An Era of “β-divergence”? Empirically testing the β-convergence hypothesis for the North-eastern states of India

An Era of “β-divergence”? 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Empirically testing the β-convergence hypothesis for the North-eastern states of India Tennyson Pangambam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8569719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper attempts to empirically test the β-convergence hypothesis for the North-Eastern states of India. In this context, β-convergence is one of the major predictions of the Solow Growth model, and emphasises on the negative relationship between the “initial or starting point of per capita Gross State Domestic Product (GSDP)” of a state and its “average growth rate” over the time period considered. This paper examines data of the eight North-eastern states of India over a time period of 14 years (2004–2017) to conduct an empirical test of the β-convergence hypothesis. Furthermore, this paper then uses the Least Squares Dummy Variables Fixed Effects Model (LSDV FEM) to test for the statistical significance of heterogeneity in the growth rates of these states with Manipur as the “reference state”. This paper finds that, rather than β-convergence, there has been a phenomenon of “β-divergence” among the North-eastern states of India. This implies that richer states in per capita terms have been achieving higher growth than poorer states – an empirical refutation of the β-convergence hypothesis. Moreover, considerable heterogeneity among the per capita GSDP growth rates is found from the LSDV FEM with the GSDP growth rates of Arunachal Pradesh, Meghalaya and Sikkim showing a statistically significant deviation from that of Manipur during the time period considered. β-convergence Solow growth model North-eastern states of India Least Squares Dummy Variables Fixed Effects Model Figures Figure 1 Introduction β -c onvergence, a cornerstone of neoclassical growth theory, posits that poorer regions should grow faster than richer ones, eventually catching up in terms of per capita income. This concept suggests a negative relationship between the initial level of income and subsequent growth. If such a relationship holds, it implies that economic disparities among regions should decrease over time. However, whether this theoretical prediction holds true in the real world, especially within diverse regions like India, remains an open empirical question. This paper focuses on testing the β-convergence hypothesis in the context of the eight North-Eastern states of India over the period 2004–2017. This region is marked by both geographical isolation and socio-economic diversity, making it an intriguing case for examining regional growth patterns. Despite being targets of several special economic packages and policies, these North-Eastern states continue to show uneven patterns of development. Using per capita Gross State Domestic Product (GSDP) data, the study first tests for pooled β-convergence across the eight states and then employs the Least Squares Dummy Variable Fixed Effects Model (LSDV FEM) to investigate state-specific heterogeneity, using Manipur as the reference state. The analysis reveals an unexpected result: rather than β-convergence, the region exhibits β-divergence, meaning that wealthier states are growing faster than their poorer counterparts. This outcome challenges the conventional expectation of convergence and raises important questions about the effectiveness of current regional development policies. By identifying states with significantly different growth paths, this study aims to provide empirical grounding for re-evaluating regional planning strategies in the North-East. The findings underscore the need for tailored policy interventions rather than assuming automatic convergence through market forces alone. Literature Review The concept of β-convergence stems from the Solow growth model (1956) This model predicts that poorer economies should grow faster than richer ones, assuming diminishing returns to capital. Barro and Sala-i-Martin ( 1992 ) pioneered the empirical work on this topic and found evidence of β-convergence among U.S. states and across OECD countries. They argued that although absolute convergence was rare globally, conditional convergence was more common. This means evidence of β-convergence was found after controlling for factors such as investment rates and human capital (Barro & Sala-i-Martin, 1995 ). Subsequent studies have expanded this framework. Mankiw, Romer, and Weil ( 1992 ) extended the Solow model by including human capital and reinforced the idea of conditional convergence. Sala-i-Martin ( 1996 ) emphasized distributional convergence and used kernel density estimation to explore income dynamics across regions. Meanwhile, Islam ( 1995 ) introduced panel data techniques to control for unobserved heterogeneity, further strengthening the empirical case for conditional β-convergence. Critics, however, point to evidence of persistent divergence in developing regions. Easterly and Levine ( 1997 ) argued that institutional quality and geographic constraints often override neoclassical predictions. Durlauf and Johnson ( 1995 ) suggested that countries may follow distinct growth paths or "clubs," rather than converging globally. Caselli, Esquivel, and Lefort ( 1996 ) developed GMM estimators to correct for biases in dynamic panel models, offering more accurate convergence estimates. In the Indian context, Cashin and Sahay ( 1996 ) and Nagaraj, Varoudakis, and Veganzones ( 2000 ) found mixed evidence of regional convergence, pointing to infrastructural and institutional disparities. Overall, the empirical literature suggests that convergence is not automatic. It is heavily influenced by structural factors like human capital, governance and openness to trade. Methodology The empirical methodology of this study follows the standard framework used in testing β-convergence. Two key econometric models are employed: a pooled cross-sectional regression to test for unconditional β-convergence, and a Least Squares Dummy Variable Fixed Effects Model (LSDV FEM) to examine heterogeneity in state-specific growth performance with respect to that of Manipur. Pooled Regression Model The equation used to check for β-convergence is a pooled regression model given by: (Log GSDP growth) = a + β (log GSDP) +E Here, the Gross State Domestic Products (GSDP) are in per capita terms for each state i for each year t. A negative value of β implies β-convergence and vice-versa. Least Squares Dummy Variables Fixed Effects Model (dup: abstract ?) The equation for Least Squares Dummy Variables Fixed Effects Model (LSDV FEM) is- (Log GSDP growth) it = a 1 + a 2 D 1 +a 3 D 2 + a 4 D 4 + a 5 D 5 + a 6 D 6 + a 7 D 7 + a 8 D 8 + b (log GSDP) it + E it In this equation, Dependent variable Log Growth rate of GSDP Independent variable Log GSDP Dummy variables : D1 -Aunachal Pradesh D2 -Assam D3 -Manipur D4 -Meghalaya D5 -Mizoram D6 -Nagaland D7 -Sikkim D8 -Tripura Manipur (D3) is the reference state. This model controls for time-invariant heterogeneity and allows the study to isolate state-specific growth effects. The statistical significance of these dummy variables indicates heterogeneity in the growth experiences of the North-Eastern states in comparison to Manipur. Analysis and Results The Pooled Regression Model Table 1 The β coefficient. Coefficients Standard Error t Stat P-value Intercept -0.11345 0.074465 -1.52349 0.133471 log GSDP 0.028106 0.013377 2.101609 0.018698 Source Author’s estimation on MS Excel. Here, the β coefficient is statistically significant at 5% level. Its value is 0.028106. β-convergence requires that this coefficient is negative so that initial lower level of per capita GSDP translates into corresponding higher per capita GSDP growth rates. But empirical evidence (β > 0) reveals the phenomenon of β-divergence which indicates that richer north-eastern states in terms of per capita GSDP are growing faster than poorer states. This provides an empirical refutation of the Solow growth model in the north-eastern states for the given time period. Graphically: Graphically, β-convergence requires the regression line to slope downwards. However, we see in Fig. 1 that it is upward sloping. This is the evidence of β-divergence. Least Squares Dummy Variables Fixed Effects Model Table 2 Result of the LSDV FEM. Source Author’s estimation on EViews. From Table 3 , it is clear that in terms of heterogeneity in the impact of per capita GSDP on per capita growth, in comparison to Manipur, this heterogenous impact is statistically significant for Arunachal Pradesh, Meghalaya and Sikkim but insignificant for the remaining states. This heterogeneity can be calculated as: Table 3 Heterogeneity among the states. State Dummy Variable Dummy Variable Coefficient Calculation of intercept Individual Intercept Arunachal Pradesh D1 -0.041 a1 + a2 -0.6499*** Assam D2 -0.0012 a1 + a3 -0.6101 Manipur D3 -0.6089 a1 -0.6089*** Meghalaya D4 -0.0325 a1 + a4 -0.6414** Mizoram D5 -0.0198 a1 + a5 -0.6287 Nagaland D6 -0.0191 a1 + a6 -0.6280 Sikkim D7 -0.0739 a1 + a7 -0.6828** Tripura D8 -0.0034 a1 + a8 -0.6123 Source Author’s estimation on EViews. Diagnostic Tests Cross-sectional dependence test The Pesaran CD test is used to determine whether the observations of one cross section (state) is correlated to that of the others. Cross sectional dependence is required to fulfil the assumption of independence of the regressors. Table 4 Results of Pesaran CD test. Source Author’s estimation on EViews. From Table 4 , it is clear that there is no cross-sectional dependence in the LSDV FEM. Test for Heteroscedasticity The test for panel heteroscedasticity of the residuals of the LSDV FEM are conducting using Panel Likelihood Ratio (LR) procedure. Table 5 Results of Panel LR test Source Author’s estimation on EViews. The results of Table 5 shows that the residuals are homoscedastic as the p-value of the LR statistic indicates that it is statistically insignificant and thus, residuals have a constant variance. Test for Autocorrelation The correlogram is used to test for autocorrelation in the LSDV FEM. Since the data is annual, lags for 2 years are used for the construction of the correlogram. Table 6 Results of Correlogram. Source Author’s estimation on EViews. In Table 6 , none of the autocorrelation and partial correlation coefficients exceed the confidence bands on either side. This suggests that the data is free from autocorrelation. Model Selection: Hausman Test The Hausman test is used to select between the Fixed Effects Model and the Random Effects Model. Table 7 Results of Hausman Test. Test Summary Chi-Square statistic p-value Cross-Section Random 17.655712*** 0.0014 Source Author’s estimation on EViews. With a null hypothesis suggesting the selection of Random Effects Model, the Chi-Square statistic in Table 7 shows that we should reject the Random Effects Model in favour of the Fixed Effects Model. Findings and Policy Implications The findings of this study present a striking deviation from the theoretical prediction of β-convergence. In the pooled regression model, the estimated β coefficient is positive and statistically significant (β = 0.0281, p < 0.05). This result contradicts the Solow model’s expectation of convergence as richer North-Eastern states (in terms of per capita GSDP) have grown faster than the poorer ones between 2004 and 2017. This β-divergence is visually corroborated by the upward-sloping regression line shown in Fig. 1. Graphically and statistically, the evidence is clear: instead of narrowing gaps, economic disparities between states have widened over the study period. To further explore intra-regional dynamics, the LSDV fixed effects model is used with Manipur as the reference state. The dummy variable coefficients for Arunachal Pradesh, Meghalaya, and Sikkim are statistically significant, indicating that these states exhibit growth trajectories distinct from Manipur. Their intercepts are lower (i.e., more negative), reflecting faster growth rates when initial GSDP levels are controlled for. Assam, Nagaland, Mizoram and Tripura, on the other hand, do not show heterogeneity in the impact of initial per capita GSDP on per capita GSDP growth as compared to Manipur. These results underscore several critical policy implications: Inequality Within Underdeveloped Regions : Even among relatively underdeveloped regions like the North-East, richer states are pulling ahead. This raises concerns about the uneven distribution of benefits from central and state-level development programs. Need for Targeted Interventions : Uniform policies may not be effective. Poorer states like Manipur and Assam may require focused support in infrastructure, education, and institutional development to enable catch-up growth. Rethinking Convergence Expectations : The assumption that poorer regions will automatically catch up through capital accumulation and diminishing returns needs re-evaluation. Institutional quality, governance, and connectivity may play a larger role. Monitoring Inter-State Disparities : Regular assessment of convergence or divergence trends should guide the design and revision of regional development policies. In sum, the North-Eastern states are not a monolithic block. The divergent growth patterns call for nuanced, evidence-based policy frameworks rather than broad-based strategies. Conclusion This study set out to test the hypothesis of β-convergence among the North-Eastern states of India, based on Solow’s neoclassical growth theory. Using pooled OLS regression and LSDV fixed effects modelling on per capita GSDP data from 2004 to 2017, it instead uncovered a clear trend of β-divergence. Contrary to expectations, economically better-off states in the region such as Sikkim, Arunachal Pradesh and Meghalaya experienced higher per capita growth than their poorer counterparts like Manipur. The β coefficient in the pooled model was not only positive but also statistically significant, offering robust evidence that β-convergence has not occurred. The LSDV model further validated this finding by showing significant heterogeneity among the states’ growth patterns, with certain states consistently outperforming others even after controlling for initial income levels. These results carry important implications for policymakers. They suggest that the developmental strategies employed over the past decade have not yielded uniform benefits across the region. Factors such as governance quality, infrastructural connectivity, and human capital development likely contribute to these differences and should be examined further. Regional development policy must shift from a convergence-centric model to a divergence-aware model. Rather than expecting the market alone to drive catch-up growth, active state intervention is needed to support lagging states. This includes focused investments, institutional strengthening, and tailored economic incentives that address state-specific bottlenecks. In conclusion, this paper highlights the critical need to reassess assumptions about regional growth in India. Even within regions considered homogeneous by policymakers, internal disparities persist and, in this case, are growing. By acknowledging these divergence trends, more effective and equitable policy measures can be designed to uplift all regions, not just the already advancing few. References Barro RJ, Sala-i-Martin X (1992) Convergence. J Polit Econ 100(2):223–251 Barro RJ, Sala-i-Martin X (1995) Economic growth. McGraw-Hill Caselli F, Esquivel G, Lefort F (1996) Reopening the convergence debate: A new look at cross-country growth empirics. J Econ Growth 1(3):363–389 Cashin P, Sahay R (1996) Regional economic growth and convergence in India. Finance Dev 33(1):49–52 Durlauf SN, Johnson PA (1995) Multiple regimes and cross-country growth behaviour. J Appl Econom 10(4):365–384 Easterly W, Levine R (1997) Africa’s growth tragedy: Policies and ethnic divisions. Quart J Econ 112(4):1203–1250 Islam N (1995) Growth empirics: A panel data approach. Quart J Econ 110(4):1127–1170 Mankiw NG, Romer D, Weil DN (1992) A contribution to the empirics of economic growth. Quart J Econ 107(2):407–437 Nagaraj R, Varoudakis A, Veganzones M-A (2000) Long-run growth trends and convergence across Indian states. J Int Dev 12(1):45–70 Sala-i-Martin X (1996) The classical approach to convergence analysis. Econ J 106(437):1019–1036 Solow RM (1956) A contribution to the theory of economic growth. Quart J Econ 70(1):65–94 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Empirically testing the β-convergence hypothesis for the North-eastern states of India\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eβ\u003cb\u003e-c\u003c/b\u003eonvergence, a cornerstone of neoclassical growth theory, posits that poorer regions should grow faster than richer ones, eventually catching up in terms of per capita income. This concept suggests a negative relationship between the initial level of income and subsequent growth. If such a relationship holds, it implies that economic disparities among regions should decrease over time. However, whether this theoretical prediction holds true in the real world, especially within diverse regions like India, remains an open empirical question.\u003c/p\u003e \u003cp\u003eThis paper focuses on testing the β-convergence hypothesis in the context of the eight North-Eastern states of India over the period 2004\u0026ndash;2017. This region is marked by both geographical isolation and socio-economic diversity, making it an intriguing case for examining regional growth patterns. Despite being targets of several special economic packages and policies, these North-Eastern states continue to show uneven patterns of development.\u003c/p\u003e \u003cp\u003eUsing per capita Gross State Domestic Product (GSDP) data, the study first tests for pooled β-convergence across the eight states and then employs the Least Squares Dummy Variable Fixed Effects Model (LSDV FEM) to investigate state-specific heterogeneity, using Manipur as the reference state. The analysis reveals an unexpected result: rather than β-convergence, the region exhibits β-divergence, meaning that wealthier states are growing faster than their poorer counterparts.\u003c/p\u003e \u003cp\u003eThis outcome challenges the conventional expectation of convergence and raises important questions about the effectiveness of current regional development policies. By identifying states with significantly different growth paths, this study aims to provide empirical grounding for re-evaluating regional planning strategies in the North-East. The findings underscore the need for tailored policy interventions rather than assuming automatic convergence through market forces alone.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe concept of β-convergence stems from the Solow growth model (1956) This model predicts that poorer economies should grow faster than richer ones, assuming diminishing returns to capital. Barro and Sala-i-Martin (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) pioneered the empirical work on this topic and found evidence of β-convergence among U.S. states and across OECD countries. They argued that although absolute convergence was rare globally, conditional convergence was more common. This means evidence of β-convergence was found after controlling for factors such as investment rates and human capital (Barro \u0026amp; Sala-i-Martin, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequent studies have expanded this framework. Mankiw, Romer, and Weil (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) extended the Solow model by including human capital and reinforced the idea of conditional convergence. Sala-i-Martin (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) emphasized distributional convergence and used kernel density estimation to explore income dynamics across regions. Meanwhile, Islam (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) introduced panel data techniques to control for unobserved heterogeneity, further strengthening the empirical case for conditional β-convergence.\u003c/p\u003e \u003cp\u003eCritics, however, point to evidence of persistent divergence in developing regions. Easterly and Levine (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) argued that institutional quality and geographic constraints often override neoclassical predictions. Durlauf and Johnson (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) suggested that countries may follow distinct growth paths or \"clubs,\" rather than converging globally. Caselli, Esquivel, and Lefort (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) developed GMM estimators to correct for biases in dynamic panel models, offering more accurate convergence estimates. In the Indian context, Cashin and Sahay (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and Nagaraj, Varoudakis, and Veganzones (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) found mixed evidence of regional convergence, pointing to infrastructural and institutional disparities.\u003c/p\u003e \u003cp\u003eOverall, the empirical literature suggests that convergence is not automatic. It is heavily influenced by structural factors like human capital, governance and openness to trade.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe empirical methodology of this study follows the standard framework used in testing β-convergence. Two key econometric models are employed: a pooled cross-sectional regression to test for unconditional β-convergence, and a Least Squares Dummy Variable Fixed Effects Model (LSDV FEM) to examine heterogeneity in state-specific growth performance with respect to that of Manipur.\u003c/p\u003e\u003ch3\u003ePooled Regression Model\u003c/h3\u003e\u003cp\u003eThe equation used to check for β-convergence is a pooled regression model given by:\u003c/p\u003e\u003ch3\u003e(Log GSDP growth) = a + β (log GSDP) +E\u003c/h3\u003e\u003cp\u003eHere, the Gross State Domestic Products (GSDP) are in per capita terms for each state i for each year t. A negative value of β implies β-convergence and vice-versa.\u003c/p\u003e\u003ch3\u003eLeast Squares Dummy Variables Fixed Effects Model (dup: abstract ?)\u003c/h3\u003e\u003cp\u003eThe equation for Least Squares Dummy Variables Fixed Effects Model (LSDV FEM) is-\u003c/p\u003e\u003cp\u003e \u003cb\u003e(Log GSDP growth)\u003c/b\u003e \u003csub\u003e \u003cb\u003eit\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e= a\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ a\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eD\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+a\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eD\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ a\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eD\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ a\u003c/b\u003e\u003csub\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eD\u003c/b\u003e\u003csub\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ a\u003c/b\u003e\u003csub\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eD\u003c/b\u003e\u003csub\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ a\u003c/b\u003e\u003csub\u003e\u003cb\u003e7\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eD\u003c/b\u003e\u003csub\u003e\u003cb\u003e7\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ a\u003c/b\u003e\u003csub\u003e\u003cb\u003e8\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eD\u003c/b\u003e\u003csub\u003e\u003cb\u003e8\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ b (log GSDP)\u003c/b\u003e\u003csub\u003e\u003cb\u003eit\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e+ E\u003c/b\u003e\u003csub\u003e\u003cb\u003eit\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003cp\u003eIn this equation,\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eDependent variable\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eLog Growth rate of GSDP\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eIndependent variable\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eLog GSDP\u003c/p\u003e\u003cp\u003e \u003cb\u003eDummy variables\u003c/b\u003e: \u003cb\u003eD1\u003c/b\u003e-Aunachal Pradesh\u003c/p\u003e\u003cp\u003e \u003cb\u003eD2\u003c/b\u003e-Assam\u003c/p\u003e\u003cp\u003e \u003cb\u003eD3\u003c/b\u003e-Manipur\u003c/p\u003e\u003cp\u003e \u003cb\u003eD4\u003c/b\u003e-Meghalaya\u003c/p\u003e\u003cp\u003e \u003cb\u003eD5\u003c/b\u003e-Mizoram\u003c/p\u003e\u003cp\u003e \u003cb\u003eD6\u003c/b\u003e-Nagaland\u003c/p\u003e\u003cp\u003e \u003cb\u003eD7\u003c/b\u003e-Sikkim\u003c/p\u003e\u003cp\u003e \u003cb\u003eD8\u003c/b\u003e-Tripura\u003c/p\u003e\u003cp\u003eManipur (D3) is the reference state.\u003c/p\u003e\u003cp\u003eThis model controls for time-invariant heterogeneity and allows the study to isolate state-specific growth effects. The statistical significance of these dummy variables indicates heterogeneity in the growth experiences of the North-Eastern states in comparison to Manipur.\u003c/p\u003e"},{"header":"Analysis and Results","content":"\u003ch2\u003eThe Pooled Regression Model\u003c/h2\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe β coefficient.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCoefficients\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eStandard Error\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et Stat\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.11345\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074465\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.52349\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133471\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog GSDP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028106\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013377\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.101609\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018698\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAuthor’s estimation on MS Excel.\u003c/p\u003e\u003cp\u003eHere, the β coefficient is statistically significant at 5% level. Its value is 0.028106. β-convergence requires that this coefficient is negative so that initial lower level of per capita GSDP translates into corresponding higher per capita GSDP growth rates. But empirical evidence (β \u0026gt; 0) reveals the phenomenon of β-divergence which indicates that richer north-eastern states in terms of per capita GSDP are growing faster than poorer states. This provides an empirical refutation of the Solow growth model in the north-eastern states for the given time period. Graphically:\u003c/p\u003e\u003cp\u003eGraphically, β-convergence requires the regression line to slope downwards. However, we see in Fig.\u0026nbsp;1 that it is upward sloping. This is the evidence of β-divergence.\u003c/p\u003e\u003ch3\u003eLeast Squares Dummy Variables Fixed Effects Model\u003c/h3\u003e\u003cp\u003eTable 2 Result of the LSDV FEM.\u003c/p\u003e\u003cp\u003e\u003cimg 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A0EEUiXhwjIAoIUya5bnj3nWp9/AR3VZyLBcCBGfcyEEHMsA0EOrzEYkwzAMwzBny+kaE7iFWxaA1mfIPGHkt/GoBxi3buFa8hkzG3oJza8daZS1HrNi+Rbgtx/J3TrLRbdZgq7Hn+u6jlqzC9cCgBbG2u0MwzAMwzAnzCkbE8DqTQtAD4/aamvC+6KOHix8WltMPgIA+L4H2zQT58SlL0YkFxqmBq3ckv9tlbP9FIKjJlFZMVERfK8xRd3D3PBsM+LXYMJW5gsY+kCY2YbWPOiLY4wyH17Dhpnok6aZMO34ufvQz8T2AL9hx/01TDuzzdPN19kxW3umH59hCeUaSc79DOszgb4YzOaz/lhfmFJT7lQ0S8kniXVpmrLvno1c0IYwaqVn52T7G+ljWH7DTOWPPJ1+3III2WZDwPdsmGG0bNOGG8nn+x7s5eS4quctew6Eoq0MwzAMw5wodBq4FgEgGA4JEuQYIMAiN5mPXLIAguWO/h3J51qGlJORDEcEOcM6EsmSklwryG9ZZCTzAAQYNBQVyAvLZCbLpVERlyxV/QBZVjAWQVuGCCdoS7LuDML8hhOpV8FQbnK8w/Edl6Jjn3yWTOl2Tz9fpJzv8cyaf972jEvxuoUzXv5ozsevz/FEyxpkOQ65QoxfA0NcsjRFvYl1OQhzWxoBIMPpJ+TIvmqJ/JLsOkbJovaoEtI0xXfRcKgf5HFtU9aVkQynP2yDcMbnhdWmQbzBDMMwDMOcIGdgTIyUrqTuFP88oSyGMmCQ5caVJ+GGCnNCZlgmUVFUMTaciBEgRgpkVLEcKYnJugUJd6QEhWWG8g2L3JFwcqPKZrLzszLRmBCxcUnWN2yj5ZJIChAjYygsNuuYzT5fsxoHM+afsT2zjs+oPVJ+PHuGoZixPicjyHXUhrBhWGQ5ijbH5tAid2gfpNflcYyJsAwsl/rJNgiXLEM+t0JrIjAmZD3t9FoePjfIavdT82YGZa32gAbkkq1pw7zRdsm88tlmaKUwDMMwDHPinIkxoVaEA2Vs+FlUWRy9jc3Uu0KZ0QwZylpUUUwyNByGz6aoO9a/kVKZfFNPSvlzMtxxmCIZ4xVuIQS5rkuu45BlGWRE3pqrlOsk6T5NMWap+ZrROJgp/zztiTyaYnxiOwaGJXcMXLVSPyRjfc6EEOS6DjmWRYYR3xlR77QYpLANYkbAcYyJKGPHLWVMRHYrRhLIMUfGR+oxyXkzNQQ7DqP8U88BwzAMwzAnyqn7TAAA9FXcMgD0HmHoOuE9RguAcWsVCZ9SAAL7wa03rXL0DHYkFerSMXUGR2NjqZD8SEFYt4WbqfPnAaWbsACgtw/h9/EMAHANi+mOjM68nwGGYcByXIhuE+mmy7PzmqahUCigXC6jXA+u4x1zw9BsY3ay8zU/87Rn1vHRUfvUks7uvRZa9TrK5TIKheDMvt2Ap3KyOC66jlKphlqziW63G9zo5MAygF69MIqlMmFdlm5WoCU/nItZxy3AWEJ6ZUXmbTUnfTSSqVBHlwC0voWHy6j93oKpZc2BczpzwDAMwzDMkLMxJgLFC+ih/oUnHVI/awGw8GlNoekw2Yy5Grbb7aJZKymMM8CzCygH95IalgXLceC6Llwh5PWix76X92Iz1/iUmuiSgOs4sIx4hl6rjnIhotzPRRiob7yTvq7X0PzUAgD0HrXHOmqfNJ69OGHcMkyWawXoGY9movQAu4OsObgr58BhR2yGYRiGOS3OyJiIvM1vPYYXXAcL66biDToAFLBkAIABJ3Gvfjqp3sIfh7DuMddsBrsqMJZQGN6e9Ax9hRbn9+X74TeLh8eywXAEodtsolmroVQqoaTr0PXRG+Fnqk5M5E3Ol4pZ23Oc8dFRqtXQDHYJSAgI4QaxH46r3If9CI3wGTjhdSn29xQKuYfHD0mOWz9j3PZkzmfCV5RPEpm3/kAxV/F5K2uhNZI9BxqA3jdt+JMrZxiGYRhmDs7OmEAJN4O78D/7SN6db2WeI9KxKs9FoV6QV1nG9CHfQyM4WqG+XnP8NZrjCesGWmUTdiNaty+vswyu+JRHtEr4xIm3dZjbs4cBx84HPewnruiV12qWpXE0N8edr5Nm3vbMMD6Rq33tqHxdh64XsLgUzx5n2vUZ7ujJ62TlNcbJvgTXssbWJIBgXWroob6oXpdJ/bqwVJTKd/1XGN0O68NrmPislcwdpYd9N36d7HDcaFy5JDpWPwzbvIyGK8bMmwvyG1jOBXMQzTucAw0ndJaLYRiGYZgskk4UJ0LSATv5ueqZwsF20tWeKUfjqPzAWVR+LP8fd1CVpJ2Jiaa6GjbW/oyrP/EGrobNYOI1pkFK3lA1/ZjNOl/p+R7PNFe3yjRyIp++PbOOz3RlMm5zClNyTWQQvUFsXDJS8gQ5wW1KyTR03o46VAuHDOU1rwY5TvoqWVkkcMyekIbXuYYO2FZb7WBNk6+GhWFRO7jvdeLVsHybE8MwDMOcKme4MxE56hR7g5pNqdmFcNNnoZHlaFz6BG5wvASYHOgrGx2lJinrNgwLjitA3Vqk/TpqXQHXCRxyZU5YrkDz5vCDN4pe60I4FuLdMYb9EYFTwHGO5cw8X6fMLO2ZZ3z0WoZ8GDAsB67oIuYSNOf61EtNdEWwvjLrInRTEet01Lp95brsfnot/dJer6Hbd+P9MSzZj0X1S3691kV/wrhpAHqP3KmPGpUe7KKvGtfIvIVHnPRaRt7IHNQLqpYzDMMwDHMSaEQznUNgGOZtwLORK7dAlotBs6Q0FBiGYRiGYSZxtjsTDMMwDMMwDMO8NbAxwTAMwzAMwzDMXPAxJ4ZhGIZhGIZh5oJ3JhiGYRiGYRiGmQs2JhiGYRiGYRiGmQs2JhiGYRiGYRiGmQs2JhiGYRiGYRiGmYtTMiY82JoGbVwyTZh2A940UbtmxPdPQeg0+B5s04z10zTt8X30PTRsE2ZibOwxhTxbg6bZ8Iaf+PAaNkwzPsay7qQcH41EvuwUrUNyvLpDJq2PdL3HxW/E52WaZDZk+8Oy4f8vNHOsUd9rwI7N7zzf3XnWiWxvdVnRXsH3RjAMwzDMuSAZEvtkcMkCCFMmw3JJJEXMg3DIMkCw3OST08e1yFD0TSaDVE0SY8vIcUkTjO3wmSv7rCg/lONER1eQMyH/KFkUb8Fx6w6LWal84+s9PsIxFPWMT2Hbw7LKvlwkxq439Rp1rXHjpi6TxiXL1BTlR8lw+jRIlBKOSZoi77Du9iBVhmEYhmGYs+WUjYlspVAIQa5ljJSb6bSS8YRK6knImomR8RQ3jGQfpfKTGAvhDPtuWA6JYSFB7lDxNSilvwZ9DLvoWsH4GRa5IyGJuhVyshi2yyI3WeaE6h4q9mc+T0kmr9O3B5csbYo1GtHOhWMMlXnDiZcZGcIGOf1RGRWupUk5hkVtxTqRdRjk9KOmgUu2Jg0Qw2rT6JEg1w6NDIvaAzYnGIZhGOZN8saMiSERpfrYuuUbMiaGyrHhKHdYQqU7+mZ7qIhntDVL4ZblwnENxzmtsIeo6s5mvLyTqjvr87NnhnV6wRkaBoZDKt3ftQLFfWgZCHKM5GcJXIu0cc+J5BhrmsJYGBEaG7HdCdcmTQvamyomyAl2Onh3gmEYhmHeLKfkMzEDeg1fOwYAoPU4eVI+PGedPO9uwrQ9jI5aB34A5Zb8b6ss89lxeX7GefFG8vC33wh8GExMc0xe7PcAANanNejJhwBKNy0AQG9fBJ94eNwCAAtusxTNOkSvdUFEoNjzoJx1E/LTEppEIOqipqo4wrXFCRngo2GW0QJgOF8r5J1U3T76zwDAwK3VCQXPCUmfCek3osH25FoZ+ROYsIcLJli7kTU7eqZm6vWJedboHgiA9ekdFJIPAZRuVqAB6O37oNgTA7fKqhIACksoakDvkQuBoE25oE1Dn4YSmoOBXCcFLV4+wbWCjvE5khhYKmDGMgzDMAzDnCRv3pgAoK/eggEAz/oY6UUebK2Acr2FXk8q6yN66LXKKBSmd9T1bBOFQhmthKxer4V6uXAM59qRcryUoXOhsBTvn9+HLLKEgsoxNcsB23sMqc+rDZAUfgOftTC+bQF+4yPUewAMB1+rrIOTqttv41EPAK5hUSQce7P6fV7pN2AW6mgNl1QPrbpcS54drN1h5tEzFae3PhFfo3qG6h0YBvHvIAD0sO/HzYshYh97GY+mxm/g84cESq6T0u+waWhAr45fVV2M/K19eNVf4W6XAOMWyln9YRiGYRjmbEhuVZwMMx4fiZ7TDz6KHgOKHbMmIhIjx9/YKaCsY05Dh1+DLFfEjiIJ97jHrEKH5uzjPsP+hcegwvYYBhnjHJgTx6bix4wmEPXJyGxYyLRHlk6g7onO1+l+nx7TrdOkA/ZwbSZ8CeJO3lGfE0HOuGNtp7o+KXJkaYx/g3DICI8VDT8a+TOk2xV15o77WkyNcMgM/TgUDthEghzbTK8PSD+j9PEnhmEYhmHOmnOxM6Gi1KTIMR8fnufBazRg2ybMj8qRt8GT8NGQr8hhuV00S3rsKJJeqqErHBjKY1anTK+HXs+A5QoI6b8CIgHhWnIno1fHR8M30sljRtn4DRNaoQ650SDQVe00RPAbn0Geuvo048jSydXty1fkgGHBcUXQ53H9PsdYLrq10nA96bVPIQ+0AZbbRGnYdR214Khb+s3/+V2feu1ruTuAHlrlAgqR41eFcgswDDlfxhJm3SDwG8vIFeroEmA4fezWCqnjSr73B3zT6iY+lfSePYLbCY8NMgzDMAzzpjgfxoTYl8dBjKXIeW4fni3PjxcKBZTLZZTrdbRaPaROPY1FIHBpQKscOVITTYHyi9bjqY9NHYvw2BMMOCKpQOrQS0103cDP4lE7djRq7DEj34NtaijI80qw3LQyn8bDF0F+55MM2SdY99AXpNtEbaRtZ/f7HGNknh2bfKxsxDlcn0N01Lp9aeTJBSsJDMHu17dmd1jwPdhmDoV6FwQDVlugqzIkGstYLDfRJQNOu58yOs29HurlRdgugRJlGYZhGIY5O86FMeFJb2Tg2uJQqfbsAsrB9oNhWbAcB67rwhUCQggEPtvnAB2L1yDPlme9KA2NpUj/JNeQ6RcdGhy9fQgAfvsRerCQpc/7DRtmQe7YGJYDNzBSJhL4QsC4hSx/6FOrW0Wi3+eZyU7t54XIGp3k/5Bao4GR1w2V+ZEh6LcfZZRRM1onNFonZVVJH+1veiAYcPq7qMUcwGV7dts2NA1ofb6FrC4xDMMwDHP6vHljIuKoO3ozHt52ZMARhG6ziWathlKphJKuQ9dHb3Of9Se9vy5gSZ7FgCMiCpEyNSce41FRkBWg9VlD+TY9NJaGb7L1VdwyAKCFzJMrsd0aH+1HvcTOzQjPNlGot9ALdwSatcgRm/EM23ZrNUMhPMm6R9G3ExdtjVDuUr3NnP76BIDCUhEagNZnW0ojzXv8EATAWBrdqOQ3TOQ0DWZDVcJH+1FwQ9TNG6mdhSSevYzFcJ20J62TEGmgs63AMAzDMOeYpBPFyTDZsXV80LpREDgr4cAr3CDKdeiIGX0+dMCOO/COHGMNchKOpCRccsKgXXN7uEaD1iUC0GUFrYs63SYDgjkj51bLDZxjk30Nc0+IcTGeyY7XJ113dC6sWFQ8QW6y36fO5HVKkTYnHbDTbRwznkOn+/RYnf76JNm2YdC6MWs0FjdOxpFIzVXkAoSow3YWw0jWypgRalw7CHQHg5x2vAbh2hMctxmGYRiGOStO2ZiYLhkKJSl+M052UhoTYYrIHSlMGcmIKJTD24gUSmEWsdttkslQKJ6T2xSOixwLVVtmGGdVAxS3aCU5+brD26+yk8pwOR3OhzEhH49fC7H1SedljSZuhwpvhIoFqAuD1iXLKpLVjhgGo8B0mWkG44RhGIZhmNPhjR1zMgwDluXAFYSuInCbXutCOAnHTxgwAudPEThNxBx1S5/AtSIFIjfnlJpdCNeBFRcIGAYsx4Xozn+EBABQaqIr3JR8w7ACH4LYx0DYpowyjiuCcQmPGSl8GkJ/h3mZeKToNOrWUetS2ql3OFZqx+23nVNfnzjGGk3OVdgm6qKmXjgjvMeQB6hmRUdtVzp/m4r2Oq4AdWuYEAePYRiGYZhTRiOief7SMwzDMAzDMAzzjvPGdiYYhmEYhmEYhrnYsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcnK4x4Xto2CZMU4OmjZJpmrAbHvxhtLmLjAdb06BpNrzkozPBh9ewFWNsw8saYN+DbZqK/MmMEeYpE8VvwNQ0aGZjFGRwKs6of0OC+bRVs+mjkWhHdnpT64FhGIZhGOYMSYbEPimEYxCACckgy02WvGi4ZAEEWHT2XXHJMpJjGk+GIxJFLDIU+cbOxzxlYoRjBILhUKJFYzij/oWISH3KjIKcCe0ZpTexHhiGYRiGYc6W09mZ8Bv4qN4DABiWCyEIRKMkhIBjGQB6aJX5De68eHYZrR4Aw4IrRGSMBVzLAAD06h+hMXwj78EutyCLuBCp/Kr5mKdMFB8Ns4xW8uMpOJv+YbT7UQjqy0RHrRtfy7EkHMhWWXBFE6VkcYZhGIZhmLeNpHVxEgx3JZRvd0e4lnyLOyHbOedN7UyE9RqUfDkfEo5v+PZ+OC8ZuwPJ/DRnmRGjN/mW48gdgww5ac6of9GdDMMi17GmWrtpJreXYRiGYRjmbeNUdib0xWvyH8/6Y8/Hl5ryjW5T+Qo3PCsfP/uuaSZMO+5v4dnyme3JXRF7eK7dhD18bR3Ii8gZPYsTk+c1Yuf1Tbsx5dl7iZ9xfr+RFBL6FGhm5E37OEpoEoGoi5qefBbn2qLMIPbla3fr0xpURUo3LQBAb18MP5unTIhnF1DvAYYj0FxNPp3E2fQP/WfoGRYcV4C6TZQWR4+mZ7T7YjhfT2wvwzAMwzDM28KpGBMofQLHANCro6CZsBsNeL4/1rCI48HWCijXW+j1kudOeui1yigUFMdV+g2YhXrkqEoPrXoBZsOHZwfyhplHzzLpN2CW64g2odeqo6yqW4FnmygUymgl+tDrtVAvT6j7uPgNfNYCAANLBQDw0X8W/b+CwpI8pjM0AucpI/EbJsotaUh0T0O7PpH+AXqtC+o2USvN30a/8RHqPQCGg69Po68MwzAMwzDnlNMxJqCj1hVwHQsGemjV6ygXCihE3syPu83Js4Mz9gp/CxIu5HH5Fh4nNPpWvQ44o7PywgnP1RdQbllwh7IEHPmSWvlGPSQpL1p3WXnbTwTPRlke+Iflisj5fYJw5dn6Xr0gdz8AQK+hO+Wb+In40qiS+u0beFPu2SjUe4Dlnpoh8Ub7F8PDF9KSgPO1ekeEYRiGYRjmbeWUjAkA0FGqNaWCLARc14FjWTAMA71eC616GYWCpnw7Hx5/omZJHk/yPHiNBmzbhPnRGCdZy0W3VhoqdHrtUwQ2Ayy3idHLZx214MhL8o16jIQ86CU0vw6cbFuPx+xO+GjI1+aw3C6aJT2mZOqlGrqBs24raREdE79hQhsq2qe0KzAOvwFTbklAqM+vHYs33r8EfuOzwPD99A0bNQzDMAzDMGfPKRoTEXQdpVINtWYT3W5Xvp0XDixDvp1PGxQ+PFv6GRQKBZTLZZTrdbRavdiRoyRG5vmWMUdfxmDdVCjD+ipuybMy6CebPUQgOL6PVjnq7xFJgUI83iiZAd+DbWpyRyDYDTl7RduDXaijBwtu94Tf0p+L/iWJ7Ep8olgrDMMwDMMwbzmnYEyEQdzGOxLreg3NTwOH2Eft2O6AZxeCI0KAYVmwHAeu68IVQl4rK08vpQgdcd81/Mi1poblwBVyNySODukX30PmyS6xLw2ca4uBITBbGYRv6dFCOct46tWD427T+Z3gVPt3TLzHsr/GLayeiECGYRiGYZiLxSkYEwUsGQDQQ/2LadXFKB4eB461jiB0m000azWUSiWUdB26Pnrr/yx7a+BEUMr323gkNVJk2y7hGMg+pOIRxNLx4hF4tolCvYVe+La+WYsc54pTkI1C6zN1FGpPDnxsh2eeMifJaffvOAzl3Vo9GeOEYRiGYRjmopG8K/ZEcIO7+gEyDItcIeL3/gtBrjO63z8eo2AULdlKXNgvXCcWETkslx2vYszd/2EbFTEJQnlhG8LnQkTqH1amjjMxigBukOMm+++SY00Xi2Mck+IqpBmNrWE5JIaFBLlhe1LxMuYpo0DMGmfirPqXIFwXE+dlzNpiGIZhGIZ5RzgdYyIZDGxMMhRK20gRH59O25iwrJFRFEtGVCFVGxNEFFFgM1JUTqhsq9qqZKQ4T0zRgRk7L4ZiDOcsk2RmY+IM+xdlWmNiOF/peWcYhmEYhnlXOIVjThK91ERXBNfDGkknByM4+07oKm780WtdCMdCvJgBIwguNrzyNeFrceLcbEK4lry9CZCOv44L0Z3uaFKp2YVwHVjJ/huzyVESnteflVITXeGm2mQYVuCLEPtYMk+Z43KW/ZuH0P/CWMLJHJpiGIZhGIa5eGhERMkP33U8W0O5BVhuVnRuhmEYhmEYhmFObWeCYRiGYRiGYZi3GzYmGIZhGIZhGIaZCz7mxDAMwzAMwzDMXPDOBMMwDMMwDMMwc8HGBMMwDMMwDMMwc8HGBMMwDMMwDMMwc8HGBMMwDMMwDMMwc3E6xoTfgKlp0MYl04Td8E436FyA3zChaRrMxinV5tmyT7aXfHIinHr7U3iwNQ2aZuN0enT6Ywacdh3BGM0iO/xemI0zWfcMwzAMwzCnzekYE9PQ66FVL6Nwmgorw5wGvgfbLM8YoduDXajLqNkMwzAMwzBvCadrTBgOBBFIlYQLywCAFsqzvN2dA73WBRGhW9OTjy4EF739bw8+vIYNs1BGayarwEdjZuODYRiGYRjm/HO6xsQ49BKaXzuQ9sRj3p1gzjW+Z8PUCijXW+gZFlzHSmbJwEfDLKDeAywnWO8MwzAMwzBvCW/OmAAAfRHXkp9F8H0Ptin9BcJkmjYaXuLEeXA23mz4gdIX+mXY8PxsnwPfa0wnf4gPzzYj/iAm7My8WfLHl1GRbL9nS1m2B/iNSH+DPme7VgTjY0b9V6b3XQnrTY4jIm1M+xDMNmYhU8+9zD19HUN/HnPMOCnoP0PPsOC4AtRtorSYzKDGs6UhYTgCzdXkU4ZhGIZhmIvNmzUm/D6eJT8L8GwThUIZrV78PEmv10K9XFAqtNj/Ah+VW5Fz6UsoKE8G+fBsDYVyPVO+ZicVbA+2WUC51YvI76FVLsB+HMsIBMq1Wr4sk1a6Z6dV1lCoR/sLoNdCvaBSlOUb8kK5hXiTAt+VU3EKnm3MQmab+/nqmBW91gV1m6iVlAtKid8wUW5JQ4KPqDEMwzAM8zbyhowJH77XgBk6pFo3UYo+9myUWz0ABixXxPwuhCuPivTqBST18V6rBTjuKH+3BpUK5zc+QrkFhXwB4VrB0asyPooorZ4dnJM3LLhilN91DLRaydPwHr6oj9of9RUJ24/WZwqFf3aMaH+FC3n4podH7bhwv/ER6j0AMOTb9aTvSq+OL45v38SYbcwCZpz7mevQa+gSgaiLU9XvPRuFeg+wXDYkGIZhGIZ5e6HTQDhkAIRpkmGRGy9MjiGfWfEHI0L5YQbXCuQlZUmEYxAAMhwxnfxQnuGQkB+QBRBgkCM/iBHKH7YnUgcMiyzHIdd1SSjKTkO8/USuFchWdCDdFpquzzHC/sbHM6w3bEeUdL2zjhlN187Y3M9TxwkRrhGV7LCNw/Uz4XOGYRiGYZgLyhvamQAMw4DluBDdZnxXAgL7wXmVVjl6tj+Swh2NpOO2sYRC9P9KQvkWbsYrHlG6Kd/w9/YhED2OdQ2LipfM+mLS80NH7dNgh6PXQqteR7lcRqEQnP23G/D8429LGEuTeysJ+2xg6iLHZeYxw+xzP1cdp014BawFN2NnjGEYhmEY5m3hdI2JMVfDdrtdNGulk1W2ri2erLzjUGqiSwKu48Ay4nf49Fp1lAvJs//M24Df+Cy4AraFcpYh1KujoGmnGxSQYRiGYRjmDDhdY2IuClgyIM/2D8/AZ6XkrsY0hPJbeJylyXmPpUIY7nQMb516hr5C//f7WW7kOkq1GppdGSeChIAQLhwZYAO9R+1TcHpWEfa5h32RfHYyiHBLIWSuMZtx7ueqg2EYhmEYhjkpzqExoWP1lgGgJ28l8vy4wu17aNhZ15BOQygfaJVN2I2o/ODqVOmdDePWarDTUcInTrxNwxKejY+kZ/OIyPWjdrT9ug5dL2BxKZ799In3OX69qrzZaprxLEhNH736RxHncR9ew8RnKV/nGccMmGPu56njdAkDDCqTCJzvhzt28xjDDMMwDMMw54ikE8WJcAKOpq4VOM9mpajj9jhnWIUDM5EYOTFnpVTbI07ViWRZ6fqHzr+ZSe00rCLZ/tkcoYdPMtsvU9TZWu2Ane1Yb5DjpMdgXJ2qMQuZae5nrWPYh+nHX8mENafkBL4XDMMwDMMw54lzuDMhKTW7EG7a3wCZjtuzoKPUlFeNJuUbw8BkSedZHbWugOsEjtUyNyxXoHkzlhEI3lCr5AMGDMuBK075atIUsv3CtRBvkgHDciGmeUuu19AVbrxPhiX7ogziNtuYhcw29/PVwTAMwzAMwxwfjYgo+SHDMAzDMAzDMMwkzu3OBMMwDMMwDMMw5xs2JhiGYRiGYRiGmQs2JhiGYRiGYRiGmQs2JhiGYRiGYRiGmQs2JhiGYRiGYRiGmQs2JhiGYRiGYRiGmQs2JhiGYRiGYRiGmYszMCZ8+F4DtmnC1DRow2TCtBvwfD9ZgHkD+Il58BsmNE2D2biY83P27fdgaxo0zYaXfHSSeLb8/tinVItnIxfIP5EANL4H25RzESbTtOGd1bQwDMMwDHOqnKox4Xs2TK2AQrmOVq+HXuxpD71WHeVCAabtgXWLN4TfgG1qKHwhkk8Y5lj4jWXkCmW0eolvfq+FcsGE7Z6IucIwDMMwzBvk1IwJv2GiUG6hB8AwLLhCQBCBwiQEXMeCAaDXKqNwWm9amfGIfbTiuh4AQK91QUTo1vTkowvBRW//xcfDF3d7IACG1Y589wVcy4CGHlqrVbA9wTAMwzAXm9MxJvwGPqpLDdVyBbrdJkq6jphap+so1ZroCgcGALTKYHuCYd4SvMd4CAIMB396UI5893WUml9j09QAtPCtx9YEwzAMw1xkTsWY8NuP5JEmy0WzNOHNsF7D164Dx3XxSSnyeXA23Gz4wXGp4Mx14rx16I+RPJPdUBzKVuc1YSvyhsxTZhzzyPMzzp2r+igJxswc5dc0E3YjepzMR8PUoJVb8r+tsswXWHTjfA7UfUi3x7PlM9sD/EZkDjU5jwrRQKb88WOURNX++dozzVhmE9apHMegjWr/Bx+eHfUzmtz/2dbJjPL9BsyczNc4sRNxBpYKyc8YhmEYhrlQ0IkjyDFAAMhyk89mwLUIABmWRQakPAAEwyFBRESCXCvyuSpZbpCXSDhG+nkib5J5yoxjHnmuNb6M4YQ9DBmNvzJFxk+ZL2hD2Na4/NnGfGJeGJRs/jxjpELV/tnbkzFGYRqOJRGRSxZAgEXRFoZ1pucp0tdUn1yyMuq1LPm9SJaZvE760dxTyR9ESpBwyNAgxygqKhNBjqnJuq12ZJwEuZZBGuT49WOVMAzDMAxz0TgFYyJUqpKK2YwExoRUhEYKashI6TTIckVMWRHuyACRStyoTZYblyRcJ8ibbO88ZcYxh7zhGCT7GC0TN9qi4+JE6xEjBTKmh4Z1JJRTlTI+25jHlffYHIpwLJJK9hxjlIGq/bO2Z7axPDljYthOw6JRtYLcqKEVLTP1OpGau2tJJX+S/OPr+YKcDCPHsKKGGMMwDMMwF5ULYEzElTPJFLsfYXnDIRF9w2xYZDkOua5LYmz75ikzjlnlTdFHESiKwwxTlEkytTExhezYmEeUYkUBtSI96xhlk27/rO2Zor8xTsqYGP/9SZeZop3hroLl0oBcssbsMAgn2DU4AWNCuFawm6FIRsJAYxiGYRjmQnIKPhMFLBkA0MP+SZytNpaQPlYtsC+dMnAz6mcRpXQTFgD09iGgo/apvDkKvRZa9TrK5TIKheBcuTLexYxlwvv/FUkeiZ9R3rCPQKuclqlpGrRCXfqmtB4HsQ3CMqdxFn3WMR9hTN2YWcdoPqZrz2mO5Rj8Pp4BAK5hUeFupC9eS3wy5TohuU46Ylb58+E3lrFYbqFHBpy2GN3iRgLCtWDs9VAvF/h6WIZhGIa54JyCMaFj9ZYBAGg9VjmWKggVcbORdmq9thi/BWpeSk10ScB1HFiGbF/IMN5F0kl2njLjOGl5byM8Rm8BPtrf9EAw4PR3UStHv8E69FIT3bYFTQNan29BsD3BMAzDMBeWUzAmAL32qXxD3SqPvyEGkBFygxuFjFurUxoO4e5HC5n2ivcYLSR3NnSUajU0uzIGAQkBIVw4llRae4/aaWNm2jKlZuTtazw1Y2/yp5Q37KMBR6RlxlMTsooT3hWKMe+Yz8O0Y3SanOZYSkS4pRBFX4TcG3iGvqKTfl/uK4yYcZ0UZpV/HE5v7BiGYRiGOR+cijEBlPCJE+xOlAvD6yljuovvw2vYMAvlQAF18PXUAcYiux9lE3YjKju4yjNqoPiN4ApMef3lMK+uQ9cLWFwaFh4xT5lxzCwv7GMP9YKpGD8PDTt5tWh8XOJXgvrDa0rVV5H2JyjoM4758NkMzDxGp8lxxnJEQWr66NU/ilw968NrmPgsuJU3TvjdGc17iO/Zw/gtI2ZbJ4QSPtmUQePqi2r5x98o0LH4Y0AD0FpdRsONryzfs2GutkAEGLfK0LXYY4ZhGIZhLhJJJ4qTRDiJa12zUuyKzYAMx+ARU1xTGpE7upknK6UdXucpM4555E268hNG0kF9wnWmSYf2yK1ZwGi8VQ7Ms4757M7H842RClX7Z2/PLGOpdsAeOsmnkkGOk7XGs+ud92rY+DoR5BjBjU5j5J/U1bCZia+GZRiGYZgLzyntTEj0Wnj+3YKROP8OGDAsB64QoG5tjjfZOkpNgnDTZ+sNw4LjxuXqta4y76gdXSQ3RuYpM4555JWaGWUMA5bjQnTDI04hOmrdwMk1VsSAYbkQwyNRAaVP4AbHhwAAz8btUMw25vMwzxidHjOOpQq9hq5w4/0xLNmPxWjGKLJe1wmc0QEABixXoHkzlnHIbOtER63bz5R/MhsFOmq7/WDsstdK4WQqYxiGYRjmDaERESU/ZBiGYRiGYRiGmcSp7kwwDMMwDMMwDPP2wsYEwzAMwzAMwzBzwcYEwzAMwzAMwzBzwcYEwzAMwzAMwzBzwcYEwzAMwzAMwzBzwcYEwzAMwzAMwzBzwcYEwzAMwzAMwzBzceLGhN8woWnaTMlsZIdJmwbfP155wIOtadA0G17y0VvJ6ffXs+Xc2qdVwQUluVbD78txvwMXAw92LgfNdjFdcBsP1TB/ZgEfXsOGaSZ+U0wbnngXxpRhGIZh3iwnbkycKX4Dtqmh8IVIPmGY88W7vlZ9D7a5ila2VRDH91BdXkVzbH4P1eVFlOst9HrxJ71eC+XFAkxHTGm4MAzDMAwzDyduTOi1LogokVxYAAALbuoZoVvTk2KmQ+yjlVAiGOZckrFWw+/L3N+Bc0+wc1Aoo9WbRq0f5W92x+f3qqtodQkwLLT7IvKbIuDaJjQAvbu/QkOMl8MwDMMwzPycuDHBMAwDAL5nw8wV5M6BYaHtWNCSmSLMlt/Dty2AYMD50wOUC1FjTEfpwS7atgYNPXzT9nl3gmEYhmFOiXNpTPheA7YZ970wTRsNLzwD7aNhatDKLfnfVlnmSx3QD89TJ/04TJi2h3ldLWL+AF4jdl7btBsYNjPGbG2J1uE3bJjRMqaNrCP2vu9NGLv5mTwvavzYGKn7G+ZLyzdhj5E/U389Wz5v+FJxjc1Dtt/C0A8otr6mnc/xa3Wcz4R6PNJ9m2etqGWPGWu/ATMn+9eY9qRW/zn2ihYcV4C6TZQXgTHWgSK/NiZ/CQ8GAxB1UStkZgIAXFt8W3d9GIZhGOYcQGeCSxZAgEVu8lEMQa4FAsYkyyVBghxD/WxEWOe4FLZn2vZJwjZajkNGSqZKzixtCUpMGgcY5IhIASJyLUORb5SMYYHZ+jv9vIwYjpFlpfMq6hbO+LbH51YyfX+HBeTnlqWeN8OJ9SEoFIxVdLxnmc/xazXsd7yts433xLwwyOlHpDsGaak8cdmDUfawEBlaWtZMuBZpGghWOy1fhWuP8k9VIIJwyNQ0Agza7M9amGEYhmGYaTlXxsRIoTTIckVEsRMk3JECOFS8AuVQrWiOFCOR1BCFS1ag4Mmi07UvJKq8GU5EiY7IjbZptrZIxtaRHAdZIGPsiIQ7Mnrm6e/M8zKp/akxGinslhsfoFHbE8bTTP1Nlkm0KdLe1FJSrLF55lMlhyJjGx27Wcd77Fhr4eehBRB+NmGs5zUYxnFWxsTQkAAZm/3p6mIYhmEYZi7OkTExeoObUuhCQoUsfIOcoaAlEUKQ67rkOg5ZlkFG5E3xPMp1VJlMIUKFTC1rclsk4+oYKpvDZ1OMXdguy52xv1PITs7LhPanxyjy9t6wyHIccl2Foj5kijbF+hswNCYU/Vaupcn1TDufavkqY2JyncnxHjfWw12I4W6DIMeQivZ0Y32CnIExIRxTlmFDgmEYhmHOhHPkMyGw3wMACzdLyWcBpZvyVqjePiYf2/bh2fJMeKFQQLlcRrleR6vVS10jOS+WqqH6Km4ZAPAM/ci5+XnbYiwVkh8pCMcOaJXjfgDDVKijBwCtxzPGljjevEw3Rjpqn1owAKDXQqteR7lcRqEQnOW3G/BiThbH7K+xhNSolj6BYyTy+2086gEwHHwS68b88zmZ+cd7urWio/ZpZYaxviD4HqrLORTqXRAZsNp97NYL2S4XDMMwDMOcCOfImDhZPLuAcnAXp2FZsBwHruvCFQJCCKk4nhHnqS3nllITXRJwHQeWER+QXquOcqGgdFKei2uLSLvk6li9ZQBo4bOgHu8LaZAYt1Zj+S/8fE411kmz8PziN6pYDq6SNSwHruiiWWZDgmEYhmHOgnNkTBSwZABAC49Tr5IDvMdoIePNcgwPj2VGOILQbTbRrNVQKpVQ0nXo+ujN9rPR9sHMKMuGb7NxDfISmbNoSzh2so5kHI94aiLrhbea482Lsk+pMQrRUarV0OwGsUqEgBAuHEsqvL1HbUhpp9NfvfYprGE94bxZ+DQWA+K05/N44z09k8baTe0ynUe86jIW6010EexGNGsopS1FhmEYhmFOiXNkTIRvhoFW2YTd8APFEQCCqzyD6zWTb4qBfiRvlB722/En8krMslTGjkmvXoi10/cbsD8KjtdYNxNK7Gm2JRy7HuoFEw0vOnbyCEgjOJaTvj53EseZl2CMIteN+l4DZngEKRwjvxFcZyqvJh3m1nXoegGLS8Pi4YNT6m8JN6U1gS/sz9ACYDifZBgj885n1loNOd54TyRyxet0Y31+8RvLWG12QYaD/oB3IxiGYRjmjZB0ojgdpnX4neJKzOj1nZHbeZIOqBOvGg2SdHydtn2SideeGjNeexprS7yO1PWmUXkJh9tJV6WO2jVbf2eel1MZo9mvwk3WkeUEHWPoHK6uU2aZUG+Q1LdtBSloQ9oBm2Ye70lrJe6AHfksMyluczp3V8O6ZAc3Nk1MyvIMwzAMw5wE52hnAvLoRZMg3PRZbsMIg1nVRm9jS5/ADY5lAACejd766rUuhGMhLsYYyhHBwfbR0Zk5uNmEcAPHYQCAActxIbrxozVn0hYApWZXOXYw1O2anhnnJYpijAwr3Ra9ltF2GMNz8LHTRqfV36FzOADr01SdmHc+x6zVNMcY7ynQa130FbJjYz3/+amzwfsWLVDyU4ZhGIZhzhiNiPgv8ox4toZyC7BcQnNmbZVhGIZhGIZh3g7O2c4EwzAMwzAMwzAXBTYmGIZhGIZhGIaZCz7mxDAMwzAMwzDMXPDOBMMwDMMwDMMwc8HGBMMwDMMwDMMwc8HGBMMwDMMwDMMwc8HGBMMwDMMwDMMwc3EiDtiapiU/YhiGYRiGYRjmDXECKv5UnIgxwTAMwzAMwzDMuwcfc2IYhmEYhmEYZi7YmGAYhmEYhmEYZi5O5JgT+0wwDMMwDMMwzPnhBFT8qTgRY4JhGIZhGIZhmHcPPubEMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHBMAzDMAzDMMxcsDHxhvAbJjRNg2Z7yUdqPFvmNxvwk8+OSdgWszG/ZM/WoGkapu0O4MHWNGiajamLMOcIH77XgG2aMDU59zKZMO0GPH/+tXRS+I0qlnOjtpmOACk+Tz6fB7+xjFwukDGvkGPgn4Pxnhnfw1Z1GctmYh5ME7bjQojsgZypv50q8rkcNMvF4DiT429hJZ9LrZusZLUHb2QtvI3MNN9R/A62qitYXs4hF/nOG4YBy2mjf1JrbAKHh4egyGI4vH8dC/kcjM0DHA2y2zANKtmX8jkYmy+PLftCcXgfP720gJziuzhMhoHK5g76g8Hcv/XT8t3tv8GlvIbK9g84GiSfvn2wMfGG0GufwgKA1meYrMP7aHzWAgBYn9agJx8zzBniezZMrYBCuY5Wr4de7GkPvVYd5UIBpu2euOE7NV4Vi3eb6Cb/Yni2+vOLir+F6nIOi1/0L5Ti6m8tI7dYRq3ZRTe+gNDr9dC6u4rFxWVYLsX7FenvsQwD5mLgb2F9JY/F/9fsSre/dR0LV8qoNXfR7cbX0d7eHh7e/QBXFk1Y7SPERPv3564zxaGUVfh/vsDRMUWlOLyP9et5LJ6G7LeVvT189Zuf40rexs6rI7wDOv6ZwcbEG6OEmxYA9FD/YsK7eb+NRz0AsHCzlHx4fPRaF0SEbo3NFGY8fsPEYrmFHgDDsOAKAUEECpMQcB0LBoBeaxUF2z31N0Aq/P4z+Q+rjf5Atq1bL+Cw/zz1eZi69QK0mJTp0Wu7GAwCGfMKmQexj1YPF8qQgL+FX9V7IAKMShv9fnwehBBo2CY0rYeHqzY8otEa8vfx8E3319jEwdEg1mZVaq3mznYtvI34L/CwF5n/afG38A+/6WIwIBQrOzjox+dLCIFGdRk57c94+ME6vOiban9/vjpViH18tZeWdfnj7/H6aIDe3SvI5+ZcJGIfX/UQN4QC2a+OBujdfW9+2ReZ4j28eHWEgeI7SX4H6ysLyGlf4Re3XRy9C1sGZwQbE2+Q0icODABoPR571Mf7oi6VN+cTnIItwTDT4Tfw0d0eCIDl9tHtNlHS9fhOma6jVGuiKxyYGoDWKqpu8k/p2WFcLUBX/D3N+pw5fXz3EXogoNLG02YZhUL8ua7ruPNgF66Vg6Y9xOMOIaWNMcwYfPc/0SMCVXbw9EEZi4X4l13Xddz58inadg457SG+7dCbNVCZs+Hyz/Bv/3sDywt5aF89QWdAvDtxQrAx8SbRa/hUnnXCZ5lnnTw8bgGAhU9jOwc+vIYN0wx8L4bJhGl7SB33DHwuzIYfHFMJ8pt28BZQ5TMxYx0BvteAOTwHPT5vFr7vwU7Ua5o2Gt6MgpgTw28/wp60JPCglNAAk+g1/KntwHFdfFJOa+2hv8Us8+v7HuzlCWW8KnI5DYV6F0RA7+5i7Axt6nPbhWiYI3+HaIXw4XtVLC/H177tuBCxfON9JnzfQ3V5Od1uN16fV80hp2mwXIJoVLGci5zPNy04IvqG08fWcg65chNEBDxclWfCLTdV/3lDL1yDBg143sfhGCvhxoMjDAaEVjkHTfOxtZxHrtSUx5serir8IHx0qssRfxgTlpuejzdGp4p8PgfD6UN4VSzn5XxrhoW2YyOfy3gWmXe/s4Xq8jJykbVhGBacZD8jdfW9Klai8vrpN+XnDx9bKwvIlx/Io0YPP8BCPgfNak919Ehf/LH8x7M+/DHZb3z5GkcDQms1j5zm4/71aeo8ROf+OlYS86BpBgwr3Gk7HMp6fUTAw5/jUj4HrbKD1wPK9Jk47NzH+sqyXNvD+TVgtQ8i6/wQ969fCmQPgK8iso+kbLXPxCEOO+u4vpKP+I8YqGwGu7SRnG81lxfxY/kLFOe72/jRpTyMjRc4cG/j+qV88J2pYPtgdBTu8Lv7uH19JTFHFWzuHGCQsTb97+7jp9cXgnE3YFR28PLgLfOpIubNIhwyAILhkEg+IyLhGASADCf61CVLvqsbkyxyIyXItaQcy5L1hclwyD2BOlxLfmZZsp50SrRnKD/5OZFryfZkpXg7mbNBkGNoco7dQfLhDAhyLY00xbwOk9WmfqIK1zbHljGcPg1kRtK09PPMZLWp7xikaSBjM5BBJPtrjmmnsRlro3DMkYzI5xPbHcnv2mPqAwgwaLM/CNooqKFqX6Udq/98Em27QZXNTWr3+8rfvxGCGmZO2d+jwYCIPLJV4wFQpVIhTdMieedENGg5pxGMTTo4mkOOZ1Mup1GxUiFDi7S1uEkHbSv72dGAiAR5do5y49Z2ZYcOwv6Nq2uetp85ghrL+XR/Kzv0eqr2C2qshOWLVNmYbo1tDctk1elRdSGvXGejVKHtVweR+iNpbZteHQ3I31qhfE6j4sbLoezws7S8eFkin7ZWFtSyX0vZCzmNihsvIu3OKBOm4ga9eH0U+f27gPhbdH0hT1rxHr14NaYv/hZdv5QnDWv05IfXdBR+3lmnSws5Kq6tkZGL/NYU79H+qyMakE+d9Uu0kFOM33AOntB+MI6d9R/RQg60trZGuVwunRdr9OSvr0b1X3DYmDgHDBXxpFZNghwDBBgU1Z/D/LBcEslfR+GSZSjkBcYEADIcN/ajqjJYZq1jmD8pP5IX8QapjYlhOw2yXBFvpxsYXsm+MWeAS5YWrMV+8tn0CCdUrg2y2tH5FSRci4zgj93QOKCogWCQ1Y4rBMJ1yAzKWO1Q0U4o99H8is/HfQYYtNmOdFi4ZJuBUZVVX/hhot3RYRNug0wtLidqTBibEYNKuGQHCmHSWJF1SEX5OHry2SPIbdjDMYgmw6iQtdmmftKipEBJVhgGnp2TY21UqB0xuLxGOI9Rw2NOQmMi0V5lUintgYIPgIqbO9SPtmXcMyISjWXZbxSpstOPGLKChGeTmZPro7h5QEeDtLyhkXHR8KpSwZ7aiIgiyGvYtByMTXR+isUKVTbbdKBcY9l1etUFymsgVLbp5cEg/p3zPaou50nTQJXtV8E8VGkhrxEqoSEQZE0ZE9JIyaFIa9svY+vU97ZoJZ8jDUXaePFayqVQdi5iZIxkJ42J8DOgSBtR+b5H64HRs7b9il4rhuPCMNGY8MnvbNFKaAyuPaEfXkdU+cCYAEDFe0/o5VFchr91nS7l5RiuPXlBL4/C3xmf/M46XV+QBkjx3j69OhoMjYlQ3otQnt+h29cDw27tCf311dthTrAxcR4IFeikhpz1eQQhBLmuS67jkGUZZISKe1LhHirp6Z0AlTERZZo6osZHinD3JVa3ypgIjacxxkIoKzMDczqchDEh3/YjUKCVuFagFDqBwhQvoywlAoPCGinUKgMh6/P0Z6Ndicw6E6SNidHb90wZIjAognYPjQmFYSBCpTj57MIaExGEINdtkGNbZBrxXUlj8yDeL6Ux4ZGd00iDQZsH6bGWivh5MiYqtDNURKZ4RoIay9JYquwcqec5LB/WO1beBWKMYj8TQpDnNqhh22SapvzOBHNV3HgZV6KnrNP3ffI8j9xGg2zbJNPUhobr7MaEP9oVKa7R2uYmtdvSoFbON81iTIx2JS68wTCO0JhIfhdVqbhGT15FdiUoakwkdiyI5Bhev0R5DbT25K8UtUGGdNbpRwu54U6GFxoTa0/or8kC/n36abg78pbsTrDPxHmg9AkcI31NrPe4BcCA80nS7dqHF/g5FAoFlMtllOt1tFo99BLXLKYwljDhtHvAfHVYquum9FXcMgDgGfrZR+IBCOwHslvl6Dn1SCpIZ/RJTuvMSVPAUlED0MP+uEPIYwnn18IvS6kTq5LSTVjQgN4+pEeBwItwTawG576TqVCXV722voWHedsWRdZJMHC1gPTZ2qmQfaWx7a6hS5Rqt7E03Tf0rUHXUSrdQe1BE7tdebOcEA3Ypoa9u1ew3Jhw7a3fxzMAhB9jUXEhnTw/P98sKpnmNqdeHYtZN+kU5W+w8qnymS+/N1SR3xtVwRs3UYEGbW8fItCYgCx5F5zOuvRlSH6fNC19zWuIruNG6Q7uPHiA3d1dDAYDCNFAdTmHP//2Paw4B1Ner3qIzvoKFnI56LqOUqmEcq2GZrObun52Ni7j43+pwMzloO19ha/u3sXq6ioWF2V8DKOyiXZfSP+omRF4sUcAFbGkK3wF3iGKxSIqG9s42G3ig4W82mm4eBW6lhwnHy/2CERr+MXP8tBUBX/2S/xjLofc3gv4ER+UtV/8DPlkgcsl/L2RQ057DnF4nHVzflANCXPm6Kh9agHo4VE70Lb9Bj5rAbA+RfLGVs8uoNySGpZhWbAcB67ryms6hZCGSRbXFqeKU3GsOpi3EB2rt4rQALQed6ZT2T1bOpyZDYyJDcW8U3io5nPIaWbCoTyOrt/Bg08taJqG3jduYFi+JVwbc5PYuGfzcNLyLgQdrC/kkdMMbB4cZa4cXb+DL39vIadp2PvGha+0QuJ01t/DB81dHBGhWKmgsrmJdrsNt99HXwg0lvPIsiEncuNLfP+6D3dLXk6Ri9wrvPfVb/DBlUWYzgnEvnjbGXM1rIxh8wEWc7lso+rH8juT+ZxRwsbEeSHYnejVv4AHeXNOT/mmP7zdyYAjCN1mE81aDaVSSV7TqY/e7j8bvw0whvnrSP4fQCROxjXlm8MRBSwZGNab/CGIpyZfk3vG6LVPUdEAtMqoesn7jBL4HuzVhzKWwK1yoNCE89vCt17GH0TvMVqgYAdNA1DA1XBNJO6KT6cmyidysb+sU0MPL0TkLe9MyL5qMLDZHyj/sJ18uy8CBSwVAaCHu194J/NGTl/ENQAanit3Pv3+8+i7+guILr832kP5vVF1pfMYD0Ggt3EnIsmNL/E6Y2dI3sqk46qhQdP28Jt7ncwbdmang2+/IgyoiI2Xr7HbbKFVr6NcLqNUKKCg+3jxZxkD5ZmIR6Wensu48fEdPNjdxdFgAPJ9+MKTMTFyGva+8WRcn2SxsRRwtagB2h72/VnLMhIdV4saNO0rPPnuCKS6T/a7b/HvgwEGxavQc6OdjefiEKlRP/Twn70BBvRjFC5rb0U8GjYmzg3h7kQLjz0PX9R7gOEgdcJpSA/74S5GgLxuswwZK/skmL2OXr0AO3JVp+81YIZHk6ybEwwAHau3DBnIr2Ci4fnxCMq+N7zGVrP5kNPZU8Inm4bcnSgvDq9ljc9RcJ1woYwWEWA4+NOdMBicjtUPg/Kry7Ab0bLBlcWrrYQBEpbpob64jIYrxqyJk7oWVdYJTbaz4UYNJ394hev4+nSUbxmA1sPdjHZvVZenkDMtffjJP1jnEh13fi93HPBwFSvLMvBhcg11tqpYLrcwIILxYTkwLKP0I2Vu4HebBjSth7tX5FiH4+l3qvhVvas++nJh0FH+0IAGDQ8/WIbdEJGdPh9+p4rl1RZoQCjeKkN/GzSTFNH5noSOO/9SCdbYB1hZttHuq9bYOlZWmzKw3Ydl6Kkthazv1B5euH4scqK80nUVzaOsnRABhUo54vA+rge7KZWdA/RDg+HyZVzWC1hcSh67iSLgj5ONyyh/WERO0/DVz69jK3HNbOf2JSzkteHVsoyKyyj/fRG5nIavfvF3qDovcTA8ynSIw+9u46cfNPHq9QDFvy+hEDEm9n67hPX26Jrew+/+iJ++V8f3r45Aa7/Az3K5t0MRTzpRMG+S+HWsWT7GocP0pBRzqB7jzK1ywJ61jolXwxpJx2+VA3bwZMLVsGlZzFkinMT1wllp6EQdKz35alhFuUlXrMKwqB3xVEw7VWd/rvos6oSdqgsgYEx9kQpnaXfogJ2UQdM4YIfyks/PKcJV3+SUTEalTYNoh0IH7Eh/pVN1xtWx466GHTpUG7R5oLr9JcEsDthQOGGHTtGVnbQj+LhnRETTXA0brW+ivAtC6Awd9nGCU3QU4VWVNzklUzEpc0ydE69vDWWGjtWhA3b47DhXw2bd5hSVPe/VsFij7VevJ38HzjMTb3OaQOiAnbzlacgUV8MOr5Gd4mrY4ho9+SHifD10yi7Svf0fLpyj/FthEL09lPBJ6IwwZldCr3UhHAtGzG/BGAYvEoGM3qP2DG9z4sxdx80mhGvJyN6ALGO5EN3pjyWVml0I14EVrxwwDFjObLKYk0evNdElAdexYCTnCAYMy4ErBKhbQyLwLAAdpeYAfcX8DoNvKcqVHuwqy0TXxMkeFdJR2+2j79owzejnQWCqwXT1yXY3YJuqdrfR351OTial36FtGaNt8udZb1PPF3rpAXb7fbgNG2ZybGAEDqcD7LbK8g1zyA1VfyHfRu8eSHnDhwYq7T6a/7cx56MvDDpuPDjCgTsK9hhSLFaw2e5j0B3j9H1RufFb7FgR/4Fn0+9Q6De+xNMDucaWE2MGFFGsbGLnYIBuaxX56Lip6gze5F/++HscODaWzejRlKKcg50D9BvLyOcCHwwi0I3fYbtiIh9mfi5wmHFM6fLH3+Og3UB1Oe4vARRRXNvEzsEu6lciPhmh7PCD5+N2KC7j4+9fou9WsbKci7d9bQcvX7ewupB/C74np8ll/OzffsBLdwvrK3JXOaRYXMPG9ksc7d7F1YXE780v/i/s79i4Pvy8iOLaNl5838QHlxbejl0JABrNd7CPYRiGYRiGYZh3nLfFKGIYhmEYhmEY5oxhY4JhGIZhGIZhmLlgY4JhGIZhGIZhmLlgY4JhGIZhGIZhmLlgY4JhGIZhGIZhmLlgY4JhGIZhGIZhmLlgY4JhGIZhGIZhmLk4kTgT8WAwDMMwDMMwDMO8SU5AxZ+KEzEmGIZhGIZhGIZ59+BjTgzDMAzDMAzDzAUbEwzDMAzDMAzDzMWJHHNinwmGYRiGYRiGOT+cgIo/FSdiTDAMwzAMwzAM8+7Bx5wYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiYYhmEYhmEYhpkLNiZOBA+2pkHTbHjJR1Phw/casE0TpqZBGyYTpmmj4fnJApn4XgO2bcI0o3I0aKYJu+HBn17UTMxXbzhuY5JpwrQbyBwCv5EYM0UK25Asy5wtvgfbNGNzY5p29txOjYdqLgfNdkGUfCaR369cpO5x6yqQl1xHw2TDzaooir+F5VwOmulAnEZ+zFnmvOJ7qC4vIxdbHxZcQZi5Z37neLL8Lazk5bj2BxklZqrDR2eriuXlHHK5UX7DsOAKocjfQTWfj8mOJwvtQbKeecpcJOQYrijGsN1XjeEY/A7WV1aQi3zPpZxBtpxxZVSFDju4v76C5VxuNCeGgUq7j4GqwGEH69dXkI/Jr6B9kCFfxeF9XL+UR87YxMsjRV8OO7itrONo+jrOM4cd3L5+PdW/nVn7d/idWs7LI2T9HODwO9z+6aQyh/jj3/0IC5H1m50q2P7hNQaxKv6I29cXkB+WN2BUNrFzMKZdZwUxJ4BLFkCARW7y0SSEQwZAmJQMixyRLBxhWjkAGWMFzcix6g3HbbpkWC4lJcxS/1zzw5wMrkWmlpyPMBlktQfJEtMhXLJNTcqx2jRQiBGOSdqEumPFXHtMfhBgUVtVUQyXbC1ol7FJ/RPPT8My2kxlzimuTWbY/1QyqNIeKOdWiWfT8lhZRxNkeWTncsNxPThKZxaNZcplrpEiVXaidXhkL+fGrqni5gHFqvFsyuWy+gACKrRzlFi385S5MHhUnTSGGy/p9RSdE40VymeOU5EqO6/jc0FE5FVpJRd811KpSJXteBnfq9JyPis/qLi2Ta8jBfyt8W1aS8hX49H6Qp5yAKG4QS9eHyXWxzqt5IN1nUqyjmnG79zirdP1hTH9e/KKXh8lCynozCFn6jI+3f/pJcqPWcejtEZP/vqKwqr8+z+lS/kxa+TJD/Qq2a4zhI2JE2FOY8K1RovBsMhxRUJZFiRchyxjtGgsVQUqOQmtWwiXHMsY5ksr9nNw7Honj5sQglzLGBkMyQEIjQnDSRsaIcIdjWGyPHMGuGQFyp1htSPzJOdW/gBbNJs9Ich1rLghqTImhBMoqQZZ7X7kj6sg1zYjini0SGB8qORNhaCGGVEkJir6s+anOcucV1yyA0XNqLQj/RDk2aEhWKH2VIqwd0xZghrLEaVAaUyMjA2jspOqI6dpMcXds3PS8ChWaKcfX4Oj/EXaPBgpf9JY0QiVHTqacl7nKXNR8Kr50RgeJMawGvQbRdp4+TpjXkM8qgYKdbGyE5nbqJwK7cQU8WiZbToYrh2fvOoy5YMy26+CMv7WUGkvVjbo4CA0LH3ytpYDo0G2VVbvUTUwAopr2/QyJj80MtZG8pX4tLWyMDJwU8bEyNAorm3Ty9eROtajdUwav/OKR+uXFoL+PUn1byHo35MfXg+VczWdhJxwDH3qROX8NSqnQ+s/GpUZjbtPnfXrGWUy8O/TTy/lScMaPdl/NTIgh58Xae3JC3o1ekCd20EdxXu0/8Obmz82Jk6EyUpxisgb9WkUe+GECnmyjtHb/WnkjAyApJxZOYl6Zxi3yHjF7IFpjAmKlp+iLuZEEU6otDsxpT3EtQLlz4kqCdkI1x7tchgWtR1Lylco/64dyN5UyQ53A+I7DePLTGKk5Fubm2Ro2gRFf9b8FCtTmbrM+UU0AiU/ow+eLd9IG5sHqflNIhqmVKiURkBcVvrxyJCIjmtKTrgDoHoWytAQ7E6EhkfcWIgSGhvR3QnVZ5OYp8zFIFTmi7TxMmMMA2Nj4u6EV5WKc3GDXqYyCtpakXKiuxMi3DUobkSUyxFedYHyYd1Hg+H/UdmOKH0jhrsQa8Fzr0oL+VyGfH/YprXtiHIZY2RIrG1skJnLkZYwJvytQBFOGRkSb32B8jlQ8d6L2I7JRcHfuk4LealQv1AYXd76JVrIgYr39pVzEuJvXZdv/4v3aF8hp7P+o5ScWcr8kFpzUTp0+0cLlEOR7u3/EFvHndvjZITl4jsZZw37TLwh/PYj9ADActGt6cnHKfRaF64FAC181hgd9PYbn6GF6eWg1IRrWbCcmygoz4tPx5nXq9fwtWMAAFqP5/BM0RdxLfkZcyaI/R4AwPr9HRS05FOgdLMCaEBvXyQfqek/Q69owXEFqNtEeVEDFHIBoPRggAERuvVCPIvvo2F/jhYRYP0SpeFTH/1nAGDgw1U9S2wmXnUR9S6huNnHgw+0ieVnzY+wTE+WaU5Z5jwj9vcAAJXf34GupXtz42YFGjT09n1MOvHv7++BAlmFCbLku4kRneoV1LsDFDcP8GA1h1y6+AwUsVQAoN3Ag6MjDKiH+mJu7FxdK4TrzUf/OUAo4lZZh6IbCuYpc1G4gS9fyzG8e2XCGC7O/p1NU8RVHcPfFH+/ByKSayqXrv/GL9eg5TTsvRAgdPDtV4QBVbD9b2XkFYvo8sff4/XRAPTwAywonqspYkn14wmgs/4e7nZf4yf3XuDBB3nkFFqdeLEHAmHtXz5W9+EXFWhaDnsv/Nl8C84J4sUeQIS1//kxCnlV/9ag5cL+ZXfQf7EHCuQsKsbpZ7/8R+QCOaHfi//iz0GZf55YJrvuQ/zx736O5qvX+Mm9f8fHiwvIRwT97P/6K14dEXq/uYpL0QeHh/jj7X9F8/URBmu/wM9yuTfmCP2m6n3H8dF+FChYN0vJh5mUbloAgN6jduBMPKecZhPNWgn6FDaAmjdTr756CwYAPOvP7kzt9/Es+RlzBoRKjiGVKxWFpWBeBfys39oIem0Xg24TtdLsC8mrBs6QhQLqLZLOkM3ySPny2/imBxCuYVFswV6OOGKbJmw329HTbyxjtSWV/N1aAZPUfH9rtvyIltmYvsz5xod4RgAZmcoS9CUUNQDPJ33vA0OQDCxFlMEY+hKMUFZkIv2tFay2BihuHOBpbXG8IXHjd9g0NGi9u/h11Y04vvvoVH+Fu10CvX8LZX2K2fG38PlDAlFofADwXXzTI4CuoSDuYz3qcGwYsFyRVvgmlumny7wtRMbwqo7xhtSN32LDzCH359/g1+tuxLneR2f916jvDjB4/0OUdS1QjnyI5xgZGCoKSyhCg/ZM4NDv4xkRULyKgnaI7+6vxx3GVQ7YN36HDUOTbarKNsmnh+is/xp3u6M2Jft2eP86ft46wt/+4QWe1q4ojRfgEOIZ5BrLWpOFqyhqgPa8j8km+3njEOJ5MP+FMf2DBu25/Buj7t+h/FtFRVxVjDUAQL+K9zUp55AAwiH6Yd16booy6rE9/OM/4je7r3H0/j38+8eLWIgaDAq++6e/waW8Bk3X8fGXr/Drx/t41fwAP1p4cyr9m6v5nUZAvqwdo2CpCJWu3j7kO9w55RybN1VvwLD/0yBvyjIL9WAn6CamN3+YtwupbBYNQ36P0ENr9VeoOhEDQbxADwSghdXVOlrdyE9/r4fW6iJypgOR/IvgVbF4twuqtKWSP/5vgcxfnyE/wjI90NoMZZjJdKq4Uu9isNbG01phvCEBANBx5+kBHNvE3lcfyLeRmgZNK6DU7OInaxvod2tYnCTI38LKlTq6A8L7m3/CnULwVtPfRw8A4SE++KCOZpdGhsDeHh4GazB2y9TEMlfSZd4G/C2svHcX3aMB3t/437izmFcrk0N03Pn+JTbtZfzlq5/jSj4ydw928ZO1ezjYreFKfk7VSLyA3AP4Br++fgXl2gPsJubiqw+uIG86OBjetnQZH39/AMdexl/+4xd4Lx/e/qSj9OAp/vYf7uHlbg2LyTfunXW8d7eL1//wBN9nGhLM+ec7bPy2i6Oj93Hv3z/G4sKkNSyNp/ffL8LQNGjYw1e/+AesOwd4ffTmvt9zfmOYi4LfiF/FGU1m5LjUSXPm9fbqKCjq0rQCCuXAkDAsuE02Jd5ddNR2B+h2u+gSgYQDU9tD6+4ilgODwpdnnADDwma7j8CvDEQCwrVhagB6d/GrRsQA8RtYXm2BipvoP4jscmThb0XylybnR7TMBvrNKcswk/G3sLLawqC4gYMHJeSmHFi/8wc8ethTvu3fe/YIbW/8cRF/awX5xTp2B4T3Nw+wG9kN8eVWHlCsYLPdx2AQWYOeDTOnQdu7i19tieF1kJPKLCvKXHT8rRUsXKlj92iA9zdeYrd+JXY0JAu/cw/ftLrKIyd7z75B2/OPP0Z7e9jrvY+1Jwc4GMijlkQ+fK+K5XwO2t5v8OstgUFQ0WG4nhQV7z3/Bq4n4u09vI/rP2/h6G//gBcP1MepmIvB4R//Fx4eHWGw9j/xz4X8FGv4Mv75v37A024P3cEAA/8+fnrp/+Dff3sV152XePWGDAo2Jt4IBSzJV6OzIeTbJxhLkBsCoZwepj1ufjK8oXpT/Z8OwzBgOS5Et8m7EswIvYbdtgVNA3rftOFTcIRqQKBuE/VydJXp0EsPsNu2Y/kBD9XFu+hSBe3dmtInJI6H6mI9kn9igWGZHq3NUIaZTAfVK3V0B2toP62hMKVC5m+t4Eq5ie6giM2dtOJu/uXP+M3qImxXER/A76C6nMdifRcDeh+VnT66iWNV+p2nOBoMQL0W6uXoDpQO/cYDuWZzGvYeufCDCiaVeaooc2HxO1hfkWN4NHgflZ0D7NavTLGjBPhb1/HeahO7g/dxb+cgMXdVLP/lz/jtB1dQdee8tz88ToMiNl4+xYPVRSxq4dGby7h840s83bGQz+ew942cC//+dVxZbWL36Ce4t/1SzmPE+Fj5P3/Bbz94D3Y7bFMH6+/dRff1P+DJ93IXZYquM+eSyK7Eb24gP8+O2OV/xn9t27i0kMPe/8eFP1DEFzkLkh7ZzDzMcCtRwPB2phmuKnUtUPL2pHnkhGWmuoUpg5Opd7ZxC/sfq3Pa25yYN0Z4W1NmLAnXGl3Fmnw2DWFcCMVtThMZXh07TeyIdP7xMSySyaLNTWOG/BVZR3jbUeq5KskyFwk3uGGp0k7fhEIU3J6kaYRKe+KVp+FtTZmxJCKyDpzg5qfUGKpSeNVreOOTkXk70/C2p2L8tifRkLE05JWhm9TuZ11POwHRoOWcRtoscSPmKXMOEY1q0A957erOTGMoqLGSp5w25grZ2G1P8l6c8KYoZfwJmUHeJFTZplcHjeBa2MhVsUmGV8eu0farg1ibsuXL255evD4iEd7MlFqjqiSve3WD25rWtl+pb7vy1mUda9v0SpnhfBPe1rT2JH4LUiQDXVrIEdae0A+pIBEjOlE5qmyddfpRIOevQYbwtqa1J3+dXCYZCKJzWz477rWu0Stl39CNTnOYQcxJoNc+hbycqTzVsR+/YaIsr0/Cp5Hbk2aVc1Kceb1+A5+1AMCA8wnvL1wkCsE2XOvzrbSvAQDv8UOAAOPEHXB8NJbl+WPbVTu++e1vpI+EcRUFHE7MP/SpMK4icT8UMyeFpSIA4OHnW8q35p3HD0EgGEv6RIdzfakILZCliggel3Ucgl3ZdBVKOtUVXKm30KX3sbbTR7dZR1npLOpjayWPXE6D1VbsbAAj/4ii3KHV5ipz8eisr+BKXe4qrO0cYLd5F6vKMZzEHl74UI+TAn3JgKZpck0p3vp2vv0KNCAUrxag6WV8aGjQtId40smYC7GPvXAuhltIe9hX/TieEPpVuV/y1b/eV/fhyUMQDVC8ejFvAitcLQKahq/+130IReTvzpOvQIOwf9kd1K8WoQVy+opx+u7bf8cgkBMeidSvvh+U+ePEMsm6v3vylXz29yXoipugJKOo2ZXtH/A6GhI7zOH9J3qDAaj4HvThTtgZk7QumHmY7Q37EFXQt1iGeYLWGWQ5bip4HIlQVlYAuTk4dr2Tx+1EgtYxb5ho0DonskaOE7QuwpididHOgSJoXWRXIYyCPT7/KL5FKmq2CtGQuxgZ8RNSzJqf5ixz7ogGrduk/jAYybSB5qJEg9YdQ1bwJl8VS2IYhA4Gbe70Y2tOeDaZ4ZvzIN7DMJhcYqcii2F+FKkSky/Ii+xuRKNsx8tE43Fkl7lIiEYQ7K24QS+nGMMshsHvUKSN2DgRCS+y67HxMvKWORq0LhGEThW0LtypQJEqGzIwnqzGJ3+rSstBwMMwboRXlbsGQJE2tl/Gdt98rzoKgBfEsRiLv0UrC+k4E/GgdRv0MtqHty5o3b1U/+YLWnePXr4cyZkuaN09ehErMy5oXXZciSSj6NeKoHX3gxgbkLsjyc2Ps4KNiRNhFMBtUkrqwiTixkJ2Mmis7j+1HCnLctyTUb6PVe/04waAjNTgyfrZmLgAuNYo0FwqGekjUMIJ8hvkqCLdRRljTMjHQdC8jBQPTifIiUaWVqSpg9nNqujPmp/mLHMecaXCmxxrmQyqtAfxuRUNMnOaVOgPEoaBZ9PyWFlTKNRjjIlocLu0/CANDYdRtOxUnmQaRq4eBb5L5QlSOjDdPGUuCiNlPtmnVKrsSIVbhMeNkkeawqNOirJhihxxGuJVaSUwNFL5UaTKdvyIklddCYyMZF6ZimvbEcNgFJgumS/ZponTl2lMyKM+oWGSko8irW2/HqvQnnu8dbq+MKZ/T16NDER/i64HEaXvRSNNkzyWNLWc45SZ8WhS5/ZPxx5vUwe0OzvYmDgRpleKVfowEQ3f3g/fwAMEGGQEOxbTIoRLjmWQkVTwDYMMy5lJ1izMV+/kcTMMgyzLIWVxYmPiQiHc2A6VnF9LPbcnaExQ5PuVqjtDtnBtMs3kWrSo3e8ns2Yzq6I/a36as8x5Rbhkm3HDzzAqat+CccYEEZHwppelYqwxQUQk5C6EmVxTFdpsR3YTQv+JxO+aMg2NiaAGzybT1GIGQrFYoXY/25gVnk3LqjIH2WXOPaEfQ3K8VGmiMUHB3FVpedkkLaLwF4sV2twZY3AJj6rLy6kyO8O34HF8X5V/LagjWcCXuxCpNmXlz2CcMUFE5Hu0vhLuYo3q2Hn5WtmHC4fv0frKSqp/28n+jTMmiIj8jlrOiwy/FgrKXJ+hzBz+En7nvnL+tl8o+nDGaKS6H41hGIZhGIZhGGYC7IDNMAzDMAzDMMxcsDHxLuPZiiBvk5PtJQUxDMMwDMMw7yJ8zIlhGIZhGIZhmLngnQmGYRiGYRiGYeaCjQmGYRiGYRiGYeaCjQmGYRiGYRiGYeaCjQmGYRiGYRiGYeaCjYl3Bg+24mamWDJNmHYDnp8sO45ALl/xxEzC92CbZmzNmaY943oL8D1UlxWyxJT3SfgNLOdy0EwHk4t4qOZy0GwXY6+r8D1Ul5dTbXKVFQQyk9/BYbLgKirzvS1Ul3PIDfOZMK1GRh0XjGD8Rn3TYJoWXEGYuXd+5xiyOqjm88hZbQwUczDE72CruozlXGTeTBOWK9TrJGxTZN4NI9kmH1sreeSiMjOThfZgQn/8Lazkc8gZDvqDsTkvAD46W1WsLOdi42MYFtp9MX4cJuFv4fpCHjljEwdHg+RTAIDfuY/12NwYMCwH7f5AMd+H6NxfV7S1gna/D+W9N4cdrF9fQT62PipoH6jkSw6DNuWjbaok23SI+9cvRfKMSxXsvD463li+SQ47uH39emoMdw6OMsdQyeF3ajkvjzDd1+g73P6bS8hXtvFDxnrC4Xf4p5+q6nidXcc8Zc6KZBQ75m1lcrTpaDIsd3JEaeGSFUa8zgrtzTBERK4VRLRWJYOs9gzhO117oqzx0lyywwiixiZlBMGWCJdsM8g7JsL2zG0Ko3an8oapQu1EZaJhjiljUCVZx0XCtWUk71S/In2btnOeTctjZakjFhOFkbNzMnJ2IiJ1FOHZQQTupHyZislo1o141Np4KlJlJ2yToMZybsw8R1OFdo7GzblHdi7oSzErkvdFwaPqhHEpbryk13N10aNqPhynDXr5+iiZgcTWypgo3EWqbEejHHtUXclPbmtkPvwJ8tdi8iVedXIZOR4+ba0sUG5Me0ZpjbZfTReN+dzhrdP1hWAeU6lIa09ekWJq03SOKcfv0Pr1YLzXntBfFZn9+z+lS/kxc/fkB3qVKDZPmbOEjYl3htCYsChL7RdCkGsZZIQLNNNAEOQ61ijf2LwM45IVKFKG1Y4YqXK9yR9ti6azJ0aGgGG1I4aAINc2I7KyhAlyTG30hyLTmBDkOgkDIdOYmL1NQ8MgU2YC0QiUbYMq7X7kj31Qhxb2ZRph5w2X7JycE6PSjvRBkBf2DRVqj1WcQ7w5ZQnyGgmDJsuYEA1aDpQ4o7JJ/eGEC/IaZmA0FGnz4CioY6TUFys7qTbJ/JMMg4Cgbg0V2jkYlz9hlFxwY8Kr5qVyVqzQzkF8/XvV0FAr0sbLWRVhQY2VQDYyjAmxRSv5HGkoUmX7ILImfPKqy5TXtGG5ARF51QXKB23dPjigQTJ/btRWOSUeVRfylAOouLZNL4frwI8YDGu0/SpcT9L4WAjWYHEjXsb3qsP2brxIGyEp/C1aWciRhjXafvF6ut+jc4dH65cWgjF8Qi9fR8ZwPRyrNXryw2sar293EnLCMfepE5XzV5Ucnzpb68HYB+tJaUx06PaPRnW8GM6rT53b1yN1vIrUMU+Zs4WNiXeGycbEEOEMDYWkjSDciBFhWOQ6FhsTzFiEEyjUhqNU3F0rUP6cqJKgZqIsO5C1qZI1MiSszU0yNE1pTIjoLoNhUduxxir+wokq88mniTYFz1WfjcO1NdK0rPzSmNEUuxkXgaFhlWEMebZUio3NA0Xf44iGKRVDQ608R2WFj4UXMSKMCu1sWlI5zTAmPDsn68h4PtyFCJ97NuVyWoZCP1L6R7sTWYRGSdRQURGRGaxzTVn3RSHcOSjSxkt1v0NjY7bdiZEhUdnYIDOXI01hTMRkp8ZQGgIaKoGyH/4/aiwkSlQXKJ+LyPOqtJDPxQySET5tBW1c234VyBvtNBQ3XijaJN/SL+Rz2c+HeLS+kKfctIbHOcXfuk4LeY1QvBdRtEd465doIQcq3tunV2M66W9dl2//i/doXyGns/4jpRw/uptRXKMnG5acU5Ux0blNP1rIyTp+SBq/Pt3/6SXKa6C1J38d7TTMU+aMYZ8JJo1ew9eOAQBoPU74QvSfoWdYcFwB6jZRWow/ZpgkYr8HALB+fwcFLfkUKN2sABrQ2xfJRynEi5EsXSXrl5aU9UJIkzeCV13E3S6huNnHgw80KIpL+s/QK1rYbPdB3SbKi5k5gbBNBFi/r01uEwDAh3gGAAY+XNWR3ZARpQcDDAaEbr0ALZrf97FV/RwtEKhyE6VphJ0zxP4eAKDy+zvQY52T3LhZgQYNvX0fEzwE4O/vgQJZhQmyhguk/xx7RgWb7T4Guy2svqeNmZMOHj8EiCrYeVCGpqhDv/MUR4MBqLWKnOK5miKWChhTr4+tlVW0aID3N/+EO4VcZtZO9Qrudgd4f+MAD1ZzyGVlvDDcwJevjzCgHu5eye43AFxb1Mc+j9JZfw93d4/wk3sv8eUH+cxxuvHlaxwNCL27V5CPZjo8xP31z9E6GoAqv8CNnAYNN/Dlq9dBW7NlAsCPC5eV60dNEUupH88iPiwV1DIKV1HUgD9/40FQ1rfmEPev/xzNoyP8ZOM/cGfx4q4V8WIPIMLa//wYhXx6jdz4xRq0XA57L3y1v0qA/2IPFMhZzKXl/OyX/4hcICfmT9V/jr33/xF/2H6Jo90Wfr40fu4nU8RVXYv/1k+kiPdmLnNysDHBKNFXb8EAgGd9RP1j9VoX1G2iVtIjnzJMFj76zwGCIZUlFYWlYK0J+Nm/81LWs5Es5W9m4SoMaKl16zeWsdqShsRuvaAuG6DXdjHoNlEvZzU4imwTYOBqZpuWIm0iwHfxqAcQrmFRSIfqoQNk6MCblJHAqwaOnYUCak3C2k4fg1b5jf0hmR8f4hkBZCiUpQB9CUUNwPP4nKYJ5oIMLOkZirm+BCOUFQyyfucpjnblfE8cP7+P5yCQsYRC4BC8rJq/6ATe+B02DQ3an+/iV1UXYvjQR6f6K9ztEuj9Wyjr2Qauv/XrIN8m/nSnkKmo+FsrWG0RfrJxgN3aYma+twp/C58/JBAVcVXH5DkE4G9dxwetAX5y7yWe1q8gP0WZkM76JSzkNGi6jjsPjvCPT17iqLmKhWkG+/A+/vXhAINBxDi48TtsGBpyf/4Nfl110R861R+is/5r3O0OMHj/Q7k+YlXsYT/rB1O8wB6l3qfEOLz/a/yme4TB+xv4jzuLyE3T/nPJIcTzYP4LGd+hwlUUoUF7Lv/GqMflUP6tojGKvH4V72tSzmFEzuWP/xs/PG3hNx9cmTyOP/sN7pl55P/8W/zjbRcHrwfD+f7un/4Bd5++xtH7f4+SHjHupixTjpY5Y9iYYMbT28fk98UMc47xqli82wVV2titjTckzgSxjx4IQAurq3W0upE/bb0eHq4uImc6EaUzidzZKBYNaYShh4erv4LtZNwixJwc/j7k3tg3+PXKIsq1JroZ8ze6PUnHnacHcCwTf/7q5/KNp6ZB0wooNbv4ydoG+t0aFjO1gA7+cLcHovex+ac7KGTl61Rx5W4Xg7Vt7NayDY63Cn8LK+/dRfdogPc3/jfuLOYnf78763jv7i6O/nEbT2uLMxkSoeL6frEIQ9OgYQ8Pf/4rVB2BwaTrdA7v4/qVOnbDthZCxe8yPv7+AI69jL/8xy/wXj68rU1H6cFT/O0/3MPL3RoWh2/cL6P8YRE5TcNXP78Ou32AfmT34bBzG9c/aOLoaADs7cNX/ih0cO83PQwG72PjP+4o38Izp8Vl/PN/vYCzfh3/5z9+iauX8sP5vvFv3+N//MMf8OL7Gt67FF3L85Q5W9iYYBjm7cVvYHm1BSpuov/gfLy59+UZJ8CQR6kG0ncNRALCtWFqGtC7i181/AzjQMed3QG63S66RCDRgKnt4eHdRSw32KA4E3o99HoGKjt9+SY5nD/PhpnToO3dxa+2xPC6Rr/zBzx62INqcvaePULby5prwN/6HA+JQJXf407Wm1d/CyurLdBPNnDwoDzD8aqLi7+1goWhcv4Su9PsMPhbuP5BC4Of3MPLL8vxY0tTcRkff/8au90euoMBBv4WVvJ/wVe/uYLlRh9HGQbF4f3rWFis4WnQ1qe1xVjdh8H6IEX5veffwPVE7HjO5Y//NzbMPHLaHr76+Xu4khtdF62XvsTrv/0JipoGrbikPDp4eP9f8XAwwGDtX/Dx0KhhzorD7zbwTWtXaYDuPf9PuJ20cTpPmbOEjQlGjQjewBlLmOawB/OO4tmxu/yjyXbf3A+bxIO9eBddqqC9W1P6a7wJ9DvyDwIFR6lGzdKhlx5gt21B0zT0vmnLY1GT0O/MXoaZDz04kgcDmwdP0VwtRNaVDv1GMH85DXuPXPhE8LdWcKXcQnfwPjZ2+nLuo8bHX/6M36wuwnZV8QQiuxK/K6nPx6OD6pW76A7WsL1by965OK901rGQV8dcsdqKe/39DtZX8lis7+Jo8D4qOwfYrV+ZQiHuYP29u9g9+kdsP61hcaLlMQWXP8b3OxbyeQ1738j5jjX3sIP1lQUUak9xNHgfa9sH2K0nDIn713FltYndo5/g3vZL6W9DBCIfvlfFyv/5C377wXuwY2NxGR9//xJ9V8bdGC6L4ho2dg6w+/X/HfnM/kV2JX5bmnwshzlRDv/4d7i6+iW+f/0T/OHJC7w+isx3Zx3X/79/we9+fhXV9iu8DkJUzFPmzEl6ZDNvKzPc5kRErgWa6pYml29zeqdxrdE1eIkUxo4Ib2vKjCXhRm5LSj5LEN6ClIrbEBLGb7Da1HeMsXe9x9OY62QjMlVZZmmTqnyK4TWwM9zONE+Zc4Ib3LBUaadvTyEieRuSphEqbeXtSVHC25oyY0lMIyu8fUl1W9PwWtgxV7lGr2896pOznCNNG3MD07jbnsY9CxgfwyKZxrT7TeFVM+MlVHbiNwyJRjUYW1CxskE7/en7Ihrj4jIkU4V2UjcrZeCHV8eGNzqFHyfaeqBak+FtTdm3P0Vve3oxZZuGV8eubdOr5PVWwU1Ps8g774S3Na09+UF9m5e3TpcW5O1KPyRvV4rQicpRZeusy1uVVLc0RcnMF968VKR7+xltTd3cNEuZPyhuezobeGeCSeM38FkLAAw4n5SSTxlmRKkZOaYTT82yfONVWApuBvt8Sxlt2nv8ECDAyPTQHlG4OpKl8j30vm1JWVfHXo1zohSuGoAGtD5vTNEmH1tBBGvbzbhlJbUrOCpjZZTx3UfSD+MC7iQWlooAgIefbynPd3cePwSBYCzpyDjkM0RfKkILZKl8TqKy5lofehkfGho0PMS3XoYnZ+BXQUU5F7KWPeyLQFWdgY68OgrFW2XlcZW3ghtfRt60xlNrdXQrTmd9BVfqTewO3sfazgF2m3exmnXs68Twcf/6AvK5jF0SAIfuN+gRgYpXh7tUnfXruFIL2rp9gN1mHauLkR2EFHvYV/04ZnB4/zou5XMwNl8qjlYdwv1mDwMirP3yBrTEzkPnyUMQDVD8sISCdtrjdzYUrhYBTcNX/+s+xFHonDyi8+Qr0GCA4lU9Y3dPol8tQgvk9AdpOd99++8YBHKOd5RwDy98UuxEjmOeMmdI0rpg3lYm70xMH7QuAu9MMBOJBq1zSAyj1h03aJ1D/aisjABxSoQj3+RnxIaIMXFnIdqmeBAzVZtGkawNspIB6CKB06IRraNlUkHrIpGxL2YU7GjQukQQuLGB5lREg9bNKWvczkT0OQyqbMYD44lg/jSM4kYM41KgSJs78TghYSRtDaBiJPaFZNq4EmMId0nG7GxcBEQjCPZW3KCXp9EP0ZC7C4o4E6Po14qgdVthEDpQJYgDMYxmrYwbkSaMOwEUaWP7ZWzNjQLQJeJchLsVKNJatIzv0XoYhE+58/B2xJVIEw1ad49eDneBjhO07h69fDmSMzloXYTMnQmizm0ZqwIo0r0n8Tggfuf2MF5F8d4+/RBsQ0xd5g+jMmcNGxPvDKExMV0ypjUO2JhgpsG14tGkY8lIH4ESTpDfICep7UeDyqWSlDXx5/REjYlZ2ySoEY3CrUiq4HTDSNeK/FllLgxuIvp0Yvwq7UG8b6JBZqDQbyYjQXs2LY+VpTpuEmGSMUFEnr08ipqsSMVY2UQ0alVSKfux41JTrGkVb4UxEQatU4xbMlV2pKIVGgfTRsUeY0wQEXnVlbFHyUaKfhi0Lp0nlda2g8Bno8B0qTxhUhgmo+jYivxZxkI02vWrKcblIuFFAselUpHWnrwaHV3yt+j6JRlc8N5+GAwwIBqALpUScrIYY0xEg8yl5QcpFZxunjJnCx9zYoYYhgHLcuAKQrfJx5uYE6TUxG7fhWXIY0ohhmHBFd3hkaipKD3Abt+FbSpk9XfRLL+Brfthm8zYx4ZhoZ1qk7yNqe/aSGQP8vfTwekAlB7sot9uwDaNWP8Mo4J2f6Asc2EoPcBuvw3bNBV9C8Zv2r7deICnwVyoZY07bjIdNx48xUHfU9ax2e6j24wGrJNXw8r5NmLHLIrFIFhet56+GlZ5XOodpPOtvM0q+fkZcuPL73HgNlBdNlPzt3NwhG49CGjX+RZfUfp4zHjk1bDSmTopfw2bOwc46tZxJRGM7caX3+OgbSccsItY29zBwVEX9fcUgdPC+BMZtzxdaG78G/77ZRvVlZXYEaRicQ3bL5+i9cEC8tNovD/7N/z3S1ct58VTNKeVk4m85vXAW8f1lfR8b2y/xOvdu7iquBp2tjJni0bR+8YYhmEYhmEYhmGm5Fj2FcMwDMMwDMMw7y5sTDAMwzAMwzAMMxdsTDAMwzAMwzAMMxdsTDAMwzAMwzAMMxdsTDAMwzAMwzAMMxdsTDAMwzAMwzAMMxdsTDAMwzAMwzAMMxdsTLwzeLA1Ddq4ZJow7QY8P1k2ig+vYcM042VN04bnjy3IvOv4QZCv1LpJZpwC3xsGkIrJEhlhc2bNH8NDNZeDZruYPirPFGV8D9Xl5VSb3FSbfGwt55BLfl+VyYKbWeE5JxiPaD9N04Ir5ghY5neOIauDaj6PnNXGYNxYzliH39lCdTmHXC7Mb8AIgoSeRH7MWeZC4XewvrKCXC43HHMZ6HHWQHFROlhfkPN9NBgjZVzdqmKHqvwywGQ6fwfrlxaQT32fw1TBzutJfezg9qUF5Cs7eH2UkfOwg9vXV5BPtungSNGmC8hhB7evX0/1b2fG/h1+dx+3ry8gH/0eVTaxczDAuCUy4jvc/ptLyFe28cPRIPlQzeEf8Xc/WkDO2MCLV0cT5hoAvsM/hXW8nrKO0yQZEpt5W3HJSoZfH5MMyyWRFEEuWUY6b6ycky7FMORaZGrp9SKTQVZ7kCyRjWtPlBWTNmv+KMIl29RkXqtNg8yMEaYoIxomaWPaVIm1SVDD1EhL5VOlCrVVFZ53XJtMLRizVArGY9pueTYtj5V1lC1LeGSbOTnWlR06ysro2bScy6qjSJWdeB2evUy5zDal84vGbPlpjjouHF6VlnNZ3wPZv6NZ+yc8qi7n5HexskOvswR4VVoZV/f261jd/tYK5cesj7VEfvKqtJDPKfKGaY22Xx1l/075HlVX8pTTQFjbplevFTm9dVrJB2s7lWSbVMUuDN46XV8Y078nr+j1UbJQms76dVoYN3eT5PgdWr++EMzFE/rr2MwhHbr9owXKAYTiPdr/4XX2XJOs43a0jlfT1HG6sDHxzhAaExa5yUcBQghyLYOM8ItjxXO6VvC5YZErokaDLCe/bAaxPcHEcckKlBzDakeMVLlu5I+/RdPZEy7ZEVn9YRlBrm1GZIUPZs0fee4kjJAMw2DEtGUmtEnDbEaBaASKeIXaI2EXCJfsQFEzKm3qD/styIuOx9EYo2+IN6csQV4jYdBkGhMe2TmptBiVnVQdUqGv0E5QR9QwKG7G8wvPJjOnkYYibR4EyqJoBEpzkSo7/cj6icgvbtJBRBtN1nGQqCOUN6zjwuFRNVCEi5WdSN8FedWw7xXaeT1t/wR5jYRxkmlMROvepoPh2vHJqy5TPqh7pOx7VF3IZ+fPaSnjYGh8rG3TK2UbsvDJ26rScrAegSxjwqP1hTzlACqubdPL15E2rYeGzxptv5qgxJ5bPFq/JJXx4tqTVP+kcbBGT354TePUbn/rOl3KB9+je0/o5VE4Rz75ndBYKdK9/VcKw9WnzlbCYJvKmPDp/k8vUT78uzHWmPCpc19RBxsTzNkx2ZgYIpyhQTGyJ8Ly2cZCaGzw7gQTRTiB0m44EcV5hGsFyp/Tz/gBHTFRlh3I2pSyhBMokMbmVPmJiER0J8OwqO1YUobSMJDMVMa1I21KPhztQsR3J7KQhokGgzb70+Q/fwx3aZTjQeTZ8s2xsXmQHssEomHKt3VGXNkOicoKHwsvYkQYFdrZtKRymmFMSMV9fB05DVTcPKCjgaBG8OZb/j+Zm4g8m3I5bfg8Xj6VOTBkRsYK0ex1XDREI1DCixv0UtEBryrfyhc3Xk58uy6iOxzFYL5zcr5VxoQIFf3iBr1UGCtedYHyYd1Hg2CXISu/T1vBDsLa9kgh9aoLlM9FZEyB71VHSmVxjbY3Lcrnckpjwt8KFOriBr1ItYnIWw/qv/di6vrPE/7W9WDM79ELxQ6Ot36JFnKg4r39McaaT1vXpVKfma+zTj9ayKWejwwNORdPNiy50zTRmBgZEmv37pGZz5OWYUz4ndvZdZwDY4J9Jpg0eg1fOwYAoPXYCz4soUkEoi5qeix3imuLEzIw7xRivwcAsH5/BwUt+RQo3awAGtDbF8lHKcSLkSxdJeuXlpT1QgAU5CfA+n1tqvwAgP4z9IoWNtt9ULeJ8qKiYJJImcG0ZcZiYKmQ/CyJj63lVbSIUNz8E2q6huPW+iYQ+3sAgMrv70DX0j24cbMCDRp6+z4mnfz39/dAgazCBFnDCe8/x55RkXO328LqexrGDaQfrOfKv2TXAU3DXmw9F3GrrEORHdCXYAD48yMXPhFuPDjC0YDQqy8iF83v+9iqfoYWEajyS5RSzZy+jouG/2IPRJQ9r7+sQNM07L3wMfFwfP859opruLdzgEG3hdX3cuOmG/5+b1R3Lp33xi/XoOU07L0QE1ZnlCKWhj+GhxDPCURFfFjWoSn6p0Q8x977/4h7Owc46rbwwXs59dwDEC/2QCCs/cvH6j78ogJNy2HvhT9x+M4j4sUeQIS1//kxCnlV/9ag5cL+TepgEX9fKiCnGkz9Kt7XNPz5Pz2IQeTXqC/n4g/bL3G028LPl/Lx724G3/3TEu4+fYW//cM+/u2DS1gYp5EH8/2H7Zd4vdvCz5cWpqrjrBjXdOYdRl+9BQMAnvUxlX+s38BnLUypBDHvDj76zwEaty4KUtHBMwF/7O+8j/6zkSzl72jhKgxowbqV+QEDV6fKL9Fruxh0m6iXsxqcJlpGWU+U0u/gGBq03l38quph5G/tw6v+CvUegYwPsapntDnA3wrzbuJPtQwl8tzjQzwjgIyIcpVAX0JRA/B80m9RMN9kYEmHevD0JRihrGDc9TtPcbQbzJ2qTIxgPVOwnlX5A8VdeyYiivse9sXIfonh76OX8QgAOtW8dAQtFFBrDrC2fYBBc1Wh7Mxfx/km/B4XcVXX1HOkX0VR06A9G81rFvqd7/F6t4W7q4tqWTF8iOcI6k4+CygsoQgN2jOBQyLQjd9hw8gh9+ff4NfrbkTpPERn/Ve42x1g8P6HKId9OXTxTY9A9GMUxH3cvh7Mt6ZBMwxU2gfKiwAuf/w9XgX9SK+FKIcQzwCiIpayXjgUrqKoAdrzPiab7OeNkTF2tTCmf9CgPZd/Y8b3bw8vfFIbVf4L/JnS43P54//GD09b+M0HV5CbUsM//OPf4efNV/gfX+zjv2vv4VJ+fLnL//xfwzryU9ZxlrAxwYynt4+J74v9BsxCHT0AhvP1xJ0Lhnm30XFntw/HNrHXWsXi8MaQAsrNLoprm+jv1pRvYEd4+EO9B5CBzT/VlG/0mfOAjvKHBjRoePjBMmxXRIxHwO9UsbzaAg0I2JO/tXFFRRovxWIRhqZBwx4efvAR7IaIKDvJOvqpOlbG1sGcLJfx8fcHcOxl/OWrX+BKPryJTUfpwS7+9h/u4WC3hsXwDbrYxx4Awlf4+c/vorkbuTFobw9fffAe8qaDg6NJtzkxx+Myyn9fRC6n4atf/BTV9gEOIrsPh9/dxk8/aOLV6wGw9wJ+dGdiVr77JyzdfYpXv/4W/3XnykRD4iLAxgRzLPyGCW1oSAh02ZJgmIn43h/wqNVV/jHqPf8Gbc9XPgvxtz5HCwSq/P7CHm96V9Dv/G9smho0bQ8PP1iMGI8aCqUmBj95X75VLy4hvdmh487TI+x2e+gOBhiIBpZzf8ZXdxdhNvpDpTNex5VUHUdj62BOmsPOH/DoYVd5pGYv/H4Hzw7Fc/nv4ho2dg5wNBhA+rP68L0qVvI5aHu/wa+3BAbT3UvKzMnlj/8D98wF5LU9fPWL9/De0BDUoN/4Eq/+x0+C79FV6Lk5f3cP/4i/+3kTr/7HF9j/tzIujT3bdHF4O3rBnDxCbovDkH98UvgebFNDod4DYMBy2ZB4J/HszPgHtst/+FT4W8tYXG2iS4Y8py8vwgCRgHBtmHt7uLu6CNvNevMV2ZX4pMSa4blHx52nB+i7YXye4OOi9NPo/r//79Offdbv4Gnbkmf0Y/4PozqWj1vHWdNZx0J+FBcgmqz20ZT3+p8fDu9fx5XVJnaP3se97bRxsPx//oLffnAFdtC3yx9/j9dHA1DvYeLI0mVcvvElvt+xkM/nsPeNC6E4YsOcJJfx8X+/wIG3jpWViA9KcQ33tl9i9+v/BxaOpfx/h39auounr36Nb//rDq5cyr81P9/HGRXmLcZ73JL/uLaIpIngN2yYhTJaPQQBkbpolpK5GAYAdCz+GNDQk+e5VYSG67WC0kl6hI7FayNZyj+q4gV6oGDdyvxAD1H/6hix/GeBD/dRYAj0d1GL+Vjo0EsPsNu2oGkaHn7eUDvLet+iBUzlV3H+0VG4pgFaD/upYH0B/j72CMCPJ81RMN9aD1H/6hj+PnqhrLkGLljPWrCes+oAQLH1rEO/8QC7uwMMBoHx2GuhXi7g0P0GPUrmH0Pok5E6siTreHoSdZwrwu/x+LPse0Sga/POaxY6Cj9GUHfyWYDYxx4IdK2Ay9ohvG96IHofGy+foqYwDp4GxsFX/3ofYjDF0aXCEorBfE/yB1FzGYVrgKbtYd/PMEbEC+wRQD9ehI4537i/MS6j8GO5K/ciKzijeCHn6Mdy/Y/v32Vc/tm/4fvvjyLfo4f4zQdX8P/r/Cf2BgPQjwu4PFFOmsM//i88PDrCAF/hF39zKR6kUP8Y//3qCLT3Wyz9aAE5rYLtH17jHISjmwo2Jpg0EWdq55NS7JFnmyjUW+iFuxHNGtiOeIcpNSNv1uOpWZY/tYWl4Gawz7diZ7lDvMcPAQKMTA/tEYWrI1mqP6zety0pK/C4Llw1AA1ofd6YKv/ZIQ2cefC+bclbTD9chX62jT4VCktFAMDDz7eUxlPn8UMQCMbSZDVHXypCC2SJCbLmnXA9WM8P/zW7DhChGHho+1sy4rDhjI4ljfDhftMDgVD5ZQkaDrG1kkcup8FqqyIlA35oGESOLM1Wx3z9PjVufCnfzCt+Q1qr8lYc/WoxMLAzxvzbhyAiFK/qmMKreib0JWNUt0L573z7FWhAKF4tRMZWKraTOcT9INJyZee1Yu4iPhXFpbkNpcLVIjRomQZM58lDEA1QvHoxL3IoXC0Cmoav/td9CIVvSefJV6BB2L/sDh7e/yl+tJCDsfECr1OTcQj3P/cwGBDWfvEz5DVWn2Mk74pl3lYmx5mYFLROOEFgOsNRRMdmmCyiQescGsU7PG7QOof6UVnKIHTR/JtT5FcQxoVQxYzIYkyZMLYFYNBmOx5bQ8arCNq7GQ1YFnLx40qkiQat26T+MCDIpEBzKqJB6+aUFcRkyIozEQ9al64jGbRuKC8ZhC6Mtq0hFoRuFIBOEbSuEcpHPKJ1rI5IPA7hkR1GeE4EurtYRAPHbdBBdMznCloXwavKOBIZcSZSdR+E464OWhfGnQCKtLF9EFtDvlel5VBWEFNiFC27SGvbLyP54wHponEplIRRtBVxJuJB6zboZbQPb13Qunup/k0btI4663RpIRdEuo7E/PA7tL4SRJwu3qN9RSyLGEE8islxJiL49+mnl7LjTKTo3B7VcQ7iTLAx8c4QGhPTJSNhSMxUPlWWeedxrXhk6FgyyEpaEsIJ8hvkJKPNRQPEpZKUFSsxa/4kYwyDTMaWGQWmS7cnSBkB3GLRrlXPLyoRIyqdDBnAL9pd0SAzp0mD7CAxf55Ny2NlRZRwFRONiaCOXFYdxbiiT0SePYpQnU7pyNTj86uD040vk67jwhENNpdKcsyHYyIaQUC3Im28nKCYTTQmZJ6VcXVvv47MxygwXTpvkGIB7SbnnyqY3Vhjgoi8ROTkRB/Wtl9PDPh3rvEigeNSqUhrT17RUK/3t+j6pbwymnVn/XpgfCRlSDnJ/ErYmGDeXiYbA4ZhkGU55Kq2HVwrlT8zsTHBqBAuWUawuzVcc5Z6vY0zJkjKsk2FLFVemiN/lLGGQQYTywi5C6Fok5PYrYjh2qRpWraxcZERLtlmuFsUjkeF2qodmHHGBAVv5KeVlWQaY4Jmr0N4NpmmFuyOgFAsUmWznTmPwmtI+REDoVjMlk9BHcsz1HHhEB5Vl5dTY7KTNJRO2pigCXWnivlyF2I5OX9rtLkT360YlvCqtBLuIkXy77w8oIEif4pJxgQRke/R+krc6JR1vFb04QLie7S+spLq33ayf2OMCSIZ0XplJTIXxSKtbWzTy6MpDfJ30JjQiBQHEBmGYRiGYRiGYSbAHiQMwzAMwzAMw8wFGxMMwzAMwzAMw8wFGxMMwzAMwzAMw8wFGxMMwzAMwzAMw8wFGxMMwzAMwzAMw8wFGxMMwzAMwzAMw8wFGxMMwzAMwzAMw8wFGxPvDB5sTYM2LpkmTLsBz0+WnYDfgKlp0MwGZi3KvEP4HmzTjK0507RnX2+QsqrLCllCHTbH9xqwzVwk/7i17sNrVLFsxr8fpmnDFSKZOcBDNReVn0w23FhIn0n5rUR+ie9tobqcQy7aD6sBN6PfFwrfQ3V5OdI3DaZpwRWEmXvnd44hq4NqPo+c1cZAMQcSH52tKpaX43Mo6xDKOvxOMHe5ML8Bw3Ky2xT2IbJODGN8H2au40Ihx3wl1j85Ju2+eswz8TvYWl/Bci7yXTIMWO0+BtMKOryP6wt55IxNHBwNEvV3sH5pIbb+4qmCndeJMocdrF9fQT423xW0DwbIWoaHnftYX8kjH53vioN2P6PMYQe3lXUcqfNfNA47uH39eqp/O7P27/A7tZyXR1Ouj+9w+28uIV/Zxg9Hg+RDBd/hn8L8r6fJjznLnCLJKHbM28rkCNjRZFguqQITp4nINZwpyzDvHK4VRLRWJYOs9sR4nyNce6KsqDThmLGosuPzu2Sbo+ipqmRsKiJUhxGvFfllsqgdDcE6MX8lnp+IXHt8PyqJfl8oXJvMSNRaZd+m7Zxn0/JYWaqIxQHCI9vMyajWmRGwPbKXgzwZqbh5EIuqKxrxqMPxVKTKTqJNnk3LOS2jDkV+IvLsGeu4UHhUTUSHTqbixkvKCvwcRXjVMWMLKk6KhE0k27OQlzKKG/TydSIyslelhXzWXICANdp+FSnjVYOI3cl8IKBIa9uv01Gat1ZoIZdVhywTGw9vfWId04zfucVbp+sLY/r35BVNFYy6c0w5fofWry9QTsN0EbD9Dt2O5p8mmvU8ZU4ZNibeGUKl3yI3+ShACEGuZZARfnGsrJwhghwj8kVjY4JR4pIVKDmG1Y6sEbne5I+2RdPZEy7ZEVn9YRkhle2hrOCBcAIl1SCrHTUCIvmNzaEc1w6UDMOidr8/zB2Xb9DmqGL5NDRYrPZUCptozJkfIGMz3m8xVMRlu6YQd85wyQ6UO6PSpv5wQAR5QwOqQu2jafrmzSlLkNdIGDQZxoRn5+QfcaNCO/34mvJsM1Doi7R5ECiLohEor0Wq7PQj8x3JX9ykg6G26JGdk8pMsbKT6oOUX6GdSB+ixkpxc4cOImXE0DCJtOmC4VXzcsyLFdo5SIx5Nex7kTZevh7fP9EYKtTFygYdHETGtrFMudxITrZ65tPWStAeqI0Jf2uF8jmNUNmmV0krIIU0THIAFde26eVwXn3yqoGcpPHhb9HKQo40FGlt+2VknfrkrQdlihv0Ytguj9ajdbyO1BHmxxptv5owfucWj9YvLQT9e5LqnzS61ujJD+PmlYiok5ATjp9Pnaicv6rk+NTZShhsY40Jnzr3FfnHGgbzlDkb2Jh4Z5hsTAwRztCgyLYnRoaE5QT52ZhgFAgnVNqdiBI8wrUC5c9RvPFPMFFWYAyEuwfJ/ydyB4ZJaHyE/08bCyFZ8mKfq4vGmC2/oIY5Ib9rk6Zp2c/PMUNDydiMKM4jPFu+kTY2Dyb2TTTMQNGPKucjorLCx8KLGBFGhXY2LamcKo2JUNE3MhXz0NgIdyeS/0/kDuSNjIOhYRAzMCIlUvIENYK39uo6iMizKZfTsp+fazyq5qXivPEyY8wDY2PS7sTQKMnYfRBDI0D9PGpIVDY2yMzlSFMYE151gfJhe5RyRgwND4UcCmXl4rJGn71QyA8Nh5FxMNzFiBkYkRLrgbx7KnnnH3/rutwJKt6jF1GjK8Bbv0QLOVDx3v5Y487fuk6XAjn7Cjmd9R8p5fjR3YziGj3ZsGghn8s0JvzO7ez8GYbBPGXOEjYm3hlmMCaISDjG2N0J1wrekjpiZHywMcEoCI2FzKNMrjV6S598liBUwpNHmYaEx4cmvfEXghwrMEwm5Y2grl+QY2pSwZxqZyA0Dk4wv2hIhThDIT/PuIGCX2mn/3gTBYqwphEqbYVyHyc0FjKPMilkicYy5UyLNtuBIRYo3mpjYjKhsj/xWJEQ1LBN0hKGy8TyqfaFxsSYnYdwdyTDQLnohEZCZSd9HGhEaJRUaEehUE9DzEjoB7scKSMgNDiCHY4JFYWGwdr2K3VerxoojVPscvg+bVVX5PpY26ZXgWUVGgtr26/Uxpa3PqpDmeF8ExoLa09+yOzfpQWpeP+gUO5DOlE5qmyddfpRICdqJPhb1+nScoXubb+ko6NBZr5h/vs/HeZ/fTQg6twe5c8wDOYpc5awAzajRF+9BQMAnvVTTtV+w0S5BRiOQLemJ54yTBQf/ecAwcBSIfksoLAUrDUBn5IPo/joPxvJ0pKPAaBwFQY05boFAK8aOFwWCqi3CJV2H4NmGZpSWAK/gc9bsv6r0fr9Nr7pAYRrWBTSAXboaGmasN2Ec6jv4tGY/FYy/5Ae9rP8v8U+esnPLgQ+xDMCyMBSIWMS9CUUNQDP1XM6Qq4PkIElHeoFoi/BCGUFg6zfeYqj3Sbq5cJ062Ac/hY+f0ggKsr1rpDXqQYOs4UCas0B1rYPMGiuIqdpke9Ldnno8vuipb4ve3J9qBaPL9eH6tGFJzLmV3Vkz6Hfx3MAKF6FjkN0ttbjztwTHLD9+9fxQesIf3vvJZ7WFpHPZVR06OKbHoHoGgriPm5fz8fqqLT7Ecf+Q4jn4XrJkFdYQhGA9lzAJ7UjfWf9EhbyGjRdx50Hr/H/b+9dutvG7nTvByBV/T2OScqSPe1TAkjKmXVZcpIedGWdd9R14dVJ6VLpHpzT7zCVDLokXlQnZUlOJznDcg2qJJKg31HKosikz9SWRALs71GWiP87AEBsABsgKbtckrV/a2EtW9gX7AvI/8MN7Oejb09xsbeKeEyy6ngOq46ExJ1SSC5AkQDpxQAG+HVcXdw+XEhGtA+S3Ydh98HQuvdIwUJC4s+jxAKWJKucIVPOrU++w8ujPXz6YB5y2LxguPWrv4zTh84jH5fJ8yYRYkIQTfcEnvhFKyK50QUKLSEkBNcMK9hUVNUSL+hib/UDlKphwTuDUUMmtYljIqiVP2Gd/eLXT9EFAdjD6uoG9o6Z0rpd7K2mIKer0J0AQj+JTL/vT48EVn6hAhKwv5pBsaV77klDKyGzugciCt6vgjeHUUd2fgPHJmGp8iesJWVOYGOJBUVRoEoSJPSw/+CfUazps+024yGBlfdVSJCw/yCDYmsAdnMvo11CdnUPZBLQs+bHpau6ahh1ZG9v4nhkYmn7j1hLxTh9bmOcoksEwhN8uDyP1Y1H6ByT2++9HvYfzCOWtnZn8tAu4/ZGB6OPD/AsSkjAur97IBD28dOfbmK3w+ys1Ovh8YN5xNJVzg5Ql2UI/QWwtOTOqcc//RCl6gCjMGUkELxmhJgQTI9RQ9pakoC+e99/ViC44iSw3jFxfHyMYyKQXkVa6mFvM4VMhKAwahnIqQ1bSAzQ2Uh6AhZj8Nz6h1pAxf7V0XqEVIfeKiItAehu4oOaASLA0CellzzpASCx9idUVQkSLLGRclYyJAnJlV2QolgCSV1E2AKQ4IfDqGcRS62jYxKWKn101lPgx5sJrB2N0Dnu4tg0Yeo1ZOS/4vFmCula+K/ik0is/RGVtARJsgLiFLN1avL+LkbvLkGRJEiKNT+4l3bNMOpZxOc30BmZWNo+Q2djHrFpGtbrodddQu6wj77J3HtaCRlZgtT7FB/WdYz1hLGD5Qd7MN/dwtmXK9FCAsBQf2Hdt0oe240+RqZp12FYwj8mj+swLzvgHm7hk2fn7pwy6sjG/4Y/fHob2VpfCArBG0GICQEf57GJcXCioZjcQBcFtI7XIdYkBIC1UhW2l3qxdcW/xBLr6DQLkCSg+6QZfMTK0FDKyEhuHIPIegTCLyQAILHegWkS6Nh+VMY9g8T9R+g0i24dICTWJqUvQJKkcXrn3FpngEGriHSauQI1j0pzgM6ffwGJuy4v+EEx2ihlYkitd2CSinxjgONQIcEhsYajZgGSLKH3VQvGpZcnElg76mPQKtr+KPafFWt+HP+f/zH9Nb1p2mXEY3zPlUKTs6+/0UY5G0Nqo4ORuYR8o4/Oxvzk9iUW7F/uFWyfHWF3NYXUOE8Cife+xFGzCFmW0HvSgkGm5RdxewOd0cc4PFpHKsZbbfJy65NnuBiZoO4eNldT9uNrAHALt977EkeNAmIx2a7jB3ik6NYneHZYtOvQoP8QdQgEPoSYEHDRvtmz/nE3hQQAo/YbWH/Zw4r/Qz+5YQmP7gaSkgRJKkLzlCa4uSSQugNI0zzvfzeJROQ3dQKpu25Z3C9I55Eje95OxHnHonsKdm3CqJWQSa1g95igFipoDo6x6wn8ZyC5yNQxBc47JIFHlmyx0XF+6STQ8R42VpIYtp5Y7b6TQuJyV/kjkUDyrgRIXZyEGe8ZJ+gR7LZFYc0PSF2cGCHP8hgn6DplvWI3GXVnjphQ8hU0Bx3srl7ivQvnHYjeCXQkkLwDSNO8/xC4XxJIvPcIRx3TEqtEoC4zP4iX53ph1MvIzq9gt2NiKbeNxuAYu6sziDcAwF2kwjohsWgJjt4pDAD6zm+xT9YjSw/iMe8PJ4k1HI1MUO9T3I7HIEuFoBEdD+cdiN4JdLqF5B0JktQLn//6CXoA6E4SCSnknQA/zjsQvRMY9N+QvAurDiNEWOin6BFA9ufHVHVcGdw+PA0zZ9RPrUfP7ljzn9++W9Z3ldTDqcE8/sZinOKvZJVzK7Scm4kQE4IgRg2/2QMAFdV/FY8zCSK4v8s8puM9dlesj9rkovWGwt5ndc+z3A7aN/sAAWroG9ouyQW3rMBKAgDt2z2rrIUkIBmo2W7RxRb/S8Zo2kG4ugBHKmilDFIbuzh2ViN2N7ASemmT6xi/I6EuIInhlOm9jywZ9QxkWUK6ynu23kDLeqMb+X+8f+2+4ZKLCgBg/7M695f59jf7IBDUxclhTmJRgWSX5b5z4sKW9Sod1S5lMb+xi2NSkG84c4R3dQbqWesF3EKT70psOIG+/QiSdb9IkW0AERTmDW2jbrkaq1Xeo1IGWk+6IBDyP78/sQ/fOO99af2Sz/kM2VuNjYVCu2z1ecdcQq7RR2d3E6vcPg8hsYJ/UiVI0j6+bYcFiyfWexXKAi43QwzsLMcRkyUUGhecsWDEgbKIpOTMfwmPf7sD3QyKkfa3j0FkQll0PqGG2FmeQ0yWkA+pY9h6gp5JIGURCUlCckGBFFXHwb5Vx0JidjF8BUguKIAk4fHvdqBz3kVpHzwGmU77whuYWFAg2eUMOP309Ns/wLTLcVecBACEA/bNYfLWsLOb1tmIrWEFkbCmdVXSx5PkVU3rqjRgy+KY1rnu1xzTOsYZ29nqdZyeMbKbRGQdNdete1zH2IAuLL3VPo+jte0jYTk4M3l0y7Hb8t64ftvCWrCmdRUajDt+ktEcD9a07pJlBbZe9WL5QIR7WfhxDeU4pnU1x4SO3QqWNa0LtoFnWje+ZiiUbzB+HLrt1i0h1LfiOqDXMq4Xw6u0QSvZBm0K5bd95n411xk7eotZG6PO3Rp27BsBhfKHfa+hXL1EGXts3a1gWdO6bTrrO/Mg3LTOdb/mmNaN62e3gmVN63x1vHWmdVuB9l3OtG6Lzs7cciab1jFM2Bo2wGW2eb1Mnh8QISZuDI6YmO5QpxUSREJMCCbTKoyD6uChBj0o9KqdXqWqP6pvuQF68LDKYnO4IoN/uAZ0rlCZeHi8KRyfCU46tg4mvWNC50/HT2/RGgfDvCPCg+I6MHbx9rfLalu+aXr7Q69RWrbEVaXva7dWpExkWSH+DQ6RYsIN9INl+w6Pd4TrUM07AmZyY9fqYFpLMATbEF1HhAfFlcfxh/C3iXM4ZnNjp+ugK7ZWykb0k+U6PpVxW4iYYI3t/GWP6/Cb2Wklr6ux51AodxgUN67Q8Kd36vAZ0Gk+52ROHVyPhuuCxhjHBQ6FcgfnrneEUafluRhJUGjrxOfvwRrQBQ5fOWEIMSF4e5ksJlRVpUKhSq1ZFYEQE4Jp0FtUUG0zxPGcK/DnW5SYIKusYppTFi8tEemtKr9uNr1jeMe5NwIHx+hObxUpnfamU9UCNQcDb0KbWdPTOA8TQKgq5avNqVdRrjR6i4ppr/BT1Tw1eSIpSkwQWb/IT1uWnygxMV4B4MwJ/+HLr2s165qYQFZRIq7JacO06cly886kJXceKwrlK81rumJlM15N4PSx/5hCTBAR6bpGpUwm0LeVxgwO4aFiwj6tlSiTYcYCIEXJUaPfJ5M3HoZGpaxXECpKjhrjX8eDGFqdShl3dcvNc8HPY2hU5tYRkv66YWhUznrFoqLk6NDfvigxQURktPnlnAZFHZcbKCYkIu6TgwKBQCAQCAQCgUAQiXgBWyAQCAQCgUAgEFwKISYEAoFAIBAIBALBpRBiQiAQCAQCgUAgEFwKISYEAoFAIBAIBALBpRBiQiAQCAQCgUAgEFwKISYEAoFAIBAIBALBpRBiQiAQCAQCgUAgEFwKISZuDBqKkgQp6kinkS7WoBn+vCyTyilC82cRCADA0FBMpz3zJZ0uTphvIRgaShlOWfo0tjkaSrIMqdhCmMuOodVQyshM+da90dL9KRkMDaVMJnBNrbBrmjU9AEOro5SRIbPXVahF5rk22P3htk1COl1ASyfM3Dqj/QpltVGKxSAXmjDDJgisOuqlDDKyW4eUTqPQ0kPnlQejjmxMhqxWMTA5GZw2yO48VNVp22AzqY5rTRvluDVOo5naZqBdLyObkSEzY6eqBTQHg/C+NdooZ7OB8WgOTGa8h9hZjiPGzonQI4/GhWnXN0R7p4xsJua7pjya/QH4dmCz5xm2d1DOxpjrU6Hmq742XGOGbTxcXkbMM0Z5NPqjGdo3xNOdh1jOBvu1cdYP/UwYPt3BQ8/Yq1DzFTT6JnjTc/j0i5D0I256LsMv8JN34pDVbZyej8Ln7pvA72IneFuZ7IDNHmqhxXezbhUCab1HgVr+PAJBq2A7WvMOlQrNaWxFbVrFiWWFlqa3qOg4SHNcrImIWkWvc/I05evVdIR7NifPhDbk/emJSK9F18HLc21oFSnNuM1y2zZt47QiZSLLCncUtlynZWv8eQ7YNrpWtB24/eVbhxKR10KjomzXo1So77fV1YqUkaWQeahQvhHRhjET6rjO6BqVMrJ1Pziu11OhUSlr5wv0qz1222cUMC3WSpSNGo9DxxnZoHo2RnJE+e6Ro8PzEZmkUWlCHuua2DZOk+fUk8eoZykeOmcVyh1e0MW03XgV0cq0HLfne+BQKHdwHhzXAG0qL8ej+3XrhM59882oL9NcLKJvfXUbO/cmpH9Jk02t2/TwnTjJAEHZopOXQaf3N4kQEzcGR0yEB/u6rlOroJLqTOpCMKVeVUPPCQR8WlSwgzu10GREqjXfrA//Ak2nJ1pUZMoajPPojAgoUDMQaenUqhZIZb8kOGKCFQVqpUkD9wzpYwGgUsWt+BLXNCG9BALy3jboNTvYVinfHDBfGkwetUIDf4OuBS0q2oGamm8ybdBJY/tjNI1Y0i5Zlk5azSdowgSBXqOMHZSp+QoNxgOok1ZLkyxJBChU6Y9CrlenmhMIgxfouyJAyTcCbbDKz1Mj0AaWSXVcV6xx8gitGcSEVrIDcCVPjX7fcx9ppcx47LbPLsiN5TQqxZzxOKT+uN8N0koZitnjYQmDCRh1ysZkkpCjwzNLEGqlOMVkEJQcHZ71yRyPt0FaKUsxmbkm+1RknjKT59TOY9QpG5dJgkK5wzO64KVXtun0Yoo2XEk0Ks9ZgbWSO6CzC2aMyo6IytHBS3Zcg7TLcxS3+/Xg7Iy5/w1qj8tRaOvkfDwWZNRpeS5m9e0BK+AMapeXrTzKFp0488PYoXtMeleYGNR+yKSPFAcG7dybo9j4/p6U/odHiIkbw2QxMUavjgWFXzO0CnagVeWuWwgEAfSqHVCrVSZwdmkV7OCvygbJfCaWVbTLqrhluSIABLVAzWrBCrICYkKnajqY30OrSJLkO2//zQrmvcmJdKrZZTqrE+MVhpDgv1WU3Drs07y/MTmoKEkk+QXINWFSf2hFKyhWK31O273otbQVLKr84Jktyzmta4yIUPPUqBSsoDJETGhF2aoj5Lxes4NS7nk3yM9XKqRKEkm+QH+cP0QAOPUrTBu8TK7jOqJrJVdEKPY4yVY/TycmHFFgBea8HI7YYFcn9LobbJ9xgm2tFKfYOI//LItGpXiMZI8w4P3Nl8sWDm75TB5HLPjzlJ08VnDr/78vNZXjMZKRo8Nzfr9cdYz6MsVjVhB+yhF1mi0SeKsKLm1bkPjEApui/E6gHN7fmBxUfidOMnJ08L0lZNoP3fQvA0tBzmpDjg6+Pw8RPq6QyG1tUToWI0mICcGbYwYxQWErEDpVVeuXWaElBNPiiIXQR5laTHDvP+fDEQuBx4YcnMCeEQp6NU2SWqBKy560nDR2SltM+FceGPSqFXiywmEqMWGVaU7dBomQ918fB12nmvOL+zTpryAtO8DPN4NBABERaUU7OG9ygnMvjlgIfZSJU5Zey5CcLlClaQs1rTgOUoP1OasGk1YG+HiEwMBa4fAH+k6a0EeZIq9vujquI3otQ7F0nrYbzjiVrCB/ajExGUdM5BvsKkDwbx60khXI5g85waSD+/jTZNHhxRETuUN+gMvDEQ+5w/PoR5cMg+qlrDWfcod0Hpn46uKIhdzBS357tTLNxWVC7oBeTn7WKRRHOOQOvp/8yJRhUL28bM3R3AF9PymDYdAOmz7kOSePGOnXrVWOKyAmxAvYAi6J1V9ABYDnA4zfjzWa+KoLAHeR0msoppmXydJpFC/1Jq3g7cbA4AVAULGY9J+zSS7ac02HQf6TLAYGz92yJP9pAEguQIXkmbeJ9Q7M411s3E/4EofRxaluLc0F0E/R9Z+5/y+oqBKk7iY+KLXgvgttQCt9gI1jAqnvYzUBSDCgPwcAFQuT+uPFAIa/LhutZL88mkxifZeQawxg7q1A4nbKVcaA/pwAUrGYDLn4xCIUyemPKKz5AVKxmAB/giQWoTpl2V2bWDvCqLOLjZXk5P4zBngBAqmLSMJAu15Chn1RP+IFbKOexeoe4d3tPjrrKcjcupz7RbHuF16ahDU/JM79Ml0d15PE2hEuOnvYXJ1inC6DUcdv9wlEChbG88eA/gIA7L/xSC5CgQTpuY4h8V+OH+58iM1jE+bSNv64loQ87cAMd/DbfROmqYTfH37YPAmJO4XaD+cQj0mQEgmsPbrAR9+e4mJvFfEYL/VVZwj9hT1uSX57kVywxuiFdc/wxmgiwx389vEII1PBQkIOnYNPH/4d5py+/fIcH35zgovdB3gnxg+3n/7STf+Jnf589wHeiQfTD7/4CX66e46///cTfLd+G3NXaLyCVysQsHRPMN7ARj9BFwCwh5WVDexZ/7HTdbG3koSUrk34whcIrioJrL6vAhKwt5pBqaW7cx+AoZWQWd21AsXuKfTxV1IC650BKsU0enurSI1350hiZfcYSqGCQWcdybBvn5mxBImiqJboQBf7qx+gWOUHsYLXiOF8Bj7Bh9kUVtZ3cXzMdHq3i/3VFOS0b/ekdgnzm8cwc4forCd/mCD/TdTxtmLUsXx7E52RiaXtP2ItGXuNwVEbn3/ahWkuYfuPa0jKMj/g9TPcwfL8BnNN8uQxHe5g+fYGOhcmlrb/A2spXp4h9OfA0pICVZIgoYfHP/0Qpepgxl2xbhDDHdxb2MTR+QhLW3/A2nyM068AMMTgBXn79mcfoVTth/StJYT86cvVPi5GvvRPf4nFzSOcf/gt/rI2f6WEBISYEMyCMXhu/UMtoNrSYT8mByIdeqtgBTbdDfxzTcgJwfUksf4na5UBXeytppByfnGWJCRXdkFOAK8uIMmEBIb2OZ7sHXN/8eo+f4KmFrbGcBkSWOuYOD4+xjERSK8hLfWwv5lCpiYExRuh20W3qyLfGGBgMp+DWhFpWYLU28QHdd3a4tGoI7u6B3p3G/1HK5Bfm6hkeBN1vKUY9WXE5zdwNDKxtH2Go/V5hPyIfCmGO7/FvmmC8v8Ln0wjCAAMd5YRT60z15RCbELG4c4y5lLrOLowsbR9areDl+cWPnl2js5xF8emCdOoIxv/G/7w6W1kQ4Pem8tw5x7emV/Hs/MRlrZO8GzjNuLcfoXVt9+9xBHTt8tz/4k//HoB2eoZzv0CAbfwq7+w6Xdwz06/XGPSD7/AT366i/O//3ec/H4Fc5xVix+bq3dFgquBswqhLsJ5GiOxfmx9aR7vYt3zyEgCifu7OG4VAADdr5pideKmoBU9e/mzR7Hl/+C8DlirDINWEek082e1gEpzgOM//wKSL1AzahmkVndxTCoqzQFMj8guIt3rYnM1hVKL/wjEK5NYQ6dZgCRJ6D5phj4aJXgN2I8YASoq/SPsribhPn2SQOK9R9ZYyBJ6X7VgkIbS/CaOzRwOO+tIhgYhr0L7DdTxA9IuIx5jPV3co9CcYc/9WTHaKGdjSG0cYWQuId/oo7PxeoWEZ1Xi1/cni7xhG+XlOFLr1jXlDs/Q2ZggJJg8F3aeo40wIcHh1id4dlhELCaj93ULesijWjeO4VM8XJ5Dav0ZzkdLyB2c4ShSSHC49Qm+OyxgLi6j97UGw3R8RUK49Sv85bDoS/8Uv1zcxNH5h/j2L2uYn4tNt7L1hnmtt43g7UH7Zs/6x90Uwh4VDeA8680+GiW44SSQugNI6OIkbFI4wvVuEonIT8kEUnfdsrgfys47DbPM2wAJJO4/QqfjiAJLQG+sJGE0n/jKN9B80gVIRWXQsZ6795fTLEKSgL3PajDoFpJ3MX4vg4vTH3dSSEz7tXFt770EknclQOriJMx4zzhBj5z+iMKaH5C6ODHsDV79GCfoOmVN2bV87iAZVoDzTkPvBK36b7BHBMI+HsRYs0EJUnIdHZNAvU3Mx2RIUgFN8xaSdwAJPet+CWsDALLvF6P+GfZnqkMEi0a9jOz8CnY7JpZy22j0j7G7ynvHJIHkHQDo4TTsFzL9BD0Q6G4StyTfM/vtb/GYTJDyPlYSUuhz9gAw3CljObWK3aMR3v14G41+B3ur85ECJCxPLCIPl+QCFAmQeqcwrt3S5i0k70iQpB5Owwwd9VNrjO5Y98yk3hnuPMTy/AoeHV3g3Y+3cHjWwe6DGQQaS2IBS5I07lvu9bEkFqAw6Y0vfof90QgmHuNnfzeHGHt/Jz7Bd+cjUO/XWHwnDlnK4/DlBUx/mW8AISYEQYwafrMHACqq/3rf+SNq9gvXxTCLa85qhuAt5/4u80u899hdsT54k4vWb7l7n9WZl5NdtG/2AQLU0De0XZILbln+l08BQPt2zyprIewN7WiMWgayLCFddd+IYM7awgEo/Py+r/yIl7Z9JBft9zI+q3G/uLVv96xdSsf9YaBuu14XQlY3jNZXlsi5hvdeclEBAOx/Vuf2R/ubfRAI6mICIa9XjkksKpDssvQJZV1qgiRW8L4qQcI+vtVC3uR0gn1lEcmJVxzEul+kyDaACEroG9rXjPe+xMXIDHx+EBH2VsOeTb887fIy5jd20TGXkGv00dndxGoqfJwSiyokyR4Pzi/L7W8fg0yCssD+kOA79/4KEn6hweBc05H5Lj4+PENnbwOrqfCXfDFTniF2lucQkyXkGxcIPGkDYNh6gp5JIGUBiWABV57kggJIEh7/bgf6iDNGB49BpgllIRFYWfbz9OE93N54hKPRu/j44AxHu5t4MB/2eNoQO/feQVyWkD98iREnih+2vkbPNO2+/S/875+46S946bWv0R2nD58zVw7/9k6Ct5XJW8NOMq0bbxcLlQrONpvWGcsQzM7n96YQ3HRY07oq6eOp86qmdVUasGVxDeI4hG4Ny5yDSgXWHI51zvZtAets9Qqo1vai7imPx4XrEcG2wWt6FmZa57pfc0zrGGfs6+mCzZrW+UzgIo3meLCmdZcsa8LWq+PzUClf8Rrj6bbxnYSIrV0dbPO74LatrGldsA3TmdbZhNbxFnCJrWH1GusZMV0er2ndNvX7zrhOMq2z/CAcX4uwSzQm+FjwGLtZT2k057pfc0zrnPoxxVayVxbWtG6LztgxmsG0buxkzZrMTcB1v+aY1jn+F7C2kz0fse7XHNO6nWD6SBwDvCuwNawQEzcGR0xMd6hcReD4TIQfwsxOwKVVcI3jAoca9KDQq2O36arfvIE1oQscVlmRH6pRYoKIESW8g+dB4ZrdBdPbh9+QbUIbeKLAFRr8g29od01o+dynef3Btk2vUdoO6Ct9X19pRcpEljUhyJ8kJohIK2Ys47pA+dahROQdExXoaz6XZ8+hTBYqDlF1XHeixIRes52mWYM6VxQE+9R3+MvUSpSNGo9DjlgYu13zhIaDY0DnL5Nz5Bwfixnz2OrAdcbmpEOYod01QivTcjxsfBXKHZy73hCMa7VrUOeY1vnzcg6fb8TY6dqfzj78hnbth/cmpg8a2nEQYkLw5pksJlRVpUKhSp5FBw56q0CqT1SoaoFa7k/OAkEQvUUF1VndYueNP+EEMUFWWcU0pyxeWj8TxASRvaKQZua4qlKh2qSBP+EY3c4TvKaqb7VijN6iYjodSN+MaIPeqlEx7azmOHnykXmuDXZ/8NoWaF2UmCAi0rXpy/IzhZggItJD6hib301iUqDvlM8II0WZsg0Ok+q4zswqJpz0zHiFHtwyNSplMoHxaIx/BffhmNlFrThoJYrH5GD9vMMRE7PmYYJSQ6tTKeOsbllpFCVHjbMLfhuuG4ZG5Ww20L5Df/t4YqJtG9v5+5B3cEzojHadylnbvZ6tO8Sl3GjvhKSf3pzwKokJiYjzUKZAIBAIBAKBQCAQTEC8gC0QCAQCgUAgEAguhRATAoFAIBAIBAKB4FIIMSEQCAQCgUAgEAguhRATAoFAIBAIBAKB4FIIMSEQCAQCgUAgEAguhRATAoFAIBAIBAKB4FIIMSEQCAQCgUAgEAguhRATNwYNRUmCFHWk00gXa9AMf14fhoZaMY20L29xYkbBjcbQUEynPXMunS5Onm88DA2lDKcsfRrbHA0lWYZUbCHMZcfQaihlZKZ8695o6f6UYUyqwz7vvwfHRwEtfkYvRh0ZWYaUrkKfJv1VxtBQymQge8a0gJZOmLllRvsVymqjFItBLjRhhvapgXa9hIxnjjh16Pw6jDbqpQwysuxel6qi0NI5c8S+hsC8cOdH05yiLUYd2ZgMWa1iYE5Mfc1ooxy3xmk0U9vsfIE+Zfp2ZE7o2zbKc3HIhQYuQuoetndQzsYgy065KtR8Fc2ByRlvAMM2dspZZGLe+ZFv9iPmIUsbD+fiiOUbuBiFpI+oYzRVHVecYRsPl5cRYz5bVTWPRn/E7/OpeIqH78whlj/Ey5HpPzlm+HQHD5fjiHnGu4JG3wR3igyf4ouHy9b9yY5F4yx0TmH4FL+8x2nf2QW/jjeJ38VO8LYy2QGbPdRCi7jGxK0CqZz0bD6BIECrYDta8w6VCs0ZvDtbxYllhZamt6iYth1HQxywW0Wvq/FM5dN0dYxduAPlO0eemtyMLC0qSpJ1rWqFBhPTX2FaRUozTrD+Ps83TX4/8tCKlIksK8SxmBzXadnq01AHbI2KGTtNyKFU+h4XW10rUlq2x4pzKP66HBduTlrryFNjNGEekkZF2b7Ot80BW9eolJGte4jnVh3FRCfsPDXCHKvJclkuZWJ23bYrtT9JPRtRh0K5Q68rsqGVKBMLn1NK7jC6jYZGpWyMZCnoeu0mKduu4MHyx3Vw8l0btDItx8Pap1Du4Jx8ptWTMdpUzsbtfg26XjsY9WWai0WMt69uo/0w4lpBSu6AXp77HLZ37k2o4yX5srxRhJi4MThiokBh4b6u69QqqK5Y8AsDvTo+pxaqpI/Vhk6tqmpPapWqPBUiuMG0qGAHd2qhyYhUa75ZH6gFmk5PWAG0U9ZgnEdnRECBE4jr1KoWSGUDeE6gr1fT4yBfrTRp4J4hfSxiVKq4FTPo1Kr6hA6nDiIivWbXE3J+MjrV0kxweq3FRIuKdqCt5ptMO3TSis545Kk5MXgmO4C+TFk6aTWfoPEH+DZaUbaCCzVPjcGAKceqQ5YkAhSq9O2AVK9Rxr4mJV+hwXju6KTVOOmJSK9lrL+HXMNkdKo5wTbeJjFhjZPTn844RQbaPvSaHejPmI9IJ61e8tXNERNG3Q7aFcof9pnxM0grZay6lW06cwTLOD1IyW3TWd8RuwZpY1Gi0PaZV4CMy6yXKOOIRoSICaNOWTt4japj65RXx3VAo/JcnGQ7ED+7cO5vg7RyluKyRECODl5e0HTxtkHtuk98hYkJo07LczGSoFDu4JSZUwa1y8tW3coWnZw7471D9+Zi9lhs0enZxXgs2jvLFI/ZY3HyktxhbNPDd9z2nTplkUHth3YdyNHB9+dTtu/1I8TEjWGymBjDiAZWT7QKToDEL0F3BEXIecHNRK/aQb5aZYJ/l1bBDv6qbGDGZ2JZRbusiluWKwJAUAvUrBZCAnmdqnaAzub3YK8o+M9PX4eF5zo556NxhUS+UiFVkq61mBgLq5A2aEUrKFYr/Yl9pdfSdqDPD57ZspzTusaICDVPjUohIpB3fu1XPcG/J4UtNpzVibH44JbHFw7+MmbDFRLO/JDeAjGha0wgr9jjdAlRoJWsX/CV7TMmWIvGX/dhpWALkqCY0EpxijnlB65Lo1I8RjJydGgHhFopTjHZFgGB9Mwqh++8oZXcYFfJ2dckc8WEVmbq4DTaqNsBd8j5q45Rt4NwZYsJtF208hzFZZCydcLtYxajXR4LLyg5OtguUDwmh4qJdvmdiLLbVH4nTjJydPC9JWTaD630yB3Q95ylhPEKBHu+/ZDeicuWKHl54WufQTv35igmgXIH3/9oqxNCTNwYZhATXGEwW36BwMERC6GPMrWYwNt/zocThIc+auQ8PsQE8Xo1TZJaoErLXhPhpLFT2mIibOWBiPSqFXiqFY+YGdfRtEVGaB3EiAGrnsDpCbSKkitoPNcza0lXg5Yd4OebwSCAiKxHfiSJkG9yg3EWRyyEPsrEKUuvZUhO22Nn2mnsIHVSfTwcIZBvjMg0HfExzWNJDo4Y8K5WTItHiAzsVZG3QUzUMhRL52m74YyT/bjSTGJCp1o2RrJk/dI/da561q7bXmnQSlbwyhEToRgG1UsZa26NhUFQXEyLUc9SPHBNPDGhUXlcx/Rtvk44YiF3wP6a70lAc3FLELzkCAIWo75Mc5k8bR2e0WhkErXdvDwxEYphUL28bAtBJ6+zwjDjKsJUYkKhzz2rGW8W8QK2gEti9RdQAeD5AAYAGAM8BwB1EUkY0GpFpNPMS2viBWwBFwODFwBBxWLSf84muWjPNR0G+U+yGBg8d8uS/KcBILkAFZI7bwEk1jswj3excT/hSxxGF6c6wL0U/RRdzplxHStJ/nWxGC181QUId5HS694XvdNp64Vcfx4bo57B6h5B2R6gs56ENLm2K44B/TkBpGIxGdKWxCIUCcALd0z5WPMDpGIxAf4ESSxCdcqyOzmxdoRRxx47Xp5ZMOr4bJ9ApFjzfTjACxCgWJ+bzkvb45dyeS9gGy086RJAd5HUd1CelJ7BqGexukd4d7uPznoK8qu25wqRWDvCRWcPm6uvME5GC193CUR3kTJ8L0irKgrNAfdF1sQnz+y6U5BnrLxdnkNcliAlElh7NMJH355htLeKuCwBQx0viEDKIpLSEE93yshmvNcU9gL2rU+e4XyaaxrqeA6rjoT0X6F1XN8XsIfQX1j33EIy5BMxuQAFEqQX1ndMVEtvffIdXh7t4dMH85AvcQM9ffh3mIvZ4/3lOT785gQXuw/wTkxmxnsBCem/8P998RDLvjnIfQH7Hz7FVjqG2F9/jY8fttC/cDYIGOLpLz/C5tEFRkv/hJWE/KPd80JMCKLpnkAHAP0EXQDAV/jndBIrG3voWn+w03Wxt5KElK5N+MIXCK4qCay+rwISsLeaQamlW3PfxtBKyKzuWoFc9xThIf8E9BNbkOxhdXUDe8dMOd0u9ldTkHm7M2klpDa6oFzTEhI/0peGIASjjuz8Bo5NwlLlT1hLypAM63OT8BU+yM5jdWMXx8fkioFebzze492Wxnn28eDBBnYnpXdolzC/eQwzd4jOevJHCyquNMYpukR2325it8PsrNTrYf/BPGLpCvoRu/bMhhXoLikKVEmChB4e//QDFKs6TJMA/QQ9AIQn+HB5Hivrj9A59l7T4we3EUtX0Z+4w1QI+il6ZNXxUUQd8XQVZ5etQ2AzxOAFYWmJGe+ffYRStW/tOGacokcEwtf4+Ce3sbL2JY58c/DxTxcwl6ng9HzEjMUt/Oovp6iWl/Gf//FzLMw5u5El8N7vn+HvP/ocp8/WcXsuxhdTbwAhJgSz0e2i27V+HdOtx+RApENvFaxfl7sb+OeakBOC60li/U+oqBIkdLG3mkLKWTGQJCRXdkGKas1zdQFTrEFwMfTn1j/UAirNAUzPfVREWpKA7iY+qBnul4xRR2Z1D6RsY7B7XwiJK4ZRzyKWWkfHJCxVOKsCvR563SXkDgcYmMx4a0WkZQlSbxMf1HWYBBjWUh6g5K35MSG9fQHIru6B3t1G/9FK9C/VNxhj8ML6h5LHdqPv69sSMrIEqfcpPqzreD164hY+eXaBznEXx6YJ06gjG/sb/vDpPDK1gVuHPT8+PjhD3zTtzwQDhlZCNiaPr8n0C8hZmKKOj2rWfBNcllv45LuXOGLGe3nuP/GHXy8gWz3DubNlrzMW357ibMSMRbuM5bgMqfdrfLwz8GzxO3y6jSd7He749F58jVb7FefHKyLEhICPsxKhLiIJ5lEUqKjqx9i9n4D70EgCifu7OG4VAADdr5pideKmoBVD92svtn68D7bLk8B6Z4BBq4h0mvmzHfgf//kXkF4xUEusWV8IFHgsKoHE/UfoNAuQJAndJ00YIMuTIrWBLuXQ7Kwj+Yr1C14jRhulTAyp9Q5MUpFvDHDMComE9bkpQUGl38HuahLu01wJJN6zx1uW0PuqBYPIeuzKNEHdPd+jV/z0QBul+U0cmzkcdtaRvG5LEu0y4jG+50qhOeI+dnRZEmvPcDGy+nZzNeXr2y9x1CxCliX0nrRg0GtRE15ufYJnjQJiMdmqI3EbS/b82D47wu7qvPUDhpUYt9770pNepym8RfwkF6BIdh2nYXUUrTq+vmQdAj63PsF3hwXMxWX0vtZg/Ld5LEkSJCjYOvkOjx7cxrzMjMU//B7fNUpuetNaKRp+8RMsrH6JZxfv4vODU2sOswLk//4N//LTBZSa57j4AabtNAgxIeCifbNn/eNuihENAHAXqbBHzx3B4TwaJRAggdQdQEIXJ2GTwhGud5NIRMZBCaTuumVxv/CcdxoC83YW7KC+4/xq6Qb+RvPJayh/Ar77yKh/hj1Yj2as+o3ukus4JgK6m0jJ8vRmd1eGBJJ3JUDq4iTMcNA4QY8A3JnU59b8gNTFiQH+BDFO0HXKipxrkzHqJWRSK9g9NqHkK2gOLLHA13p3kQyr0BEcPWu8eZftwZfeqH+GfbIf3WENsOz50TEJ1NvEfMyaH1OZ3d1UEovW4ym9U4RNoVcmuQjFGb/xvXoHyVsSf+4w6aPfKZvEHSQTCKnDFhy9U1ugXiduIXlHgiT1cBpmSqmfogcC3bG+Y3hd8IORWLAEhN231vVFjHdiAYon/RDa112Y5hK2Tr7D+oPbiI1/MPAKkMe/24E+Yh+PenMIMSEIYtTwmz1YqxD/et/6W2IVv1ABYA/faN7kY/yrGYK3n/u7zGM63mN3xfrASy5aa1p7n9XBixe1b/YBAtTQN7RdkgtuWbwvVu3bPaushbA3tKMxahnIsoR0lfdGhIHmky5AQOHn9y9VPmCgnrECvmIr7IvvZt1HyUUFALD/WZ0byLS/2QeBoC4mJr5wnlhUINllBd458ZV1yQEEALRLWcxv7OKYFOQbA3R2N7DCe/kzsYL3VQmQ9vGtFvLmp/OOhP2S9o79QmahGeKU7En/FvDel8wvrd5jbzX2Gt/9MFC3HYpDVzyME+udCmUBl5shQ+w4dTT4rsTD1hO7jkUkE6t4X5UgSY/xbTtkvJ33KpTFywngWyt4X5HtOoh7TeP3KpQFJLgR7tUmuaAAkmQH08H3PtoHj0GmCWUh8cory16G2Ln3DuKyhPzhS+6jccPW1+iZptW3iRW8r8qQpcc4eGrC5KQfv1dhj4V7tT2cGsSfI1cB//ZOgreVyVu7TjStaxWsv0OlQpV1yNZJr7rO2P5sgpsOa1rnMzt8JdO6Kg3YsiJN6xiitm0dO1OrVHC2eSXyulr7toXlElHH2FfBXwfp1GKM0/Jh29+y6LVrvzWs17TOZ+oWaTTHgzWtu2RZE7aGtXwhwr0sAozdrBXKV7xGero93hKcrWQZ3wkolHe2QbXTO8Z6bPpIHMO8t2Br2ACX2Bp2bFgHhfIN1sNDJ63meknkGxPM2yK2hnXdrzmmdXXbtA6g3OG5VYezpSsUym03qD+emwYZ9RJlbUO6cfowQreGtbZGnbYOf9brAWtat+U15buUaR3DhK1hXfdrjmmd438BxgPC2eYVCuW2Dl3zQjLI2CnTsu0dwnpGjL0poNCWpw6vm7by+Qm9/JEGUIiJG4MjJqY71BBF0Co4Ttf8Iyyf4IbTKnidoT2HGvSg0Ktjt+mqP3JnDeICh1VW5MdpRKBvnXZECe+I8KBgiazD517NOaY2s3srxIQzprZYCxyqJazY5uk1SsuSNR5933hrRcpEljUhCI8UE45vhL9czuExoXMEAicdQIqnLp97NeeY2szupooJvTZ2ofb6STg+E8E+Hfft9hlxYkYvEWLCOp2lWNR4+wzttJIjQIJpAZCSOwy20U+UmCAirTxFHZx81watPA6q/W2zAv1zd1wZ1+qtkwkibYKYICLX6TpQr923PkO79sN70elzB/TS4z7nGtP5044PrgfFm0OIiRvDZDGhqioVClVyvL3C0PUWFVSvqFDVAlUnZRTcbELmDXfaRIkJssoqpjll8dL6iQz0LfRWkdJp5v5QVSpUmzTwJwzjMnXYbWgOpq7l7RET5IypV8ipap6aPGO/KDFBRKRr05flJ0pMjFcZvOPGPXz5deeamCBTUfKuWZ4PXStSOm0ZFLLpm4MQd3YeQkxwzel0rUSZTLBvG/1+IC2XCWKCiMjQ6lTK+Mc7R43xr+a+9IZGpaxXdCpKjiqOKd0kJogJeh11XHUMjcrZbKB9h2cX3j5/zWKCiMho16nM6dvDU/4ql2G0qbwcvNbtQ55zOlkrF+0yLWeDcyo8z5tDIrqyT2AJBAKBQCAQCASCK4x4AVsgEAgEAoFAIBBcCiEmBAKBQCAQCAQCwaUQYkIgEAgEAoFAIBBcCiEmBAKBQCAQCAQCwaUQYkIgEAgEAoFAIBBcCiEmBAKBQCAQCAQCwaUQYkIgEAgEAoFAIBBcCiEmbgwaipIEKepIp5Eu1qAZ/rwAYKCW5uThHkVo/uwCgaGhmE575ko6XQyZbxMwNJQynLJ01jbHQC0jQw7MT95RRIux3DG0GkoZmTlv3RstnSneR3ieaax8NJRkGVKxhSjnH0Oro+RpUxrpwrR1XHEMDaVMxjNe6XQBLZ0wc+uM9kxlGW1ev1ZD0wdpoxSLQS40YYYM4LgO2alDhRpVh9FGvZRBRmauS1VRaOmhc2TmOq4VBtr1ErKe9klQ1QKaA32m9hntOsrZWKCfmgNzynLaKM/FIRcauDB5OYZo75Q515pHczAA195r2EZ5OYuY7H6GqGoezb4ZMt5OHWw7nDzhdeyUs8jEvHMq3+xjxEt/3Ri28XB5OdCHjf4opA9DGD7ll3M2Ane4McTTnYdY9swpJ0+f85nwFL/8uznExp83/iOPw5cXMD15hnj6xSx1vGH8LnaCt5XJDtjsoRZa5DUm1qmqBtPxjwK1PHkFN55WwXa05h0qFZozuHe2ihPLskrTqZqWPC7I4UeBmrZFql5Ne5xxw8tnL8nrtjxNnjF6i4pp29E0yjG7Fn1d+ag6rjqtouXkHWgX07ZpG6cVKRNZlteBWK9lSI7q1wbfsXiMrlExLVvjz3PNJiKt6HXG9R5KoA5dK1JaDp+7CqeeWeu4XmhUysgR8x+kbJ9RiPGzB72WtZyzOWVY/cR3LB5jaFTKxKxr4Tpga1TK2ucD5TPXyubTSrZjdzAtoFDu0H9NVh3h8xakbJ966jC0ckQdICV3SBfTdOBVRSvTcjysfQrlDs4pwsDapT1rOW0qL8ejx2LrxDtP2g/pnbgcSOceOTr4/pzcatr08N7kOl7+iOMnxMSNwRET4YG+ruvUKqikOhO0EJaSg1618xWo5VUhghtPiwp2kKMWmoxIteab9aFdoOn0RIuKTFmDcR6dCehdYRCJXrUD2AI1nYLGf1Op0BwwwTlTvlph6vWKD7XivSZ9LHxUqgz8wb5OrZpPGIWJCb02vq4877ok57p4ma86LSragbOabzJt0Elz2oY8NUf+/uOhzVaWXqOMzPQrM3ZaMW19easV6gcCRjtNzSeCOEG+JVasNEql4bkmVzQoVOmPPNckAaTkKzQYTyidtFraLotJz6mj76vDKs+b5zqhlezAWclTo++d/1rJabtC22cX0e3Ta3ZArVC+0WcCdKYcZZvOuFGnTlq9NB4ba7yDYkIrxSlmX+thv0/meCwM0koZW8hY12pl1agUj5FsB/Rn43lukFZyhE+ODs/dsdNKcYrJICg5Ojzz1VF28ii0fWrXYdQpawfISm6bzvqOsDRIq7vpt5z01w6NynNxuw8P6OyC6cNyluJ2Hx68vGACdB5tXzlOnxvUZsv53i2nXX6H4vZYHJyeMfe/Qe3ysp1Hoa2T87HYNXbu0VxMIuQO6Pvz6CsiImo/jKjjoVvH5ycvpxLUPwRCTNwYJouJMWNhAJpOTzhlq1QVQkLgQ686QXjVE4Q7tAp28FdlgwQ+E8sq2mVVJpXliBIryB//NTK/k4cVK+7qBz+P9au7JNnn7QSuyABBLVCzWrAC3RAx0SpKgTKYs1SUJJKQn05EXTHGKy4hYkgrWr9Iq5U+p+1e9Fq0AGDLGpnB//tSU1GWSUKeGj4h44gAa/zy1KgUrEA0ICZ0qtm/qCvcOohIK5IsS+PzWlG22hAoy2IsHMbnZ6/jeqFRyRYA22d8MeSIjUmrE9HpnHry1BgHkRa6xogIJU+HlYIVgAfEhCUMrGvlB+aOEHBWJwwnmLdFjD+LP70rPhix4M9TdvJYqxPO/5E7pPNgw8mo24FyyPmrjlFfpnhMIihbdMqILgetPEdxmbNC4MOoL1tBvrJFJ5xyHOHgltOm8jtxkm2xwCuazeOsHDjiYLrVhDY9HNfBFwvj8j6fprwfBiEmbgwziAki0qvqlKsT7uNPqlASAg6OWAh9lKnFBNL+cz6cYD/0sSE7cA8Lyi2mEAB+dJ2qzsqEp2ynLK8o8eCsKjDBsl5Lk6QWqOKsMkx13Rx0nWrOL+75GfNeEVp2QJ9vBr+8iexAWJII+SY3uGZxxIH/USYmwXRlefo1GNTrtQzJaXv8TDdYD6Z1Av2IVQFnJUKpUH/UChUw4cxaBzfFtcYRCRMfUQpD16nmrEzkG95HkIhIr2cpls7TdqNvja9WsoLXgJiYjCMOcodW8On/fwCtRPGYbAX63ARBHPGQOzynC1OjcjxGMnJ0eD5h5eaa4oiF3AE/2CatTHNxmZA7oJfcVSeLNlsOL1m7bD2elDug77kJgjhiInfwPVmLEAbt3JujmBQuDmbFERNuHW8e8QK2gEti9RdQAeD5AFHvxxq1f8ZGF4BaxZ/XE/7TghuPgcELgKBiMek/Z5NctOeaDoP8J1kMDJ67ZUn+0wCQXIAKKXLeGrUPsNklkFrBn9aT/HJstJL9omIyiY1dQr45gLm7AimQqYtT3VrOC6CfoOs7k1jrwDzexcZKdP1haCX7xc5kEuu7hFxjAHOPd11XHQP6cwJIxWIy5OITi1AkAC/Cx9TCmh8gFYsJ8CdIYhGqUxZnsNqlGGJsvx4OYO6uQvZ1bGLtCKOOPX68egL0cKLbD0P5MU7QdU4NB3gOApRFJO0XjjPsS7yRL2BPWcfbhlHHZ/sEIgULCUw5Hhbtctwd70cjfHx4htHuKmKyb7w/eYaLzh42V1OBuTATwx38dt+EaSr2fB9Cf2Fde+j8Ty5CASC90GHQFC/Ss3UkJEhDHc9BIGURCem/8NT/0va1fwHb7cOFpMS97ZFcgALJ7sOw+2BofVeRgoWExJ9HiQUsSVY5w9ByGIY7+O3jEUamgoWEbJU51PB114RJd5A0/jd+dc+eg85YNM5CXuoPYfgFfrdv1XE77LrfAEJMCKLpniB8AxsN/24pCVT/vA4hJQRXHw2fb3ZBpKLyp3WEfX9bWMGpoqqW2EEXe6sfoFRld45JYPV9FZCAvdUMSi3dc78YWgmZ1V0r+OueRtxLs2BAfw4ointd+6sfoFgNCzIF02EJX0+/PvgAxdqr9GsCK++rkCBh/0EGxZYOduMto11CZnUPZBLQO4E+OEEPAOErfJCdx+rGLo6Pya2/18P+agpyuorBOODw1zEI1JFl63ibRIVRR/b2Jo5HJpa2/4i1VIwfTHJxxluBKkmQ0MP+gw9Qqg5gerfReT0Md7A8v4GOc61JGT7N8uoMd7B8ewOdCxNL2/+BtZQM2ThFjwDCE3y0PI+V9UfoHDM7RPV6ePzgNuLpKs5G0+5mJZjIcAf3FjZxdD7C0tYfsDYfQ0wCYJyiRwTCY/zsZ5t4dMTsENXr4fFPFzCXqeD0fDR5LIZf4CcLG3jm1JGKW3X8CAgxIbg0Ru032AOAwv8LsSghuA4Ytc+wBwIK/4b1xKRP3QTWOyaOj49xTATSq0hLPextppBhBEVi/U+oqBIkdLG3mkKK2eIvubILcoJTdQFhizOzkcCa57pqSEs97G+mkHmlwPemk8Da0Qgdpl8zstOvg5AtISeTWPsjKmkJktTD/oMUUsy2jsn7uzDfXYIiSZCURVfc9nrodZeQOxxgYBKsR5J16FoRaVmC1NvEB3V9fE3eOuYDdYzYOkIWba4bRj2L+Dg4P0NnY37GQCqBtWcX6Bx3cWyaMPUasrG/4fGn88jU+hi9RkEx3FlGPLWOI/taj9ZTgdWPV2W4s4y51DqOLkwsbZ/iaH3eW4c9pz4+OEPfNGESgciAoZWQjcmQep/io9oA5mUnumDMcOce3plft4P8EzzbuI24PRZD/YW1ba+Sw9bhGS5Gpn1/GzDaZSzHZUi9X+PjnQEuRuFjMfziJ3hnfg3f2XV8t34bc7PdAK8VISYEfHRrWRyq9eUThFmV+Nf7/pOCm4JWDPVxKLbCPwh/HJhViX+5P/tycGIdnWYBkgR0nzSZx2QSWO8MMGgVkU4z6dUCKs0BOn/+BaSZK5uBxJp9XZJ1XZN/zxJMQ2INR80CZFlC90kLxqVVWgJrR317fjCPISh5VJoDHP+f/+H+Qu080gIFlX4Hu6tJZvUsgcR7j6yxliX0vmKvya0jM6mOq0a7jHiM9Wdxj0KTs6+/0UY5G0Nqo4ORuYR8o4/Oxvyrty+xhmcNa7x7T1ow6DWoiWEb5WwcyfUjjMwl5A776Gy8ZiExbKO8HEdq/QgX5hJyh2c42mCERHIBimTNqe3TI+yuzls/eAAAbuHWe1/iWaOIWExG7+sW9GkepRLwGT7Fw3tzSK0/w/loCbmDUxwxQgIAbv3qL3h5YYK6+/j0ASv4buHWP/we3zVKmIvL6H2twTA5K0XDp/jlvTmk1r4b1/Fs48cVEhBiQhCG9s2e9Y+7Kf7jS9o31qqE+guschMIBACQQOoOIKFrPc/NwxGud5OIXixIIHXXLSvwIQsA+qn1fgJv3mrfWqsS6vtYja4oHOedjO4pvDZZCSTuP0Kn4/yKTCD7nYhh6wlzTZesdxLOeyeRjyVeRRJI3pUAqYuTMOM94wQ9AnCHM6YerPkBqYsTI+RZHuMEXaesaYYi4e1XXpHTYQuBjgnTWWno7rnzgwC6m4T7qsddJMMu0L4mKfDIklXH0aQ6Qoq9Dhj1MrLzK9jtmFjKbaMxOMbuaurVhYRDYtF65Kl3irApNC3DnTKyqRXsdkbWtfY72Au8c3ELyTvWilLo/NftR9/uJJEYiwCL4U4Zy6lV7B6N8O7HTh3ziHF/vLiDZNg7JY7g6J2+gmj+sXD78DTMnFE/RQ9k92HYytwt67tK6uHUYB4tZDFO8VeyyrnlK2e48xD35lfw6NkF3v14C4dnHew+uD27cEwsWKuI9liwlzH84pe4N7+CL8d1HGH3gVes/Gj438gWvK3MsJvTeGvY8K1eWwWQ2MFJMA0Tt3N9Q1vDRp2zcHd5CtstauwpwXhNOH/jl6tTbUKZRJN2c3LLCDOmm7S96lVm0rX/cFvD2rsgIXz3p7GhXUh5Y0J3c3K3cuVvy+pu62qZyvn/70/P1MXszDRbHf7z1wPWT8LrETEtOtVso7ewXZ903zatoUzYzUkrZSnmXOuhvQNUCLNvDev83fWHyB2y3gN+DKpnLcMza3cn/3lrt6N4TLa2VuVcw1Xnx9sa1vm76/WQO/CaBXpxdnKK2HnJMbRTtujkpbv7VvvhPU8dUe34MRBi4sYwWUxMb1onfCUEs8Ca1lVJH8+ZVzWtq9KALSvStI7vK+HHNaDjmNYx5nQeYeAIAX8e1tk6JFAeEykmmICbZ1rHOGOHiY2rDWta5zNp4xnNRcKa1k0uy3W/5pjWOcIEUwThEWJifA4K5RtMHY5ztgSPMPCkr3iN93TbJE/yX5OnDkZ06RoVHefoa7wtrF6zzd6UbTp7hTa47td+QaKTVsvYfRguNsZEiIlJ4iAIa1rnM5QLMa0b+0Io29MF/45YgEK57Qb1GWM8o16irGyJ6lCxceVhTeu2vH14adO6LTo7c8sJM62bJED8jA3rAqLAoPaO61LOio1xHp/AuEoIMXFjcATAdIcaKiTI63btPycQ8GgVvE7PnkMNelDo1bFzdNUf/LOGb4HDKivwYcu6XfuDPR+uKOEfvBWI6Dw892sfE8QEOXWEtjvM0O6a0PI5Sfv6L980vW3Ta7ZpnEqVvq9vtSJlIsvyCgOtmJnQr1P8Ch4lJuw6HIfq4BH0h4hOD1I49UTnCdZxfXDM5Pxt4hyORwTjdO13xdZK2Yh+clYAmAw8QsWEY1oXLDdwsL4RWmkcRAbSQaHcIStuXPERTMupw1YHrjM2J53tvn1xPZWEhVamZdvl2982K3A/d8fVqNPynGUuGDCba89Qzti0zp+Oc4zdrt3ViUAa+/Ca2TmmdcF0gWNKR+0fAiEmbgyTxYSqqlQoVKk1abWhVbDyqFWalFQgGKO3qKDaZojjOVfgz7coMUFWWcU0pyxeWmKCdebxpCj0VpVbfjMis94qUjrN3FOqSoVqc6r6phETRER6q0bFtLOa41xXPvK6rg16i4ppryhz2hZoXZSYIPsX+WnLIiJdq82UPsAEMUFku2anLSdzAATFv/LgRXfawAS+ipJ3jfI46FqRMjPUcS3QSpFBsOeYQkyQPd6lTLBvG9MKrjAx4fzdf128w29CZ2hUynoFoaLkqDH+dZytQw6Wxzt8jtZGSB0Vx4zvumNoVM56xaKi5Ojw7MLbh1FigojIaPPL8TuOOyZ2/n7nHb5A32g/pOWsvWroqcP3yJrz2JO/PN7xI4oJiYj7molAIBAIBAKBQCAQRCJ2cxIIBAKBQCAQCASXQogJgUAgEAgEAoFAcCmEmBAIBAKBQCAQCASXQogJgUAgEAgEAoFAcCmEmBAIBAKBQCAQCASXQogJgUAgEAgEAoFAcCmEmBAIBAKBQCAQCASXQoiJG4OGoiRBijrSaaSLNWiGP68PQ0MxnfbkTaeLk/MJbjavc94YGkoZTll6iG3OjOkNrYZSRmbSW/dGS/enjMCoIyPLkNJV6AE7HwNavYRM2nsPptNFtPSISgwNpUwGsidPAa2QdlwrIto2c+uM9iuU1UYpFoNcaMIMjJuDgXa9hIxnjjh16Pw6jDbqpQwysuxel6qi0NIRWs2Yaa4pqo7BFHVcA4w2ytksZNntd1UtoDkw+X3OxUC7XkY2I0OW3bGzyhn4yjGwsxxHjEkXfhTQuAi7jjbKc3HE8g1cmGyK4Qzl5wPlD9s7KGdjTH4Var5q9cc44RA7y3Mz1DEKacM1YNjGw+VlxDzzI49GfzTF/B9i5947iE/ZT4cvRzA92Z/i4T1O3WcjuEM+xBc/maWOi0Adv+TWccHU8SPhd7ETvK1MdsBmD7XQ4rpb61WvK7D3UKnQ8ucQCCzXdMvRmneoVGj6LUgjaBUnluUpbcb0ejXtcSWdlJ5Pi4qOe6pa8TkQt6iYjnbJVStBl2O9Fn1d+amu64rSKlKacZvltm3axmlFykSW5XMUZtE1KqZlywk71M1ao2LGThNyKJW+xylX14qUlqXQPEpoXcw1SVHX9Ip1XAe0EmVC26dQvjEKOhkH0Kjkcx32H8r2GV2MTYR1qmdjJEekd488HZ5zHLQNjUqZmFWn3/WajBnKz3nKN+pZioc6gyuUO7wgy/zaoHo2PkMdQcfwa4FWpuV42H2pUO7gnBlXHgbVl+coNmU/HXx/QU5xRn2Z5kKdz9m6Ddq5N0sd5+M6qP2Q7kW27yX9SObXREQkxMSNwRETBQqL93Vdp1ZBJdWZoAFl4AoSr9iw8lmTOrx8wU2lRQU7uFMLzcC8sT4cCzSdnnCDdLXQpME4j06tYpopyzkxY3q9age1KhWaA+ZLlUmvVphyeOhUSzNBj09MtIr2ObVAzcHAk69VdASDSpUBKw6sdkgAqXlOOyQQkGfafZ1oUdEOEq22OW3QSWPbNppGLGmXLEsnreYTNCHBt1aUrcBMzVNj4J0jWjFNsiQRoFClbwd+em0cBCv5Cg3Gg6eTVuOkZ8uzr2k8l0KuyV9H31NHJqKO64JGpZgVSCn5BvXHAblOWslpX54aF9Ht00p24K7kqdHve8duXI5C22duoBiJUadsTCYJeTo884tUg7S6TwAFxMQExuXnvOUbdcrGZZKgUO7wjJkTBmnlLMVkiaBs0+mE/iBiy8rR4elFuNC+0mhUnouTDJCSO6CzC+f+tvrDEl05Ong55bjyMOq0PBcjCTk6OLlghGubyu+4dbt9blC7vOzWzYiPUIwdujeu49xTx0O2jrGoNKj9kK2DER9vGCEmbgyTxcQYvToWFB490SrYwVGVs2qhU1Xl5BHcePSqE4RXuUF4q2AHf1U2MOMzsSw7UFcrVlnjVYYQAeBP7/+/L7UtTFix4scVEoVKhVRJ8okJpwy/WHBpFSWSJN/qRKtIUqAsB7fO67g6MV5x4bbNCt6t/uhPDHT0WtoO9CtMwOnCluWc1jVGRKh5alQKVlDJDdw1KsoySVBDA3NHbDirE2PxwS2PSHeCfea8Z5VBmXRNl6vjOqHXMuMA+Yw3rrZIULbP7F/jeTiCxBILvGSeciZGZRqV4rFxeexlGVqJMjF3/A4rBev6ZxITVvkyp3ytFKeYDFK2T+kiUJ5G5XiM5KlWGZy0Cm2feuu4Thj1ZYrHJIKyxQTaLlp5juIySNk6maH/Wdq2WFFoyxPkE1G7TO/EZYKyRSeBut3VjtzB9xNWDhzBoNDWyUvPPDZ27lkrH8oWnbwMjmn74TtW+z4/oZfhN8APihATN4YZxAQxjzOxymAqMaFSNXhScINxxELoo0ytghVMFpqBD0k/TrAf+qhRq+iWZc6ePhRdp6qzMhGR1iNGnFWOkCA5DEdMeITBVGIiXKBcZVp2gJ9v+r+IbbSiHQg3JwbCjlgIfZSJU5Zey5CcLlClaYs3rUiyfPnA2wns840RmaYjPvLUCKyGhDPbNV2ujuuEE+TnGyEBr1aygvV8gxNcT8/Eesa4jydZwsOb2KhnKZbO03ajb42XVrKC3anFRHT5oRgG1UtZa67kDuk8MrB0H3/ii5LrgyMWcgfeIJxJQHNxmZA7oJeTVaIPVxBwxchUYsISIdxrIyL28SdlKygIHLEQKkjaD61ryB3Q99wEPzziBWwBl8TqL6ACwPMBxu/H3v9XVFUA3Q38c1Fz/w4DWvGfsdEFoP4Cq4nxCcGNx8DgBUBQsZj0n7NJLtpzTYdB/pMsBgbP3bIk/2kASC5AhWTPWys9oGJhqvRBtJL9ImsyiY1dQr45gLm7AolTmFHLYHWPoFQG6KwnIfFrjMao47M9gMjXX/f/BVUVkLqb+KCowX3f2oBW+gAbXQKp72M1EdLOK4sB/TkBpGIxGXLliUUoEoAX/DFyscebVCwmwO+IxCJUpyy7DxNrRxh1drGxkuSO60wYdXy2TyBSrPEbDvACBCiLSDIvbY9f/A15AXumazKmqeM6v4Dt3McKFhISvz8SC1AkCdJzd1xnxqjjt/bYLYTNH5vhzofYPDZhLm3jj2tJyLI38a1PnuGis4fN1RRk7gVHM6l8P+3yHOIxCVIigbVHF/jo21Nc7K0iHgvPN9z5EJ8ej2AubeM/1lIT67i6DKG/sMctGfKpm1yAAgnSC+s7ZpYpMtz5CL8+vsBoaQt/WJtHzN9P//BrfK7GEPvrr/FxuYn+yHlJfoinDz/Cp50LjJb+CfeTMvxZHYZffGyn28IfPkn5xo1p36T5/2KAIU2zycTrR4gJQTTdE7h7yySwfqyjWlDR3VtBcrzrQBIre12ohSr043UILSF4O7CCGEVVLbGDLvZWP0CpytmtRyshtXkMyjctIcH7wJ+EUUcmtYFjIqiVP2E9wX4xJrDWGaBaTKP3eBWp8W4gSazsHkPJVTDorCN5qYoFrwWjjuz8Bo5NwlLlT1hLypCME3QBEL7CB9l5rG7s4viY3MC+18P+agpyuorBZbdjmaqO+Ver423HqGP59iY6IxNL23/EWjIWERy18fmnXZjmErb/uIakLPMD2EtjlU9Tlz+E/gJYWlKgShIk9PD4px+iVB1gFDrebWw5bfiPNaQm1nFTeYqtX3cxGi1h6w9rmI/x+ukWPvnuFJXSMv7zP36OhXjM3kktgfe+fIa//+hznB5t4HaclxcAnmL718d2HZ8gFY+FpLvahN8vAgEHQ/t3fLXX9f8ZANB9/hWal9rnUyC4iiSw3jFxfHyMYyKQXkVa6mFvM4UMKyiMGjKreyClgsEj/qrFJIx6BnJq3RYS9sqGrxxD+xxf7XW5vzB3XzxBUzOCIkfwRjDqWcRS6+iYhKVKH531lPdXyF4Pve4ScocDDEyC9YixDl0rIi1LkHqb+KCuv9r2juM6+oE6Mq+rjrcQo76M+PwGjkYmlrbPcLQ+j1hEZDTc+S32TROU/1/4JOLX5svilG/mpi3/Fj55do7OcRfHpgnTqCMb/xv+8OltZGt9rqCYvY6byXDnt3hsjmDm/ic+SYX30/DpFr7e78Dk9HXvxddoaQMQ5xwADL/4HfZHVh2/SsYQsZh0pYm4ZQQ3Gt36tQvqIpynLYxaGsmVPXShotrS7S8r+wurVYDa7WJjJYmi5i1K8BajFT17+bNHscX/8Ly2JNbRaRYgSUD3SdN+nEJDKbWJY8qj2VlH2JM6oRgaShkZyfVjEKkoNAfobHCERD2D1OoujklBpTmA6bn3ikj3ethcTaHY+nGWuG8sRhulTAyp9Q5MUpFvDHDMComE9QifBAWVfge7q0lmjiSQeO+RNadkCb2vWjB4SnESgTpSgTqOXrWOH5J2GfGY16/DOQpNdo/+14zRRjkbQ2rjCCNzCflGH52NaCHhWZX49f1LPcIUDbMq8S+XLP/WJ3h2WEQsJqP3RIMeeOyFWZX49f1r/HjTDw2zKvHpfcRk/sQY7tzDwuojPLt4F58fnGI0Mu3PZgNGu4zl//s3/MtPF1BsnuPCYxoB76rEp+8hFj35rjTX98oFPyjaN3vWP+6m7MeWDDS/6gJQUdWPsX6ffZgpgcT9XRy3CgCAvd/UJjzbLLg5JJC6A0jo4iTMi80RrneTSER+ryWQuuuWxY0x9FN0Qfa8tdIDXZxOlX4KnHcsuqfQQTBqn2EPBGAPq4yRkCRJkJLWSgO6m9ZjBFIRLSaQM+olZFIr2D0mqIUKmoMOdleSnCVuA62vugCpqAw6WPekSSBx3w5IJQn7n9WuXrAYSQLJuxIgdXESZrxnnKBHAO5MGiN7vKUuTgx7g1c/xgm6TlnBjp4Jd/xMKHl7/FaDQtDiLpJhFTpioGc9Usq77Ol4E3X8GDj3cQ+nBvP4Fotxih4R6O5042rUy8jOr2C3Y2Ipt41G/xi7q77VJB7tb/GYTJDyPlbCnl9/Fdrf4jERzFctP7kARbLGO/B50D7Avl3H/eQr1HFluIXkHQmS1MNpmCmlfooeCHTH+o6ZqslPrbE2Fet9B34/DdH6ugfTXMLWyTNsPLjNiLNbuPUPv8d3jSLm4jIe/+4L6COfIeDTAzw27ToSYSsfTPsmzf87KdySQt4b+YERYkIQxKjhN3uwhMO/3vedjAgKBTeP+7vMr+TeY3fF+khLLlpvHOx9VmdeHHbRvtkHCFBD39B2SS64ZfFetNS+3bPKst+4Ti6ogATsfVabIr2BWsZ64TrsF36j+cQSH+oCeGH/tGilDFLruzh2ViN2N7AycVnj7bz3kosKAGD/s3ow8AHQ/mYfBIK6mJj4UntiUYFklxV0HfeWNWVIwaVdymJ+w1opyjfc8QuUmFjB+6oESPv4Vgt589N550GxVoEDZUziTdTxQ/Lel7gY/5rrPfZWY5AlILGg2GI5ZFy/3QcRQVlIICTqG9MuL2N+Yxcdcwm5Rh+d3U2spjhjx6H97WOQSVDeX0HiBwja2t8+BpE5oXzX0Trf4DsfD1tP0DMJpCwi4euP9sG+Xcd9671Hz9nrSXJBASQJj3+3A338ArRL++AxyDShLCQgTZgfDk+/tfP8030k5Un91MOpwbqOT8fTg8cw7ToSEe+tOPOfK0jYcm5P377Xjn97J8HbyuStYSeZ1rUK9t+hUrXl3f9VbxXG+VSxN6zAA2taVyV9PD1e1bSuSgO2LJ4JnSd9ZWJ61/2aY1rHOGOHbjXLote4W8NO8lXg4WwXC6jWdqHMOZ1xj+Y5Z199WNM6n6lbpNEcD9a07pJlRW7D6ng2hHtZBHDKg0L5itdIT2dM6aytZH15HSZc06Q6MtPUcaVhTeu2vaZ8M5jW6TXX0O0sfJ/OEMJ9JSYy1daw4b4Sflz3a45pXd1uI0C5Q/92pG+Hr0QQ1rRui876zjy/rGldhK+Ej3bZ2rYVUGjr4JRGTGKj7bpyB7d8DfeVCMKa1m3R6ZljLihM6wRvHNe9eppD5TrPucZ0oQfXg0Jw42kVKG0H4sFDDXpQ6FU7vUpVv9tcqzixLE+OGdO7IoN/8A3tOHDFhCtuJh551s/C56rNO2YQJ1cORhAFD9Xy3GCbptcoLdvmf33feGtW8BwsxylrQkAdGbg7ng7+cjkHk18rOgEvJ53t6hysiyHymixeuY6rjuZzk/YcCuUbIzfo02u2czRrUOcKkmB+38Hzq9AZt+uAn8AEphETrNv1FOVrJVc08A6udwTrdj3R0O6aobmBu78vAIVyB+euEeHYzTpELLBu1xMFiOtDEazXPngeFKzb9TQioP2Q7kW27yXfg+INIcTEjWGymFBVlQqFKvkWHXzo1iqEapvajfMWAqsVAoEHvUUFzrzhTpsoMUFWWcU0pyxeWpo9vd6qctM3Q9Jz4YkJxySPc/8FDo+YIPveK1I6cF15qvpWK64leouKaa+QU9U8NXlGfFFigohI16Yvy09U4D5eAeCMl//w5deda2ICfkXJu8Z0UURdE4Oua1TMXLKO64CuUSmTCbSv4Xcj54kJx9jOP068gycmHEGgbNPZhBWQANOICa1E8Zg8U/mGVqdSJu0RkYqSo8b4l2sfWnlcx+mUdVwrDI3K2WygPw79/TFJTLRtkzueCOBiWKsQWf+9l6PtwzPPasUYx2guxNWai9Gmh8uc9p1y2vCGkYhmfcpLIBAIBAKBQCAQCMQL2AKBQCAQCAQCgeCSCDEhEAgEAoFAIBAILoUQEwKBQCAQCAQCgeBSCDEhEAgEAoFAIBAILoUQEwKBQCAQCAQCgeBSCDEhEAgEAoFAIBAILoUQEwKBQCAQCAQCgeBSCDEhYDBgaDUU02mkJQnS+EgjXaxBMwx/BoFgegwNxXSamVcS0ukitMtMK0NDreibp+k0ipoOrnGOoaGU4dStc1Pb6TOB9K2w9AAMrYZSRg7cN/w8Gkoym9Z/FNAKWAAZ0OolZNLefOl0AS1d96W9hth9LgfaRvwxDcVAu15CxjMWbj8Fy5o1fQhGHdmYDFmtYmBOytVGKRaDXGjCDIyzjdG2+oOZJ6o6S39MUce1whqnbEaGLLvjpKoFNAfTjJOB+nIcMSZv+FFAc2RGlNlGeS4OudDARdhYD9vYKWeRkWV3Tqsq8s0BdzyG7R2UszHm+lSo+SqaAxOc5HyGO1iei0FWKzgLXP8Q7Z0yspmYr//yaPYHeCssx4ZtPFxeRsxzz+TR6I+m70MAGD7ll3M2QthwY/gUD+9Nn2f49As89MxHFWq+gkafnx4Y4ukXD7GcDY5f46zPnVNvFL+LneBmorcKpPpdQDmHWmgRz7BYIIikVbAdrXmHSoXm9PadeqsYURZILficoyPTW3WzyfVqOsKlOpieiKhV9LotT8wz0Qk7T01vI6iYliLqAKmVa+xy3CpabuGcdgEq5ZvmlG3TqJiZ1E99xi121vRhaFSUZascpUL9qAy6RsW0bI1/iKO1Xst4XG69h0L5xii6P3SNipnoOq4XGpWc9gT6wzqU7TO6iGymTrVsjOSIMtwjT40wh2hDo1ImZvct39Ha0EqUiYXPKyV36HHYNurZCHduhXKHF1PNwXI8RjLAcbjWqDyh7cr2adD1+zqhlWk5bt+DgUOh3ME5XYz8mTi0L1HOjHnaD+9RPGq8D17SuaeONj28F48ev60Tehl9A/ygCDEhIL2qjiekqhaopetewaDr1KoyYqPQYs8KBBNoUcEOjNRCk5lbOrUKqv0BXKCp9IReHQsDtVClwbgwnVpVJ6BXqTJwCmtRkal7/GfSGQFQYAL3WdN7xYda8eZxhY91Tc4pvWbn8QufEFpFyUqv5qk5GDBn7Ovi1HF9aFFRtgIvNd+kwbhDdNLGbctTczS5bVrRDjjtfmJGiSlLpUrfCrQmpbe+vN30fHSqZZhAIlRM6KTVLNE0TssN9F1houQbgf6wREaeGtz+mLaO64VWsgNhJU+Nvm+cSo7wUmj77ILTJ1Oi1ygbk0lCnhpnvPHWSauXKGPPVatvOWLCqNvlgJT8NvX7jvAzSKtnbNFgXevIZNMrlDs8Y8bKIK1kiwxlm87CxA0RERlUzzLBpk9MaOU4xWQQlBwdnvXJZOsoO0JGoa3TaUTLVUSj8lycZICU3AGdXTj3htU+K3DP0cHLC/LrAC9tXzlOHxrUZsv5ni2nTeV33DxuvxvULi8H8hg792guZn3HKFve9MZYlCi0dfJyLI7bD9+huD1+B6feOdJ+6NSh0OdMnjeNEBM3Hb06FgmF1oQ1B09a/0mBgI/uBPlqlQm0XVoFO5CsskECn1bR/iIPCcLHgb19fvx/tcKv2y5Prdh1OysG3PQ6Ve3VAXelwf3buAw/dpnsyoGnXm4mFkvgSBFiwREb05V3tRgLK7XCBM4uTsCvVvoT2qbZoiQ8+GfLGpmOiIlOL4/T+8+SR0jkKxVSJYkkjpjQtSKlnSBUyVOjUrACYF6grxVJlqUQUWLXJyGwOjFTHdcKjUp2sL3NDfJdsTF5dSIMto5gwKlrjIhQ8nRYKVgBOEdMaKU4xaQQocGuQuSs81rJCvSV7TPOyoBGpXiMZOTo8JzfdlZI5La3KS3LJHnEhLNiodB2iFhwxIaydT1XJ4z6MsVjEkHZolNOP2nlOYrL1q/3vDFxMOrLVqCvbNEJp5x22Qrq2XJmyfPyQqede3MUkyJWEtoP6Z24zJxv08N34iT7BIY3i13H5yFlvgHEOxM3HKP5FboAUGhh937Cf9pLYh1/blVRbbXwr/f9JwUCPvpJFwBQ+Lc1JCX/WeD+P+YBCeieTHruX8O3ewChgOaj+5A4ZSXWOzBNAu2uQJIA/bQLEFD4t3UkOOnv/7xg1X2qWzJ5KlQsJAFvcSreX034/maTXIQKCd0nTRggAAb05xjn4WdiuY9HpgmTjrGRlCKT30lNU97VQj/pAQDy/7aGBGdQ3/vHPCRI6J4YiH5b4D08Gtn9lJIju+FOKgEJ96dPn+SPbbs0j81jE0qlj0erMmReIgAYvEBPyWO7OYB5vIfV29IrjpOCxSS8Zbz2Oq4K7+HLixFM6mJzPnqc7qb44xSNgfryA+yZJpa2/4i1ZCz4Mqn+Aj0lh61GH6PjXTy4LXM/f4A2vn1MMCmPw9+vIMaZELc+eYaLkQnaf4C4LOG9L89xMSJ0N+e96YdD7JR/iz3ThJn7Od6T+fd+u3wbm8cXeHfrFI8exCAHLv49/P78AiPqYvN2LHyOjj8/IhJcUfTTHkCE3P/8BMlYcI6897McJFlG79SIfDfEOO2B7HJScrCcf/j5x5Dtcpx3FIzTv9p5fjUxj1u3gn+6n4DMG4zEAhRJwl+/1mCYJgj/gP/9/TlG1MWnC3OIcbI43EndgvQjjV9g2gluEgaaX9mB3j9Opw4S99exfv8+JsgOgcDGwOAFQFCt4IdHchEqADzXYYR/zgPGAM9BgLqIJIbQakWk08xLk4EXsA0M7KA9GPzbJBegQgKeD2AAwP1/QUWVIHU38UGpBffdaQNa6QNsHhNIfR+rAWXSRage0U/QZc8YLXzVBQh3kdLr3pe202kUWtO8TMpg1PHZHkAU0cdXFgP6cwJIxSJPaQJAYhGKBOCFPUaXxajjs32y+inhC8R52OlNp1996Y16Fqt7Jpa2+zhaT0UGaYm1I4w6u9hYSU6O1d6z5+BfnTnozAYDbWcOLv0CKwlvcDlTHW8T43FVsHCJWNiof4RPj02YS9v441oSMU5UlPjkGS46e9hcTUGOqmCo4wURoCwgKQ3xdKfsfWE84gVsh3Z5DvGYBCmRwNqjC3z07SlGe6uIcybYcGcZP90b4b9/foqjdZ8YmZbhDn67b8I0FSz45tT1YAj9hT3+YT+2JBegQIL0wvqO4ff+0PquIrsfeAUlFrAkWeUMCSAMMXDqToQITE8e5+eQHk4N4r8UbpyiN043BcMv8Lv9EUamgtth1/0G4Nw2gpuDDutH4+sYhAhuHPqptYqGr/BBJomVjT10rT9YdLvYW0lBTlcZETArCax3BqgU0+jtrSI13jUjiZXdYyiFCgadDWaFJYHV91VAAvZWMyi1dLDrK4ZWQmZ11/rS6J5a58biYg+rqxvYO2YuttvF/qrThikaYdSRSW3gmAhq5U9Yv5bBwBvAqCM7v4GOSVArf8RaMvgLoodJ6dslzG8ew8w1cLSejBQSs5PA2lEf1UIaf338U+vXTnsO3t89xru5bQyO15F6vZVeT4w6src3cTyyVxVSsehxDdDG1qfHMM0lbP9xDUmekpgF/QQ9EAhP8OHyPFbWH6FzzASNvR4eP5hHLF1FP7DbEsaB8dKSAlWSIKGHxz/9EMXqAKZ/i592Gbc3j3Hx0QGevYKQWL69gc6FiaXt/8B6KmJ1TfAauIX7/6RCliU8/tlPUG6eoW+6wmH49Je49+ARzi9MoHcKY5KoGH6Bnyxs4Nn5CEtbf8BaKh65cvFD8op3jkAgELxhul10uyoKzQF0670vEOnQWwWkJQDdTXxQm/HXfQZD+xxP9o65+bvPn6CpeR/HSqz/yfolGV3sraaQclYZJAnJlV2QolorL+oCkgAM6xknQC2gYv9K6bahiLQk2W0w+L9c2Rj1DOTUui0kBuis37BfpKfEqGcRS63bwmDyKsLE9EYd2dU9mEvb6D9aif6l+pIY7c/x1X4XvAnQe/4Vmlr03LgJGPUs4vMb6IxMLG2fobMxP3MgZdR/i30iUP7fsJaUX19A1Ouh111C7qCPvmna97hh/bgQkyH1PsWHdT0oEHALnzy7QOe4i2PThGnUkY39DX/49DYytQFGTvrhDpZ/uofRf/8cp4/4j1NNYrizjLnUOo4uTCxtv8LKhmAmbv3qD9jOxBGTenj8swXcjrlbByfe+z3O//5dKJIESVlAQgr/cWj4xU/wzvwavjsfYWnrBN+t38bcrDfAa+S13TuC60gSiyoAdDHxcXWBgIdW9PgCsEex5f+ifEWSC1ZQDhXVQQe7K0nmcbsEEvd30WkWIUmw3k+4RPVGLYPU6i6OSUWlae297gn0e11srqZQbLG/GFmrGYNWEek0U5gtFjp//oXnOdbEmv1ex7H9SIp7Bon7j9BpFiBJ7DsWPgwNpYyM1PoxiKzHJjobQkgEMNp2P3VgOv3kFwYs/vQNXvo2SvObODZzaBytIxla2OUx6lnMr+zh2FzCdsP6RXo8B7Ui0n/7Kz5dTaHYmsF/4CrTLiMe43uuFJqcPfeNNsrZGFIbHYzMJeQbfXQ25sPHNRRmVeLX7/GfX5+V5KL1OA0UbJ8d4ZHz4wIA4BZuvfcljhoFxGIyek9ak395vvUJnjHpdSIQ2ijf3sTxxUc4eLaOec47ApEM23i4HEdq/QgX5hJyh2c42hBC4s1xC7/6yyn6WhnLWebRKCWHrcMzHP35/8FcPCI0Hz7FL+/NIbX2Hc5HS8gdnOLZxo8rJAAIn4mbznhb2Gm3Z2oVrPRqVfhNCIhaBXebRN/heEc4uzWFekm0Cu4OTP5zLONtYb1bs3rQq7ZfgZXG2TUp4PPg4OzeVGiSaTo7M7Fby/qI3O2Jz6zbwJJes9vg95og0u1tPwGQmq9Qc9qLuMK07B2W8s3gTihE9u5GkkTIN6felYjXT1E5p01v+T8E5zr/CNu+ldmxKbDTkrNbkxK6w1T0bk8MoXVcMbRSqMdCvuHdfUivubsqKfltaoSM01Q49SrbdDbrDjhaydo9yL9j03ib13z47kvjNFE7NDH40g/qzhalwf4KHjk6PHe3yzXqZco62w7ntqkx3rb2euPs1pQ74O92RFqZ5uIyIXdALwMmES5tthxesnaZ3rHL+d5O4OzWlDv4fnIer3kEl/HWsZz0xs5DWna2Hc5t0aGzvfAVQIiJG0+LCvYHz+StYd20anVCWoHA5vVtDWsF+5hGHNjB/mxbw/K2fvXBERNOHfytYXWqecr0/59Dq0iSJAW2SmX9JPJNXl3Xk9e3NayFVsx4+2lCnlnSvzkxEdz6dczbJiamhPWTyDfCtuqdnlfaTjZMTJBBddscLn94zr9GrUTxmGyLmMHYSC8Xkn68lay93at+STHB+knkDs/o4i2YEw7XZ2tYcywW+FvDGuOtY3MH33uM61yjO4VyB6eR7fgxEGJCEDCtq7YmmNaJVQnBTLCmdVXSWaO5WU3rxs7RKhWqrBu7Tnq1QGl7jrqBOmtCV/Ga3HFM6MY+FlCp0mTN4bzO2x7hwF4TG+Trlmu1dc+4gfI4ePanJ51azC/keUZsTAq4rzesaV2FBmPVN7tpnV6zjebUCYG2zVgcTJk+Er1m/Wo+Kcin6EDf8bYAFKo0vOKG9ZJQQr0vbCLquG7oNdvsTdmms8hGT4vXV2LmEkPFBHMOCuW3G9Qfz1uDjHqJMvbKgCMeXPdrjmkd44wdJjY8GHXKxv0+E1YdcUaQTCrm+sGa1m3RGWsUeGnTui06O3PLmc60botOPXmCpnWOj0RAFBhterhsGw8qW3TykllRclYrfH+/SggxISAiIp0VC1GHEBKCy9AqjAPx4KEGH4EaP9KkUtW3pOCKAP6h+h8nYkRA8LDqdpO7qxPBtPbBWeWIvia/2Zy7OhFMax1e8znHtC6YLnDkp3yU6qrRckVU8FAtYcW2S69RWpZsd2qnbx3TOn9+zpFvMqZ1nPP+Y5qA/DWJCXZ1InAdzvHKdVwnnMCf0w/+I9+wTNfGbtYhYoF1u75McB0lJogs5+rQ+QxScocec7ix0zUnLRBmaMeBKyYc07pguYEjd0jngV/Lrwma4x7NaRcUyh2cu48hGXVanovZTtM+kTZ2ofaXwSnnknncVQZ/Wiu915zOMa3zp+McnEej3hRCTAgY7BUI1V2psA6V1EKVWu5PygLB7OgtKvjmlqoWiPt0XYSYICLS9RYV0sGyqtzCrLqLnPQtTtlEurUKwUlf9a1WsFh5mPtGValQbQaEh0MgvV1Hc+Crw3nsyXNPhhzXVUyQM0ZeUaaqef77Czwx4bxb4e8T3pFv0qg5S/opAvLXJibImoOaNQfZsVeUPFUmPIo1ZmId14SIdyoCx7Ri4lXel6DJYoKIyDA0KmUyvvHLUaXR546HodWplEl75qSi5Kgx/qV7CnhiQitbj1X5+4p3XGcxQURkaFTOZgN9eHh24e3DKDFB1ioBt5wQB3EiO8/y9HmM9kNazjI/GigK5bYP6cwvbscrGZzx8h8/opiQKMoOUCAQCAQCgUAgEAhCiNh/SiAQCAQCgUAgEAjCEWJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJl2+vq4AAAAtSURBVAQCgUAgEAgEAsGlEGJCIBAIBAKBQCAQXAohJgQCgUAgEAgEAsGl+P8BUkAIW6Htj2kAAAAASUVORK5CYII=\" width=\"580\" height=\"358.094\" style=\"width: 580px; height: 358.094px;\"\u003e\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAuthor’s estimation on EViews.\u003c/p\u003e\u003cp\u003eFrom Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it is clear that in terms of heterogeneity in the impact of per capita GSDP on per capita growth, in comparison to Manipur, this heterogenous impact is statistically significant for Arunachal Pradesh, Meghalaya and Sikkim but insignificant for the remaining states. This heterogeneity can be calculated as:\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity among the states.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy Variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy Variable Coefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalculation of intercept\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndividual Intercept\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArunachal Pradesh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea1 + a2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6499***\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssam\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea1 + a3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6101\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eManipur\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.6089\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6089***\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeghalaya\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0325\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea1 + a4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6414**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMizoram\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0198\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea1 + a5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6287\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNagaland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0191\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea1 + a6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6280\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSikkim\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0739\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea1 + a7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6828**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTripura\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0034\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea1 + a8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6123\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAuthor’s estimation on EViews.\u003c/p\u003e\u003ch3\u003eDiagnostic Tests\u003c/h3\u003e\u003ch2\u003eCross-sectional dependence test\u003c/h2\u003e\u003cp\u003eThe Pesaran CD test is used to determine whether the observations of one cross section (state) is correlated to that of the others. Cross sectional dependence is required to fulfil the assumption of independence of the regressors.\u003c/p\u003e\u003cp\u003eTable 4 Results of Pesaran CD test.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"460\" height=\"210.139\" style=\"width: 460px; height: 210.139px;\"\u003e\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAuthor’s estimation on EViews.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFrom Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it is clear that there is no cross-sectional dependence in the LSDV FEM.\u003c/p\u003e\u003ch2\u003eTest for Heteroscedasticity\u003c/h2\u003e\u003cp\u003eThe test for panel heteroscedasticity of the residuals of the LSDV FEM are conducting using Panel Likelihood Ratio (LR) procedure.\u003c/p\u003e\u003cp\u003eTable 5 Results of Panel LR test\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"654\" height=\"170\"\u003e\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAuthor’s estimation on EViews.\u003c/p\u003e\u003cp\u003eThe results of Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the residuals are homoscedastic as the p-value of the LR statistic indicates that it is statistically insignificant and thus, residuals have a constant variance.\u003c/p\u003e\u003ch2\u003eTest for Autocorrelation\u003c/h2\u003e\u003cp\u003eThe correlogram is used to test for autocorrelation in the LSDV FEM. Since the data is annual, lags for 2 years are used for the construction of the correlogram.\u003c/p\u003e\u003cp\u003eTable 6 Results of Correlogram.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"678\" height=\"182\"\u003e\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAuthor’s estimation on EViews.\u003c/p\u003e\u003cp\u003eIn Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, none of the autocorrelation and partial correlation coefficients exceed the confidence bands on either side. This suggests that the data is free from autocorrelation.\u003c/p\u003e\u003ch2\u003eModel Selection: Hausman Test\u003c/h2\u003e\u003cp\u003eThe Hausman test is used to select between the Fixed Effects Model and the Random Effects Model.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Hausman Test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest Summary\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-Square statistic\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCross-Section Random\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e17.655712***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eAuthor’s estimation on EViews.\u003c/p\u003e\u003cp\u003eWith a null hypothesis suggesting the selection of Random Effects Model, the Chi-Square statistic in Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows that we should reject the Random Effects Model in favour of the Fixed Effects Model.\u003c/p\u003e"},{"header":"Findings and Policy Implications","content":"\u003cp\u003eThe findings of this study present a striking deviation from the theoretical prediction of β-convergence. In the pooled regression model, the estimated β coefficient is positive and statistically significant (β = 0.0281, p \u0026lt; 0.05). This result contradicts the Solow model’s expectation of convergence as richer North-Eastern states (in terms of per capita GSDP) have grown faster than the poorer ones between 2004 and 2017. This β-divergence is visually corroborated by the upward-sloping regression line shown in Fig.\u0026nbsp;1. Graphically and statistically, the evidence is clear: instead of narrowing gaps, economic disparities between states have widened over the study period.\u003c/p\u003e\u003cp\u003eTo further explore intra-regional dynamics, the LSDV fixed effects model is used with Manipur as the reference state. The dummy variable coefficients for Arunachal Pradesh, Meghalaya, and Sikkim are statistically significant, indicating that these states exhibit growth trajectories distinct from Manipur. Their intercepts are lower (i.e., more negative), reflecting faster growth rates when initial GSDP levels are controlled for. Assam, Nagaland, Mizoram and Tripura, on the other hand, do not show heterogeneity in the impact of initial per capita GSDP on per capita GSDP growth as compared to Manipur.\u003c/p\u003e\u003cp\u003eThese results underscore several critical policy implications:\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInequality Within Underdeveloped Regions\u003c/b\u003e: Even among relatively underdeveloped regions like the North-East, richer states are pulling ahead. This raises concerns about the uneven distribution of benefits from central and state-level development programs.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNeed for Targeted Interventions\u003c/b\u003e: Uniform policies may not be effective. Poorer states like Manipur and Assam may require focused support in infrastructure, education, and institutional development to enable catch-up growth.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRethinking Convergence Expectations\u003c/b\u003e: The assumption that poorer regions will automatically catch up through capital accumulation and diminishing returns needs re-evaluation. Institutional quality, governance, and connectivity may play a larger role.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMonitoring Inter-State Disparities\u003c/b\u003e: Regular assessment of convergence or divergence trends should guide the design and revision of regional development policies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003cp\u003eIn sum, the North-Eastern states are not a monolithic block. The divergent growth patterns call for nuanced, evidence-based policy frameworks rather than broad-based strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study set out to test the hypothesis of β-convergence among the North-Eastern states of India, based on Solow\u0026rsquo;s neoclassical growth theory. Using pooled OLS regression and LSDV fixed effects modelling on per capita GSDP data from 2004 to 2017, it instead uncovered a clear trend of β-divergence. Contrary to expectations, economically better-off states in the region such as Sikkim, Arunachal Pradesh and Meghalaya experienced higher per capita growth than their poorer counterparts like Manipur. The β coefficient in the pooled model was not only positive but also statistically significant, offering robust evidence that β-convergence has not occurred. The LSDV model further validated this finding by showing significant heterogeneity among the states\u0026rsquo; growth patterns, with certain states consistently outperforming others even after controlling for initial income levels.\u003c/p\u003e \u003cp\u003eThese results carry important implications for policymakers. They suggest that the developmental strategies employed over the past decade have not yielded uniform benefits across the region. Factors such as governance quality, infrastructural connectivity, and human capital development likely contribute to these differences and should be examined further. Regional development policy must shift from a convergence-centric model to a divergence-aware model. Rather than expecting the market alone to drive catch-up growth, active state intervention is needed to support lagging states. This includes focused investments, institutional strengthening, and tailored economic incentives that address state-specific bottlenecks.\u003c/p\u003e \u003cp\u003eIn conclusion, this paper highlights the critical need to reassess assumptions about regional growth in India. Even within regions considered homogeneous by policymakers, internal disparities persist and, in this case, are growing. By acknowledging these divergence trends, more effective and equitable policy measures can be designed to uplift all regions, not just the already advancing few.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarro RJ, Sala-i-Martin X (1992) Convergence. J Polit Econ 100(2):223\u0026ndash;251\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarro RJ, Sala-i-Martin X (1995) Economic growth. McGraw-Hill\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaselli F, Esquivel G, Lefort F (1996) Reopening the convergence debate: A new look at cross-country growth empirics. J Econ Growth 1(3):363\u0026ndash;389\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCashin P, Sahay R (1996) Regional economic growth and convergence in India. Finance Dev 33(1):49\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDurlauf SN, Johnson PA (1995) Multiple regimes and cross-country growth behaviour. J Appl Econom 10(4):365\u0026ndash;384\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEasterly W, Levine R (1997) Africa\u0026rsquo;s growth tragedy: Policies and ethnic divisions. Quart J Econ 112(4):1203\u0026ndash;1250\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam N (1995) Growth empirics: A panel data approach. Quart J Econ 110(4):1127\u0026ndash;1170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMankiw NG, Romer D, Weil DN (1992) A contribution to the empirics of economic growth. Quart J Econ 107(2):407\u0026ndash;437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagaraj R, Varoudakis A, Veganzones M-A (2000) Long-run growth trends and convergence across Indian states. J Int Dev 12(1):45\u0026ndash;70\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSala-i-Martin X (1996) The classical approach to convergence analysis. Econ J 106(437):1019\u0026ndash;1036\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolow RM (1956) A contribution to the theory of economic growth. Quart J Econ 70(1):65\u0026ndash;94\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Manipur University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"β-convergence, Solow growth model, North-eastern states of India, Least Squares Dummy Variables Fixed Effects Model","lastPublishedDoi":"10.21203/rs.3.rs-8569719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8569719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper attempts to empirically test the β-convergence hypothesis for the North-Eastern states of India. In this context, β-convergence is one of the major predictions of the Solow Growth model, and emphasises on the negative relationship between the \u0026ldquo;initial or starting point of per capita Gross State Domestic Product (GSDP)\u0026rdquo; of a state and its \u0026ldquo;average growth rate\u0026rdquo; over the time period considered. This paper examines data of the eight North-eastern states of India over a time period of 14 years (2004\u0026ndash;2017) to conduct an empirical test of the β-convergence hypothesis. Furthermore, this paper then uses the Least Squares Dummy Variables Fixed Effects Model (LSDV FEM) to test for the statistical significance of heterogeneity in the growth rates of these states with Manipur as the \u0026ldquo;reference state\u0026rdquo;. This paper finds that, rather than β-convergence, there has been a phenomenon of \u0026ldquo;β-divergence\u0026rdquo; among the North-eastern states of India. This implies that richer states in per capita terms have been achieving higher growth than poorer states \u0026ndash; an empirical refutation of the β-convergence hypothesis. Moreover, considerable heterogeneity among the per capita GSDP growth rates is found from the LSDV FEM with the GSDP growth rates of Arunachal Pradesh, Meghalaya and Sikkim showing a statistically significant deviation from that of Manipur during the time period considered.\u003c/p\u003e","manuscriptTitle":"An Era of “β-divergence”? Empirically testing the β-convergence hypothesis for the North-eastern states of India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 05:36:01","doi":"10.21203/rs.3.rs-8569719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb599c36-66b7-40b4-9dc2-0860c45e82c6","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-13T05:36:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 05:36:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8569719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8569719","identity":"rs-8569719","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}