Econometric Approach Research Articles

Does supplemental irrigation enhance smallholder monsoon season rice yield? Evidence from Bangladesh

AbstractIrrigation is one pillar of the Green Revolution that drove dramatic agricultural productivity gains across Asia. In Bangladesh, irrigation uptake has been so significant that 97% of dry‐season rice is now irrigated. While most Bangladesh monsoon rice is completely rainfed, supplementary irrigation is sometimes employed where late monsoon onset is potentially yield‐limiting. Station‐controlled experiments provide a narrative of positive yield benefits from supplementary irrigation. In contrast, statistical evaluations of actual farm experience mostly show no yield benefit and lower profitability for supplementary irrigation adopters. To add evidence on this controversial practice, we evaluated data from 2012 and 2015 Bangladesh farm household surveys with causality econometric approaches that control for differences between supplementary irrigation adopter and non‐adopter groups. After controlling for self‐selection and endogeneity, we found no statistically significant yield benefit for supplementary irrigation. Our results support scepticism about the profitability of supplementary irrigation. As such, we recommend careful consideration of the mixed evidence on effectiveness in future supplementary irrigation project benefit cost analyses. Further evidence over a longer time and accounting for a broader range of crops is also important moving forward.

The Sovereign Wealth Fund and Nigerian Economic Development

The study specifically investigated the impact of sub-national government sovereign wealth funds on the economic development of Nigeria. An ex-post research design was used for the study. Economic development was proxied by per capita income, while sovereign wealth funds were proxied by their three components: stabilization funds, future generation funds, and infrastructure funds. Data for the variables were obtained from the Central Bank of Nigeria for the period 2012–2022. An econometric approach was used to analyze the data. Findings showed that of the three components of SWFs, only future generation funds impact positively and significantly on economic development. In view of the findings, it is recommended that the government ensure effective and efficient management of the various funds by putting in place proper regulatory measures so as to attain the goals for which the funds were established.

Econometric modeling of the mutual impact of import-export operations in the Republic of Azerbaijan

The recent economic changes have led to the formation of new bases for the interaction of the domestic economy with the world economy, which has significantly increased the role of external economic factors in the country’s development. The volume of foreign trade, as well as the structure of export and import of goods, is mainly determined by the state of the country’s economy and production. Therefore, it is essential to analyze the impact of economic factors such as the volume of industrial production and domestic consumption, the volume of gross domestic product (GDP) and per capita income, the ratio of domestic and world prices, and the real exchange rate of the national currency. The primary objective of this article is to statistically analyze, reflect, and forecast the volume of export and import during the transition period of the economic development of the Republic of Azerbaijan. The econometric analysis of foreign trade operations, specifically import-export activities, in the Republic of Azerbaijan for the years 1995-2022 has been conducted in this research. Econometric research approaches were implemented using the operations of the Eviews 8 program package. Regression analysis, correlation analysis, construction of graphs, Descriptive Statistics, Dickey-Fuller test, Granger test, Cusum test, and Cointegration were performed using the tools in the Eviews 8 application package for evaluation.

Validation of the 5C questionnaire to assess the hesitancy towards adult vaccination among the Hindi speaking population of Northern India

Background and objectivesTo enable an accurate assessment of vaccine hesitancy among adults, a validated Hindi vaccine hesitancy questionnaire, encompassing multiple sub-domains of vaccine hesitancy, is a pre-requisite. The following study was conducted to translate and determine the reliability, content, construct, and concurrent validity of the Hindi Adult Vaccine Hesitancy scale (5C) among adults in Northern India.MethodsTranslation-back translation of the 5C tool (which comprises five domains of vaccine hesitancy: confidence, calculation, complacency, constraint, and collective responsibility) to Hindi, followed by cognitive interviews among the target population, was done. Item-wise (I-CVI) and full-scale Content Validity Index (S-CVI) were determined through a panel of experts. The robust econometric approach of Confirmatory Factor Analysis (CFA) was employed and construct validity were tested among 150 participants. Discriminant and Convergent validity were examined using the Average Variance Extracted (AVE). The reliability of the domains was assessed through Composite Reliability (CR).ResultsThe I-CVI and Kappa statistics of all tool items ranged from 0.8 to 1.0. The S-CVI/AVE was calculated to be 0.97. The composite reliability (CR ≥ 0.70) and convergent validity (AVE ≥ 0.50) coefficients were found to be adequate. AVE of the latent variables was greater than the squared values of the latent variable correlations, indicating adequate discriminant validity. 5C tool in the Indian setting demonstrated a ‘good fit’ established through confirmatory factor analysis.Interpretation and conclusionsHindi version of the 5C scale is a valid tool to assess vaccine hesitancy among the Hindi-speaking population North Indian Population. Willingness to take the recommended vaccines was positively correlated with ‘confidence,’ ‘calculation,’ and ‘collective responsibility’ and negatively correlated with ‘complacency’ and ‘constraint’ domains.

Strategic resource management for economic sustainability: Assessing the impact of technological advancement and energy efficiency

In the last two decades, numerous empirical studies have been done exploring the relationship between natural resources and economic growth. Still, the contradictory findings lead the policymakers and governments to adopt inadequate and inappropriate policies for sustainable development. Following the trend, the present study examines the nexus of natural resources and trade with the economic performance of the top global economies during 1988–2021. Using the second and third-generation econometric approaches, this study found the validity of the long-run equilibrium relationship between mineral rents, trade, oil rents, and economic performance. This study employs the cross-section augmented autoregressive distributed lag estimator and concludes that natural resources are adversely affecting the economic performance of the region. However, trade has a beneficial impact on the economic performance. The results are found robust, where energy efficiency and technological advancement are progressively influencing economic performance. This study suggests resource management and improved trade policies to attain economic sustainability.

Economic Welfare in Central and Southeast European Union Countries – An Econometric Approach

The importance of economic aggregates per capita: final consumption expenditure and Gross Domestic Product, for the well-being of the entire population of a country determined the analysis of the economic convergence in the countries of the Central and South-Eastern European Union (CSE). The econometric approach of convergence as a steady state of these variables was performed on panel data models with an error correction term. Establishing the long-run and short-run equations describes the convergence of welfare in the CSE region and the choice of the best model.

Dynamic efficiency in MENA stock markets during COVID-19 outbreak and vaccines

PurposeThis paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.Design/methodology/approachThe authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.FindingsThe results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.Originality/valueThe results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

Climate finance in developing countries: green budget tagging and resource mobilization

ABSTRACT Information on the benefits of green budget tagging (Green budget tagging refers to itemization and linking of every budget item with climate action, mitigation, adaptation, or both) in developing countries could help boost the uptake of this practice and in turn help these same developing countries to mobilize climate-related development finance. We apply double differences in means econometric analytical approach to evaluate the association between green budget tagging (GBT) and target outcome variables of climate-related finance and CO2 emissions. The results show that all 32 developing countries that have undertaken green budget tagging are also associated with larger inflows of climate-related finance. Among the adopters, climate-related finance inflows have been significantly larger for countries with externally-initiated green budget tagging than for those with internally-initiated tagging. The results also show that a minimum level of climate-related finance is required to achieve CO2 emissions reduction. However, the current mean yearly inflow of climate-related development finance falls short of the level that drives emissions reduction. These findings point to the importance of green budget tagging and to the need to upscale climate-related finance inflows to propel improvement in environmental quality. Although green budget tagging is a useful tool for mobilizing external development finance, current external finance inflows are insufficient to achieve ambitious emission reductions. Immense effort is therefore required to mobilize the adequate scale of climate-related finance from both external and other sources.

Willingness to pay for renewables: Insights from a meta-analysis of choice experiments

Renewable energy policies contribute to solving some of the current environmental challenges, such as climate change mitigation and decarbonization, and people around the world are usually willing to pay for a greater presence of renewable energies. That willingness to pay (WTP) can be translated into a gain in social well-being. But not all renewables are equally valued, nor do all people experience the same gain in terms of well-being. Over the last 25 years, many choice experiments have focused on analyzing social preferences for different external effects associated with renewables. This article presents a meta-analysis of choice experiments to examine the evidence on WTP for renewable energies. We examined 63 articles across 28 countries, with a total sample of 52,948 participants and 84,821 observations. We used three econometric approaches (Generalized Least Squares, Weighted Least Squares, and a Panel Data Model) to estimate the willingness to pay of households for renewables around the world. Results show that the promotion of renewable energies is socially demanded, with an average minimum WTP of between €113 and €124 per household per year. The results also show that onshore wind farms are the preferred renewable energy source for electricity generation, whereas people are more reluctant about offshore wind in comparison with other renewable technologies. Finally, some geographic patterns are found, with people from Asia and southern Europe showing, respectively, a lower and higher willingness to pay for renewable energies.

Minimum Мixed Savings on Cycle of Money

This paper is about the utility of the cycle of money with minimum mixed savings. This means that it examines the crucial points of tax policy and public policy that are the best for the increase of consumption and investments, subject to the case that there exists mixed savings at the minimum level. Therefore, an analysis is based on the utility of the public sector and the utility of the uncontrolled enterprises. Thence, it is plausible to extract conclusions about the utility of the cycle of money, showing the points and the behaviors of any economy when there are mixed savings and omitted escaped savings. For this analysis is applied the Q.E. method and its econometrical approach.

An Investigation of the Effect of Covid-19 on Efficient Market Hypothesis (EMH) Anomalies: Econometric Approach

The research aims to analyze the influence of the Covid-19 crisis on changes in the daily return pattern of the LQ45 Index, and to see whether the daily return pattern of the LQ45 Index is efficient as argued by the EMH (Efficient Market Hypothesis). The observation period was before and during Covid 19. Data analysis used an econometric approach to test any EMH anomalies, as well as running the ARCH-GARCH model due to the use of dummy variables. The analysis results show that Covid-19 has no effect on the daily return pattern of LQ45, The Week Four effect is proven, but The Day of Week effect, Monday effect and Weekend effect are not found. Another finding that the trading day anomaly testing model is sensitive to the error term distribution, also suggests that good news or bad news in volatility not only depends on the asymmetric model but also the choice of error term distribution.

Which employers pay a higher college wage premium?

PurposeThe authors' work aims to identify the employer-specific drivers of the college (or university) wage gap, which has been identified as one of the major determinants of the dynamics of overall wage and income inequality in the past decades. The authors focus on three employer-level features that can be associated with asymmetries in the employment relation orientation adopted for college and non-college-educated employees: (1) size, (2) the share of standard employment and (3) the pervasiveness of incentive pay schemes.Design/methodology/approachThe authors' establishment-level analysis (data from the Basic Survey on Wage Structure (BSWS), 2005–2018) focusses on Japan, an economy characterised by many unique economic and institutional features relevant to the aims of the authors' analysis. The authors use an adjusted measure of firm-specific college wage premium, which is not biased by confounding individual and establishment-level factors and reflects unobservable characteristics of employees that determine the payment of a premium. The authors' empirical methods account for the complexity of the relationships they investigate, and the authors test their baseline outcomes with econometric approaches (propensity score methods) able to address crucial identification issues related to endogeneity and reverse causality.FindingsThe authors' findings indicate that larger establishment size, a larger share of regular workers and more pervasive implementation of IPSs for college workers tend to increase the college wage gap once all observable workers, job and establishment characteristics are controlled for. This evidence corroborates the authors' hypotheses that a larger establishment size, a higher share of regular workers and a more developed set-up of performance pay schemes for college workers are associated with a better capacity of employers to attract and keep highly educated employees with unobservable characteristics that justify a wage premium above average market levels. The authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.Originality/valueThe authors' contribution to the existing knowledge is threefold. First, the authors combine the economics and management/organisation literature to develop new insights that underpin the authors' testable empirical hypotheses. This enables the authors to shed light on employer-level drivers of wage differentials (size, workforce composition, implementation of performance-pay schemes) related to many structural, institutional and strategic dimensions. The second contribution lies in the authors' measure of the “adjusted” college wage gap, which is calculated on the component of individual wages that differs between observationally identical workers in the same establishment. As such, the metric captures unobservable workers' characteristics that can generate a wage premium/penalty. Third, the authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

Analysis of Long-Term and Short-Term Relationships between Electricity Consumption and Economic Growth in Industrialized Regions of Russia

The purpose of the proposed study is to identify long-term and short-term cause-and-effect relationships between industrial electricity consumption and economic growth through comparative analysis of two neighboring regions with approximately the same industrial potential - the Sverdlovsk and Chelyabinsk regions. To solve this problem, an econometric approach is used, based on the method of testing the boundaries of autoregressive and distributed lag (ARDL) models, which determines the presence of cointegration between series. The use of this method is indispensable when studying regional problems due to the insufficient length of time series of economic indicators in the region. The variables in the comparative analysis were industrial electricity consumption, industrial production volume, economic growth rate, per capita income, and average annual number of employees. When analyzing the data, it was revealed that significant cointegrated variables for the Sverdlovsk region are the rate of economic growth and electricity consumption. Accordingly, for the Chelyabinsk region these variables are the volume of industrial production, electricity consumption and the average annual number of employees. That is, the electricity consumption of the Sverdlovsk region in the long term does not depend on the volume of industrial production and the number of employees but depends only on the rate of economic growth. In the Chelyabinsk region, accordingly, in the long term, electricity consumption depends on the volume of industrial production, the number of employees and does not depend on growth rates. Thus, the regions that, at first glance, are similar in industrial potential differ in the cause-and-effect relationships between economic growth and industrial electricity consumption. The use of causality tests made it possible to identify long-term and short-term cause-and-effect relationships between variables. The results obtained in this study illustrate the explanatory and predictive capabilities of the econometric approach in the context of analyzing cause-and-effect relationships in the economy of two neighboring regions and its energy system. These results may be important when analyzing electricity consumption and energy saving in the industrial sector of the economy of these areas.

Среднесрочная межотраслевая эконометрическая модель экономики города Москвы

As the largest Russian region with high socio-economic indicators, Moscow affects economic development of the whole country. Therefore, the present study aims to develop a model to forecast main indicators of the Moscow economy. To accomplish the task, it is necessary to: a) choose a suitable model and forecasting methods; b) transform available regional statistics into an appropriate form; c) select an algorithm for assessing the impact of detailed budget expenditures on the Moscow economy; d) combine the obtained results to construct a forecasting model. The proposed medium-term forecasting model of the Moscow economy includes both interindustry and econometric approaches. The study justified the use of cross-validation metrics for selecting optimal econometric forecasting models. An algorithm for converting budget expenditure data from detailed expenditure codes into economic activities and product data was developed. We assessed the impact of Moscow’s budget expenditures on the economy considering intra-city interindustry connections. According to the model calculations, two complex macroeconomic forecasts were used as scenarios: the base forecast of the Ministry of Economic Development of Russia (April 2023) and the lower-growth forecast of the Institute of Economic Forecasting of the Russian Academy of Sciences (March 2023). The scenario of the Ministry of Economic Development assumes that, in 2023–2025, the Russian gross domestic product (GDP) in constant prices will increase by 1.2, 2.0 and 2.6 %, respectively. The gross regional product (GRP) of Moscow is expected to increase by 0.5, 0.8 and 1.2 %, respectively. The calculations show that, depending on these scenarios, Moscow’s GRP in current prices will grow up to 30.9-31.7 trillion roubles by 2025. Moscow budget expenditure multiplier for GRP is estimated as 0.76-0.77 for 2023-2025.

Modeling Consumer Choice between Public and Private Health Care in Russia

Demand for public and private health care services is formed through consumer decision-making in two situations: to seek or not to seek medical help and which medical institution to attend. Theory suggests that choices in these situations occur sequentially and are explained by Andersen's behavioral model. This study tests these theoretical assumptions using three econometric approaches on the Russian National Health Telephone Survey 2022 data: (1) two separate probit regressions, (2) sample selection probit, and (3) nested logit. Nested logit confirmed the relevance of all demand-side characteristics (Andersen's model factors). Being male, older, and having health problems were associated with decreased probability of seeking health care and choosing private clinics. Being married, having a high level of education, being employed, having a medium or high monthly income, and living in larger cities were associated with increased probability of seeking health care and attending private clinics. Robustness check of the nested logit model accounted for the varying levels of availability of health care facilities in different cities. Despite the differences in the availability of public and private medical organizations, trends in consumer behavior remained the same. The author proposes to expand the practice of using nested logit models to analyze the behavior of consumers of medical services using data from Russia and other countries. Recommendations for increasing the efficiency of the Russian health care system and reducing inequality in its use by different consumer groups include transforming attitudes of Russians to their own health and promoting the accessibility of health care.

The Empirical Evidence of the Phillips Curve in Bangladesh during 1991-2022: An Econometric Approach

The Empirical Evidence of the Phillips Curve in Bangladesh during 1991-2022: An Econometric Approach

A methodological framework for exploring SME finance with SAFE data.

Research on small and medium-sized enterprises (SMEs) access to bank finance is vital for the euro area economy. SMEs heavily represent the European business sector, employing around 100 million people and accounting for more than half of the Gross Domestic Product. Research studies in the field often rely on the ECB/EC Survey on the Access to Finance of Enterprises (SAFE). Many studies employ probit or logit models with categorical dependent variables derived from SAFE. The research findings show that hardly any study employs the simpler linear probability model (LPM), with a dominant lack of research providing evidence that justifies the model selection process and suitability. However, it is well known that different econometrics models can lack consistency and frequently yield different results. Yet, the literature has no consensus on the best econometric approach. In addition, there is a lack of robustness tests in the literature to ensure model validity, underlining the need for a comprehensive review of the methodological framework that dominates SAFE data use. This paper addresses the identified research gap by introducing a robust methodological framework that helps researchers identify and choose an appropriate categorical model when using SAFE data. The study adds significant value to the extant literature by identifying four criteria that need to be considered when selecting the appropriate model among three common binary dependent models: LPM, probit and logit models. The findings show that the probit model was appropriate is all cases but that the LPM should not be disregarded, as it can be used in two cases: when considering the interaction between monetary policy and debt to assets and monetary policy and innovation. The use of the LPM is justified as a less complex econometric model, allowing for clearer communication of the results. This innovative, robust approach to choosing the appropriate econometric categorical dependent model when employing SAFE data contributes to support policy effectively.

Does corruption affect the impact of financial development on entrepreneurship? Evidence from emerging economies

Purpose: This study examines the relationship between financial development, corruption, and entrepreneurship in a sample of 21 emerging economies from 2008 to 2020. Methodology: Utilizing the Generalized Method of Moments (GMM) econometric approach, we explore the interactive dynamics between these variables. Results: Our findings indicate that higher levels of corruption are associated with increased entrepreneurial activity in these economies. This can be attributed to the prevalence of corrupt practices, such as bribery, which serve as a means for entrepreneurs to overcome barriers and initiate businesses. Conversely, while financial development has a positive influence on entrepreneurship, its impact is not statistically significant. However, when considering a combined effect of financial development and corruption, a positive net impact is observed. This suggests that corruption can facilitate access to financial resources for entrepreneurs in these emerging economies. These findings support the notion of the “grease the wheels effect”. Conclusion: This study provides valuable insights into the complex interplay between financial accessibility, corruption, and entrepreneurship in emerging economies, informing policymakers and stakeholders on strategies to foster entrepreneurship and drive sustainable economic growth.

Does Similarity in Philippine Free Trade Agreements Matter in Trade?

This study proposes using text-of-trade analysis to understand the design of the Japan-Philippines Economic Partnership Agreement and the European Free Trade Association (EFTA). It examines the similarities between these agreements and other free trade agreements of Japan and EFTA member countries and how these similarities impact Philippine trade. The paper illustrates how text analysis can complement conventional methods, such as the gravity model and other econometric approaches, in assessing trade agreement impacts. The results reveal that similarities across trade agreements, whether at the document, chapter, or topic-specific provision levels, which strongly reference sustainable development, can influence and foster trade.

Understanding Intracity Housing Market Dynamics: A Hybrid Econometric and Machine Learning Approach

Understanding Intracity Housing Market Dynamics: A Hybrid Econometric and Machine Learning Approach