scholarly journals Food security and trade policies: evidence from the milk sector case study

2021 ◽  
Vol 123 (13) ◽  
pp. 59-72
Author(s):  
Maria Bruna Zolin ◽  
Danilo Cavapozzi ◽  
Martina Mazzarolo
Keyword(s):  
Food Security ◽  
Empirical Evidence ◽  
Rural Areas ◽  
Time Series Data ◽  
Income Elasticity ◽  
Series Data ◽  
National Bureau ◽  
Content Type ◽  
Trade Opening ◽  
Urban And Rural Areas

PurposeMilk is one of the most produced, consumed and protected agricultural commodities worldwide. The purpose of this paper is to assess how trade-opening policies can foster food security in the Chinese milk sector.Design/methodology/approachThe empirical evidence proposed in our paper is based on time series data from the National Bureau of Statistics of China (2019) and FAOSTAT (2020). Differences in income elasticity between urban and rural areas are estimated by OLS regressions. The data also provide empirical evidence to assess to what extent and to which countries China is resorting to meet its growing demand.FindingsPer-capita milk consumption of Chinese is rising. The authors’ estimates show that milk income elasticity is higher in rural areas. China is also progressively increasing its dependence on imports. Producers who benefit the most are those from countries implementing trade-opening policies.Research limitations/implicationsOther methods could be applied, by way of example, the gravitational model.Practical implicationsTrade agreements and the removal of barriers could be effective responses to protectionist pressures and to food security concerns.Social implicationsThe case examined is of particular interest as it intervenes on food security and safety.Originality/valueThe paper adds value and evidence to the effects of trade on food security in a country with limited and exploited natural resources addressing a health emergency and environmental concerns.

Peer-to-peer-travel: is Airbnb a friend or foe to hotels?

2019 ◽  
Vol 13 (4) ◽  
pp. 443-457 ◽  
Author(s):  
Oswald Mhlanga
Keyword(s):  
South Africa ◽  
Empirical Evidence ◽  
Time Series Data ◽  
Theoretical Perspective ◽  
Hotel Industry ◽  
Disruptive Innovation ◽  
Series Data ◽  
Bottom Line ◽  
Data Set ◽  
Content Type

Purpose The sharing economy has caught great attention from researchers and policymakers. However, due to the dearth of available data, not much empirical evidence has been provided. This paper aims to empirically assess the impacts of Airbnb on hotel performances in South Africa. Design/methodology/approach Using South Africa as a case study, the study measures the impacts of Airbnb on hotel performances on three key metrics, namely, room prices, occupancy and Revenue per available room (RevPAR). A difference-in-difference model is estimated using a population-based data set of 809 hotels from 2016 to 2018. Findings The results reveal that despite Airbnb significantly and negatively impacting on hotel occupancies it has a non-significant effect on hotel prices and RevPAR. Although from the theoretical perspective a disruptive innovation business model such as Airbnb can possibly have a negligible effect on hotel performances because it may attract a different group of customers and create a new market, the empirical findings of this study fail to support this theoretical hypothesis. Consequently, the findings diverge with newly developed knowledge in other markets and point to nuanced and contextual complementary effects. Research limitations/implications Although some interesting findings are revealed into his study, some caveats remain. For instance, the study relied on data from hotels not from Airbnb. If the data of Airbnb can become available, it would be interesting to further examine whether the aggregated RevPAR of Airbnb can compensate for the aggregated loss of hotel RevPAR. This type of analysis could provide a broader evaluation scope regarding the overall effect of Airbnb on hotel performances. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel performances. Practical implications While hotels have launched a campaign to portray Airbnb as being commercial operators looking to compete illegally with hotels for the same segment of customers, this study shows that the rhetoric has been exaggerated. Airbnb, and more broadly, vacation rentals do not represent a war with hotels. They represent an answer to a different need. Indeed, the study reveals that Airbnb’s offer is a mere supplement to the market contrary to media rhetoric that it is meant to substitute hotels. The study has several implications for practitioners. First, these results are important because they serve as evidence against news articles that claim Airbnb is driving hotels out of business. They also show that if current trends continue, employees in the hotel industry in South Africa do not need to be concerned about losing their jobs because of Airbnb’s emergence. It is also important information for investors who may be concerned that Airbnb is hurting the hotel industry’s bottom line. Second, as the share of Airbnb listings on the accommodation market varies dramatically between cities, it is likely that eventual regulations/restrictions should be introduced in the provincial levels, while most of the cities continue benefiting from the increasing number of Airbnb visitors. Originality/value To the best of the author’s knowledge, this study is the first in South Africa to provide empirical evidence that Airbnb is significantly changing consumption patterns in the hotel industry, as opposed to generating purely incremental economic activity.

scholarly journals Food Security Challenges in Nigeria: A Paradox of Rising Domestic Food Production and Food Import

2014 ◽  
Vol 18 ◽  
pp. 38-46 ◽  
Author(s):  
G.T. Ahungwa ◽  
U. Haruna ◽  
B.G. Muktar
Keyword(s):  
Food Security ◽  
Food Production ◽  
Management Practices ◽  
Time Series Data ◽  
A Priori ◽  
Series Data ◽  
National Bureau ◽  
Security Challenges ◽  
Postharvest Management ◽  
Domestic Food

This paper examined the food security challenges vis-á-vis the paradox of increased domestic food production and food import in Nigeria. The study used time-series data from National Bureau of Statistic, Central Bank of Nigeria, Nigeria’s National Dailies and CIA Factbook reports. The trend analysis showed that the share of agriculture to the total Gross Domestic Product, GDP had a downward trend, especially from 1960-1979, where food import hovered around 2.92 % from 1960-74 and up to 9.85 % in 1975-79 of GDP. The result depicts an undulating trend in the contribution of agriculture and food import values to 2009 where food import rose astronomically from N2.6trillion (3.83 %) in 2005-2009 to about N20.6trillion (25.02 %) in 2010-2012. Results of the regression analysis confirmed that agriculture has a positive relationship with GDP, and contributes significantly with a coefficient of 0.852. The paradox however is that food import negates the a priori expectation as it is found to be positively related to the GDP: as food production increases marginally, food importation increases asymptotically. The paper recommends that reliance on food import could be minimized through increased budgetary allocation to the sector, and improvement in postharvest management practices that have hitherto, aggravated food insecurity in the country.

Exploring the dynamic contribution behavior of editors in wikis based on time series analysis

2016 ◽  
Vol 50 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Linghe Huang ◽  
Qinghua Zhu ◽  
Jia Tina Du ◽  
Baozhen Lee
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Time Of Day ◽  
Observation Time ◽  
Series Data ◽  
Clustering Methods ◽  
Content Type ◽  
Free Rider Problem ◽  
Series Analysis

Purpose – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings – There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions. Originality/value – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Has the low level of economic growth spurred informal sector activities in Uganda? An empirical analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stephen Esaku
Keyword(s):  
Economic Growth ◽  
Informal Sector ◽  
Shadow Economy ◽  
Time Series Data ◽  
Series Data ◽  
Content Type ◽  
Low Level ◽  
Revenue Collection ◽  
Long Run ◽  
Short Run

PurposeIn this paper, the authors examine how economic growth shapes the shadow economy in the long and short run.Design/methodology/approachUsing annual time series data from Uganda, drawn from various data sources, covering the period from 1991 to 2017, the authors apply the ARDL modeling approach to cointegration.FindingsThis paper finds that an increase in economic growth significantly reduces the size of the shadow economy, in both the long and short run, all else equal. However, the long-run relationship between the shadow economy and growth is non-linear. The results suggest that the rise of the shadow economy could partially be attributed to the slow and sluggish rate of economic growth.Practical implicationsThese findings imply that addressing informality requires addressing underlying factors of underdevelopment since improvements in economic growth also translate into a reduction in the size of the shadow economy in the short and long run.Originality/valueThese findings reveal that the low level of economic growth is an issue because it spurs informal sector activities in the short run. However, as the economy improves, it becomes an incentive for individuals to operate in the informal sector. Additionally, tackling shadow activities in the short run could help improve tax revenue collection.

scholarly journals Interest Rates and Foreign Investor Activity in Kenya: Evidence from Nairobi Securities Exchange

2020 ◽  
pp. 75-82
Author(s):  
Loice Koskei
Keyword(s):  
Interest Rates ◽  
Inflation Rate ◽  
Time Series Data ◽  
Multiple Regression Model ◽  
Series Data ◽  
Foreign Investor ◽  
National Bureau ◽  
Treasury Bills ◽  
Monthly Data ◽  
Deposit Rate

Interest rates play a key role in attracting foreign investor activity in the country. This study investigated the effect of interest rates on foreign investor activity at Nairobi Securities Exchange in Kenya. Monthly data was collected from Nairobi Securities Exchange, Central Bank of Kenya and Kenya National Bureau of Statistics. Time series data for eleven year period spanning from January 2009 to December 2019 was used.  The multiple regression model results disclosed that interest rates as measured by lending rate had a positive and statistically significant effect on foreign investor. Inflation rate results had a negatively but statistically significant effect on foreign investor. The results for exchange rate had a negative but statistically insignificant effect on foreign investor activity. The deposit rate results indicated a negative and statistically significant effect on foreign investor activity implying that commercial banks deposit rate has an effect on foreign investor activity. The results for 91-day treasury bills specified a positive and non-statistically insignificant relationship with foreign investor activity pointing that for 91- day treasury bills do not affect the foreign investor activity at Nairobi securities exchange in Kenya.

An improved morphological weighted dynamic similarity measurement algorithm for time series data

2018 ◽  
Vol 11 (4) ◽  
pp. 486-495
Author(s):  
Ke Yi Zhou ◽  
Shaolin Hu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Morphological Changes ◽  
Series Data ◽  
Similarity Measurement ◽  
Improved Method ◽  
Control Measurement ◽  
Content Type ◽  
Basic Work ◽  
Measurement Algorithm

Purpose The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate. Design/methodology/approach Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system. Findings The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement. Practical implications This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis. Originality/value In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Sensor Review ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Jinghan Du ◽  
Haiyan Chen ◽  
Weining Zhang
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Correlation ◽  
Data Recovery ◽  
Series Data ◽  
Learning Method ◽  
Content Type ◽  
Spatio Temporal

Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

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