Finance and Growth (LLB, 2024-2025) PDF

Empirical Banking Finance and Growth Levine, Loyaza, and Beck Stefano Caiazza 2024-2025  Levine, Loayza, Beck (LLB, 2000) While past work shows that the level of financial development is a good predictor of economic growth (King and Levine, 1993; Lev...

Empirical Banking Finance and Growth Levine, Loyaza, and Beck Stefano Caiazza 2024-2025  Levine, Loayza, Beck (LLB, 2000) While past work shows that the level of financial development is a good predictor of economic growth (King and Levine, 1993; Levine and Zervos, 1998; Neusser and Kugler, 1998; Rousseau and Wachtel, 1998), these results do not settle the issue of causality. Although LLB does not fully resolve all concerns about causality, it uses new data and new econometric procedures that directly confront the potential biases induced by simultaneity, omitted variables, and unobserved country-specific effects that have plagued previous empirical work on the finance-growth link. Methodologically, the paper uses two econometric techniques: (1) Cross-sectional instrumental-variable estimator (2) Generalized method-of-moments (GMM) dynamic panel estimators  Financial intermediary development Numerous theoretical models show that economic agents may form financial intermediaries to mitigate the economic consequences of information and transaction costs. The theory further suggests that financial intermediaries influence savings and allocation decisions that may alter long-run growth rates by providing these services to the economy. To evaluate the empirical predictions advanced by a variety of theoretical models regarding the relationship between finance and growth, therefore, we would ideally like to construct measures of the ability of different financial systems to research and identify profitable ventures, monitor and control managers, ease risk management and facilitate resource mobilization. It is impossible, however, to construct accurate, comparable measures of these financial services for a broad cross-section of countries over the past 35 years. Consequently, to measure the provision of financial services, this paper constructs three indicators of intermediary financial development.  Financial intermediary development indicators LIQUID LIABILITIES equals liquid liabilities of the financial system (currency plus demand and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP. This is a standard measure of ‘financial depth' and, thus, of the overall size of the intermediary financial sector (King and Levine, 1993). This commonly used measure of financial sector development has shortcomings. It may not accurately gauge the effectiveness of the financial sector in ameliorating informational asymmetries and easing transaction costs. COMMERCIAL-CENTRAL BANK equals the ratio of commercial bank assets divided by the commercial bank plus Central Bank assets. COMMERCIAL-CENTRAL BANK measures the degree to which commercial banks versus the central bank allocate society's savings. The intuition underlying this measure is that banks are more likely to identify profitable investments, monitor managers, facilitate risk management, and mobilize savings than central banks. PRIVATE CREDIT equals the value of credits by financial intermediaries to the private sector divided by GDP. PRIVATE CREDIT isolates credit issued to the private sector instead of credit issued to governments, government agencies, and public enterprises. PRIVATE CREDIT is a broader measure of credit issuing by financial intermediaries, and its time dimension is twice as long, 1960- 1995. While PRIVATE CREDIT does not directly measure the amelioration of information and transaction costs, we interpret higher levels of PRIVATE CREDIT as indicating higher levels of financial services and, therefore, more significant financial intermediary development. Descriptive statistics Histogram - Income per capita and financial development https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups  Cross-country analysis Growthi = α + β Financei + γ [Conditiong Set ]i + ε i Period: 1960-1995 Growth = Real per capita GDP growth Banking = (1) Liquid Liabilities (2) Commercial Central Bank (3) Private Credit Conditioning Set = Log of initial per capita GDP The Simple conditioning information set School attainment The Policy conditioning information set Government size Inflation Exchange rate premium The Full conditioning information set Opening to international trade Political stability Ethnic diversity  Endogeneity and Estimators { } ^ −1 β 2 SLS = X ' Z ( Z ' Z ) Z ' X X ' Z ( Z ' Z ) Z 'Y −1 −1 βGMM = ( X ' ZWZ ' X ) X ' ZWZ 'Y −1 Where W is the weight matrix=S-1, i.e., the inverse of the covariance matrix of the moment conditions. The problem is to find out good instrument(s) in the appropriate numbers. Just-identified case: dim(Z)=dim(X). The number of instruments is equal to the number of endogenous regressors. Underidentified case: dim(Z)