The Scan-LM to Test Instability in the Constant Coefficient of Spatial Autoregressive Models

This paper presents a test based on the principle of Lagrange Multipliers to identify spatial instability in the constant coefficient of regression models including substantive spatial dependence. The test has been adapted to the Scan methodology. Its main advantage is that it identifies areas with differential behavior without the need to provide information about their location, shape, or size. The study shows the utility of the test, reconsidering the results obtained by Mur et al.(2008) about instability in the distribution of per capita income in European regions.

Estimation of Spatial Lag Model Under Random Missing Data in the Dependent Variable. Two Stage Estimator with Imputation

The main goal of this article is to propose estimators for the Spatial Lag Model (SLM) under missing data context. We present three alternatives estimators for the SLM based on Two Stage Least Squares estimation methodology. The estimators are eÿcient within their type and consistent under random missing data in the dependent variable. Unlike the IBG2SLS estimator presented in Wang and Lee (2013) which impute all missing data we only impute missing data in the spatial lag. Our first proposal is an alternative version of the IBG2SLS estimator, the second one is based on an approximation to the optimal instruments matrix and the third one is an alternative equivalent to the first. Thorough a Monte Carlo simulation we assess the estimators performance under finite samples. Results show a good performance for all estimators, moreover, results are quite similar to the IBG2SLS estimator suggesting that a complete imputation (as IBG2SLS does) does not add information.

Where Are the Poor Located? A Spatial Heterogeneity Analysis of Monetary Poverty in Peru

Measuring poverty is a first step to the design of effective public policies, however, it is also essential to know where the poor are located. The main objective of this research is to evaluate the spatial heterogeneity of the factors that influence monetary poverty for each district in Peru. We apply a Geographically Weighted Regression (GWR) approach, which allows us to capture the non-stationarity of the hidden data and to provide coefficients for each district, unlike the OLS model. This research mainly uses the Poverty Map and the Population and Household Census of Peru, both from 2007 and 2017. The overriding findings of our results indicate that female headship, secondary education, electricity, and sanitation services are directly associated with poverty reduction at the local level. For 2007, significant effects are mainly concentrated in the districts of Pasco, Lima and Cajamarca regions. For 2017, the results show a shift towards districts of Junín, Huancavelica, and Cajamarca regions. Likewise, it is highlighted that the highest mean negative effect on poverty is generated by Secondary Education in the GWR estimates; while malnutrition represents the highest mean positive effect on poverty for the level and intercensal models. Finally, the empirical evidence found in this research can help establish better policy designs at the district level.

Price and Spatial Distribution of Office Rental in Madrid: A Decision Tree Analysis

In this paper, we assess the drivers of office rental prices in the municipality of Madrid with a sample of 4,721 offices in March, 2020. The estimation was performed using the decision tree approach, which was built with a random forest algorithm. This technique allows us to capture the strong nonlinear component in the relation between price and its drivers, mainly geospatial location. Through a stratified analysis, we find out that the willingness to pay high rent in the center of Madrid is a feature of particular relevance to medium-sized offices. For diferent reasons, we also find out some office clusters located far from the city center with high rent for both large and small offices.

Human Capital Constraints, Spatial Dependence, and Regionalization in Bolivia: A Spatial Clustering Approach

Using a novel dataset, this article studies the spatial distribution of human capital constraints across 339 municipalities in Bolivia. In particular, five human capital constraints are evaluated: chronic malnutrition in children, non-Spanish speaking population, secondary dropout rate of males, secondary dropout rates of females, and inequality in years of education. Through the lens of principal components, spatial dependence, and regionalization methods, the municipalities are endogenously classified according to their similarity in human capital constraints and geographical location. Results from the spatial dependence analysis indicate the specific location of significant hot spots (high-value clusters) and cold spots (low-value clusters). A regionalization analysis of the constraints indicates that Bolivia can be regionalized into seven or eight geographical regions. The article concludes discussing the potential complementary of these two analyses and their usefulness in identifying the location of policy priorities.

Spatial Diffusion of Civil Liberty

This paper studies the existence of spatial diffusion of civil liberty among neighboring countries. For that purpose, we first combine different exploratory space-time data analysis approaches to find that this phenomenon is spatially clustered and that a convergence process is at work among the world countries from 1985 to 2010, with a structural change by the end of the Twentieth century mainly due to the appearance of the Internet. Second, we specify a spatial autoregressive panel data model for a sample of 130 countries, for 1985–2000, and 172 countries, for 2000–2010. Results provide evidence for spatial diffusion of civil liberty, though it is not constant along this time span. The spreading rate is 0.34 in the first sub-period. After 2000, it reduces to 0.21; that is, countries only “catch” 21% of the average changes in their neighbors’ civil liberty levels. Additionally, religious culture, urban agglomeration and GDP explain the levels of civil liberties in the world.

Empirical Identification of Intra-Urban Subcentralities: A New Methodological Approach with an Application for a Developing Country

We present a new empirical approach for identifying sub-centers within urban areas and apply it to the São Paulo metropolitan area (SPMA). We use geographically weighted regressions (GWR) to overcome the limitations presented by previous methods, which rely on previous knowledge of the employment distribution and use arbitrary threshold values and band sizes. We find three SBD in 2002 and only two in 2014, suggesting that SPMA is polycentric but presents only one business core that concentrates more than 90% of all employees working in an SBD. We apply the widely recognized method of McMillen and Smith (2003) to our database and compare the results. Our method is more conservative in identifying areas as sub-centers (SBD) and presents lower standard errors.

A Simple Test of Spatial Autocorrelation for Centered Variables

We present a simple test of spatial autocorrelation based on the skedastic structure of the spatial series. Its distribution function is known for all sample sizes. Moreover, it is very simple to obtain, specially in a case of small samples where the new GQsp test has great power, higher than other alternatives existing in the literature.

Development of Regional Economics Studies in Peru: Contributions and Criticisms

This research presents an overview of the evolution of regional economic studies in Peru. After a brief introduction, the document presents a summary of the different conceptions of space in Regional Economics through time. In addition, the document shows the origins of Regional Economics, as well as the factors that explain the interest in the development of regional studies in the Latin American context. This document also explains the importance of the geographical space of Peru for regional research. Indeed, the country is the perfect setting because Peru has a wide geographic diversity (ecosystems, microclimates) throughout its territory, it has implemented a variety of public policies to propose economic growth measures, and it has many social issues to propose territorial policies (migration, crime, health, employment, among others). Despite these characteristics, regional research in Peru is relatively less than in other Latin American countries. Finally, the document offers the contributions and criticisms of the regional studies in the Peruvian context.

Local Public Investment Drivers in Peru

Around half of Peru’s public investment is made by local governments. Through the estimation of a dynamic panel data model for 1796 local governments between 2010 and 2018, we find that the most important drivers for local public investment are: (i) availability of funding sources, especially those associated to non-renewable natural resource revenues; (ii) variables associated with each local government’s capacity to plan, budget and execute public investment; and (iii) political budget cycle effects, especially during the year following local elections. Furthermore, we extend our analysis by differentiating between local governments with reelected and non-reelected authorities, and by grouping local governments according to their economic size. Most of our results are shown to be robust across different specifications. Results from this paper can be used as a starting point to design mechanisms that make public investment more stable and predictable in a context where reelection banning can further deepen public investment’s fall.