Real Rate Research Articles

U.S. economic uncertainty shocks and extreme capital flows episodes: An empirical analysis of emerging and developing economies

AbstractWe use the two‐way fixed‐effect panel logit model to examine the impact of U.S. economic uncertainty shocks on the probability of extreme capital flow episodes based on quarterly data from 71 emerging and developing economies from 1998Q1 to 2022Q4. According to the findings, U.S. economic uncertainty shocks has a negative effect on the probability of gross capital surges, gross capital flight, and net capital surges, and has a positive effect on the probability of gross capital sudden stops, gross capital retrenchment, and net capital sudden stops. Moreover, we find differences in the factors affecting net and gross capital flows, which are usually more closely related to earnings factors dominated by real economic growth rates. Additionally, the sample's heterogeneity is analysed in accordance with the exchange rate regimes. Our results differ from traditional views, as floating exchange rates do not act as a buffer against extreme capital flows. Finally, capital flows are classified into direct investment, other investment, and portfolio investment, and it is found that U.S. economic uncertainty shocks have a significant impact on the extreme flow episodes of other and portfolio investment.

Detection of the Corn Kernel Breakage Rate Based on an Improved Mask Region-Based Convolutional Neural Network

Real-time knowledge of kernel breakage during corn harvesting plays a significant role in the adjustment of operational parameters of corn kernel harvesters. (1) Transfer learning by initializing the DenseNet121 network with pre-trained weights for training and validating a dataset of corn kernels was adopted. Additionally, the feature extraction capability of DenseNet121 was improved by incorporating the attention mechanism of a Convolutional Block Attention Module (CBAM) and a Feature Pyramid Network (FPN) structure. (2) The quality of intact and broken corn kernels and their pixels were found to be coupled, and a linear regression model was established using the least squares method. The results of the test showed that: (1) The MAPb50 and MAPm50 of the improved Mask Region-based Convolutional Neural Network (RCNN) model were 97.62% and 98.70%, in comparison to the original Mask Region-based Convolutional Neural Network (RCNN) model, which were improved by 0.34% and 0.37%, respectively; the backbone FLOPs and Params were 3.09 GMac and 9.31 M, and the feature extraction time was 206 ms; compared to the original backbone, these were reduced by 3.87 GMac and 17.32 M, respectively. The training of the obtained prediction weights for the detection of a picture of the corn kernel took 76 ms, so compared to the Mask RCNN model, it was reduced by 375 ms; based on the concept of transfer learning, the improved Mask RCNN model converged twice as quickly with the loss function using pre-training weights than the loss function without pre-training weights during training. (2) The coefficients of determination R2 of the two models, when the regression models of the pixels and the quality of intact and broken corn kernels were analyzed, were 0.958 and 0.992, respectively. These findings indicate a strong correlation between the pixel characteristics and the quality of corn kernels. The improved Mask RCNN model was used to segment mask pixels to calculate the corn kernel breakage rate. The verified error between the machine vision and the real breakage rate ranged from −0.72% to 0.65%, and the detection time of the corn kernel breakage rate was only 76 ms, which could meet the requirements for real-time detection. According to the test results, the improved Mask RCNN method had the advantages of a fast detection speed and high accuracy, and can be used as a data basis for adjusting the operation parameters of corn kernel harvesters.

US recession prediction using statistical and natural language processing methods

This study mainly predicts the recession in the United States. We build our model based on the data of more than ten recessions experienced by the United States since the mid-20th century. Our research can be divided into two parts, one part is a machine learning model constructed using econometrics theory, and the other part is a text analysis model based on natural language processing (NLP) techniques. We collected quarterly data from January 1, 1950, to September 1, 2020, to examine each historical recessionary period. We select key macroeconomic variables such as real GDP growth rate, unemployment rate, and interest rates as variables to build the machine learning model. Depending on the data type and model accuracy, we adopted three models, Support Vector Classification (SVC), Naive Bayes, and Logistic Regression, where the SVC model has the highest accuracy, above 80%. Regarding NLP models, we choose the reports based on Bank of International Settlements central bank speeches (BIS) to complete the relevant analysis. We evaluate bag-of-words and convolutional neural networks in conjunction with Epoch loss to determine how well the model's predictions match the actual data. Although we have debugged the NLP model many times, its accuracy still needs to be higher than that of the econometric model. How to effectively improve the prediction accuracy of the NLP model will be the main problem we hope to solve in the future.

The Current State and Future Outlook of the US Economy

The study examines the current state of the US economy after the interest rate increases implemented by the Federal Reserve from January 2022 to July 2023 to reduce the rise in inflation. Although the Federal Reserve has successfully reduced inflation during this time period, the thesis of this paper is that future interest rate increases would be damaging to the economy and result in a recession. The five key economic indicators reviewed to assess the current state of the economy are (1) national debt, (2) real GDP growth rate, (3) inflation, (4) interest rate yield curve, and (5) unemployment. The conclusion of the paper is that the US Federal Reserve would damage several components of the economy if interest rate increases continue into the future, and it would increase the likelihood of a recession. The benefit of continuing with this monetary policy would be to decrease inflation from 3% to the publicly stated target of 2%. However, costs associated with this would be significantly higher since it would result in an increase in the US national debt and annual budget deficit because the servicing costs of the debt would increase dramatically. This would have a major impact on the US economy and eventually lead to an economic downturn.

Effect of water saturation on Cerchar abrasivity index (CAI) of clay-rich rocks at different scratch lengths

Predicting the cutter consumption rate of mechanical excavators using the Cerchar abrasivity index (CAI) results is possible. However, changing operational parameters in the field may cause differences between the estimated cutter consumption rate based on CAI test results and the real cutter consumption rate observed in the field. One of the essential parameters in the field that affects the cutter consumption rate is the increase in the water content of the excavated rocks. In theory, an increase in the water content of rocks is expected to reduce their abrasivity. In addition, the prediction of cutter consumption rate is based solely on CAI test results conducted on dry surfaces. However, especially during the excavation of low-strength clay-rich rocks, the excavated rock material can adhere to the cutters and block them, leading to a higher cutter consumption rate than initially predicted. This study aims to analyze the rock-pin interaction in CAI tests conducted on wet surfaces of clay-rich rocks and investigate the effect of water saturation on CAI at different scratch lengths. CAI tests were conducted on dry and wet surfaces of 20 clay-rich rocks with low strength at 10, 30, 50, and 70 mm scratch lengths, respectively. The relationships between the CAI test results obtained on dry and saturated rock surfaces were statistically analyzed for each scratch length. A positive linear correlation was found between CAI values obtained on dry (CAIDry) and saturated surfaces (CAISat) at each scratch length. The percentage variations in CAI values at each scratch length were analyzed in detail. It was found that 90 % of samples have higher CAIDry than CAISat at 10 mm scratch length. However, it was observed that as the scratch length increased, the relationship between the CAI values obtained in dry and saturated conditions changed, and the proportion of samples with CAIDry higher than CAISat decreased to 65 %, 55 %, and 25 % at scratch lengths of 30 mm, 50 mm, and 70 mm, respectively. In CAI tests conducted on saturated surfaces, it was observed that the excavated rock material adhered to the test pins, and as the scratch length increased, the increase in CAISat values was considered to be due to this situation. This study revealed the importance of considering the adhesion potential of rocks to cutters at water-saturated conditions for the accurate prediction of cutter consumption in the excavation of clay-rich rocks.

Effect of government revenue on economic growth of sub-Saharan Africa: Does institutional quality matter?

Following the approval of sustainable development goals at the global level, the link between fiscal policy, institutional quality, and economic growth has attracted special attention in economic literature. This study scrutinizes the effect of government revenue-institutional quality interaction on the economic growth of 43 Sub-Saharan Africa countries for the period of 2012-2022. Methodology-wise, the study employed the System Generalized Method of Moment (SGMM) to analyze the panel data gained from dependable data sources; the World Development Indicator and the Heritage Economic Freedom Index. The novelty of this study emanates from the estimation technique designated and the introduction of revenue-institutional quality into the economic growth model of SSA. The result of the study reveals that government revenue adversely affects economic growth while institutional quality positively enhances economic growth before interacting with each other. However, the interactive coefficient of government revenue and economic growth positively affected the real GDP growth rate of SSA countries over the study periods. Precisely, before interacting with institutional quality, a percentage change in government revenue, keeping all other things constant, leads to a 0.0866 percent decline in economic growth while it marks a 0.2329 percent upsurge in economic growth in the presence of institutional quality. The result of the study further shows that government revenue promotes the economic growth of the region when combined with institutional quality. On the other hand, foreign direct investment and openness to trade were the key sources of economic growth whereas the population growth rate adversely impacted economic growth in SSA countries. The policy implication of the study is that SSA needs to strengthen government revenue management. Further, the finding of the study implies that SSA countries need to improve institutional quality through promoting efficiency of the regulatory quality and the size of the SSA governments. In addition to this, the fast real GDP growth rate of SSA countries demands improved institutional quality indicators such as the rule of law and extended access to the open market.

Threshold Impact of Remittances on Real Exchange Rates for WAEMU: A Panel Smooth Transition Regression Approach

This paper explores the nonlinear relationship between workers’ remittances and real effective exchange rates for West African Economic and Monetary Union (WAEMU) member countries using the panel smooth transition regression (PSTR). The PSTR was estimated with one transition function and one location parameter as dictated by the diagnostic tests. The diagnostic tests reveal that the relationship between workers’ remittances and real effective rates is nonlinear. The estimated threshold value is 3.07%. The results reveal that increases in workers’ remittances have a depreciating effect on real effective exchange rates in regime one, associated with threshold value of less than 3.07%. However, in the second regime when the threshold value is greater than or equal to 3.07%, increases in workers remittances have an appreciating impact on real effective exchange rates. These results substantiate that the relationship between workers’ remittances and real effective exchange rates is asymmetric and as such should be modeled accordingly. Above all, the results confirm the presence of Dutch disease effect in the second regime where the estimated coefficient on workers’ remittances is negative and statistically significant. However, there is no evidence of Dutch disease effect in the first regime where the regression coefficient on workers remittances is positive and also statistically significant. Policy implications are discussed.

Capital flight and the real exchange rate: evidence from resource scarce MENA countries

ABSTRACT We analyze the determinants of capital flight in three resource scarce MENA countries namely Egypt, Morocco, and Tunisia. Our methodology involves both the linear and nonlinear autoregressive distributed lag (ARDL) cointegration approaches, with a focus on asymmetric relationships between capital flight and the real exchange rate, to distinguish the impact of real appreciation and depreciation of domestic currency on capital flight. The results based on nonlinear ARDL approach using annual data between 1977 and 2019 indicate that in Egypt, financial openness is negatively related with capital flight, while real depreciation seems to increase it in the long run. Our findings also reveal that the real GDP growth rate, institutional quality as well as inflation are important factors affecting capital flight in Morocco, whereas financial openness and the real exchange rate influence capital flight significantly in Tunisia.

A modified HJC model for geological materials subjected to blasting loadings

In this paper, a modified HJC model is proposed to address the problem that the HJC model cannot simulate tensile damage cracks. The modified HJC model modifies the strength surface, strain rate effect and damage model of the original HJC model, respectively. The modified model is embedded in LS-DYNA software for single and multiple finite element validation. The effects of different parameters on the modified HJC model stress–strain curves are analyzed in the single finite element validation. In multiple finite element verification, uniaxial and triaxial compression experiments, Brazilian disc splitting experiments, SHPB dynamic compression experiments, and SHPB dynamic splitting experiments are performed. The results of single and multiple finite element numerical validation illustrate that the modified HJC model can well simulate the pre-peak and post-peak nonlinear mechanical behaviors of geological materials such as rocks and concrete. Compared with the HJC model, the damage pattern of the modified HJC model is closer to the real damage pattern. For the strain rate effect, the modified HJC model can reflect the real compressive strain rate effect and improve the sensitivity of tensile strength to strain rate. The results of the numerical simulation of the blasting funnel illustrate that: the modified HJC model can simulate explosive tensile cracking well and can explain the formation mechanism of blasting funnel due to the distinction between tensile and compressive meridian surfaces and the addition of tensile damage. The modified HJC model is more applicable to the numerical simulation of engineering blasting.

Firm-level uncertainty and monetary policy transmission: analysis based on investment behavior and annual report text

The loose monetary policy has not translated into actual credit increments, and the real investment rate has declined. This paper extracts the level of policy uncertainty perception from enterprise management analysis and discussion based on text analysis methods to identify the investment intention of enterprises. It is found that enterprise uncertainty perception can weaken corporate investment intention, thus hindering the promotion of loose monetary policy on investment. On the one hand, this paper verifies that uncertainty perception can weaken the effectiveness of monetary policy implementation at the micro level of enterprises; On the other hand, this paper finds that merely being based on the financing constraint theory may not be able to explain the capital investment behavior of Chinese companies. It also requires examining the investment intentions of enterprises themselves, enriching the research conclusions of enterprise investment theory.

The neo-Goodwinian model reconsidered

This paper examines the relationship between aggregate demand and the wage share in the US using a vector autoregression methodology. It finds evidence of Goodwin-cycle effects – that is, profit-led demand and a profit-squeeze effect – in baseline estimates using assumptions traditionally used in the aggregative literature. However, estimates that examine the relationship between demand and the two components of the wage share (the real wage rate and labor productivity) indicate that these results are highly sensitive to ordering restrictions relating demand and labor productivity and that different types of shocks to the wage share may have differing effects on demand. The results suggest two possible interpretations of the initial Goodwin-cycle finding, depending on the assumptions used to identify the demand–productivity relationship. One suggests that the initial estimates reflect a causal relationship in which the effects are driven by a positive effect of productivity on demand and a negative effect of demand on productivity. The other suggests that the initial Goodwin-cycle finding may be spurious, as it interprets contemporaneous pro-cyclical variation in labor productivity as a profit-led demand effect, thereby obscuring an underlying wage-led relationship.

A Financial New Keynesian Model

This paper solves a standard New Keynesian model in terms of risk-neutral expectations and estimates it using a cross-section of longer-dated financial assets at a single point in time. Inflation risk premia appear in the theory and cause inflation to deviate from its target on average. We re-estimate the model based on each day’s closing prices to capture high-frequency changes in the expected path of the economy. Our estimates show that financial markets reacted to the post-COVID surge in inflation with higher short-run inflation expectations, an increase in the inflation risk premium, and an increase in the long-run neutral real rate, 𝑟∗, while long-term inflation expectations remained well anchored. Our model produces long term inflation forecasts that outperform several standard alternative measures.

Non-contact heart rate measurement from face video based on group sparse representation

Non-contact heart rate measurement based on face video is rapidly developed due to its comfort and wide application. However, it is difficult to extract the pulse signals for non-contact heart rate measurement due to the various interference factors, such as illumination variation, head motion and face expression. In this paper, we propose group sparse representation to reconstruct the pulse signals, then estimate the heart rate based on the fact that the real heart rate is consistent at the same time from different sub-regions. Specifically, we formulate the reconstruction of pulse signals as group sparse representation problem and require the raw signals of all the sub-regions to be similar sparse representation. Firstly, we use the face detection algorithm to obtain the region of interest (ROI) and divide it into sub-regions, followed by the distortion compensation of color signals from different sub-regions. Then we extract the sub-region chrominance signals and select the high-quality chrominance signals to construct the raw pulse signals matrix. After that, we construct a mixed pulse dictionary containing discrete cosine bases and wavelet bases considering the periodicity and pulsatility of the pulse signals. Finally, we conduct group sparse representation to reconstruct the pulse signals and estimate the heart rate via spectral analysis based on the reconstructed pulse signals. Experimental results on three public datasets show that this method outperforms most existing heart rate measurement methods.

Machine learning for seismic exploration: Where are we and how far are we from the holy grail?

Machine-learning (ML) applications in seismic exploration are growing faster than applications in other industry fields, mainly due to the large amount of acquired data for the exploration industry. The ML algorithms are constantly being implemented for almost all the steps involved in seismic processing and interpretation workflow, mainly for automation, processing time reduction, efficiency, and in some cases for improving the results. We carry out a literature-based analysis of existing ML-based seismic processing and interpretation published in SEG and EAGE literature repositories and derive a detailed overview of the main ML thrusts in different seismic applications. For each publication, we extract various metadata about ML implementations and performances. The data indicate that current ML implementations in seismic exploration are focused on individual tasks rather than a disruptive change in processing and interpretation workflows. The metadata indicate that the main targets of ML applications for seismic processing are denoising, velocity model building, and first-break picking, whereas, for seismic interpretation, they are fault detection, lithofacies classification, and geobody identification. Through the metadata available in publications, we obtain indices related to computational power efficiency, data preparation simplicity, real data test rate of the ML model, diversity of ML methods, etc., and we use them to approximate the level of efficiency, effectivity, and applicability of the current ML-based seismic processing and interpretation tasks. The indices of ML-based processing tasks indicate that current ML-based denoising and frequency extrapolation have higher efficiency, whereas ML-based quality control is more effective and applicable compared with other processing tasks. Among the interpretation tasks, ML-based impedance inversion indicates high efficiency, whereas high effectivity is depicted for fault detection. ML-based lithofacies classification, stratigraphic sequence identification, and petro/rock properties inversion exhibit high applicability among other interpretation tasks.

Cost of Illness of Chronic Kidney Disease with Hemodialysis at Dr. Moewardi Surakarta Hospital

Hemodialysis is needed for a long period of time and also requires a lot of money. The purpose of this study was to determine differences in patient characteristic factors on direct medical costs in chronic kidney disease (CKD) patients with hemodialysis procedures and to find out the average cost of inpatient CKD treatment with hemodialysis. This research is a non-experimental study with data taken retrospectively at Dr. Moewardi in the period July-December 2022. The research subjects were CKD patients with inpatient and outpatient hemodialysis procedures. Cost data were then analyzed using descriptive analysis, the one sample t test was used to compare the average cost of hemodialysis treatment with the INA-CBG's rates, the Mann-Whitney and Kruskal-Wallis tests to determine differences in real costs in terms of age, gender, length of stay, frequency of hemodialysis and number of comorbidities. The results showed that there were 75 patients with CKD with hemodialysis inpatients. The cost of illness for CKD patients undergoing hemodialysis is IDR 995,141,718.00. In inpatients there are differences in total direct medical costs on gender, age, frequency of HD and LOS. The difference between real costs and INA-CBG's rates is found in groups N-4-10-II class II and III, N-4-10-III class III, N-1-12-I class I and III, N-1- 12-II class I and III and N-1-12-III.

Academic Discourses on Causes of and Solutions to Poverty in India

This paper analyses academic discourses about factors that cause/perpetuate or eliminate poverty. It finds that the academic debates on the subject have been dominated by economists and have concentrated mainly on economic backwardness and/or unequal distribution of productive assets as the major causes of poverty in India. The consensus that emerges from these suggests that growth is important but other factors such as the growth of employment and real wage rates are equally important since the majority of the poor in India are rural labour households with marginal or no landholdings. The alternative discourse by political scientists highlights the importance of political factors and unequal distribution of power and resources in causing poverty and therefore suggests land reforms, empowerment, the need for inclusive policies/politics and peoples’ movement as some of the solutions. Sociological studies of poverty bring out the role of social institutions such as the caste system (a system of institutionalized inequality) and patriarchy in bringing a disproportionate burden of poverty on certain disadvantaged sections of society such as the scheduled castes and scheduled tribes, women, etc. State level studies highlight the importance of ‘Quality of Governance’ while village level studies provide useful insights based on the people’s experience of falling in and out of poverty. The paper thus finds that the understanding of causes of poverty is affected by – (a) Academic Disciplines of the researchers; (b) Ideology of the researchers and (c) the level of enquiry, i.e. national, state, district or village.

Characteristics and Effects of Chinese Herbal Medicine in the Management of Female Infertility: A Hospital-Based Study

Background: In Taiwan, Chinese herbal medicine (CHM) is used to treat female infertility. Evidence indicates that the absence of monotherapy efficacy assessment and comparison with mainstream interventions may lead to the improper use of CHM for female infertility. Methods: A retrospective cohort study enrolled female patients at a hospital undergoing CHM intervention to treat infertility from 2012 to 2020 in order to determine the outcomes of CHM monotherapy for female infertility. Kaplan-Meier analysis under strict assumptions was used to estimate the cumulative probability of pregnancy and live births after CHM. Cox hazard regression analysis was used to estimate the hazard ratios of prognostic variables, namely, the woman’s age and diagnostic category. Results: 694 women met the inclusion criteria and accounted for 2,145 cycles. A total of 190 pregnancies resulted in 125 live births, all of which were singleton births of babies with 16 perinatal complications requiring hospitalization. The real cumulative pregnancy rate and cumulative live birth rate (CLBR) for the total population after 10 cycles were between 27.4% and 35.2% and between 18% and 22.1%, respectively. Compared with the live birth rate corresponding to patients aged under 35 years, that of older patients, particularly those aged 38–39 years, was significantly lower (hazard ratio: 0.19, 95% confidence interval: 0.11–0.33). Women with other diagnoses, namely, uterine problems or endometriosis, had a greater probability of a live birth than did women with tubal pathology (hazard ratio: 6.31, 95% confidence interval: 1.99–20.07). Conclusion: To the best of our knowledge, this is the first retrospective study to employ life table analysis to determine the CHM treatment outcomes in terms of female infertility. The study established a basis to compare in vitro fertilization (IVF) with CHM and identified the advantages and disadvantages of CHM for treating female infertility. Although the CLBR of present study is lower than those reported in IVF studies, CHM in treating female infertility can still be beneficial to women aged younger than 38 years or with diagnoses other than tubal pathology and worth recommendation by reproductive specialists according to the promising results gained from the strict criteria. However, in order to determine the optimal timing, possible mechanism, corresponding side effects, and the efficacy of CHM combined with IVF for treating female infertility, rigorous research is required.

New Empirical Evidence on Factors Influencing the Yield on High-Grade Municipal Bonds

We investigate the impact of federal budget deficits and other factors on the ex-post and ex-ante real yields on high-grade municipal bonds. The estimation results reveal that both yields increase with the real yield on 30-year Moody’s Aaa-rated bonds and provisions in the Community Reinvestment Act but decrease with net capital inflows, the real GDP growth rate, and the average effective federal income tax rate. Most importantly, both yields are increasing functions of the federal budget deficit. These results support limiting the size of federal budget deficits to avoid the excessive crowding out of private investment spending.

Assessing the benefits of high-speed broadband: Lessons from Australia's National Broadband Network (NBN)

This study reviews the economic benefits analysis conducted in 2014 for the National Broadband Network (NBN), one of Australia's largest ever public infrastructure projects. The review identifies that the NBN's benefits were overstated due to error by an estimated $17–20 billion in 2014 present value terms, an amount sufficient to eliminate the NBN's forecast net economic benefits. To provide an updated view on the benefits of high-speed broadband in 2022, a stated choice experiment is conducted, finding that the WTP has grown since 2014 at an annual real rate of 8.8 percent for download speeds and 11.5 percent for upload speeds. Even at these growth rates, however, the benefits of the NBN remain materially lower than forecast in 2014. Further, the analysis finds high heterogeneity in the household WTP, with smaller and lower-income households reluctant to pay a premium for speeds much faster than 50 Mbps, indicating that the adoption of the fastest available broadband speeds is likely to remain low. The study highlights the potential value of cost-benefit analyses, but also the risks, particularly where they are relied on to justify enduring extensions of government ownership and control. The paper concludes by identifying learnings for policymakers and cost-benefit analysis practitioners seeking to assess and optimize the economic value of public infrastructure investments.

Analysis of Turkish Economy from Macroeconomic and Socio-political Perspective (1923-2023)

The evaluation of economies provides an idea about the direction of countries and determines their international reputations. Economic evaluations hold significant importance for this reason. Evaluations often utilize macroeconomic indicators like real growth rate, unemployment rate, and inflation rate. On the other hand, to honestly evaluate economies, a long-term perspective is required, and it is vital to maintain continuity and have a broad scope. During this process, many factors, such as developments in the external world, geography, language, religion, ethnicity, and customary practices, can have an impact, in addition to macroeconomic factors. However, none of these (except for macroeconomic factors) have as much impact on the development of economies as the institutional factors that form the basis of the socio-political structure. As property rights, freedom of expression, press freedom, judicial independence, and equality of opportunities in the economic, social, and political fields expand in a country; experts anticipate that institutional quality will increase and positively impact economic operations. Findings indicate the necessity of conducting macroeconomic and socio-political performance measurements to evaluate the Turkish economy properly. For 1924-2023, a macroeconomic performance index (MPI) has established for measuring macroeconomic performance, and a socio-political performance index (SPI) for measuring socio-political performance.
 The findings obtained from the studies reveal that in Türkiye, more than ensuring only macroeconomic stability is necessary to achieve satisfactory and lasting economic success. Compromising fundamental rights, freedoms, and socio-economic justice (e.g., income distribution) has achieved stability, but only with temporary effects. The development structure and macroeconomic gains solidify the economic outlook through socio-political developments.