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The financial system of an economy, especially banking, facilitates efficient allocation of resources from savers to borrowers for productive investments, and thus promotes economic growth. State-wise bank credit in India shows a growing divergence, despite the aim of central planning to reach a degree of convergence in macroeconomic performance over time. This chapter analyzes how diverging bank credit affects macroeconomic performances of the Indian states, through an alternative approach of composite indicators-based rankings of states adopting the methodology of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) that is used in operations research or more specifically MCDM (multiple criteria decision-making). A composite indicator of the states’ annual macroeconomic performances has been constructed taking indicators of output growth, per capita state domestic product, inflation, and fiscal indicators for years 2006–2018. States are ranked by both macroeconomic performance and bank credit to states, and the correlation between the two indicators, known in the literature to be interlinked,is studied here to understand how the availability of credit or lack of it has influenced State level macroeconomic development in India. The results thus show that wealthier and better performing states continue to attract the larger chunk of bank credit, while weaker states have not been able to catch up. An important policy implication would be to place even more emphasis on higher levels of credit growth for weaker states, particularly infrastructure credit, to achieve a degree of income convergence throughout the Indian economy.

This study aims to analyze the imbalances in the public finance structure of Pakistan’s economy and highlight the need for comprehensive reforms. Specifically, it aims to contribute to the empirical literature by analyzing the relationship between fiscal vulnerability, financial stress and macroeconomic policies in Pakistan’s economy between 1971 and 2020.

The study develops an index of fiscal vulnerability, an index of financial stress and an index of macroeconomic policies. The fiscal vulnerability index is based on the patterns of fiscal indicators resulting from past trends of the selected variables in Pakistan’s economy. The financial stress in Pakistan is caused from the financial disorders that are acknowledged in the composite index, which is based on variables with the potential to indicate periods of stress stemming from the foreign exchange market, the securities market and the monetary policy components. The macroeconomic policies index is developed to analyze the mechanism through which fiscal vulnerability and financial stress have influenced macroeconomic policies in Pakistan. The causal association between fiscal vulnerability, financial stress and macroeconomic policies is analyzed using the auto-regressive distributive lags approach.

There exists a long-run relationship between the three indices, and a bi-directional causality between fiscal vulnerability and macroeconomic policies.

This study contributes to the development of a fiscal monitoring mechanism, which has the basic purpose of analyzing the refinancing risk of public liabilities. Moreover, it focuses on fiscal vulnerability from a macroeconomic perspective. The study tries to develop a framework to assess fiscal vulnerability in light of “The Risk Octagon” theory, which focuses on three risk components: fiscal variables, macroeconomic-disruption-associated shocks and non-fiscal country-specific variables. The initial contribution of this work to the literature is to develop a framework (a fiscal vulnerability index, financial stress index and macroeconomic policies index) for effective and result-oriented macro-fiscal surveillance. Moreover, empirical literature emphasized and advised developing countries to develop their own capacity mechanisms to assess their fiscal vulnerability in light of the IMF guidelines regarding vulnerability assessments. This study thus attempts to fulfill the said gap identified in literature.

The study constructs a composite indicator to rank macroeconomic performance of countries and a separate composite indicator to rank countries by inequality using the TOPSIS methodology of Multiple Criteria Decision-Making Analysis. The intuitive idea of TOPSIS is to formulate an ideal solution with respect to each individual policy variable; the relative rank of any country is then determined, using a suitable distance metric, such that the best performer simultaneously has the shortest distance from the ideal solution and the farthest distance from the non-ideal. It uses the composite indicator based rankings together with the KOF Globalization Index and sub-indices based rankings to examine the overall relationship between globalization and macroeconomic performance of countries and reduction in inequality; the impacts of trade and financial globalization for 1990–2018 across countries and groups of the globe. It shows that though highly correlated with growth, globalization may not necessarily lead to an improvement in overall macroeconomic performances of countries when one also takes into account unemployment and inflation. Economic globalization is seen here to mostly coincide with rise in income inequality. Observations also support the fact that countries, even if they are not highly integrated may reap sufficient benefits of globalization for macroeconomic performance and inequality diminution given supportive policies.

The purpose of this paper is to analyze determinants of institutional quality based on six separate indicators of governance: voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption.

The determinants under consideration include measures of economic freedom by the Cato Institute and the Heritage Foundation, indicators of policy quality, real per capita gross domestic product (GDP), risk rating, and the degree of openness.

Five measures of institutional quality increase real GDP growth significantly across Middle East and North Africa (MENA) countries. In contrast, institutional quality has a negative impact on the growth of private credit and private investment. Further, the combined evidence does not suggest that improvement in institutional quality is a major factor in attracting foreign direct investment flows to MENA countries.

The research provides startling evidence that illustrates how institutions have impacted macroeconomic performance and the underlying roots of institutional quality. Addressing shortcomings in institutions should top the policy agenda in an effort to drive the growth process.

Improving institutional quality will distribute the benefits of growth and enhance macroeconomic performance in the MENA region, which is rich in endowed resources. Nonetheless, the region lacks fundamentals of economic management and quality governance to utilize resources in the most efficient and productive fashion in order to maximize the welfare for a growing population that is constantly seeking productive opportunities to secure employment and a higher real standard of living.

The MENA region is understudied and worthy of much more empirical work. Many cross‐country studies of the determinants of growth omit oil‐producing nations. Focusing on this oil‐rich region is an attempt to fill this void. Unlike previous literature on the relationship between institutions and growth, the paper's approach to the issue analyzes micro foundations in the transmission channel between institutions and economic growth.

This study aimed to know the effect of cross-sectional risk, which comprises business-specific risk and stock market volatility, as a variable for estimating macroeconomic risk in Indonesia. This study observes public companies in Indonesia and Indonesian macroeconomic data from 2004 to 2020. In this study, the author uses term spread as the dependent variable that reflects macroeconomic risk. The cross-sectional risk comprises financial friction (FF), cash flow (CF), debt–service ratio, and stock market volatility as independent variables. By using the Autoregressive Distributed Lag (ARDL) Model method, this study shows that business-specific and stock market risk can estimate macroeconomic risk, so that it becomes an early signal of economic shock, such as recession or high inflation, in the future. The model in this study also examines the cross-sectional risk relationship with other macroeconomic indicators, such as the Consumer Confidence Index (CCI), money supply (M0), and Indonesia’s trade balance (TB).

The objective of this paper is threefold. First, it aims to empirically study whether firm-specific/idiosyncratic uncertainty, macroeconomic/aggregate uncertainty and political uncertainty have an adverse influence on firms' investment decisions in Pakistan. After establishing this, it scrutinizes whether the uncertainty effects on investment are different for firms of different sizes. Finally, it investigates whether any heterogeneity exists in the uncertainty impacts across different industries.

The empirical analysis is based on an unbalanced panel data of 468 nonfinancial firms listed at the Pakistan Stock Exchange (PSX) during the period 2000–2018. Departing from the literature, the paper builds a time-varying composite volatility/uncertainty index based on the principal component analysis (PCA) by utilizing the constructed volatility series for sales, cash flows and return on assets to gauge firm-specific uncertainty for each firm included in the analysis. Likewise, the paper develops a PCA-based composite index for macroeconomic uncertainty by using the conditional variance series of consumer price index (CPI), industrial production index (IPI), the interest rate and the exchange rate obtained by estimating the (generalized) autoregressive conditional heteroscedastic, (G)ARCH, models. Finally, political uncertainty is measured by political risk components maintained by the Political Risk Services Group. The empirical framework of the paper augments the standard investment equation by incorporating all three types of uncertainty. Firms are grouped into small, medium and large categories based on firms' total assets and the size indicators are generated. Next, the indicators are multiplied by each uncertainty measure to quantify the differential effects of uncertainty across firm size. Firms are also differentiated by sectors to explore the sector-based asymmetries in the uncertainty effects. The “robust two-step system generalized method of moments (2SYS GMM) (dynamic panel data) estimator” is applied to estimate the empirical models.

The results provide robust and strong evidence of the detrimental influence of all three types of uncertainty on investment. Yet, it is observed that the strength of the influence considerably varies across uncertainty types. In particular, compared to firm-specific uncertainty, both macroeconomic and political uncertainties have more unfavorable effects. The analysis also reveals that the effects of all three types of uncertainty are quite different at small, medium and large firms. Specifically, it is observed that although the investment of all firms is influenced adversely by magnified uncertainty, the adverse effects of all three kinds of uncertainty are quite stronger at small firms than medium and large firms. These findings support the phenomenon of size-based asymmetries in the effects of uncertainty on investment. The results also provide evidence that either type of uncertainty quite differently affects the investment policy of firms in different sectors.

The findings help different stakeholders to know how different types of uncertainty differently affect corporate firms' investments. Further, they suggest that firm size has a vital role in ascertaining the adverse effects of uncertainty on investment. The paper identifies to which type of uncertainty investors and policymakers should care more about and to which types of firms and industries they should concern more during volatile times. Firms should have more fixed assets and expand their size to mitigate the detrimental effects on investment of internal and external uncertainties. The government should enhance the political stability to induce firms for a higher level of investment, which, in turn, will result in higher growth of the economy.

The originality of the paper is credited to four aspects. First, unlike most previous studies that have utilized a single volatility measure, this paper constructs composite uncertainty indices based on the weights determined by the PCA. Second, it examines the effect of political uncertainty over and above the effects of idiosyncratic and aggregate (macroeconomic uncertainty) for an emerging economy. Third, and most important, it provides first-hand empirical evidence on the role of firm size in establishing the asymmetric effects of uncertainty on investment. Finally, it provides evidence on the industry-based heterogeneity in the uncertainty effects.

This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania.

The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations.

The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle.

Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary.

This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.

The cost-to-asset ratio is a vital efficiency ratio for any financial institution, as it measures its operating expenses to its asset base. This study uses this ratio to evaluate the efficiency of defined benefit pension plans (DBPPs) in the Republic of Congo using financial and macroeconomic indicators.

Under the financial indicator, the authors apply vector autoregression (VAR) to a dataset covering 120 months from 2011 to 2020. In addition, the authors use 12 years of data from 2009 to 2020 and the random effects model under macroeconomic indicators.

Assets and costs together Granger cause the efficiency of the DBPP. However, there is no Granger causality from the combination of assets and costs on the DB public and industry PP efficiencies. The random effects model results show that macroconnect level variables significantly lower the cost-to-asset ratio, thereby improving the PP's efficiency. Macrodisconnect level variables significantly increase the cost-to-asset ratio, thereby deteriorating PP efficiency.

The study is limited to a developing economy in sub-Saharan Africa, which may hinder the generalization of the results. Future studies could use panel samples from sub-Saharan Africa so that inferences could be drawn for the continent and comparisons made with others.

To the best of the authors knowledge, this study is the first in sub-Saharan Africa to assess the efficiency of DBPPs using financial and macroeconomic indicators.

The purpose of this paper is to investigate the determinants of interest rate in emerging markets, focusing on banking financial institutions in Uganda.

Using the net interest margin model, interest rate was estimated by applying a panel random effects regression method on 24 banks, while controlling for bank-specific factors, industry and macroeconomic indicators. Data were drawn from annual reports provided by Bank of Uganda Depository Corporation survey from 2008 to 2016.

The results indicate that liquidity, equity capital, market power and reserve requirement have a positive effect on interest rate. The study further finds that operational efficiency, lending out ratio, concentration, public sector borrowing and private sector credit have a negative effect on interest rate. However, credit risk does not influence interest rate.

Studied banks are grouped in one panel data set; future studies would focus on the differences in banks and establish how these differences affect interest rate. Future study would also focus on how the determinants of interest rate in Uganda are compared with those of other banks in other emerging market countries.

Bank managers need to take interest in equity mobilization because it is a reliable and cheaper source of funding bank operations. Banks should emphasize efficient operations to reduce on the cost of doing business. Government should utilize funds borrowed from banks in efficient ways to improve economic growth. The central bank should minimize the use of reserve requirement as a means of controlling money in circulation.

This is the first paper that uses annual report data from several banks and periods to investigate the determinants of interest rate in an emerging country.

With the growing interconnectedness of global markets brought about by globalization and technological innovation, there is a heightened worldwide risk of money laundering, posing a considerable negative impact on economies and social equality. Therefore, the primary purpose of this research is to examine factors that underpin the pervasiveness of money laundering risk.

By using a cross-section sample of 84 countries, the study uses ordered logit and multinomial logit regression to test and explain the role of main and varied determinants of money laundering risk covering countries’ economic, social, regulatory and corporate environment.

The authors conclude that, overall, the macroeconomic indicators are less relevant in influencing money laundering risk than the other factors adopted from the Basel report. Nonetheless, the volume of exports and the exchange rate were robust in both the ordered and multinomial regression analyses alongside financial secrecy, auditing standards and corporate transparency. While more financial secrecy and a higher volume of exports were found to increase this risk, the other variables showed a negative relationship. The authors further conclude that it is mostly less secrecy, more transparency and better auditing that could gradually transform a high-risk country into medium risk.

This study recommends the implementation of publicly accessible ownership registries to address the issues around secrecy, transparency and auditing misconducts. Additionally, the general strengthening of laws and policies in these three domains is also necessary alongside the application of current technologies, such as machine learning, for the detection of money laundering.

The authors believe this study uses advanced econometric techniques rarely used in the literature on money laundering. Separating the impact of economic and social/regulatory is also valuable