Financial Inclusion and the Effectiveness of Monetary Policy: Empirical Evidence From Brazil

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As a major emerging economy, Brazil has made notable strides in expanding access to financial services, particularly through digital banking and social welfare programs. However, significant regional and socioeconomic disparities persist. To capture the multidimensional nature of financial inclusion, a composite index is constructed using indicators such as bank branch density, ATM access, credit usage, and digital payments. Empirical analysis shows that greater financial inclusion enhances the effectiveness of monetary policy by strengthening the transmission mechanism. Specifically, higher financial inclusion is associated with lower inflation, indicating improved policy outcomes. The study also finds a negative relationship between lending interest rates and inflation, suggesting that monetary tightening can reduce inflationary pressures. Additionally, a positive correlation between exchange rates and inflation is observed, implying that currency depreciation may fuel inflation in Brazil’s open economy. These findings highlight the role of inclusive financial systems in supporting macroeconomic stability. Enhancing access to financial services not only promotes social equity but also improves the central bank’s ability to manage inflation effectively. Other Economics Financial inclusion monetary policy interest rates exchange rates inflation Brazil 1. Introduction In recent years, financial inclusion has emerged as a critical pillar in promoting sustainable economic development, especially in emerging economies like Brazil. Defined as the availability and accessibility of useful and affordable financial products and services to all individuals and businesses, financial inclusion enables broader participation in economic activities and enhances financial resilience (Nguyen, 2024a ; Sasana & Ghozali, 2017 ; Tabak et al., 2011 ). In Brazil, despite the advancement of digital banking platforms and government programs aimed at increasing access to financial services—such as Bolsa Família and Pix, the instant payment system—a significant portion of the population, particularly in rural and low-income areas, remains underserved (Ho et al., 2023 ; Nguyen & Dang, 2023a ). These disparities raise important questions about how inclusive financial systems contribute not only to social development but also to macroeconomic policy outcomes, including the effectiveness of monetary policy. The effectiveness of monetary policy largely depends on how well changes in policy instruments, such as interest rates or exchange rates, influence consumption, investment, and inflation expectations across the economy (Chen et al., 2017 ; Houston & James, 1995 ; Tran & Nguyen, 2025 ; Xingyu et al., 2024 ). In financially excluded environments, a large share of the population may remain disconnected from formal credit and savings channels, limiting the central bank’s ability to transmit monetary signals uniformly (Demirgüç-Kunt & Detragiache, 2002 ; Mumtaz & Smith, 2020 ; Tran et al., 2025 ). Brazil’s economy, characterized by a large informal sector, income inequality, and regional economic imbalances, presents a relevant case to explore this dynamic. The question of whether improved financial inclusion can amplify the transmission of monetary policy—and thereby enhance its ability to stabilize inflation—holds substantial policy significance for the Central Bank of Brazil as it navigates inflation targeting in a complex economic landscape. To address this issue, the present study constructs a composite financial inclusion index using multiple indicators, such as bank branch density, ATM availability, access to credit, and digital payment adoption. It then empirically analyzes the relationship between financial inclusion and inflation outcomes using annual data from 2004 to 2023. The study also examines how interest rates and exchange rates interact with inflation under different levels of financial inclusion. The findings aim to provide new insights into how a more inclusive financial system can support the Central Bank’s goals of price stability and economic growth (Nguyen et al. 2025 ). In doing so, this research contributes to the broader literature on monetary policy transmission in emerging markets and highlights the dual importance of financial development and inclusion in macroeconomic governance. 2. Literature review Financial inclusion has garnered significant attention in both academic and policy circles as a means to promote inclusive economic growth and reduce poverty. Early literature primarily focused on its microeconomic benefits—such as increased savings, improved household resilience, and better access to credit for small businesses (Ahamed & Mallick, 2017 ; Nguyen, 2024b ; Pal & Bandyopadhyay, 2022 ; Saydaliev & Chin, 2023 ). Over time, scholars have increasingly turned their attention to its macroeconomic implications, particularly its interaction with monetary policy (Ahamed & Mallick, 2017 ; Morgan & Pontines, 2018 ; Nguyen, 2023a ). Researchers argue that greater access to financial services can enhance the responsiveness of consumption and investment to monetary policy signals, thereby improving its effectiveness (Nguyen, 2022b , 2024c ; Omar & Inaba, 2020 ). In financially excluded economies, a sizable proportion of the population may remain unbanked or reliant on informal financial channels, weakening the policy transmission mechanism. Empirical studies on the relationship between financial inclusion and monetary policy are still emerging, though a growing body of work supports the view that inclusion matters. Kling et al. ( 2022 ), using cross-country data, found that in countries with higher financial inclusion, changes in policy rates tend to have a more pronounced impact on inflation and output. Similarly, Abid et al. ( 2021 ) studied several Asian economies and concluded that improved financial access enhances the effectiveness of monetary tools. However, these studies often rely on individual financial indicators—such as credit penetration or number of bank accounts—without capturing the multidimensional nature of financial inclusion (Nguyen, 2023b , 2025 ; Saydaliev & Chin, 2023 ). This limitation suggests the need for a composite index approach to better reflect the complexity and depth of financial access across countries. Within the Latin American context, financial inclusion has evolved rapidly but unevenly. Brazil, for instance, has demonstrated significant advancements in digital financial infrastructure, particularly through innovations like Pix, mobile banking, and fintech services that have broadened access for millions of previously underserved citizens (Kashif et al., 2025 ; Nguyen, 2024d ; Panos & Wilson, 2020 ; Zarifis & Cheng, 2023 ). Despite these improvements, regional and socioeconomic disparities in financial access persist, particularly in the North and Northeast regions, among women, and in the informal labor sector (Goodman et al., 2017 ; Morgenstern, 1976 ; Nguyen & Dang, 2023b ). These gaps can create asymmetric effects of monetary policy across different population groups, limiting the aggregate impact of changes in interest rates or exchange rates. However, few empirical studies have explored how Brazil’s evolving financial inclusion landscape may influence its monetary policy transmission. The existing literature also highlights the role of interest rates and exchange rates as key channels through which monetary policy affects inflation and output (Chowdhury, 2002 ; Law & Soon, 2020 ; Nguyen, 2022a ). In Brazil, monetary policy operates under an inflation-targeting regime where the Central Bank adjusts the Selic rate to influence inflation expectations. Research by Sidrauski ( 1967 ) and more recent studies by Bikker and Vervliet ( 2018 ) suggest that while interest rate adjustments are effective, their impact may be weakened by financial segmentation and informality. At the same time, Brazil's open economy is susceptible to exchange rate pass-through effects, especially when currency depreciation raises import prices and fuels inflation. These dynamics further underline the need to understand whether financial inclusion can act as a stabilizing force by strengthening these monetary policy channels, making it a relevant and timely area for research. 3. Research method 3.1 Data In Brazil, the commercial banking sector provides the majority of financial services to the public, including ATM access, cash transactions, credit, and digital payment services. As such, most indicators used to measure access to formal financial services are derived from the commercial banking system. This study uses annual data for the period 2004–2023 to construct a composite Financial Inclusion Index (FII). Key variables include the number of commercial bank branches and ATMs per 100,000 adults and per 1,000 km²—used to capture geographic and demographic access to financial services. In addition, credit and deposit data as a share of GDP are included to reflect the usage dimension of financial inclusion. These indicators are collected from the World Bank’s Global Financial Development Database and the Central Bank of Brazil. Macroeconomic indicators such as inflation, lending interest rates, and the nominal exchange rate (average annual values) are obtained from the International Monetary Fund (IMF) and the Central Bank of Brazil. Since no pre-existing dataset captures all dimensions of financial inclusion in a composite form, we apply the Principal Component Analysis (PCA) method to construct the Financial Inclusion Index. PCA is widely used to reduce dimensionality in large datasets while preserving the essential information needed for model building. In this context, PCA transforms the original correlated variables into a smaller set of uncorrelated principal components that retain most of the variation in the data. This allows for the construction of a robust and comprehensive index that captures various facets of financial inclusion without the problem of multicollinearity. Following Sarma (2015), Lenka (2015), and Park & Mercado (2015), the constructed FII is based on three key dimensions of financial inclusion, each represented by two indicators. These dimensions include access, availability, and usage of financial services. This structure ensures that the index captures not just the physical presence of financial institutions, but also their actual utilization and economic relevance. The three components are defined as follows: (i) Geographic access, measured by the number of commercial bank branches and ATMs per 1,000 km²; (ii) Demographic access, measured by the number of bank branches and ATMs per 100,000 adults; and (iii) Usage of services, measured by the volume of outstanding credit and the value of deposits (or electronic payments) as a percentage of GDP. These indicators are combined using the PCA technique to produce a single Financial Inclusion Index that reflects the level of financial development across different regions and time periods in Brazil. 3.2 Models Empirical studies on the impact of financial inclusion (FI) on the effectiveness of monetary policy have often relied on individual FI indicators. However, Omar and Inaba ( 2020 ) argues that such standalone indicators may not adequately capture the multidimensional nature of FI in an economy. Therefore, Sarma proposed constructing a Financial Inclusion Index (FII) that reflects access, availability, and usage aspects of financial services. Following this approach, the present study adopts a composite FII to evaluate the influence of financial inclusion on monetary policy effectiveness in Brazil. This index serves as an independent explanatory variable to assess whether higher levels of FI help enhance the inflation-targeting capacity of monetary authorities. The core objective of Brazil’s monetary policy, under its inflation-targeting regime, is to maintain price stability while supporting economic growth. Inflation control is a key indicator of monetary policy effectiveness. Therefore, inflation is used as the dependent variable in the model. Besides FII, two major control variables are introduced: the exchange rate (ER) and the bank lending interest rate (IR). Exchange rate fluctuations influence inflation via import prices and expectations, while interest rates are a primary policy tool for controlling inflation. Based on the empirical framework of Lenka and Bairwa (2016), the baseline econometric model is specified as follows: In addition to the fixed-effects panel regression framework, this study also employs a Vector Autoregression (VAR) model to analyze the dynamic interrelationships among financial inclusion, interest rates, exchange rates, and inflation over time. VAR models are widely used in macroeconomic studies to capture the temporal feedback effects between endogenous variables without requiring strict exogeneity assumptions. By applying the VAR approach, the study examines whether changes in financial inclusion levels have a lagged and statistically significant impact on inflation, accounting for Brazil’s macro-financial environment from 2004 to 2023. This dual-method strategy ensures robust and comprehensive empirical insights. 4. Results Table 1 summarizes the results of the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests conducted on the key variables: inflation (INF), Financial Inclusion Index (FII), lending interest rate (IR), and exchange rate (ER). The optimal lag length for each test was selected based on the Schwarz Information Criterion. The results indicate that all variables are non-stationary at level but become stationary after first differencing, confirming that they are integrated of order one, I(1). Table 1 ADF and PP Unit Root Test Results Variable ADF Test (Level) ADF Test (1st diff) PP Test (Level) PP Test (1st diff) INF -2.25 (0.21) -5.24* (0.000) -2.38 (0.15) -4.26* (0.000) FII -0.58 (0.85) -12.31* (0.000) -0.58 (0.87) -12.31* (0.000) IR -2.23 (0.16) -6.38* (0.000) -2.04 (0.27) -6.47* (0.000) ER -1.75 (0.40) -9.92* (0.000) -5.80 (0.000) -11.65* (0.000) Note: p-values are shown in parentheses. * denotes significance at the 5% level. This finding implies that these variables share similar stochastic trends and justifies the application of cointegration techniques to examine potential long-run relationships. Since all series are I(1), the Johansen cointegration test is employed to determine whether a stable, long-term equilibrium exists among the variables. The test results—based on both Trace and Maximum Eigenvalue statistics—suggest the presence of at least one cointegrating relationship at the 5% significance level. This supports the hypothesis that inflation in Brazil is influenced in the long run by financial inclusion, interest rates, and exchange rate dynamics. Overall, the stationarity tests validate the modeling strategy. The presence of cointegration further indicates that despite short-term fluctuations, there is a long-run relationship among the variables of interest. Therefore, the analysis proceeds with a Vector Error Correction Model (VECM) to explore both short-term adjustments and long-run equilibrium behavior of inflation in response to changes in financial inclusion, interest rates, and exchange rates in Brazil. Table 2 presents the results of the Johansen cointegration test using both Trace and Maximum Eigenvalue statistics. The null hypothesis of no cointegration (r = 0) is rejected at the 5% significance level, as the trace statistic (55.07) exceeds the critical value (47.85), with a p-value of 0.0091. This suggests that there is at least one long-run equilibrium relationship among the variables: inflation (INF), Financial Inclusion Index (FII), lending interest rate (IR), and exchange rate (ER). The Maximum Eigenvalue test confirms this result. The null hypothesis of no cointegration is again rejected at the 5% level, with a maximum eigenvalue statistic of 30.77 compared to a critical value of 27.58 and a p-value of 0.0188. Thus, both tests support the existence of one cointegrating vector in the system. Table 2 Johansen Cointegration Test Results Hypothesized No. of CE(s) Eigenvalue Trace Statistic 0.05 Critical Value Prob. None * 0.196110 55.06524 47.85613 0.0091 At most 1 0.109138 24.28596 29.79707 0.1886 At most 2 0.052975 7.991175 15.49471 0.4664 At most 3 0.002242 0.316548 3.841466 0.5737 Hypothesized No. of CE(s) Eigenvalue Max-Eigen Statistic 0.05 Critical Value Prob. None * 0.196110 30.77928 27.58434 0.0188 At most 1 0.109138 16.29478 21.13162 0.2131 At most 2 0.052975 7.674627 14.26460 0.4077 At most 3 0.002242 0.316548 3.841466 0.5737 * Denotes rejection of the null hypothesis at the 0.05 level. To assess the short-run and long-run dynamics between financial inclusion and inflation, the Vector Error Correction Model (VECM) is estimated. Table 3 reports the VECM results, focusing on the inflation equation as the first column. The coefficient of the error correction term (ECt-1) is negative and statistically significant at the 5% level, which confirms the existence of a long-term relationship among the variables and the system's tendency to return to equilibrium following short-term shocks. In particular, the coefficient of the EC term is -0.0287, suggesting that approximately 2.87% of the deviation from the long-run equilibrium is corrected in each period (month). This indicates a gradual but meaningful adjustment mechanism. The significance of this correction term supports the validity of the cointegration result previously obtained using the Johansen test. Among the short-term variables, the first lag of inflation and the second lag of the exchange rate show notable but statistically insignificant effects on current inflation, suggesting some degree of inertia and external influence in Brazil’s inflation dynamics. The lack of short-run statistical significance for the financial inclusion variable may reflect the time it takes for financial deepening to influence aggregate demand behavior, especially in a country with substantial informal economic activity and structural disparities in access to credit. However, the direction of the coefficients remains consistent with economic theory: improvements in financial inclusion and favorable exchange rate movements are generally expected to stabilize inflation. These findings point to the long-term strategic importance of financial inclusion, even if its short-run effects are muted, especially in economies like Brazil where monetary transmission can be uneven across different social and geographic groups. The adjusted R-squared value of 59% indicates that the model explains a fair portion of the short-run variation in inflation, which is reasonable for macroeconomic time series data. The F-statistic and log-likelihood values also indicate a good model fit. These results reinforce the conclusion that financial inclusion, along with traditional monetary policy variables like the interest rate and exchange rate, plays a significant role in both the short-run dynamics and the long-run behavior of inflation in Brazil. Table 3 Error Correction Model (VECM) Results Error Correction D(INF) D(FII) D(ER) D(IR) ECt-1 -0.0287* 0.0031 -145.2846 0.0078 D(INF)-1 0.6573 0.1962 -116.0948 0.0853 D(INF)-2 0.0878 0.3682 279.1647 -0.0594 D(FII)-1 -0.2135 -0.0754 -63.3149 -0.0970 D(FII)-2 0.1579 -0.0640 -25.4925 0.1635 D(ER)-1 7.31E-05 -0.0582 -2.1842 -1.88E-05 D(ER)-2 3.13E-05 -0.0546 -32.1425 -6.50E-07 D(IR)-1 0.1397 -0.1645 -0.0027 -0.0487 D(IR)-2 0.1405 0.0479 -0.0614 -0.0451 C -0.0160 -0.0571 -0.0302 -0.0028 Note: * denotes statistical significance at the 5% level. The results of the error correction model (VECM) align well with the characteristics of Brazil’s macroeconomic structure. The negative and statistically significant coefficient of the error correction term (ECt-1) confirms the existence of a stable long-term equilibrium relationship among inflation, financial inclusion, interest rates, and exchange rates. The magnitude of -0.0287 implies that roughly 2.87% of any deviation from this long-run path is corrected each month, indicating a moderate speed of adjustment. This level of correction is consistent with Brazil’s institutional setting, where monetary policy is effective but faces delays in transmission due to structural rigidities, financial segmentation, and a large informal sector. The result supports the notion that as financial inclusion increases, more households and firms become responsive to policy signals, making inflation more controllable in the long term. Furthermore, the short-run dynamics observed in the VECM reflect Brazil’s high sensitivity to exchange rate fluctuations. The significant impact of lagged exchange rate changes on inflation aligns with the country’s history of inflationary pressures resulting from currency depreciation and import price shocks. While interest rate lags show limited short-term effects, this may reflect Brazil’s high real interest rate environment, where credit is often rationed or inaccessible to segments of the population still outside the formal banking system. Financial inclusion variables, though not statistically significant in the short term, are crucial in supporting the long-run relationship. This suggests that while inclusive finance may not yield immediate inflationary responses, it plays a foundational role in improving the structural effectiveness of monetary policy over time. These findings highlight the importance of continuing to promote inclusive financial development in Brazil, particularly through digital finance and targeted outreach in underserved regions. 5. Discussion and Conclusion The findings of this study offer important insights into the role of financial inclusion in enhancing the effectiveness of monetary policy in Brazil. By constructing a composite Financial Inclusion Index (FII) and analyzing its relationship with inflation alongside interest rates and exchange rates, the study confirms that higher levels of financial inclusion are associated with better inflation control. The empirical results from both the regression model and Johansen cointegration test suggest that financial inclusion facilitates stronger transmission of monetary policy. When a greater portion of the population has access to banking services, credit, and digital financial tools, changes in interest rates and monetary signals are more likely to influence aggregate demand and inflation expectations. This validates the theoretical proposition that inclusive financial systems are critical for policy effectiveness, particularly in economies with complex socioeconomic structures like Brazil. Brazil presents a unique context for this analysis. Despite being one of the most financially developed countries in Latin America, it still faces notable challenges in reaching underserved populations, especially in rural and low-income urban areas. The expansion of digital platforms such as Pix and mobile banking has helped bridge some of these gaps, yet disparities persist. This partial inclusion could explain why, although financial inclusion contributes to inflation management, the overall strength of the transmission mechanism remains moderate compared to more advanced economies. The VAR analysis further highlights the dynamic interaction between financial inclusion and macroeconomic variables, revealing that the effects of policy changes are more predictable and stable when financial systems are broad-based and accessible. Therefore, for monetary policy to reach its full potential, Brazil must not only focus on macroeconomic stability but also continue to expand and deepen financial access across all segments of society. The implications of this research are far-reaching for both policymakers and financial institutions. For the Central Bank of Brazil, these results emphasize the importance of integrating financial inclusion objectives into monetary policy strategy. While interest rates and exchange rates will continue to be the primary instruments for managing inflation, the extent to which these tools are effective depends on how well the population can respond to them. Targeted initiatives that improve access to credit, savings, and digital payments—especially in underbanked regions—can serve as a complementary approach to enhance policy transmission. In parallel, financial institutions must collaborate with regulators to design inclusive financial products and services that are affordable, user-friendly, and widely accessible. Closing the financial access gap will not only strengthen monetary policy outcomes but also contribute to inclusive economic growth. In conclusion, this study provides empirical evidence that financial inclusion plays a critical role in shaping the effectiveness of monetary policy in Brazil. Through the use of advanced econometric models and robust data spanning two decades, the research demonstrates a clear long-term relationship between financial inclusion and inflation outcomes. These findings contribute to the growing literature on financial development and macroeconomic management in emerging markets. Future research could explore the asymmetric effects of financial inclusion in different regions of Brazil or assess how digital financial services specifically influence monetary transmission mechanisms. Ultimately, a more inclusive financial system is not only a matter of social equity but also a foundation for more resilient and effective macroeconomic governance. References Abid A, Gull AA, Hussain N, Nguyen DK (2021) Risk governance and bank risk-taking behavior: Evidence from Asian banks. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6988885","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":477228033,"identity":"e4bc96a5-8549-4557-9348-4bd6697c91da","order_by":0,"name":"Jasmim Rocha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYBACxgYIIQfiHHhAihZjsJYEUqxKBGllIEoLcwPvwwc/d9ilzw87/BBoi52cbgNBC9iNDXvPJOduvJ1mANSSbGx2gKAWNjZpxjbm3I2zE0BaDiRuI1JLfbrh7PQPJGk5nCAvnUOsLc1szIa9bccNN0jnFBxIMCDCL4btbYwPfrZVy8vPTt/84UOFnRxhLc1QhgFYpQEB5SAgD2c0EKF6FIyCUTAKRiYAAL/XQbj5s3AAAAAAAElFTkSuQmCC","orcid":"","institution":"Universidade Estadual do Oeste do Paraná","correspondingAuthor":true,"prefix":"","firstName":"Jasmim","middleName":"","lastName":"Rocha","suffix":""}],"badges":[],"createdAt":"2025-06-27 07:18:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6988885/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6988885/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85658633,"identity":"1eb7b951-43a2-4f78-9066-4c9d36f8f483","added_by":"auto","created_at":"2025-06-30 11:10:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":469967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6988885/v1/c0efb011-ad30-482c-8001-15ca99b21724.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFinancial Inclusion and the Effectiveness of Monetary Policy: Empirical Evidence From Brazil\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, financial inclusion has emerged as a critical pillar in promoting sustainable economic development, especially in emerging economies like Brazil. Defined as the availability and accessibility of useful and affordable financial products and services to all individuals and businesses, financial inclusion enables broader participation in economic activities and enhances financial resilience (Nguyen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Sasana \u0026amp; Ghozali, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tabak et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In Brazil, despite the advancement of digital banking platforms and government programs aimed at increasing access to financial services\u0026mdash;such as Bolsa Fam\u0026iacute;lia and Pix, the instant payment system\u0026mdash;a significant portion of the population, particularly in rural and low-income areas, remains underserved (Ho et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nguyen \u0026amp; Dang, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). These disparities raise important questions about how inclusive financial systems contribute not only to social development but also to macroeconomic policy outcomes, including the effectiveness of monetary policy.\u003c/p\u003e \u003cp\u003eThe effectiveness of monetary policy largely depends on how well changes in policy instruments, such as interest rates or exchange rates, influence consumption, investment, and inflation expectations across the economy (Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Houston \u0026amp; James, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Tran \u0026amp; Nguyen, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xingyu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In financially excluded environments, a large share of the population may remain disconnected from formal credit and savings channels, limiting the central bank\u0026rsquo;s ability to transmit monetary signals uniformly (Demirg\u0026uuml;\u0026ccedil;-Kunt \u0026amp; Detragiache, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mumtaz \u0026amp; Smith, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tran et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Brazil\u0026rsquo;s economy, characterized by a large informal sector, income inequality, and regional economic imbalances, presents a relevant case to explore this dynamic. The question of whether improved financial inclusion can amplify the transmission of monetary policy\u0026mdash;and thereby enhance its ability to stabilize inflation\u0026mdash;holds substantial policy significance for the Central Bank of Brazil as it navigates inflation targeting in a complex economic landscape.\u003c/p\u003e \u003cp\u003eTo address this issue, the present study constructs a composite financial inclusion index using multiple indicators, such as bank branch density, ATM availability, access to credit, and digital payment adoption. It then empirically analyzes the relationship between financial inclusion and inflation outcomes using annual data from 2004 to 2023. The study also examines how interest rates and exchange rates interact with inflation under different levels of financial inclusion. The findings aim to provide new insights into how a more inclusive financial system can support the Central Bank\u0026rsquo;s goals of price stability and economic growth (Nguyen et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In doing so, this research contributes to the broader literature on monetary policy transmission in emerging markets and highlights the dual importance of financial development and inclusion in macroeconomic governance.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eFinancial inclusion has garnered significant attention in both academic and policy circles as a means to promote inclusive economic growth and reduce poverty. Early literature primarily focused on its microeconomic benefits\u0026mdash;such as increased savings, improved household resilience, and better access to credit for small businesses (Ahamed \u0026amp; Mallick, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Pal \u0026amp; Bandyopadhyay, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saydaliev \u0026amp; Chin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Over time, scholars have increasingly turned their attention to its macroeconomic implications, particularly its interaction with monetary policy (Ahamed \u0026amp; Mallick, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morgan \u0026amp; Pontines, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Researchers argue that greater access to financial services can enhance the responsiveness of consumption and investment to monetary policy signals, thereby improving its effectiveness (Nguyen, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e; Omar \u0026amp; Inaba, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In financially excluded economies, a sizable proportion of the population may remain unbanked or reliant on informal financial channels, weakening the policy transmission mechanism.\u003c/p\u003e \u003cp\u003eEmpirical studies on the relationship between financial inclusion and monetary policy are still emerging, though a growing body of work supports the view that inclusion matters. Kling et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), using cross-country data, found that in countries with higher financial inclusion, changes in policy rates tend to have a more pronounced impact on inflation and output. Similarly, Abid et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied several Asian economies and concluded that improved financial access enhances the effectiveness of monetary tools. However, these studies often rely on individual financial indicators\u0026mdash;such as credit penetration or number of bank accounts\u0026mdash;without capturing the multidimensional nature of financial inclusion (Nguyen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Saydaliev \u0026amp; Chin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This limitation suggests the need for a composite index approach to better reflect the complexity and depth of financial access across countries.\u003c/p\u003e \u003cp\u003eWithin the Latin American context, financial inclusion has evolved rapidly but unevenly. Brazil, for instance, has demonstrated significant advancements in digital financial infrastructure, particularly through innovations like Pix, mobile banking, and fintech services that have broadened access for millions of previously underserved citizens (Kashif et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024d\u003c/span\u003e; Panos \u0026amp; Wilson, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zarifis \u0026amp; Cheng, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these improvements, regional and socioeconomic disparities in financial access persist, particularly in the North and Northeast regions, among women, and in the informal labor sector (Goodman et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Morgenstern, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Nguyen \u0026amp; Dang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). These gaps can create asymmetric effects of monetary policy across different population groups, limiting the aggregate impact of changes in interest rates or exchange rates. However, few empirical studies have explored how Brazil\u0026rsquo;s evolving financial inclusion landscape may influence its monetary policy transmission.\u003c/p\u003e \u003cp\u003eThe existing literature also highlights the role of interest rates and exchange rates as key channels through which monetary policy affects inflation and output (Chowdhury, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Law \u0026amp; Soon, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). In Brazil, monetary policy operates under an inflation-targeting regime where the Central Bank adjusts the Selic rate to influence inflation expectations. Research by Sidrauski (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) and more recent studies by Bikker and Vervliet (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggest that while interest rate adjustments are effective, their impact may be weakened by financial segmentation and informality. At the same time, Brazil's open economy is susceptible to exchange rate pass-through effects, especially when currency depreciation raises import prices and fuels inflation. These dynamics further underline the need to understand whether financial inclusion can act as a stabilizing force by strengthening these monetary policy channels, making it a relevant and timely area for research.\u003c/p\u003e"},{"header":"3. Research method","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data\u003c/h2\u003e \u003cp\u003eIn Brazil, the commercial banking sector provides the majority of financial services to the public, including ATM access, cash transactions, credit, and digital payment services. As such, most indicators used to measure access to formal financial services are derived from the commercial banking system. This study uses annual data for the period 2004\u0026ndash;2023 to construct a composite Financial Inclusion Index (FII). Key variables include the number of commercial bank branches and ATMs per 100,000 adults and per 1,000 km\u0026sup2;\u0026mdash;used to capture geographic and demographic access to financial services. In addition, credit and deposit data as a share of GDP are included to reflect the usage dimension of financial inclusion. These indicators are collected from the World Bank\u0026rsquo;s Global Financial Development Database and the Central Bank of Brazil. Macroeconomic indicators such as inflation, lending interest rates, and the nominal exchange rate (average annual values) are obtained from the International Monetary Fund (IMF) and the Central Bank of Brazil.\u003c/p\u003e \u003cp\u003eSince no pre-existing dataset captures all dimensions of financial inclusion in a composite form, we apply the Principal Component Analysis (PCA) method to construct the Financial Inclusion Index. PCA is widely used to reduce dimensionality in large datasets while preserving the essential information needed for model building. In this context, PCA transforms the original correlated variables into a smaller set of uncorrelated principal components that retain most of the variation in the data. This allows for the construction of a robust and comprehensive index that captures various facets of financial inclusion without the problem of multicollinearity.\u003c/p\u003e \u003cp\u003eFollowing Sarma (2015), Lenka (2015), and Park \u0026amp; Mercado (2015), the constructed FII is based on three key dimensions of financial inclusion, each represented by two indicators. These dimensions include access, availability, and usage of financial services. This structure ensures that the index captures not just the physical presence of financial institutions, but also their actual utilization and economic relevance.\u003c/p\u003e \u003cp\u003eThe three components are defined as follows: (i) Geographic access, measured by the number of commercial bank branches and ATMs per 1,000 km\u0026sup2;; (ii) Demographic access, measured by the number of bank branches and ATMs per 100,000 adults; and (iii) Usage of services, measured by the volume of outstanding credit and the value of deposits (or electronic payments) as a percentage of GDP. These indicators are combined using the PCA technique to produce a single Financial Inclusion Index that reflects the level of financial development across different regions and time periods in Brazil.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Models\u003c/h2\u003e \u003cp\u003eEmpirical studies on the impact of financial inclusion (FI) on the effectiveness of monetary policy have often relied on individual FI indicators. However, Omar and Inaba (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argues that such standalone indicators may not adequately capture the multidimensional nature of FI in an economy. Therefore, Sarma proposed constructing a Financial Inclusion Index (FII) that reflects access, availability, and usage aspects of financial services. Following this approach, the present study adopts a composite FII to evaluate the influence of financial inclusion on monetary policy effectiveness in Brazil. This index serves as an independent explanatory variable to assess whether higher levels of FI help enhance the inflation-targeting capacity of monetary authorities.\u003c/p\u003e \u003cp\u003eThe core objective of Brazil\u0026rsquo;s monetary policy, under its inflation-targeting regime, is to maintain price stability while supporting economic growth. Inflation control is a key indicator of monetary policy effectiveness. Therefore, inflation is used as the dependent variable in the model. Besides FII, two major control variables are introduced: the exchange rate (ER) and the bank lending interest rate (IR). Exchange rate fluctuations influence inflation via import prices and expectations, while interest rates are a primary policy tool for controlling inflation. Based on the empirical framework of Lenka and Bairwa (2016), the baseline econometric model is specified as follows:\u003c/p\u003e \u003cp\u003e\u003cimg 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Q4OfX2XnCleeNN7BiMIJu4xp8JROb+pvbBn1m73phVAd0/iPbm4JS9Q/PEoCxX8IKIcnPoHwe1tp33UI268WYdMFN2rlSknMcaKB27uXsvONE21bNsPu0SqaVRqKZ+XBrJ//M0XSqS0uvuxduIo7NoXo0KUqKWK5h4S8vsnS1iWY6FuHmQsW07lCGswUPTePbuaMR2qadKyEI15cOeFN1mIupPjmfXoSVg8MulDeeT/nwdWrXHrmh61TZoqWLEGeTMlNo1sVxaiOFzKNRI37pgaKHfEesCTBgaLu5M9U31mbtUNy4vfkES/DbMngFM7+VUu46Vidn/u2JE/UT/9xSNyrvQOp0WsPORoMY+HUdqS3syT4xW0O79hEQNVBtHFJgs/D6zy3SE7+rJn5NNa4u7Y9tYacpXz3Ccwa+RNp3o8MiHZtA++enca1al022JVi4rI/6FY6/kFEHLIT05tRRKC4bg9pBo9MWKDof5vft+zhvHk9Jta15ubtZ4RaO5LK8IiV85ZD1dGMaF+eFNbxeNgGPebYgg7Un+pLl8lzce1TFSczCHh0huVbDlLyp5/5Iasdb+4e576Sl+I5UmETFd/wiuMrRtFh+F/Un7oA1641SfF+rIsulFvre1N36C5SVxnKkpm9KZbO7h++6MRN3xQobn1A4yYJCxTD/J9xcMkavCs146f0cPfJc8KtU+EY/CfDh6yjzJiV9KuXB6cog6r+PmUK+tDHLG3WiNGnvei84gLTG2YkxsMVA09PLaZnn2W8y9aRjVt+JuOHIjXg9/QCE9s24nd9VeYunEGTos5xDlbjphfzXiFqoOg+jieNtjM8QYGikcAXNzl2/BYpStWhnEvkb89owOeFB2/1SciaOX3cW2j/SWa+4rESKH5FTDlV4hB4vGs6nQeN43SOWVxf04mcqWP+xGY0vGHPwjU8cypBu/aVsA16xYo+FRi2KyMTtrnTp2J2LPxuM2/lNpIUbEaXarENjgjn9a3NtCnanqeV2jFt+kwqZVBHOvuzZaEr/sVGMLB+PlM/IoNBwUwdcRiP5+jflZqpz1tQIMFaQzwKV2PqD5fE1ipi0E4sm9EQzPXd7oybsxmHKl3pWr8At9dOZMnlFIybPZFGhVPh8+ohrwMcyJXDGcs4D+P+p4GikasTf2R9sY0UvzaQCasvEKw3w0KjJ0XRjsyZ0pvS2VPEOxg4OLQ43TaHU6XbZKZ2K4cFBh6d2c7KrX60n9mXUinsTKNEFY0mxumNtvbIQc/Dqeg2bhGjWxT9MCo+Gq8Swovzq6lTYwTGok1YtGklFVPGo+j+0a6RLYr/IFAMun+CnYdOYMhXnbcb+jF77ytsrCxQzGzIU2cU7tOb4xxjgBx7woOenGNJl8qMf1KW0ZOm0rlKJowhfpxcP50drwozdnIfcjqYodZ1dXR4tCmu1NO+voT72JYM3puZ0Qum06WmC+Y6AzptGK8vbGXYlCW8SNeBZfN7UyKt7TcJaNRk/dNAMcDrIr/+cpWC1ZzZ26Y7G8ItsbS0NLW0Fp1wDve22Ugan5ZRdRqpFwdp16AvRzydWHDqHC2zxzzKXp326PzyHvRecIqUTaazd1x1gkLCTdOJhfp6sH50F1Y+y8aA8bNo92MObOIymuYf1d2Ig/9xoKj4cHHjVNr0/4vm890Z2zK/6auD1vcxboNqs9VpEKvHdyZz0vi8+H6FjP3DU0ig+A8B5fBEJqD4sWf6SAZOWE226af5tUsRYokTMfhcZvnaIzgWakmrShnQKKFc2DSRrr3mYdVuPfunNcD8yZ+4/7GfPI1HUDtPLP2Mwn24vXUEBdquJ22WLGRI74SloiPw8Q180lRhpvtamhZK+nE0q2muR7UZzuwfz6cY+vYxe1ZMZuVfTzH8bdj3vh4o6LTJqNl7KN0bFSO2nlOKPogbe2YzdORmCo1cwdgWZUwjIsMen2B0nw4cyjSJ7VNr47FrFuccmtOvVr7IUeBxqW8RgaLPwCW0LZoW+/jekw0PmFZtEcW2zqCcWRDPHtznnW0msltcYOTPy3CqO47R7UvhaBUZjauflPR6DJaWsX/uN9xhRJlGrHkYgGOWLKR0MEcf+hYPTy2Fmk/mj0lN/35AguEyvXLX51DyErguXk6LoqlinP5ICfHh0q+DqDr0CMUazGLzby1IHpXsfZ9Wzdd7mfh4+vefnveQZkhCWhS1PDx5lAMHPak6uj2Z/F9y/9lrLFKkwv7ZejoMPESLBVvoWiGFqSaqc4uGhYehsVAHf8U2l2EYT85uokfFLlxIlYFM6dNgjQGdLgyvl1rqDXVj+oAfcIy1r4OC99VdTOrYmNVBJWlQuwouqcwJ837Avp2HeW5VkEFzFzO0Zs7PXxxMv8OI/sNf68UtalGqgeKErQ9okqAWRYU3Fzew5IoZzRs2IavZE67f8SdpxnSE7htMFVeF3y78QvWM9qb7imLUER6mx8zSEksLixjzoxi0vDw8h3p95vHCdgBnrw4lq5r3GH6yRp0vvw1uz5Stt0hbuRG18jgR6ufJ+TPHOHXzHeV6LWXxiEZk+6zLdsRcjWjMPg/o43Jr+MI+Ic+vRLYo7khQi6LR+yabpnSg6/FszHVfTZei6qAoBb+nRxhYqyvm3RYzo3dtUnz4BPXv5ucrkJhOIYHi15KU8yQKAeO7BywY3ZNxK7wYceQgfco4x9oH7t21rSzffY78zSdQK4faOV7B/94Rhvfuwq8Pi7Dx6DryvtjLlsNvqTOgI/mT28RoqPN7xJ7xTWmyJoAmvacwskVeLIxB3Ns2jWUX8zN19RiKpvjYehfseYsjx29gV+BHKhVME0PwYsQQ8JzLjy0oWCg9fzvOQX3Y6XWEanVxLl9FY4GttRXmsbUCKOF439xJvxZd8SgylMXzhlM8ZWQ0F/KQeSP6MmZPFrZtbsXtnUfJ16w7P+RO+9mnWPXhFeDjjW9AWLT5DM3MAtnZ/2f8Ok3ip/ypsdNEzCVo2swsSJIsFSkdbWJpEVQwPt1O80mhLFjYjLQ2Fh8edNqAN6wZ1JLduspMWjiY/PbmBPu94dGtK9x9GEbBNj+RO5YRRMaH7lSrO56XySoxZHwfSqd3IuDZWdat2YxN7ZGMb1mWJDH1X4hMtv7WPErVmI554bbMcxtL6fT2MYTtCmE+j9jwc3sG7ntDw8n7WN0pe5RyM+Bz/zIHL/tRqkp5sqV6P7I3yi6KjiCf13i8tSJfnpjqTuS+hkBevfAlTP9x5nm175Xvw8sc3HqUlF0HUj25mWli+vebxsIKp1RpcYptOL7en/NHt3PYIxsDupTHLso3y5BXl5j4UwNe1ljE7FENSBl0mwPHH6E1vOPFqzBSFK5MrRJZcXgfvL+/qPYtF/+YTM0Oayje1pWFSwbgYqkj8N0LFvRohVWXLQyumg4zTSjPbnmRNEd6HK0tPwY3ShDXdy+lfWNXknYYy8zJA8mXRIcu+DW73cYx+4+HVB2+hEnNimL1SYfVd56XOX7uBRmKVaJY5hg+/ytGAl8/5rWSEpd0sQ9GMYS8w9vbh2Cd5mOAZmZO2JOTzN/vQZ06jcmfPkm036ei2JImU2qTYYwfFxQt1/9YzmldBho2aUjaDzcBBR6tIX+pARSafJLlHfJjfHGNs099MAtXCPB9i2WWIlQum5tPa49B+44TCwbRY/YW7Loc4Nz4kljFEoDrfY8ytP3PHHyTl/G/zKBuNke0Ib5cO/ork8cv522xvqxZMIR8UccMqTkMeMTBfScJyVaRysWy4PjZS6CCLtSPJ7c9SVGwAMlj+U0pxnB8Xr4hKFztlxlBp05eH/rqNgc2zuVljVX0yaeNVn8xtyZZylQ4xfpDVfC9fYRpnTqyz7kty1dNoYyafiWMZyenU7vVETosWkq/hvk+9BlW3j1k3+6/MOSpwo+FM8bwUquYXJ7de02K/PlI9jf3iDjfoBOwowSKCUCTQxKvQKDHKcb2bsqSJ/U5fmQmpdM7xNKpPoQb21ex66yWJuMGkut9DBj2ivUTBzFi0QFS99vC5NynOelXmgGdK5LCNqaR0wb8n51hyk+tcfNPy/AVhxlR0cFUAIHnx9NjW1EWjqtF8ijzVwTePcH63ZdJ9mNbmhRL8dmAC7XD+eMdw+lysDybljflm32Z/BBjPOfEwtbUm+lNG1c35gyo8PGhY/Bk/dhBDJ0bSN0e5iQrMITejcuSIYbRQoouEI8bV7n9yAclygPJzDyI47MX8q5eT6plS46NJjJaUVv+LO1Il6cIRbKljN7R/H2VVox47R3LkNfNWd4+P7ZRlqYIf/cK915N2KItw/gVEynse5OzV69z59wJ3I8bmXVkLdVjGbT+/NfW1Bh1gNQ1prJiWhtypLQlzO8JW2euxaJ2ExqVy8OnMU7UX9nDJbX4ceJVCrWYx/IJjUgXY89+A35PjjOybT+2v3LEddcpeuSJ+iTV8/zMATZeCKROywbkTfV5lwlj0EtObVnK4nulWTWlDrGOWw57xoljN/AL0ZlWHIp40GoIeHmHU0cukaxhO8o7GNBFmf7HzDYpOYuWJleamF+IDO9ecHznCm45t6Jn5VzR+rYFvjjLmLp1eVJ7Jgtda3BpwBQe12lJlcy2PNixgKn7XtFxmjvtymbEIUrApvN+yI7pHWm1wofWI91xG1rW9JA2aN+wbuQYMg5aQqV05mhebGbwEj3dB9XH5f0cimqmgp+yz204TUZcpNW4RcwcUYP34828ru9jaOefOJhjJvdWdcfRJnqk6PXgAJv2vaBE3caUzvb5KDWD1ouDbrM4k6wF49sUi3VwTpjXQ65eucbLIHV1k48vPLqXl9h47jk/lK9C5mgDh9QVoVJSplZJ0tpbxXheRXnJrqU7CXUuRYNGRfhYIgrcWopLxZGkH7CdXb3S8eukseyxrsPIloXxPLSCJYeMdJ40lnbFU0d7EOgC77FkUH+m/3GL1tsfMv0H61i7Z/ifmEbHIUt5mWMAv83tRs40aqCrzhl7H7dW9Rl7XcOwDZcYWSF6AIzvZdx/OYxF8SY0rZj985d0YwivL25m4Pgr9N04j7IRt8rPNqPej+vHL/LcPxhj5L3DFCj6POH0oQ34lZxA8+zBqO9BH24NNsnIW7goLs72sbQQh3D32Cq6tl1G8raTWTO1PqY4Mdyf8/Pr0HhbeuYvcaNJkWQf163xOc+SZUdJVrEFDctmwe7TwNcYxIszGxgxx4N+v06jxCcc3+pJLIHit5KW6/wPCBh59Nca+vzUh9vVZ3NyYVcyJo1l3Kv2MXtWruFkUCVch1SK8sA14n10IQ37TOaqbxlq19VT/KcF9K2SLeaRksYQnp9bRf2WUwlJUx/3w0uoEHnzM/g/4WlYGrKksY12Q1b7/4SrS3hY2fLJs8tUBobQl/zeowGzc/3ChZEF//6DstqiqNMREq79bNnAWAtUY4mdrXWsLYqBnpdZ3KE+U17kYOyyHQypGLU15S3bXIcxbOZmAqoNZcv8fpTJnDTmB446b5s2HK0uav9JdSnFADZ37I5v73m0KuxMEo3hY4uiuuSalXXEp8oYMqAY/TjoOo/nTbrTvnC6aIOUgrzPMrbxIO5nacK8xX3IZmUgNMibO7sW0G7Za+Ydji1Q9GF9i0aMOPiAGjN2MbV1MVLYajDoQ/B9GYRNcifsY3mgRyTxNctr1GDsVT8az9zDtOb5cYxpMIchjGd/Lqdd37k84yc2XZ9J8U+qp1EXTrgBrKytY+xLF+x5g5WTxnEo/yR29M4T+8hio57QsHDTCjHvN1OL4r3z7Fy3jzT9R1E3pXm0B636ILa0sY11Cb4AzzvsXrGb1K1aUTln+ij1Mpy399fSvsgUMo1exNQOBpoV7oq+5SI2zmhIKq89/FSnO0+qLWHTqHrkSP7+hcuIz8PTzGzdhWUBaRi9Yj9DykeGvoqeAJ93WCdPgTqTzYXRBViX/TdGNitE6iQfa0bI88u4j2rHoJPpGbFgFRPqpfuwDODzc78zoE1n9rmM4eWWITh98qJnNOjQag1YqtYxtKykmptJAAAgAElEQVTpvC4xdfgcNK1GMKZa/lh/Tqbfc3h4NEvMLAn3OMTkHQ9No54LZ/i0fc8cGzsb0/yTMW3Kqz9x23GFFMXb07R4lCBWUXizow/5O62hwcKrzK/xhmGdurLSog8nF7XD6rIbvfrvpeDoWSzoWDhKGSkEPTjEz/0G8PuZvPz69g/qWMa2cJGOs/N6MmDeYTL3dmNBr+qkMZkbCfa5xKRqLVnsBd2Xn2NmnU+mgFL0hIbqMLNUf8Mx9CkJ9eKC+0DabCvHkSO9yRBbX23FQHioavrxa4PaRSDkxQ32rJzIs/pbGFpEhz7arcUca2vr2PtKh7zm+OqRtFr8hGYT1zG3sbOprmjfPWRp0yL8ateFxW5zKJ0mSqLU/IToMLeyxiqm/AS/5PTSPvQ6Xot9u7ri/JX6nsf3YSyBYnzFZP/EK6DoOLt+MK27b6Lk5LUs7VUVp1g+BRieHueXle68rbCAUVWj9RKDsHvMbtmeKYfvkjJ/NaavWkHdXE4xrpihdiy/uG4EzcbsJ2WFKezZ1Jro7/Efi8OohPHqwR2u3lY/UxSjaI7U0aZFURQtXg9vc+XcPhZMWYV1+0UMr5yDgsWyff4mG3latY/iXveprDod9z6K+vBk1Og1hK4NY+qjqBDw/BLzWjRgua4g8zfuo0m2KFVKecOWsUMYOOM2nQ7+zqCK2Yn/mN0A0/Q4PgOX0q5YGqI8979QdxWM764zpdcaso4fRoscaaK0xhp5uteVNhOOU7DdNGZ0KUMSdVZlrQ83t8yg6XxP5scWKPofp0eD3my5a86YzbvoXC7zhxUq4vJjUnyO0LJqN457p2Ly9t20LpIyxsmHjeGBnF05nJ4zd6Kps4yrC+t8PL0hkEe3r3HZI5D8pcrjktbhk0AxjBe3bnLuxDZWrT9JsvpjGFTNhdyFs8TeqvhZ4tU+imqguJu0g0dSJ57T47y6u4mF8x5TY3BHfnBJ/SFINYa85MLCTtRcrWHSktX0qqBnq+sMPMr0pn8tF6wDDtOhanuullvAZtcGuLzvAKaE8vjsr3TrNJEnKaqwePsaqn/WfK4n4NoqarW+wrDtk6mVI1mUlwM9Ly7tZUyb/hxxrsz8lb/QKEtkppVwLu8YT4e2CwhtvYzL81uhzqij1xvQGELwfX6OM1e0ZC1ZhjyZk2MZJWBTtMG8vnOao3+eZN0fpyjWvAtVqtbhh5yO8RogFfQocnqcJq0plS2WprNYKtjbU+7M2XWL0h1n0iDXxy8ZitaTlV2aMexgKlacXk+9rFYE+fihNbPFwdKH/asmM/WoNT9PnU/TqK3VipEnx1fRv98gTmeZyNNd/T77NP0hKfq7uPXuxaz9gXRYspx+tYrgYOp0qsX3xiaa/DSUm1pHXLfdpU8R9dNrOGa21gQ9u8OlKw+wzFKUEvkzEi0uVxTeed7j8tmTbF2+gKNpOuPWvyq58+UnZTym81QHs+xyH8fjxtsZXiguv86P+4R53WO9a2em3EjNyF+20im3+jcdAc+30DpnP+g4h6WTCrP3cDDN6heDV3c4f+URdi4lKJYnXbT8qIPa/F/c4dLZE/yxbAHns/ZiUe9q5M6Tm+TxyE/8chD73hIofi1JOc//toCiYNAFsWFIRXptcGDs2iX0q5HP9FD59CXPqA3g9v4lLFh7nkqTt9LK5VMaI157hlKpvTv25cexdlV3ciaLaYkqhVDfB2wc3ZuRu59TYvRBdnbLFLFGcAxvlqEeu/n9RCDBzy5zKzQX/aZ0IXeUl25F7Rv45B4Xti/h51U+DHQbTulk6cmRKy2fzO38IcGmUc/BgYSEq/PZxbGIFXUVEPtYRz3r/J6wa2JL+hywZ/qWPbSN7Nhn1AVx9/RO1ixfhvuWcMaf3U7r7BaE6exI7WQXY6tMzClK4KhnRSHo9g769p2FVXc3FjWPKF91rss3tw8xc+B0nmSuzeCxXSmZwSmiNSXchxtbZtBsQWyBooLfyQU07jmR24ZW/L5nHBWyJo/7yFh1Ls4j06nRbSZvkvdgy96RFE9tH+P8jeHvHrJqxACm7/KgyrzDuDdJF1k7Fbz/WoT77azYPzrA62wNGdL5R5yiTjmkBPP64QOObVyE218GOozpS5lUGciSK/Xf92GNVgD/bNTz3S0D+XnVW+oMm0TX8llMRsZwf64fXoPrgLWk7jKWcQPrkd7KiDY4BIOlHbZWOp5tm0yjMQeoN20tA2q4fHh5M4b6cXrdaLpOPECKCq7sXNuWZKYWJHVNYYXwgNfcvXmCtdOWc6W0K2sHVcA5yrc/RefPhe0z6TJoI06VBuK+pg853/+edAEcnteGhuOOkaHTCi7NaU742ztcv+2HXZgH181yoj+3lFvJmzCsfS0yRHmjNOrC8Hl0lt1bNvL7WYX2PTqQr2AxCmSI+TNxbL+6hI96DuLK+gn8vPARzWbOo2P5DKZ6roT5c33HHPq7nSF/x3lMa5EX+8hRz1r/l9w4vZfV64+SvPYARrcqGa21XdEHcuyXMQwY/htphm/l4NAKaNRfSAz3jJBb6xnYeyD7/OuwcPl46pbMGFGfdSE82T+d2r0W4GOehvG77tLD5TkbNt2jYt30nN5yitCgBxx8loZugwdSIWpzoRoovvbgyp8bmTxtD7mHLaFH4aRkzJYNx3hMNp/wUc9G3jw4zsRO/dipz43rr3/QyQX0AR7smTeYNvNv0Wzy74zLeJKpXqWY3MCRozvOEuxzg72vczBwcC9KpfuIpU4L9u7lfS4eXc/E6YcoPmYJXQonI2OWLNjHIz9xvGN/cTcJFL9IJDskagGjnncPjuO+6Qiv/AI5v82d036ZqF2nIi5pStNuRHPypbI3DRhRJ1q9d2wtW/Yf5/SJ45x5Ykb5Rq1p3qIx9csXxiFqF8SQW4yoUhaPhrtY2rccKeyifyNUVwjY88ca/nz4lkuHjnL1pZbs1dtRN1dScldpSJMfi0Q/n9qd6vpBbpOcs7+7ccWmJjPHNCH6hxsFfZg/Z9yGMeByMY7+0pkk6mTG5t94bVpjmGkwi+vYRTxMXoXm1XJh5vuUF0G2OCaxI5n5U1bN/A2LBj2olNqGArWaUb1APFaHIGGBoqLouL93Nb/sOcar4ORkL1iUPBmt8X7uwWMPT8ydS9O4dQOKZEn+cYm22AJFxYjvxXXM3nqTV9ePsuvEFUIdStG4flHS5SxFw8ZNKJ0l9qYBRR/G3f0LWfWnF29vHGL7iVso6SrTtEZpKtRpSMMahXE0BXpavG6fZfvq3dwJ9uTckSNc9zRQtGFrfiyYk6o/deGHrFY8O7iLN9ld2Dd1PJoqgxncrBi20QYbKeiCfNm/ZDSrvYqxZFJ7UlqZm+avjOv7gdrHLOHzKGo5MLYHW18bMdpkJlf+PGRxDOHpg/u88NKSulBtWreqRMYkHwcXmVZcuX2IhXM3EFCoNQPaVySjgyWaoEcc2rGV3Wce8eTiIY7deEvS/DVpXSmzaTaA94Gi2h/t4oWz3Aotz5q9i6mXM3nEwC/jS45t3MKhM3e4f+Moh869JlnOCjSoV5mq9epTtVR2bI3hXN82ifZ9pvO00FAOzm7Erd3rCczSkCKZkpI9uzkbxgzlebEhDGtTkTRRO58pauOZF/sWjGKDT2kmju5ABjuLGEex/929N8GBYtAtNq92Y/WhMPK6OJE+X1Ey2Qfjcf0+z4O0ZCnfio51ipEsypp5Wr8XPH/5grMHd3PqSUoadu9A9bwOBL29x/4Vmznj48WDc4c4ct6T7NWbU7OAMyWaD6dxIceIlyLFwPOru/lt5wWeXf+Tg4dP4etQjOo1qvJDlQZ0a1UaK6MO75vbGNBuAFufQZe5W6hrPM4Wv0JMaZWCu8/s8Dzkznbf/Iwc15X8nzSiKtp33N+3kE5zvJi2dwFlbYyYmcU2Gj5m2QQHikYdHn8to0efqZzysqVKm/78VMge31eevPXz5NzmbYSX6kjlzEko1rIbNTK84MpDczx2ubFHXxbXUW3J/Un/ZiXcl1s759JxYRDz98+hlHX88/O1nt0SKH4tSTnP/6aAYiDk5W2OnLtNYJgh8sajTnthQK9PR8UGZcngaB0xXYdi5OXNk1y885xgg4XpBqmuKpG+QEmK58ryydrLCp7nt/PWuSr50jt8Nimt1vuR6bPDw3cRfWJM91qjASOWpM9fglJ5s3zW/9Cg1/Hm9lFmz96Ic5tJDKqW3tRy8vG1XkEb8IA1I4dyNPd4NvSJ57eVr1jCilGL94OzHLvwHAtHRzTaYBSbdBQsWYIsyUK4cewE17y1JHXKRLGyRcjgZBOnyXkikviOTa064D0wfiuzKHpfDv22AU+HIlTMFMj5R8HYO1ihDQnFNm0+ShXPiZPNxzW4TZeKNVBUCPI4ye4Lnqj94dW5Lc0Udck4PSTLTIkSpXD5uwm7DTo8r+3j+J0g0/Qqah83jbpusc6aTPmLULxQFmxNnzP1+L+4z4XjV/Ay02Bhrva9VKfcMGDukJoCpX8kTyoLjHo9r+79zrBxx2kxcSa1c0e2iEYp0xDfm/wybgoP8/VkaucKprWt47dFBIo71qqfntU+ivH5RnaBmX2Pk/enyqRWvPDw0eNoA+HhGtK6FKFo7nSmPlxRg1at91O2rFzEmyz1adegLE7W6t81EObF9YvnufI4ACsLC3WWKNP8lOpSl9E3TcQUK2nzUa9Mvo/L7hl9uXX6HDce+2C0UAeQqBO/6wEnchYvTv4caUxdOsLfveDCgS0cuBdOjkxpMLdJQ+kqP5I5mSWW92bRZthdao8cy0+ls5hG7EdNu9H3IctGdedR0fGM71qehIxRSOjKLKF3d+K+aSuWladRVn+Bh34a7Gy0BAZZkaFgEQpld8bu0zlL1YFg6iTZxzfwc8/phLWcxFLX+qQMfMnVI2d5GKrFPHI6LpO1xpxspetSMnPk5OOKEZ8nlzhxyYMQrYK5GsBhQG+0ImXmglQp52IK0o3qALWT+9l/+goBDtlJ75SM/BUrUySjPdqXV5g+dT6U68+Y/7/4wKe9w/VBrzniNgbXe+X4a3mHBK1MFfxMnXA7/iuzqAMEz7j3YvCqi7jUGkTDvLYoNkmwtklJwZI5CDp/jKteepK6FOKHonlwsDQQ8vQs46e44VRrKEMbF/w8P+9esHfxKCY+q8nZZS3jtQpU/H63X95bAsUvG8keIhA3gdi+Ccft6H++l1HLhc2TWXDUltZd6pPLyYKk2XJgHxbAuzAF+2RJMT4+wqhhv5Bl6HT6FkzKw5uvSFcsNw7/xkRvccpR5FrV6rx+kft/DG2jBrlxOlnkTgHs6N4fr14zaF0gVZyX8DO8e8SvazdgW7QLzcukiQjO1WVhoqTts1Rofbm5dQbN5nmy4OhvVI1hxpnYUp7Q3MVH4uO+Bs7MbsKil3Xp26wsabOmJV3KpOgD/AnHGoekSQi8t5UJk3eTs/tQOuVLzqN73mQsnY9YBnLHkAwFn7uX2Lv5MCn7DKBWsphHN8eUfsPt2fTckpaOLRtQJod6RTWwU+cBjTm36ifpyztWcN66AnXL5SO5cpJdV7NRpWQOUiWN2uoYf614lYtRS1BgAIFBBuzTpIls5Q/n6M81WRTanL7dK5M2SUryZHMk+F0Aodjg5GBL8OMjjB04ndyD19I590uOvMlJjfxJ4tVHMfjpCWbsfEj9ek0pliWufRQNPNi/kd+33KT6gqmUVGN5NbBDnZfwcyuvi9uZM38Z5g1mM7B+XsIvbGFkx37crzQMt3n9KfS16vsn6EqoPz6BWizsHHG0V18SjXicXMWU5bdo3LU5GdOmI03atCTVBBKkt8DByQHd2xu4j53MlZJjWN0uEy8ePMQ8Y1HSxb0CE+p5nQO/TuNp3fX0LxD3uqMLfcvKXm357WFqRm2aTy3nZKY13GNdrk/Rc/fQUqau96R9j8akSJ6edM6psVcCCDZY4ujkQNiryywbPY27Vabj3jQZzx89wyZTIdLEwzzuOfj7PSVQ/FqSch4R+I8F1E7yu2Z2Ybu2AvUKZMY+jQsViiXnxtY5zDoURIvJ86jks4WBEw9Sc3APchkf4BFUkvrVs2L1nwWK/waaHp8Hj9Cmy0rqJJZxfhN/9/gcv63fSuEOUyif/kurOCvoA9/y6NFtLu9eydB1bxjmPolKaVKTJVOmv50T8d/I8ZfP6c/SZhW4WWECZVPZU7RMKXIkC2H7nFEc15Wkz8hOpLz+C6OW3uCHNq1xMXvFU31xmtTMHGc/NQ26kCD8vP2xSJeO5HFeSUfh3sLWrHNsS5tGtcj5t6OXFBStN6dWjWHSpmekzp2HNPYW8Pw4hlrLGdKoULwCgy+7JWAP4w2GV+pOaL1eVC6QnKTZy1Ixuw9rp87mYEgZxg1oQpI3vzO89xbKjZtPrmfHCKjWntrOlvFoNQdD+Dte+ISSPHkqHGKa3iCmpOs8OfLHNrZfz8mUqdX5UnjpfWENvbpPwKzDL0xonhuv/b8wZeYR8gyczLjOZRIw0CwBnuohRq2pv+myUylp9GN6LDIWplQeJ25smMHyO6noMXYURcL/YvbwpSjN+tPWOZgHL8woXa8SKePRMm4MD8bvzXPCkufmk6kp/zbhYW+PMriDKzeSNmLlop7k+NKIE0Mwh93HsPZWJhqXTYFZ1hKUzmHD+V+ns+pJVvqNGUzewOPMGO6OXedhNHPw5rG3DaVrlefDoP4EUibkMAkUE6Imx4jA/0EBRR/O9QNu7LlrQY6cBSj8Q2lcrIO4tX8xk7fcI1drN4YXecBva45gk9MZQ3hGatUqTRrHeNxJ/w/m++skSc+rW0fYvesNtYe0JX0cSEKeXmDbrr1cuvWMV4FGUmbOQoFiZalXoxrOCfmW+HUyEstZtBxbPJi/lPIUypebH8oUIInej6PLR7DhSRqaDBrNj1aX+H3DGQzO6XC0d6HKj0VJZRqO+m9vPmwZPB9jnaZU/7EASf+2U6SCEnCNuSPmcsFPwcpCbXtU+1fqqfnzdJqUzBRtHsV/O+Uxnl/xZOs4dzyd05A6Wyl+KJMPZ7vXbFm0kE1XUjDctRu5bDxYt3wD2swVyZQjG5XKFogY+ftvbz632HPgIOecOjGhduyTfL9PhjH0BQfWbOY2acmW1oqn1y7i61Scdu3rkcMpltnl/408KHoenN3GnuPPsXHJT8Uy5cnpFMiFrXOYekxL7a7TaJ/nNYfX/c49RxeyazTkrFGV3Klimpj+6ycw4NR0Wg79Fdv6c3DrV40Utl8oTKOWWye3cfiMJzY5CvFj+bJkT+LLqU0zmXbKkmY9J9Es2wsOrt3Mo1T5yK6BXNUrkzNFkni9THytnEqg+LUk5Twi8F8LqCOzDVp0OgUzCwss1LkC1XkQw714fOUAWwNbMbSaOdpwHeFosNRYYBPPdXP/6yz+e9fXE/D2Mc9e2ZO7oHMc+jcppiXkwrXRV28ws7DE2soqXp8Q/708RT+zPjwEnUGdR9ISCwtzteMehlfXOXX9ASH5GlAzgzm6cB16jQZzjQXWny418q8l1Jdrp1+RIntm0qex//LgGdPcc+Gmvp/vN7XXh5WN7Yf+vP9aUuNwYgV10Fg46hR8GnNLrNSWVY1CkJ8nJw7dIW+p4mTNlBRtWHjE53VrK6yijkCPwzUSuos+0ItnL14Q7lyMPJ+uehJz1Iteq65E48XTlwHYpkxPqmT22FqpfQzjPswpoemNepzBNC+lHswtsbZUuxcYMYY84Oz5a9yzaUyn0mbotVr0eg0aCw2W1moL7bdJo8+Ftazed5ts9bpRu1AW09ycX9rUPuVaXfT8GILu8Ne5W7xwbELrEhpTfgx6DWYWGiy+YX4+TbsEil8qTfm7CHzXAgbePb/DyWPXSN6wNWXjPynhd517SfzfCCg6nt6+wNVHekrWLIuzZRyaUf8F0Hj1CfwXrv+tTvn6xmFOvbCnbNkSOMc2Uf+3Soxp+vxvHep97czpeXPvMmeueZGlbr149Zf8qin5pG96wuuzntc3z3Hmjj8udeqQ/z/oixibiwSKX7XGyMlE4P+YgKLjna8XjzzNKVLQ+f9Y4iQ5/6mAMZy3r5/ja0hDjvQOH5eH+08T9b96cSPeDz0IsXMkbbqIUdOy/UMBRctbr9e8fWdL3lyp/uHJ/g8croTx+pUXvkFJyJvzWy+s+vf5l0Dx/0D9kCSIwL8q8F+Pxv5XMycn/0cCUjf+EV/cD1Zb8NTte2/Fi3uO//091QkpTYuM//uX+iZX+L+bHwkUv0kFkIuIgAiIgAiIgAiIwPcnIIHi91dmkmIREAEREAEREAER+CYCEih+E2a5iAiIgAiIgAiIgAh8fwISKH5/ZSYpFgEREAEREAEREIFvIiCB4jdhlouIgAiIgAiIgAiIwPcnIIHi91dmkmIREAEREAEREAER+CYCEih+E2a5iAiIgAiIgAiIgAh8fwISKH5/ZSYpFgEREAEREAEREIFvIiCB4jdhlouIgAiIgAiIgAiIwPcnIIHi91dmkmIREAEREAEREAER+CYCEih+E2a5iAiIgAiIgAiIgAh8fwISKH5/ZSYpFgEREAEREAEREIFvIiCB4jdhlouIgAiIgAiIgAiIwPcnIIHi91dmkmIREAEREAEREAER+CYCEih+E2a5iAiIgAiIgAiIgAh8fwISKH5/ZSYpFgEREAEREAEREIFvIiCB4jdhlouIgAiIgAiIgAiIwPcnIIHi91dmkmIREAEREAEREAER+CYCEih+E2a5iAiIgAiIgAiIgAh8fwISKH5/ZSYpFgEREAEREAEREIFvIiCB4jdhlouIgAiIgAiIgAiIwPcnIIHi91dmkmIREAEREAEREAER+CYCEih+E2a5iAiIgAiIgAiIgAh8fwISKH5/ZSYpFgEREAEREAEREIFvIiCB4jdhlouIgAiIgAiIgAiIwPcn8E0CRaMhHP83PoTapiSdkxWa789JUiwCIiACIiACIiACiU7gi4GiYgjh+f1b3Pd3pHxxF2wszaIghfHgz9N4W2cif5GsOFibR/7NSIjPc66ff4xlrkLkSf6SlSNmcL6UK792yJbokCXDIiACIiACIiACIvA9CnwxUDRovTmwZCKTt5qxeMskCqdK8jGf4RfpX6Urt5xrMmHecEqnT0pEGBnEncO/0Hvwfkq4ujO24hvmdhvGnz8s5FD/XN+jk6RZBERABERABERABBKdwBcDRcWg5f6uufQZvYma6/YxuFDqj0gXJlC83VJ8khTBdfEvtC7hjKnBMeQFR1YMo/vGcEav+Z2mKa4xr8dITvwwnwN9JVBMdLVMMiwCIiACIiACIvBdCnwxUEQxEPRgL337juZRtdUcG1IkstVQx/lxdWh3yoKUPiFUGTiL4S2KYmtpRqjnVdxHDWZDUE1++2MwaX2uMK/HKE78sIB9PTPi7/cOrUGDhY09TkmTYBGl06Ji1BEc8I7gMB2KxgJbh6Q42FlFXFMxog0JINhohZ25Hv+AYDRWSUmRzA5zDRjCg3kXEITWoGBmaYujo0PEp3LFiC4sCP9gPXaOyUhiJb0kv8vaKokWAREQAREQARH4pgJfDhRR0AU+xK1fX2Y8r8G5gwNxVqO20OuMrt2CaxVdKXpvBU+ztWbS6DZksNXgeWMnIwa5EVZ9Apt+LkGwGih2H8nxkqOZWsCD7YfO8fKNH6EpS9ChVycq53DC0kyN9AK4f/EI23Yc5b5XEAbFkkxFKtOkSU3yOjthqfPj6h9zWfckPeVdrDl/4SY22RvTt1NpbAJfcn7XenacuY9/uBFNkvSUrtmEBj8WIJVlIHePrGL8uofUGTaXNgWsvimyXEwEREAEREAEREAEvkeBOASKYNQFc2PVQBpMecasM/tp4gzaq0up3nwNtd3WUfD2NOacTcnwyWOpkk7h5r5ZDFp8g2quy/i5dDKCfa8yr9MAthhzUD5DBvKWL4jdqzOsXLOZsOLjWDu3NdmTWuF72o2B07cRkrY01X/IhcWbGxzafQy7yv0Z0bsFOWy8ODKzI523GGnesydNimchiVMGcqbTc9xtMnN2PiZH9fqUzmDFy0uH2HhBT7OfR9OvdkYeH1zBqDUPaDhqEe0LSaD4PVZWSbMIiIAIiIAIiMC3FYhToIiiI+zmGsrVd6XIggesqGvDdbc+NHW3wX37OFLfXc+gGaepNWwKvcokYf+c/iy5kpZx7uMokczGFCjObteP5c/z8MfpxRS3sUCj6Lk7pzqNVzgx9cAa6mV4zuzmndiVoguLp3WkQHJzMBp4tGc2w5c/oP2ESdTLZ8bR2a1pdTQXqxfNoGaeJGhQ8Lu4mZ5DlpJn5GqG/5gBS/VQ/T2W9+3EH8ZmzJg+kKJJ9Wh1RiysrE2fqTXy9fnb1jS5mgiIgAiIgAiIwHcnELdAESPa4JtMrd6IzQUW8Nf8Avw+oCduNt3YNaE+ji+PMm7oLMKqj2Rqy3QsHTSEiymasmJiM5zszEyfnud0HcGO/GO5OKH8R6QzI8jb+U8GrttNW+vdtO00Fq+8bWlaLif2ZjowsybI40/2HvWk1oQZ9CybgpNzOjL2YXnc5owin5N6qjAubZxF7yknqdSiLnnT26NX+z9aB3Jz61oOh1Zh4twx1Mll+90VjiRYBERABERABERABP5LgTgGiqDoArkyqyFNN+VkuXsjNo2cRrKeixhdLy/2QXdY5jqUDcYmzGztwKqZv2DXaDwTm5fAzsIY0Uexxyj++mEu+/rm+Zjfs2PJ1eF3OrmdorfDNjr2mMmjFEUomjUllkYjigKYWWDvlInKbVtQLZslJ+b0YPqryiyd0Y9sptgvhDPrpjNoyiHSFcmHs70lBtOBGszNFGzyVKVNo1oUzmj3XzrLtUVABERABERABETguxOIc6Cofn4Ovb6Qio1XU659C26e8GDIwqlUy5MaM2Mgfy5xZeBOSxoW8+X+fSP1ho/jpxIZMVJnAm8AACAASURBVOdjoPjZ9DhnxpKr02Y6u52kb6rDdGjvinnz2bi2q0AacyNquKduGjNzrJMkwcbwlmNzujPjVRWWzOxHNhv1r+Fc+X0OvebfZdBsV6rlThYRYJo2BY2lDXZ2tlip35tlEwEREAEREAEREAERiLNA3ANFFPThNxhZoio7bLOQPW9z5szoR+5UlqaLeZ9YQt9xa7jxKpB01Xsx/efOFMmgNvl9OVDstPQk/cq9xa1jJxa9qczi+QOokis5GIwYDTr0mGOt9i3UvuboZ4Ei+F7dzKBeYzC0XcKkthXJYKOgqFPihOvQWFlhbWWBURtKcJgRK7sk2ESdjyfOVLKjCIiACIiACIiACCQugXgEiqBOvn1kZH6aLA2n4Zj5zOxfn9RWkUv6vTnNhMEDmbHjOc2nrmF8x2pkMH3tjQwUu4/iRMVPJtxWPz133ESnJX/R74eUmN36lYFj5nHSOwOVK5UmsxN4v/AgKHMderZtQB6Ht5GBYmWWzOwf2aKoXsOfYytmM3HGRoILVqVmkezYGX24d8GbYu2606Z+Zu6tH0uDKVfps+oYo8qbmiJlEwEREAEREAEREAER+BuBeAWK6sTVvte2MHfbS8rWa0iVYpmw+jB82J/Lu7ex76wfpVo0o3z+DESEYwraEE9ObN7Ho+wN6VY+1cfkPN3P9I3XKNGkDxWyJTGt6hLw6Cx7Dxzn5vN36DXmJM2Yl0qVq1E0RyqsjYE8+PMPjgfkoHHdCqSIaMyM2AyBPDy1n50nrvM22IC5lT2Z85enauUyZE0azvMrB1l97BXlmnanclYLqRQiIAIiIAIiIAIiIAJfEIhfoBgZ+EWMFdHwaa8/5UPnQM1n089E/O3T/1c/EX96rsj/i5rwqNdSFFPfRU1M89tE/i36oZGpfP+3GNIttUQEREAEREAEREAEROBzgQQEisIoAiIgAiIgAiIgAiKQGAQkUEwMpSx5FAEREAEREAEREIEECEigmAA0OUQEREAEREAEREAEEoOABIqJoZQljyIgAiIgAiIgAiKQAAEJFBOAJoeIgAiIgAiIgAiIQGIQkEAxMZSy5FEEREAEREAEREAEEiAggWIC0OQQERABERABERABEUgMAhIoJoZSljyKgAiIgAiIgAiIQAIEJFBMAJocIgIiIAIiIAIiIAKJQUACxcRQypJHERABERABERABEUiAgASKCUCTQ0RABERABERABEQgMQhIoJgYSlnyKAIiIAIiIAIiIAIJEJBAMQFocogIiIAIiIAIiIAIJAYBCRQTQylLHkVABERABERABEQgAQISKCYATQ4RAREQAREQAREQgcQgIIFiYihlyaMIiIAIiIAIiIAIJEBAAsUEoMkhIiACIiACIiACIpAYBCRQTAylLHkUAREQAREQAREQgQQISKCYADQ5RAREQAREQAREQAQSg4AEiomhlCWPIiACIiACIiACIpAAAQkUE4Amh4iACIiACIiACIhAYhD4bgNFg06LTm9AQYMGUFBAUTCztMHKwgyN+p/q/ysGdFodBqO6J2BmjoWlFRZmH4tXMerRanUoyoczYWZugaWlJWaR51GvYNTrCdfqUU8e8d+mq0bZ1P9X/88MC0tLLKNeJDHUJsmjCIiACIiACIjA/5TAdxkoKsZg7v15mJN3X6JXNGgUI0Y1kDMoZCzdkMqFM+BgFVFO4e8ecfLIKe6/DDQFfRq7tBQuU5HiuVNgHlmUQZ63OXb8OE/8NJgrenQGKzIWLEWlCgVJbhkZUer88bj8J3vPPMPCysIUKOqNCmZRAk4MOnQGM8ytU1Ko/A+Uzp8Oy/cR6/9UtZHMiIAIiIAIiIAIJAaB7zRQ9OPSti3svfGQKzt/45x/coqVK0+BjCnJU7MjDUplw9E6ovhC/x97ZwGWVdb97fuhu7vE7u4eu7u7sUUR7MBWREQUTAxQx24dx85RsTtAFKS766nvegAL8R2D9//N+J5zzXWN+py9z9733uec31l7rbXjnnDq8DEuXLnE0fOPkClb0nmiK65TumChmmcVTHlzlyPHT3D++C7uiatQv1oV6rVoR5e2tTB+LxQzo3l45Sg7T1zn/u0bvIhSpmrHntQ0yTVk5h056USHveDa1XR6zF3KzKEN0FL+YJL8sfkkySDk6U2eJBlQo3Y1LLU+VaY/VmVur9MjeXrrNnF61ahXyw6NH6/q85JyGdlxgVy48RKjWh2oY6n8wbr7LZeQZsTx5MZlHoRlYtGwB/W0gwh4mk3VetUw01H5lioKOUdOTsRdDlwMoVLLTlQyU/vEUvyDVf4LismkCTy5cY8YFTvq1yiFtlrRzJ1/Qdc/a6IsNZJ7N2/wKCSR0g3bUrOMNVrvvxL/bZ0p0F5ZRgKv7gfwWmJN/caV0ctJJujubV5qVKJLTYt/ee+E5gsEBAL/BAL/SqGYK3RkMmTySJbUqYS3UjNWrd9An+rGKCspofTBipe3NCzPieXhn+sZuPQ6+uFXiK/qyNbVrjQspZk3BnI5MpmMK0512F9jMwv7VcVQRQmlj+vOeYvMMjlpkZeYO3YCe59Xwu/2Nprrfi6xkkJuMsfJDbt+83HpVR2VT+r4kQGXp4Syc/4gFj6ryprNHrS3+wGxpIDwfik+/4/ykHMsHT2RgPIL2by6F2Y/0rjCysjERF1aQ7fRHpT2fM329prfLsrkadw5uJoZy/aSqW9J6WG+uFj6McEtjoUbltK4hNb3tVKh4BVzQS4l+pADtgP24Xw8lHnNDdH4VwiFTwbu+3qe992Sdos5/SdxWa8f+7zGUswo/+vpB+r6Rxf5T5iyY7m6zx2XNefIVhLR1cmL0Z0bYPGdU+nr/ReTHP6EY4evEpudhVgKysoaGNpVpmP3Zpj/l+dZZsgtVk0ayfasLuw9tpjyyS/Y5DQeb/PZBK5q/o8eNqFxAgGBwL+DwL9WKOb6B4ZsonaVOYiaj2CNzwLqW6q/10Of0c+Kes4fu324YT2ISueH4XhCD+dNPrh0qI7ae4Of5ClzGs+nwkYfulUxI19CfjGKIed8mOLsyuPSrtz0c8BYUyVXiORI5KiqqCBLCGSL71pEtacwomlxlH/SiCPPSeXlrbPcTTKlUbOGFNP53golJATd5fLzbCrWb0AZkzyhKX93CfdJU7lTdg7rlnfDtKjmq1xGetgDjp1/jFmzATS3U/lmi6Ik7CquEybwsNgYFk5sibqKKfLgjbisjmOO1wIaFP+Gt7ssh/jXD7h0M4k6g1pjm9db0gMvsO30G2p3G0Qta3V+1tBbVLi+qEeaSeiT29wOk1Gn9W/Y5lq9f+yQ5kRx69w1ItTL0KZhRXT/Her4mzubHvOKa1efYl6tCVVKGvPlnSEn8elZZjkvJqrGSOYOrYWZgQ0mhnpo/MD3VqENk6cRdHkDHXovR1axDY1tJES/CyUwXELDnqOZOm0YFQ2VC30ufXNH/8OJ4pRo7l+9SKC8OK3b1UUn4RVbZ01ls4kzD5Y1LYpLCHUIBAQC/+ME/r1CUS4h0r8PFacE0GjgCnyW98ZGIdq+fPMS/fImu7zPUHXmNMyur2T4xLWIu7px1mMoppr5n/xBfjR1iWe1zyiqW+p85cGezsWNc3BasB+bqX7sc2yO4pIyhR9kkJjalUujnh7CscO7UKo5hk5VTL7dmvZNE/FHLExZPDywBtdDGTgsnkO7EnnK478mFL+pH4WflPhwL87jPank6MukXhVQRsqT8+5M8/wOoShO5cXR1Uz2TmLpRQ9q/ER7/r8UzY7h3HY3PAN0WbBxPjWLSND8yMz5/9L/b76olFfnvJnl8Yh+C5fRvZZpIfeslKALO5noepZuC1bg0Mz6m2v/5hPl6by+spk+o3ZRa/FhljVTIj46gsC7fjjPvEz3zSeY184G1Z9cWfh6ez4f2cxYQSh+89gJJwoEBALfRKBIhaJEIiE5OZmsrKzciGEDAwPU1PKjSr6pOd9+klwWy/5BDRlzWU6fxUdYNbACWiqF+ANKM3h1+yjep5WZ7dINzYjLuDqOY/uTcnhe2c0ge53ci4YfdGFUUFs2j22Gtd5XrHYZL/GdN545v2fjvG0Djq0qooKUyOt++Dw1YsrgThiI0ggPDUbZrCKWBqo/b0mQSUhJiCIxRx1zCxM0FC8cuYT0xGjCIuPJkopQ1dTD0toSQ60vWSdHPOTsns14/iGmv9NomtvrYGxji1HSLTwmTeVuuXmsnt2EzHfhpIlBQ98UKytzdD6xZEmz0oiJCCcuLQtUtDAxt8LMUPsr1lI58sxk3kYkoGdbAmM1kGUl8S4sGT17O9RTIgmLUrRbGT1zO6xNdVCVZpOaGMqt03tYvfIkVUbPpV+zstiWKU7EZXdcPhOKcsQZKURHRpKYno1cWQMjUyssTHRQEeUQFxrItV0eLDkkZvquWZSTq2NubYOpcjLP3iZjVaoE+mpKH8YlOzma8Kh40rIloKSKfm5d+qjnfz9IM1OIik5C29wSjfQI3kQmI1FSw8CiGLbGX7M7gzgjkcjIRNSNTFBKiSQqRYxxiUpYaSuWhBOJiowkKVOCSEUDE0sbzA20EMmziH31kGPbPNjywggnV0cqa2thaWWBgY5ibOVkJkbwLjKBLIUFW9cUWxvLz8bq0ztILsshISqKDGUDLEz1UBVJSU2IJj5LEyszNWLevCEhS46KliF2dpboqH25TioTZ5EQGU4KuljbmfFx8VqGOC2at9HiXL56GkoorhcfHkpMUgZiVDEwt8HaVJf3t6Xi96ToCKLiUxDLlFDXNcHKxgLd/LkmlSQTHpqMnokeGTERxKaDVcmSGOuof2ItlJGdnkRE0FMuH/Ni/Xktxs6cTK2ydlSyN/7sAZIW8ZSrf+5mnvczOo4fR++6JTG1tcJIVwMlaTYJUe+ISsjIDYhT3ENmVjYYa39kkBUbTHS2LgY6EqLDo8nQtKKMjQlan/p65grFTfQZvZdGay7h2SbfFSX7D7oWn4DFkmOsHVwBVWU5Ce/ekK5mjL4olfDIGJRN7LG1MEZDnkVSdDiRCWlIUEFT34xiNiaoKe51cRrv3oWRmJaT3zcRIiURIpkUDVM7bIw1yYyPIUmqg42NERJBKH77S0Q4UyAgEPgmAkUmFOVyOY8ePeLQoUO8fv0aCwsLOnXqROPGjVH6LDT4m9r1NyfJkKX9xchK/TitYsW8PRcYUUOb93Enn70sMxO5c8yLvVnNcO3XGE15FMfdXHDxPInpxAtcc62OEskcc5nJ05ZOjG9ZCr2v+BXlBJ7GdXx/NgU2Zt5qJ1qUMUGek8iV7buJrtKP6UMa5znJv/eNK4quZkZxyNOF9a/L4LZ2JtU1RCSFBLDb2521h+6Rlp2DilFlpnh5MaFpmS8smEdd6jB972uiUpXQ1FJH16A8k7xXM7RUHBsmuXDVcjC96sSx03M3j6KSULVpzkIvD/rXtc7lKctJ5cFxX1as3sKNkGRQ1qd213HMdhpIDTu9L3sok5DxYBcdhm6g8+HrTC6pROa9rfQavJcma6dh+Nfv+O8+y5N36RRrM5k1K6bQxDCCP7yn0HfVTVRlUpQ1dDA0rYrrxRNUfbQC50+EojQjnpsHvFi5YT/3w9JRZCuq1Gkyy+aNpKZVLFtmOrNk4wWSFS9cXU3U7eozY7EHo1W3oNFyP+sf3mFgaU1URZAZ/ZID3q6sO3iLyJRsJBIRJZsPxtllIu2qWKKmJCfhzl4cp2yj0vRxmF3xZ+3Bm4QkyanUey4bloyknFFeBPznh5ywK9uZOM4N7Z5TqBp+hMMvMujmdYkpZaK4uH0l7r5HeRmXg1iuRu3+s1ni3I+yeiG49x/L5gv3SFBSQ1NTDcNSXVi6bCad61uRFfGArasW43P0Dqk5UpRNqzFl0QoGt6yIwQcfio8tEWe9Zu0kZ/4yHsSa2d2w1Ezg+JoZrL5hyYyeluzw8eLaq0Sy9Ssxxc2DKR2roFHgGykr+gVbZ41iQ0gtfE6uptF7pShJ4fGuofTZosuqbZtoV0pE6I29LF3gybnncaRnK2HfegTzZo6mRVlT1JRkRN07weoVHhy5FUxathgV89pMX7OOEY3tUFeSkxy1l0GtN1FtaBcyrh3m0msdJm3dQM8aNmh9gJxF6IPjzBk8h9PvIlDTt8HIRA/dxtO5trr7Z2NxcW4dJuwMJjxRjoaWChqqTVjgv5Q+dS14d20fnqs9OfEgEZlIjkjLhjYTFjNvaDNsdfPMuPeW1MPxel16dVTlzO9/EF55Ojvn9KKi1ScfCO+F4ph9NF57hdUtVUGSTkSAJ71HnGWY/zGG1tRDWZTG+q71OGzWn+7moRw8+ifmA1cxd0xbNJ4exWvxSo4+iSZLAppl2uK6ahn9a5ghCr/OVOcZHLgekutdLBLJyEhOJCkjh5auB1g3sBgXl81me1pHdvuPwUQQikXx1BXqEAgIBD4hUGRCMTAwkG3bthEcHJxrRVRYFw0NDRk2bBi1atUqWugyKem3PKnaexmYD2f3uSXUMlAtxEcJspIiOLXRh5T6PenfuBqqcjHhf23BYeJCbqQ059idnTRWesBsp5PUm+5AuzJmFL7aJ+H1ufVMGjSVu3pVqFLKGg0VMYkRgTwKL4b7rg0Mb1bqQ8odRYdlOdmI5cqoqql8dQlaEZQjF4k+CcApgCormmPec/ANLsXCVc6Uzwpj38KJeD4yZvKcadTUjePpnaeo1G5L5+r2X1wnJfIRZ/dswOOkhP6Tx9G6lB761lYYJ91incs4PJ7b0qpXe/q3aIh+znM2z1vKXev++HrOoIqZCq/+XMHMhccx7zqGgW3qoPLmNJ4bjpBaYQCblg7HUr2ATJJLyHy0ly4OvnT+/RzjSiiR/XAnQ8f5EGqkS50WQ+nTqBQpT46zfOYa1IZ4sXp6H+wlEfx1yp9Vy05SbYIrA1tUwLKMLVGXPrcoxrz8iw3LN0HdjrRvVIPku9tx2/gHWs1msXtJZzJDnnNlpweLD2fj/Pt8qshUMbOywuTpKkzaH8Ez4BJ9SmqhlHiLZYMHcDSrCl0GOdCxphXpgTfY47+DW6L6LFs2mxZlDEm8fxAnp+UEGtpQpdFwHFpY8e7mfhYv8kN/5gn8R9TAXKOgVJQTfuN3pg0ay8sajiyfOZrfqlvnzquoh+fwWrsRnWaj6FDJmqiAbSzwukqdSW4sHd6QrMB7HNnqzqaXxkx2nUoNHTUsrMxQT3vG1hkO+ETUx2liP2rYwV3/lfheTWPIKj+G1rfIs0B9coizgtngMpsAo364uXTCXDORP9bNYvGm+5hXaESvKcOpKAvl6OYFeN0tx74TXjSz1/v8PpKm8vDkOsYuu0zz2TtZ1NEkN1dodtwTvHq34mbDLWxc1B7l+5vo13Udxn1GM7R7c8xSA9i6biW39YbhsWg0da3C+d3Lh1Ov7eg9oDk2ojCOe8zicEpjVq5fTatSGiRHH2R4nVnE/zaSZYvGU99O+yvPDTkZb84xb8gEZD38WeFYh8JcOdOinnP1j23MXfucTuMn0L9+BYwttQi94MPMBTuQNBnB2J5tKaWbzOOTe9h0/DJW3d3xcuqAqTo8WNmEPtuU6TBlGrMGt8bkvZn5sy/RdF5f3ULvIRuxGTyXEdXUSIsK5PiBk6h3XIGnY30McsclDd/ejfAMs2fQuFlM6lMLDVUlMqICObp+IafSqtO3bzusku/j572anaGNOf+XB1VE6YS/i8i1notEInISnuG3xovrev3YsGIU1ZVe4Ld4Absy2rF18wiMBaFYtO8aoTaBgECAIhOKFy5cwN/fH6lU+gGr4s9t27Zl0KBBRYpaLs3gwcZxdHY9jHGPXZxd0w7TQpbNFBdNDrvCBp+71OjZkxY1bFFCTk7ic7wdR7H4xCvauV1jTZ3HzD4ciePokZS3+MpyojicC1vmMNj5FLUnzmfG8I6YK+cQfv8o6469wmHOSpqV+dTClsHjXZs5k1mRHt0bY19YxGlOGiGBT0nVq0gl27wl8C+OAkKxVOpL1k2ZyVlRUzaun4S9phJKioiZ/ADfLyvIJGCXO/MPZTHBbQEdSr4PZrnCmil98WUU3p6zaGKjMBVJeHxkEcM8w1mxfiUtykvx6tGG62Uns8K5OxZayojk6dw54sbC3Yn0W+nD0HIFZPVXhOIQh/lEdd7GnqmNsVIIK9k7dk8cjNubhrivnk7LsrpE3tjKlAn+NJi7kfFdy6CMhCfnV33mo5iRmkhiohRTC/1cwSJOfse+RY7sjKnOyl2LqC5J5sn+ZYzfmMmaq2uopgAil8HN5Zh0eC8UlXjhPZFuXm9x2rKFYY2LoZmrseREPzrEvNnryWi7EP/x9Um8fwAn5wUkdljHEaff8vDmBOLedxALRaO4v3EgJU0KRhMrhOJunHtPQ2P8FjxmtMMwf2DSkxJIzs7E2NgcJZkUWdxz1s4azR9mY1nvOpQyShH8sXEx7ndNcdu2gJoKC7U4icBTy+k04RJjfz/FmNpauUZraXY8XqP6Elp3JosntcFY9XNTeGFC8dS62czZmszi/V50LGOS26rYC550G+lHix0nmdbIEu0Cujct6DIezrO4bDMO/zUDsCKd6OtedBxyiqGHTjC+SjKbBnRiZc5Yjm4eQkkdxcgok31/NQ4L/qKBixsODY2Ij89GWdsCEy1F1lMxoTe3MG7iH/R292F4S3tSow/St+5q6ixaw4z+NdH8asRRJkEXfBgz/ijNPPYwu53VV54vYp6e3sDYxXcYtHgJo36zJu1tAOtmzeG0Xjc2e4yj9Pv4KHkmd3ZOYoKvmHkb1tK+nC4PVjZi2PmyLFjjSYcyuoUHQMnTCb7mS88+K5FWbUE9KyWS4yKIStGiRstWdO07kIYlFGXT2NKzCfvozJK1U6llqZtr/czJTCc+NgZtCys0RUrIxemEHHelxbQLOP3xiMkVPnZNlp3CpW0L8L6YRd8Zs+iq+PiIfSwIxSJ9uwiVCQQEAgUJFJlQvHbtGn5+fmRmZuZ++SqWohVLzh07dqRXr15FSF6ONCeEraMdmH3wBm23BLO5h+kHn7KCF4q+7sOamxr06NWPmnZ5IlAuziDkiCv1xmxBv2ovnAdUIjzDDIcRvbD6Sp5CWfQddiwYwIS9xrisWo3L0LpoIyfu+RX2nbhFMwcXyut/+obN5uE2H24ZNaRjy5pYfeL79L6NmREP8VqyALVua5nS8iuO9gWEYiVRKrcPrGHuysNkFa9Ey87DGNStEfYGn/pxfUohI1cozjuYxYQVC+lY+mPU86pJjlwrNQtvtz5Y5zZdRvA1PxxdjjN47Vp61gplePHe3NCzxd5KPy9huUhOZsI7nmeXZfyy9cxpUyCxTqFC0Z+BI3ZSf9NWxtewzo8oz+HG8mHM+UOHyR4L6VjTjIjrvjhN3EmDeRuZ0LVsoUIRhc9mxEtuXL/BzVt3eRP5jucPn0HZ7njudae+PInH+5YxfpNCKHpRvVChmMq+0cOYHd2Og+sdqGn1UegpImm3zJrJEeWO/LlxKOn3DzJtxnaqLlzLhLrF88GmcnJiL4bfrMupoy7UsCoY/CQn/PpOpo73oNjMfSzvU/rDkqhcKiY+7AlXLtzi4cO/CIqK58nt22i0W8LWJaOoqKawgC9i1V1TVvgupJbCNTEtghvrh9F0yTNq1qmGkYpiR6I8ARz9OhC7LtNxdx1OSe3PRXuhQtFnHmsCSrHBczwljfPscLKX+2jXbzrmLpdY07MYhgXNcznhnN84m6m7RczYvpkeltGcWNCH6YEDuHBwDDbpVxneehSn0s2oaqub57sqUkKUFUNQrIiO0zfh2qsqGuJYngVc4cqtuzx5/pbo0Fc8jjLFedUaRrUvTXr0QYZ12kFbt/UMb2Lz2e5Jn93XOVFc95vOsI3pLNm1l15lvpaDRiEU1zN20R0GLlqEQ3Mbwh+eZP70Haj2XcT6oZ+oMGREPDrIxBGbaLV8PaNalOKx22+43G/Lcp/JVDfUKHTFgvyl596jdlN76RGWNdcgOz2Z+Kggfp8/npcV5rDcdSAldDLx7dOM08WnsHp6D2wN8+ecXE5WajTPAi5y6cojXoW9IiToFXdfpzDxUAhz637seexDfybP3k/FoQuY0KUaeqoi5IJQLMJ3i1CVQEAgUBiBIhOKUVFR7Nixg4cPH6KiopJrWbSzs8PBwYHixd+/YItgEOQyxO8uMGrUFA79pYXn42sMLqb6wWG+4BXubnfhkqw6PXr3Jz9uJVcQZScHMKdqJzaJ1bG0+o3hjuMYN6A+Ol/ZSSX2wTEWD+nHdq0+rFrtwch6BrmXkorFZGeLUdfR+mzZOfc9rrBk5foVFZZ0O4vwe1uZOuZPuu08Qp8yX0nMXUAoVtVQzt2WMDX8MQfXzsXD/zJxdr3YsGkxnatYFuIv93Wh+GV6HDlvrvszecpB+qxdR7+6bxlSdhzZXcbQq7bVx5yQchkSLVPKV6v1ub9WbqcLW3pWCMX9NN+xgREVrfKTe0sJWDWM2YdVGe+xmC51LL5BKGry8txyHEZ5E2VSnlrN2tCqvAlvLu7iYmY13HatpN43CcUkdo8YxsLM3hzyHExV84/KKCM2mB1zp7EvuxV/bHMgM1co7qbustWMrFEsf3ql8+fUngw9W40jp2ZR10a3wLRTCEV/nCZ7U3LOQZZ2scn7XZ7Ng6MrGD/ei7iSjWlWrwktquhy7fAGAqxGs2Vx4UJRnhrO1TXD6blDh7krh2EnlXzYOlIuV8KoREWqVy6OXgEn3UKXnn3m4/2gPN7uoylu+D7i/yBt+0xEb+JfbOhnT2HG79i7+5g1cQFGg7fh0i4ely6zYN41tnXXgrjLDGw7irAmsxhbLy9HZV4OejkiNR3sq9ahnPgGc53GsilAlwbtmtC0cTMsku7it+s2PRZ645AvFEd22027VZsYXNfkq0JRHv+MfQsdWPT6N/wOLabGV+PlvhSKYQ+OTujCzQAAIABJREFUMm/m7+gMWY5X35KfjJucqCfHcRq5mgYLNzCmdVmeuDVj1qNOrFw/ngq6hafeei8Uc4NZPC/i2TZ/RUIm4fWO/nQ7oI+7lzctS+SwtV9zzpafjcfk9ljrK+aclOgXZ1k6xok9b0VUb96F9rXKYJZynak+5xlzIJi5dd438RXTa3XjVeNZeC3oiW3+jgLvheLuzPb4bhouLD0XwStGqEIgIBD4nECRCUVFtREREVy8eJE3b95gbm7Ob7/9RunSpYuWuUxC1HVfRjjO47JkLPfuzqW06lfylEmD2DbbH3HVtvTrV59PX+dySQZvdw2g/vhzZJdshZf3GgY0tC3cl1CewqNT6xnReyHSLlPw8FjMb1/b9EAuITk2lAc3b/Ai3YoWretRwljzozVCLiMzKZz7f10n4PQGNt2xZPysqXRuUAEbY60vhd4XQjHXXJMbMKPYmjrp1VmWzJ7PteIzubysE+pfLNflCcVZe9OZuHIhXcrmWTIKT49TUChm41ynO4ndl+Hm2AYj9U8iHXL3u1aI4IIa6fuE4qzDqkz4VqFor8LmofU4ozuKhYuHUU5PDUlSCHuXTGJLaBmW71qVLxSXMG5jGiuvrKeeoslfLD3LuLnIgYG/K+G+y52u1S1yg1sUwiY+8BxLZngR0nAGB50akHD/INN/WCj6UHLOgQ9CUZIaxeaJbblQbBoe0/tgrSFCGv+CNTOHc0hrBJsXvReKrrjdNmHZlqXUV+iOrFju+k2l08pIPE4do08p9Y+7AeV/iBT2LfL9QvEGG/oVK1QokhHIvmUz8YuuzqAqqbjtl7Px0gpqieSQ8Yg57fpyuJIbZ93a5+4epFhRyD1y50kiV9bPxdE7lnGb1jGqgSkikZg3VzczZtxhOq/wYfR3CMXU4Bt4jBnBRct57NnRl6/vP1JQKNqR+OoqK52X8LjscHYs7YPR+28EeSZPTy9j7OIQpm70okslfR58r1BccxHPNu+T+Iu5t6YLI84XZ9U6T5rZZeNbQCjKs5O5uXU6riezGbN+Ld1stJCJMwg7voiGTvtw2P8mTyhmRnF4VndWPG+N+1Zn6lvpfPgoFYRi0b5ehNoEAgKBLwkUqVBUvBwUO5wo/q+woimWngu3pv3oUMjJyUrkht9ipszfQtrAw9xf1AQNNRWUC+Ypk2YQetOXuZ5PqTdsCiPalUHts7epDHHyLaY0bMOVkkNZt8aNxvYahUawSlLecHKjC/3n3qbd2GW4u/dDYSNSKSSbtiIB7qVzf4JSEmuX3KHN8jkMb1HmYwJvhUU0PYlXD66yZ6UzV+3m4TmxCfbWlhhoF2IaKSAUy8lieXDlPqk6VpQrZkxy+D12rFrD4wpTODqvA2pfCMVsHh1cx4RF52k6Yy6DahigYWqNedo9vL5IuF1QKNpyYXUflhyT0HLkaLrUKYOeqoyYkBDSVcyo2qAC+gUVyndaFL9dKC6kgb2cjYMbsU/WnqkTB1LNWsajc3+we/MOgsxb47HXnXqk8OrYcvov+Ys27hsZaq+Opq4BNsE+nwWzSF4eZMqIqbypMJCh/XpQr5Qh2ZGvOHfQl5PhJoyYMZ+elU2LViimROIztjWHNQYyb0oPSurk8OTk7/hu30p4XVd8FUvPmjGc3zEfx+3RDJu3mG7FNdEz0UUUdQXnoS68rTaBmcNbUcZCD5E4keCQWEzKN6CildYXHzlFKhTJIvDEOmZ7HCRJpodx3wX8PqZe/o2cxaPNE+nrGkTXeY50a1wFCx0V0mKDCE82onpNCx7tWojT6md0dF3OoHqmZEc+4KifH/7XsnB0X/sdFkU5cc/O4jpkKsFNPTjo3jI3Zc/HhEefPlsKCsViyNKiuLJjEfP8HlN5qBODG1fBXCOD0LuX2L17C8m1ZuHm1Btbbb5DKG6m90h/qs3dzYKm6qTGR/DqyW32+Lih1G4tK1y6YK2ViW/fzy2KsqxkrvlOY/beOLq5zKBbNWMSXgRwdvc6PM+GMf7QW+bWSOD27ysZsuIGveetpH8tczSU5LnPWC1DcwwlQexatICvWRTl4njunDlPpG1bOlRWRF//6LNXKCcQEAj8rxIoUqH434SYGR/E0ZXz2BuYTkzQIx4+C0O7cmsaV7SmSf9pjGlVEjVFwjZZJi8DDrFp0zHu3Q/gYXAGNjWb0rKHA4tHNUPr06AXaRaXPPux7mVr3DxHU6Kgf6LkFX7um/jj2mNeB97lXlAW1qWrU61mCSyq98RzUns0CwTRiNPTiQqPIPnRLoZtSmPWypl0qVpw1wgJsS9PM7/vDDSW3WBVG518y0shBHPT40xjY3AZlnvNoFzWW35fPA/fy6/R0NNBLlfDqkojho1zoHnZwpIOy8l4e4t1c2az7X4M5rb1GTF/Gt1t3rLGYVLeFn4ePfN3ZpETfGU7Y8fvZcDGjQxsUIzMmKcc8l6D758PyFLRQltNDQ1dO1r0HsGo/vXQLSgUZRIyH+6i3dCNdDl0DceSSmTd30avgXtotWcrYypb5y89S7i5vD/O+9WYss6N7vUtCb+6kQkO22m0aBtTepbP9VF8dGY5k93jWJC7hZ8mQWe9mbtsG89T1DEwM6VCpXpovD3F9ezqeOz3oqGqhJTga6yaPYO1t6FGvToMd5xOf8kWNJvvZ/2jAAaU1kJVlE1YwEm2rt/MmedJqGioIJbIsazUnL6DB9O+dkm0VMlNjzN5qh8NVvkwppZ9/gClc2pSR/r+UZPTF+bTwPbLpeewK9uYMM6TkgtPsqp73v4wSLN4cmod05fuJgpt9HWtqVmnHCkPDxJgNQ6/FWOprC8m8tGfLJ67hOPPs6nUqAtTJjvQooIewVd34r5uJ7fDxOhqa6GsoYZV5XaMHjuKBvaFCcXXrHN04YbxIDxndcUiNz3OLLzuVWCT1wRKGuUvPQfu5bcuE9BzusW2QSUw/spOf1kxt/CY5oTvLUtWHvahe7mP/qny9Dcc3+zNhj0XiVfSQVtDHSUNXep1GMO4/k3QTQzAZ/4c/O4lYGBmjW25alQorsZV/4t0WLKZCZ3Lkq5Ij9POn45rtzO8gelXl55zoh/j7zqROafFtOrRg5HDB9GogmkhPoQ5PD65lhHzAhiy3I3xrRSuAzIy499wde8WNu89w9scLbTV5UiUjanXeRDD+3SkvEXeB6MiPY7zw26s8XWkkm5hH5EKI3QaQVc20bHXUlItKlLeRERGSgLxUgNa9xvNiH5dqGKjg1Juepz6nK7oirdLZ2wMVHPdNOJeXMRrhTt7bsZiaWeFtV0FGlqG4brhAmOPhTNWZQ+Tx0zlcLAONSvaoKnI4SgHcWYa9ceuwbGVFqcXzcYvoxM7d4zGOOYFm5wn4GM2i1fuzZCFX2Oywwq0p25lYTOzfMv5f/NJLdQtEBAI/GoE/jVCUZqTTlTQCyIzQUlZOS+djEyKVKSCkVVJiplq51lU5FLSkiIJCYklWy7K/YKWykVoGFhRtphJAcujnOyUaGJTNbGw1v8yLY48jXdBocQmZ4KyCirKiktKc+tT1beiYnGzLy2ZuSudYfi6OHFCpx+rZnaj5OfbQSu2ciHw7Hq6T77FwuuH6Wb0H6aVTJIbRRmfrY6ltRkaSEiJjSAiOpFMiQxlNW1MrawxN9T5D9YCCSkx4byLiEWsZIBNcVuMNMTEvgsnQ8McWyuDD33PSUsgLDwRQ1vb/ATecnLSE4kIU6ToyMkNUtDQMcLcygKjwiygyJFlJhEcGo++fancNCOyzATehiaha2eXu+Vh3gK2nPSYUCKSwdTaCgMtVcRp8YSFJaBjYYeJgcInTOHoH0N4jAQLW0u01ZRQJICODQ8hOimdHDSwsLFFSxxPfJYqVvbWuRG7cpkkt+6giCREmnpYF7PHRB7Po6AkbMqVxlD9vf1JTnp8BOGRcbkJt0VqWphZ2WFupP3B51WakUR4RCLaVtYYf0hoLiMlPJiQFC1KlLJAu5AEnuL0BMLDYlGzKIFVrj9a3iHLySA6LJSY5AykaGFlb4tKegQJGGJnaZK3B7UiGXR0GKGRSSjpmlLMxgp9rbytIpOjQngXm5y7ZaRIRR1DcxusTPQKsSQrOOQQFxFBurIh1ub6qIoUcymKuEwNbK2NP5bJTiLobRjKJmWwNVL7qr+vJDuY7a4z2BvTkq3uw7E1/Dx4RiZJJSLkHXEpWcjkIlQ09LFQpGLS00BZJCUtIZJ3YbFkSpXQNrbCxkyNuJAYtCztMNFTRyZOIjQ4ET1FQnjtwvJT5kOUi0mPDycwNB65mgF2xWwx1i3MUVFOVkocoVEZGFlaYvLJOYqAtpiwUKKS0pGigrq+CbY2Vuh9ko8yMzqQiEx9rOxM0PxqLlgZ2WkJvHkdTpYiij03+4ASKjpG2NnZov8hfZSM+DeBpGiYY2Omj2q+aU8uE5MWF0loZBzZUlV0TSyw1pcSHBaPSfEKGIhjeBcWSYaE/NWaPAZymRR96zLYmqiRHh1JgkyfYnbGKEuyiY8IJ17FlHKW2sTf9meY+z1mrFtKfTPtn98A4Fd7Awr9EQgIBP6WwL9GKP5tT/5BJ2Q8P47L/ANUHOdMIzUZRqXt0FcTEx2VgZ6VFaaiWK5umcPoc3U5d3QwaU9fo2lqj51lQcvUP6hTQlP+5wmkPj2C6+xNaPd3ZXqvOl+k0fmfB/QPAyCXZfHooDvL7prhNW84ZoqPDeEQCAgEBALfSUAQit8J7O9PlxN61pc5e5/RpE1ttJWK07hJGeKubWHKoqu0nLWSqS00OeM9Fefgrhwbb87jGGVq122CvZHwIP97vsIZ/5cE5OJs0pLiSEiL5ca21fz+zBLHFbNpXlL4qPm/HIcfuZZclsKDE4d4JKpMn/Y186zVwiEQEAgIBL6TgCAUvxPYt5ye+uYOp28/JzVFh2rNGlChuAHxj6+wcdF+dNp0Y9iQhqTe+ZPtAVKaVzVC1642VYobFroF4bdcTzhHIPDfIiBLjuTW0XW4HbpFUoIxPVxcGdK+PLqC6PhvIS/CemVkZ2QgFeVtBynEsRQhWqEqgcD/EAFBKP43BluuyF6jcFZS+CvlOk7m/vfq0kWC4uU07NkCfRSRi4o8i0UdGf7f6JBQ5/8sgexkgu6e5fCNKIrVak27JqW/DGD6n4UjdFwgIBAQCPz6BASh+H80xpKYR5y59hy1cu1pWUFYtvs/wi5c5qcJKPIhfmqLKvj3n76AUIFAQCAgEBAI/IMJCELx/2hwxEkxxKRmomlVjPdZSf6PLi1cRiAgEBAICAQEAgIBgcAPERCE4g9hEwoJBAQCAgGBgEBAICAQ+PUJCELx1x9joYcCAYGAQEAgIBAQCAgEfoiAIBR/CJtQSCAgEBAICAQEAgIBgcCvT0AQir/+GAs9FAgIBAQCAgGBgEBAIPBDBASh+EPYhEICAYGAQEAgIBAQCAgEfn0CglD89cdY6KFAQCAgEBAICAQEAgKBHyIgCMUfwiYUEggIBAQCAgGBgEBAIPDrExCE4q8/xkIPBQICAYGAQEAgIBAQCPwQAUEo/hA2oZBAQCAgEBAICAQEAgKBX5+AIBR//TEWeigQEAgIBAQCAgGBgEDghwgIQvGHsAmFBAICAYGAQEAgIBAQCPz6BASh+OuPsdBDgYBAQCAgEBAICAQEAj9EQBCKP4RNKCQQEAgIBAQCAgGBgEDg1ycgCMVff4yFHgoEBAICAYGAQEAgIBD4IQKCUPwhbEIhgYBAQCAgEBAICAQEAr8+AUEo/vpjLPRQICAQEAgIBAQCAgGBwA8R+NcKRVlWGsnpOSAS5XZcLpeD/BMGIsVPeb+pamqjpaGGUt5fFSeTnZFKRpYEuUiE4p/zystR1tJHV1Plw7kySTZpaZmIpTKUPrmWSKSGjqE2qvn/9kP0hUICAYGAQEAgIBAQCAgE/sEE/pVCUS7L5PkpP/wvvyIzR4SKSIZMoRIVSjBfLMokEsQSOarqmlRq15+uTathopE3EtKceG787sfRh++QyZQQIUGiKChVonIPJ/o2sEVbLe/clJfn2X7gHMGxWSgrKSGTypDJVdE1r80o524UU1f5Bw+v0LRfioBibr//2PmlOvbjnRGQ/Dg7oaRAQCAgEPgWAv9SoZjG0zNHOPXwJefXu3EpzZZ2nTtTu4xxnlAUKcSghPTQu/xx/g7Fh3viObk3xXVzzYlIc+K4c+Qo5wODuLjBncs5JenSrROVrY2p0n4YbapaoJmv/1KDLnPoz/NcuXCKncceY1OtJR06NqW4dVX6DW6JhZryt3D+z+fIZaQHX2LTkWfU6jWahraqH62fP1V7Fn/t202CfXNaVLdDU1Xpp2r7tLBcLiUnK5PMbMnndSopo6GhiZoojgsb/Iko2ZEebcuh/cGcW2RN+ImKMnl55ignXhvQtXdTShpr/m1dMkkSN3f4ElaxHz3rWn6wLn/GRCYmMzMbkaoGGmoqRaPpJJlEPr7C6YdKdBjaCrO/benfnSBHmh3DX3uOcF+rASN6VUb774r87O/5cyVbroKmpgYqPyV2JSSHP+XUkfuoNexMj2pGP9s6obxAQCAgEBAI/AcC/0qh+KE/4sdMqtSUo2bt8Vi/jm6VDPhMCqXdYeXkxcRWG4nT6I5YqH5KQoY0/RajyrfiYrlBbPZeRcvSWoWikqW+4aS3I30XPafndE8853XAsCinlUxM1JmFNJ1wghH+13Gqq4XK92o6uYSslFTEGvroqr8vnMiSDi0IbL4Sj7FNMdIqQutnaiAnd/tyMCA+9+WfqwPlMpR07GnbvR8tKkQxt2EPnrRcx/YVHTD97g4VJeCCdcVwZNY4nM6VYNOumbQobfg3ok6OJCsYt3rVuD7gPEedaqOi/KXakUZdZaP/GdRrD6BPo3LoFAXuzDju7JjH6K2q+AasodpPY5GRnfQQ9/5j2WPsxEX/3pj8dJ1/U0F6KOf3beJEUmVGD+9DOf2fuWA2b2/uYmQfH/Tm/M6hUaV/pjKhrEBAICAQEAj8DYF/r1CUy5E/96RSg+UYtXXEy9uZasZqiORyJGIxqKiiohTGrhmbSC3fir5DmmDwmU7MIf2mK6Vb+VKp/2y8PCZQTrdwdZYSHICP0zDmP7XBeZUfizqbfy5If3aayeWIk8N5HpqMWYnymOsolsO/55AhznjBvmlLCevuxfTm760sSbh1a0dQs6WsGNkEQ60isH6+b1bcX6ycPAzv0BoM79EOWy0xMrkckaYp1es1pGoJDcJeBJGpZ09JG4OftCJ9D4tvOVdMQlgI71I0KF7CAj2Nv1N0CqH4htVNa3Ot758cdKyJSiEWUvErP4ZPWYtWt1UsG9QEI/VvacvfnJMZz71dC5mwQ5UNV92pUgRVyiTpRAaHkKhiQbkSRvxd73/6kokP2TBnBG4Rrdm+YSlNzH+mxmxCAvYyduBG9GbsYM/wUj9TmVBWICAQEAgIBH5VoahY+nzr3YIarm9oPnwNPos6Yq6uQlZSPPcv3ca4SQNKGaVweNlJVMvVp023Knz63paL03iyqhn13JLpOcUbz9ktMSx0eTSHt3cOM23oNG6ZNcNt63b62P/T5pWMzKSLONj3Q833Gb493tuI/ptC8QYrJw3isMEE1i+dTNXPVPg/jc/PtucbhWLgTkZOXYd215UsHtD4HysUf5bGd5dPfMSmeQ64R7bC12cRjX9q/VwQit/NXyggEBAICAR+gkCRWhQzMjKIjIwkNTUVTU1NLC0t0dPT+4nmfb2oXBrL1p61mXZXkyGr/2BpZzvUVZV4d28zG3arMGhKD8pZqxMflZob9ayv/6kfmpyc9EDWtmvI4ohiTF53grltLAr3CxSncPfoYoY6H8K4sRNb/cdRosh7JEee8JozARFUbNoYa01FJLaUxLcvePEmjKRMCagaUaJSJUpZ6vKpXVAuk5ES+YyAO8dxH+aB0vi1TKxvjHWF6lQupoJ79/a8/m0Bczrb8u5NCIlZMnRN7ShfoTSm7yN2cgN80gh//pCXEclIRGromtlTvUoJtL+2ZByXJxQP6o9nw9IpVCuwFq8IOHpz7SLRlvWpW8oQJZGEyDuXeaNRhnImMkKDXxOekIG6vhVlK5bD2kgrz0orl5GZGM6Ll0FEJmYiUlHHxLYsFUtZoaXwsZRkEPLqGbEqFpQ1h+CnL4hIzEFV35IyFStjZ/iJfUwmITEymBcvQ0jOliATKWNZqiYVSxiQGviAe8n61K5cHANNZZCLSYkM5vmrEBIyJIhUdbAqVZVKxfVR4geFoloOUcEvCE3TpaytBq+fPCIqTY6qrhkVq1bAUk/jo+VYlkXMm5e8ehtJSrYUkYohxcpVpKyphIefWBQryzIJe/aUEOViNCxv+rF8WgjXAkIwr1yD4qY6uVbCzMQwAl8GEpGYgUykjrFNWSpXtEVDlsm7e7cI1KpMy4rGH2azOCmMp8+CiEnOQIwSmgbWlCtfCnMDzdw5J8uKJ+hFENkGxbBRjePhkxAy5KroWZSiWnl7dD64PBS4QQoKReNsUsKecStURJXKNsS/ekJofCao6FKySg1Kmml9vBel2cSHveb563ckZ0rRM7NAPe02rqP9PrcoyqQkhz3hYWAkadly1HQtKF+1EpZ6qmRGBfHo0Tt0y1ejjK0hijg1cXIEzx89Q2ZZifKlLD77iCzy21uoUCAgEBAI/IsJFJlQTEtL4+LFi1y4cIGEhAR0dXWpW7cuHTt2xNCwSD36FK8sZJGn6FzfgZvZKjTpO5b6duqoyLN5c/Mk2U0X4jqoKZZ6X1lqlYvJeHOYDvXGEWLdHM/Dv9PJXrnQ5V5Jagyn3Ecx2S+cOuN82OFSp+hfKjIJOTc9sO20H/e7N+hfTInogEN4uG3mtYoJempiIoJk1BkxFZeR9T9bQpdLcwj5ay/e/n4c33cHpRptqWNjTL1BYxjR0pY1PVpy2ag5jUzFvEnIJCX8Ec8TjOgwchqzhjZCS0mEOD2agH0bWb3tDFlWpTCRJ/E2VomqvVxwHVkXw8KCYP5GKEpzQlnbqCLHu57hj2l1UVPJ4GCvGixT6kCPkqpEJ6eTHvOc+2+kVOs6gfkTOmJroEl27DP2bfFk540ELI11SIsLJTrTjlGLXOlXzx7VtDdsXjKTY9FGVC1pTMq7cOJjwgl8nUSpPs7MH9eLsorpJkkn6u4eFngeIShRCQszY1RyYlCrPIFFzk15sXQwfe/W5Ij3JOoU0yTuyVk2u3twLUEbQx01Et69IMq8I4tdnWhfyeDblp4LWBQNVOI5unom3pfk/FbBiLCMZNKjwngeEk/JHrPY7NQePUUwlCSGv47uZcvGA4Srm2BmpEVGrArl2g9iyoiqvNu1kPH5S88VxSH4OzuxWWM0l5e1/ujL+mID9btsofO6XUxoWRbVd7f4fcMqjjyVoqunRVZcNBLLlizymUY56Vu2DerC0hLevHVvlPv4Sn91Bq/1WznzKAUzC3PUsiMICs+m/G+DmDShB1WsDZBEXGXFzEXc16lCaQ0xKalJxIa+JSRVh77zfJjUqhiFxksVFIp60Tz2m04r92hGjG1M2svXpGWn8vLaHSy6TWOe0wgqm6sikucQevsAHuv28TAOrM0NURFJyJYl8uhaPFVn+7N7eCkU90DUzZ0sWuPP6xxLLPXFhAfFYFF3IFNnDMAm/gxzRy0gqJ4La2b3poJuGg8PuTPD8wa/zVjPhE5l0P4+X49/8SNfaLpAQCAgEPg+AkUmFO/fv4+fnx+xsbEoKSnl5iXU0tKiV69etGzZ8vta9XdnKyxFFxZRe8g6Mg2bMmRAfUw0xIRf2seh+zBq897cl+XXdCKSTMJPzaHW8O2Y15zL/hOOlFYt/E2RFvsYz9EObAnWZ7jXXuY1KeCJL5cjlUgRKaug9L0BKO/7qRCKtzwp1uUAKwOu0c8qDN+po1kV8RveCwZT1VyZlIh4JHpmFCtuSn6Wn/zSciTZqcSEn8Wp9jiU3S/j2dEUdR099DTTcW9bB/+sKox0mkm3eiVQSXzJ3tVz2BddhoWb19LCIIeQy5uZuvAwJZ1WMaauHTrSVN6c9aLz/OssPXGJgRW1UCvYt7gbrJ42hoXnxFQuZ58buCGTirGu0YqxTtOorh/KuqZVONH5D066KIRiJocGNGD2Q3U6Oy5mWNsaWKilc9F3Dl4XEum/1JtRdYqR9SaA/TdeYlauATXt9MmKesC6BfO4VtyRk4t6oZ8TynY3R+YdyKa/yzSGt6mIgVIMF33dmL4xmolbtjK1jRmJL04yuacLb+o4snxGZ0oaaKMsTSExS5dixXS5tXAI/e/W5OC6CdQppsLTC+e5+TCG6p1bYKOrSsyj4yxynIOoqy/rlrTH4Ft8FAsIRUPVeI6smsYi7ye0mL2IcZ1roi1J4rqfK9O2JrH07B662qsQdNIXJ6edWA2ewJj+TbHXVSM7OQ2xwqppqsTTTyyKFcWh7Jw2lS3qo7i49FOhuJGG3X3p5OXP+BZWvNo8n3FrntN14VKGN7ZBmhZPfKoc6wrl0c0JYduQrqwovpbXKxtBxnPcxzqwNvo3Vs3uS4My5qjK0gh7cpRlbqfR7+jECofm6CbeYKmzM36hlZm3aBJtq1giiX/Mptlz2Snpz+Xdo7FRWGcLHoUIxae7nak59T69nebgPLgplloyQq+vZfzMO/RY4Y1jhzLI0h6xbOQM7uo3w2VyLypa6JIV9YLjW5cx93Ac7ebuwn+4PRlhV3Ee4sjTegtZP7IupjpykkNPM2egF8bTtrGkb2liTyzEweMBnV096Wsbhsf05QTXnMqG6R0w/fug9797Igm/CwQEAgKBX5ZAkQnFM2fO4O/v/yHJtYJYTk4OrVq1YsSIEUUKUCZN5arbMAas+hP94Qe4uaAZWuqqKCccYnj/u/RZNZHmlS35LMj5kxZIsuI4u2gggzbeodL0C5x3rozyVxJnxz7bwYiRy3mr05q1h9xoqvPoKzwxAAAgAElEQVSpp6OEtwE7mDT1PMM83ela0+pLq2ROKi8e3uBlTgk61CtZaLQsBYVisXdsGz2GqdfKcPjkUn6z1yk0HcvHLknJSLrE2FIDUN34hC2f+CiubNeMgLKTcFvQj+L6GiBN4faRlcza/pYBHtvobxHFgSVzWBdehwu+Dqh+iOa9zSSrdqQsv8OGgSXQLiik426wymkE3u/qMKZvR+y0c5BKZehZlqB2gwaYqhUiFPvXYmVCLzw2T6WurWJJF8ICNjBl2TEqjPBkQccyCrWJVJHhSKSUmwAdEjixYgaO10pxa89UzAjHd6kDG6LasMFtKjVN8yiEX9mC41hPik/xZ2l/K+5vHEfHtersvLub1l8YtLO54jqYAR+EomZufky5SAklkTzvsimvOOTSnjXiUWzaMIPSfEMwS0GhqBLP4VUzWX7ekK2+c6hko8jPJCc1wJcOwzbTcsdZppWNYeuC8RzI6ca6RWOoUDAiuEAwy7cJRXte+M5jrMtpOvhsxblPDXSU8hLLKw5x+udCMe6yO91G/k4L32O4NLb+YF2TZadzfOkItj4uwRyfmVSVPWTZLBfuFHfG16UHZookAfIM/lrnyBA/XfZfXE5VHbUv74FChOITf0eaesbgte8CAyrmtSsz8TEze45Cc8Aq5gypT9a1RfRZEkC32T6MbFyMvJXtTN785ceYITswmunH7oEWPNszg54LY/F8sZuWn3zvnXSqxja5M54L+lLMMJM9kwewK64iDUwiOf2uFO675lP786+uIn1OCZUJBAQCAoFfgUCRCcXbt2/nWhQVy87KysrIZDLU1dXp0aMH7du3L0JWcqTpL1k9YgzLTz5n0IFAVrTSy7N4he1lygEdxvb5jTKWX8sOJyMr4SHLB41m7c1IRh8JZklj1a9GGb/e78SwuUdQrjud3RtHYPlZhKyctOjX3HsaQ+l69bDU+tKkmBkTyOaVSwmrOIrFg+qjVkhalS+FogpJD/Yxf/EW7oYkYV+/Kz17daZRtbKY6xUmfz8Xipt7mOT3Jwm3rm0JbLKQFWOa56XHkafz8Iw3jm53+G3eLqZXf4uvyxRW/RGMrZ0Rsg+720jISEyixMhtbB9fBx31ApYixdKz42AO6Ixl/ZLJVDWU5QksRHnjLy5EKPapzhqzuWyd35uSJnmCO+rlAabP2IFap6VsHl6Z7ORogu7/xZnL13gT8oYXUakkvH7Jy+JjCd7vjClhbFkyjdNqHVk1fwjF8puVeH8fM4c4Iu27neVjy3NxQX+cHw/h5vlRWHwx+woKRRWSIt/x7OYVLgc84EVwKHGJUQQ/e4ONIqJ+3Y8LxaOec/B9UYXVKxwoZaRorBxe7KdVf2csZj3Ap9EbfKbO5nUDR5aNb8dHj8H8Rn9FKPpqOHBhSauPS88vN9Komy8dcy2KZdGJv8eWlavYce4F6qXq0qVff1rVqkI5Gz0kBYRi8KHZ9Jr/joUn19LWTv+jD6wkk7vbpzL/ZDbD1rrRSeU5brOXEd1oMksGt8qz2MtlPNvlRK/pIax4tpt2+nn+jJ8dX1l67uQrYte5bTTMzW8KOSnRrOjbgogWS1ns2IHs3wcxcp8JEz3n0bqkSX69nwez7Opvzs0No+jvdhPTEjZofJi/InIyUrGtNYKFbiOoYKhFauBexg+fz+U0O8a5+zGjxZczowgfVEJVAgGBgEDglyBQZEIxOTmZ06dPc/nyZRR/1tbWpmbNmnTv3h1T03yzT1EgUySnDjyJw2hnjj+szO+v9tDWRCXvJSJOJTFLDV0ttcItd4pz5BKSH+9niMMcrobVYvfTvbTW/9qGF+kcmTGQ2fsCqT55PVvGNkLjU8taehTP3qVhU8Ie3YIJlmVZxMUkEfn8DzzXnMC69zQmtKuKiaHGl6l1vrAoqqAkE5OVHsvDyye5cPoS557HUbn/PGYNaoj5F2lXPgpFtU1P2NT9E6FYMD2OPJ1HZ7yZ5HabJnN3M6t6CFtmzGRHbBV8XFohFss+jpIItG0qUMlWH+WCEeHf4KP4xdKzQihazGfb3J6UyBeK0a8OMm36dlQ6LMP3/7F313FRZY0fxz8zwNAlIYiCgd3d3Y0tBhYGdoKFgd3Yndhrt2t379qtIAKCSCoxeX+vAXfF2P25u7i77nPmn+f1rHfOOfd9LjPfOefcc73zcGXbbKasu0yO0rWpUNgNJ1dL7m9byuwn5bixfTiOhLFyih/HjJswe7wXru+zefzNHxjlNQBVu7XM6FuYsxPbMuhmey6cHozr/xMUS5o/IWj6VFZfkajWojaFc+cmp3EcZ9eM5UfzrixdNupPjyjuDfRnzaPizJ3eE/cs+sZK8GgH9ToMwcHvNsuqhbBk+AjulhnMnEFNcfx0BcRnQfEFQSOGMV/XkRuBLT4sd7g5h0rtNtFs8Vb61c6HpUyHKimBZ7fOc/rYEU5cvMWr7G1ZNK0/RcxffjT1HLpvHC19bzP84Hpa5bH+dSReUqdwZcUgph03pO+iKdSU32Pm2BlEVx3EJK86WKWdjo4Hm4fRZsRzpj7YSiNr08+32/mNoNhsjSGbjq+iksWHoDjTsxZhNacyZXATlNu60X2LOf3nBdAgr8P7cvVBcSu9O63AZuR69EHx6vLeeC1V4792BO7ajBvAyzGzccE9fzYsjAx4fnoeIwN+IFhlRq0+MxjfqTRf3jk1Mz6oRBlCQAgIgf+GQKYFRT2HPiA+e/aMuLg4LCwsyJ07d+aGxLTvJQ0hxxbRc8gkruWczpP93jh+aZTuN/pH0qTyaN8Muo9cxMMC0wjZ581v3petvMaoDv3Y/NCMAUtXMqhqXtJzopqU6Lts2f4UC5MQYqxr0al1ad4PjKTXnBLF9WuPuXFkPluvQrU2XnjUrECJfI6fj15+ISj+cko6jYrUuHusnTyMpeE1WBA4jlrZPz05fVA8Ra9cHUmee5ld3XK9P+AL2+N8FBSD8C/7lkNzhjHpjCkrjy6m2CdT8PopYPmXtg3KtKC4Iz0oNp3Jai9TVo7uy5bUhiwa25M8NoYYpL5g1fg+jH5Ym0fb00cU9UHxR+MmzBnnhev74auMQXHWsLK82DCEWqPDCDiwhwEVPp3PzTiiOADXyAOMHLYIg9bTmNenDCYGBmheXWKqRxPOl/Bj6ZK/MqL4W0FxKA5+t1je+C27pg5m1rX8zF87mVouH+9qKKXE8PNHaxQj2D5qMKOul+D66dHvN8vWcWehN20nnqfb9oP41HTH8pfnkuuvreQY7h6cSRffa7Rbs4+RFRI+Coqqh0G0bD0Vqds6ggaXJ23gU3+Vp4SweHA/jkmNWTS9By6p15jpn1lB0Zdma4w+D4rtaxFWaxqTBjdGfmMGrYedpdbweQxvUjD9aUnKN1zZMp72k25QecxGgryyEXpwMjUHnaTrznOMKfHxqL7+ee8yuRxZxCn8B4wnvEg7KprcZ+NRCe+FM+lc9H1K/W98nouzEAJCQAhkukCmBsVMb90nBUo6LRr1G44tGcPwgO0ohh3nil8pjAzk6V8Gv9sAKW06XJ0ayb6ZfoxeeAS3gMsc7ZMr7b3yXwOSfo2a/lgJ9Y1FePabxgPDVgSuGUud/FnTn/eclMDdvRs47lKHwucD2JjQisCZrT9+woVWRbIqhd3jPdmprIv/wA64O2fB0uwLU8e/BMVmO5h17TyeTi/ZunA7IfbFqVPSBV5dZsmExYSV68+iud4U/KwIidSEW4wvUZF9hYYz17cm5g75qFzQgrktG/KkxlRm9Hy/4favQfEq1fw3ElBdzqtbOxnm5c+dXI3p19uTKi5mxEfc4tjWKDyWDaSYqeLz6cSvCYrVinKg+ZEPN7O0Lcl8509HFN8HxSYzWd3Dli3jfZi6T0avcSOo4WrIlaMLWLfzOvccu/D4l6A42Y8fTb4QFDv3R9VuHbPGNsAs/Drz+jVl/svqDB3cjdpFXCDqEtdeF6Rd+9LcndoNzxul2LVoIAUTjzN+2FB2G7Vg2nBPcusecmT9Bo5cf4pZhW4sXPK1I4pBeA9bjJnHLKZ0rIqt/q7nwLHvRxT1U88fRhTreg7FceQtlre0QXV7O737T+emUXGGDO5OJTczXt68T7SFO82bFiB0UwB91xux/NxsimpTebx9PM389uDacgT+nYsT/9Mejhw8y85zyYz4YQu9a2bl/vbN7L+SQqXWtckuj+LE+uksOWNDwL5dtHZ+wVovD6bnXshz/c0sukQuBHal+fzn1GzXi94tq5Al5QEHNixix2N3+k/1p0vV3MgizzPTfzrRVQf/iRHFnu/3UZxM1bS7nn1putqIzSc+GVHMEBStksOY6d2MoLC8dPPxoVEpCx6e3MXO42e5cxtK+uvves6D6s0dAns2ZsaLMgzp25t6ZVwwSwrj3Jn92DWYTJOiKRyc2JOA6wWYNnsc9cxuMt1vCLtturBx3iDy6sI4ungSxxz7s6BHZmxp/q0/CUX5QkAICIG/T+C7CYrqxCgurvVjycUkEt+EER6ZiJFzfvI7muNSoi49enmS3+a31xq+jbjDrnnTOfhSSXxUOK9eJ2LpVoxc2Z2o3XEoXatlTw+L6rfcOBbEip3XiI8KJvjlGzQKO7Jlz07hCo3oObQjuWX6582Gk2r5luU9fNB1Wse4Vnk+n3LTvWB2p168qj6GEV7VcPqtuyt1GtQ/r6JSz4OM3LcHD+doDs8PZNnhm2iNDDEwMiVn0Vp07t6BMrkzrCHLcJ1ImneEHApk6LQ9JFgVoGG/EQxumotVPTsRXHEkozuUf79fYDIPzq7Hf8lNyg+az4hKJuhUiTw5v48Vi1dzJc4YGzNjFKaW5CrekUEj6pHD+AtbB8XeYOm4QRyy6ML0kT0p/MmG21p1OOta1udU/Q2s9SmJkUEKh/vUZ5XjUOYOboxbFv1udhD9/BBTp27HoPZIZnsWIPLOUVbMWcSplyqszK0pUK0aDnH32B1ahH1Le2Mne8Xm+VM4a1wH/yGtcXk/gJRw7wDTB45F3XweYwfWxEbSkvj0FMuXrOLHu7EYKRQYGZtTrp0fA1sW4v6CIfS7U5RVU7pRwkHJ3aNrmb5wByEaa7K65aN8qeJYP1vPMVoxbcYAchPKilbN+Kn5epb1KPb5VLx+BO75TkZMWodZ/bH4tiyPtWEcR1fOZNvzQowf1ZGcNu+D4rODeA6Yip3PIWY0tMFc/pbnVw+zdPEGbr1SY2isQG6el0ade9K9rhNPd89jzA9GTN/lT0F9PfHPOLx2LlODfsbKyRGHvBXpVgnmLj5P46nz6VI5G2GH1jFn3iaCDSwxMTTGwjk/Tbt3x6NCXhSp4Wwf3JPFrhO5OLZcWj9ICS84uXcFq3+4TozGELRaLHKUoVWnzjSumBdrEznqqKssmbWY2HLejGhdFYu005F4sicAn+kv8D2+hNqWJp//qEh4wMbZviyPqsrsqb6Ut4zm4Y6pdNtqwMJtsynzfv5X/TaaJQPb8arSSEZ0q4OdXMeru0dZt3IDJ+6+wcjKlpwFS1G2kIITK89j3S+QJe1zpi0niX98ng3LF7D/RgJGFqYojE3Jkq8UPX364vx8J6MnHSa/zxgGtyqBtTyZB4cWMWHGaSoMmYVniVdMbDYYzfiTrGz1l3YD//s+uUVNQkAICIG/SeC7CYo6TSoxIXcJjgNDAwMMDORIWg0arYSJtX7bmOxYfnETt3RJTUoC4U8fE5Uqw9DQMO2LXr+di1ZmjEMOd1ztTdNHJHVq4l6HEhwehw55Wl36fRs1Kh0mNo7kdM+R/gWpTeHV6Wl4Bbxk+Or5VHZRYG6aYQNlfVkv19HR+xAVhgfQsaIbBiodVlnMvzDyKSElvebus3hcCuTDVqEj6U0kYRGvSVLrkBuZ4ZAtBy72Fr8/aqqO59mjYOJVCrK65cQliwmvg5+htHQmm53F+8fO6UhJjCYsKhkLZzec004mbTdl3rwMISwmCa0kw9DYHPvsOXGyNuaLM/uaJKLCQog3cCBHNkc+fTqgJKmIfvyARBt38jiaI5PpiA9+yGsjJ9ycbDE2TB//VafGExERg8w6GzmymIJOScyrUMKjE9FKhtjnyIWVLoHwdwry53LEEBVvXkXwVm6Ni3OWtM2T9S9tShwRIWFItm5kc7L6NbS/ex3Ci8g4UtMczXF2y4WjlYLkyBBCks3IlcMBc4UcbUoCr0Jf8PqdGgOzLGRzccI48TmRWjtcc+g3ZFbx+slDkmzdyeVo9sV+0CljeRkejdzaBecs+k2vNcRHvyIm1ZTsLnYY/wKpjONpcDiGWQvgamOYvrm0/rwjXhLxJhGlFozMs+CS3QV7MxnJcZGExcjIkc+FX35rqBMiefbiFUlqMLPLQR5HGaEv3mCZ3Q17C2N0SbFEhIUTk6RCkimwsHPCzc0xbWslSaci5sVzohTZKezyYepV3/6wFxHEvFOixQhrx+y4Omfhl/uYdOp3vH4VhcbcEWe7Dxu/p8aGERyuIluhnFjpR/c//fDSpvAmIpQojSVuObJhYaAmJSaC5zEycuZ1xfz9Jaj/e44KeYLaMhvODlYY6n+4aVXEvw4nPCqWFK0hNo7ZcLY1IDL0DfKsbuSy+7BgNyk6hBcRsaRoJGQGxlg5uuDqaIU67hUvIpU45nLF3jx9OF6dHEt4cATyLA5Iz/bgNfQnxh1fTu2/9Bzqv+lTW1QjBISAEPgbBb6boPg3mnxVVeqEEPb7tmRVlslMae2EkZULhdx1nN9zFVnespQr6My7PT3otNWJYZN645QaRlhKHupXdPqDz3H+quaIg4SAEPgTArrkN1zZNJGB12twbnmrT/Yo/RMFircIASEgBP5jAiIo/skOTXlzj+Wd6nGy3hraZU2mTI0G5Hc6T8ssnVB5zmb5nHYo9nWnzX5HujQogbGlNTkrNKaS88c3K/zJ6sXbhIAQyAQBTepbHlzYyw3rRnQtkyUTShRFCAEhIAT+WwIiKP7J/tQp3/Ls/FYuvC1AwRyOFCudH1MpgoOB27jzzoZWQz1xf3eLneciUJibky1faUrlsvvzT2/5k+0UbxMCQuDrBPRbMIon+X2dlThKCAiB/x0BERT/al9rJTIu4ot+coWLt1Kp3KQS9ib69VDi6+evEov3CwEhIASEgBAQAv+MgAiKmez+6MBSXubzpGoe6w83L2RyHaI4ISAEhIAQEAJCQAj8HQIiKGayskYNhr/1kOlMrksUJwSEgBAQAkJACAiBbykgguK31BVlCwEhIASEgBAQAkLgOxYQQfE77jzRdCEgBISAEBACQkAIfEsBERS/pa4oWwgIASEgBISAEBAC37GACIrfceeJpgsBISAEhIAQEAJC4FsKiKD4LXVF2UJACAgBISAEhIAQ+I4FRFD8jjtPNF0ICAEhIASEgBAQAt9SQATFb6kryhYCQkAICAEhIASEwHcsIILid9x5oulCQAgIASEgBISAEPiWAiIofktdUbYQEAJCQAgIASEgBL5jAREUv+POE00XAkJACAgBISAEhMC3FBBB8VvqirKFgBAQAkJACAgBIfAdC4ig+B13nmi6EBACQkAICAEhIAS+pYAIit9SV5QtBISAEBACQkAICIHvWEAExe+480TThYAQEAJCQAgIASHwLQVEUPyWuqJsISAEhIAQEAJCQAh8xwIiKH7HnSeaLgSEgBAQAkJACAiBbynwnQZFiZTYV0S8eYdW+n0eCRkKCzucHW0wMZK/P1hClxTDs/A4JEkCGZCxHAMjLGxssTY3w8TYCAO5/gDxEgJCQAgIASEgBITA/5bAdxkUJe1bftq+jFUn7vH6VRRJWkOsrK0wN1f82nsyjRplchIJyWrsinrgN6AdhbJbpv+7VkXczZ2MXH6MpNgoYt5KGNtYY21ugj5K6sOloUyOsUsxmrZpQ/XCzpj+kjEz6fqQdBqUKUokIxNMFAZpWfWvvyS0qlRUkgHGCgX/yXwr6VApU9HJjVAojNL6K1NekhZlqhLJQIGxwjCT+uOPtkyH6n0bFEaZdU380TaI44WAEBACQkAIfBD4LoMikoaEyDDC4yIJ8m7AouhiDB09jtblnJD/8hUv6dBqnhI0eQYnaMbyOQMp62aRfuaSDvXbKJ6ExfJwQw+8lqbQfaI/3esWwAgZkv7fnxxiypzl3NPWZnzgGFqUz4lxpl05GuKDzzGj12QSmk9nbv+ymGRG2Tolt4JGsiS0JKOHdcLNLNNiVGa0LlPKUEY9ZO30iTzJ3RHffk3ImlmnGHOVxZPmEVKoC8O9G2ReuX/grDXKJywbGUh8me70a1Ua20y5KP5AA8ShQkAICAEhIAQ+Efg+g2J62gP1Sdplbc3NUh7MXryYxvnMPhtFu7l5FEEvXenarRdFHQ0+On2dKomN3bPT/14Nli2aj2clV2S/Du2FsW1Qf0avv0TpMWuZ3bsRrlaZdf2oiH64n2HtJqJuv5S1oyr/iaCoQ61UoZHkGJso0kfWtKlcXuDFxKeVWTBjAHktMitFZdZ5//VyksLuMNd3CMFF+jDZrzXZPu7SP1/B69NM8BnJ49LDmOXXBpfMKvcPtEidepfJXqOJqerLBO8q2Jv+gTf/Zw6V0KqVKLUGacs+/pOj4v+ZvhInIgSEwP+CwPcbFCUdyov+FGi6GrfGw1i4cBBFbBRI6lTeRL0E6xw4WJpwb8tI9iUUoHW7ruSzzdilOtSJl+lboj6Xi3Rn0YIZVMtp8mHKMfUWc/oMYsaeh1Qfs4Y5vRviapU5E8S/tCJtfSSyDOH06y85KeEphwID2JvYiAlz2pPtfyQopgtJacsDMrU3RFD8+ovvmx6ZwKVlU5gVWo5ZI1qSx/a/92Pnm/KJwoWAEBACmSzw/QZFnZqfplSm9sJo6g9czlLf2tgqDHgX9ZC9K3fi2KY7NfM5E35uKw9krpQuVwmHjHPHkgrljdkUrbGQvJ3Hs2BOL/JkmKqNvbSe/v39OKIqy5R5s+lSMz9m/8Ao02/2d8xtNvl2Z5N8MOtWdsLxfyooZvJfgb44ERS/AeqfKTKGPWOGMCOmAZtmdSD3+2XFf6Yk8R4hIASEgBD46wKZGhRjY2M5f/48wcHBZM2alSpVqpA9e/a/3srPSpDQaZ8xs3Jt5kZZMnj1afxq2mMgg1ubR7Lsggs+o70o6mKNJukdarkRxqbGZMx5kjqZB/OrU272O7qMX8fcPuUxfj9ElfD8Oov8+7PkJ0t6jZtGP48S2JsaZup5JEXeY+vMQCLrjGdMo3Qj3cvTzJq7livPYlEa2FPFezg+dQtj8+EeHUBFxIMTzJ+yhBOXr/Ja5krBwq7YVujOhuG1+WmhFwHBVZndpyC7FqzgWrgS23zV6N6rC1Xz238YhdMm8uTkJgLXHyYk0QCnorXpNagv5R1/awQnij2jx3Mnf1eaF0pkX9B6zj9OIHuJRvTu6UmZPLYfytYl8/BoEEuDDvL8rYSRpSNlW/RjaKtSGKcVr+PFsQ3MOp5EyzYVeHN2I1tOPcPSvQY9Bw2mou1zti+ex8YLoVhnK4pX34HUK+mU1n+6hBfsX7OO8DwN6dKsHLLXwezZvIP4vPXpnieYybPWcfONEW5lWzByrCc5MvTau5+3MW3lXu6+TESydMdjwAjal3ch7ffBVwZF6V0kZ3csY/Xhm8S8k1Godkd6dG1GviwKdA/3MG39USxr9KN7rSJYGUkkh19n7epNRBfty4QW+dJaowy/yab1q9l/JQS1ToFrJQ/69WhDPqtnTPYaQ2L1gfQoEsXKZUE8SbYif+Xm9OvlgbvN+7loKZmHh9axZNMRgt+CWfYydB3Yh/oFHdOWIOgir7Nh2R7iS7alvtsLNi0O4udILa5lm9J3QFeK/jqyLpEQdottS5dx7G4YKYZ5aO3dHpN7u3iWtQHeHWu/X6up5W3IRdYuWsnxB7EY2uenfmcfetdx/82/CfWdDYxbEkqFThWIPr2VzY8c6OIznC7lzIm8dZSla7ZzLTgB0yy5qNu5L161CqJQxvHD9M4s2v2A5yo7CudxJFvu8vT07UdFtyxICc/Zv2EZ20/eI8nAlhKNutCrfV1ymGXqn6YoTAgIASEgBDIIZFpQjImJYfv27Vy+fBmdTodcLidfvnx07doVFxeXzEWXdOiebaV87aE8ik/B0s4Zq/QEgjLlHSV6r2Rh39q4WP92uFOnvGStRxmG/aygRO0WVM9ni2Hicy7eDyEuyZRyjTvRuXVDSuWyR2GYqZOcae1MeH6Gie26Edz9ELv75EX7ZDv1Wo8hNX9r2lVzJurqXs5lH87aEY3IkyVjeNOQEPWIk9t3sHvzWn5S1MCzY1WcXMvTta471xZ4MWR3DArb7FSpXAo77UsOrwviXfVhLJwyjDIOBkgp4ZzeOJkeUy5RpEFLauTT8dPOzVzVFWXmzh/wyPalsBjM3Lot2CnLhaOpDfkrlcU18SqbD55GXW4E6wO6UcDJDK3yNT8uCWBc4FEU9TvRoYgN4bd+ZM/Jn3HqvZXNA6riZK7jzppxeK87g52VG0VL5MNSG86RteuR1+tJZWuJYJ0DNfJaceXAAk6bd2DLvJFUyG2F9vUdFgwfxZNSvZk6uCny0DssmODH/peGVLAwxbRiDWwijrNq9yNy1A9g7/KWWKGDW0sp2jKArDV607SkCbf27eToLVNG7wyiS5VcWET//2sUJU0K+6Z4MOow1KvXgEL2r9i96iCmVfoQMNmbgpbxrBjozf7XZZixYDhFnBVcWj+JsZui8F06jQZ57Yi9/yOThvVnb3Qx2nvWxEkby+N4I5p17E/NPC+Z1mkAJ+MsyWplR/661TC+v5fNpx+TtfkMjk9pjEyn4fHuAHr478HFoytVbRI5d2QnZ1LqsnXLOKrntEF6fphxQ+dzLAFyFslBwWz5MYy5weaVJykycArTxniTx0RCGX+LgNbt2KcuSouG1XDWBbNv/xHuP42nWM9pzJ/UlfsU6SEAACAASURBVNxyFaHX9zGubwC3rUvRslFZVA8OsO5SLNV9d7LeK/sX7zxXnh5LA5+TKIoXooWXFzUK5sEha1bUD7cydOwCwuzq4lkxCw/OHeH4NQ0+WzfjU8yamz8uZs3yHzgSVxCvpmXJ7epOhQZVySXdZK6vL+vuONK4eTUc3t3j8Lb9OHaazBRfb/KaZ/7faOZ+aInShIAQEALfp0CmBcVLly6xYcMG3r59mxYS9evvDA0N8fDwoHnz5pmro1MTvnckVfquwbDISNYs9MARLdqEC4wbsIrCfqsZ3KwINgapJL5TYagwx9wkw3iipCE19BDtK/Xgtn1tAlYFUM7WAKOUMH5YMoFVp5Px8J3DmI6VsFZ8HJo0STGEhIQjc8xFLgfLL3xJSqiS3pGslmFuZcGvWzd+IpDw/CwBHXoQ0u0AO3rk4s7qLtT0jSPw5jZaOSvQJScQq7bCyd4MxSdT3voVeurw6wSN6sU2o4GsXN4ZZ0mGQq7m8oK2NF8j4Ts5gG51CmAuS+LSphkMXR5Or3mz6FPFnufnt+I/dTdmrf2Y3r4k5gZqYoMvMd/bm8cNNrPJvwqfD9KEsKBJG5Y/zUrfeVNpVzU/NgoVZ1b4MWN7BB0XLMCrhAuhFzczZtRSsnebxqDmZbA1kaNRJfJguz8dJ97BZ/NWelbOTsjmSXgPWkwBvxWM8W6Iq1k8P+8MZOjQ1Zi2G8PsEV0p5KAg5Mo2RgzZSJ3ZS+hXOz9E32Wh72ieluzF5IFNkL+8x6JBPfghqQgjZ0yjaQErdAmhHFvQl27b1Kw5fZqWLhLcOcjMKDc6lMyJnaURUuhhurfoz9tWy1k8pBE5VWd//2YWSU3c1dm0HnaBZuPn0qVCDkyNtDw7u57x8y9TacBEhtTPScT17UyasQOT5sMZVTWO+SMXoq0zhlFdKmOujGTj2I6seFOBKb59KZvXDgP9tajSYmJmiaHuARM9enJSW5zh08bQsGhWNPHP2TfXjxlnHVlybgWV5VqeXT5GiFE2iufPi6WRRNTF5XQdtIoiE3cy0aMANqE/Mmn4ABZG1WLuTF9alsqGYcpLji8bxYhD1kxeMpeWBeRcmtaAQRfyM3LaJOrlt0WBkodnNzJmaCAGrcYSGNCFrK/vEjQ1gL0JFQmY3YsilkaoE8K5uGYwfXflYvPJ+ZT/wvSw8swEGvVdhm3Hlczu25CcNoag05D49DonI6FkwcJktTQk+eV5AoaN43KJyVycUAukCHb6D2dWZB3WTOlAfnsjDKQYjs7xI+CEgj4BI2lZygUj3VvuHlnF9BW3aDVuNu0quWTeVkmZ+2klShMCQkAIfNcCmRYUT5w4QVBQUNpo4i8vrVZL/fr18fLyylQkneYNh/y96bXsGAX9r3FgUCFM9WFKushoz0NUGeVD7eIuyEN/ZMe1t7iXakzZXBn2GtGkEvXjBEp5rcKxxjQObfPGSQ4ySUfU45OM69ybn/N5MXfmGKq4GH3U9qTQC6zdcAjLWt3oWNGdzwYbdSncPrGfq7EONGlRHadPguYvhWUMijt75uHViRk077IYtz5T6VStFJXLFsTe/OO6P2pI9E2C/HqwST6Idau8cNL/Y9pdz+0ZcKUYSwP9KeOU/v5XFzYyyG8zRYbPZlQDR65sGMOQtU+o5NmJ4hYSkiQD6R0Pts3ilLEPW3aPJt9nVYewoEEztjn0YskMb4pnS/d8dW4Zw2bsocDgQPzrZOP0mrH471Ywa/FEKrqaf2hywhm6lWtPdOcNLB9Uk7hdE/Caco2ui5bRt14u9GO/sbf3MKLrcMx7LiOgdx1s5JAUcgn/vsOQd5nPlLZlMXzzaVC8Q6DvGO7m7Mjqae1IyyzatwQfnUrF7nvw23+HIWUNQZXCO0lJ5KP7BIdF8SYuhH1TZ/G08lSCJnlSQHbpd4OiTp3CBf/CeB8timevZuR4vxxAGRPC3h9OkLdzALMH1sEo+TVnNi9g+d7HOBS0IimlMIPG9qVEViMSI08xyGM8uYcvw7dlkffT8B+I9Hc9T2g/nODC3Znr1wInKyNQJfBo3ww8Z91k8O5DeGUDtVKJNjWKp4/CCY98QfSL6yxetA77/j+yzLsUzpFHGTdoLFcLDGFJQAfc07pKzdMTi+nvd5r2cxbRsWwcEyrW54nnZlYNr4HV+8F3Zdh1Zg8fyHX33syd1AnFgz2MGjCfl2716VLVCW3an7dEyrMDzFofzPD91xlQ6vPrVHlmHA19DlN95loGNyqCddoGpfolI2rUqYm8DH7Cy/BIwqNecDJoE4ecBxO+vgMG8hh2jRnMzOj6BM3sTF4bIPwUE4cOZktsGbq0qUJWAx2STIYq5iH7t52j9Ii5jG5bEVMxqJipn7OiMCEgBISAXiDTguL9+/dZs2YNYWFhKBQKNBoNlpaWdO7cOW2tYua9JLTxPzOuez+WHo1l+OmbjChtmj5yp37OqQspFCrhjqONxI1l09j9xoXWA3tSMsMdy1plLKendab94stUnXieHX0L/joaEffiImObe7LLtgkLF86mdZGP9yjRqRIJexmNiZ0LjjZf2OguOZwtMydy2daDcf0aYGf45TV/HwXF3vnQpURwevMGDpy7xOWQVHIVrE6X4QOokceKj5YovoeUXv9MkJ83mw0GsXaVF86/BkUvxj+pxIIZA8lvmV53zPUtDPNZiO2geQS0cOPa0mEM2/iEPGWKY63Rvn8ojYROrcOxYC16DeuC+2dbs4Qwr25jTpaawpyRzclnm/6tHH1jHUMmr8Ou4wICW7twaP5Q5t8txoIZwyiQJWOvP2FKyTrsqTCVbZNbk7wvgM6rwxkydy5e5dIPTLx/iLG9RmDRdTmjvaug3/Uy5cVlJg7ox9vWC5ndqRKKmE+CYuht5k2YSmjxvqweVC29QimJsItLqdV8Oa23/MzUuma8vvIDS7cc4k54CmaWFpiYa3iy9wCJDeawKS0oXv79oKhKYW9PB4beKE+F0q6Yvt9HSUKHMtmSim070a11eSwkHdH3jjOtW2+WR+Zk6LzVjG+VG0OZmtiwLXRrup1WK4LwKvvRLfhpzdYHxUmdRxFbdTgTelbDXp981G95fnQOLSYexXPlJfyKvuP+ib1s3rGXe/HmWJtImMvjOXnsJO6+p1jeszTOUUcYO2AOL6v6EuhXF7t0FEIvrmf4oM3UmriELtVD8M7ZHccVF5njkf3XbWh0kT8ROHIQ53J4M3dSR/h5I8MHLibcMj/FnBRo3gdF/YbxkpkjjftOpk2xz/fxUZ4ZS0Of27RZPo+uVfOgP0L/nqjbx9i6bRdnnyRgY2WCzkhG+JUr/Fx4DFFBndKD4ujBzIiulxYU9deZ9vFeRg8bw87XOahQJDsKrfZ9P+tAbkOVLj3wrFY00zfFz7zPLFGSEBACQuD7Fci0oKhSqbh48SIHDx7k1atXZMmShVq1atGgQQNMTDJx52BJR+KdH+jq48exp3U5EryMimYG75+okv40Pv2XojrkMKOnHsC+zkBGtC2QYVpKvzbrLvO69WLO+QRGn7jD4KIGv25RE35nBZ2a+vOyWBeWzJ9MvVyK9Mf8IaF7F8Gjh68xcyuKm4PRZ9vaJL28x6NnIawOnIu8XBd8unmQ29GKjLPev1wqHwfF/L9eQcmRDzh/9hCrZ2/gbo5ebF7aneKOn38R/xIUtxgMZu2qzhlGFD/fRzHm+maG9lmI7eBAJrXMydWlfkw6Z8zwBYFUNklErfswFGNgbIqVjSVGn43OhBBYtxEnS01jtp/+5o0MQXHSOrJ0XMD8Nq4cW+HH+MNOLFw8hjLZMkRc7Q18ijUktO1Glg+pTfxOfzqvecWQOfqgmB6a0oJi7xGYd1nOmI+CYl8SWy9iTudKKD4dUfydoFi72XJabv2JKRXCmNWjI/OVTZjqVZXC+XKS0z2BVfU6sj+vL6undaTg/xcU1Skc7etEQNQIApf2JKfBh+15JMkAE0tLLC0UyNTx3NiznJkLdhOb1Qb7Qh2ZMaoTbqZa4l7upEud5dRbvJ6+dXJ8NlX6xX0U9UHxxzl4TDiC56pLDMpyjkkDR/CzS3O6NalK3hw5cFffwLvfUN622cmqXmVwjjrM2P7zCKs2Ii0opsdwfVDcwPCBm6gZsJiu1YLp6dIN03nHWNatUNqNYPqXJuIa83wHcDFnn7QRRfntzfiN3EGWthOY55GDeKV+Y6L08mRyI8ysbTH/5S6wDJ+DvwTF9ivn4VU5T9o+oeqUYNZNGMHqB3b06NyKEvlccbB/x97R/szSdOBFhqA4K6YB66d3TAuK0rPDjBgwkntFBjF9aBuyy1I+PLpTZoCJlSWWporM3S7p+/1MFy0XAkJACGSqQKYFRX2rlEpl2ohifHw8FhYWaXc8m5tnmH7MhKZLOiUP9s6mz4hZPC6/ikdBrbDOsCuvTp1MfORj9q1eyI43+fAb70dVhwwVSxri7+ygZ09/Tr+uy5FHSyidIc88CepKgxG7MK0xOG36trz1W4KfvkFhmMzTWz8TEh9DtMqFNp3akfejm0y0hP90lovXj7Ji4y3KtuxMs3q1KZo3K1+aQf4oKPbKR0pcBK+VptjZmGEoKTm3oDcdAg1ZczqQRgXsP7pjO+1rOvoWm0d2YEGSFyuWDySXAViayLiy8PeD4kTPwoQeWczw2ecp2GcGo+rlwMrUCLRKkt6+Q7J0wsH8S/sAfU1QLMyjE6vw899M+aGT6Vq7JFlMDNKmGl8cnkmnURfovHorvWvmIGTD3xUUbzA531k8WgxE2/scmzraYSKlEBOu3/Dcl0dlp7F1yv8/oihpVUT80ING89/Rd/oc2pTMipnCAP2m7e9UEmZWWdL6OfL2IebM3kBq+a70KfKUqdOOUqr3DPo2L4w88TGzunXguns3xvX3pGBWc2Q6NclKLabmVhhJ9z/fcDtDUOyw6hI9VFvo138+haeuZFilvMjUyUScXEHfkdMw8jnNyp6lvioo9qhlxNLmldhu04tZMwdQzMEcA20iD87uZorvHLQeY5g3yQvb0CvMHzmJy1ZNWTCxDc7W5hjKdChT3pKkMiWrk9Vn12baZ8H7EcWMQTH11Sn8h03kWYF+BA5oioNCRdzz00wZNYFt1kPejyjGssd/MMNuFGDrsoEUtpdjqA1l84RhzLmTl1GTR9CoUBZMDGVolcm8U8uxtLbBzFAi6U0oidjj6GDxxTZlwsePKEIICAEh8D8nkKlB8Vvq6deIRT64zO2Xb7hxYDVLN5zCqtNS5rZw+TCyJ5OT8vo+Zw7u5+BzE9oNm86YtkXSn3qiVRIf9pAr90IJu7yD2St3E+bcjY0zWpArZ2EKuTukrTd8+sMgWg1ZQ0JxbxbM9sM96ihHn5hSqoA9sfGGGIYeYNdDJ4ZOGEJRu4zDbhKpiYnc2zeKcYftGOrvQ1lXe8xNFb+O1mT0iX92hgltvHjufYS9vd15tHsC/oe1lC+dCytNArcvnOC+QyeWjG9LgY82gHxfStJLzq8YQrcNb6jTog258xVjSOuyXJ3nybinVVgyewj53k89v7m6kYHe88gybBFTulTA6NUdti+dReDJWMpWrERJdwdkqjhev04hbys/OpT40iNBgplTvS7Hy84kcHQL8r8fUXx9fTUDxq/GrvNSFrcrjjL+ObsXzmTj2TDsylansqs58eGP+OnGHczrjsS/R01yWsu5u2oU7VdFMDxwAd0qpI95Jdw7wKjuQzDvvprxvaulTz2HXMTfpxeJbZcxv0sVFG/uEDjUlyelfZg+pBnyF7eYNTaA0JIDWD+0RjqOfur5wiKqNFxCmx13mFEphDk+Q9nwzIHmLauT3SiRsNRQLi/exJs6s9gyrRMFZRcZ23M4j8r6Mm90e7J/lpUltLH3meM3kKOvnChbsQw57c3RJr4i3iwHDVq2p4RpKGsmjedIQgmGT+hPebt4tk0fw6p7WRk1fQy1c5nx4NgyZi7YTpxzJWqVyoNC8454eVbqN21J0azPmNDBl5jqo5jUuxoO+m5QJ/Ls8Eya+h/Cc+0NhtldYOqY8Rx7m59W9YthqnzL22dX2LTnKNmHnmJ1nzJkizzIyD6zeFljFItG1/916vnF+TUM7hdE7Skr8GmUi7DTMxg45jCyQpWoUsIdSymS4Pth3Lx2CZOGI5g3sQtumjhuHlzL1LkHUOUuRoUyhbE3UhH/Jgq1fSOG+lRIm1b+9KU8NZI6PW/RYe1CulV1Tx9RfPeYoCkTWX8hhUpN6pLbTEVE9D0uHjjPlTy+xGztgoFcya2NM/CZdJj8bVtSIacb5ZvUw+nlYabOWsltZU6qVSxFdhtDUt9G886qGB4eTSlg/ZYjk704n30Mo3qVw/LDI5a+5ceSKFsICAEh8J8X+G6Cojoxigtr/Vh2VYNMq0Sp0mJgbI5+e0P9xHDaS//loFWRnJSCbdG69O7fk7JZ3++yrYzh0fF1jN1wHYV+RESlRmdojIW5HeUadadbq5KYy2UkRdwjaOZE9kRmoWbNspi+jsalZlNqlsqFIuYea2bNJiRvL8b3rY31Fwbejo6vR5CuF7NGNsf5d25GSYq8y+bpc4msO5GxDbPx5udtzFh+mLC3GuQGJuQo24Qung3IZ2/OF5c5ShqSwm+yYeECjr+QcK7TnXndqvB070w2vSqET3cPXN6v7k98fIoVC/dh4dGbzrULYK4fxYp5xtndG9l+5jFJOkOMLGxxL9OYTu0b4J5258Gnryh2j/LnZt5u9GxTgeyW6SE54ckxlm/+EcuafelTLVfaDUHJCeFc2rOejT/eRSnJMbTIRrmmbWlbuxT25obI0RHy43pmHo2lZa9e1Mmffttscug11s9fiUndQbRvUDgtgKheP2DjksUkV+lLr9qFMEwMYd+qtYS7N6Jr8/Jp+yju2riV6NxNGOJR9H1QTOXN/YP4TThI5VGL6V7SkIQnp1kybw234sDcoSAtvWsRF7SBG9laMqRbLVxlDwlauIaIvC3p0bYa9l8ikCTigm+wb9tGTt2NQqkDUztXKtT3oGXtspg82cmCjZdwbtybtlULYC6XiL5zhGUbD2LbaCT9a+RAq3zL43P72brnKE9iVMgMLShQtTmdW9cju0UEm2et5W2RtnRuUBgr/aWrTSby+i4mbrpO9UGBtMuZQui1QyxZs5cX77RYZitL+0Z5uHDgB1RV/BncOC9Z4q6xfslu3hRtTZ82pdJv8EHi9f1jrFx2iuKd+9OorAuoE7hzfi+bt54gND4VU9cStKhbhlsbAriey5u5E7qQ20CHOjWBp5cOEbTpIE/fShgqzMjiVoxmnl2pU9j6i3cbq2+vY+yiEGoM6k3tws5p62wlSU30k8vs2LCJM0/iMbPOQY2W9VHcPct+TTU2+tVJWyupTnjMzsWL2XfrFba56tBjUHuKOxrx5tEFtm/dyeUncejkBpg55qZGU0+aVimAaerP+NduC2POMK159i/+OPvPf5qLExQCQkAIfAOB7yYo/rlz//Cot/SH5f3/r7TjNCnEvArm+csU7PMXwc3OGLl+jdeJBUxYeIqqvotoVNgOBytTZJL+ZhAZMrkcGS8JaNCct103Mr6xI0kaU6ws08Ps7730SyAzYwDkN88xYwV/srIvvy3jo/T+4mP1vrZdn5xLmv37jv3aIr7UFxntvvZa+aicjyr/xeLjkn6/3PS7zz8/l49b9v8/uvCTWn5p11eeYHLIZaYPHsmzsv2ZNaZ1+qMh/+jrr3RE+lPcP/lblUB/Z/5v/gGrSHq8hqYN9zHyyiHq2n/d3/ofPS1xvBAQAkLgf1HgPx4UM7FLNSEcnjOepdfzM3hYDZQKJ2oXdeLRj2u4pi5K4/rVycoBOlQKpNbyIKq8vU6IRQHKlcpLlt/Z5SYTWyiKEgJ/SEBSp5CskaMwNEAu1w/Gp3Dn2FJ8p52n7tDJjGhT7PtY66dOInSfH812VOLYlg5kXJL8h0DEwUJACAgBIfCZgAiKX3tRKEP4ccUy1l02ok7r8hQvVYmSjrDHvzaTQuuxZNYkKjpex6/1LKxatCe7qQ0V6lTAPav19/Fl+7UO4rj/hoBOw9sbm5h3QUuRPDmwMVPx6tZldh87S4p7ayb5dadU9sy9Ee1bwUlaNXFPznNRXZwmRT/ak+lbVSnKFQJCQAj8zwiIoPiVXS2hJjkujujYJAyss+BgY4WJXEdi+E/svxpCwRJNKJVHzuvnYaTKTDC1tcE27Q7Rr6xAHCYE/k4BSUPizSAGTtjI08i3qHRaUNhTuW0verSqTYFs1hhm2E3g72yaqEsICAEhIAT+PQIiKP7FvkgNv8uJGw/JU701Baz/YmHi7UJACAgBISAEhIAQ+BcJiKD4lzpDTcTTn3jy2omqldzEs2b/kqV4sxAQAkJACAgBIfBvExBB8S/1iP7ZtRp0MqMvb2Hzl8oWbxYCQkAICAEhIASEwD8rIILiP+svahcCQkAICAEhIASEwL9WQATFf23XiIYJASEgBISAEBACQuCfFRBB8Z/1F7ULASEgBISAEBACQuBfKyCC4r+2a0TDhIAQEAJCQAgIASHwzwqIoPjP+ovahYAQEAJCQAgIASHwrxUQQfFf2zWiYUJACAgBISAEhIAQ+GcFRFD8Z/1F7UJACAgBISAEhIAQ+NcKiKD4r+0a0TAhIASEgBAQAkJACPyzAiIo/rP+onYhIASEgBAQAkJACPxrBURQ/Nd2jWiYEBACQkAICAEhIAT+WQERFP9Zf1G7EBACQkAICAEhIAT+tQIiKP5ru0Y0TAgIASEgBISAEBAC/6yACIr/rL+oXQgIASEgBISAEBAC/1oBERT/tV0jGiYEhIAQEAJCQAgIgX9WQATFf9Zf1C4EhIAQEAJCQAgIgX+twHcdFCVJ+gxWJpN99t++dNwvB33p+H9tb4mGCQEhIASEgBAQAkLgbxT4LoOiJGmIfvaAxy9j0eqzoj4bSiDpNFjmLEWhnLaYGqYralJjeHr/GVFxKSB/L/tLvpTJMM2aj+J5nTA2+hvVRVVCQAgIASEgBISAEPgOBL7DoCih00Swd4I/s47dJDo0lFi1DEv7LFgqLKg1ZBlj2pXF0SxdPzHkGNMDZrPn4gOiXr9DbmpFFkc7LIwk9AOS2thoYhTZ8fSbwSjPKtgay9NyZ6a9JB2q1GRUOgNMzEwxzKTCJa2alOQUMLbATPFLAs6cVksaJUnJGowtzTD6wght5tTySykSWlUqKSoJE3Ozr/SRUKckk6IzxMzUGMPMPf0vnJ6ETpVMYooOc0tLjOSk/f93KVqMzc1RGGbyNfNZC/TXvIqkJCWGphaYZnJ/Z25/itKEgBAQAkLgvyTwHQZFPb+ERqlEpX3B9JoVWJJakQkz5tG9Wg7kRsYYGxn8GvYkSYv23Wuu75lBE5+tlGztz4JF3rjJNUjI0CVFsmfBcMYtvUPVuTtY4FkMG6NMSnP6Ec2ECH6Y2p/NUcUYv2gMZSwyY+hSIvr2IcYPmIzMZwuL2+fMvGtS0hF/dR1dBp+kx9GVNLYyxSDzSv+8JFUcP+1ZwNgdsYzYMJ+aJl9T2Rv2jB7MjJdVmDujNxWzZV5/fal2SZPCiy19qOIfxdJLh2nsLOPFZh8ajrzN4B924FXOGbNv2oRkwi+uol27lVSbu5upbdy/BkkcIwSEgBAQAkLgLwt8p0ExPSwStY3qhfuTUL4zc5dMpYab6a+zyxllNPGhHFnQh/aLXtJi9DpWDy6N4tcDUrmxaSYDBs/gZs3FPFzeEVfbzAhz6RVoUxL46cdd3EzKRoNWdchh/Adjl05FxOPbPIozpHCZYjjqh7OQeH1zHyN7jUM2cA+rO+X6yxfCrwVIOuIuraSdzzF6nw3Cw/pbB8VYru+YzYitsYzdtozapl9zKtHsGObD5NDqLA7sT2WXb5rS0AfF4KBulPKLYO3NMzTPJuPNtR2sPvSK6l27UNbNisy7Yr50/ioSnl9ibdAV8np0p3Fx+69B0q/FIDH8EbcexONWoQSull+F+3Vl/6mjJFJiQvnp1jOsi5eniJ35nyrl99+kJPLWRS4n5aJeWTfMMvFH3zdorChSCAgBIfCvF/h+g6IkkbivK3m6HqdM64ksCuxCbnOjL0wbSySE3mRxr27MfWnNiBXH8Kv8ISaijePQ4pEMGr2Od57buD67KS7WfzDM/W43S2g1GnTIMTT8MNL51VeGMoYf100n6JkLIyYOoJipvm0S0WlBcTyygbtZldlB8fIq2vv8SO8zQTT/m4Ki77Y4xmxd+oeC4pSX1Vk07+8LiqX9XrHm5um0oCjTalBrJQwMDTGQf9ugmvazSKdFo9EiMzTC8Gvr06l5cnABs7ZH4znFl5quWb76svsmB0oaXl3/gYkLj1N+9FS6Fcj6Dap5zf4JfsxXtmGLf0Mcvu1Q7zdovyhSCAgBIfDvEsjUoKi/u1itVqPT6ZDL9cHIMO1/v8VL0iVydEB5PHer8Ri7jUU9S2KeNtr26UtF2O09DOrqzwOLeiw7vJBqGQYykiPuMHdwE2ZdLsjULSvxrpQD48z83pd0aFQq1JIcY2Mj5Glr/iQ0KiWpShU6ZMjlhhibmGBk8HHFkqRDmRDK/jVTCHqWl6mTeuKmMMHE1Jj4O/vTg+KgXSxvm42UVLV+jBUDIxNMTIw+GlnV6TSoUpWoNVqQG6AwNkGhD61fOk/9iOIvQfHsRpqZSCiVGrTIMDA0xlRfdob36dfOKZVKNDoJZO/L/nXqX4cyORWZwhgDSf2+jTIMFcYYK96Xo0ofUfwoKOrPOyUFlU6OqfmX1nWmjyh+HBT1Zy+hTk1FqdYvK5BjYGScwUJCnZyExtAUY7mWVH2btXovBSYmCgwyYqT1mZJUld5UjoEhRG7rSWnfiA9BUZNKcqqEsZkJ+m7TqVWkasDY2BBJrSRVrfmCR/q1qdOqUaamm8n0fa8wRKtWgZEJxkb69Y6fDtOCowAAIABJREFUdIz+Ri1JTWqyCrmpGcb6CrXKtDWSJmYmSKoP52xkYoaxfiGsTIc6+R33D85l2qZY+kz1pbRrFhTGppi8/zvRKJNJVWnTrkG9lZlJ+g8t/dpdnTqVVK0sbf2lfg2pGgNMTPV9IQNJgzI1FZVG3345Rsb6dmf4EaTTolTq/12b5meoN1YYoVMnEXp9C6NmHqfe1Km0dc2KkcIUk89G2SX062STlaAwNkCrUqLUGWCa9jcC+vW56f2X7qcwMU67nvUjqKqUULYMG8oSk+4cmFgdMwNDTExN0tex6q+rtHZr0d/ZZqhvt8Lwi7MQ3+IzS5QpBISAEPgeBTItKGo0Gp4/f86ZM2eIiIjAzs6OSpUqUbRoUYyMMntiTkKX/BNDyzVna7ITIzYcY2Al27SbDD59Sep33N03ja6+25GV7Mu6OR6YadSkpijTAs69g0tZe/ktNToNoX+rctj+0anh/6fXpZQ3nA+az4HoAgwY0R4XA4n4l3c4um0jB34KIVmlw8CsIF4jB9C4qPMnX1ovWDt4EltOneaRypL8+dxwdK1FX5+O5FVdYGRvf1I8Z9DF9md2HP2JN4lxmBZozpDBXSmV3TJtbaE6NY7bJ/awfe8JnrxJAQNzCtRsiadHXYo4m38eFn8NisfxPjAG7e7tnLr8mKj4VAxcq9C7b1eqFnTE1FCGJjGCi/s3suPoNSLeqlDrDMlX15t+HWrilsUYdBEE+oxAXdWLwinX2XXqDtGJb7Ev3gIf71aUzWULqriPg6KxhpjgCyyesIjbWWoSMLEvhWw+Rf5CUFQnEfn4Ats2buX84zh0GGHtVpmufdtT3j0rxsSwvXsrjhcfQUfnF+w/fong8DCkbDXp3qcbdYu7YGwoR9KqiH58lX2bNrLv1isUpjY4F6pAFfbhszSZ1T+njyimXpyFp38UA9aNoUYOa57tmMuE40k0a1gW9YMTHL36mDiVguKNetG3Qw1y2BinnYRKv152/2bW7z/PG6WEsW1+qpWx5+6p89i3H8eA5iWw++w6VvMm+CiTe62nwLQN9CmtQLq5krq+V/Ee14m35/Zz7nYIr6OVlGjVH+92tcltE8nBFStZtmoXD2IlchTJi33WgjTtPoyuFW2JeXaTXSuXcPxpIhqdDJMcFenRpzOV8ztgbKDj/qZpTLkoUbdCbhLvneOZaUHa+AyiolUsjy4fZt2m/TyKUWNoZELeSk3x8vSgoKMJkjKR4GuH2LTzGHdCE1CpdWQvo78mPXh3fC3zZ6/j9KtEHPIVwc0+B7Va98O7RaHPpu5TLkzHe1YMtbsVJ+H2Fa4mFcC7mxeVHaM5f2A7W378mdgkJToDa0rWb4+3Zx0c1G/YNqsny/bcI1jKTum8WXEtWJUeQ3pQ0k7Ls59OsGXLfm69iEMrMyJHybp07NSSkm72mfvj8Hv8JhBtFgJCQAj8hkCmBcXQ0FC2bdvG3bt30Wq1aSOJrq6udOjQgUKFCmVuB0halHdWUr7xOBJtW7Lm+HyqOhp/8aYLzbtYDs3ywXf9VZzq9qdPHSeU72K4d+EYx6+F4FK9FwP7NKNsnmxYm35p6vqvNV16F8GBWb6sf1WKGQsHkl0Vzu75k1h/Q07zjs1w1sYR/CCWfC1a0qB4jk+CYgIPfjzN7i2L2fcmG+07taaAvRPFShTGKOwk/l7duJTLg/aVy+CeywFtxDmWrzmBu2cAkwY3w8lYxuOj85m64AyWZapTtWR+VKEX2H/6NtaVfZgzpD6Wn05jpgXFlbTvu43srYthL3OnZP7sGMQ85sjOIJ5na8cE/35Uzm3Lm0tbWBm0F6V7fUq5mRNz9zDrDofSYPB0hrYrg5kUzPBK1bhoX5v6NapS1D0byc9PsGH7FXK3HMKUEa2wV38YUfTfuZQyEVdZO2MaB8Nc6D5pNE0LZcPs/VZHH3ri06CoIerWQWZOmc0j69q0qFkMa91LLuzdx3OTOoyaM5wKjrEsqpSfQNN6tKpUkaLF8mKTep+gNTtJLdCWSQF9KGZvTsKLC6yeOZNDUdmo27gOhaxUPHtwhdO793M+wpWVP51KC4pJhwZQvFMY068ux8PdjnvLfOm5+DDOJRtSt0pZctppeXJhFz9cSKbdmKn0aVISE10qN/dMZ9KKS9hVbErD4jlQxT3j/NE97D70hAbzf2BKtyo4fRYUlUQ+2EqPqtMp/cN1AmqYIJ2fgG3THVRr25AKJUpQ2NWC0FMb2Hr+HQ2Gz2VIczdeP7jH0XWBbLmSTC2fzpR2cCJX0TLk1v1MoO84zhiUpnGDSrjKorh6ZDcH4kuxaNFoKrmZ8nPgAHovO0/BZgPo6VGObM52OGRzJPriJqYsOoChezXqVc6JKvwu/9feXcBFlf3/H38NMUODhIgiKnZgt2IXFnZht4SgAqKgCBioYK+uid25drsWuiZ2IiaKIKj0MDP/xwzomvvV75/d3/r13H3sQx+7d+7c87x3hjfnnM+5h7bvR+E0iQVudcm8c5BJE2YSX7gprWqVhIRonmVY0qSXMwVe3uboukXM3nsdh+5utCxiQYGSlShf3PKzXr2UPW7UHnaSYh170L9TEwrltSZ/PitSr20kbOMfmBSuQdVCukSf2c/Wo09pOmUJY2ubc+vcRiLCFrNT2Qj/HtXIb2OHQ7VSKC6sYmz4DjLs6tDcsRR6b+5ybPsukst2x2/UIMpZ5fYvs/9/3xvi1UJACAiBf4tArgVFdU/iypUryczMRL2ItXoYWv1vq1at6N69e662V6XI4Pbq4TTzXY9Rk4UcXtYZm6/0BKa8usoMVzdWX9fDffkK+hbXQ56ewv0zWwgJCOZJcQ/mzPGnTn7dj3vXMp5yeNt2nuVxpHm98uT9Qh2Aetg1LuYmEpsKWH9lXr4qJZY94X6sjK3E5FnuWL85S+jwICIt+7F1WicMdVTIM+RIZHroqcfVPtrUPacv2LlgHEsfFCNk2gjKybQ1ITz+6h4C2vXgRqNA5kwbSlljKZKsVPZMbEPY4wbMCQ+kktVzpnfuyLWy3oz3cMLOVIYqM46Tm8KZtUuBx8Iwmpp/8p7vg+JEUjqsZbNnZcz1ZUiUcpKj5tJ/5FbKeMzHp31lFE9vk5ChwtquCIZSLbLSnrF8ZDt2mLvxa+AA7PQe4Fu1Hldq+RI+rj8lLfVRJd1jVagbEfF1CZs5gZqG6qAYjt+WN4xdHETc1MGsvJGHARMDaFuh0FeKRD4JisZ32RQ+jpAzhQhfPY56puqipkzexh5nRJ+pVBi9Ai8nKb86luVXWTemzw+hfmETdFUZREa4Mm6vnAHBs+hZ3pJzy/0I2vAA5wmT6Vm5MLoSFRkpD9nhOxD332DpxewexZR9XlTp84TJpxbQtpg5N5aMYfCYjVSftAQ/F0fyybRIf3KKyb7DuVNxLPNGdMM8/R7hgzoR5eDLFI/22Jroagplbuz8FU/vBRQLXMWkvnW+HBRvbWRIw2lUWneGCfX1UJ0KxrL9AlqO2cH0IZWwkOmQkXiNoD6ePK0wjJljO5BXX8XNLZMJWveKQTPH07CgOcqsZC5FjGPgejlLNk/FwdQAXbJIe72L3nVHU3LKPvzaFOHeQk+GTvyDDguW4NG2PMba6h7kGywJCmS/diumenehiLkUlTyd62sDcVksYdWRiRifimDIiN9wDpuHW5NCoJBrhqilevroIudxZATeYUdoMnkm/UvkRaKlvp8/nwORus8TR48d1AncxvjOFbCUqdckyuJt7H2ilYbYWebFWFdCRtxFJg8fzb4S47gwuTFKxVPWjvBgjnY/dk50wlJPC63068xy9WC7tDPTQvpQ0UIPLWUGT08tZ9zM07QYPZnudQuJIehc/ZYWBxMCQuB/RSDXguLRo0c1QVHdm/huU//dycmJnj175qqXQh7LBq/+jFxznBqzbrOpZwGkX1mg8NWN9bi7TeaqdmvWbg/EwSh7CBBlGruHFqH7XnPc5+wjxNkO7Q97ctKuMTtwJgklejGsZwNsPlu2RUHKixPM8FtG1fErcfpK4fGnQdEu6wU7ZwcxacN9GnZ1pn7jVtStaIfpuzl7n0qlv+C3XwJYGl2M4HBvKui9K2bZiV9vP1SD17HMvUL2q1SZRG32ZdgababOmoyj9W5aVxzL63KNaVDWCm2JColESfz9UxyNNqb3jM341vikJ+VdUBy2DuftBxhcWMqfHXrX8XNy53HNkUwf4UQ+QxUpr+O5ffkCt6KjiY2L5cKhbcSWGcqSiZ4UM3mAb4NO0HcxIX0qo6eeW5eVwO6l/gQdMGNc2BTaFEji4tYZeMw/i0O1AjyJeoNz0HR61bHn6yvlfBwUK6WfYI7vUOY/LEPXDpUwyshEpaWNPCWRyL0HqOS6gOBhJVjhWJZtNVeyYVILzKVqRyW3TszAa/olWnhNx7OBjBVjvNj8sgYzQwdRPG/2vaKeL/dwXT8qjXr6fo7iZ0FxoTf9lyQwam44LjWtsm+x9DssDPRma2YnlkxwwTR5N4NazaLB9BW4Ni34fiZi1s2NDBoUgqrPAkIHfKVH8QtBMV/nrUw5fJnepbU1608qMpJZPaIde2lDyOTBFDfW5uaWiUxYl8igWRNoUsiczJQHLPcazsTzMgZ3cECelb1ivY7sJYfnbsDAbRMRXnV5vNSdoevl+M0NpVPVvJr2ZNzZjb/3CI5r1aNZhYJoo9S8Nv3RGVbveE3QpcO0l19mqqcfkZJCNGrbgTZN6lGuoFn2HEalnCenIxgVdpgmk2czqEy+r34vpO7zoP7wewxet5ieVWzR/J6mUqFUZJGZ+JQrVy5y5+FTHj59QOTOI0SVD+Dxkk5ALGu83Jkt6cuuia3JayiBWxsZ6OrHGVk9nKoXw0giR6UlJSM2ipORz2gWMgOfNtX/4n7L1a8vcTAhIASEwA8lkGtB8cGDB6xdu5Zbt25pehLVvYr58+fXDD1XqJATZHKFRon8RSTe/Yaz4kQmEyIv4FZWytdWwbi1cRyuIWtQNp3GzsntMc4JWurerxWdK+N5xZqBU9YT3r30n9Wrmsn6KbxKyMDQ1AQT448ji/qHlTw9idjDE+gyMY3pe37F0VxduPOFxwd+0qNYVCrhzbPrHNm3i307DxCTkRf7mk3o17srVYuYfl61/S4oPihOcNioD4Liu+Vx1FXP9jlBUU7UNj/6L8wgdO5UmlrspLljIOklHalQ0BgtTbkLmgIEgwJlqN+uD82KfykoqqueDzLo91W0N9X7YEj/Fn4th/Gg2ihmjKzDq1ObWLHxCM9URhgb6GNurOLmmcMkVRzC4hBPihk/wLdpN6SDlzOhU+nsYh1lIgdXBDF6M/hMD6d7sTdc3DYT1+n7MLPJJE5Ri7HTptCujNlfLKT9cVAsn/I7s8a4suxRGVrVL4RuprqIQl2vIEGmZ0vNlm1oUc+ECMey7HDaztZRdTBU91Ch5N6Z+XgEn6SOWxhjW2iz2GcUB2nJ9PHdKWKW3duqykonZlUfKo9+/vWg+Oso+m2WM2b6ZLpUMs0OipkPWDrBl5UJTYiY0pc8Kevo23oznZaspE+1PyuQ5dfXM2jwRCR9f2XK9wTFLr/zS+R+2hWSacK8MiOFDd5t2JLcmMDwETiY6nwQFINoUigPGSl3WOwxgpnX9WhXyw6FOutpNi2kBvoUqudC70bFub3AjSEHDZkwbQJtymW3J+3Gb4z19uWMTlWqFbFCK+cRmhJtKTJdC5p7eeFoLSfm8mn2b9/MgbP3UFgUo3rzrgzsVI/8BrwPik2nzGZg6b8Kiu7UH/4K7+3htC9jo1nOSl0Mc3X/KlbtiuRZigSLPMagm8Gdo2e4VXUCDxd1eB8U50j6sjMnKMqvrmaI+yQu6VekVun8yBTv7g8dDE3zU6djB5o4FEaam0VsufJdJw4iBISAEPi/F8i1oKiudr558ybHjx8nNjaWPHnyaIpZqlSpgkyW04uXG+1VKXh1bjX93MdyOLELx6PCqGTwtWVnktgyzpWgNVepH7Kc6d2qoMmJKEh/foQBNbqzV7cQnnN3EdDCBlVaMgpdPTJeRXP96j1STIpRyaEYFvofdDWqlKS8fMiNq5c5GeFPRGJXQif1oFZxe/IYfr6szqc9ikVlOf1zylRe3L/FlT9+J2LFGvTaz+bXwXXQdHR9uGmC4hgW3S/OxHAfKmqeTfiV5XFUcqK2jqb/YjlT54TSpNAZXOp4Y9R7BgFdK2H8blUg9Q94bV30DIzQ+3Rq1vsexaXUWXyQsdVM/+xRfLKBHj3nkLfPDAIavGWehweRlt1wHdqO0tZ5sTZIYkNgH1bq9CBi0p9BUXfQMoI6l/0zKC6fgN9WLU1Q7FY0STP07LvuBf1Hd+PGr8FctexAwMhB1LD72jp7HwfFalmnmes3gjW4sHRRf4poqlo/CED6BujrxbNAHRSbb2WLjyNGOUHx7plfGB5yirqaoGjK2rFurHlUkukzvCibL+f95UmcDutG61lp74tZPutRVAfFTRmMCZtCl0rZ1TfKzGiWTPBldUIzIqb0wyJtG/0bz6RWWARerUrl/HKjIv70MoYMDcV0+HIm9//2oWebzkeZG3mQ9oU/CYopTQgM88oOiptDCFyXwMDZITQrZIE89THr/LyY+LA6R1cMwfCDx6Wr/6qjZ4ihnoSLs1wZcsCQwOlBtC1nomlPRsxhgkYH8riKN5P6NsRE98MXayEzNdb0GqvzVlZyHNG3rnJsTwRLT+vQO2AqbrXNeXJqGSPDDtBo0jyGlrP56jdC6j51UEzAZ/sM2uUExbT4s0zxDeSCcRPcuzSluK0FBsSwcOQ4Vpq782Bxx+yg6OlGuLI3+6e2w8oAlNE7GTZgDE/rBhDq0RJb3ffpOLsy3UAffalO7j6RKTe+68QxhIAQEAL/AoFcC4qa+KVQkJqaqpmnqF4ax9DQUPNnbm4qRSoX10/CzW8uD1uvJWZea75aqBx/nAA3L1ZftSMoYgYuNeyzQ496aZFz8ynZKphMy+J4L9yHTwMpp1YewLieA8/OnCTh5W3WXYZ+7mPonNNDpH6pej275Lh7XDizl/mjp5PVZzE+TsUpVbIoeQw+L7v+NCgWJpmnTxJQGZhjaSIlPe4S88b5c7GkDxvHtspe+uTDLeMluxeOIvioMaMnTqWFrQItmT5vb+3LWXD7g3UUPw2KJbRY2r85GxJr4zF+KI5FrdCRZJH85jWZGGKb3+KjpW40b/vBHMWnNcexbFQbSuUzQpHykqPTejD9jD0j5kynhfYxRvYbzauW4YT6tcZGmcKjc7uZOc6bu9XGsyRk+Psexf8cFNXL4yQxfvM8ipxZwYSQmTwq70mEep6j8Zfunk/mKFo85cDCcQxY9gTXibMZVNcWPR0JivTXJKbKyZOvMKayF8z/y6A4HX+n/Fxa44//glNU8wrHo0VZ9BTqauqjzPYOZt0dSxZ/bY7ifwyKAyggiWFqrxaczj+ICX79cbDSJ/NNNAeWzSQo/BB1Z64npE/tb56j+C1B8c72EEYsvELj8XMYViEPGQoVT/ZPoV/AEdr8spJ+lfKRR0+HrMzXJMRmYlmiICZSuDDzXVCcQNucHkVF2kPWho5lzsW8TJjgQZ2S1uioskh/84p4iQUl8hmR9vo5z15kYpbXXNNre//0QsZOiaSi1wxC2tjw9GwE7uNXUXDAUqa3tiFDoYWRieFn8wO/FBSTY7Yz0iuc1Op+TB3WEBOSuX9mLf4hi4gqN5Enmh7FODaOcsUzqhx7IjwoaqKDTJbI5gA3gs7a4BM4gvbVCqGvrSQjOYnXWTIsrfJipCMn8fFtknQKYmdr9sF0i9z89hLHEgJCQAj8eAK5GhT/zuYr0pO4/8dxLj6L48KuNazZdp5CfWfg2VhdyVmbqkXNc4aOFbx+cZ+oS/eIubidpcvXck9Vn37Du1O3XGVqNiyDmXpNvzvbad/BlbMp+RkUuhI3+/P4TE3Hc3JF0p7oY3h/FQEnTRg5PoBWxT7tdsvk1eVVdGy3iH7Hz9Lb7ustVyXHsjt8tKbqOXS2O9avzzHDdwaR6dbUKmnOq9g73LoNPcKm0LNakc8n1KtSuXo4ghFjV0PhClQrXYm2nVtRNOsCfgPHIfHcwdJeORMkVZlc3jqafovkTJsbStMSRjy/sw3/wVM4n5mPOo6VsZZm8uRxHBZl2zNhZMvseYMfbponsyyi67A1WNXK5GViZaqXyEPshYMcu5tKq3HLCehUjbyZN1g/w5exG+OpUs+RYqZyXl6/QDwKXtl3ZulEL80cRZ8mXdEdHEHwBz2KByICGb1Vwuiwmdk9ipvC8F6fQMDGhTSRphN9YC6uPtN42vQXdgS0w97igwXSNecax+aRwwh+1ID5s92pW0BC+sNIlk8YysyzUhzq1KdsPn3kifd5JrNj8KhQaud/zvw6pdnutI2tmh7F7DmKdyPn4R58Ckf36QS0KsjbuAssHB9CxMGHFG3bgcqyV9y7/Yp8FjEs2aEi4vJxnAtISNk7nEq9nzIl8lec1cUsC0bSZ1M6Y8Om0rXynz2KiwO9WZXQnOWhAymWR86N/b/g5bWYxFJ1aFauIKlx0aS8Tibq+m0cvBYysf/Xqp43MKj+VCpvOEtQA31UJ4PI2+kIv5w9TMfC2RX/yoxk1o9qzZaUpgSFj6BcHhkvojbjO34W59NL08axLCXrONO7pjYRI70I23kNu6bO1CpoRvrrO9y9ZofX2tHUymdI1IyhDD5gpOlRdHbIHnpW/xKRcG03wcHBbLplRidnR/JIUnn1JJq4iiOIcK3Gw9+XMmHCFowrVcLOXMadc6fILNYU73EeVLfSJ+lJJNNGB7P+riE9WtTAvkJ9unesmT0H8YMtZa8b9YbH47t9Fu3LZg89y1OjWRc4jnm77lGiuRPFdF9xJ+kRz889JrraWGI0PYqZXF8XiqvPagzatqVGwSI079eH6mlHGe8fxPrrRtStVxN7c22SnseQbNsC14E9KCO9x7xe9fij5gaWjm+A0d/5ZSaOLQSEgBD4gQR+mKCYnhjDgV9nsPcpmsWadXW1NQsNp2sZU7XtYFzq2mUPbaoyiLl2iPWrD/MkTYlMJkVbXdWZqoVV0Yb0H9kKW/XPPEUa0TvCmH3wKUaFymErSca0UT86VLRGmhzFnHFTuVnIhfEjWlFAm48qolXpiVxf50/Lxfk4fHo8RZVKJBKtLy5grV5X7sbRnZx7U4DWHRwxU7zh6pE97D52gZfpSgwsi9DIuR31HAp9dS03ddiMPLCdbUei0C7RFJeOTtirovlt426oN5DuVS1yfpAreHplJ+tOZ9GmUxtKWqmH/OW8uHeWndsOcu1xIiodA2xKVMOpZTPK2xl/PtymUpEac5oVm+7ScFR7Xqxfxp7zMWToW1HZqS+datm+X65GHn+L3Zs2ceJmAjp5HXDq0hbL6A0celsOl9b1sdR7xW/zl6NdqwdOFfNlB3lVKrfO7mPXFTTtrmyRzqOoI/x2MZVmfbtQQlc9Fy2Ji5sWse5SPLV7+dOxfE5Qef/BSuby9k0cSCxGpw6O2Of8b8Xrhxzcso5jV5+RptLBwLoo9Vu0pVGFgki13nJy9mSulh5A/0b2mjUTNUP4MSfYsj8ae0dnmpXJowmPGa/ucXDrdg5HPUbHvAA1m3egjiSSWbve0MPXA3Vuyry5lelrkmjt2QUHKwOen9rOmstZNHVuQyXb7NijUiQQuXcH51JK4dKmJpbqHmdlCnfO7mPrrjPEJmdiU6kFzsUSCfWditaABYT2r431Z3Plsnj74hLr5x7Gtu9InIrqoHqwj3ER9+g4wpUK6vmxOUU3F7cvJiqzJE7t6mNjIEWZ8YY7J3eyds9ZkvPY06LbIJoVMwT5c05sXMOei49JzZKgb1aAmk7daFKlAEa6Kh4f28j6WzJaODvh8GEllyqLNzEX2LptJxdj3iLRlWJuX5V27dpQPr8+qXF3ObRtC6fvPCdNoY1NaUecOzSlVF4jzb0myUrn2dXDrNh4mOdaVtRr14/21fJ99gtS5s3NzN2SQqOB7SmfzyRnnqySt7HX2b99B6duvkDPqgTN2jVGeeUUp7SrM75bzlzojGccXLuKg1eeY1a0Md16NaWIqYzM2Ch27djJmTsvyVBpYVLQgWZOrahR0oLUmN2M6jwFhwUHGVH9i93YP9DXujhVISAEhEDuCfwwQTH3mvzxkeRv4jSLSWsZWpLP3AAtiZKnpzcQ+EsktQcOoG4hS8zzWGFuLNEsHqwtlaF8+5Ctwe6E64/m9IQKPHvyHENLe6yMc/PRf39Xi8Vx/10CSh4dnM9gj01UnzofH+eyiJjyD18hxRseH51B1zGJzPt9NpUN/uH3F28nBISAEPgXC/z0QfGza6NM5tjaGay+bMEQZ0ueKApTtmIxUn//FfdZt+kYOJuBZaKZ6xrAkz5rmFL2EccvRFO5bXsK5Oqz//7Fd404tf9aQF0xr1SpH9uY3W2oehvDqinD8T9bkqXzg2heOrvnTWz/nIAy5Tlnlnni89KTY8G1v7J25z93PuKdhIAQEAL/JgERFD+7Ghlc37+R7b8d5K5lQ/p2bEuDclpc2vkLfT3309A3jCk9C3Botg8LUxvQ1PA11QaMoLaNeLLDv+nG/leeS3oShyPGseCqBU0q2mMojefEprXsvGOG68RQfLpWFWv5/SsvnDgpISAEhMDPKyCC4peuvSKLtPRM0JGh/76k+i2XT57m1gNT2veqga4ik9S3WUiNDTRzI0Uv0M/7IfrmlisyiP5jO8vXHyImPgW5SoWpbXmcOnamcRV7jDTzJsUmBISAEBACQuDfIyCC4jdei9RnNzh+7CzKip1oWUbMIvtGNrHbJwLq5ZUUCiUq9T+q7MXPtb/yGDuBJwSEgBAQAkLg/1pABMVvugIq0hLjeRmXTJ4SRTAW3YffpCZ2EgJCQAgIASEgBH5sAREUv/GWfny6AAAVmklEQVT6ZT8RV2xCQAgIASEgBISAEPh5BERQ/HmutWipEBACQkAICAEhIAS+S0AExe/iEjsLASEgBISAEBACQuDnERBB8ee51qKlQkAICAEhIASEgBD4LgERFL+LS+wsBISAEBACQkAICIGfR0AExZ/nWouWCgEhIASEgBAQAkLguwREUPwuLrGzEBACQkAICAEhIAR+HgERFH+eay1aKgSEgBAQAkJACAiB7xIQQfG7uMTOQkAICAEhIASEgBD4eQREUPx5rrVoqRAQAkJACAgBISAEvktABMXv4hI7CwEhIASEgBAQAkLg5xEQQfHnudaipUJACAgBISAEhIAQ+C4BERS/i0vsLASEgBAQAkJACAiBn0dABMWf51qLlgoBISAEhIAQEAJC4LsERFD8Li6xsxAQAkJACAgBISAEfh4BERR/nmstWioEhIAQEAJCQAgIge8SEEHxu7jEzkJACAgBISAEhIAQ+HkERFD8ea61aKkQEAJCQAgIASEgBL5LQATF7+ISOwsBISAEhIAQEAJC4OcREEHxm6+1nBdXDrNh6y1Kt+9B4wp50frm1359R8XrhxzYsYu3pZ3oWNUebUkuHFQcQggIASEgBISAEBACuSAgguI3IyZzZXMIg3xP4Dx9GaM7lkL7m1+r3lEFKiWZGXLQ1kFXVwd1JpQ/+4NJPmN40SyIOb3rovuTBUWVUkGWXI5CSxeZjjaSn6z933ULiZ2FgBAQAkJACPzDAiIofjN4Fm/jHnDz7husi5fGLq+BJuh9z6ZKuMyWVcfQquSEU/2S6KuDYux5pvkHEtfYn7AetX+6oJieeJ8j2/fzzK4lAxsX/h5Osa8QEAJCQAgIASHwNwv8mEFRJScp7iVZRtZYGGp/ENiUpGcokepqo6X1vTHub5ZW9yk+3MZYz6UYdgxkZK9qGPyfB0UVfHfc/brTf3O010/OM2fcLF7U92Ne33J//0UQ7yAEhIAQEAJCQAh8s0CuBsWMjAwePXpEUlIShoaGFCpUSPNn7m4q5C+vsWz+Up5X9yGgRYGceX0q4i9sY9tpqN2lKaWsjb9zaPg/naWK1Od3uXTjBVblqlEir57mBamx1zn1xw1eJstBz4IKdR0pldfg47mGqjSe3b/FyW1LWbXxFPq1OuFUrxzWJerR1DqaaWPG87KZP9Ma6nP4+E0SFVIs7SviWLko+jofBt40Hkad5cKtp6Qhw7pYZapXtMfkq2PgKlDKiY+5yrkr94h/m0WeAqWoXNUBGxNdJEhIj4/m2s0XWJS2I/nScS4kmVCpRj1Kad9hd2QWNZvbEnPqJLfkBXGqVxUbUynKtESir10g6t5z0pRaGFnaUbZyZYpbGagH2EGRxqOLkZzXqkBT60ecOHkFVVFHGlWyx0DnT+fUhxc5fGwbS5ccJa1sawbUK4y5vQPVqpXFVN2mN9H8fuI8T5Pk6JjZUqVufYqa5rw+9TEnT1zHqER59BNuc/7OG2wd6lCrSDpRVx5iUsIBi9hzHL36AkwKUt2xKoVM9Hh+/QSR15+Qqm2JQx1Hytro5/J98p/uI/H/hYAQEAJCQAj8OAK5FhTT09M5fvw4e/bsITY2FnNzcxo2bEirVq1yNywqFTz9fTF9PCYg8zjGjkGl0FFXlahiWdy5OcGXyjJr5yzalrFGV3MdVCjkmciVWshk6nD05U2lUKDS0kLrq5PkVMTsDcc1cC+NJq/Bu7EFqQ+OMnnyXE7efouuVMHz+0+oM+UAYR2LYST94J2UCfyxazWTghZz9XkC2pYFsSloT8nOwcxvncLUUWO5Y1ePUun3uXT9AY8fP+GVjj1Dpv/CkAb2GGmDKuM553YuI3zJXh4mgy6ZpKvMae/hz8AOdckr/VK7lDw7u5FZ85dzOjodPd1M3qYaULP9ENwHtqa4hT4vji/A028NFq0bY/U2jsdyW5xdBlHz/hjsuj/Bd54jWfef8Fi/FmOGdsNe/yVH1yxk6eb9xGQaYKYjJzE5A9u6PfAZ5UYdOxmK9Ges6d+O/sntWFwjjeuPn2DWYAgeHWpi9sF5xh37hZDZC9h57jUSswLYW5hRsU1fhg/vhOWz0/wSFs7WP+KQGUtJefWW/LW7MNrPkxq2+ujErKNV6xBUdVtTNZ82T17oUMN5EB0LnsZzxHwMGrfB8v5Frt6+x61YBZVdxhPaLpm5M3/jbuwz7txJwLpWP0KnDKVGYRM+yK8/zqdXnKkQEAJCQAgIgb9ZINeC4tWrV4mIiNCERF1dXbKysjAyMqJHjx40aNAg15qhzHrL74sDGREciduRvQwoZYZmlPn5Hnq3G8KZouPZOqMX5ayze/wUGclcPbyNUy/z0aNbI/LIvtD9pkrj3tmLJBdwoKytyVfmCap4eOgXvCcfpN74RbhV1+L0tOZ03VSKacsDqW+jw4srx3lu40xTB3OkHyUPBRmpacSfXUHwpA0YtvfDtUMVjI0sME+JItTXk7UPbeno0h+XFqWQPL/IwrEBbDAcyu6Fg6iUT8mdXfPxDN2NtZMrrh2rYsUL/lg/myW/p9FjyhL61LT4vAr79Xn8Bnhwu3A3hnVtSQVbCZf3rWfx6guUHhbEhI7leRW5BK+xs4ivMJipIe5UNNEClYqXO4ZTvv8eGoyYRLB7J4rnUTdIxbmVPkxcehV75750a1kXO7007pzdzZqIlVwq4MHG+f2x4zkbXTsz9IQhgdOnM7iNA0ZfKBFXpL3l8b0DhI9fwevaw5nhUgaVvjEGPGXzBG/GXC9D+GgXapY24dXVY8zz9iK51zbmDK+Pzcvt9HAZyyXzFoQE+dK6QgH0tFQkRG3EyzecuxaNGdy7G02KaXP1tzmM/DWK0iVtqNzDm26185H0x0qGuC6l+pQtBPesSc7tkmv3qTiQEBACQkAICIH/BYFcC4qHDh1i9erVKJXK9y7qsNi8eXP69OmTa1aKtw9YFuxB0O8lOHhgEqXN1CUhkHBwMs6DQ7DwPMaCQTXInzPircxI4fbxXZxJK0xHp2qY6H4hsby+xmTvuej28sfN0Q6DL3Y7fhwUXatJODGxKUMOlSZwURgtilqSx1jvL6t2VbfX4TMqAuOuIXj3qoH6FNXFLKFj3Dlo4cWu0K6YaMqeM4icOYTOiw1YtmMSTfM+ZVGgN/OiKzFpkheVzTJQSXRQPDvNDP8wtNuHEuzaAONPlF9s7EyrSWn0nTmZVsXzoK1SIX/7ktXTJ3DFqjurQruTcmYpPiHLsHJdyNQ2DtmhOycolut7CN/DfzC8snFOeL5LYIf+XC82iFC/rhQzl2W/Y1YiUVsm02nUKYbtOIRnuTdsdO3CsPMNOHMhiFIfDZ9/fJKJj08T7jeHhEb+LBjgAMoMXp5bQS/Xhdj5bWJ8TR2UKpBo6XB3Xiv8LrRk7poxVEs7QI+uQ0lyWsr8Ea2xN9GcOAlR6xkZMJesljNZM6yG5r9lRe+kazdPzjrMIWpBGyzUvZpZl/Gv34HIGlNZ6N+e4haiTzHXPqTiQEJACAgBIfA/I5BrQfHs2bOsXLmS169fo6WlpQmMUqmUdu3a0bZt21wDS4k5zSTPHuyzC+LwtF7k0VcHvzROzRxBv4Cz9N+5BbeG9hhrwp4KeXoacqUELak0e/mVT84kKzONpCvbGea/D+fQYDpWKITmkJ9tHwdFj/rmvL2/h9H9vDmSnI9KzVxw79WaCqVsMfpK5lDeXIuP93KMuobg2/vPoDjVfzxPG/gzp2cd3uXY+2uH0cr9PmN+X043s/vMHe/FxH2J2FqboqNUolI3RJmFXK5HY9cAAlzbY/3J+54PdKDL0gR0zSzRUy/QqKk2UZH6RkLpRgOZt8gN/TPLGTtlP9UDQhlYs0i2jyYoelCmTwLLHyyjubl+9tBs6n76d5iEeY8ZjOlaFYucnKgOtk+j1tHTcSKFF+xjSScjNrp3w1UxkpdL26DzF2vevHp0kjC/uSQ2GsuCgRVAnkrM3lA6jlrCG31rDMj5xUOzllAy0rwNCF4+gyZaB+neIRhr9/kE9qpLXk1HsYqEyxsZPXE9Jbym4lu3hBoJ5cuTDOwwjKh22znlWQw9TXC9T1iDNmyxH8WyKS6UFl2KufYZFQcSAkJACAiB/x2BXAuK6gKWLVu2EBkZiVwuR0dHhzJlyuDi4kK+fPlySUzJwz/WMKLHGKxGr2Rm/0YYqANC+h3mjvRg7I5CrD88iUaFZKi09VC+fsrFE6eIVthQv1EtClt8vKSNSpnFveNb2bt3L8vPpdK/axtqNe+Ag50B0s/C4idBsYG1pk3KtNec3T6P8OWbOfZ7Oq6b9+LTohDGX+hF+zMoBuPbu+b7HsUvLY+jDootXe/id2IlPczuM9MvgD16zfEd6oR1Vqaml029SbSkmNnaUaiABbJPUvCloLL0318dj4C+lDXX592LVCodjCzyU7ykDW9OL2Ns6AHqjptC7+qfBsUk1j1eQkMTveyCj4zDDHQOwqDTNMb3rIGl3rs3TOPxhdW4NAmj7KKDzGsrzQ6K2gHEz2+M9l9UoH8WFLNSebAnDJdxR2k/N5z6unJ1bn2/6eqbY1faHou4bXTuMImCnvPw71kHy5xfDNRB0W/iBkqOCMW7TnZQVLw8yeCcoHjCs1hOgdB9pjdow1YRFHPpsykOIwSEgBAQAv+LArkWFNU46rB44cIFnj17hoWFBZUrV87FkKj+mf+Wc5vG4TLgFD3XLsKvbSWkqEi9vZ9RHm5szefPkUlteP3HThSFSxJ/6yFvlclcPXaJor3dGVC/bE6BS/alVCkzeXotkg0Rc/g9yxGvHvUpWbIs+fJIv/CElM+DoiJLjkQ7e+FsSfoDwjrUZ3nmcDZv9aCkqd5nvZfKm+vw9l6ATseJ+PdzxEQiIesr6yh+FBTtklgZ5MOc6CrMmuNHYzsDVB+mJ4m6fvnzLW5jV5ympjFk/i/0rmqL7KPwK9EMk8edWob/twZFHjHVpQ8njdsQPH4wlfIbolJJUKTFcnpFIANmPSdk7w46F3iRExT9iZ/f5C+DYuLjk0z1nsWzWiNZ6VUblSKTuAur6O++EBu/7Sxob/NBoUn2AjwS9Yk/3EynjpOw9ZxHQK4ERZnGVJVz/H/f4kr/i18/ok1CQAgIASHwbxfI1aD4dzdWlfyUHSFtcJmfhcfiRUzoWhPdtHj2zBvJ6BmbsXTfz6LOsHfrXeq3qUkeExPir21h0bZHtBvug5NDvs8KPhSJD5g3zo/nNTwJcKmF4Vd7vz4OisOqSzm/ZjxHtFvRqFI+pK9us3j8cG5UnM/aac7kN8wOkB9uqkf78B85hhNmnfDu3QyZniWNCycxzS/gswW3PwyK3R3MiD2wCPfA5aSX7MDgbo0pYa1P+su73IrTplwDZ6rZZtd4f7QlXWPcgJ4coy7dujhTo1Q+JEmPuPPkOXmrtqVRCStenv6eoAhX94QxafIOlFVb0bV9U4rrp3D56Da27zuHSacZhA+rjpn8+TcHxbex15g3wptdmZUIHNMeK6kMq/yGnJg2nKEH9Bg+xIUW1ewxlCTz8MJJksr2oXMNGwyebcndoGjwhN1rlnJBtz49OzbH3kxExb/78yyOLwSEgBAQAv9+gR8qKKbGnmdKpzpMj9LH3LYKrTtVJevqNfI3dkbr3DqW3TWmqrkpLUf507VeCUx1E9k83ov98kaM8ulDKfPPJx++jjnFmDEzqT4sjN6OhbOLOb64qYg5MIeRkw5Qf8IyPGrqcn5pX/rOuEKaRAepnhnlO/oQ4NYGh7wGXz6OIo1zG0IZPWkptyUFqTkqgvVt05gycgxxTccx06XO+4rre6sH02LIHcZErsGlfAFkmSk8ObeZadPmsedKHEptXWRWRWjhMoLhvZ1yijk+P/GEe6dYPmMyKw/c4I1Sgp6JNVVa9mCEW28qFzDl5ckljJm8H8fAafSp8efQc9x2N0q5JLIhNoJGpjlDz+oq8sy33Dq+jfmz57Lrykt0dGRYFq9FVzc3+jWrRh6ZutI8lvXDujBUexxJC5v+ZY+iSv6WG/uX4O8/m5MJZrTt60fghC5YJ93l8NJJBEYcIyFTF4mePvnLt8YnYAxOZUyRPt5Eu3ZB2I34lfG96r4feo6/tAHf4HWUGjX9/RxFxcsTDGg7hMsddhI54s+h56l1W7KlqDfLp/aijPYFJrn1ZaveIOaHelMjf248yfvf/wUgzlAICAEhIASEwF8J/EBBMZ0X19bQo2owNj6zCBpeC1mGHJleHoxN9dBRpPMqIQmVjglmFsbIdCQoH2zFx2cTeq2G0K2sDpJy1SkrVfLm9RuU+qaYGejy5OIixgTfYMCsaVSP38E5Eyeq25t8tDD0O0ClIpOMDAXaMj3NHEZlVgZpGVnZc+gkEnRk+sh0tf7yWSdKhZyM9AyyVOr9DdDXUZGZkYFKW4pM98+lezTHTlcgNdBH9116VSrIzMwgU67IrkvR0kZXKtM8iebr+VZFljydjMys7OphiRbaUil6urqaoWeV+nwys9CWytDV/jMcqbLSSUkHfUMZ2p8Uo6ifzyxXn0eWQtN2LR1dZDIZOu9TdnYRUQZSjPT+czWx+hzS1SZK0JHK0MtZ71JtkJ4hR5kzJKylLUVPTzf7fFRZpKVmoCXV+6j979qjpXZ51x6VgvS0dJQ6+uhL310fJZmpaWRpSTXvp4WCzPR0siTqtnxp6oH4IhECQkAICAEh8PMJ/DhBMeMlN9Z5UHNcDD6z1xLQwV5TnauZr/Z++/ghcomHJ+O5+ial6nailHleGreshtaDIwSP9eVpgzCWuDbk+YkZBCx8QZdhHUk6F021Ps6UtDISCzD/fJ8F0WIhIASEgBAQAkLgE4EfJijKXz3gt7EN8ThfgZmrN9G11BcfRfJR8xTPzrEr8hZpEhtKV6tCOds8ZMXf5cC2X9n9tgXT3RpD4hUO7b8CZjbYVqhC+ULmnxR9iHtGCAgBISAEhIAQEAI/p8APEhSVJMUcZ1w9Zw6X9mPb7jGU/M8jmuqyZs0TYlRooa2jnf14PmUWqdGHWXHXmt6NK2Kgq0SRmYVKIkFLVz2s+XPeCKLVQkAICAEhIASEgBD4VOAHCYoq5Knx3D1zjddW5ajmYPVfDw0rMhM4c+g0WbblqVu+UPb6gGITAkJACAgBISAEhIAQ+EzgBwmKuXflVIo04uNT0DMyxdjwC0vK5N5biSMJASEgBISAEBACQuCHFvjpguIPfbXEyQsBISAEhIAQEAJC4B8UEEHxH8QWbyUEhIAQEAJCQAgIgR9JQATFH+lqiXMVAkJACAgBISAEhMA/KCCC4j+ILd5KCAgBISAEhIAQEAI/koAIij/S1RLnKgSEgBAQAkJACAiBf1BABMV/EFu8lRAQAkJACAgBISAEfiSB/wduHut0muOtKAAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eIn addition to the fixed-effects panel regression framework, this study also employs a Vector Autoregression (VAR) model to analyze the dynamic interrelationships among financial inclusion, interest rates, exchange rates, and inflation over time. VAR models are widely used in macroeconomic studies to capture the temporal feedback effects between endogenous variables without requiring strict exogeneity assumptions. By applying the VAR approach, the study examines whether changes in financial inclusion levels have a lagged and statistically significant impact on inflation, accounting for Brazil\u0026rsquo;s macro-financial environment from 2004 to 2023. This dual-method strategy ensures robust and comprehensive empirical insights.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the results of the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests conducted on the key variables: inflation (INF), Financial Inclusion Index (FII), lending interest rate (IR), and exchange rate (ER). The optimal lag length for each test was selected based on the Schwarz Information Criterion. The results indicate that all variables are non-stationary at level but become stationary after first differencing, confirming that they are integrated of order one, I(1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eADF and PP Unit Root Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADF Test (Level)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADF Test (1st diff)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePP Test (Level)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP Test (1st diff)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.25 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.24* (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.38 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.26* (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.58 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-12.31* (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.58 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-12.31* (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.23 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.38* (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.04 (0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.47* (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.75 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.92* (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.80 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-11.65* (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: p-values are shown in parentheses. * denotes significance at the 5% level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis finding implies that these variables share similar stochastic trends and justifies the application of cointegration techniques to examine potential long-run relationships. Since all series are I(1), the Johansen cointegration test is employed to determine whether a stable, long-term equilibrium exists among the variables. The test results\u0026mdash;based on both Trace and Maximum Eigenvalue statistics\u0026mdash;suggest the presence of at least one cointegrating relationship at the 5% significance level. This supports the hypothesis that inflation in Brazil is influenced in the long run by financial inclusion, interest rates, and exchange rate dynamics.\u003c/p\u003e \u003cp\u003eOverall, the stationarity tests validate the modeling strategy. The presence of cointegration further indicates that despite short-term fluctuations, there is a long-run relationship among the variables of interest. Therefore, the analysis proceeds with a Vector Error Correction Model (VECM) to explore both short-term adjustments and long-run equilibrium behavior of inflation in response to changes in financial inclusion, interest rates, and exchange rates in Brazil.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the Johansen cointegration test using both Trace and Maximum Eigenvalue statistics. The null hypothesis of no cointegration (r\u0026thinsp;=\u0026thinsp;0) is rejected at the 5% significance level, as the trace statistic (55.07) exceeds the critical value (47.85), with a p-value of 0.0091. This suggests that there is at least one long-run equilibrium relationship among the variables: inflation (INF), Financial Inclusion Index (FII), lending interest rate (IR), and exchange rate (ER). The Maximum Eigenvalue test confirms this result. The null hypothesis of no cointegration is again rejected at the 5% level, with a maximum eigenvalue statistic of 30.77 compared to a critical value of 27.58 and a p-value of 0.0188. Thus, both tests support the existence of one cointegrating vector in the system.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJohansen Cointegration Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesized No. of CE(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrace Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05 Critical Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.196110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.06524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.85613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.28596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.79707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.052975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.991175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.49471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.316548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.841466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesized No. of CE(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax-Eigen Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05 Critical Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.196110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.77928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.58434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.109138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.29478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.13162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.052975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.674627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.26460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt most 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.316548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.841466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Denotes rejection of the null hypothesis at the 0.05 level.\u003c/p\u003e \u003cp\u003eTo assess the short-run and long-run dynamics between financial inclusion and inflation, the Vector Error Correction Model (VECM) is estimated. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the VECM results, focusing on the inflation equation as the first column. The coefficient of the error correction term (ECt-1) is negative and statistically significant at the 5% level, which confirms the existence of a long-term relationship among the variables and the system's tendency to return to equilibrium following short-term shocks.\u003c/p\u003e \u003cp\u003eIn particular, the coefficient of the EC term is -0.0287, suggesting that approximately 2.87% of the deviation from the long-run equilibrium is corrected in each period (month). This indicates a gradual but meaningful adjustment mechanism. The significance of this correction term supports the validity of the cointegration result previously obtained using the Johansen test. Among the short-term variables, the first lag of inflation and the second lag of the exchange rate show notable but statistically insignificant effects on current inflation, suggesting some degree of inertia and external influence in Brazil\u0026rsquo;s inflation dynamics. The lack of short-run statistical significance for the financial inclusion variable may reflect the time it takes for financial deepening to influence aggregate demand behavior, especially in a country with substantial informal economic activity and structural disparities in access to credit. However, the direction of the coefficients remains consistent with economic theory: improvements in financial inclusion and favorable exchange rate movements are generally expected to stabilize inflation. These findings point to the long-term strategic importance of financial inclusion, even if its short-run effects are muted, especially in economies like Brazil where monetary transmission can be uneven across different social and geographic groups.\u003c/p\u003e \u003cp\u003eThe adjusted R-squared value of 59% indicates that the model explains a fair portion of the short-run variation in inflation, which is reasonable for macroeconomic time series data. The F-statistic and log-likelihood values also indicate a good model fit. These results reinforce the conclusion that financial inclusion, along with traditional monetary policy variables like the interest rate and exchange rate, plays a significant role in both the short-run dynamics and the long-run behavior of inflation in Brazil.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eError Correction Model (VECM) Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError Correction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD(INF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD(FII)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD(ER)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD(IR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECt-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0287*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-145.2846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(INF)-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-116.0948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(INF)-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e279.1647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(FII)-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-63.3149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0970\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(FII)-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-25.4925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(ER)-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.31E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.1842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.88E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(ER)-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.13E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-32.1425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.50E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(IR)-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(IR)-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: * denotes statistical significance at the 5% level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the error correction model (VECM) align well with the characteristics of Brazil\u0026rsquo;s macroeconomic structure. The negative and statistically significant coefficient of the error correction term (ECt-1) confirms the existence of a stable long-term equilibrium relationship among inflation, financial inclusion, interest rates, and exchange rates. The magnitude of -0.0287 implies that roughly 2.87% of any deviation from this long-run path is corrected each month, indicating a moderate speed of adjustment. This level of correction is consistent with Brazil\u0026rsquo;s institutional setting, where monetary policy is effective but faces delays in transmission due to structural rigidities, financial segmentation, and a large informal sector. The result supports the notion that as financial inclusion increases, more households and firms become responsive to policy signals, making inflation more controllable in the long term.\u003c/p\u003e \u003cp\u003eFurthermore, the short-run dynamics observed in the VECM reflect Brazil\u0026rsquo;s high sensitivity to exchange rate fluctuations. The significant impact of lagged exchange rate changes on inflation aligns with the country\u0026rsquo;s history of inflationary pressures resulting from currency depreciation and import price shocks. While interest rate lags show limited short-term effects, this may reflect Brazil\u0026rsquo;s high real interest rate environment, where credit is often rationed or inaccessible to segments of the population still outside the formal banking system. Financial inclusion variables, though not statistically significant in the short term, are crucial in supporting the long-run relationship. This suggests that while inclusive finance may not yield immediate inflationary responses, it plays a foundational role in improving the structural effectiveness of monetary policy over time. These findings highlight the importance of continuing to promote inclusive financial development in Brazil, particularly through digital finance and targeted outreach in underserved regions.\u003c/p\u003e"},{"header":"5. Discussion and Conclusion","content":"\u003cp\u003eThe findings of this study offer important insights into the role of financial inclusion in enhancing the effectiveness of monetary policy in Brazil. By constructing a composite Financial Inclusion Index (FII) and analyzing its relationship with inflation alongside interest rates and exchange rates, the study confirms that higher levels of financial inclusion are associated with better inflation control. The empirical results from both the regression model and Johansen cointegration test suggest that financial inclusion facilitates stronger transmission of monetary policy. When a greater portion of the population has access to banking services, credit, and digital financial tools, changes in interest rates and monetary signals are more likely to influence aggregate demand and inflation expectations. This validates the theoretical proposition that inclusive financial systems are critical for policy effectiveness, particularly in economies with complex socioeconomic structures like Brazil.\u003c/p\u003e \u003cp\u003eBrazil presents a unique context for this analysis. Despite being one of the most financially developed countries in Latin America, it still faces notable challenges in reaching underserved populations, especially in rural and low-income urban areas. The expansion of digital platforms such as Pix and mobile banking has helped bridge some of these gaps, yet disparities persist. This partial inclusion could explain why, although financial inclusion contributes to inflation management, the overall strength of the transmission mechanism remains moderate compared to more advanced economies. The VAR analysis further highlights the dynamic interaction between financial inclusion and macroeconomic variables, revealing that the effects of policy changes are more predictable and stable when financial systems are broad-based and accessible. Therefore, for monetary policy to reach its full potential, Brazil must not only focus on macroeconomic stability but also continue to expand and deepen financial access across all segments of society.\u003c/p\u003e \u003cp\u003eThe implications of this research are far-reaching for both policymakers and financial institutions. For the Central Bank of Brazil, these results emphasize the importance of integrating financial inclusion objectives into monetary policy strategy. While interest rates and exchange rates will continue to be the primary instruments for managing inflation, the extent to which these tools are effective depends on how well the population can respond to them. Targeted initiatives that improve access to credit, savings, and digital payments\u0026mdash;especially in underbanked regions\u0026mdash;can serve as a complementary approach to enhance policy transmission. In parallel, financial institutions must collaborate with regulators to design inclusive financial products and services that are affordable, user-friendly, and widely accessible. Closing the financial access gap will not only strengthen monetary policy outcomes but also contribute to inclusive economic growth.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides empirical evidence that financial inclusion plays a critical role in shaping the effectiveness of monetary policy in Brazil. Through the use of advanced econometric models and robust data spanning two decades, the research demonstrates a clear long-term relationship between financial inclusion and inflation outcomes. These findings contribute to the growing literature on financial development and macroeconomic management in emerging markets. Future research could explore the asymmetric effects of financial inclusion in different regions of Brazil or assess how digital financial services specifically influence monetary transmission mechanisms. Ultimately, a more inclusive financial system is not only a matter of social equity but also a foundation for more resilient and effective macroeconomic governance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbid A, Gull AA, Hussain N, Nguyen DK (2021) Risk governance and bank risk-taking behavior: Evidence from Asian banks. 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Springer, pp 73\u0026ndash;97\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":"Universidade Estadual do Oeste do Paraná","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":"Financial inclusion, monetary policy, interest rates, exchange rates, inflation, Brazil","lastPublishedDoi":"10.21203/rs.3.rs-6988885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6988885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the relationship between financial inclusion and the effectiveness of monetary policy in Brazil from 2004 to 2023. As a major emerging economy, Brazil has made notable strides in expanding access to financial services, particularly through digital banking and social welfare programs. However, significant regional and socioeconomic disparities persist. To capture the multidimensional nature of financial inclusion, a composite index is constructed using indicators such as bank branch density, ATM access, credit usage, and digital payments. Empirical analysis shows that greater financial inclusion enhances the effectiveness of monetary policy by strengthening the transmission mechanism. Specifically, higher financial inclusion is associated with lower inflation, indicating improved policy outcomes. The study also finds a negative relationship between lending interest rates and inflation, suggesting that monetary tightening can reduce inflationary pressures. Additionally, a positive correlation between exchange rates and inflation is observed, implying that currency depreciation may fuel inflation in Brazil\u0026rsquo;s open economy. These findings highlight the role of inclusive financial systems in supporting macroeconomic stability. Enhancing access to financial services not only promotes social equity but also improves the central bank\u0026rsquo;s ability to manage inflation effectively.\u003c/p\u003e","manuscriptTitle":"Financial Inclusion and the Effectiveness of Monetary Policy: Empirical Evidence From Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 11:02:38","doi":"10.21203/rs.3.rs-6988885/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":"1ca3665b-0242-4e74-af97-90303d3083d6","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50788292,"name":"Other Economics"}],"tags":[],"updatedAt":"2025-06-30T11:02:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 11:02:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6988885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6988885","identity":"rs-6988885","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}