Important Socio-economic Indicators Research Articles

Bangladesh's pathways to climate-resilient development: A methodical review

Climate resilient development pathways (CRDP) are procedures that reinforce sustainable development and reduce poverty and inequalities while meeting adaptation and mitigation targets. The study systematically assessed Bangladesh's efforts to combat climate change and evaluated its progress toward CRDP with a degree of confidence approach. It also assessed climate change vulnerability, national climate change plans and policies, climate finance conditions, and environmental sustainability. The study revealed that the recent climate change plans and strategies have transformative, social inclusiveness, gender responsiveness, climate resilience, institutional flexibility and coordination, and target setting and monitoring attributes from a moderate to large extent. However, to implement its recently developed climate plans like the Mujib Climate Prosperity Plan, Delta Plan, NAP, and NDC, where funding is essential—the nation needs USD 533.7 billion. The study suggests that though the country has implemented several plans and strategies in recent decades, uneven progress has appeared in some important socio-economic and environmental indicators as well as SDGs which signifies low return against investment. It can be concluded that ensuring maximum socio-economic and environmental return on investment, long-term climate finance, strengthening local govt. organizations, adopting region specific and holistic approaches with private sector engagement would advance CRDP attainment as well as a nature positive economy and system transition in Bangladesh.

The influence of socioeconomic factors on female entrepreneurship in Southeast Asian countries

Purpose Female entrepreneurship discussions will broaden and diversify as a result of global shifts. Studies of female entrepreneurship must take into account differences between male and female entrepreneurs due to the historical, cultural and social specificity of developing countries to narrow gender gaps, identify barriers, fine-tune support systems, release dormant potential and provide information for policymaking. This paper aims to measure and estimate the most crucial socioeconomic characteristics that Southeast Asian countries leverage to advance women’s business initiatives. Design/methodology/approach Using a panel data model whereby the Female Entrepreneurship Indicator Score serves as the dependent variable and the ten most important socioeconomic indicators serve as the independent variables. Ten southeast Asian countries are analyzed using the panel fixed effects approach of Method of Moments Quantile Regression (MM-QR) from 1980 to 2021. Findings It has been found by empirical panel quantile regression using the MM-QR method that the following indicators positively affect female entrepreneurship in southeast Asian countries: the assets indicator, the pay indicator, the workplace indicator, the mobility indicator and the a woman can sign a contract in the same way as a man indicator. But the parenthood indicator, the unemployment indicator, the school enrollment indicator, the men and women have equal ownership rights to immovable property indicator and the marriage indicator all have negative effects. Originality/value This paper uses a new method called MM-QR to look at how the most important socioeconomic factors affect female entrepreneurship in Southeast Asian countries. The results obtained will also add to and broaden the small amount of research that has been done on female entrepreneurs in developing countries.

Building a Model Linking the Indicator of the Standard of Living of the Population with a Set of Indicators of Socio-Economic Policy in the Regions of Russia

The work contains a study of one of the components of the quality of life - the standard of living of the population - using the example of an analysis of the regions of the Central Federal District. Using Rosstat data, a list of the most important socio-economic indicators characterizing the standard of living of the population was formed, and then an integral indicator was built on them. This indicator can be used as one of the criteria for the performance of regional authorities. The dependence between the obtained indicator and the lag values of variables characterizing the level of economic policy development is shown: human development, innovative activity, transport infrastructure and a number of others. On this basis, an econometric model is built that has a high statistical quality, since the unbiased estimate of the determination coefficient R2 is 95% and the model approximation error is 5.4%. Estimates of elasticity coefficients of explaining variables in this model make it possible to streamline the degree of influence of the corresponding directions of economic policy on the indicator of the standard of living of the population. The presence of lag explanatory variables allows you to make forecasts of indicator values with a planning horizon of 1-2 years.

Predictive Analytics Executed through the Use of Social Big Data and Machine Learning: An Imperious Result

Instability in important socioeconomic indicators can have far-reaching effects on global development. This thesis offers a set of one-of-a-kind big data analytics algorithms that operate on unstructured Web data streams to automatically infer events, knowledge graphs, and predictive models, allowing for a better understanding, definition, and anticipation of the volatility of socioeconomic indicators. This paper we presents four major results that expand previous knowledge. Given a large volume of diverse unstructured news streams, we first describe novel models for collecting events and learning spatio-temporal features of events from news streams. We explore two different kinds of event models: one that is based on the concept of event triggers, and another that is probabilistic and learns a generic class of meta-events by extracting named entities from text streams. The second piece of work investigates the challenge of gleaning knowledge graphs from time-sensitive data like news and events as they happen. Event graphs produce a condensed depiction of a chronology of events pertinent to a news query by characterizing linkages between them using "event-phenomenon graphs," while spatio-temporal article graphs capture innate links between news stories. In this paper we present the various result outcome for predictive result analysis.

Estimating freight production/attraction using nighttime satellite imagery

An appropriate understanding and accurate estimation of zonal freight production (FP) and attraction (FA) volume is one of the most important topics and the initial step in developing the origin and destination (OD) matrix. In general, zonal FP and FA are difficult to be obtained as observed data, particularly in developing countries, owing to the lack of large-scale freight surveys. Thus, several proxy indicators are often used for estimating FP and FA, such as the gross regional product (GRP), population, and the number of establishments. However, these indicators do not often estimate the zonal FP and FA accurately and are even not available readily. The nighttime light intensity (NLI) data that is human-induced light emission data and one of the open alternative data, possibly improves the accuracy of FP and FA estimation. However, few studies have examined the relationship between NLI and FP/FA. Therefore, the objectives of this study are to examine whether NLI can be a significant indicator for estimating zonal FP and FA relative to other socio-economic indicators and to examine the types of NLIs that can accurately estimate the FP and FA through a case study in Japan. As a result of the analysis, NLI data are identified as a significant variable for the estimation of FP and FA among various socio-economic variables. Although some of the indicators related to secondary industries provide higher accuracy, NLI exhibits better estimates than the popular indicators such as GRP and population. In addition, we propose three types of total NLI that multiply the unit NLI by the zonal area and this generates higher accuracy of unit NLI, the average light intensity of all pixels in a zone. We have also found that the use of habitable area for the estimation of total NLI provides the highest accuracy among the four types of NLIs including unit NLI. Through the analysis, NLI data is sufficiently applicable for estimating zonal FP and FA, relative to other important socio-economic indicators, although space transferability should be examined in other study areas. These findings could help estimating FP/FA particularly in countries and regions with limited statistical data available.

Effects of Education and Income on Incident Type 2 Diabetes and Cardiovascular Diseases: a Dutch Prospective Study

BackgroundEducation and income, as two primary socioeconomic indicators, are often used interchangeably in health research. However, there is a lack of clear distinction between these two indicators concerning their associations with health.ObjectiveThis study aimed to investigate the separate and combined effects of education and income in relation to incident type 2 diabetes and cardiovascular diseases in the general population.Design and ParticipantsParticipants aged between 30 and 65 years from the prospective Dutch Lifelines cohort study were included. Two sub-cohorts were subsequently created, including 83,759 and 91,083 participants for a type 2 diabetes cohort and a cardiovascular diseases cohort, respectively.Main MeasuresEducation and income level were assessed by self-report questionnaires. The outcomes were incident type 2 diabetes and cardiovascular diseases (defined as the earliest non-fatal cardiovascular event).Key ResultsA total of 1228 new cases of type 2 diabetes (incidence 1.5%) and 3286 (incidence 3.6%) new cases of cardiovascular diseases were identified, after a median follow-up of 43 and 44 months, respectively. Low education and low income (<1000 euro/month) were both positively associated with a higher risk of incident type 2 diabetes (OR 1.24 [95%CI 1.04–1.48] and OR 1.71 [95%CI 1.30–2.26], respectively); and with a higher risk of incident cardiovascular diseases (OR 1.15 [95%CI 1.04–1.28] and OR 1.24 [95%CI 1.02–1.52], respectively); independent of age, sex, lifestyle factors, BMI, clinical biomarkers, comorbid conditions at baseline, and each other. Results from the combined associations of education and income showed that within each education group, a higher income was associated with better health; and similarly, a higher education was associated with better health within each income group, except for the low-income group.ConclusionsEducation and income were both independently associated with incident type 2 diabetes and cardiovascular diseases. The combined associations of these two socioeconomic indicators revealed that within each education or income level, substantial health disparities existed across strata of the other socioeconomic indicator. Education and income are two equally important socioeconomic indicators in health, and should be considered simultaneously in health research and policymaking.

Social vulnerability assessment of Glacial Lake Outburst Flood in a Northeastern state in India

The Himalayas are experiencing rapid climate change that is significantly contributing to glacier retreat, formation of new glacial lakes, and enlargement of the existing ones. The presence of glacial lakes in the Himalayan region makes it a potential threat for the downstream community, as the catastrophic failure of the lake causes the Glacial Lake Outburst Flood (GLOF), which significantly damages the low-lying areas. Devising appropriate risk reduction strategies for the downstream community to the GLOF hazard is the prime concern for the policymakers. In this study, we have assessed the social vulnerability of the downstream community of Chungthang town of Sikkim Himalaya. Many settlements at Chungthang are potentially exposed to the future GLOF of South Lohnak Lake, one of the fastest-growing glacial lakes in Sikkim, India. A sample size of 90 households have been selected and classified into migrated and permanent residents for the study. Primary data have been collected by using a semi-structured questionnaire. Results revealed that, in Chungthang migrated population are more vulnerable because of their low adaptive capacity and high exposure to the GLOF of South Lohnak lake in Sikkim. The study suggests some important mitigation measures to decrease the vulnerability of the vulnerable community by improving some of the important socioeconomic indicators.

A Novel Consistency Calibration Method for DMSP-OLS Nighttime Stable Light Time-Series Images

The Defense Meteorological Satellite Program-Operational Line-Scan System (DMSP-OLS) nighttime stable light (NSL) products have been widely employed for studying human activities and urbanization. However, due to the lack of on-board calibration of the OLS sensors, narrow radiation range, and different satellite platforms, the NSL products suffer from several limitations such as saturation effect and inconsistency problem. To solve these issues, this study proposed a spatio-temporal adaptive pseudo-invariant pixel (STAPIP) scheme for NSL time-series consistency calibration based on eight-year DMSP-OLS radiance calibrated (RC) products. The unsaturated pixels were calibrated with the temporal adaptive pseudo-invariant pixel (TAPIP) method, while the saturated pixels were calibrated with the spatial adaptive PIP (SAPIP) method. To verify the proposed method's effectiveness, we applied it in China and compared its results with the results of several existing NSL calibration methods. Then we further analyzed the correlations between the calibrated NSL time-series images and several important socio-economic indicators, such as population, gross domestic product (GDP), and electricity consumption (EC). Results showed that the NSL time-series images calibrated by the STAPIP method have the best consistency and highest correlation with socio-economic indicators at both provincial and city levels. Thus, this study can provide an accurate and stable NSL time-series product for research on human activities and the urbanization process.

The Pursuit of Happiness: A Calculation of the Hidden Costs of Being Black in the 21st Century

There is no shortage of evidence documenting glaring disparities on important socioeconomic and health indicators between White and Black Americans. Persistent racial disparities are the consequence of a historic system of structural racism. Given ongoing inequities in nearly every realm of American life, we aim to calculate the contemporary cost of racial inequity. We contribute to a growing body of literature concerning the “hidden cost” of being Black by employing a novel methodological approach and centering a paradigm of intersectionality ( Crenshaw, 1991 ; Shapiro, 2004 ). Specifically, we account for the disparities in health and income between Blacks and Whites by using the compensating and equivalent surplus frameworks to calculate willingness to accept (WTA) and willingness to pay (WTP) estimates. We estimate the WTP to avoid the disparity in health, income, and wellbeing between Black and White Americans, to be between approximately $38,000 and $45,000 per year per person using the Behavioral Risk Factor Surveillance System as well as the General Social Survey, respectively. These estimates can be interpreted as the annual willingness to pay by an average White person to avoid the disparities in income and health experienced by the average Black person.

МОДЕЛИРОВАНИЕ УРОВНЯ БЕДНОСТИ В РФ НА ОСНОВЕ НЕЙРОСЕТЕВОГО ПОДХОДА

The article raises the question of modeling the level of poverty as one of the most important socio-economic indicators. A review of publications by domestic and foreign scientists-economists proves the relevance of the topic chosen for the study. Today, the time series apparatus acts as one of the popular tools for studying the dynamics of the poverty level and the factors that directly influence it, but classical statistical forecasting methods impose rather strict assumptions on the construction of models. The article discusses the possibility of using automated neural networks of the STATISTICA package for analyzing and forecasting a time series composed of annual data reflecting the dynamics of the poverty level in the Russian Federation over the past 20 years. The study took into account the strengths and weaknesses of the use of the neural network apparatus for predicting socio-economic processes. The construction of economic and mathematical models was carried out by building automated neural networks, custom neural networks and the method of multiple sampling. When choosing the most preferable model, a multidimensional criterion was used. The comparison of the real poverty level with the values obtained using the models is made, the quality assessment of the developed models is calculated, the poverty level forecast for 2021-2022 is constructed.

Development trends and assessment of system efficiency &#x0D; of interbudgetary relations in the Republic of Uzbekistan

The study is devoted to topical issues of the distribution of competencies between the budgets of different levels, first of all, with the aim of effectively fulfilling the tasks they face, forming a reliable and stable revenue base for local budgets, equalizing regional incomes, as well as the conditions and mechanisms for providing them with financial assistance. The purpose of this article is to generalize the existing ones in the world, as well as to improve and develop methodological approaches to assessing the effectiveness of the use of fiscal equalization funds, which is undeservedly paid little attention to in the research of Uzbek scientists. The article analyzes the existing system of interbudgetary relations of the Republic of Uzbekistan, identifies the problems and trends of its development. Based on the application of these approaches, as well as mathematical-statistical and expert methods, conclusions were drawn about the presence of a high level of differentiation of the regions of Uzbekistan in terms of important socio-economic indicators and about the effectiveness of the transfer policy carried out in the republic, designed to smooth out these differences. The work will be useful to employees of the relevant authorities of Uzbekistan, decision-makers in the field of formation and implementation of socio-economic policy at the regional and national levels, as well as a wide range of researchers and readers interested in the peculiarities of organizing the system of interbudgetary relations.

Visible minorities in the Canadian Labour Market: Disentangling the effect of religion and ethnicity

Studies on labour-market disadvantages of ethnic and visible minorities in Canada have focused, primarily, on earning differentials leaving other important socioeconomic indicators such as employment and occupational distribution insufficiently examined. These studies have rarely included religion as one of the explanatory variables, despite the presence of sizable religious communities and considerable religious diversity in Canada. Given the rise in anti-Muslim sentiment and the increase in Islamophobia, religion becomes an important factor. In this study, we argue that the Canadian labour market excludes/includes individuals based on their physical visibility and religious affiliation. We analyse data obtained from the Canadian 2011 National Household Survey. The analysis supports the existence of a hierarchy of labour market outcomes predicated on both visibility and religious affiliation. It is suggested that the existing labour market inequality among the various ethno-religious groups is shaped largely by physical visibility and cultural proximity to the dominant group. The results provide evidence for a ‘Muslim penalty’.

Regional Development and Banking Activities

This article is a continuation of studies devoted to the interaction of the region and its banking system. The research object in this article is the banking system of the Rostov region, the Southern Federal District, and its impact on the acceleration of socioeconomic development of both the regions.The subject of the study is interaction of banks with the real sector of the economy and the population of the region. The authors analyzed the scientific sources exploring the interaction of the banking and real sectors of the economy and methodological approaches to the analysis of banking activities’s effects on the region and its socio-economic indicators.Based on econometric modeling, quantitative and qualitative regularities of the indicators are revealed. As a result, authors concluded that the growth in the total volume of profits received by existing credit institutions contributes to the growth of such important socio-economic indicators like Gross Regional Product, internal costs of research and development, labor productivity, fixed asset investments, per capita income, growth of deposits attracted by credit institutions, as well as the growth in the amount of debt on loans extended by credit institutions to legal entities and individuals.

Estimation of Child Mortality Rate in Sistan and Baluchistan Province, Eastern Iran, From 1962 to 2015

Background: Child mortality rate (CMR) is one of the most important health and socio-economic indicators and one of the targets for sustainable development goals to be achieved by 2030. However, due to incomplete registration of deaths in most of the developing countries there is limited knowledge on child mortality. Objectives: The current study aimed at indirectly estimating the trend of CMR in Sistan and Baluchistan Province of Iran using summary birth history (SBH) method. Methods: The SBH data were extracted from censuses 1986, 1996, 2006, and 2011 and demographic and health survey (DSH) 2000. The collected data were analyzed using maternal age cohort (MAC) and maternal period (MAC) methods. These two estimates were smoothed using Loess regression. Finally, the final trend of CRM (with an uncertainty level of 95%) was obtained using Gaussian process regression (GPR). Results: Trend of CMR in Sistan and Baluchistan Province had a decreasing trend from 1962 to 2015; the rate of CRM was 336 per 1000 live births (95% confidence interval (CI): 306 - 366); it was 247 (95% CI: 264 - 279) and 182 (95% CI: 177 - 187) per 1000 live births in 1970 and 1980, respectively. Moreover, CRM in 1990 was 115 (95% CI: 114 - 118) per 1000 live births and in 2000 it reached 67 (95% CI: 66 - 70) per 1000 live births. In 2005, CMR in Sistan and Baluchistan Province was 50 (95% CI: 48 - 53), while it reached 35 (95% CI: 31 - 40) per 1000 live births in 2010 and 22 (95% CI: 17 - 29) per 1000 live births in 2015. Based on the results of analysis of annual reduction rate (ARR), the median ARR during 52 years was approximately 5%. Conclusions: The results of the current study showed that the survival of children in Sistan and Baluchistan Province considerably improved during 53 recent years and achieved the MDG 4, as CMR reduced by over two-thirds. Nevertheless, according to sustainable development goals (SDG), CMR should be decreased further in the years to come in Sistan and Baluchistan Province to reach a favorable level.

Impact of labour migration on entrepreneurship ecosystem: case of Eurasian Economic Union

Labour migration is one of the most important socioeconomic development indicators. The problem of a steady decline in the working-age population size has changed a role of migration that has an active impact on the human potential development. With the help of an empirical example from the Eurasian Economic Union (EEU), this research paper presents specifics of labour migration. A review of literary sources points out to an ambiguous impact of a number of factors on dynamics of migration flows, employment, and unemployment. Correlation-regression modelling of migration processes for the EEU in various configurations has resulted with the following. First, a revealed direct relationship between a natural growth (decline) in the population, a number of vacant jobs and the population migration indicator. Second, researchers have established an inverse dependence between GDP per capita, consumer price index, minimum wage, unemployment rate, and population migration indicator. Thirdly, they have shown that a direction of migrant flows depends on such factors, as GDP per capita, number of vacant jobs and minimum wage. Results of the analysis show that an increasing difference between an average wage in the region and across the EEU, and minimum standard of living leads to decreasing numbers of migrants from a particular region of the EEU. In the EEU, for the population main reasons for employment abroad include unemployment in rural areas, no regular income, and lower wages compared to neighbouring countries. The discussion explains an essence of contradictory consequences of the labour migration impact on a development of national economic systems in terms of the completed academic and empirical research. In this regard, it is reasonable to consider labour migration as a global economic phenomenon and this needs further research in terms of factors that influence it.

Transport indicator analysis and comparison of 151 urban areas, based on open source data

IntroductionThe accurate analysis and comparison of transport indicators from a large variety of urban areas can help to evaluate the performance of different adopted transport policies. This paper attempts to determine important transport and socio-economic indicators from 151 urban areas and 51 countries, based on comparable, directly observable open-source data such as OpenstreetMap (OSM) and the TomTom database.AnalysisThis is the first, systematic indicator-analysis using recent, open source data from different urban areas around the world. The indicator road kilometers per person, sometimes cited as infrastructure accessibility is calculated by processing OSM data. Information on congestion levels have been taken from the TomTom database and socio-economic data from various, publicly accessible databases. Relations between indicators are identified through correlations and regression models are calibrated, quantifying the relation between transport infrastructure and performance indicators. Three sub-categories of cities with different population sizes (small cities, large cities and metropolises) are defined and studied individually. In addition, a qualitative analysis is performed, putting five different indicators into relation.Results & ConclusionsThe main results reconfirm previous findings but with a larger sample size and more comparable data. Good correlation values between infrastructure accessibility, socio-economic indicators, and congestion levels are demonstrated. It is shown that cities with higher GDP have generally built more infrastructure which in turn reduces their congestion levels. In particular, for cities with low population density (above approximately 1500 inh. Per sq.km), more roads per inhabitant lead to lower congestion levels; cities with high population density have in general lower congestion levels if the rail infrastructure per person ratio is high. Furthermore, these cities increasing railways per person is more effective in reducing congestions than increasing road length per person.

A Big City as an Independent Central Place System, a Case Study of Moscow

The paper studies a big city as an independent central place system. For this purpose, the author has developed and tested a methodology for quantitative assessment of central functions based on an integrated set of the most important socioeconomic indicators. This methodology helped to prove the possibility of not only measuring the value of central functions in different parts of a big city, but also of determining the affiliation of all the system’s elements with different hierarchical levels or a service area. The study was based on the model of Moscow’s central place system. The study evaluated how the factor of the Russian capital’s considerable territorial expansion has influenced the dynamics of its central place system from 2009 to 2015. We have found that there is a strong interrelation between Moscow’s radial–circular structure and a relatively uniform reduction in the value of central functions with increasing distance from the city center, which is disturbed by the largest highways, which concentrate a significantly higher value of the key factor compared with neighboring elements.

Neonatal Mortality in the Eastern Mediterranean Region: Socio-Demographic, Economic and Perinatal Factors, 1990 - 2013

Background: The neonatal mortality rate (NMR) is considered amongst the most important socioeconomic and health indicators of a community development. Globally, it has been reported that the daily mortality rate during the neonatal period is much higher (30-fold) than the post-neonatal period. In the Eastern Mediterranean Region, about 40% of the under-5 mortality occurs in the neonatal period. This study aimed to explore the association between the socio-economic, demographic and perinatal care related factors, and the neonatal mortality in the Eastern Mediterranean Region. Methods: The secondary data for the 1990 to 2013 on neonatal mortality rates, socio-demographic, socio-economic and health factors were extracted from official websites, including united nations children’s fund (UNICEF), world health organization (WHO), and united nations educational, scientific and cultural organization (UNESCO). Data were collected using the following keywords: ‘neonatal mortality’, ‘perinatal deaths’, ‘child health, education’, ‘expenditure on health’, ‘safe water’, ‘sanitation’ and ‘health indicators’. We used secondary data analysis to explore the relationship between the possible explanatory variables and neonatal mortality using correlation coefficient analysis. Results: There was a noticeable decline in neonatal mortality rates (NMR) among the Eastern Mediterranean countries from 1990 to 2013. The neonatal mortality rate had a negative and significant association with literacy status of both sexes, annual growth rates, antenatal care coverage, birth attendance by skilled health personnel, Gross National Income (GNI), total health expenditure per capita, safe drinking water and adequate sanitation facilities. However, the neonatal mortality rate had significant and positive relationships with maternal mortality ratio, total fertility rate, population size, total births and low birth weight. Conclusion: Despite the fall in the neonatal mortality rate, it remains high in the Eastern Mediterranean countries. Socioeconomic and other related factors appear to play a major role in determining neonatal mortality and there is an inverse relationship between socioeconomic status and the neonatal mortality rate. Neonatal health in Eastern Mediterranean countries needs a more effective and innovative approach to sustain the rapid progress in the overall reduction of child mortality.

Relationship between socioeconomic vulnerability and ecological sustainability: The case of Aran-V-Bidgol’s rangelands, Iran

This study combines the Me-Bar and Valdez’s vulnerability assessment techniques with geographic information systems (GIS) to assess socioeconomic vulnerability and its relation to ecological sustainability in winter rangelands of Aran-V-Bidgol County, Isfahan province, Iran. Socioeconomic data were collected using a researcher-made questionnaire and ecological sustainability parameters (including plant, litter, soil, sand and gravel cover and forage yield) data were collected through field measurements. Results showed that “local problems”, “number of rangeland users”, “rangeland user’s access to rangeland experts”, “utilization pattern”, “livestock type”, “economic status of rangeland users”, “livestock number” and “increasing prices” were the most important socioeconomic indicators influencing socioeconomic vulnerability in the rangelands. In addition, a significant relationship was found between the socioeconomic vulnerability index and ecological sustainability parameters, including forage yield of annual forbs and total forage yield. Findings further indicated that a combination of the social and economic indicators with a focus on the local scale dimensions of vulnerability could create a useful tool for decision-makers, practitioners and rangeland users whose main recent concern is vulnerable rangeland ecosystems.

Socio-economic and Customer Preference on Evaluation of Materials used for Interior Decoration in Selected Hotels in Nigeria

The study investigated effect of socio-economic characteristics on Hotel customers’ preference of interior decoration in Abeokuta, Ogun State, Nigeria. Out of six hotels, two were selected and ninety respondents were used for data. Questionnaire was used to solicit information. The data was analyzed using descriptive statistics of frequency and simple percentage. Chi-square used to test for the hypotheses formulated at 0.05 level of significance. The findings revealed that majority of the respondents were male and also had tertiary education. It also revealed that majority of the respondents were married. The study also revealed that there was no significant difference between the utilization of quality materials used for interior decoration and customer’s preference. The study concluded that important socio economic indicators were examined. Among recommendations made is good quality materials for interior decorations should be encouraged by the hotel keepers to facelift and maximize customers’ preference.