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
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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
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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.
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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.
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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
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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
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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:
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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
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 = (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].
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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
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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
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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). The Regional Distribution of Spanish Unemployment Rate: Spatial Analysis. Regional Studies: The Journal of the Regional Studies
Association, 39(3): 305-318.
[2]
Arbia, G. and Quha, D. (2007). A Class of Spatial Econometric Methods in the Empirical
Analysis of Clusters of Firms in the Space. Discussion Paper of regional science.
[3]
Bailey, T. C. and Gatrell, A. C. (1995). Interactive Spatial Data Analysis. Essex: Addison Wesley
Longman Limited.
[4]
Berhanu Denu, Abraham Tekeste, and Vander, H. (2005). Characteristics and Determinants
of Youth Unemployment, Underemployment and Inadequate Employment in Ethiopia.
Employment Policies Unit, Employment Strategy Department, Ethiopia.
[5]
Berument, H., Dogan, N., and Tansel, A. (2008). Macroeconomic Policy and Unemployment by
Economic Activity: Evidence from Turkey. IZA Discussion Paper No. 3461.
[6]
Beln Solomon and Kimmel, J. (2009). Testing the Inverseness of Fertility and Labor Supply:
The Case of Ethiopia. Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of
第 16 页 共 6 页
Labor, IZA Discussion Paper 3949.
[7]
Bräuninger, M. and Pannenberg, M. (2002). Unemployment and Productivity Growth: An
Empirical Analysis with in an Augmented Solow Model. Economic modelling, 19: 105-120.
[8]
Cressie, N. (1993). Statistics for Spatial Data. John Wiley & Sons: New York).
[9]
Copus, A., Hall, C., Barnes, A., Dalton, G. and Cook, P. (2006). Study on Employment in
Rural Areas: Final Deliverable, Consistency of Rural Development Contract No. 30-CE
-0009640/00-32.
[10]
Darper, N. R. and Smith, H. (1998). Applied Regression Analysis. Third edition, John Wiley
and Sons, New York.
[11]
Dominicis, L., Florax, R. J. and Henri, G. L. (2008). A Meta- Analysis on the Relationship
between Incomes Inequality and Economic. Scottish Journal of political economy. Scottish
Economic Society, 55(5): 654-682.
[12]
Elhorst, J. P. (2000). The Mystery of Regional Unemployment Differentials: a Survey of
Theoretical and Empirical Explanations. Research Report No. 00C06, SOM, University of
Groningen, Netherlands.
[13]
Elhorst, J.P. (2003). The mystery of Regional Unemployment Differentials: Theoretical and
Empirical Explanations. Journal of Economic Surveys, 17: 709–748.
第 17 页 共 6 页
[14]
Girma Earo and Vanden, M. (2006). Poverty and the Rural non-Farm Economy in Oromia,
Ethiopia: Contributed Paper Prepared for Presentation at the International Association of
Agricultural Economist Conference, Gold Coast, Australia.
[15]
Graaff de, T., Florax, R, J. G. M. and Nijkamp, P. (2001). A General Misspecification Test for
Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity, Journal of Regional
Science, Vol.41, No.2, pp.255-276
[16]
Haining, R. (1990). Spatial Data Analysis in the Social and Environmental Sciences. Cambridge
University Press: Cambridge.
[17]
Haining, R. (2003). Spatial Data Analysis: Theory and Practice. University of Cambridge:
Cambridge.
[18]
Hussmanns, R., Mehran, F. and Verma, V. (1990). Surveys of Economically Active Population,
Employment, Unemployment and Underemployment: An ILO manual on concepts and methods,
Geneva.
[19]
IMF (2009). Letter of Intent, Memorandum of Economic and Financial Policies, and Technical
Memorandum of Understanding, Ethiopia.
[20]
ILO (2006). Labor Force Supply Classifications. Labour Force Survey User Guide
Volume 5: LFS Classifications.
第 18 页 共 6 页
[21]
ILO (1982). Resolution Concerning Statistics of the Economically Active Population,
Employment, Unemployment and Underemployment. Adopted by the Thirteenth International
Conference of Labour Statisticians, 2000 edition.
[22]
ILO (1998): Guidelines Concerning the Treatment in Employment and Unemployment Statistics
of Persons on extended Absences from Work; in: Current International Recommendations on
Labour Statistics. Sixteenth International Conference of Labor Statisticians, 2000 edition, Geneva.
[23]
ILO (2000). Resolution Concerning Statistics of the Economically Active Population,
Employment, Unemployment and Underemployment. Current International Recommendations
on Labour Statistics, 2000 edition, pp.86-87.
[24]
Johnoston, J. and Dinardo, J. (1997). Econometric Methods. MCGRAW-HILL International
Editions, 4th edition.
[25]
Kelejian, H. and Robinson, D. (1992). Spatial Autocorrelation: A new Computationally Simple
Test with an Application to Percapita County Police Expenditures. Regional Science and Urban
Economics,22: 317-333.
[26]
Kosfeld, R. and Dreger, C. (2006). Thresholds for Employment and Unemployment: A Spatial
Analysis of German Regional Labour Markets, 1992–2000.John Wiley and Sons, New York.
[27]
Lahiri, S.N. (2003). Central Limit Theorems for Weighted Sum of Spatial Process under Class of
Stochastic and Fixed Designs. The Indian journal of statistics, 65(2).
第 19 页 共 6 页
[28]
Lesage, J. P. (1999). The Theory and Practice of Spatial Econometrics. Department of Economics,
University of Toledo.
[29]
Lee Gallo, J. and Kamarianakis, Y. (2004). ESDA and Spatial Econometrics Modelling for
Study of Regional Productivity Differentials in Europe union (1975-2000). 7th AGILE
Conference on Geographic Information Science.
[30]
Lesage J. P. and. Pace, R. K. (2009). Spatial Econometric Models. In Fisher, M. M. and Getis, A.,
editors, Handbook of Spatial Analysis: Software Tools, Methods and Application, pp.355-374,
WMX Design GmbH, Heidelberg, Germany.
[31]
Maierhofer, E. and Fischer, M. (2001). The Tyranny of Regional Unemployment Rates
Conceptual, Measurement and Data Quality Problems. Paper presented to the 41st Congress of
the European Regional Science Association, Zagreb.
[32]
Maria, D. E. (2011). Spatial Unemployment Differentials in Colombia. Discussion Paper 14, JEL
Classification: R23, C14, C23, C31.
[33]
MOFED (2010). Trends and Prospects for Meeting MDGs by 2015. Ethiopia.
[34]
Montgomery, D.C., E. A. Peck and G.G. Vining (2001). Introduction to Linear Regression
Analysis. 3rd edition, John Wiley and Sons, New York.
[35]
Niebuhr, A. (2003). Spatial Interaction and Regional Unemployment in Europe. European Journal
第 20 页 共 6 页
of Spatial Development, EJSD-ISSN 1650-9544, 5:2-24.
[36]
Nijkamp, P., Cracolici, M.F. and Cuffaro, M. (2007). Geographical Distribution of
Unemployment: An Analysis of Provincial Differences in Italy. Tinbergen Institute
Discussion Paper 065/3.
[37]
Overman, H. and Puga, D. (2002). Unemployment Clusters Across Europe’s Regions and
Countries. Economic Policy, 17(34): 117-147.
[38]
Rey S.J. and Montouri, B.D. (1999). U.S. Regional Income Convergence: A Spatial Econo
Metric Perspective. Regional Studies, 33(2): 145-156.
[39]
Stehrer, R., and Foster, N. (2009). The Determinants of Regional Economic Growth by
Quintile. Wiiw Working Paper no.54, the Vienna institute for international economic studies.
[40]
Smirnov, O., Anselin, L., Syabri, I. (2002). Visualizing Multivariate Spatial Correlation with
Dynamically Linked Windows. In: Proceedings of the SCISS Specialist Meeting “New Tools
for Spatial Data Analysis”. Santa Barbara, California, USA.
[41]
Taylor, J. (1996). Regional Problems and Policies: A European Perspective. Australasian
Journal of Regional Studies, 2: 103–131.
[42]
Topa, G. (2001). Social Interactions, Local Spillovers and Unemployment. The review of
Economic
第 21 页 共 6 页
Studies, 2(68): 261-295.
[43]
Trendle, B. (2006). Unemployment Variation in Metropolitan Brisbane: The role of Geographic
Location and Demographic Characteristics. Australasian Journal of Regional Studies, 2(12).
[44]
UN, (2003). Labor Supply. Regional Stocktaking Review.
第 22 页 共 6 页