Identification of sources of variation in poverty outcomes

The international community has declared poverty reduction one of the fundamental objectives of development, and therefore a metric for assessing the effectiveness of development interventions. This creates the need for a sound understanding of the fundamental factors that account for observed variations in poverty outcomes either over time or across space. Consistent with the view that such an understanding entails deeper micro empirical work on growth and distributional change, this paper reviews existing decomposition methods that can be used to identify sources of variation in poverty. The maintained hypothesis is that the living standard of an individual is a pay-off from her participation in the life of society. In that sense, individual outcomes depend on endowments, behavior and the circumstances that determine the returns to those endowments in any social transaction. To identify the contribution of each of these factors to changes in poverty, the statistical and structural methods reviewed in this paper all rely on the notion of ceteris paribus variation. This entails the comparison of an observed outcome distribution to a counterfactual obtained by changing one factor at a time while holding all the other factors constant.Economic Theory&Research,Labor Policies,Markets and Market Access,Environmental Economics&Policies,Poverty Monitoring&Analysis

Peers, Parents, and Attitudes about School

Educational attitudes are an important component of adolescent development linked to long-term educational success and as a component of noncognitive skills. This study focuses on peer and parent roles in shaping adolescent attitude development. First, I explore the relationship between an adolescent and their friends' attitudes and whether this influence is heterogeneous. Second, I ask whether parents can moderate the friend effect. I find that adolescents with poor attitudes and whose friends have particularly poor attitudes are especially at risk of developing low educational attitudes and that working with parents can serve as a channel to decrease the risk

What Makes a Classmate a Peer? Examining Which Peers Matter in NYC Elementary Schools

Generalizing the group interaction model of Lee (2007), we identify and estimate the effects of student level social spillovers on standardized test performance in New York City (NYC) elementary schools. We leverage student demographic data to construct within-classroom social networks based on shared student characteristics, such as a gender or ethnicity. Rather than aggregate shared characteristics into a single network matrix, we specify additively separate network matrices for each shared characteristic and estimate city-wide peer effects for each one. Conditional on being in the same classroom, we find that the most important student peer effects are shared ethnicity, gender, and primary language spoken at home. We show that altering classroom composition changes the impact of these networks. Particularly, low ethnic diversity is correlated with low impact for shared ethnicity. We discuss identification of the model and its implications for within- and between-group test performance gaps along several demographic traits

Language knowledge and earnings in Catalonia

This paper investigates the economic value of Catalan knowledge for national and foreign first- and second-generation immigrants in Catalonia. Specifically, drawing on data from the “Survey on Living Conditions and Habits of the Catalan Population (2006)”, we want to quantify the expected earnings differential between individuals who are proficient in Catalan and those who are not, taking into account the potential endogeneity between knowledge of Catalan and earnings. The results indicate the existence of a positive return to knowledge of Catalan, with a 7.5% increase in earnings estimated by OLS; however, when we account for the presence of endogeneity, monthly earnings are around 18% higher for individuals who are able to speak and write Catalan. However, we also find that language and education are complementary inputs for generating earnings in Catalonia, given that knowledge of Catalan increases monthly earnings only for more educated individuals.Language, Earnings, Immigrants, Endogeneity, Complementarity

Essays On Robust Estimators For Non-Identically Distributed Observations In Spatial Econometric And Time Series Models

This thesis proposal consists of three essays on the estimation methods and applications of spatial econometric models and one essay on the generalized autoregressive conditionally heteroskedastic (GARCH)-type models in financial time series. The first essay discusses the heteroskedasticity robust generalized method of moments estimator (RGMME) for the spatial models that allow for spatial dependence in both the dependent variable and the disturbance term (SARAR(1,1)). First, we show that the maximum likelihood estimator (MLE) is generally inconsistent in the presence of unknown heteroskedasticity. Then, we extend robust GMM approach in Lin and Lee (2010) to SARAR(1,1). The large sample properties are rigorously studied and presented for the RGMME. Through a comprehensive Monte Carlo study, we compare the finite sample properties of the RGMME with some other estimators proposed in the literature. The second essay focuses on the GMM estimation of the spatial autoregressive models which impose a moving average process for the disturbance term (SARMA). We extend the best GMM estimator (BGMME) of Liu et al. (2010) to the SARMA models and provide the best set of instruments for the SARMA(1,1) and the SARMA(0,1) specifications. The large sample properties are rigorously studied and presented for the BGMME. The finite sample properties are investigated through an extensive Monte Carlo study. To confirm our results from the Monte Carlo study, we replicate the results for the SARMA(1,1) specification in Behrens et al. (2012) in an empirical illustration. The third essay investigates the effect of foreign direct investment (FDI) on economic growth through a spatially augmented Solow growth model. The current literature on the relationship between FDI and economic growth uses canonical cross-country growth regression specifications that are derived from the textbook Solow growth model for closed economies. We claim that these specifications cannot reflect the relationship between economic growth and FDI, because they model each country as an isolated island that does not interact with the rest of the world. On the other hand, a spatially augmented Solow growth model allows for technological interdependence among countries through spatial externalities. The modified growth model yields regression specifications that properly account for spatial autocorrelations. We construct a panel of 85 countries for the period 1980-2010 and estimate the modified specifications with the tools from spatial econometrics. Our findings indicate that FDI inflows have a significant positive effect on the growth rate of host countries. The final essay proposes a flexible distribution for the maximum likelihood estimation of the GARCH-type time series models. The new distribution can better account for the potential skewness and leptokurticity in the driving noise sequence. We study the large sample properties of the new estimator following the methodology presented in Francq and Zakoïan (2004). To investigate the finite sample properties of the new estimator, we first conduct a Monte Carlo study. Furthermore, to test the relative out-of-sample predictive power of the new estimator, we test for its prediction power on two data sets using the methods described in White (2000) and Hansen et al. (2003)

Spatial model selection and spatial knowledge spillovers : a regional view of Germany

The aim of this paper is to introduce a new model selection mechanism for cross sectional spatial models. This method is more flexible than the approach proposed by Florax et al. (2003) since it controls for spatial dependence as well as for spatial heterogeneity. In particular, Bayesian and Maximum-Likelihood (ML) estimation methods are employed for model selection. Furthermore, higher order spatial influence is considered. The proposed method is then used to identify knowledge spillovers from German NUTS-2 regional data. One key result of the study is that spatial heterogeneity matters. Thus, robust estimation can be achieved by controlling for both phenomena

Contribution of Climate-smart Agriculture to Farm Performance, Food and Nutrition Security and Poverty Reduction in Ghana

This study examines the drivers of individual and joint adoption of crop choice and soil and water conservation strategies and how adoption of these strategies impacts on farm performance and exposure to production risks, using a multinomial endogenous switching regression model to account for selectivity bias due to both observable and unobservable factors. The study also examines the determinants of climate-smart practices and how adoption affects food and nutrition security among farm households in Ghana, using an endogenous switching regression approach to account for selectivity bias. It also examines the impact of adoption of sustainable land management practices on consumption and poverty outcomes using multivalued treatment effects and generalized propensity score approaches, while considering adoption intensity within a continuum. Finally, the study assesses the impact of adoption of SLM on farm households’ technical efficiency and environmental inefficiency stochastic production frontier and data envelopment analysis models. The results revealed that farmers’ adoption of crop choice and soil and water conservation leads to higher crop yields and reduction in exposure to production risks, with the largest impact on yields coming from joint adoption, an indication of complementarity effects of crop choices and soil and water conservation strategies. The findings also showed that adoption of climate-smart practices had positive and significant impact on food and nutrition security in terms of household dietary diversity scores, household food insecurity access scores and farm revenues. Moreover, the effect of adoption of SLM on per capita consumption and poverty outcomes is nonlinear and differed among adopters at different intensity levels of adoption. Also, adopters of SLM exhibited higher levels of technical efficiency as compared to non-adopters, but they were also found to be using higher levels of herbicides that might have environmental implications

Die Rolle von Kommunikationskanälen für die Nahrungsmittelproduktion und die Wohlfahrt der Haushalte: Empirische Evidenz aus Nord-Ghana

Lack of information on innovative agricultural technologies continue to be a major constrain and cause of low technology adoption and productivity among farmers in sub-Saharan Africa. The emergence of new communication channels such as ICTs offer some prospects to boost agricultural extension delivery and lower the barriers to information diffusion among farmers. However, not much is known about the impact of the new communication channels on food production and welfare via its role in improving farmers’ technology adoption. This study in chapter two contributes to literature by employing copula functions, to examine the impact of participation in ICT-based extension channels on improved technology adoption, specifically the new Rhizobia inoculant technology, and its impact on farmers’ technical knowledge, yields and farm net returns. Low technology adoption persist among smallholder farmers in developing countries. This has been attributed to lack of understanding about the adoption decision-making behavior of farmers, leading to inability to develop appropriate extension policies that can enhance technology adoption. In chapter three, this study contributes to knowledge by employing dynamic treatment effect model to analyze farmers’ adoption decision-making as a dynamic process, one that comprises a series of multiple decisions made over several stages or periods. The inability to develop appropriate extension policies has also been attributed to the disconnection between empirical studies that examine adoption of technological packages and studies that analyze management practices of those packages. Chapter four of this study attempts to bridge the knowledge gap by employing the stochastic frontier treatment effect with endogenous mediator model to simultaneously estimate the impact of technology adoption and extension participation and decompose their interaction effect into direct and indirect effects. The use of farmers’ egocentric information or social networks to diffusion information about new technologies leading to adoption is voluminous in the literature. However, the impact of the egocentric information networks on the technical efficiency of farmers appears to be over looked in the literature. This study in chapter five contributes to literature by employing spatial stochastic frontier analysis to investigate the impact of egocentric information networks on farmers’ technical efficiency, productivity and its distributive mechanisms among farmers in the network. The findings in chapter two reveal that ICT-based extension channels are equally effective as the conventional extension channels, and in some instances, outperform them. The study found that ICT extension channels lead to higher yields, farm net returns, and knowledge gained, relative to conventional extension channels and non-participation in extension programs. The study in chapter three further reveal the existence of significant impact heterogeneities across different adoption stages, with the long-term benefits of adoption outweighing the short-term benefits. The study found that there exist unrealized potential gains at some stages in the adoption process, in particular, at knowledge acquisition and trial stages, which extension policymakers can target in order to maximize adoption impacts and save resources to expand extension outreach to benefit more farmers. Chapter four of the results show that the direct impact of technology adoption alone contributes 72% to farm productivity and 73% indirectly due to improvement in farmers’ efficiency, leading to overall welfare improvement of 77%. Similarly, the direct impact of extension participation alone contributes 28% to farm productivity and 27% indirectly due to improvement in farmers’ efficiency, resulting in 23% improvement in farmers’ welfare. The findings suggest that, it is insufficient and less beneficial to provide extension services to farmers without the provision of improved technology. Finally, the study in chapter five reveal that 19% of farmers’ technical inefficiency depend on the inefficiency of the farmers from whom they seek farming advice. The results also show that inefficient farmers tend to depend on efficient farmers in their egocentric information networks to improve their level of efficiency. In general, the study provides empirical evidence to inform effective extension service delivery policies, towards attainment of the Sustainable Development Goals (SDGs), in particular goal two and five, which seek to achieve zero hunger and equal access to extension services by all for enhance agricultural productivity

Essays on identification and estimation of networks

This thesis consists of three chapters that explore the estimation and identification of networks from observable outcomes and covariates only. This problem is equivalent to estimating the spatial neighbouring matrix from a spatial econometric model. Under three settings, I show how the networks can be recovered entirely from observable non-network data. In the first chapter, networks are treated as a source of unobserved heterogeneity and dealt with data collected from observing many groups in one period of time. The proposed method estimates the probability that pairs of individuals form connections, which may depend on exogenous factors such as common gender. I derive a maximum likelihood estimator for network effects that is not conditional on network observation, accomplished with recourse to a spatial econometric model with unobserved and stochastic networks. I apply the model to estimate network effects in the context of a program evaluation. The second chapter assumes the observation of one group over many periods of time and estimates the networks as a collection of pairwise links. We estimate the spatial neighbouring matrix with recourse to the Adaptive Lasso. Non-asymptotic Oracle inequalities, together with the asymptotic sign consistency of the estimators, are presented and proved. The third chapter shows how the procedure developed in the preceding paper can be used to classify individuals into groups based on similarity of observed behavior. We propose a Lasso estimator that captures the block structure of the spatial neighboring matrix. The main results show that off-diagonal block elements are estimated as zeros with high probability. We correctly identified US Senate’s blocks based on party affiliation using only voting data. Empirical research on social and economic networks has been constrained by the limited availability of data regarding such networks. This collection of papers may therefore provide an useful tool for applied research