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The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

This paper aims to determine causal relationships between the level of productive credit, real deposits and money demand – all of them in real terms – and Gross National Product between 2006 and 2020.

The vector autoregressive technique (VAR) was used, where data from real macroeconomic aggregates published by the Central Bank of Ecuador (BCE) are correlated, such as productive credit, gross domestic product (GDP) per capita, deposits and money demand.

The results indicate that there is no causal relationship, in the Granger sense, between GDP and financial activity, but there is between the growth rate of real money demand per capita and the growth rate of total real deposits per capita.

The study shows that bank credit mainly finances the operations of current assets and/or liabilities. In addition, economic agents use the banking system mainly to carry out transactional and precautionary activities.

The study attempts to estimate farm subsidies the governments can save by transitioning to a millet-based production system, replacing GHG emission-intensive crops.

It updates a 131 × 131 commodity input–output (IO) table of the year 2015–16 into 2021–22 using the RAS procedure and simulates the economy-wide impacts of replacing rice and wheat with pearl millet and sorghum using consumption and production approaches. It then quantifies fertilizer, electricity and credit subsidy expenses the government can save through this intervention. It also estimates the potential reduction in GHG emissions that the transition could bring about. India is taken as a case.

Results show pearl millet expansion brings greater benefits to the government. It is estimated that when households return to their pearl millet consumption rates that prevailed in the early-reform period, this could save the Indian government Rs. 622 crores (USD 75 m). The savings shall be reinvested in agriculture to finance climate adaptation/mitigation efforts, contributing to a sustainable food system. Net GHG emissions also decline by 3.3–3.6 MMT CO2e.

Indian government has been actively aiming to bring down paddy areas since 2013–14 through the Crop Diversification Program and promoting millets (and pulses and oilseeds) on these farms. The prime reason is to check rapidly declining groundwater irrigation in Green Revolution states. Regulations in the past in these states have not brought the intended results. Meanwhile, electricity and fertilizers are heavily subsidized for agriculture. A slight shift in the cropping system can help conserve these resources. Meanwhile, GHG emissions could also be brought down and subsidies could well be saved. The results of the study indicate the same.

A less warm society is what governments and nongovernment organizations across the world are aiming for at present. Financial implications affect actions against climate change to a greater extent, apart from technological innovations. The effects of policy strategies discussed in the study, taking a large country as a case, when implemented appropriately around the regions, could help move a step closer to action against climate change.

The paper addresses a key but rarely explored research issue – that how a climate-sensitive crop choice will help reduce the government’s fiscal burden to finance climate adaption/mitigation. It also offers a mechanism to estimate the benefits within an economy-wide framework.

This study aims to revisit the empirical debate about the asymmetric relationship between oil prices, energy consumption, CO₂ emissions and economic growth in a panel of 184 countries from 1981 to 2020.

A relatively new research method, the PVAR system GMM, is applied.

The outcome of the PVAR system GMM model at the group level in the study suggests that oil prices exert a positive but statistically insignificant effect on economic growth. Energy consumption is inversely related to economic growth but statistically significant, and the correlation between CO₂ emissions and economic growth is negative but statistically insignificant. The Granger causality test indicates that oil prices, CO₂ emissions, oil rents, energy consumption and savings jointly Granger-cause economic growth. A unidirectional causality runs from energy consumption, savings and economic growth to oil prices. At countries’ income grouping levels, oil prices, oil rent, CO₂ emissions, energy consumption and savings jointly Granger-cause economic growth for the high-income and upper-middle-income countries groups only, while those variables did not jointly Granger-cause economic growth for the low-income and lower-middle-income countries groups. The modulus emanating from the eigenvalue stability condition with the roots of the companion matrix indicates that the model is stable. The results support the asymmetric impacts of oil prices on economic growth and aid policy formulation, particularly the cross-country disparities regarding the nexus between oil prices and growth.

From a methodological perspective, to the best of the author’s knowledge, the study is the first attempt to use the PVAR system GMM and such a large sample group of 184 economies in the post-COVID-19 era to examine the impacts of oil prices on countries’ growth while controlling for other crucial variables, which is noteworthy. Two, using the World Bank categorisation of countries according to income groups, the study adds another layer of contribution to the literature by decomposing the 184 sample economies into four income groups: high-income, low-income, upper-middle-income and lower-middle-income groups to investigate the potential for asymmetric effects of oil prices on growth, the first of its kind in the post-COVID-19 period.

This study examines the impacts of the Ethiopian developmental state model on the competition, efficiency and profitability of banks.

The competition, efficiency and profitability of the Ethiopian bank are measured using Panzar Rose, data envelopment analysis and financial ratio. Fixed-effect panel regression methods are applied to test the direction and strength of association between the Ethiopian developmental state model and the competition, efficiency and profitability of the country's banks while controlling bank-specific market structure and macroeconomic factors.

The Ethiopian developmental state model embeds the state-directed financial system, which affects the banking industry using a range of credit allocation instruments. Of which, directed credit schemes, interest rate control and the lack of financial freedom reduce the competition and efficiency of banks. The National Bank of Ethiopia (NBE) advances to the government and the sale of Treasury bills to a captive market enhances banking competition while negatively affecting banking efficiency. Interest rate control and the lack of financial freedom lower banking profitability. Unexpectedly, directed credit schemes improve banking profitability.

As with any study, this one has limitations. The intra-period comparison of efficiency is based on balanced data. Future studies can use methods that can measure the efficiency of banks using unbalanced data. The computation of the yearly H-statistic is constrained by the small sample size. The use of high-frequency data for measuring competition can provide us with better insights into banking competition in Ethiopia. Furthermore, there are a number of methods for measuring banking competition, efficiency and profitability with different assumptions. Approaching the subject of this study by applying different methods will offer different insights.

The contributions of this study to practice are at two levels. First, at the policy level, it enhances our understanding of the impacts of developmental state model policies, as implemented in Ethiopia, on the banking industry and therefore provides suggestions to policymakers to reform the sector's policies. Second, it offers input to the management of banks regarding the factors that impact the industry.

The banking industry is often studied in the context of financial liberalisation. The originality of this study lies in investigating how the competition, efficiency and profitability of banks are affected when operating in the context of significant state interventions in the industry.

Academic research has intensively analyzed the relationship between market concentration or market power and banking stability but provides ambiguous results, which are summarized under the concentration-stability/fragility view. We provide empirical evidence that the mixed results are due to the difficulty of identifying reliable variables to measure concentration and market power.

Using data from 3,943 banks operating in the European Union (EU)-15 between 2013 and 2020, we employ linear regression models on panel data. Banking market concentration is measured by the Herfindahl–Hirschman Index (HHI), and market power is estimated by the product-specific Lerner Indices for the loan and deposit market, respectively.

Our analysis reveals a significantly stability-decreasing impact of market concentration (HHI) and a significantly stability-increasing effect of market power (Lerner Indices). In addition, we provide evidence for a weak (or even absent) empirical relationship between the (non)structural measures, challenging the validity of the structure-conduct-performance (SCP) paradigm. Our baseline findings remain robust, especially when controlling for a likely reverse causality.

Our results suggest that the HHI may reflect other factors beyond market power that influence banking stability. Thus, banking supervisors and competition authorities should investigate market concentration and market power simultaneously while considering their joint impact on banking stability.

Financial inclusion and digital finance go side by side and help enhance agricultural activities; however, the magnitude of digital financial services varies across countries. In line with this argument, this study aims to examine whether financial inclusion enhances agricultural participation and decompose the significance of the difference in determinants of agricultural participation between financially included – not financially included households and digital finance – no digital finance households.

This study uses Pakistan’s household integrated economic survey 2018/19 to test hypotheses. The logit model is used to examine the effect of financial inclusion on agriculture participation. Moreover, this study employs a nonlinear Fairlie Oaxaca Blinder technique to investigate the difference in determinants of agricultural participation.

This study reports that financial inclusion positively influences agricultural participation, meaning households may have access to financial services and participate in agricultural activities. The results suggest that the likelihood of participating in agriculture in households with mobiles and smartphones is higher. Moreover, household size, income, age, gender, education, urban, remittances from abroad, fertilizer, pesticides, wheat, cotton, sugarcane, fruits and vegetables are the significant determinants of agricultural participation. To distinguish the financially included – not financially included households’ gap, this study employs a nonlinear Fairlie Oaxaca Blinder decomposition and finds that differences in fertilizer explain the substantial gap in agricultural participation. Likewise, this study tests the digital finance – no digital finance gap and finds that the difference in fertilizer is a significant contributor, describing a considerable gap in agricultural participation.

Empirically identified that various factors cause agricultural participation including financial inclusion and digital finance. Regarding the research limitation, this study only considers a developing country to analyze the findings. However, for future research, scholars may consider some other countries to compare the results and identify their differences.

The accessibility of fertilizer can reduce the agricultural participation gap. However, increased income level, education and cotton and sugar production can also overcome the differences in agriculture participation between digital finance and no digital finance households.

This is the first study to decompose the difference in determinants of agricultural participation between financially and not financially included households.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.