans and the real effective exchange rate. This means that deterioration in international competitiveness of the local economy (as reflected by an appreciation in the real effective exchange rate) may result in higher levels of non-performing loans. Our evidence shows that changes in real income as reflected by growth in real GDP exert a significant negative effect on NPLs. We also find that commercial banks that are aggressive and charge relatively higher interest rates incur greater NPLs. However, contrary to expectations, the evidence reveals that large banks are not necessarily more effective in screening loan customers when compared to their smaller counterparts as there is no significant relationship between the size of a banking institution and the level of NPLs it reports. The remainder of the study is organised as follows. Section II provides a comprehensive review of the empirical literature on non-performing loans. Section III describes our panel dataset and econometric methodology. In this section special attention is given to the motivation for selecting each variable included in our econometric model. Section IV presents the empirical analysis, and Section V concludes with several policy implications. II. LITERATURE REVIEW Over the last few years the literature that examines non-performing loans has expanded in line with the interest afforded to understanding the factors responsible for financial vulnerability. This situation may be attributed to the fact that impaired assets plays a critical role in financial vulnerability as evidenced by the strong association between NPLs and banking/financial crises in Argentina, East Asia and Sub-Saharan African Countries during the 1990s. In this section we review the existing literature so as to formulate a theoretical framework to investigate the determinants of non-performing loans in Guyana. Keeton and Morris (1987) present one of the earliest studies to examine the causes of loan losses. In the latter paper the authors examined the losses by 2,470 insured commercial banks in the United States (US) over the 1979-85. Using NPLs net of charge-offs as the primary measure of loan losses Keeton and Morris (1987) shows that local economic conditions along with the poor performance of certain sectors explain the variation in loan losses recorded by the banks. The study also reports that commercial banks with greater risk appetite tend to record higher losses. Several studies which followed the publication of Keeton and Morris (1987) have since proposed similar and other explanations for problem loans in the US. Sinkey and Greenwalt (1991), for instance, investigate the loan loss-experience of large commercial banks in the US; they argue that both internal and external factors explain the loan-loss rate (defined as net loan charge offs plus NPLs divided by total loans plus net charge-offs) of these banks. These authors find a significant positive relationship between the loan-loss rate and internal factors such as high interest rates, excessive lending, and volatile funds. Similar to the previous study, Sinkey and Greenwalt (1991) report that depressed regional economic conditions also explain the loss-rate of the commercial banks. The study employs a simple log-linear regression model and data of large commercial banks in the United States from 1984 to 1987. Keeton (1999) uses data from 1982 to 1996 and a vector autoregression model to analyse the impact of credit growth and loan delinquencies in the US. It reports evidence of a strong relationship between credit growth and impaired assets. Specifically, Keeton (1999) shows that rapid credit growth, which was associated with lower credit standards, contributed to higher loan losses in certain states in the US. In this study loan delinquency was defined as loans which are overdue for more than 90 days or does not accrue interest. Studies that examined other financial systems also provide similar results to those in the US. For instance, Bercoff et al (2002) examine the fragility of the Argentinean Banking system over the 1993-1996 period; they argue that NPLs are affected by both bank specific factors and macroeconomic factors. To separate the impact of bank specific and macroeconomic factors, the authors employ survival analysis. Using a dynamic model and a panel dataset covering the period 1985-1997 to investigate the determinants of problem loans of Spanish commercial and saving banks, Salas and Saurina (2002) reveal that real growth in GDP, rapid credit expansion, bank size, capital ratio and market power explain variation in NPLs. Furthermore, Jimenez and Saurina (2005) examine the Spanish banking sector from 1984 to 2003; they provide evidence that NPLs are determined by GDP growth, high real interest rates and lenient credit terms. This study attributes the latter to disaster myopia, herd behaviour and agency problems that may entice bank managers to lend excessively during boom periods. Meanwhile, Rajan and Dhal (2003) utilise panel regression analysis to report that favourable macroeconomic conditions (measured by GDP growth) and financial factors such as maturity, cost and terms of credit, banks size, and credit orientation impact significantly on the NPLs of commercial banks in India. Using a pseudo panel-based model for several Sub-Saharan African countries, Fofack (2005) finds evidence that economic growth, real exchange rate appreciation, the real interest rate, net interest margins, and inter-bank loans are significant determinants of NPLs in these countries. The author attributes the strong association between the macroeconomic factors and non-performing loans to the undiversified nature of some African economies. More recently Hu et al (2006) analyse the relationship between NPLs and ownership structure of commercial banks in Taiwan with a panel dataset covering the period 1996-1999. The study shows that banks with higher government ownership recorded lower non-performing loans. Hu et al (2006) also show that bank size is negatively related to NPLs while diversification may not be a determinant. III. ECONOMETRIC MODEL AND ESTIMATION PROCEDURE Based on our review of the literature it is clear that there is extensive international evidence which suggests that NPLs may be explained by both macroeconomic and bank specific factors. In this study we employ a reduced form econometric model that is similar to Jimenez and Saurina (2005) to ascertain the determinants of NPLs in the Guyanese banking sector. The model is a simple linear regression function that links the ratio of NPLs to total loans and key macroeconomic and bank specific variables. The general regression equation is of the form: lnNPL_Ai,t = 0i + 1lnNPL_Ai,t-1 + 2lnL_Ai,t + 3SIZEi,t + 4"LOANSi,t +5"LOANSi,,t-1 + 6"LOANSi,,t-2 + 7lnRIRt + 8lnRIRt-1 + 9lnINFt + 10lnINFt-1 + 11"GDPt + 12"GDPt-1+ 13lnREERt + 14lnREERt-1 +  + i,t i = 1,& N, t= 1,& T where: lnNPLi,t and lnNPLi,t represent the natural log of the ratio of NPLs to total loans for bank i in year t and t-1; "GDPt and "GDPt-1 represent the annual growth in real GDP at time t and t-1 respectively; lnRIRt and lnRIRt-1 denote the natural log of the real interest rates (measured as the difference between the weighted average lending rate and the annual inflation rate) at time t and t-1; lnREERt and lnREERt-1 indicates the natural log of the real effective exchange rate at time t and t-1; lnINFt and lnINFt-1 indicate the natural log of the annual inflation rate at time t and t-1; SIZEi,t is the ratio of the relative market share of each banks assets that capture the size of the institution at time t; lnL Ai,t is the natural log of the loans to total asset ratio for bank i in year t; "LOANSi,t, "LOANSi,,t-1 and "LOANSi,,t-2 represent the growth in loans for bank i in year t, t-1,and t-2 respectively; and i,t is the white noise error term. In the model, the coefficient 0i captures the idiosyncratic behaviour of commercial banks. The fixed effect coefficient allows for detecting those factors affecting NPLs that do not change over time. The model is estimated using pooled least squares with a fixed effect estimator. Researchers who utilise this estimation technique argue that it is more efficient than the ordinary pooled least squares since it accounts for heterogeneity that is often present in panel datasets. In order to minimise the effect of heteroskedasticity, the White robust standard errors are computed. We also estimate our model with the dependent variable on the right-hand side with a lag of one year. This is done to overcome the persistence exhibited by the ratio of NPLs to total loans over our sample period (Figure 1). Additionally, we follow the general to specific approach to arrive at the parsimonious model. The rationale for considering each variable is provided in the ensuing section of the paper. Motivation and description of variables Macroeconomic variables The existing literature provides evidence that suggests a strong association between NPLs and several macroeconomic factors. Several macroeconomic factors which the literature proposes as important determinants are: annual growth in GDP, credit growth, real interest rates, the annual inflation rate, real effective exchange rate (REER), annual unemployment rate, broad money supply (M2) and GDP per capital etc. This study only considers the growth in real GDP (GDP), annual inflation (INF) and the real effective exchange rate (REER). There is significant empirical evidence of a negative relationship between the growth in real GDP and NPLs (Salas and Suarina, 2002; Rajan & Dhal, 2003; Fofack, 2005; and Jimenez and Saurina, 2005). The explanation provided by the literature for this relationship is that strong positive growth in real GDP usually translates into more income which improves the debt servicing capacity of borrower which in turn contributes to lower non-performing loans. Conversely, when there is a slowdown in the economy (low or negative GDP growth) the level of NPLs should increase. The literature also provides evidence of a positive relationship between the inflation rate and non-performing loans. Fofack (2005), for instance, shows that inflationary pressures contribute to the high level of impaired loans in a number of Sub-Saharan African countries with flexible exchange rate regimes. According to this author, inflation is responsible for the rapid erosion of commercial banks equity and consequently higher credit risk in the banking sectors of these African countries. There is also evidence in the literature of a positive association between NPLs and real effective exchange rate. Fofack (2005) reveals that changes in the real effective exchange rate have a positive impact on NPLs of commercial banks that operate in some Sub-Saharan African countries with fixed exchange rate regimes. The author argues that this result is due to the large concentration of loans to the export-oriented agriculture sector, which was adversely affected by the appreciation in the currency of these countries during the 80s and early 90s. The macroeconomic variables are included in our econometric model both contemporaneously and with one year lag since adverse shocks from the economy may not impact immediately on the loan portfolios of banks. Except for GDP, the natural logarithms of the macro variables are used to estimate our model. We were unable to take the log of GDP since there were negative growth rates during our sample period. Additionally, while we allow the macroeconomic variables to vary over time they are the same across institutions. Bank Specific Variables Apart from macroeconomic variables, there is abundant empirical evidence that suggests that several bank specific factors (such as, size of the institution, profit margins, efficiency, the terms of credit (size, maturity and interest rate), risk profile of banks (measured by several proxies including total capital to asset ratio and loans to asset ratio) are important determinants of NPLs. This study only considers four bank specific variables owing to data availability. These are: real interest rate (RIR), bank size (SIZE), annual growth in loans ("LOAN) and the ratio of loans to total asset (L_A). The impact of real interest rates on NPLs is extensively documented in the literature. In fact, several studies report that high real interest rate is positively related to this variable (see for example, Jimenez and Saurina, 2005 and Fofack, 2005). We construct this variable by subtracting the annual inflation rate from the weighted average lending rate of each bank. The variable is included contemporaneously (RIRi,t) and with a lag of one year (RIRi,t-1). Excessive lending by commercial banks is often identified as an important determinant of NPLs (Salas and Saurina, 2002; and Jimenez and Saurina, 2005; Keeton and Morris, 1987; and Sinkey and Greenwalt, 1991; and Keeton, 1999). The variable to capture credit growth is constructed by finding the annual percentage change in the loan portfolio for each bank (LOANS). This variable is introduced into our model contemporaneously and with up to two lags. Like the growth in real GDP, we were unable to take the natural logarithm of (LOANS) since there were periods when some commercial banks provided lower credit to the private sector. We expect this variable to have a significant positive relationship with NPLs since the literature shows that rapid credit growth is often associated with higher NPLs. The empirical evidence relating to the impact of bank size on NPLs appears to be mixed. For instance, some studies report a negative association between NPLs and bank size (see Rajan and Dhal, 2003; Salas and Saurina, 2002; Hu et al, 2006). According to these studies, the inverse relationship means that large banks have better risk management strategies that usually translate into more superior loan portfolios vis--vis their smaller counterparts. There are also studies which provide evidence of a positive association between NPLs and bank size (see Rajan and Dhal, 2003). In this study the SIZE variable is constructed by computing the relative market share of the asset of each commercial bank. There is also evidence in the literature that shows a strong positive relationship between NPLs and the ratio of loans to asset (L_A), which captures the risk appetite of banks (see Sinkey and Greenwalt, 1991). The supporting rationale is that banks that value profitability more than the cost of higher risk (represented by a high loan to asset ratio) are likely to incur higher levels of NPLs during periods of economic downturn. In this paper, SIZE and L_A variables are included contemporaneously. In addition, our bank specific variables vary with time and across institutions. The methodology for calculating each variable and the a priori coefficient signs are given by Table 1. Table 1: Summary of variables used in regression model Data issues In this study we use a panel dataset that consists of firm-level data for six commercial banks that operated during the 1994 to 2004 period. The dataset also includes macroeconomic variables such as the annual inflation rate, real effective exchange rate (REER), and annual growth in real GDP over the period of analysis. The firm-level data were obtained from the Annual Reports of Commercial Banks while the macroeconomic variables were obtained form the Bank of Guyana Annual Reports and the International Financial Statistics (IFS). The time period covered by our panel is selected for two primary reasons. Firstly, data for NPLs before 1994 were not available. Secondly, several local commercial banks (in collaboration with the government) embarked on an exercise to restructure their NPLs to the rice producing sector during 2005. This initiative saw a sharp contraction in the impaired assets of the banks and can therefore distort the econometric analysis. Stylized facts The adoption of the Economic Recovery Programme (ERP) in 1989 saw the liberalisation of the Guyanese banking sector and the expansion of the real economy which in turn encouraged many commercial banks to extend credit rapidly to the private sector. The average real GDP growth rate during the 1991-97 was 7.1 percent. The ratio of credit to GDP rose from 19.8 percent in 1991 to approximately 50 percent in 1997. However, these trends were reversed after 1997 due to political instability, a slow down in the real economy, and unfavourable external circumstances which contributed to a sharp increase in NPLs. The total NPLs of the banking system which amounted to G$786 million in 1994 expanded to reach G$21 billion (or 45 percent of total loans and advances) at end-2001. The strong co-movement between the ratio of NPLs/total loans and our macroeconomic variables (GDP and REER) depicted in Figure 1 clearly reflects the sensitivity of impaired loans to the real economy and adverse external shocks. Figure 1: Trend of growth in real economy (GDP), real effective exchange rate (REER), inflation and the ratio of NPLs to total loans  Source: Bank of Guyana Annual Report (various years) and International Financial Statistics (IFS) Figure 2: Trend of growth in loans and advance, loan-to-asset ratio, real interest rate the ratio non-performing loans-to-total loans  Source: Annual Reports of Commercial Banks (various years). Given the widespread default on loans during the mid-1990s several commercial bank adopted a cautious lending stance. As a consequence, credit growth slowed significantly after 1998 as commercial banks shifted to safer investments, mainly treasury bills. These trends in the lending policies of local commercial banks are reflected in Figure 2, which shows a contraction in credit growth and the ratio of loans to total assets. Based on Figure 2, credit growth reduced continually from 47 percent during 1995 to below 1 percent in 2004. The ratio of loans to asset also reduced from 45 percent to 31 percent over the corresponding period. The strong co-movements between NPLs and the various bank specific and macroeconomic factors are not only clearly visible from the Figures above but are confirmed by our correlation analysis reported in Table 2, which presents the correlation coefficient between NPLs/total loan ratio and the bank specific and macroeconomic variables for our panel dataset from 1994 to 2004. Table 2: Correlation Analysis VariablesSmall BankLarge BanksAll BanksBank specific variables Loan to asset ratio0.210.190.20 Credit growth-0.33-0.28-0.33 Real interest rate0.060.070.07Macroeconomic variables Real GDP growth-0.40-0.39-0.41 Inflation rate -0.26-0.27-0.28 Real effective exchange rate 0.100.090.09Source: Bank of Guyana Annual Report (various years) and the Annual Reports of Commercial Banks (various years). The results show a positive association between the loan to asset ratio and NPLs. The magnitude of this relationship is fairly strong suggesting that banks with a greater penchant for risk taking incurred higher NPLs. According to Table 2, real interest rates also have a positive relationship with non-performing loans. However, the association between real interest rate and the ratio of NPLs/total loans is weak as reflected in the low correlation coefficient. Growth in loans and advances exhibits a fairly strong negative relationship with non-performing loans. While the literature suggest a positive association, our peculiar results probably reflects the conservative lending stance adopted by commercial banks after 1998 due to their bad lending experience with the rice sector and the general decline in the real economy. The social/political instability that characterised the late 1990s and which continued during the early to mid 2000s may also explain the contraction in loans and advances extended by the banking sector. Our correlation analysis reveals a fairly strong negative relationship between NPLs and growth in real GDP. Contrary to the existing literature Table 2 shows a negative association between inflation and the ratio of NPLs to total loans. This unique situation may be explained by the indexation of wages to inflation in Guyana. Table 2 shows that there are positive co-movements between the ratio of NPLs and real effective exchange rate. However, the magnitude of the correlation coefficient indicates that the co-movements between the real effective exchange rate and NPLs are not as strong as those between growth in real GDP and non-performing loans. This suggests that international competitiveness had a less pronounced impact on NPLs when compared to the slowdown in the real economy during our sample period. IV. DISCUSSION OF RESULTS In this study we employ a fixed effect panel model to identify the determinants of NPLs of local commercial banks. Tables 3 and 4 summarize the results of our regression model which is estimated using pooled least squares with a fixed effect estimator. Our model is estimated with a balanced panel dataset that consists of both macroeconomic and firm level data from 1994 to 2004. The variable L_Ai,t which represents the risk appetite of the commercial banks is positive and significant at the 15 percent and 5 percent levels of significance in our general and parsimonious models (see Tables 3 and 4). This means that banks which are high risk takers are likely to incur greater levels of NPLs(see Sinkey and Greenwalt, 1987). The variable SIZEi,t (which represents the size of the bank) is positive but insignificant. This evidence which is inconsistent with previous studies (Rajan and Dhal, 2003; Salas and Saurina, 2002 and Hu et al, 2006) can be interpreted to mean that large banks are not necessarily more effective in screening loan customers when compared to their smaller counterparts. Similar to previous studies, however, we find a significant positive contemporaneous association between the real interest rate variable (RIRi,t) and NPLs (see Sinkey and Greenwalt, 1991; Fofack, 2005; Jimenez and Saurina, 2005). This indicates that when a commercial bank increases its real interest rates this may translate immediately into higher non-performing loans. Table 3: Regression results This table shows the results of the fixed effect regression model which is estimated with the ordinary least squares estimation technique and a balanced panel dataset from 1994 to 2004. lnNPL_Ai,t = 0i + 1lnNPL_Ai,t-1 + 2lnL_Ai,t + 3SIZEi,t + 4"LOANSi,t +5"LOANSi,,t-1 + 6"LOANSi,,t-2 + 7lnRIRt + 8lnRIRt-1 + 9lnINFt + 10lnINFt-1 + 11"GDPt + 12"GDPt-1+ 13lnREERt + 14lnREERt-1 +  + i,t i = 1,& N, t= 1,& T where: lnNPLi,t and lnNPLi,t represents the natural log of the ratio of non-performing loans to total loans for bank i in year t and t-1; "GDPt and "GDPt-1 represents the annual growth in real GDP at time t and t-1 respectively; lnRIRt and lnRIRt-1 denotes the natural log of the real interest rates (measured as the difference between the weighted average lending rate and the annual inflation rate) at time t and t-1; lnREERt and lnREERt-1 indicates the natural log of the real effective exchange rate at time t and t-1; lnINFt and lnINFt-1 is the natural log of the annual inflation rate at time t and t-1; SIZEi,t is the ratio of the relative market share of each banks assets that capture the size of the institution at time t; lnl_Ai,t is the natural log of the loans to total asset ratio for bank i in year t; "LOANSi,t, "LOANSi,,t-1 and "LOANSi,,t-2 represent the growth in loans for bank i in year t, t-1,and t-2 respectively; and i,t is the error term which should be normally distributed with zero mean and constant variance. In the model 0i is fixed. VariablesCoefficientStd. error 1)t-statisticsProb.Bank specific factorsLnNPL_Ai,t-10.2313290.0910672.5402060.0171lnL_Ai,t0.9334470.6086201.5337110.1367lnSIZEi,t0.1578750.3836930.4114620.6840LOANS-0.0108620.002624-4.1388430.0003LOANSi,t-1-0.0126950.002079-6.1064730.0000LOANSi,t-2-0.0086160.002497-3.4504960.0019RIRi,t0.8069530.5025831.6056120.1200RIRi,t-10.3813300.4260800.8949750.3787Macro-factorsINFt-0.0020970.086632-0.0242110.9809INFt-10.0701320.1289100.5440400.5909GDPt-0.0495540.036561-1.3554060.1865GDPt-1-0.0065140.034384-0.1894550.8512REER1.5914871.0460191.5214710.1398REERt-12.6899590.8405463.2002540.0035Fixed EffectsBank 1-9.781574Bank 2-9.920805Bank 3-9.678762Bank 4-12.54788Bank 5-9.963629Bank 6-10.88915Diagnostic testR-squared0.9565051) Represent Whites robust standard errors. Table 4: Regression results: the parsimonious model This table shows the results of the fixed effect regression model which is estimated with the ordinary least squares estimation technique and a balanced panel dataset from 1994 to 2004. lnNPL_Ai,t = 0i + 1lnNPL_Ai,t-1 + 2lnL_Ai,t + 3SIZEi,t + 4"LOANSi,t +5"LOANSi,,t-1 + 6"LOANSi,,t-2 + 7lnRIRt + 8lnRIRt-1 + 9lnINFt + 10lnINFt-1 + 11"GDPt + 12"GDPt-1+ 13lnREERt + 14lnREERt-1 +  + i,t i = 1,& N, t= 1,& T where: lnNPLi,t and lnNPLi,t represents the natural log of the ratio of non-performing loans to total loans for bank i in year t and t-1; "GDPt and "GDPt-1 represents the annual growth in real GDP at time t and t-1 respectively; lnRIRt and lnRIRt-1 denotes the natural log of the real interest rates (measured as the difference between the weighted average lending rate and the annual inflation rate) at time t and t-1; lnREERt and lnREERt-1 indicates the natural log of the real effective exchange rate at time t and t-1; lnINFt and lnINFt-1 is the natural log of the annual inflation rate at time t and t-1; SIZEi,t is the ratio of the relative market share of each banks assets that capture the size of the institution at time t; lnl_Ai,t is the natural log of the loans to total asset ratio for bank i in year t; "LOANSi,t, "LOANSi,,t-1 and "LOANSi,,t-2 represent the growth in loans for bank i in year t, t-1,and t-2 respectively; and i,t is the error term which should be normally distributed with zero mean and constant variance. In the model 0i is fixed. VariablesCoefficientStd. error 1)t-statisticsProb.Bank specific factorsLnNPL_Ai,t-10.2536780.0846412.9970960.0052lnL_Ai,t-0.9277150.411203-2.2560990.0310lnSIZEi,t----LOANS-0.0108170.002462-4.3943260.0001LOANSi,t-1-0.0125560.001892-6.6362340.0000LOANSi,t-2-0.0084320.002234-3.7746990.0007RIRi,t0.5928640.3890241.5239790.1373RIRi,t-1----Macro-factorsINFt----INFt-1----GDPt-0.0518730.029788-1.7413940.0912GDPt-1----REER1.4139990.7901451.7895420.0830REERt-12.5836510.7475483.4561660.0016Fixed EffectsBank 1-7.507684Bank 2-7.574109Bank 3-7.542794Bank 4-10.57218Bank 5-7.730316Bank 6-8.824282Diagnostic testR-squared0.9565052 1) Represent White s robust standard errors. The variable which captures the relative credit growth of commercial banks ("LOANSi,t) is negative and significantly related to NPLsat time t, t-1 and t-2 respectively. Based on our results, it therefore follows that commercial banks which extend relatively higher levels of credit are likely to incur lower non-performing loans. It is important to note that our results are contrary to the international evidence which suggest a positive relationship between credit growth and NPLs (see Salas and Saurina, 2002 and Jimenez and Saurina, 2005). Based on Tables 3 and 4, the real effective exchange rate (REERt) is positively and significantly related to NPLs suggesting that the international competitiveness of the domestic economy is an important determinant of credit risk. In other words, whenever there is the deterioration in the competitiveness in the local economy the level of NPLs emanating from the key export oriented economic sectors is likely to increase. This evidence is not only consistent with Fofack (2005) but highlights the high levels of NPLsthat were reported for the agriculture sector as a result of lower commodity prices in the late 1990s. Consistent with previous studies we also find a significant negative contemporaneous relationship between "GDPt and NPLs (see Salas and Suarina, 2002; Jajan and Dhal, 2003; Fofack, 2005; and Jimenez and Saurina, 2005). Similar to these studies we interpret our findings to mean that an improvement in the real economy is likely to see an instantaneous reduction in the non-performing loan portfolios of commercial banks. Our results suggest a mixed relationship between inflation and non-performing loans. The variable has a negative relationship with NPLs at time t but a positive impact at time t-1. This means that high inflation in the current period should see a reduction in the level of NPLsin the banking sector. However, high inflation from the previous period causes commercial banks to incur higher non-performing loans. Apart from the mixed effects that inflation appears to exert on NPLsthe coefficients of the inflation variables are not statistically significant in our regression model. V. CONCLUSION AND POLICY IMPLICATIONS This study attempted to ascertain the determinants of NPLs in the Guyanese banking sector using a panel dataset and a fixed effect model similar to Jimenez and Saurina (2005). Our empirical results support the view that macro-factors, such as, the real effective exchange rate and growth in real GDP impacts significantly on the level of NPLs. In particular, we found that the real effective exchange rate has a strong positive association with the levels of NPLs reported by commercial banks suggesting that whenever there is a deterioration in the international competitiveness of the domestic economy (as reflected by an appreciation in the real effective exchange rate) this translates into higher NPLs. We also find evidence of a significant inverse relationship between GDP and non-performing loans. This means that strong performance in the real economy results in lower non-performing loans. Our results show that the impact of growth in real GDP on NPLs is instantaneous. The empirical results, however, reveals that inflation is not an important determinant of NPLs in the Guyanese banking system. With respect to the bank specific variables, we find that banks which charges relatively higher real interest rates and have a penchant for taking on risk tends to experience greater loan delinquencies (or non-performing loans). This is evident from the significant positive associations between NPLs and the RIRi,t and L_Ai,t variables. However, contrary to international evidence our results show that large banks are not necessarily more effective in screening loan customers when compared to their smaller counterparts since there is no significant relationship between the size of a banking institution and the level of NPLs. We also find that banks which are more aggressive in the credit market are likely to incur lower NPLs, which conflict with previous studies. Based on our findings, it means that commercial banks should pay attention to several factors when providing loans in order to curtail the level of impaired loans. Specifically, commercial banks need to consider the international competitiveness of the domestic economy since this may impair the ability of borrowers from the key export oriented sectors to repay their loans which in turn would r(GMNOP+ 4 L 署sdUdF7Fh)h 3CJaJmH sH h)h!CJaJmH sH h)h CJaJmH sH h)hCJaJmH sH hCJaJmH sH h)h|;YCJaJmH sH h= CJaJmH sH h)h= CJaJmH sH h)hv]CJaJmH sH ,jh)h(30J5CJ UaJ mH sH h)h5CJ aJ mH sH h)hec_5CJ aJ mH sH h)hv]5CJ aJ mH sH OP_   ! " VI$d`a$gdRd$d`a$gd>8$a$gd$a$gd L r   6 7 8 d  ӵӋӚӵӵ|maRh)hCJaJmH sH h- CJaJmH sH h)h56CJaJmH sH h)h.CJaJmH sH h)hbt<CJaJmH sH h9CJaJmH sH h)h_nCJaJmH sH h)h 3CJaJmH sH h)h3gCJaJmH sH h)h!CJaJmH sH h)hDECJaJmH sH h)h CJaJmH sH    ! " / 8 = [ p w "#UX0^sʻvg[[[[vvh CJaJmH sH h)hCJaJmH sH h)hCJaJmH sH h)hCJaJmH sH h9CJaJmH sH hyCJaJmH sH h)hHlCJaJmH sH h)h9CJaJmH sH h1/CJaJmH sH h)h1/CJaJmH sH h)hCJaJmH sH h#CJaJmH sH    Mb̖̖̖̽xiZiNhg|yCJaJmH sH h)hcJCJaJmH sH h)h4CJaJmH sH h)hLgCJaJmH sH heshCJaJmH sH h)heGCJaJmH sH hMCJaJmH sH hCJaJmH sH h)h CJaJmH sH h)hHlCJaJmH sH h)hHlB*CJaJph)jh)h 0JCJUaJmH sH 1F[]rxyz~lx|TUVamɽɱɥzk_khRdCJaJmH sH h)hHlCJaJmH sH hMpCJaJh^yCJaJhLzCJaJhmN,CJaJhZhZCJaJhMCJaJmH sH hab8CJaJmH sH hCJaJmH sH hmN,CJaJmH sH h@.!CJaJmH sH h@.!CJaJh!h!CJaJh!CJaJmH sH "mrTVt#$%8=SYx{vvgv[[v[Lvh)hvCJaJmH sH hRdCJaJmH sH h)hCJaJmH sH h)h[gCJaJmH sH h)h5CJaJmH sH h)h!CJaJmH sH h)ho1CJaJmH sH h)hHlCJaJmH sH hA)CJaJmH sH hCJaJmH sH hCJaJmH sH hMCJaJmH sH h)hV[oCJaJmH sH   !+-AGYr~8=GH  jyjjy[h)h0;_CJaJmH sH h)h' CJaJmH sH h)hCJaJmH sH h)hHlCJaJmH sH h5z}CJaJmH sH h2SCJaJmH sH h)hB|1CJaJmH sH h_6CJaJmH sH h HCJaJmH sH h)h[gCJaJmH sH h)ho1CJaJmH sH hRdCJaJmH sH "jr{ 1QRj&:\ĵ⩞teVVVh)hnCJaJmH sH h)h5CJaJmH sH hM CJaJmH sH h)h56CJaJmH sH h)hArCJaJmH sH h)h56mH sH h5CJaJmH sH h)h' CJaJmH sH h)h"XCJaJmH sH h)hCJaJmH sH h)hHlCJaJmH sH h)hTzCJaJmH sH Id"x$&()+3-c-d-/11 $da$gd $da$gd>8$d`a$gdw1l $da$gd8 $da$gd>8$d`a$gd>8$a$gd#a$d`a$gd5@MYmrtz>]ej{|⩸sg[[h(#CJaJmH sH h,CJaJmH sH h2CJaJmH sH h"-CJaJmH sH h)hU<CJaJmH sH h)hBI8CJaJmH sH h)hbCJaJmH sH h)hTCJaJmH sH h)h-CJaJmH sH h-CJaJmH sH h)hHlCJaJmH sH h)hy)CJaJmH sH !27=[egxP T Z![!\!m!p!q!t"w"""ǻǬ֬֬֬֬sdUh)hGCJaJmH sH h)h0MCJaJmH sH hbCJaJmH sH h)hbCJaJmH sH h)h.CJaJmH sH h)h8CJaJmH sH h)hHlCJaJmH sH h(#CJaJmH sH h)h5XQCJaJmH sH h-CJaJmH sH h)h{ZwCJaJmH sH h)hMCJaJmH sH """"###%#&#.#@#O#P#Q#]#`#e#~##x$y$$$$⸩ӚsgXL=h)htlCJaJmH sH h VCJaJmH sH h)hDCJaJmH sH h~CJaJmH sH ha2CJaJmH sH h)h!pCJaJmH sH htfCJaJmH sH h)haCJaJmH sH h)h&CJaJmH sH hR^CJaJmH sH h)hGCJaJmH sH h)h0MCJaJmH sH h)hHlCJaJmH sH h)h>ACJaJmH sH $$$$$$%K%R%W%a%w%%%%%%%%%%%%%&&&&&&6'C'W''ٲپym^h)hytUCJaJmH sH h`X@CJaJmH sH h)h 7CJaJmH sH h)h$<CJaJmH sH h)hG)CJaJmH sH hCJaJmH sH hMCJaJmH sH h)hHlCJaJmH sH h{CJaJmH sH h~CJaJmH sH h)htlCJaJmH sH h VCJaJmH sH !''''''(((((((((((())%)I)h)p)q)w){)))))))))ٻ||pah)h-CJaJmH sH hHlCJaJmH sH hL!CJaJmH sH hCJaJmH sH h&xCJaJmH sH h)h-CJaJmH sH h8CJaJmH sH h)hHlCJaJmH sH h)hDCJaJmH sH h)hytUCJaJmH sH h`X@CJaJmH sH h/CJaJmH sH ") ****9*:*;*I*O*X********* + ++ +@+D+z+|+++++ӸөӸǩǩ|m|m|^O|m|h)hLWCJaJmH sH h)hUCJaJmH sH h)h?CJaJmH sH h)hw6CJaJmH sH h)h CJaJmH sH h)hqrCJaJmH sH h)hHlCJaJmH sH h)hTCJaJmH sH hV CJaJmH sH h)hKCJaJmH sH h)h-CJaJmH sH h)h/1CJaJmH sH +++++++,,,,,---/-0-2-3-8-9-b-c----------. ...!.񻬝ʂvʂʂʂjhMCJaJmH sH hKhCJaJmH sH h)h9CJaJmH sH htCJaJmH sH h)h56CJaJmH sH h)htCJaJmH sH h)hArCJaJmH sH h9CJaJmH sH h)h CJaJmH sH hw1lCJaJmH sH h)hHlCJaJmH sH $!.:.;.H.Y.Z._.a.b.i.j.m.......//+/./3/@/E/R//ĵ񵚵sdXLhMCJaJmH sH hCJaJmH sH h)hI CJaJmH sH hXoCJaJmH sH h=CJaJmH sH h)h{$CJaJmH sH h)h.qCJaJmH sH h?CJaJmH sH h)hHlCJaJmH sH hrthHlCJaJmH sH hrthpwCJaJmH sH h)hpwCJaJmH sH h)hcJCJaJmH sH ///00000 0000(0002040>0D0J0ͻ͇ueSeC1C1C"h)hjb6CJH*aJmH sH h)hjb6CJaJmH sH "h)hwG6CJH*aJmH sH h)hwG6CJaJmH sH "h)h,/6CJH*aJmH sH h)h,/6CJaJmH sH "h)hJ3z6CJH*aJmH sH "h)h06CJH*aJmH sH "h)h2wY6CJH*aJmH sH h)hJ3z6CJaJmH sH "h)hHl6CJH*aJmH sH h)hHl6CJaJmH sH J0L0N0V0\0b0d0f0r0x0~00000000000000000000011ͻͩͻ͇ͩ͗ͩuueeSe"h)hh)6CJH*aJmH sH h)hh)6CJaJmH sH "h)h 6CJH*aJmH sH h)h 6CJaJmH sH "h)h]6CJH*aJmH sH "h)hT6CJH*aJmH sH "h)h&|6CJH*aJmH sH h)hT6CJaJmH sH "h)ho6CJH*aJmH sH h)ho6CJaJmH sH  1 1111$1&1(10121:1>1F1L1P1T1X1\1d1f1j1n1r1~1111111111޼zocWh)h"&6mH sH h)h 6mH sH h)h mH sH h)hz6CJaJmH sH h)h]6CJaJmH sH "h)hHl6CJH*aJmH sH h)he26CJaJmH sH h)hHl6CJaJmH sH "h)hh)6CJH*aJmH sH h)hh)6CJaJmH sH "h)h]6CJH*aJmH sH  1122222 2@2x22222222222222233ϿᠰscQBcQh)hGCJaJmH sH "h)hG6CJH*aJmH sH h)hG6CJaJmH sH h)hoHCJaJmH sH h?CJaJmH sH "h)hwG6CJH*aJmH sH h)hwG6CJaJmH sH h)hwGCJaJmH sH h)hHlCJH*aJmH sH "h)hHl6CJH*aJmH sH h)hHl6CJaJmH sH h)hHlCJaJmH sH 33b3d3n3t333333333333}4~444444444444444444444óҡҡҡucuuóҡ"h)h 6CJH*aJmH sH h)h 6CJaJmH sH hj4zCJaJmH sH h)hHlCJH*aJmH sH "h)hHl6CJH*aJmH sH h)h"&6CJaJmH sH h)h-CJaJmH sH h)hHl6CJaJmH sH h)hHlCJaJmH sH h)h CJaJmH sH &4444455 5A5B5G5J5L5P5S5W55555555°…°vfVI°hGg6CJaJmH sH h)hG6CJaJmH sH h)hjm6CJaJmH sH h)hjmCJaJmH sH h)h-CJaJmH sH hxCJaJmH sH h)h"&CJH*aJmH sH "h)hHl6CJH*aJmH sH h)hHl6CJaJmH sH h)h"&6CJaJmH sH h)h"&CJaJmH sH h)hHlCJaJmH sH 555556 6264686D6J6N6Z6^6d6f6h6p6|66666Ĵp^ԤpL<h)h"&CJH*aJmH sH "h)h 6CJH*aJmH sH "hGghHl6CJH*aJmH sH "h)hHl6CJH*aJmH sH h)h;6CJaJmH sH "h)hG6CJH*aJmH sH h)hG6CJaJmH sH h)h-6CJaJmH sH h)hHl6CJaJmH sH h)hHlCJaJmH sH h1>CJaJmH sH h)hHlCJH*aJmH sH 6666677$72747:7J7b7v7|77777777788868񦗋{i{]]NhDh1>CJaJmH sH h2-"CJaJmH sH "h)hP_6CJH*aJmH sH h)hP_6CJaJmH sH h},CJaJmH sH h)hP_CJaJmH sH h1>CJaJmH sH h)hHlCJH*aJmH sH h)h-CJaJmH sH h)h 6CJaJmH sH h)hHl6CJaJmH sH h)hHlCJaJmH sH 68>888888888888Z9]9d9e9x9}9999999999ֻyyyydUIht >CJaJmH sH h)h 8CJaJmH sH )jh)hU}?0JCJUaJmH sH h)h(7CJaJmH sH h)h]CJaJmH sH hjJCJaJmH sH h},CJaJmH sH hYe-CJaJmH sH hHWCJaJmH sH h)h[*CJaJmH sH h)hHlCJaJmH sH hDCJaJmH sH hDhDCJaJmH sH 18;;;;;;>A|CEHH,L-LMRU3X4X5X6X7X8Xdgd>8 $da$gdXo$d`a$gd>8 $da$gd>89999 ::::$:)::::::::;G;;;;;;;;;;;;徯ymaQDQDQhi5CJaJmH sH h cmhXo5CJaJmH sH hXoCJaJmH sH hHlCJaJmH sH h<CJaJmH sH hF]CJaJmH sH h)h`CJaJmH sH h)hBCJaJmH sH hGgh/oCJaJmH sH h)h/oCJaJmH sH hV"CJaJmH sH ht >CJaJmH sH hxCJaJmH sH h)hvCJaJmH sH ;;;<4<=<A<N<[<e<m<z<{<<<<<<<<<3=7=h=j====>>> >0>вУвДДvgXgh cmh0)kCJaJmH sH h cmhjfKCJaJmH sH h cmhA!CJaJmH sH h cmh_UCJaJmH sH h cmhJCJaJmH sH h cmhCJaJmH sH h cmhQNCJaJmH sH h cmhzKwCJaJmH sH h cmhHlCJaJmH sH h cmhHl6CJaJmH sH h cmh6CJaJmH sH  0><>>>>>>>>L?l?p?????@@@qAvAAAAAⵦyj[Lh cmh;ACJaJmH sH h cmh|;YCJaJmH sH h cmhktCJaJmH sH h cmh&:CJaJmH sH h cmh2=tCJaJmH sH h cmhG$CJaJmH sH h cmhG$CJaJmH sH h cmhQNCJaJmH sH h cmhp.CJaJmH sH h cmhCJaJmH sH h cmhHlCJaJmH sH h cmhqCJaJmH sH AAAAAAAABBBB CCCC5CJ^J`JfJhJJJJJJJJJJJJKK K³znnn_nSh&yCJaJmH sH h cmhCJaJmH sH hMCJaJmH sH h cmh"CJaJmH sH h cmhM5 CJaJmH sH h cmh/CJaJmH sH h CJaJmH sH h cmh[CJaJmH sH h cmhQb\CJaJmH sH h cmhd&CJaJmH sH h cmhHlCJaJmH sH h cmhHl6CJaJmH sH  KJKdKKKKKKKKKLLLL$L%L'L-LRLVLLL'MⳤvfWKWKWh&yCJaJmH sH h cmh`CJaJmH sH h cmhS6CJaJmH sH h cmhSCJaJmH sH h cmhG6CJaJmH sH h cmhGCJaJmH sH h cmhYCJaJmH sH h cmhHl6CJaJmH sH h cmh6CJaJmH sH h cmhCJaJmH sH h cmhHlCJaJmH sH h cmh/CJaJmH sH 'MMMMMMMMMMMMMM N!N$NTNXNNNNNNNNNӷӖӷӇxi]xQxxh^cyCJaJmH sH hiCJaJmH sH h cmhM5 CJaJmH sH h cmh`CJaJmH sH h cmhrCJaJmH sH h cmho#CJaJmH sH "h cmh^6CJH*aJmH sH h cmh`6CJaJmH sH h&yCJaJmH sH h cmh`CJaJmH sH h cmh19CJaJmH sH h cmh)CJaJmH sH NOOOQOTOPPPxPPPQQQQQRRRRR'RURaReRfRĦė|m^RFhDCJaJmH sH h&yCJaJmH sH h cmhHlCJaJmH sH h cmhHGCJaJmH sH h^cyCJaJmH sH h cmh>"CJaJmH sH h cmhXCJaJmH sH h cmhCCJaJmH sH h cmhrCJaJmH sH h cmhvCJaJmH sH h cmhKNNCJaJmH sH h cmh)CJaJmH sH h cmh ?CJaJmH sH fRkRRRRRRRRRRSS S SS'S9SYS]S^SqSSǸ|pd|U|ָpָFh cmh CJaJmH sH h cmhYCJaJmH sH hDCJaJmH sH h&yCJaJmH sH h cmh'CJaJmH sH h cmhCJaJmH sH h cmhVFCJaJmH sH h cmh)UWCJaJmH sH h cmh6 \CJaJmH sH h cmh)CJaJmH sH h^cyCJaJmH sH h cmhHGCJaJmH sH h cmh&pCJaJmH sH SSSSSST$T(T1TTTTTTTUU6U@UDULUMUZU\U]Uĵө❩papRCRh cmh1lNCJaJmH sH h cmhW;CJaJmH sH h cmh+f0CJaJmH sH h cmhx?CJaJmH sH h cmhY!CJaJmH sH h cmh;CJaJmH sH h&yCJaJmH sH h^cyCJaJmH sH h cmhhCJaJmH sH h cmheCJaJmH sH h cmhHlCJaJmH sH h cmh6 \CJaJmH sH h cmh^CJaJmH sH ]UjUoU~UUUUUUUUUUUVVVV VVĵ|paQAph cmhHl6CJaJmH sH h cmh6CJaJmH sH h cmhCJaJmH sH h^cyCJaJmH sH h&yCJaJmH sH h cmhbCJaJmH sH h cmh31mCJaJmH sH h cmhHlCJaJmH sH h cmh ?CJaJmH sH h cmhx?CJaJmH sH h cmhW;CJaJmH sH h cmh+f0CJaJmH sH h cmhFVCJaJmH sH VV2VSV|VVVVVVVVVVW W W0W1WAWEWJWKWNWeWxWWWWWWWWǝǍ}n_n_nSh 9aCJaJmH sH h cmhXCJaJmH sH h cmh<sCJaJmH sH h cmhS6CJaJmH sH h cmhHl6CJaJmH sH h cmhWbCJaJmH sH h&yCJaJmH sH h cmhCJaJmH sH h^cyCJaJmH sH h cmhbCJaJmH sH h cmhHlCJaJmH sH h cmhU?CJaJmH sH  W2X3X4X>X?XBXCXLXtXuXyXzX{X|XX˾zjz]M>/h)hCJaJmH sH h)hGCJaJmH sH h)h$W5CJaJmH sH hOf5CJaJmH sH h)h5CJaJmH sH h)hjB5CJaJmH sH h)hOf5CJaJmH sH ,jh)h$WCJUaJmHnHsH uh5CJaJmH sH h>{5CJaJmH sH h|;?5CJaJmH sH hg5CJaJmH sH h)hHlCJaJmH sH h^cyCJaJmH sH 8X9X:X;XX?X@XAXBXzX{X|X}X~XXXXXXXXXXXXX $da$gd>8dgd>8XXXZc\r\aaabbwbxbybzb{b|b}b~bbb$a$gd[ $da$gdBY$a$gdx$d`a$gdN# $da$gd,F$d`a$gd>8 $da$gd>8XXXXXXXXXXX YYY#Y$Y%Y&Y'Y+Y9Y`YYYYYYȹ}nnYMMMMh-/CJaJmH sH )jh)hx;0JCJUaJmH sH h)hCJaJmH sH h)hjCJaJmH sH h)h#gHCJaJmH sH h)hB%CJaJmH sH h)h-cCJaJmH sH h)h>{!CJaJmH sH hCJaJmH sH h-/h-/6CJaJmH sH h$W5CJaJmH sH h)h:CJaJmH sH YYYZZ)Z*Z4ZAZBZZZZZZZZZZZZZZZĸvgvXIX:vh)h0CJaJmH sH h)hSCJaJmH sH h)h{CJaJmH sH h)hxH+CJaJmH sH h)h8CJaJmH sH hlQCJaJmH sH hGCJaJmH sH hCJaJmH sH h)hCJaJmH sH hCJaJmH sH h)hHlCJaJmH sH h)hCJaJmH sH h)hx;CJaJmH sH h)hKnCJaJmH sH ZZZZ[[[[[![4[5[?[G[X[][^[_[[[[[[[[[[[[⸬pppĸaaRah)h"CJaJmH sH h)hCJaJmH sH h)h-/CJaJmH sH h)hCJaJmH sH h)hACJaJmH sH h)h%hCJaJmH sH h-/CJaJmH sH h&yCJaJmH sH h)hHlCJaJmH sH h)h8CJaJmH sH h)h{CJaJmH sH h)h8FCJaJmH sH [[ \\\"\,\b\c\r\v\\\ ]]>]E]S]i]o]w]]]]]]]⻯{ococoWcWcWcKcWhr#ZCJaJmH sH h(CJaJmH sH hGCJaJmH sH h qCJaJmH sH hznCJaJmH sH h^8CJaJmH sH h%CJaJmH sH h,Fh,F6CJaJmH sH hHlCJaJmH sH h8&gCJaJmH sH howCJaJmH sH h)h_bCJaJmH sH h)h-CJaJmH sH h)h CJaJmH sH ]]]]]]]]]]]]]]]]]]^ ^ ^^^{^|^^^^^^^^^^^_"_*_ĸ葠yjh)hZMCJaJmH sH h@OCJaJmH sH h&yCJaJmH sH h)hN#CJaJmH sH hSsCJaJmH sH hRCJaJmH sH h} CJaJmH sH hr#ZCJaJmH sH hGCJaJmH sH h,CJaJmH sH h(CJaJmH sH h,FCJaJmH sH %*_G_m_t_y_z_______`````0`6`F`H`X`````a aaaʾrrcʲWWWKhH CJaJmH sH hv\!CJaJmH sH hxhRCJaJmH sH h bh`6CJaJmH sH h`CJaJmH sH h5WCJaJmH sH h,FCJaJmH sH h+=OCJaJmH sH hDJCJaJmH sH h:RCJaJmH sH hRCJaJmH sH h)hZMCJaJmH sH h)hN#CJaJmH sH h BSCJaJmH sH aaaaa|aaaaabbbbbb+bHbvbwbbbbbbbbbyuj_jTjIjh[h(3CJaJh[hfvCJaJh[h[BCJaJh[h{CJaJh^a h)hz, h)h7jh)h UhxCJaJheCJaJh&yCJaJhxhCJaJhxh: CJaJhxh,(CJaJhywCJaJh)hf`CJaJmH sH h)hN#CJaJmH sH h)hoqCJaJmH sH bbc cEcFc?gAg_gigtggg $$Ifa$gd\@ $Ifgd\@ $da$gdOjY $da$gd BS$d`a$gdN#$a$gdywgdyw$a$gd[ bbbbbbbbbbbbbbcc cEcFccccccc$ddddddddddɺsdXssshST|CJaJmH sH hywh9:CJaJmH sH h9:CJaJmH sH h CJaJmH sH hRCJaJmH sH h@OCJaJmH sH hywCJaJmH sH h)h,(mH sH h)h{CJaJmH sH jn(h)h,(Uh[hubCJaJh[h+CJaJh[h{CJaJh[h CJaJ"dde ee&e'e,e6eBeFeVereteue{eeeeeeeeeeeeeeef ff͵͑ͅvjv^vRhCJaJmH sH hMCJaJmH sH h&yCJaJmH sH h)hwnvCJaJmH sH hE3CJaJmH sH hW(rCJaJmH sH h9:CJaJmH sH h<CJaJmH sH h[CJaJmH sH hCJaJmH sH h BSCJaJmH sH hoCJaJmH sH hywhST|CJaJmH sH hST|CJaJmH sH  f#fEfHf[fofsffffffff?g@gAgGgHgJg^g_ggggyjy[yOG>hM5CJaJh)h\@5h)h\@5CJaJhOh_ CJaJmH sH hOhlCJaJmH sH hOhOjYCJaJmH sH h)h\`CJaJmH sH h)hN#CJaJmH sH hvh BSCJaJmH sH h BSCJaJmH sH hICJaJmH sH hv.CJaJmH sH hF$$Ifl\ !  t!644 lalii jjjj,j0j1j;j@CJaJmH sH h+CJaJmH sH h)hyCJaJmH sH h)h{xCJaJmH sH hOhCJaJmH sH hOh(wCJaJmH sH h)h(wCJaJmH sH h)hlCJaJmH sH jkkkYkZk[kkkkkkkll6l;l?lblll.m/m0m⵩paaRaCh)hCJaJmH sH h)h@CJaJmH sH h)hR4CJaJmH sH h)h[<CJaJmH sH h)hoCJaJmH sH h)hY(CJaJmH sH hvCJaJmH sH hJFCJaJmH sH h)hCJaJmH sH h)h%LCJaJmH sH h)h2CJaJmH sH h)hyCJaJmH sH h)hZCJaJmH sH 0m1m4mQmSmjmwmmmmmmmmmmmmmmmmmǸӑsdXI:h)hzCJaJmH sH h)h5 aCJaJmH sH hCJaJmH sH h)huCJaJmH sH hehCJaJmH sH h)hf<CJaJmH sH h)hfDCJaJmH sH heCJaJmH sH h&yCJaJmH sH h)hq*KCJaJmH sH hCJaJmH sH h)h2CJaJmH sH h)hCJaJmH sH h)hiCJaJmH sH mn n nnnknunvnwnxnynnnnnnnno(o;sdUUI:Ih)h8PCJaJmH sH h\ CJaJmH sH h)huCJaJmH sH hehCJaJmH sH h)h2CJaJmH sH h)hExCJaJmH sH #jhg0JCJUaJmH sH h)hCJaJmH sH hcI'CJaJmH sH h)hf<CJaJmH sH h)hzCJaJmH sH heCJaJmH sH h&yCJaJmH sH hCJaJmH sH (o1ohmCJaJmH sH h)hu8krlrvrrrrrrrrrrrrrrrr/s3sVsWscsgsjsksQuTuWu]uaubu:v;vʻʻ品q品eVhuvohuJ$$Iflrp ! t0,"644 la $$7$8$H$Ifa$.BThvx=kdK$$Iflrp ! t0,"644 la $$7$8$H$Ifa$xʂ؂ $$7$8$H$Ifa$ $If^gdTD؂ڂ,K>000 $$7$8$H$Ifa$ $If^gdTDkdK$$Iflrp ! t0,"644 la,:<J\n=0 $If^gdkdL$$Iflrp ! t0,"644 la $$7$8$H$Ifa$n=0 $If^gdkdM$$Iflrp ! t0,"644 la $$7$8$H$Ifa$ƃ؃=4 $IfgdTDkdbN$$Iflrp ! t0,"644 la $$7$8$H$Ifa$ B5 $If^gdTDkd6O$$Iflrp ! t0,"644 la $IfgdTD,>R`b=kd P$$Iflrp ! t0,"644 la $$7$8$H$Ifa$bp $$7$8$H$Ifa$ $If^gdTD„քK>000 $$7$8$H$Ifa$ $If^gdTDkdP$$Iflrp ! t0,"644 la 0B=0 $If^gdTDkdQ$$Iflrp ! t0,"644 la $$7$8$H$Ifa$BVdfp=0 $If^gdTDkdR$$Iflrp ! t0,"644 la $$7$8$H$Ifa$ƅ=0 $If^gdkdZS$$Iflrp ! t0,"644 la $$7$8$H$Ifa$ƅ؅=kd.T$$Iflrp ! t0,"644 la $$7$8$H$Ifa$BkdU$$Iflrp!  t0,"644 la $IfgdTD *+,- $IfgdTD $$7$8$H$Ifa$ $If^gdTD-.5?@K>0' $IfgdTD $$7$8$H$Ifa$ $If^gdTDkdU$$Iflrp!  t0,"644 la@ABCJB5 $If^gdTDkdV$$Iflrp!  t0,"644 la $IfgdTDCJTWX_ilmt~†ĆžsdYKhr"hk5aJmH sH hG!5aJmH sH h)hJBCJaJmH sH h)hL7CJaJmH sH h)hL7CJaJmH sH h)hL7CJH*aJmH sH  h)hrh)hrCJaJhrhrmH sH  h)hTDh)hTDCJaJh)hTD5CJaJ h)hH hrhHhrhHmH sH h)hHCJaJJTUVWX4kdW$$Iflrp!  t0,"644 la $IfgdTD $$7$8$H$Ifa$X_ijkl $IfgdTD $$7$8$H$Ifa$ $If^gdTDlmt~K>0' $IfgdTD $$7$8$H$Ifa$ $If^gdTDkdX$$Iflrp!  t0,"644 laB5 $If^gdTDkdlY$$Iflrp!  t0,"644 la $IfgdTD4kdNZ$$Iflrp!  t0,"644 la $IfgdTD $$7$8$H$Ifa$Bkd>[$$Iflrp!  t0,"644 la $IfgdTD$$7$8$H$Ifa$gdTD$7$8$H$If^gdTD $$7$8$H$Ifa$ $7$8$H$If†%^SD<4$a$gdG!dgdk$d`a$gd>8 $da$gd0=kd.\$$Ifl\p! t0,"644 la $%$.8:DJRT\bjlx~ˆȈʈԈֈވ"*.68@DLRZ^jltxh)hG!6H*mH sH h)hG!6mH sH h)hG!CJaJmH sH hk5aJmH sH hr"hk5aJmH sH h-&5aJmH sH DRT|ʐ֐ $$Ifa$gdN $IfgdN$a$gdkgdG! $da$gdG! &,  (0ċ΋Ћҋڋ TUZ]_cf؍ٍۍDFXjphG!mH sH h)hG!H*mH sH h)hG!6H*mH sH h)hG!6mH sH h)hG!mH sH N.Z`68<DPRh֐ؐ (lnx~ȑʑ֑ޑôshshsYhV hkCJaJmH sH hV hkmH sH h)hkCJH*aJh)hkCJaJhM5CJaJ h)hkh)hk5CJH*aJh)hk5CJaJh)hkCJaJmH sH hG!hG!mH sH h)hG!6H*mH sH h)hG!6mH sH h)hG!mH sH h)hG!H*mH sH "֐ؐ KBBBBB $IfgdNkd\$$Iflrp ! t0&644 la (:LI<++$$7$8$H$Ifa$gdV $If^gdNkd]$$IflFrp ! t0&644 laL^ln:- $If^gdNkd^$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV ȑʑޑ:- $If^gdNkd_$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV ޑ:kdj`$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV FHT`’ ^`fprēƓ̓ԓ:<DL\^h &(*8LPRTbvz|~ʕױױױhV hkCJaJh)hk5CJaJhV hkCJaJmH sH hV hkmH sH h)hkCJH*aJh)hkCJaJ h)hkF$8F$$7$8$H$Ifa$gdV $If^gdNFH`tK>---$$7$8$H$Ifa$gdV $If^gdNkd>a$$Iflrp ! t0&644 la’֒:- $If^gdNkdb$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV  ,:- $If^gdNkdb$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV ,>P^`r:- $If^gdNkdc$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV rvz~:kdd$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV 6kdbe$$Iflrp ! t0&644 la $$Ifa$gdV $IfgdNē$$7$8$H$Ifa$gdV $If^gdNēƓԓؓܓK>---$$7$8$H$Ifa$gdV $If^gdNkd6f$$Iflrp ! t0&644 la:- $If^gdNkd g$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV ,:<LP:- $If^gdNkdg$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV PTX\^h:- $If^gdNkdh$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV hz:kdi$$Iflrp ! t0&644 la$$7$8$H$Ifa$gdV Д$$7$8$H$Ifa$gdV $If^gdN "$&(KB6666 $$Ifa$gdV $IfgdNkdZj$$Iflrp ! t0&644 la(*8LK>-$$7$8$H$Ifa$gdV $If^gdNkd.k$$Iflrp}& | t0&644 laLNPRTb?2 $If^gdNkdl$$Iflrp!  t0&644 la $$Ifa$gdV bvxz|~.kdl$$Iflrp!  t0&644 la $$Ifa$gdV $$7$8$H$Ifa$gdV ~ $$Ifa$gdV $$7$8$H$Ifa$gdV $If^gdNʕK>-$$7$8$H$Ifa$gdV $If^gdNkdm$$Iflrp!  t0&644 laʕ̕ΕЕҕ?2 $If^gdNkdn$$Iflrp!  t0&644 la $$Ifa$gdV ʕΕЕҕ "$&FNPdtx~ؖږܖޖ㦙~rcTcTh)h -|CJaJmH sH h)hbCJaJmH sH hkCJaJmH sH hmCJaJmH sH h)hkCJaJmH sH hmCJH*aJmH sH h)hkCJH*aJmH sH hJ`hkmH sH hJ`hmH sH h)hk5CJaJhV hkmH sH  h)hkh)hkCJaJhV hkCJaJ.kdo$$Iflrp!  t0&644 la $$Ifa$gdV $$7$8$H$Ifa$gdV   "$ $$Ifa$gdV $$7$8$H$Ifa$gdV $If^gdN$&FHJLNKBBBBB $IfgdNkdzp$$Iflrp!  t0&644 laNPdxK>-$$7$8$H$Ifa$gdJ` $If^gdNkdjq$$Iflrp!  t0&644 laxz|~ږ;0 $da$gdmkdZr$$Ifl\p! t0&644 la$7$8$H$If^gdN$$7$8$H$Ifa$gdN<JNnprtƗܗAGRñӥzk[z[zOOhI<CJaJmH sH h)h -|6CJaJmH sH h)hdDCJaJmH sH h)hdD6CJaJmH sH h)h -|CJaJmH sH h&yCJaJmH sH h}QCJaJmH sH "h)hI6CJH*aJmH sH h)hI6CJaJmH sH h)hICJaJmH sH h)hbCJaJmH sH h)hZCJaJmH sH ؘLPQəΙӸӎpapaRC4h)h#CJaJmH sH h)h6CJaJmH sH h)hjCJaJmH sH hY_hF]CJaJmH sH hY_hI<CJaJmH sH h)hCJaJmH sH h)hZCJaJmH sH h)hbCJaJmH sH hY_CJaJmH sH h&yCJaJmH sH h)hsCJaJmH sH h)h -|CJaJmH sH h)h7+CJaJmH sH h)h+mCJaJmH sH ږflbr  ST$a$gdD$d`a$gd& $da$gd  $da$gd>8$d`a$gd$d`a$gd>8 $da$gdmΙϙљԙޙߙz֚!#.6IRo³೧›৛ttteVh)hCJaJmH sH h)h)<CJaJmH sH h)hPCCJaJmH sH hxLCJaJmH sH hAgCJaJmH sH hY_CJaJmH sH h&yCJaJmH sH h)hBCJaJmH sH h)h_CJaJmH sH h)h6CJaJmH sH h)h#CJaJmH sH h)h#CJH*aJmH sH optěӛ>BLZĸsdUF7h)hJ%CJaJmH sH h)h2^fCJaJmH sH h)hVCJaJmH sH h)hGj CJaJmH sH h)h6CJaJmH sH h)h_CJaJmH sH h*CJaJmH sH h)hqgCJaJmH sH h%CJaJmH sH h&yCJaJmH sH h)htyCJaJmH sH h)hCJaJmH sH h)h::CJaJmH sH h)hjCJaJmH sH ZrΜ֜؜ڜ ô~o`Q`B`oh)hb CJaJmH sH h)h YCJaJmH sH h)hGUNCJaJmH sH h)hVCJaJmH sH h)hCJaJmH sH hY_CJaJmH sH h&yCJaJmH sH h)h3CJaJmH sH h)hGj CJaJmH sH h)h#CJH*aJmH sH h)h#CJaJmH sH h)hJ%CJaJmH sH h)hCJaJmH sH .3M]gijnԞޞ&*+3Ӧyj[[OC[hY_CJaJmH sH h&yCJaJmH sH h)h+MCJaJmH sH h)h#CJaJmH sH h)hGUNCJaJmH sH h)hCJaJmH sH h)hNXCJaJmH sH h)h ]CJaJmH sH h)hnCJaJmH sH h)hCJaJmH sH h)hp0CJaJmH sH h)h!CJaJmH sH h)h CJaJmH sH 34SV=>~Ϡ"$FJKR[jŶԶzk\kPkPkPkhY_CJaJmH sH h)hZ(}CJaJmH sH h)hMCJaJmH sH h)hp^qCJaJmH sH h)hCJaJmH sH h)hhCJaJmH sH h)h-CJaJmH sH h)hBCJaJmH sH h)hNXCJaJmH sH h&yCJaJmH sH h)h+MCJaJmH sH h%h+M6CJaJmH sH jlmq cg|ܢϣӣnϤӤԤ@DELUdefǸָָ֬֠yyyjh)hECCJaJmH sH hr/CJaJmH sH h)hRrCJaJmH sH h&yCJaJmH sH hQCJaJmH sH hY_CJaJmH sH h)h`nCJaJmH sH h)hoCJaJmH sH h)hMCJaJmH sH hVCJaJmH sH h)hyCJaJmH sH $fzƥ˥ޥRpǻǯǔrcSA"h3hd6CJH*aJmH sH h3hd6CJaJmH sH h3hCJaJmH sH "h3h6CJH*aJmH sH h3h6CJaJmH sH h3hdCJaJmH sH hQCJaJmH sH h&yCJaJmH sH h{DCJaJmH sH h)hdCJaJmH sH h)hCJaJmH sH hMCJaJmH sH h)hmCJaJmH sH ٧ݧާߧ@DFjklux¶ªwhYJY;Y;h)heCJaJmH sH h)hcCJaJmH sH h)h[*CJaJmH sH h)hmCJaJmH sH h9CJaJmH sH h&yCJaJmH sH h)hdCJaJmH sH h\ CJaJmH sH h3CJaJmH sH hQCJaJmH sH h3h3CJaJmH sH h3h},CJaJmH sH h3hdCJaJmH sH h3hdCJH*aJmH sH ʨϨب%R$&ARv 4:Ck岦妲th\h[*CJaJmH sH hxLCJaJmH sH h8yCJaJmH sH hkCJaJmH sH h'CJaJmH sH h9CJaJmH sH Uh CJaJmH sH h1!+CJaJmH sH hQCJaJmH sH h)hA>CJaJmH sH h%CJaJmH sH h)heCJaJmH sH h6nCJaJmH sH esult in higher non-performing loans. These lending institutions should also take the performance of the real economy into account when extending loans given the reality that loan delinquencies are likely to be higher during periods of economic downturn. Finally, banks should constantly review the interest rates on loans since loan delinquencies are higher for banks which increase their real interest rates. The Bank of Guyana should also expand its monitoring framework to include macroeconomic prudential indicators such as GDP and REER when assessing the stability and soundness of the banking system. Future Research Since our results for Guyana are encouraging, the authors will replicate this study for other countries in the Caribbean. In order to extend the literature on non-performing loans, the authors plan to incorporate corporate governance and the regulatory environment in our future research. This decision is motivated by two primary reasons: (i) the financial crisis in the US was blamed on deregulation and the weak regulatory framework in this country; and (ii) ethics seem to at the heart of the financial crisis in the US. While there are reasons to suspect that other financial crises occurred because of these factors, they were ignored in previous studies. REFERENCES Andreeva, Olga (2004). Aggregate bankruptcy probabilities and their role in explaining banks loan losses, Working Paper 2004/2, Norges Bank, February. Bercoff, Jose J., Julian di, Giovanni and Franque Grimard (2002). Argentinean Banks, Credit Growth and the Tequila Crisis: A Duration Analysis (Unpublished). Fernandez de Lis, Santiago, Martinez, Jorge and Jesus Saurina (2000). Credit Growth, Problem Loans and Credit Risk Provisioning in Spain. Working Paper No. 0018, Banco de Espana, October. Fofack, Hippolyte (2005). Non-performing loans in sub-Saharan Africa: Causal Analysis and Macroeconomic Implications. World Bank Policy Research Working Paper No. 3769, November. Hasan, Ifetkhar and Larry D. Wall (2003). Determinants of the loan loss allowance: some cross-country comparison. Bank of Finland Discussion Papers No. 33/2003. Hoggarth, Glen (2003). Assessing the strength of UK banks through macroeconomic stress tests. Financial Stability Review, Bank of England, June. Hoggarth Glen and John Whitley (2003). Assessing the Strength of UK Banks through Macroeconomic Stress Tests. Financial Stability Review, Bank of England, June, pp. 91-103. Hu, Jin-Li, Yang Li and Yung-Ho, Chiu (2006). Ownership and Non-performing Loans: Evidence from Taiwans Banks. Developing Economies, (Forthcoming). Jimenez, Gabriel and Jesus Saurina (2005). Credit cycles, credit risk, and prudential regulation. Banco de Espana, January. Jordan, John S. (1998). Problem Loans at New England Banks, 1989 to 1992: Evidence of Aggressive Loan Policies. New England Economic Review, January/February, pp.24-38. Kearns, Allan (2004). Loan Losses and the Macroeconomy: A Framework for Stress Testing Credit Institutions Financial Well-Being. Financial Stability Report, CBFSAI, pp. 111-121. Keeton, William R. (1999). Does Faster Loan Growth Lead to Higher Loan Losses? Federal Reserve Bank of Kansas City, Economic Review, Second Quarter 1999. Keeton, William and Charles S. Morris (1987). Why Do Banks Loan Losses Differ? Federal Reserve Bank of Kansas City, Economic Review, May, pp. 3-21. Pain, Darren (2003). The Provisioning Experience of the Major UK Banks: a Small Panel Investigation. Bank of England Working Paper No. 177, January. Rajan, Rajiv and Sarat C. Dhal (2003). Non-performing Loans and Terms of Credit of Public Sector Banks in India: An Empirical Assessment. Occasional Papers, 24:3, pp. 81-121, Reserve Bank of India. Salas, Vincente and Jesus Saurina (2002). Credit Risk in Two Institutional Regimes: Spanish Commercial and Savings Banks. Journal of Financial Services Research, 22:3, pp. 203-224. Sinkey, Joseph. F. and Mary B. Greenwalt (1991). Loan-Loss Experience and Risk-Taking Behvior at Large Commercial Banks. Journal of Financial Services Research, 5, pp.43-59. Sorge, Marco (2004). Stress-testing financial systems: an overview of current mythologies. BIS Working Papers No. 165, December. Sorge, Marco and Kimmo Virolainen (2006). A comparative analysis of macro stress-testing methodologies with application to Finland. Journal of Financial Stability, 2, pp.113-151. Wooldridge, Jeffrey (2009). Introductory Econometrics: A Modern Approach, 4th edition. Mason, OH: South-Western Cengage Learning.  This paper was done when Sukrishnalall Pasha was an Economist in the Financial Stability Unit of the Bank of Guyana and is currently being updated by the authors. It has also benefitted from the invaluable research assistance provided by Ms. A Singh and Mr. A. Dorris.  Sorge (2004), for instance, reports that the stress testing literature makes extensive use of non-performing loans and loan loss provisions to assess the vulnerability of the financial system.  See Hu et al. (2006) and Wooldridge (2009).  The data for GNCB are excluded from the panel. In 2002 GNCB was merged with GUYBANK, where the former took over the non-performing loans of the latter. The merger of these financial institutions therefore resulted in a significant growth in the aggregate NPLs of the banking sector which was not related to the performance of the domestic economy or credit policy of GNCB. Since the aim of this exercise is to determine the relationship between the growth in non-performing loans and key macro-economic and bank specific variables, GNCB was excluded to avoid the distortion that may be caused by the inclusion of this financial institution in our analysis.  The labour unions in Guyana usually negotiate wage increases based on the inflation rate. During the sample period, the unions were able to successfully negotiate higher wages for their member that covered the rate of inflation.     PAGE   PAGE \* MERGEFORMAT - 1 - `abr{| ;[`,KOƺ~r~c~W~hQCJaJmH sH h&hNCJaJmH sH hT^CJaJmH sH hxmCJaJmH sH hNCJaJmH sH h*= CJaJmH sH hCJaJmH sH h$eCJaJmH sH h&CJaJmH sH h h&5CJaJmH sH h)h6CJaJmH sH h6h6CJaJmH sH hyBCJaJmH sH O\mt   "%./CEĸpppaRaRaCh)hCJaJmH sH h)hb?CJaJmH sH h)hK]CJaJmH sH h)hBCJaJmH sH h)h CJaJmH sH h)hDCJaJmH sH hpdnCJaJmH sH h)hpdnCJaJmH sH hQCJaJmH sH hNCJaJmH sH hNCJaJmH sH hCJaJmH sH hxmCJaJmH sH h CJaJmH sH EOPQRT]dn$%+.<ĵĵĦĵyjy[yOy@h)h&dCJaJmH sH hWCJaJmH sH h)hb?CJaJmH sH h)hZsCJaJmH sH h)h{CJaJmH sH h)hBCJaJmH sH h)hCJaJmH sH h)hF}CJaJmH sH h)h^YCJaJmH sH h)hsmCJaJmH sH h)h3.CJaJmH sH h)hCJaJmH sH h)hK]CJaJmH sH noJKvw,-cd3$a$gdDo (<GIJKRĵӦӦӦyyjy[yLh)h`CJaJmH sH h)h3.CJaJmH sH h)hITCJaJmH sH h)hzCJaJmH sH h)hACJaJmH sH h)heCJaJmH sH h)h]CJaJmH sH h)h1>CJaJmH sH h)h,GCJaJmH sH h)hQ]CJaJmH sH h)hb?CJaJmH sH h)h{CJaJmH sH R[`ftuvw-3?F9ĸ⩚||m^^^h)hW*CJaJmH sH h)hYCJaJmH sH h)h6rCJaJmH sH h)h2|FCJaJmH sH h)h7[CJaJmH sH h)hF}CJaJmH sH hDCJaJmH sH h)h1>CJaJmH sH h)hITCJaJmH sH h)hb?CJaJmH sH h)hCJaJmH sH 9Vad  "st}ĵĵyyyyyyjjj[[jh)hqhHCJaJmH sH h)h CJaJmH sH h)h&dCJaJmH sH h)hZsCJaJmH sH h)hb?CJaJmH sH h)hDO>CJaJmH sH h)hITCJaJmH sH h)hYCJaJmH sH h)hW*CJaJmH sH h)h1>CJaJmH sH h)hoCJaJmH sH #/49@GlmӸqgcXPXE> ht h ?ht h ?mH sH h ?mH sH hl>h ?mH sH h ?jh ?0JUhDhD>*CJaJmH sH hDhDCJH*aJmH sH hDCJaJmH sH h0CJaJmH sH h)h0CJaJmH sH h$WCJaJmH sH h)hCJaJmH sH h)hF}CJaJmH sH h)h CJaJmH sH h)hW*CJaJmH sH 34mntuwxz{}~ &`#$gdHl$a$gd"$a$gdB%$a$gd]_$a$gdDqstuvxy{|~˼ޱޔ~x~xppgphR=mHnHujh ?U h ?0Jjh ?0JUjh6wUh6whgh ?mH sH hY_h ?mH sH  hx;h ?h@a!h ?mH sH hU}?h ?CJaJmH sH %jhU}?h ?CJUaJmH sH h ?mH sH h]_h ?mH sH h ?jh ?0JU($a$gdD$a$hDhD>*CJaJmH sH h6w5091h:pHl/ =!"#$% @=`;agjz#_a; @ NN.;x} |}A D@#b (d1"o-? H!QG+@Th}EV%RVVQZ?gwgLfqwu;{26~Cp'_FHxYQS!z Ŀ-LkpTtv,0l%"8΃͕״ ;E7TƳxH6 Ǝ{?.T,z-/ciC1cǰ㖖ґc;c( )-[o[dB.FC[gn PQ9`ay}כݫmiFm6:%'~~"`Z,Z.?MmB{=M&s9K K ^w dk<܇2_1^(77Tj'KJJ2,LSY+oGa7ɧr JXx.,YڌŪX dخW̓c*Kqu5 U9PgZPwKBX @vܛ,2lkQ <- L(|jaNey|yE2Wwʞ_g0s}[%ܛ#q]b ~0[Ӷ<-U$dв59Wh?νٵV3!K` ֔yq&A΂>hwF>jR=+g0Y34::ksy7u2<0ޠoC~8\9=~Mk~}5~ km gXV&4m7 ʍ2Gv;}!II]Q7hL֫MH3l0%Qw45UY9,PX?v,ˀg _ (Mx;Zط-e#>K^=F=ּX,5Ys彇iV Mz Y2=K^5s]ۍYϛ2rfzg.<~?<-s2fI57]M1Zz|JB&k}9r_aU(zx(t>_j7Yl^ u'衾y2/K9+~ERJR .:U/ؽ}z\j>+73lE>4o|B"x6xIiVh=;{<-7 lUh:_y\ Sߗfm堫V$1ymWh BTm"]{|BxoJiڸov,=.OKUĭe5/<:2g~´vI^a֛ax+%kMh~ӗ]dr].)3OSR=N:n9øݫit\X0Y2>MmZBn%U<%iJJ9Ɵ%ϙ)8ι;/|h8;Adst-Z0wsòoh;1LFsGc+2~Ez]͗b7B\OKL!ItQ Y}|jr:TMNe7|p\M^<͌1z9ޡ9XRqi%)\ 3:kb͘' y8C5x\$%^ޯ  yT$c~>Hڎұg;0[Cz|V^k/P@pVxZl62WPX\P^ q=xwlZuiGEGJf;oxڴGaZw0R0'Wۍ|^̗9"s>4Wn e%|pKǹ)FGӬqKLqKC=R>_nn/[IxnG<Dtk62~=>OKg.ڹ5?~36σRΑjUX] 5a!o$o~ԼE-_ Ź2σuK:Bg@ |-)OK%h׀EENI_N14p_i&ft̄T:ì&66QF muWߣ/OKC0cQKh;2Tȳо9^ >_fgoxڧi=Z.f6ͯϛjidگ MiO؇3__aN/rȪ-ZbG|yZkt ˝ CHOK%/Ԛ%և Q%<ͮ<.@>a>4_9.m<y0AKziƞ״UV8O}m.! %k-wB Vi^ZzI=yZkk>=>OKmk->%i&alo6$pFVN̦csIlr|琴Ͼ>K?pz>=if8*2PX;9 7疐w4pN 2q6ۍ/ p;oX.ǘZ)l|\㑓c|֎[jݱ iYma!=~I|cG:A}-Ok3T_ҧ0}=kL3H71#71My9;y#o;vZ*_߫alV^fp>[4߲u~W{~kuC!c-e5n|Pktss?/<އ^cF?m? ӎ?s\HG<֬)DYE[=٫ϗY_ϻz/9Ix7ؽ}z\f_lc9e+S ~}^F9'Im~2IYyZZq-"4ǒZS2яuS$OcHO3?ZHesO DA{A6{ArCϖ0/:3SCLKKF8Iv `~CM~<{\ wZ`}mNHRF8RmEsꔣ'/RG0vz^#Y#amjն- Sp 2.\jy6zrIɔ=[a|:p^42ROQ}|.ӦSY/doY4ziz_1dAƂq!4yԺz`F9'xoؼDyZQ_#2OoUeܼ?#_W;@-`ϔԟ Ưims34+$iv'h9ø iQFõmy/k ̓ r!&_n98>q?1"ƏiMD11<ycXDh+aߝnnAtabc?xcxz?n {&N>\˧F-Љ+dKm(onET@cm,5clBߜ؅Gx)A<4IP>n_=Wqlsý[Uk).jЙ]ˊ.U wħ8_;E[Mpt/Lü MΚV;{5X[7Ŕ (FrtEQ<.7yμE5uϛ6\u݇>Th\PP ;tBii !b/Z>T~nـ,) MZ2o CEu0,9ujpu;TJϯT>R(PU*K%K(Aoq9K*{&~ٞvNyxz蹶G%ct:c|M!핓|W^ b2"Z+J4ƭrdQm<>q 0DVnLaKd165N`0vb[m,d[~EB|=cGc8rd|"2x]>iXT}a2y+]6SŪW >X0wo};択plFU {ZНGXb왴FĖmCmjd0\/ci! \ﷱko/+@X&b n62W$(fy Pۛ`bpK:F3)b})}qY(XU?D";kXޓ~M.!QtL:b%Џ:{%l_h+jo'Q}Kee:@'N8e x] T~y\2nXGo}ux a 1.(Ǯ=,yngN/lNB s yy+0 afK0 f%`S:!.a2Y` \盒'u{vtS12J>I6Yk|iDg 4P:+lSکjMKAX[(&^}ZJ{[ۏ19E2ZZ"/o瘓-4ySO9W"&7c:K6gJe2iU2iKg?\:mOӷ.nqpqHyd\F+0KO%u/ _y>F}{0m]m'~x r"|~e?0:\/r +5BH=u?8pp4#-݇U_U?Vr|P.me~ixuqEl\Mq:HZIppoB'zjS~Ü_Jttx߸hlQ <~Gy5_]YXسTO1׼ U a܄I~8Q0^j}>7w#oB6>qL~ǹIdc|zŊJ۫+FakuwԜ?0JG 9@N:w?<|P3aNzI0tI8]8B`l-~N7.p8]oBߞ}3_ 3nS v9ݷ?1.yG_N.."P.Dg9$m <ō+.,#NfLgaЧw8Np{qv9ݯ<GibE3n0 ot3p>97n=osSw_<;CzNp}mNpAmn2nz*|v~1N{y;~ٰ5_m*a+g_.p6ni{+t^wݰV;7w;!6v)9~b=|اu2Ûב#V6˗HI|> vم^\9mD|^2Ms: *߫#6i*bmDKܛ6bfz5L}p0>PDž n6ٶ7<.j e!aXQl ;&f>Dީ 믂&]Dݴutj"8꺕t`Z BjV~nַC;._sj+o_sq䥘_GD Bm/`r;~U9Zg .~p̮~/7/֮>ϳ=B dlWFvcK<_j_Qk/\WpKѯ G'ߛEx`<~~O@ОO[2Pe-Aw|2']?΍ gnos2?3ok{";˻z)?K WG7z_uy]ok]Kn.] +y:lsƵC$)teB6CZ*I?avw廞G5Qa`^oeeXʸ0(.gs,ӭ$^+"(Y%gsL3e/{v¦grX}Z粹e/6g1dsT1=.W ?$/柙&C^!1d_nZ~qEי"9ZX2^MZ1i(G̣gAլ_:gL9{Ykby[cǘͿ˒ͿKc]sQRNE#=w^yM+OϨ$ondϹ̭}uAnP_'g0;[iq0[7(Vp~%ܱea+)zާ$Yl'?}{~OOT@!M}jϏ7ܞwz{` {~+:%cgb{*]{~'w׉1{_ 0JIX>6m|ܞ(=?>raعDNA`̀.ggܴe6ǒ>VɀA$-9 7cR`&`vVؗ$MDkd}{˭? vģ4&*iMoc,UC-YvA \i '`>z nzA弓an`c1BT[y X ]>6pm6>Vq$gsLeEu>61/- !5|lXͣ =y]>v";*i{dc^*{So'3ddTvb!8#$0ٱĘ82g|𹈛:>>Ib 5N7R2Ӥˆ}ߋLj57Ӌlc?&_3 Og3~gѿ\V-m/뢶-k7@]_'Y351W^a(wK_'_0$_b&_0!as `^W~$cSl9W5ΰ]ۏ]ex{^&$ka|"o?GM UC8o*_}`$(~oL}?$C$ߧmΪcbn=ۋ<6mѤ3oQs:k4192>%?LFh}9Kɢ:OPCiY^sh3 %ߔ6pF)sB6_[$79Rs <^9cs:N/ut_uyr|aY^Z_Y]HTҨoBfGJ qYX_}}=qj)8p_F6Ll`{mO],iLfCdc,0&P0J8T6Ni1i0JqNwh㱴|V WvyD.m§Ǵc1b ]nc屮bH0X15 X{PQV}xߣ޸%Sb?V2~Ϡ^c{Ԧ^6:+)^̅_ڎ_/|qHšg$JܦZ$.?T^ˡѡн1|mlq1zr/tq>W*3b>Qq[gG=뇊7'|M յMmE6ԗڪntb|gRidkQLUͯ;ذ<KԺ8ƿaA͹cכ7\}ځ=tq3 Y,ͨv FA ˖/Uղ//oc-HK26_ZJFߐ䷣N[ .~pr~qGXٖcCb^5غey9Τ "˯Y"/Ss~b){e}'U_bо/bV(-cN$>7 ߾$=s l4[,~p5ab-^ξ =l*y1/;%#d[q=s9F~T5--"f_UXa .H+I>z.P`GV33eXAƫ1ݼ^a^/ѳ&e ?0ϕgZyl hu: QWU tmrFd2n-<[}&lKo9q*\1ƭ=~eK?UxPu6t_̦Ü_GHd~%!ٜr^ȸ+a2|4kiZ7ϧ׹icnqk9 p>&fS:}{u(P9ڜ:QvJq*Ic}dRŦ쬯zu󢙰] {oAgmeW6?h+K b=܊=JWB]ӯ)8vD [$n$!Aάpn~R퀋/+jgnxmuAMS - me;me]w<=V-6;`oNleh#[Y"1%J*[ٓV;Q/Imei z:{{Ǵc/1[9oU.O8ov}O}Oe]{Z ֺVyumԵUr4}u|;ֵZ|z>'Vҷ*қ]^1۱}=Ϗ\͈IFNDTnR%o̸c'MԬZSs3Ά-tV;e{oذ=Iͺr鍩E}oԕ^OV.suoFf/7bWvUu(6^O>Q |2|a`h}75*]懗R®7ai+&~+[o˿3ͺ7v0Dto ٞ$er?Lӌ}|WXC~5wZ̳t wZ%ߔ B>S 6)/F:IoMZ~ya>*fsqMAm90`4-L%ѭ['b0GJ0n}){iٳ6<;֟?(G%/篵yǙ7(嚾4~d T_G Yw'1 !I 9`6nTHƍfoˉcQ!)O19;vCV고c[cQ2k'ݺؼ-KMNzFeȖG>{8?4ؑGžvȌJ(HmIsN=stf;iI/k9I~zITJ[of{1^t3`NwL/}i75'ͮ|t;e1wvJvkע^J5w`[m&qcq;ًK4sAzsj\<"Ľ8b\uo 2S7Bss^Lܔ]u4h^l ٕu)y+_!7+|il_`$t'{qq63̧%Ƿ79$~j/H<(R`6θr0?`̵9f1f`_(%^0枢NghxY%sƄ=_`/P&|pl y_bоgh?t65c9fg'Z֟x|X޷rgh,cb++q8afK0s{iL[0H/?Y q8 IesA# {~Vf /Z?zK~p ?=t^ D:!į+~a#.C.:WK Y0J8T~NԽjBulvk1E?xjZ[^P'pq)q0$vyP腀9eb ]^G1/1҉/@{Xa+^:Hell`9^2lgx\6^:0HmvyDQ^Jalgx:7D?(-]^: 6^:*n0H0҉.6gh/ҟ9ÿzL/؜!*3f3$vyldb~a6I0҉(f?Us&ɹA#;Y^$>/t6@-<ߑ^^:)l㥓|>c;f;>NV|>J^@EZѲGd^ aIkk$SɔtKPնK:v}wjJIihRII$U|h ֹI6O- n/u"ds'G%\/|>n!A+|9>z1靘ǿhD|l[ӭ>1>q6w gޕu?*j󼜥z!Ey-$=nzʋc/L!,ыc{Jja,o~{^1.KoP8'֎YYb_Nܻb:qv=do;^#-e*Ck\"ꝓiï.,ס$u[/_1 sǾy6x"a<< ''3~uƧ/jx![vL7o9]O4l1mk\Qkʸ>䈛.ؿ{B r0=wӹ}݄d))a Ruƪ,wP&xux(Pi1C1zL5qe"FHw;S/k';e2(:u=kyrנXlN%X79XIz2\@whRW(ϳx?c=rއu5?שmD׼mNal-&Ü"|cs^dGeæEy7'/Rzα*S 6)/F:k:+%:S7>K͔_¦_l;4j: ƣ%}67jƎ  bc l{\2ee0`67,gɸ丩-Ofls$>ӏCX~tώqt\F"͹ľ oX/+s)OZ򾋽y 5nřf0*ٵip>I>3TsHu^f>^p5*z5}8ƐtTHzpЊiy7PTgdˬHu^rT,BT}bFF>|!skдmY& _Lv}i%}*};O61h=1\Y0J8T>Nckq'mp-QSO -pAo;qz|Hy : }/SL NX!^ +ocmO`S3$4杰rr 'g9 c ٟ)Bߖy|rF%t{v9a% 'o-t@ϡoo d.'0_`׳1Q23k$v9ظydܴ 0/`儕CqqŠSsYh)љǡr‹}&ѿ8 ߟN6gH^ͷv2g^gvp`f$ ~YP=䶘?îF8W?ÙlmIg3[CEQyx !OQ"DnѴ֟TI8ư .~-'~spA)S1%?m%lwmn(Dd OTQP  3 A 3"`"'^J*kaT'D @='^J*kaTلU >I>l'x] |TŹ.C6 1L|B1CLQAjjJ)hWPk((Oubh Viz R93g9gg7Y Nv|of)!j#XrpB6BHɘ P'%ABWv"ׇ .7O0 H  }g/}\Rql$t'$O'2!W%|Vk=4d"ĬG>5*'2L8@vc;d]K;v w8 I3GBV쳅}N&װF2tYh>iKbB` V|8!?&KP}UC{[E6vc6){T5l+0}@{X o_  67Uڇ'$.O%f۫`ouǝ2^y6:1 x-4a$AU̶s}gf=^B7^ĬI/HK&ԞoJCI^6ߌ{i YWܦaT&)uGn(E@yWBt!ٯf"j١QGn9\jȈo@ez;hq A=mElʶl{oAOrՈ5BW~Jh~Jy9!= ԮkZLCJ~r?hrXKQ>Q9ud>nDms3yH[@^J7}|>cc 7w}ȶWmM#JyCŨ^J]u#G>Tmm|.e[_,@pp2qas8 #ް1y'ᅥRu}ȻKFL;̍7xv޵a@j.o|.?TSp#^wǗw |v^yov㥈y{ 2$5o XՍ _4z2/%3[|">^|gO韙3lhcsង%L9xx\mk#5Džǩs\g gc2eGy=6fGcƓl0_;ɘ!@?r;Gr.P7$9`}(].PL.Px.P!b}(=j}(_>/b}.}#8 x0'vbQ\<ܑځm/tyc[G ]~]ޑ^v+y G? V;x}ZZ.[ug-.^+yvNzU9[^%T.[ Q]ޡ^<&y/W+x'ty9xITEיt.v׽ [>޽F㺲$ HYK%]D6DŽ6}||D 5T2u)ÿg|g݇r~?% mJ}X'2ǀ?  ?5ȷ~"8*6*>=Zz4h}ϳx>M0'|[d?jle+V|$=rId_#c۞u9muƶ>>Lgy(}TE~ml;]Ev >; . quPlg3vY1;gUS 1+仑ùCn$i>W~Ey_Ӱ s1K?f|w8qn8`s7?ُܾF~b=!=SRǤ9b~rΙp<w0M)pC?D"{('>p) S-vY1;gȜw;9~$_~' {(Q oҰB~v0cw| 3w2 4ɿܾOA ?Z]Sȧǘi=+|Vo.7:s#'|sF?#i_8!9 O@!{(~=A W=C\~f 4a.f,78[gvP=8WuИ LxQ,4cϱ>`6adS7qX+8o<6?9 =gkok[}_xAϵ 4dgWG؞uOY_|g!fI0|9.ӓkL:҇\ʶ$\rm)ͣ3_k>, {|CV?^G>E3/>EzݓF{7"ƚ&+0t^ uOh܎)ڑy\t_g̶ٶf}n8*D;~ xQr/`[xqptXl_ows;>w)S׍|L/c/=[q Kn\r)f&F!z]:$Jyo@s$Z MI>ys&&_`n[;515$VMG A=kO T`jbcC`>&"T9M̹mW 7K ÚeO;sT_`\Y)Lt8/9%"ۼZڝj7XR~)Ern-d_쿅|D8lT[pP`ĹsFM̅_ '41q>#0W#ML_*0?5f]H3<`>nljWH1Չd\P;9a  ib E2`9Ty Y&0G*0ɬy xT_9C`sQ|DML| ks&&"Oib9ͬ-],0(0E3 fYL(0P`^ˬ-W*02cb3^)e/C'5|^Ǥ/}r^+prD4yb5}ʻX\W'E}I=x{*)*ZDXVljTPNlKνAc=h۽VD߆mC{范#'hs47zŃu0Q%846wZâާ*Ӿ>*L^g P>zK zm^V z,Mb7-YzSH7қiU j^OW-bxkL׍4##+F#+,ԫY>zxd^Vn;RoXꍀGV-czyn䋓ZE];ҭtGtn)uq-beyDD[&9VlU*lmXe{VA,De[eVYUVkͶf[e˭V4[p0a z  {^7I^5ԃzOz5PWe`̥1zBG zC"$DTFc zxIMaCwg; G?˷| pԖs8f/eE (vuP99Fwû..cX[v}~a6veu\˰3wCw0O a2v}I +x2*cl7YŰ&[yӁt:úo D0U6ȁgCRΡ>dkGHU6*eG-ÖQKﰥwv}NeGGD_5ȧM@G5[eEY^^;eVyGxh1=ڱ )RHtd-VrQV+Qe b`+JmzxyYOY~lK(tJzjm`ɲVGtYdY?`+wo󲞛-׺ǫߝz:ŝ g]e@G(+R) wئ} W;5IDH{ Y\;PHǑt %9;W9@Up4I)V^^ :b0dʭ)_#[4Y3r#dO G!lY` {avYug~kIǚ[ M_$)|TIYYnR5d,N53+NLYiffVC׏"7ɲ4Y3()Z/"N/Z})W^':Z WE<ǝN Rk=&gA>O=Y'ANtz!d=%1pi ]@Vo{rOY26+6||[tQ֘U(J~lL%%%I FɜְU(Yt1J:O%-yQX6J6ۃ5ZZFIe3~:a]yAÎdLY:_XeEYBA7ɲi]Ѧgo^mq~Q2Fl(čF`՟#^n4JjU2^Cq8VU*^gFyw^!l!5:r8>Ba#$zDO5[QmwoqXk%[8:Pm==z>(Q Vt1b w>!mͷ6wF?W1T3*];/7_T!ɻL2ėo^`6z[g{W.cg I >[dr +k4fv2Vyږ>RP9F )@pn>v ?]Zz> Wɿz&=onyҏE.ϲ?|,장[-s<#>ZKX_:Q [96R¼V\>m>Ldc=ȕ쳾kիO2sWo|CwO/7#-m~*A9C5޶嫺UYS9ly\> ߶G*~}w)~sP\kdx̅^|հ7! Hjޡ.;W4x!6[0yk5xnB~wɻXw 2Ļ N񙼿9yhtᝉxg"ޅbWZڅ7x_{8y{jָ#b?Թ߬{:oߊ9׻!k҂׳2uO.f3o+dnעj΋?kP!#y9"y-FHɗ 9'X?v=4V+NNnZ3y*xyܭ I&Uw׭mJ〓w6o׭K&@im[_1!'[ڼ;x2yN^[}ɻ*l~ks[Ns#&/u{6P[{_{60Gr=)f~;Iיtr+Uߊ(s|\ū4_v /C_3]qz]&)V:ua!6OJ:/Ͽ˸O׆ɶǂ,}dۇ#q/&}kdۇošxݚl <>nWG>qC=͸|kdۇAl3Óml3a,^=tں\)9ϯ8$ErnFG=xn|?9~M&IvRތ䳑Ha~w'OeߓE]b{:}/{5<[|9s%ľ RٗzEbW:*a] a+oB8J^h_AfYOe#.:,\Wq?x}Tg\| (FωvK}/q%qigN{}mb>(vR.Dخ/Ha!?(raG\tY.F|(~>Z_+N#HBXe_¾!jzEbW:*-Wj2;YIy#9+ d_+=~R~G\tY.F|(~y>}q4NIX$ErnOaOaC{yi=r1+|G jY(p;8BK> ,f ~qigN}qAI)˨ʾ(}Cm b贳\rJ'_]d~B{F\Z }losi%.n^>uc 8~ ӿEuԍ/?JĽ:fS7#irkkяvԍ/>495ćŵ:l`^Y&G@o̯A~gS7499\;w1Zutԍ/)M>/:l`?Ns|(MuL7:s4/s|zGcg ?Q ԗi\IquSo&Cp)g:m\L2V|MrJ?cĈbM˚ S\Y8GY(!f4 k%F;\oQ3xeq{[a~mڠ{c|>k!(śp Q9'jy)X|T`~\)̏fecMIs`OD#9F`.R`nĜ#ڈ\$0W+0jb.@~&0w*0ļC`U`>ق3ܡսWܭ9}\ٖa[jQ`Y0 _)0_Ĕ9Eb(0*0kbV ?]6⡇9g{I/Fϑ7oHa?Cyȏh7*\ưtDf{k҅~0xf7MOs=51g#?51F*0㚘 ̝ 4K`U`kb6c>#&fsHmtyf6> ̳hf|Siz_@QQ.(Qt"#:v@:y6ssnܣs 8>(0w*0_М|&Ř3|(O ̥ cRSY!.yDYaݿ"N4~uRF3WC jML|ݤV`NU`S- Q-ss&&sWy&&>'xE`~0ö?8ļf6q,9A9U9A`6*03lKssl]-0R`^EK* mU%ʩ^>f'o]5r~|k1j Dڊ߿\|;vʹ@G{h5DغoH$n F3~M܍qpQoqV= Lp+=-;ld[Tp Tos7"0rReDd lD  # A#" `2 K* \~F&( `!K* \~F&f8;H7x] tE<$8 #Q2EX~~Kd xrB2 V+lH&v${ټuIsHf_{~6{~깎r9V1Q}9k6/农OtMc-6cf퍟/#G"c+&o{U}C#}ťosD},?ܡ?A!}̠1>/W|-'/IO*|J;V | D,7}H}G~~1]ݫ{ʯ㷯/?~ Z~+\ᷗO>_~ N~)kˏ1$|Ol}]Z/?~ +UQ_~ f ~%:~=epsљn4mv1s["#^. Ė2Mlj旫0 f>wTp{EԜHsфT((752%|huE'_RG/__8#wWWg>G]/V+G](7>qKr_(/rS;]3 p T|~')G](7>q~uA.~|jLᷳ¯|4/rS|TA$~m!% 'InT4Uȸo&2` I=C=?zG>C]Ϗ~KP!hc!dc*ɮP_lc/J1s|,N\s}86I:csm ]9nK'>WׯZ˭Jz?ttu;~w*>s;ϤK~;/Vw~PoX1 oSwzHo_F*ޢ;)Iׯm~' +Im|~ ~Ot]|/xvsT4p zS籼Gr3;3Vzr/_S;^T^ԆnRRÏ[؅8k9C&%MJV>c%RR濇_ee?>X2>1@ Ł7 /o4y ߱҉H &3PSО??dݡWFY1\ˈ; èШ4y  %,6(< 46FW1QB*ix?/Azn_g#2dgY5w߾&ϋ۫ 5Pz,F,}_(xvy]O }3N76E۾>I϶,"N7GvCI 3~ؒs29;Ox?2sw _ؽǎ/wAȾ D8nxE}-9?g-s4zc紊Vd뼀@zn ٯA=Կ(qg5OG]z'~ؒs29;O\f;/0N;M#7e(d?C"N7|90- 10O:%gesw8zƚi[ Ƌ0^l@UH ~ 'D8n@E& c\,wr?vNKn4; mB!=G!q8q𽛁Qk4'쏯,wuD-bBv|/@znF=qkG^\ 8ʙֻ?αS!; ~5w ^}g#0qỢհωy-9?g-ss˧2<^]<*~ۧҖ=n؟b擿?p+!f;53\gxɟώf[8h N|g\gC ?ʶ%Tw>% 'l`#գ)8urm37-v]Hh N|g\{G/Ǚ~l̶M, xm9d6FGSp; ^dɯټf%qB _,auTw_c bʇƩGSp;%: ~'߾~o_]㷯SJy|> 9yР\K Wua_{Q}Goj;6$'#&H8AUo#Fſ"!~΂=p_RqdC1rlx~&yE;{Dٺ)~w^zhZ_'m#kf|#Yp4:7G#c#|΢RVX[ζh?~(}ߩ/k,|Hiċ?Y[FR4߉>}ݓ}EtOt c]ɱ+v|[pRphrA N=sܯ ΃ _irDgu ͚og`bqTp<%8sjrD܀sz 59/^\Y #(9gs&dę.8(8}!=P>3g[gPbNpDy&$TlU9CqvW+8WKgXYs37Ƀ1֬{( ZHYxKrd d߃;w; E10OpX% 0?g+ȵ$\7Ra4htl;59' Ν?9 a>+8Gjr1mܯXu|$8넪59KY<`g`b<)8 <9S.xޑkurkLUpnkV3MyXag'_%3KAakr5L'E(X_k g59_} ÏUV.V>W)W;&x4"1* Ebnǜ{49)v3\'|9q349Q#8?Sp>ɉ1y\pCyH_ Γ N~pއ<%8(8sѷ;XMN^Tї#V 59嚮9ǫx*aX*>xﱾ-8Qphr@&ljG ΑS:cOo/ MN<'ojrqWp $֗7 ̠Lu<L,se3_s0\L3Y`b}>Ep^|O3 g]qf Ԑg=K>>=C>#s.Hq>4),t'oL`xs$O+O$aˊo?#-rb,=3֞4WNͽ+Id/lcE$'3҂g=w>8lfho:,۴orWmh%YoXA*B3T:JO8H$k+_.q}#-n6Zn Yen X9ncI 7eJGw]p_\֯4Va_hhJ5¥[}M$n -z/q- en 9n c ܠ0lb2npdXE/|Q s[|KВ-dҒmd[-aKvؒ{\͒ud,גXKVfʬllzqVg=7@̑3q M*L@Mܠ (+q5&n098bp`;H\qm$n q$nq %n]Ue>>rr-a|>r/ytQfhn|% T5o%%|`jv]Q͸vlϸ ;G:s+ҩ|Ymϸ[赌Vth0xڋg\3ҫ|s\Q|5/暙+]9l"8W2’tȪjJ M٫6)[{*IK ^ y#e5 R[EB_#^,Ǵjڳ,tR` no!o.z\q7>/)to #ek R)IRuȖpbyYOYߐ.uYD޴0oY)[-V~p[- 6l  .ָuK!R 0RvĒ}!dy \Kr!K*;n}^S֩ Pu] ؙ^ \/I$%9dmmpSw!]Fʮd]yytVus*ᢷw󲞲NU$ץLn)ɒفQ"|YpbyYOYmE)+dB]ۮm#y l6\6Xn}^Si7)s;RE{ { n!{)pNmmpI7@z]LBsm[/Hr bܺ9q3F&[I)E3q?HdA^rk./&k?>YЌA]%l?]9bWyMʭf9b`{~2]51+>jʸ󚘕[ X9Ұ#l%6󚘕[ 坊.f3"kbVn54c}Ʊ lYf&fVC"*F6VyM*z f@ͶLz;htguekuuWCwurZY_;_.{5k? I9.Go;$e4rM#^?w8Z֔2||.Q&Y  mH^C=6! $Z[KcS-IssƁΐUH`lВ$@nHVjgH&lCR $݄w%nHf1[QjckKOՎO`a[ n]M{vϓ#'G[.[  iHY}~I=S Ksҁ9d襑^Q ziE@ɐ%idW& ' h'wm>'}R5-b=B|e]I~2 H~2pL}5h j"9缈'*_攃[ԗ>ٰZlbtR*> b!Endnote ReferenceH*TT & List Paragraph ^m$CJaJmH sH PK![Content_Types].xmlj0Eжr(΢Iw},-j4 wP-t#bΙ{UTU^hd}㨫)*1P' ^W0)T9<l#$yi};~@(Hu* Dנz/0ǰ $ X3aZ,D0j~3߶b~i>3\`?/[G\!-Rk.sԻ..a濭?PK!֧6 _rels/.relsj0 }Q%v/C/}(h"O = C?hv=Ʌ%[xp{۵_Pѣ<1H0ORBdJE4b$q_6LR7`0̞O,En7Lib/SeеPK!kytheme/theme/themeManager.xml M @}w7c(EbˮCAǠҟ7՛K Y, e.|,H,lxɴIsQ}#Ր ֵ+!,^$j=GW)E+& 8PK!Ptheme/theme/theme1.xmlYOo6w toc'vuر-MniP@I}úama[إ4:lЯGRX^6؊>$ !)O^rC$y@/yH*񄴽)޵߻UDb`}"qۋJחX^)I`nEp)liV[]1M<OP6r=zgbIguSebORD۫qu gZo~ٺlAplxpT0+[}`jzAV2Fi@qv֬5\|ʜ̭NleXdsjcs7f W+Ն7`g ȘJj|h(KD- dXiJ؇(x$( :;˹! I_TS 1?E??ZBΪmU/?~xY'y5g&΋/ɋ>GMGeD3Vq%'#q$8K)fw9:ĵ x}rxwr:\TZaG*y8IjbRc|XŻǿI u3KGnD1NIBs RuK>V.EL+M2#'fi ~V vl{u8zH *:(W☕ ~JTe\O*tHGHY}KNP*ݾ˦TѼ9/#A7qZ$*c?qUnwN%Oi4 =3ڗP 1Pm \\9Mؓ2aD];Yt\[x]}Wr|]g- eW )6-rCSj id DЇAΜIqbJ#x꺃 6k#ASh&ʌt(Q%p%m&]caSl=X\P1Mh9MVdDAaVB[݈fJíP|8 քAV^f Hn- "d>znNJ ة>b&2vKyϼD:,AGm\nziÙ.uχYC6OMf3or$5NHT[XF64T,ќM0E)`#5XY`פ;%1U٥m;R>QD DcpU'&LE/pm%]8firS4d 7y\`JnίI R3U~7+׸#m qBiDi*L69mY&iHE=(K&N!V.KeLDĕ{D vEꦚdeNƟe(MN9ߜR6&3(a/DUz<{ˊYȳV)9Z[4^n5!J?Q3eBoCM m<.vpIYfZY_p[=al-Y}Nc͙ŋ4vfavl'SA8|*u{-ߟ0%M07%<ҍPK! ѐ'theme/theme/_rels/themeManager.xml.relsM 0wooӺ&݈Э5 6?$Q ,.aic21h:qm@RN;d`o7gK(M&$R(.1r'JЊT8V"AȻHu}|$b{P8g/]QAsم(#L[PK-![Content_Types].xmlPK-!֧6 +_rels/.relsPK-!kytheme/theme/themeManager.xmlPK-!Ptheme/theme/theme1.xmlPK-! ѐ' theme/theme/_rels/themeManager.xml.relsPK] M<.H\88 999<L  mj"$')+!./J0113456689;0>ADFH K'MNfRS]UVWXYZ[]*_abdfgij0mm(oqkr;vxy~CʕΙoZ3jfOER9UWXYZ[\]_`abcdefghijklmnoqrstuvwxyz{|}~  I18XXbggggh3hYhhh|pր ^.x؂,nbBƅ-@JXl֐ LޑF,rēPh(Lb~ʕ$Nxږ3V^p  /5<!!/X"$agjz#_ah;7"$^J*kaT'@(  V  3 A #" `?B S  ?G87"5$t%oFDm0&oFt1'oFT/(oFTT2)oF*oFtr*+oF|R),oFC-oF,".oF<,/oF00oFT*1oF\*2oFܛ)3oFg.4oFԑ/ nMMT9     ## pRVV9  9*urn:schemas-microsoft-com:office:smarttagsplaceB *urn:schemas-microsoft-com:office:smarttagscountry-region= *urn:schemas-microsoft-com:office:smarttags PlaceName=*urn:schemas-microsoft-com:office:smarttags PlaceType8*urn:schemas-microsoft-com:office:smarttagsCity9*urn:schemas-microsoft-com:office:smarttagsState      PVW^gmr{\bgpcj ;"A"##$$y&&''e(i(~(((((((( ))f)l)$*+*****d+g+h+l+++++, ,5,9,33$3)383>3V3]3R5X5A7G7Y>`>k>q>?%?????@@ @@"C'CBCICQCSCDDEEEE``aaaaaavb{bbbbbcccccd dd&d-deerfvfffffffgg-g1gggGhNhhhi iiiiiii%j&jRjVj]kekkkDlJlllmmoo\p`pxpypppppqqqqrqxq1r8rrrrrksssssssttAFXl]Nz= 68MM&p!&' ?,FNg)yET^akp~ ? J12Jr  LD/SGzr~x;%L \(]]jZknyz ##(#-';H]_{ +.<dNi1l%u 37 W W \ Bf : g V _ t t   Z  - c k  p : M h     M5 *= V {Z m VG$>>zCB2GXXkw&%(6A>@![+7~l6wMSbD2]d}A-@8dO_g~.Zjmp} 8-9D8P?Y^ab";_CqDQK]sm(w X7&:oQ$epZs n )`ktTz@@"EI ],5:h= ;*0=[{[;JMPjbrf!E#,62fl /R=T=&|}   3* K6 b Gj !!!@.!G!|T!Y!v\!a!@a!b!!m!>{!""""2-"O"'k"r"9###%#*#N#QR#W#l#o#v0$3$g$t${$%%%G!%/"%/%H%g%-&3&EJ& X&d&cI'tL's'Y(,(G(AJ(mW(fY(t(4):)7A)A)N)+n)y)***[*!^*E+1!+i$+_)+xH+}+,!,mN,},L -- -7#-Ye-u-.z8.p.v./,/-/4/D/BF/(0.:0]0`0d0+f0o0p0Pr0/1B|12D2S2Y2[2e2?z2 33374y4w4R43{45 5_5h$565o\5M6u5656M6Ww6w6 7q(7(7H7L7N7Tw78>8BI8pO8EZ8ab8Tq8#9'9h9:@%:"9:::wH:Z:p:|;W;Y;b;<<U<$<1<1<I<f<i<bt<|<===E =M==4=a'=-=97=:=RG=Vl=t >l>1>DO>?3?|;?@@@`X@a@n@8}@>A2AIANAOAfAyB*BW3BNB[BjBBECIeCkCslClDTDDq EN3E=E@ESEjVELFFFF FF2|F}FGwG,G5GHGeGHH}H H#Hr8HdKHVNH#gHqhHhH{HI I|I.IhN1lNuN`On-O+=OqVOz0P=PIPQ&Q5XQ #R:RJRxWRZR:S2S BS1aSjSITq"TV#TKTRTqsThUU,UAUNUU_UytUVV V8V9VDVKVLVzxV+yVW$W5W79W`=WHWLWSW)UW"XE%XrX YBY%Y\Y#Y|;YcIYaYcY+dYOjYqqY`vY2wY]Zr#ZD[?[@[e[7 \6 \ \\b$\3\M\Qb\F]]]]n*]].]U]wg]k]v]-w]4^5^R^@S^^_,_7_0;_T_Y_ec_f_ *`2`J`;o`5 a.a 9aKahab bUb_b nbubc-cmGcncdLd2@dodee eee6e$f?f9GfOf2^f`ftf}f3g% g0g8&g)gAg,Mg[gkgogxg%hT1hEhKhS]hSi[ix]ijcj~jk0)kMk$ltlw1l?lflk}l}lm"m31m;mHm cm[ n*6n8n_n`npdnsnx{n'o/o0o ToV[oeouvovo p p!pLpSpMvpfqq qq.q0q;q0Zqp^qKmqoqAr{%rW(rRrSYr2{res<sEsSsXsT}s}s2=tItF[trtst uJuUFuIuTuv,vwnvv(w HwzKwQLwCNw{ZwowGx&y7;yHy^cysyg|yJ3zj4zP{ {v {{1{*>{>{_{k{| | -|ST|Z(}47}F}\}5z}~~2.~m4~\I    $3pI[\`b qyj-)0G;W`1"z,]CTni $#-A:QabKs|`ehlQP^9&<??moqLz^p.JByKcd*g I>q 1%8h{uc,G = z /;K+MZbiqv~3 * ?8y #'1/03W#aWd6rqrz" on~_tK-ELY,`ckmvh"&9GX]^/ %"-4F OZ7[[ja U'z5zs %6&u;>KTjW[{V""1fMp{ :AfDDEJz';yG#bMB%GBhJVimty \ PCwix{-9q' $&dDOFVunH {j!J%&2<n-{x)4(W*Rzno $`>oFqL@O8PYZgq9&IWP}QX^Rdl<4%?Y/'5y}} %l@J_vw',_67<0=T^opxhDGa6nuhXo} @|7DVFq,.Gb_b&3 n7 _DWZsm~l$l*9:+m^t%l/^68`:IH^Q:X[hj`E34Qls?mCQ-S> 6=KQ]n~*19XhqN17:>>\$F7JJ6zxC8FQ'[Knw(#8LWb.nz )?Spx30p$+l:5CG/Ruy'1B6:>oqwx>kCei{~0&1> H6 3:DTTp `xmB&d&xW r-^8pwyp')-0WJi J, CKHl|tt{h).IL ),q@e=KMtK^zr/2Z,t[<P_mjp 3.=,Fk:p'a2\@lYL!D3z5}J~:ju o)<O6RaRZoVy*80;AZ_1gss>"%DVGgUwr" %'8:<BJKmnCMb?KRbjm{([*8oNP(`fvxz% :np~ZA!)qu7QS!Lg|oL'D,"`;o BXqgExAAEB.|R Y5oHZQ]rjBr.9|<Po=3;w>MR[\k/?GPHUW^Yb"},5Y(3L4T7=*9<<qDf`,c#GUNxmi% Dc/mH1{DFHwwi F(PzF<Lf80o1KE]cnwl"C#G)1NXCz VALis`8r5;ih '!BJFGTq36HXbs^y -yw9\iq4()= (-5=A]AD"f'(7+ 8 @B)SE7^8GTp "&R-D1Y8H:b?rRcF9CQ]CnrY!#&(3'@DFRUuYZhBMpt{m/ECG az|@'()+,S2S3S9S:S<S=@AOPefgijklmopqstuvwx|}8x@x0x2xh@x6xp@x>x@xFx@xJx@xPx@x`x@xxxzx@x~xxxx @xxx@xxxxxx0@xx<@x@UnknownG* Times New Roman5Symbol3. * ArialC PMingLiUe0}fԚ7GeorgiaA BCambria Math"1h+֦+֦H4FO4FO!4d++2qHX ?Hl2!xx INTRODUCTIONsukrishnalallspfalleyne Oh+'0 $ D P \ ht|INTRODUCTIONsukrishnalallspNormal falleyne2Microsoft Office Word@F#@X 1@m1 @m1 4F՜.+,0 hp|  bogO+  INTRODUCTION Title  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcefghijklmnopqrstuvwxyz{|}~Root Entry F_Q9 Data *&s1TablednPWordDocumentSSummaryInformation(DocumentSummaryInformation8CompObjy  F'Microsoft Office Word 97-2003 Document MSWordDocWord.Document.89q