Constant Prices Research Articles (Page 1)

On the basis of recent studies on changes in the structure of economies, it is becoming problematic to assess the process of deindustrialization. It is not clear whether deindustrialization, when industry is the main engine of economic growth, is detrimental or beneficial to development and economic growth. The aim of this study is to analyses changes in the share of industry in total output, as well as the structure of the economy's output itself in its development, and the impact of this process on the level of GDP per capita. A statistical analysis of changes in the structure of global production in the Polish economy between 1995 and 2023 was used, which showed that although the share of industry in total global production at current prices remains almost constant, its real share in total global production volume at constant prices is increasing very strongly, while also having the largest and fastest-growing real share in total global production volume of all sectors of the economy. Hence, the level of global production volume for the entire economy was mainly shaped by the real share of industry in total global production volume. A multifactor econometric analysis also showed that during the period under review, the level of GDP per capita was most strongly determined by the real share of industry in the volume of total output.

This paper examines how the COVID-19 recession changed whiskey purchasing in Iowa. This paper examines its impact on whiskey purchasing in Iowas control-state market. To decompose recessionary channels, we analyze SKUs with changing prices separately from those with fixed prices. Stock keeping units are divided into two groups: constant and changing prices. For SKUs that changed price, we compute arc elasticities by comparing each pre-recession year (2017-2019) to 2020 at the SKU-month level. For SKUs with constant posted prices, we track quantity growth and its acceleration from 2019-2020 relative to 2018-2019 to identify demand shifts. Summary measures of revenue, bottles sold, and litres are also reported. Price sensitivity rose for a subset of products in 2020, with several items showing clearly negative elasticities of meaningful size. Demand shifted when prices did not change. Lower-priced items gained volume and higher-priced items lagged. Revenue and bottles sold increased in 2020, while total liters declined, which points to smaller packages and higher prices per liter. These results show that the recession operated through both price and non-price channels. These findings offer actionable insights for managers and policymakers on portfolio and pricing strategies during crises. Furthermore, they lay crucial descriptive groundwork for future research aiming to establish causal estimates with richer datasets.

This study aims to: (1) identify and analyze the development of Third Party Funds (DPK), inflation, savings interest rates, Gross Regional Domestic Product (GRDP) at constant prices, and regional expenditure across Indonesian provinces during 2019–2023; and (2) examine the influence of inflation, savings interest rates, GRDP at constant prices, and regional expenditure on Third Party Funds in the same period. The research employs panel data regression analysis using EViews 12 for data processin.The results show that (1) the highest average growth of Third Party Funds (DPK) was recorded in South Kalimantan (11.89%), while the lowest was in Banten (-10.87%). The highest average inflation occurred in East Java (3.7%) and the lowest in Papua (2.1%). The savings interest rate peaked in 2019 at 1.17% and declined to its lowest level in 2022 at 0.37%. The highest GRDP growth was found in North Maluku (16.41%) and the lowest in West Papua (1.16%). Similarly, North Maluku also recorded the highest regional expenditure growth (14.08%), while West Papua experienced the lowest (-17.24%), reflecting economic disparities across regions in Indonesia. (2) The regression analysis reveals that GRDP at constant prices and regional expenditure have a significant and positive effect on Third Party Funds, while the savings interest rate has a significant and negative effect. In contrast, inflation shows no significant effect on Third Party Funds.

Background: Comprehensive data on the economic burden of cervical cancer treatment remain scarce in China’s less developed regions, necessitating this study on hospitalization costs and expenditure trends in these areas. Methods: Employing a multi-stage stratified cluster sampling approach, this study enrolled 10,070 cervical cancer inpatients from 72 healthcare facilities in Gansu Province. Clinical and expenditure data were extracted from hospital information systems. Rank sum tests and Spearman correlation analyses were performed for univariate assessment, while quantile regression and random forest models were applied to identify determinant factors. Results: From 2019 to 2023, the average hospitalization duration for cervical cancer patients in Gansu Province was 16.12 days, with an average hospitalization cost of USD 3862.08 (2023 constant prices, converted from CNY at 1:7.0467). During these five years, the average inpatient costs per hospitalization increased from USD 3473.45 to USD 4202.57, and the average daily hospitalization cost rose from USD 230.53 to USD 241.77. The average drug cost decreased from USD 769.06 to USD 640.16. The main factors influencing hospitalization costs included the length of hospital stay, whether cervical cancer surgery was performed, hospital type, hospital level, and the proportion of medications. Conclusions: Our findings indicate that cervical cancer is a considerable economic burden on both families and society. This highlights the need to control the length of hospital stay and optimize the allocation of medical resources, in addition to strengthening cervical cancer screening and HPV vaccination in underdeveloped areas, in order to enhance the efficiency of prevention and treatment and ensure medical equity.

Research Originality: This study presents a novel perspective by examining Indonesia’s economic growth over three crisis periods. It uniquely highlights how global economic uncertainty can strengthen Indonesia’s growth resilience when met with credible domestic policy responses. Research Objectives: The research investigates the effects of exports, imports, production value, interest rates, economic globalization, exchange rates, and state obligations on Indonesia’s economic growth at constant prices. Research Methods: Using quarterly time-series data from 1991Q1 to 2024Q1, the study employs a Dummy Variable–Autoregressive Distributed Lag model. Empirical Result: Exports have a direct negative effect on economic growth but when influenced indirectly by the global crisis and the pandemic, exports can actually contribute to growth. On the other hand, imports directly boost growth, but their impact is negatively affected by the global crisis. Additionally, interest rates support long-term growth but hinder it in the short run; however, crises may moderate this impact positively. Implications: These findings underscore the need for policymakers to craft dynamic, adaptable economic strategies that can safeguard Indonesia’s growth against future global shocks and uncertainties. JEL Classification: F41, E32, O53

Purpose. The purpose of the study is to identify latent indicators derived from business expectations of agricultural enterprises regarding production barriers (obstacles) and to assess their impact on agricultural output dynamics. Methodology / approach. The principal components analysis (PCA) method was applied to reduce dimensionality and eliminate multicollinearity among independent variables and to aggregate subjective assessments of production barriers reported by agricultural producers. Regression and correlation analysis are used to examine the direction and strength of relationships between the principal components and production volumes. The data are from the State Statistics Service of Ukraine for 2015–2024. Results. Two latent components (PC1, PC2) summarising the structure of production barriers were identified. The principal components reflect the opposite poles of farmers’ expectations: on the one hand, financial and material constraints, and on the other, the absence of constraints. This confirms that business expectations can be reduced to integrated latent dimensions that summarise the presence or absence of barriers to production. PC1 and PC2 revealed a strong positive statistically significant relationship with actual production volumes, which indicates their significance as aggregate indicators of business assessments. At the same time, the negative insignificant correlation of PC1 with production in constant prices affects the ambivalence of its interpretation, since its structure simultaneously contains the influence of both favourable and restrictive factors. Additional components (such as PC7) demonstrated a higher significant correlation with production volumes in constant prices than the leading components. This means that secondary, less dispersion-significant latent factors may be more informative for explaining the dynamics of production volume in constant prices. The business expectation system in agriculture has a multidimensional nature, where key barriers and incentives do not always coincide with the most variable factors, but may have greater predictive value for assessing future production. Originality / scientific novelty. The study presents a novel combination of PCA and regression analyse for interpreting business expectations as a latent indicator of production conditions. The scientific contribution lies in identifying latent indicators of subjective evaluations that are statistically linked to real production outcomes, even in the absence of direct objective measurements. Practical value / implications. The results can be used to develop a risk monitoring system in agriculture based on aggregated indicators. This approach improves the accuracy of assessing sectoral conditions and contributes to the development of more targeted agricultural policies, aimed both at overcoming critical barriers and enhancing adaptive capacity of producers.

Inequality in economic growth across regions often reflects structural imbalances and the limited effectiveness of current development policies. Thus, it is essential to investigate the factors that influence regional growth dynamics. Economic growth is defined as the change in income and the production of goods and services within a specific region or country over a designated period. The varying levels of economic growth, measured by Gross Domestic Product (GDP), are expressed at constant prices during a defined timeframe. This research aims to analyze the impact of Profit-Sharing Funds (DBH), labor, and Gross Fixed Capital Formation (PMTB) on economic growth in East Java. The analysis employs Geographically Weighted Panel Regression (GWPR) to assess how each independent variable influences the dependent variable across various districts and cities in East Java. The findings reveal three regional classifications. The first classification includes regions where none of the predictor variables significantly affect economic growth, which applies to 37 districts including Trenggalek. The second classification identifies areas where LABOR does not serve as a significant predictor variable in the same 37 districts and cities across East Java. Finally, the third classification highlights regions where Profit Sharing Funds (DBH) are significantly relevant to economic growth, with the exception of Trenggalek. Additionally, Gross Fixed Capital Formation (PMTB) is identified as a significant variable for economic growth in the districts of Lumajang, Jember, Bondowoso, Situbondo, Probolinggo, Pasuruan, Sidoarjo, as well as Probolinggo City and Pasuruan.

The uneven distribution of economic activities causes certain sectors to concentrate in specific locations due to geographical factors. Kalimantan Timur Province, rich in natural resources, has long relied on the mining sector; however, sustainability issues and spatial leakage require reassessment. This study aims to identify the latest sectoral agglomeration patterns using the Locational Quotient, Locational Index, and Specialization Index to understand the region's dominance, concentration, and specialization. Implementation methods involve the use of secondary data from the 2024 Gross Regional Domestic Product at constant prices from BPS Kalimantan Timur Province, which was processed and analyzed at the regency/city level to identify sectoral agglomeration patterns. Analytical techniques used in this study consist of the Locational Quotient to identify leading sectors, the Locational Index to measure sectoral concentration, and the Specialization Index to assess economic specialization. Research results indicate that, geographically, Gross Regional Domestic Product accumulation is concentrated in the central and eastern coastal areas of the province, aligning with the core-periphery model and economic agglomeration patterns. Regencies rely more on the agricultural and mining sectors, whereas urban economies are more diversified. This pattern confirms that regencies generally depend on natural resource exploitation, while cities are more developed in the manufacturing and service sectors. These findings highlight the need for economic diversification strategies in regions still dependent on extractive industries.

Regional economic growth is a key indicator in assessing the development of a region. Identifying superior sectors through economic structure analysis is essential in formulating effective development policies. In this context, economic agglomeration plays a role in enhancing efficiency and regional competitiveness by optimizing access to labor, infrastructure, and business networks. Previous studies indicate that primary sectors, such as agriculture, forestry, and mining, still dominate the economy of Jambi Province. However, to achieve sustainable growth and strengthen economic resilience, sector diversification and inter-sectoral integration must be considered. Over time, the contribution of each sector may change, necessitating an analysis of the current sectoral conditions. This study's objectives are to analyze sectors that have comparative advantage, measure regional sector growth, and determine sectors with competitive advantage in the districts and cities of Jambi Province. The implementation methods involved the use of secondary data on Gross Regional Domestic Product (GRDP) at constant prices in 2024, obtained from the Central Bureau of Statistics (BPS), covering all districts and municipalities in the province. The analytical techniques employed in this study were the Location Quotient (LQ) method, to identify sectors with comparative advantage, and the Differential Shift (DS) analysis, to evaluate sectoral growth and competitive advantage. The results showed that regencies in Jambi are still dominated by primary sectors such as agriculture and mining, while cities are superior in the services sector and processing industry. The phenomenon of emerging sectors was also found, where some sectors experienced positive growth but were not strong enough to be categorized as comparative leading sectors. The corporate services sector in Sungai Penuh City recorded the highest comparative advantage, while the construction sector in Kerinci Regency showed the fastest growth. However, some regions still have limitations in the number of leading sectors, both comparatively and competitively. Therefore, a sector diversification strategy is needed, especially in regions that are still dependent on the extractive sector, to strengthen the regional economy and increase economic resilience towards sustainable growth.

This study aims to analyze the potential of business sectors that support economic growth in Gowa Regency. This study uses a quantitative approach using GRDP data at constant prices from 2010 for the period 2019–2023. The analysis method used is the Location Quotient (LQ) to identify basic and non-basic sectors. The results show that of the 17 sectors, 7 are classified as basic (e.g., Real Estate, Agriculture, Information and Communication), which contribute significantly to GRDP and have significant potential for job creation. Meanwhile, the remaining 10 sectors are classified as non-basic (e.g., Manufacturing Industry, Construction). Although they have high GRDP values, their low LQ values indicate contributions below the provincial average and require strengthening strategies. These findings recommend prioritizing basic sectors and strengthening non-basic sectors in regional economic development planning.Keywords : GRDP, Basic Sectors, Non-Basic, Economic Growth

Sustainable economic growth can be maintained in part through integrated economic development planning to foster leading sectors. This is also necessary for Banten Province, which experienced a contraction in economic growth during the Covid-19 pandemic. Although Banten’s economic growth rate has increased year by year—except in 2020—this does not diminish the potential of its leading sectors. This study aims to :1).Identify the leading economic sectors in Banten Province that meet the criteria of having competitive, comparative, and prospective advantages. 2).Project the Gross Regional Domestic Product (GRDP) of Banten Province for the years 2024 to 2026. 3).Calculate the amount of investment required by Banten Province to achieve the targeted/projected economic growth during the 2024–2026 period. The data used in this study are secondary time series data spanning ten years from 2014 to 2023, consisting of GRDP data for Banten Province, Indonesia’s GDP, Banten Province’s Regional Budget (APBD), and Gross Fixed Capital Formation (GFCF) data. These were obtained from the Central Bureau of Statistics (BPS) and BPS of Banten Province. Based on the calculations of Location Quotient (LQ), Dynamic Location Quotient (DLQ), and Shift-Share Analysis using 2014–2023 base data, the leading sector in Banten Province is the Water Supply, Waste Management, Waste and Recycling sector. This sector qualifies as a base sector according to the LQ, shows rapid growth as per DLQ, and is identified as a fast-growing, geographically advantageous sector with a positive National Share value.The most suitable trend model for projecting the GRDP at constant prices for Banten Province is the quadratic/parabolic trend model, as it has the lowest mean absolute error. The GRDP projections at constant prices are as follows : 2024: IDR 510,576.42 billion; 2025: IDR 521,638.02 billion; 2026: IDR 531,801.37 billion. Based on ICOR calculations and GRDP projections, the required investment for Banten Province is: 2024: IDR 14,770.21 billion; 2025: IDR 51,880.84 billion; 2026: IDR 47,667.93 billion.Keywords : : Leading Sector, Location Quotient (LQ), Dynamic Location Quotient (DLQ), Shift-Share Analysis, Incremental Capital Output Ratio (ICOR).

This study aims to identify the leading sectors that make a significant contribution to the economy of Gunungkidul Regency and Yogyakarta City. Using a descriptive quantitative approach, the study utilizes secondary data in the form of Gross Regional Domestic Product (GRDP) at constant prices from 2012 to 2023, obtained from the Central Statistics Agency (BPS) of Gunungkidul Regency, Yogyakarta City, and the Special Region of Yogyakarta Province. The analytical methods employed include Location Quotient (LQ), Shift Share, and Klassen Typology to measure comparative advantage, growth, competitiveness, and the classification of economic sectors. The analysis results indicate that there are six leading sectors in Gunungkidul Regency and twelve leading sectors in Yogyakarta City. These findings reflect the differences in economic structure and development dynamics between the two regions. Based on the results of this study, it is expected that local governments can optimize the development of sectors that are not yet leading but have potential, thereby encouraging more balanced and equitable economic growth across all regions.

Economic disparities between regions in Indonesia necessitate an evaluation of the impact of infrastructure development as a catalyst for growth. This study analyzes the effect of infrastructure development by the Ministry of Public Works and Public Housing (PUPR) across four sectors—road construction, water resources, public works, and housing—on Indonesia’s regional economic growth. Using a quantitative explanatory approach, secondary data from 18 provinces (54 observations) were collected for 2022–2024. Data included PUPR’s budget realization and Regional Domestic Product (GRDP) at constant prices from the Central Statistics Agency. Multiple regression analysis assessed sectoral impacts. Road infrastructure significantly boosted regional economic growth (? = 0.573, p = 0.000), contributing 37.82% to growth variation. Water Resources (? = 0.158, p = 0.034) and Public Facilities (? = 0.189, p = 0.019) also showed positive effects, contributing 6.35% and 9.41%, respectively. Housing development had no significant impact (p = 0.792) and a negative partial effect (–2.18%). Collectively, the four sectors explained 51.4% of GRDP variance (R² = 0.514, F = 12.934, p = 0.000). Road, Water Resources, and Public Facilities infrastructure are pivotal for economic growth, while Housing policies require reevaluation. Findings advocate prioritizing high-impact sectors to foster inclusive and sustainable regional development.

This research aims to identify leading sectors through the Location Quoetient (LQ) approach and Shift Share analysis. The LQ approach is used to determine the base sector based on structural advantages, while shift share measures the competitive advantage of the economic sector at the local level compared to the provincial level. The data used is the GRDP of Malang City and East Java Province for 2020-2022 at constant prices. The results of the analysis show that of the 17 sectors analyzed, only two sectors meet the criteria as leading sectors, namely the financial and insurance services sector and the education services sector. These two sectors not only have LQ values >1, but also show DK values >0, signaling superiority in economic structure and growth. This finding emphasizes the importance of strengthening the strategic services sector as a driver of sustainable economic development in Malang City.

Regression models are widely used in Economics, particularly when the data involved are rates and proportions. The Unit-Lindley regression model is defined for data restricted to the (0,1) range. In regular problems, inference based on asymptotic theory can be unreliable when the sample is small. This is the case of the maximum likelihood estimation and the Wald test. Corrections of biases in the maximum likelihood estimators and adjustments made in the test statistics are a widely used way to solve such problems. In this article, we obtain an expression to the correct the bias and a formula for the second-order covariance matrix for the maximum likelihood estimators in the Unit-Lindley regression model. Numerical evidence shows that the corrected estimators are less biased and that the Wald test based on second-order covariance is more accurate. Finally, an application to economic data is presented, in which the Growth Rate of Real GDP per capita is modeled as a function of openness in constant prices. Keywords: bias correction; modified Wald test; second-order covariance matrix. Unit-Lindley regression.

Poverty is a state of deprivation experienced by individuals or groups with monthly per capita expenditure that is insufficient to meet basic needs. Based on Indonesia's poverty profile released by the Statistics Indonesia (BPS) in March 2024, it was recorded that 9.03% of Indonesia's population was declared poor, which is still far from the poverty reduction target of 6.5% to 7.5% targeted in the National Medium-Term Development Plan 2020-2024. One of the efforts that can be made to end poverty in Indonesia is to analyze what factors affect the poverty rate. The method used in this study is Bayesian multinomial logistic regression using the Markov Chain Monte Carlo (MCMC) Gibbs Sampling algorithm and the response variable used as a measure of poverty level is the poverty line which is an official indicator sourced from BPS. The results show that after 20,000 iterations, the Markov chain reaches a stationary state with the results of the credible interval test supported by the deviance test results stating that the factors that have a significant effect on the poverty rate in Indonesia in 2024 are GRDP at constant prices and average years of schooling.

Topicality. Ukraine is currently undergoing a simplification of its structure and a decline in its technological capabilities. This process is accomplished by increased wear and tear of fixed assets, decreased investment activity, deterioration of production, transport, and energy infrastructure, stagnation innovation, degradation of the commodity structure of exports, and a significant loss of human capital. These multifaceted challenges necessitate a comprehensive analysis of the ongoing processes and the identification of their specific characteristics in the context of capitalization. Aim and tasks. The aim of this study is to analyse the process of deindustrialization within Ukraine's manufacturing sector and to identify its distinctive features in the context of capitalization. Materials and Methods. The research applies both general scientific and special research methods, including comparison, generalization, synthesis, system analysis, and logical-dialectical analysis. The information base of the study comprises monographs, specialized literature, information and analytical materials, academic journals (both domestic and international), and statistical data from the State Statistics Service of Ukraine. Research results. The article examines the concept of “deindustrialization” and highlights its core features: a decrease in industrial output at constant prices; structural shifts within the manufacturing sector marked by a declining share of high-tech industries; and a deterioration in the quality of industrial output. The main drivers behind deindustrialization of Ukraine's manufacturing sector have been identified as follows: increasing depreciation of fixed assets and insufficient investment; simplification of Ukraine’s economic the structure and a reduction in its technological complexity; a decline in employment in the high-tech sector; structural transformations at the sectoral level of the manufacturing industry; declining labour productivity within key manufacturing segments; worsening of the commodity exports; deepening liberalization of foreign trade; stagnation of the innovation process; and loss of human capital. Additional destructive factors influencing the development of the manufacturing industry since 2013 include: the loss of traditional markets for Ukrainian manufacturing products, particularly in russia; difficulties in reorienting production and exports toward developed markets due to intense competition and protective trade barriers; underdevelopment of the domestic market for manufacturing products and the passive role of the state in addressing these issues; the rise of global protectionist sentiments and trade trends, in contrast to Ukraine’s continued liberal access for foreign goods in its domestic market. Conclusion. Based on the analysis of the deindustrialization of Ukraine's manufacturing industry, several specific features of this process in the context of capitalization have been identified. These include: a reduction in human capital due to labour emigration and a shrinking supply of highly qualified workers; loss of physical capital due to excessive depreciation of fixed assets and limited capital investment; stagnation in intangible investment assets and a corresponding decline in the technological level of manufacturing; deterioration of the institutional environment due to further liberalization of foreign trade and ineffective state policy.

PurposeThis study investigates the extent to which bilateral trade with China and the United States (US) influences the productivity of trading partners.Design/methodology/approachThis study uses panel data estimates to identify the export and import policy channels separately and then their combination with trade integration and trade balance at both the country and sectoral levels between 99 countries and China and the US, incorporating institutional quality and geopolitical risks. The sample period covers the years 2002–2019, and the two-step generalized method of moments (GMM) is employed as the main estimation method.FindingsTrade with China boosts total productivity at constant prices through exports and imports, especially in manufacturing, but reduces welfare-relevant total factor productivity through total trade and trade balance, particularly in agriculture. In contrast, trade with the US consistently enhances all productivity across all channels, except for agricultural imports, which lower welfare-relevant total factor productivity. Institutional quality amplifies the positive effects, while trade uncertainty and US–China tensions reduce them.Originality/valueThis study provides a comparative, channel-specific and sector-sensitive analysis of trade-productivity links with China and the US, offering timely insights for policymakers involved in navigating shifting global trade dynamics.

The Phillips curve is one of the most widely debated economic patterns. Its practical application, including for adjusting monetary policy, still causes significant disagreement among economists. In this regard, understanding the nature (essence) of the Phillips curve is an urgent task. The purpose of the study is to substantiate the hypothesis that the Phillips curve is based on a different pattern than is currently believed among economists. Methods of analysis and synthesis, system and logical analysis, were used. The empirical basis of the study is based on statistical data of the US economy for the period from 1980 to 2022. The essence of the study: real analysis of economic indicators (real wages, real GDP, etc.) in the vast majority of cases takes precedence over nominal analysis of economic indicators (nominal wages, nominal GDP, etc.). These two analyzes are the same if prices remain constant. It was during this period of Phillips’s study of the British economy (1862–1913) that prices remained virtually unchanged. The rest of the Phillips curve (1914–1957) was heavily influenced by non-economic factors and may therefore be less significant. Since Phillips originally defined his curve as an inverse relationship between nominal wages and unemployment, at constant prices this means that there is an inverse relationship between real wages and unemployment. This dependence is explained by the author by the fact that the UK economy already had a cyclical pattern, when during economic growth real wages rise and unemployment falls, and vice versa. Conclusion: It is quite reasonable to believe that the above curve shows an inverse relationship between fluctuations in unemployment and fluctuations in real wages.

The COVID-19 pandemic has caused significant disruptions to the global economy, including Indonesia, as reflected in the fluctuations of Gross Regional Domestic Product (GRDP) across various provinces. This study aims to analyze the differences in provincial GRDP in Indonesia between the pre-pandemic period and the pandemic period (2020–2023) in order to assess the impact of the pandemic on regional economic resilience. A comparative quantitative approach was employed, using the Wilcoxon Signed Rank Test on GRDP data based on constant prices, obtained from Statistics Indonesia (BPS). The analysis revealed a statistically significant difference in GRDP before and during the pandemic (p < 0.001), indicating a substantial economic shock. Sharp declines were particularly evident in provinces reliant on the tourism and manufacturing sectors, while provinces with more diversified economic structures or supported by agriculture exhibited relatively greater resilience. These findings highlight the importance of regional economic diversification and the strengthening of more resilient sectors as key strategies for confronting similar crises in the future.