Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

scholarly journals FDI and long-term economic growth in Egypt

2017 ◽  
Vol 13 (1) ◽  
pp. 65-74
Author(s):  
Saif Alhakimi
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Direct Investment ◽  
Direct Investment ◽  
Time Series Data ◽  
Government Expenditure ◽  
Wage Structure ◽  
Series Data ◽  
The Impact

This research paper aims to empirically analyze the impact of FDI on the long-term economic growth of Egypt. An empirical model was developed to explain the aggregate output, including total labor force, capital stock, foreign direct investment, government expenditure, and the real exchange rate. Annual time-series data from 1990–2013 were then used to estimate the model. Prior to calculating this estimation, the properties of the time series were diagnosed, and an error-correction model was developed and assessed. The overall results suggest that foreign direct investment makes a positive, yet weak and insignificant, contribution to the long-term economic growth of Egypt. This finding warrants further investigation to explore the possible reasons behind it, such as the degree of spillover that FDI has on economic growth and its impact on employment in areas like job creation, wage structure, research, and development.

scholarly journals Dampak Kebijakan Harga Pembelian Petani Gabah terhadap Kesejahteraan Petani: Suatu Simulasi

2013 ◽  
Vol 13 (2) ◽  
pp. 125-142
Author(s):  
Eka Intan Kumala Putri ◽  
Novindra Novindra ◽  
Nuva Nuva
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Time Series Data ◽  
Supply And Demand ◽  
Series Data ◽  
Government Purchases ◽  
Real Price ◽  
The Impact ◽  
Purchasing Policy

To control supply and demand rice and increase income to farmer, Government assigned a rice purchasing policy. This study apply 2SLS model with time series data 1971--2009 to simulate and forecast the impact of rice purchasing policy on farmer welfare. The responses of rice real price at farmers' level towards the changes of real price of government purchases and rice production is elastic in the long term. Demand of rice is signicantly in uenced by the price of rice, population, and demand for rice at t-1. The increasing of rice purchasing price (HPP) on grain at 9.54% and 15% lead to an increasing of farmer's surplus IDR163,512,308,700 and IDR257,292,128,790.AbstrakGuna mengontrol keseimbangan konsumsi beras dengan produksi padi dan meningkatkan pendapatan petani, pemerintah Indonesia menetapkan kebijakan Harga Pembelian Padi (HPP). Studi ini melihat dampak kebijakan HPP terhadap kesejahteraan petani dilakukan melalui simulasi peramalan dengan Two Stage Least Squares (2SLS) data time series periode 1971--2009. Secara ekonomi, respons harga riil gabah petani terhadap perubahan HPP dan produksi padi bersifat elastis dalam jangka panjang. Permintaan beras dipengaruhi harga beras, jumlah penduduk, dan permintaan beras t-1. Simulasi peramalan terhadap peningkatan HPP gabah 9,54% dan 15% menyebabkan surplus petani meningkat masing-masing Rp163.512.308.700,- dan Rp257.292.128.790,-.

scholarly journals The Impact of Infrastructure on Tourism: The Case of Sri Lanka

2019 ◽  
Vol 15 (7) ◽  
pp. 174 ◽  
Author(s):  
A. M. M. Mustafa
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
The Government ◽  
The Impact ◽  
Adf Test

This study examines the impact of infrastructure on tourism development in Sri Lanka with greater emphasis on road network. The time period used in this study are ranging from year 2005 to year 2017. The annual time series data are analyzed by using statistical package, E-Views 10 after the preliminary calculations by using Microsoft Excel. The unit root of the variables is tested by ADF test to test the stationarity of the time series data used in the model of this study. Co-integration is tested with the use of Engle–Granger. The relationship of causality between the variables is found by test of Granger Causality. The results show that infrastructure has significant short run as well long run positive impact on tourism. Two-way causal relationship is found between tourism sector and infrastructure. Further, this study recommends that the government should play its role in improving the infrastructure facilities to increase tourist’s arrival in Sri Lanka.

The Effects of Oil Prices Changes on Output Growth and Inflation: Evidence from Turkey

2013 ◽  
Vol 5 (11) ◽  
pp. 730-739 ◽  
Author(s):  
Pelin ÖGE GÜNEY
Keyword(s):  
Time Series Data ◽  
Output Growth ◽  
Oil Price ◽  
Series Data ◽  
Negative Effects ◽  
Price Changes ◽  
Asymmetric Effects ◽  
Price Increases ◽  
Causality Tests ◽  
The Impact

This paper investigates the effects of oil price changes on output and inflation for the case of Turkey using monthly time series data for the period 1990:1–2012:3. Recent studies suggest that oil price changes may have asymmetric effects on the macroeconomic variables. To account for asymmetric effects, we decompose oil price changes into positive and negative parts following Hamilton (1996). Our results show that while oil price increases have clear negative effects on output growth, the impact of oil price decline is insignificant. Similarly, oil price increases have positive and significant effects on inflation. However, oil price declines have not a significant effect on inflation. The Granger causality tests also support these results.

EFFECTS OF TREND AND PERIODICITY ON THE CORRELATION DIMENSION AND THE LYAPUNOV EXPONENTS

2008 ◽  
Vol 18 (12) ◽  
pp. 3679-3687 ◽  
Author(s):  
AYDIN A. CECEN ◽  
CAHIT ERKAL
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Lyapunov Exponents ◽  
Correlation Dimension ◽  
Time Series Data ◽  
Logistic Map ◽  
Model Identification ◽  
Series Data ◽  
Largest Lyapunov Exponent ◽  
The Impact

We present a critical remark on the pitfalls of calculating the correlation dimension and the largest Lyapunov exponent from time series data when trend and periodicity exist. We consider a special case where a time series Zi can be expressed as the sum of two subsystems so that Zi = Xi + Yi and at least one of the subsystems is deterministic. We show that if the trend and periodicity are not properly removed, correlation dimension and Lyapunov exponent estimations yield misleading results, which can severely compromise the results of diagnostic tests and model identification. We also establish an analytic relationship between the largest Lyapunov exponents of the subsystems and that of the whole system. In addition, the impact of a periodic parameter perturbation on the Lyapunov exponent for the logistic map and the Lorenz system is discussed.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

Does Public Debt Service Expenditure Crowd-Out Economic Growth? Empirical Evidence from an African Developing Country

2019 ◽  
Vol 64 (3) ◽  
pp. 23-38
Author(s):  
Talknice Saungweme ◽  
Nicholas M. Odhiambo
Keyword(s):  
Economic Growth ◽  
Public Debt ◽  
Time Series Data ◽  
Policy Formulation ◽  
Debt Service ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
The Impact

Abstract This paper contributes to the ongoing debate on the impact of public debt service on economic growth; and it provides an evidence-based approach to public policy formulation in Zimbabwe. The empirical analysis was performed by applying the autoregressive distributed lag (ARDL) technique to annual time-series data from 1970 to 2017. The study findings reveal that the impact of public debt service on economic growth in Zimbabwe is negative in the short run but positive in the long run. The results are suggestive of the existence of a crowding-out effect of public debt service in Zimbabwe in the short run and a crowding-in effect in the long run. In view of these findings, the government should consider fiscal and financial policies that promote a constant supply of long-term finance, long-term fixed investments, and extension of a government securities maturity structure so as to ensure sustainable short- and long-term public debt service expenditures. The study further recommends the strengthening of non-distortionary revenue mobilisation reforms to reduce market distortions and boost domestic investment.

scholarly journals Political Stability and its Impact on Economic Growth of Pakistan (1988-2018): A Time Series Analysis

2020 ◽  
Vol 6 (1) ◽  
pp. 273-282
Author(s):  
Majid Hussain Phul ◽  
Muhammad Saleem Rahpoto ◽  
Ghulam Muhammad Mangnejo
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Time Series Data ◽  
Political Stability ◽  
Series Data ◽  
Explanatory Variables ◽  
Long Run ◽  
Short Run ◽  
The Impact ◽  
Positive Effect

This research paper empirically investigates the outcome of Political stability on economic growth (EG) of Pakistan for the period of 1988 to 2018. Political stability (PS), gross fixed capital formation (GFCF), total labor force (TLF) and Inflation (INF) are important explanatory variables. Whereas for model selection GDPr is used as the dependent variable. To check the stationary of time series data Augmented Dickey Fuller (ADF) unit root (UR) test has been used,  and whereas to find out the long run relationship among variables, OLS method has been used. The analysis the impact of PS on EG (EG) in the short run, VAR model has been used. The outcomes show that all the variables (PS, GFCF, TLF and INF) have a significantly positive effect on the EG of Pakistan in the long run period. But the effect of PS on GDP is smaller. Further, in this research we are trying to see the short run relationship between GDP and other explanatory variables. The outcomes show that PS does not have such effect on GDP in the short run analysis. While GFCF, TLF and INF have significantly positive effect on GDP of Pakistan in the short run period.

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