Disparity Spatial Modeling of Unemployment and Labor Force Participation: A Case Study of Central, South, Sidama, Amhara and Oromia Regional States in Ethiopia Thesis Proposal Report Student Number:LS2208201 Students Name:KALEYA, KASAYE KELKAY Major: Statistics Supervisor: Prof. Yi Bowen Date: 2024-09-10 School of Economics and Management Beihang University, Beijing, China Contents 1. The basis of topic selection ....................................................................................................................... 1 1.1 Significance, Necessity and Frontiers................................Ошибка! Закладка не определена. 2. Current Research Situation /Cutting- Age of Topic Selection ................................................................... 1 3. Research plan ............................................................................................................................................ 4 3.1 Research Objective ............................................................................................................................. 4 3.2 Research content ................................................................................................................................ 5 3.3 Key Questions to be Solved ................................................................................................................ 5 3.4 Research approach............................................................................................................................... 6 3.5 Technical route to be adopted ............................................................................................................ 14 4. Expected Objectives and Achievements) ................................................................................................ 14 5. Thesis Implementation Plan .................................................................................................................... 15 6. References ............................................................................................................................................... 16 1 The basis of topic selection 1.1 Background and Significance To investigate the disparities in regional production and productivity substantial effort has been made by questioning “what are the most determining factors contributing to disparities in income growth rates among countries and regions over the world?” Spatially disaggregated analysis of the labor market appears to provide beneficial insights internal forces and the way external forces are transmitted via economic social and political linkages (Mairehofer and Ficher,2001) [1]. Regional sciences make use of spatial data to tackle problems faced by cities and regions based on statistical or econometric methods (Anselin,1988) [2]. Several factors such as trade between regions, technology and generally spatial spillovers can be cause to geographical dependence with in regions (Haining ,2003[3]); Therefore, appropriate models that incorporate spatial effect must be used (Haining,1990; Rey and Montouri, 1999; ward and Brown ,2009) [4]. One can realize that large disparities are observed in the geographical concentration of production and clusters of economic activities due to factors such as stock of human capital, investment, trade, foreign direct investment and low levels of political corruption (Dominicis et., 2008).[5] Kosfeld and Dreger (2006) [6], argued that the role of work force is a key variable in many growth models and countries with high levels of work force may potentially attract more firms there by increasing the demand for labor which in turn raises wages and incomes. Neibur (2003) [7] in his study revealed that a negative shock affecting a regional labor 第 1 页 共 6 页 market in Europe. As the result argument by Taylor (1996) [8], suggested that reducing regional disparities might lead to higher national output, lower inflationary pressure and might produce large social benefits. A study by Girma and Vanden (2006) [9], demonstrated that rural poverty remains a key development challenge for Ethiopia in general and the neighboring regions to Central and South People regions in particular. According to Ministry of Finance and Economic Development (2010) [10], Economic growth and distribution of income are the major instruments for reducing poverty and the nature of growth has the most significant effect depicted in various Ethiopian government development policy. Moreover, today reducing poverty and income inequality have been taken to be primary indicator of Economic and social development in place of emphases on economic growth; Ethiopia does not only need strong economic growth, but also robust expansion in the quantity and quality in the employment opportunities particularly regional labor force which plays vital role (Brehanu et al., 2005; IMF ,2009) [11]. However, negligence of spatial autocorrelation in regional data may cause misleading results. Therefore, when dealing with regional data, existence of spatial autocorrelation must be explored. If there is spatial autocorrelation in the data under study, then an appropriate model that take it in to account must be used. Ward and Brown (2009) [12]; and Haining (1990) [13] argued that taking in to account the spatial nature of the data; exploratory spatial data analysis and spatial process models would be used to analyze the data. Therefore, this study has been designed to introduce measures of spatial autocorrelation and spatial econometric techniques to analyze the dependence of regional unemployment and participation rates Central and South People Regional state of Ethiopia 第 2 页 共 6 页 and to trigger wider research of importance of local interactions and social networks contributing to the regional labor market payoff. 1.2 Significance of the Study It is fact that geographically close districts with similar socio-economic characteristics and vulnerability dimensions are more conducive to grouping task forces, such as the formulation of parallel policy initiatives. Thus, this study may have the contribution to assessment and evaluation of the work force and its productivity across regions or districts of Ethiopia. It may also provide information on spatial distribution of Economic participation and an accumulation of unemployment that can be useful for planners to distinguish geographically targeted preparation of development plan, monitoring, and evaluation of economic and labor force policy. Moreover, this study would suggest policy options for policy makers and development partners to adopt enhancement of economic participation, and minimize unemployment in relation with other development indicators to tackle economic Vulnerability. 2. Current Research Situation A better livelihood realization level is a priority to remain competitive in global economy. This would require a great deal of achievement in production and productivity; however, low productivity is the key reason for persistence of poverty in developing countries like Ethiopia. Wide disparity in Labor force participation and unemployment imply in efficiency in the national economy as a whole and might affect both aggregate unemployment and national output. Despite the challenging aspects, little systematic analysis has explored key 第 3 页 共 6 页 labor market issues in Ethiopia in terms of important policy questions about how to facilitate job creation, productivity growth, and labor market efficiency (UN,2003). Even though most of reports and research papers pertaining to regions outline labor force status and dynamics in cross-section at household levels, they did not take in to account main spatial effects: spatial dependence and heterogeneity in key economic variables such as Labor Force Participation and unemployment at regional level and /or country levels of administrative structure. 3. Research plan 3.1 Research Objectives This Study has the following objectives. 1. Determine spatial dependence in the Labor Force Participation and unemployment rates. 2. Investigate spatial association between unemployment and Labor Force Participation. 3. Explore and identify clusters of districts with statistically significant spatial autocorrelations. 4. Assess a best fit model to the Labor Force Participation and Unemployment. 5. Figure out major factors causing geographical variation in Labor Force Participation and Unemployment. 6. Contribute to the provision of factual information for policy makers and researchers based on the empirical result. 第 4 页 共 6 页 3.2 Research content 1.Title Page 2. Abstract 3.Introduction 3.1 Background 3.2 Statement of the Problem 3.3 Significance of the study 3.4 Research Objectives 3.5 Research Questions 4. Literature Review 5. Theoretical Frame Work 6. Research Design and Methodology 6.1 Research Approach 6.2 Data Source 6.3 Data Collection Method 6.4 Data Analysis Techniques 7. Expected Outcomes 8. References 3.3 Key Questions to be Solved . This study is highly inspired to seek solutions to the following research questions: 1. Is there a spatial spillover Effect in an unemployment and Labor Force Participation? 2. What kind of spatial association do unemployment and Labor Force Participation exhibit? 第 5 页 共 6 页 3. What are the statuses of spatial clusters in unemployment and Labor Force Participation? 4. Which spatial model could fit to the Labor Force Participation and unemployment? 5. Which factors cause spatial variation in Labor Force Participation and unemployment? 3.4 Research Design and Data This study is to be conducted in Central and South People Regional States of Ethiopia. Cross-sectional secondary data spatially aggregated at district level across Central and South People Regional States on all variables is be exploited to conduct the investigation. Data for the study is extracted from Census projection for 2030 Population and housing census database. To sample unit of analysis in fixed design especially for irregular shaped polygon, the analogue of the classical situation in the case of spatial data is the surface considered as a single realization(experiment) of random spatial process (Anselin,1988; Haining,1990;2003) [14]. Spatial econometrics literature mainly focuses on increasing domain asymptotic under fixed sample design (Cressie,1993; Lahiri,2003) [15] and model-based approach to spatial sampling (Haining,2003) [16]. Considering these issues and particularly by assuming increasing domain asymptotic and permutation (Griffith,1988) [33] 100 districts in both regional states were selected together with the following variables. The dependent variables are LFP_R: Labor Force Participation Rate, the percentage of population aged 15 years and above, both employed and unemployed to both economically active and inactive people and UNEMP_R: unemployment rate, percentage of unemployed population over the total of economically active people. 第 6 页 共 6 页 The Independent Variables and variables used in ESDA are: POP_T: Total Population of district, SEX_R: Sex Ratio, POP_U: Proportion of population in Urban, EIN_R: Economic Inactivity Rate, EMPL_GOVT: Percentage of government Employees to Total Employed Population, EMPL_SEL: Percentage of self-employed people to Total Employed People, UNPD_FW: Percentage of unpaid Family Workers, ACTA_P: Percentage of Economically active age population, DEPD_R: Dependency Ratio ; AVENP_HH: Average number of person per conventional household; PAGD_G5 : Percentage of people aged 5 and above never attended school; CDR_1000: Crude Death Rate; URBRU_100: percentage of population migrants from urban to rural areas to total migrants lived in the place of survey for at least 6 months, MUNEMP_R: Male Unemployment rate ; FUNEMP_R: Female unemployment Rate; MMR_100000: Maternal Mortality Rate; CBR_1000: Crude Birth Rate ; MIGRU_100: Percentage of Population migrants from Rural to Urban Areas, LFPR_M: Male Labor Force Participation Rate, LFPR_F: Female Labor Force Participation Rate. 4.2 Methods of Data Analysis 4.2.1 Standard Multiple Linear Regression Analysis Multiple Linear regression analysis will be used to estimate models to describe the distribution of response variable with the help of number of independent variables. In Multiple Linear regressions, a linear combination of two or more predictor variables is used to explain the variation in response Variable (Montgomery, et al., 2001) [17]. For cross-sectional data, the basic assumptions of the Multiple Linear regression Model analysis 第 7 页 共 6 页 are: Linearity, Normality, Homoskedasticity, and Multi- Collinearity. Parameters are estimated by fitting models to the sample data using Ordinary Least Squares (OLS) method, and to test significance of each independent variable and overall model t and F tests are used respectively at 5% level of significance. A number of Tests and checks help us to ensure that analysis has proceeded within the bounds of the basic assumptions. Condition number, K, is used to detect sever multicollinearity (Draper and Smith, 1998; Johnoston et al.,1997) [18], Jarque and Bera test is used to test normality of errors (Montgomery, et al.,2001) [19], Breusch-Pagan, Koenker-Bessett and White tests are used to test Homoskedasticity (Montgomery, et al., 2001) [20]. To making comparison between or/and among models, in the same class but differently specified R2. Log likelihood, AIC, SC and SF of regression are used under the study (Montgomery, et al., 2001; Draper and Smith, 1998) [21]. 4.2.2 Spatial Data Analysis In Statistics, Spatial data analysis or spatial statistics includes any of the formal techniques which assess entities using their topological, geometric or geographic properties that manifest them in space: Location, area, topology, spatial arrangements, distance and interactions (Anselin, 1996)[22]. Spatial data set consists a collection of measurements or observations on one or more attributes taken at space (Haining,1990) [23]. The spatial data instruments are raster and vector; and there are three types of spatial data (Spatial point process, Geostatistical Data, and Area (Lattice)data). In the context of standard econometric spatial models’ lattice data types are data for which aggregated value of spatial points of observation on each region at a time is used for analysis. Quantification aspect of location of spatial data is based on location information from cartesian space and contiguity 第 8 页 共 6 页 (Lesage,1999)[24]. Contiguity information is quantified as contiguity (spatial neighbors) matrix which contains elements of 1 and 0; the matrix is denoted by W and constructed based on Rock contiguity, Bishop Contiguity, and Queen Contiguity (Anselin, 1988; Lesage, 1999) [25]. Row standardized Queen Contiguity matrix called Spatial Weighted matrix (W) is used for quantification of location under this study. Lesage (1999) [26] stated that in a regression context, spatial effects pertain to spatial dependence (Spatial autocorrelation) and spatial heterogeneity. Spatial dependence is expected when sample data observed at one point in space is related to values observed at other, whereas heterogeneity is simply structural instability in the form of non-constant error variances (heteroskedasticity) and/or spatial varying of model parameters (Graaff et al.,2001) [27]. Fundamental Problems associated with analyzing spatial data and modeling spatial process are: ecological fallacy and modifiable area unit problem (King,1997) [28], asymptotes in spatial stochastic processes (Anselin,1988) [29], boundary value and spatial sampling problem, properties of spatial connectivity, spatial non stationary, and others statistical perspective problems. In practice these conditions are likely satisfied by most spatial weighted matrix which is based on simple contiguity, increasing domain and infill asymptotic approaches (Lesage,1999) [30]. Exploratory Spatial Data Analysis (ESDA) is a set of techniques aimed at describing and visualizing spatial distributions, identifying a typical localizations or spatial outliers, detecting patterns of spatial associations, clusters or hot spots, and suggesting spatial regions or other forms of spatial heterogeneity (Haining, 1990; Baily and Gatrell 1995; Anselin 1998).[31] Spatial autocorrelation can be defined as the coincidence of value similarity with location similarity or dissimilarity (Anselin, 2000; Anselin,1995)[32] which can be measured 第 9 页 共 6 页 by global and local indicators. The Global Indicator is Moran’s statistic, I, which measures similarities and dissimilarities in observations across space. Where, I =-1 perfect negative spatial autocorrelation, I = 1 is perfect positive spatial autocorrelation, and I =0 signifies no spatial correlation. Inference on Moran’s I take normal assumption and randomization or permutation approaches (Anselin, 1995; Cressie,1993) [33]. Measures of local autocorrelation are used when there is no global autocorrelation, and incase where measure of global does not enable us to appreciate the regional structure of spatial autocorrelation. The analysis of local spatial autocorrelation is carried out with two tools. First, the Moran’s scatter plot which is used to visualize local spatial instability (Anselin, et al.,1996) [34], and second, local indicators (Ii) which is used to test the hypothesis of random distribution by comparing the values of each specific localization with the values in the neighboring localizations which is depicted by Local Indicators of spatial Association (LISA) maps (Aselin, 1995)[35]. In addition to the univariate, multivariate spatial autocorrelation and LISA are also analyzed by employing a bivariate Moran’s I statistic and local measures. The bivariate spatial autocorrelation centers on the extent to which values of one variable observed at a given location show a systematic association with another variable observed at the neighboring locations (Smirnov et al.,2002). Standard multiple linear regression (OLS) model with spatially autocorrelated residuals may violet the independence assumption for error term, consequently regression parameter estimates are no longer BLUE, consistency 第 10 页 共 6 页 and unbiased, so statistical inference is unreliable. Hence an important issue in empirical spatial analysis is how one can detect the presence of spatial effects, and moreover, how one can distinguish between spatial dependence as a nuisance and a substantive spatial process (Anselin and Grifith 1988). The following ESDA are applied to check the presence of spatial autocorrelation in OLS regression model residuals. Moran’s Test for regression residuals (Cliff and Ord, 1981; Anselin, 1988) [36], Lagrange Multiplier (LM) Tests; LM-error test (Burridge, 1980), LM-lag test (Anselin, 1988) [37], Robust Lagrange Multiplier Test for a spatial error process robust to the local presence of a spatial lag, and Robust Lagrange Multiplier Test for a spatial error process robust to the local presence of a spatial error. In all of these tests discussed above the null hypothesis is stated as there is no spatial autocorrelation in the OLS residuals, and large values of test statistic ( with degree of freedom one lead to rejection of null hypothesis (Anselin, et al.,1996; Kelejian and Robinson,1992) [38]. Spatial regression model in econometrics approach is employed to model economic activity and unemployment rates; in the spatial linear regression model, spatial dependence can be incorporated in specification in two distinct ways; as an additional regressor in the form of a spatially lagged dependent variable, Wy, providing spatial lag model, and in the form of spatial lag error structure, W ,providing spatial error model. In a simultaneous specified model, the focus is on the complete spatial pattern; particularly simultaneous autoregressive models assume that the response at each location is a function not only of explanatory variable at that location but also of the values of the response values at neighboring locations as well (Cressie, 1993; Haining, 2003) [39]. The simultaneous spatial lag regression model of dependent variable Y for observation i and k predictors is: 第 11 页 共 6 页 Where, is a spatial autoregressive coefficient which is scalar. the k explanatory variables and intercept are , k = 0, 1, 2, 3…, k with associated coefficient denote the (i,j)th element of W, and , is the error term normally distributed. The matrix notation of the model is Y = WY + X+ , where is the vector of error terms which is independent and identically multivariate normally distributed with mean vector zero and constant diagonal variance -covariance matrix 2In. Spatial lag regression model is appropriate when we believe that the values of dependent in one unit i are directly influenced by the values of dependent variable found in i’s neighbors. The spatial lag term must be treated as an endogenous variable and proper estimation methods must account for this endogeneity; implies OLS estimates are biased and inconsistent due to the simultaneity. Thus, based on assumptions, the spatial process is stationary and possibly isotropic property over space and W is non-stochastic and exogenous to the model; therefore, maximum likelihood estimation with usually attractive asymptotic properties of estimators is appropriate (Anselin, 1998 and 1999; Anselin and Bera, 1998; Lee and Kammarianekis,2004; Pace and Lesage,2009) [40]. Employing an analogous arrangement in spatial econometric lag model, the spatial error model for observation i is could be specified as: Where, is a spatial autocorrelation coefficient which is scalar, and independently and identically normally distributed with mean zero and constant variance. The matrix notation of spatial error model is Y = X+u, where u = Wu+. Thus Y = X+Wu+, where 第 12 页 共 6 页 = (I - W) u. This type of spatial regression model is appropriate when we believe that dependent variable is not influenced directly by the value of dependent as such among neighbors but rather that there is some spatially clustered feature that influences the value of dependent for single unit and its neighbors but was omitted from the specification (Anselin, 1999[41]). The maximum likelihood estimation technique was suggested in concept of asymptotic properties of estimators for estimation of parameters (Anselin, 1988)[42], and the estimator for spatial autocorrelation parameter is obtained from explicit maximization of concentrated log likelihood function. Most of statistical inference principally hypothesis testing in spatial models is based on Wald(W), Lagrange Multiplier (LM) and Likelihood Ratio (LR) tests that relying on optimality properties of maximum likelihood estimators and functions of estimators (Anselin,1988; Lesage and Pace, 2009). Each test statistic is asymptotically distributed as chi-square (2) with 1 degree of freedom (Pace and Barry ,1997) [43]. Further diagnostics for normality, heteroskedasticity, and presence of spatial dependence are also assessed for both models. Likewise for models’ comparison; R2, Log likelihood, Akaike Information Criterion (AIC), Schwarz Criterion (SC), and others are often useful. The lower value for AIC and SC, and higher value Log likelihood signifies the model is best fit (Draper and Smith,1998)[44]. 第 13 页 共 6 页 3.5 Technical route to be adopted The technical route to be followed when conducting this research is as depicted below. Methodological Frame Work - Quantitative approach Data Collection -Central statistical Agency of Ethiopia -World Bank - GIS data Inference and Diagnostics - Wald(W) - Lagrange Multiplier (LM) - Likelihood Ratio (LR) - Model Comparison Spatial Modeling Techniques -spatial regression models -spatial clustering analysis -geographically weighted regression Analysis Plan (GIS Software) - Data Processing - Data cleaning - Spatial Analysis using GIs Feasibility and Ethics -Publicly available secondary data - Adhere to academic integrity Figure 3.1 Research Process 4. Expected Objectives and Achievements) 4.1. Comprehensive Understanding: Achieve a nuanced understanding of how geographical factors influence unemployment and labor force participation, contributing to existing literature on regional disparities. 4.2. Data-Driven Insights: Generate data-driven insights that can inform policymakers about the specific needs 第 14 页 共 6 页 and challenges faced by different regions in Ethiopia. 4.3. Spatial Econometric Model Development: Develop a robust spatial econometric model that can be used as a reference for future studies on regional employment issues in Ethiopia or similar contexts. 4.4. Recommendations for Policy: Formulate targeted recommendations for regional policies that address identified disparities, potentially leading to improved labor market conditions. 4.5. Contribution to Academic Knowledge: Contribute original research findings to the academic community, particularly in the fields of regional science, labor economics, and development studies. 5. Thesis Implementation Plan Activity Choosing a Thesis Topic Expected Date 24/04/15-24/04/30 Collecting Information for Topic 24/05/01-24/05/30 Formulating Final Topic 24/06/01-24/06/05 Undergoing Literature Review 24/06/10-24/07/15 Writing Thesis Proposal Report 24/07/16-24/08/31 Applying for Oral defense of Thesis Proposal 24/09/01-24/09/06 Conducting Oral defense for the Thesis Proposal 24/09/19-24/09/20 Undergoing Data Collection, Analysis, Obtaining Results and Drawing conclusion. 24/09/21-24/12/31 Writing Midterm Assessment Report /100% completed Thesis/ 25/01/01-25/01/30 Applying for Oral defense of the Midterm Assessment of Thesis 25/02/01-25/02/05 Conducting Oral defense for Midterm Assessment of Thesis 25/03/06-25/03/15 第 15 页 共 6 页 Remarks Applying for Final defense of Thesis 25/04/01-25/04/05 Conducting Final defense of Thesis 25/04/06-25/04/30 6. References [1] Artis, M., Lopez, B. E. and Delbarrio, T. (1999). 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