<p>The main objective of this study was to identify ways in which transaction costs can be lowered to improve small-scale farmers&rsquo / participation in and returns from agricultural output markets, with specific reference to small-scale mango farmers in Limpopo province. This study hypothesizes that transaction costs are lower in informal spot markets and increase when small-scale farmers sell in more structured markets (formal markets). This study builds on transaction cost economics (TCE) to demonstrate how to overcome transaction cost barriers that small-scale mango farmers face in the agro-food markets. The approach to collect primary information was sequenced in two steps: first, key informant and focus group interviews were conducted and, secondly, a structured survey instrument was administered in two districts of Limpopo. A total of 235 smallscale mango farmers were interviewed. A binary logistic regression model was used to estimate the impact of transaction costs on the likelihood of households&rsquo / participation in formal (=1) and informal (=0) agro-food markets. STATA Version 10 was used to analyse the data. This study found that a larger proportion of male than female farming households reported participation in the formal markets, suggesting deep-seated gender differentiation in market participation. The average age of small farmers participating in formal markets is 52, compared to 44 for those in informal markets, implying that older farmers might have established stronger networks and acquired experience over a longer period. Farmers staying very far from the densely populated towns (more than 50 km) participate less in the formal markets than those staying closer (0 &ndash / 25 km and 26 &ndash / 49 km), which implies that the further they are from the towns, the less the likelihood of farmers selling in the formal markets. Farmers who own storage facilities and a bakkie (transportation means) participate more in formal markets compared to those who do not own these assets, which suggests that these farmers are able to store mangoes, retaining their freshness and subsequently delivering them to various agro-food markets on time. Households that participate in formal markets have high mean values of income and social grants. However, this study found that the likelihood of a household&rsquo / s participation in the markets is less as income and social grants increase. This suggests that households do not invest their financial assets in order to overcome market access barriers. A large proportion of households that own larger pieces of arable land participate in the formal markets, which implies that they are able to produce marketable surplus. Households that have a high mean value (in Rand) of cattle participate more in formal markets than in informal markets. However, this study found that the likelihood of a household&rsquo / s participation in the markets does not change with an increase in the value of its livestock. These findings suggest that households do not sell their cattle in order to overcome market access barriers. Reduced transaction costs for small-scale mango farmers in Limpopo should improve their participation in and returns from the agro-food markets. Policy interventions to support this need to focus on: access to storage and transportation facilities, enforcement of gender equity requirements in existing policies, and better access to information about markets.</p>

This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies. / February 2009

Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC. Patient-reported scores of FCC were high, but did not differ significantly among primary care models. After accounting for patient characteristics, practice characteristics were not significantly associated with patient-reported FCC. Provider-reported scores of FCC were significantly higher in Community Health Centres than in Family Health Networks. Higher numbers of nurse practitioners and clinical services on site were associated with higher FCC scores but scores decreased as the number of family physicians at a site increased.

The aim of this paper is to analyze the impact of a conditional cash transfer program, Progresa, on school attendance in Mexican rural and urban areas. Within recent years, conditional cash transfer programs have become one of the most accepted remedies for poverty alleviation in many countries. Progresa was developed as an economic experi-ment, with randomized selection process, treatment groups and control croups. For this reason, the impact of Progresa is ideal for economic analysis. There are clear evidence of disparities between urban and rural school attendance rates in Mexico, hence the pro-gram’s effect on school attendance rates have been studied in the two regions. There are several reasons why one would expect different outcomes of the program on school at-tendance in rural and urban areas. Expected returns to education and the opportunity cost of investment in schooling in different regions are thought to affect the household’s optimization problem differently. The impact of Progresa on school attendance rates is estimated by a logit regression model analyzing household data within the household optimization framework. Mainly, Progresa has a positive impact on children’s school at-tendance. However, it may be concluded that Progresa has no significant effect for older children in rural areas. This result is assumed to be explained by the different conditions poor families face in different regions. If rural households’ optimization problem indeed looks different; this might suggest that the design of conditional cash transfer programs such as Progresa is crucially important depending on the region of implementation. / Syftet med denna uppsats är att analysera det villkora välfärdsprogrammet Progresa och dess effekt på skolnärvaro i mexikanska rurala och urbana områden. Under senare år har villkora välfärdsprogram kommit att vara en av de mest accepterade formerna av bistånd för att minska fattigdom i de flesta länder. Progresa utvecklades utifrån ett ekonomiskt experiment, med en slumpmässig urvalsprocess samt en experiment- och kontrollgrupp. Med anledning av detta är Progresa ett utmärkt program att studera för ekonomisk analys. Skolnärvaron i mexikanska rurala och urbana områden varierar stort och av denna anledning har effekten av Progresa studerats i de båda regionerna. Det finns många anledningar till varför vi bör förvänta oss avvikande utfall. En förklaring kan vara att utbildningens förväntade avkastning och alternativkostnad påverkar hushållens optimeringsproblem olika. Effekten av Progresa på skolnärvaro är beräknad med en logit regressionsmodell där hushållsdata analyseras inom ramen för hushållets optimeringsproblem. Huvudsakligen har Progresa en positiv effekt på barns skolnärvaro. Dock, och vad som bör noteras, är det faktum att Progresa inte har en signifikant påverkan på äldre rurala barns skolnärvaro. Detta resultat antas förklaras av fattiga familjers olika förutsättningar i rurala och urbana områden. I det fall rurala familjers optimeringsproblem skiljer sig från urbana familjers optimeringsproblem, torde detta innebära att strukturen av ett villkorligt biståndsprogram, så som Progresa, är av största vikt och bör anpassas ändamålsenligt.

The objective of the present study is to forecast annual rainfall taking into account the periodicities and structure of the stochastic component. This study has six Chapters. Chapter 1 presents introduction to the problem and objectives of the study. Chapter 2 consists of review of literature. Chapter 3 deals with the model formulation and development. Chapter 4 gives an account of the application of the model. Chapter 5 presents results and discussions. Chapter 6 gives the conclusions drawn from the study. In this thesis the following model formulations are presented in order to achieve the objective. Fourier analysis model is used to identify periodicities that are present in the rainfall series.1 These periodic components are used to obtain discrotized ranges which is an essential input for the Fourier series model. Auto power regression model is developed for estimation of rainfall and hence to compute the first order residuals errlt The parameters of the model are estimated using genetic algorithm. The auto power regression model is of the form, ( Refer the PDF File for Formula) where αi and βi are parameters and M indicates modular value. Fourier series model is formulated and solved through genetic algorithm to estimate the parameters amplitude R, phase Φ and periodic frequency wj for the residual series errlt. The ranges for the parameters R, Φ and wj were obtained from Fourier analysis model. errl't= /µerrlt+ Σj Rcos(wjt+ Φ) Further, an integrated auto power regression and Fourier series model developed (with parameters of the model being known from the above analysis) to estimate new rainfall series Zesťt=Zµ Σ t αi(ZMi-t ) βi+µerrl+ Σj Rcos(wjt+ Φ) and the second order residuals, err2t is computed using, err2t = (zt –Zesťt) Thus, the periodicities are removed in the errlt series and the second order residuals err'2f obtained represents the stochastic component of the actual rainfall series. Auto regressive model is formulated to study the structure of the stochastic component err2t. The auto regressive model of order two AR(2) is found to fit well. The parameters of the AR(2) model were estimated using method of least squares. An exponential weighting function is developed to compute the weight considering weight as a function of AR{2) parameters. The product of weight and Gaussian white noise N(0, óerr2) is termed as weighted stochastic component. Also, drought analysis is performed considering annual (January to December) and summer monsoon (June to September) rainfall totals, to determine average drought interval (idrt) which is used in assigning signs to the random component of the forecasting model. In the final form of the forecasting model. Zest”t = Z µ Σ t αi(ZMi-t ) βi+µerrl+ Σj Rcos(wjt+ Φ) ± WT(Φ1, Φ2)N(0, óerr2) The weighted stochastic component is added or subtracted considering two criteria. Criterion I is used for all rainfall series except all-India series for which criterion II is used. The criteria also consider average drought interval Further, it can be seen that a ± sign is introduced to add or subtract the weighted stochastic component, albeit the stochastic component itself can either be positive or negative. The introduction of ± sign on the already signed value (instead of absolute value) is found to improve the forecast in the sense of obtaining more number of point rainfall estimates within 20 percent error. Incorporating significant periodicities, and weighted stochastic component along with average drought interval into the forecasting formulation is the main feature of the model. Thus, in the process of rainfall prediction, the genetic algorithm is used as an efficient tool in estimating optimal parameters of the auto power regression and the Fourier series models, without the use of an expensive nonlinear least square algorithm. The model application is demonstrated considering different annual rainfall series relating to IMD-Regions (RI...R5), all-india (AI), IMD-Subdivisions (S1...S29), Zones (Z1...Z10) and all-Karnataka (AK). The results of the proposed model are encouraging in providing improved forecasts. The model considers periodicity, average critical drought frequency and weighted stochastic component in forecasting the rainfall series. The model performed well in achieving success-rate of 70 percent with percentage error less than 20 percent in 4 out of 5 IMD Regions (R2 to R5), all-India, 17 out of 29 IMD Subdivisions (S1 to S5, S7 to S9, S18, S19, S21, S24 to S29) and all-Karnataka rainfall series. The model performance for Zones was not that-satisfactory as only 2 out of 10 Zones [Z1 and Z2) met the criterion. In a separate study, an effort was made to forecast annual rainfall using IMSL subroutine SPWF -which estimates Wiener forecast parameters. Monthly data is considered for the study. The Wiener parameters obtained were used to estimate monthly rainfall. The annual estimates obtained by simple aggregation of the monthly estimates compared extremely well with the actual annual rainfall values. A success rate of more than 80 percent with percentage error less than 10 percent is achieved in 4 out of 5 IMD Regions (R2 to R5), all-India, 18 out of 29 IMD Subdivisions (S1 to S8, S14, S18, S19, S22 to S24, S26 to S29) and all-Karnataka rainfall series. Whereas a success rate of 80 percent within 20 percent error is achieved in 4 out of 5 IMD Regions (except R1), all-India, 25 outof 29 IMD Subdivisions (except S10, S11, S12 and S17), all- Karnataka and 8 out of 10 Zones (except Z6 and Z8)(Please refer PDF File for Formulas)

<p>The aim of this paper is to analyze the impact of a conditional cash transfer program, Progresa, on school attendance in Mexican rural and urban areas. Within recent years, conditional cash transfer programs have become one of the most accepted remedies for poverty alleviation in many countries. Progresa was developed as an economic experi-ment, with randomized selection process, treatment groups and control croups. For this reason, the impact of Progresa is ideal for economic analysis. There are clear evidence of disparities between urban and rural school attendance rates in Mexico, hence the pro-gram’s effect on school attendance rates have been studied in the two regions. There are several reasons why one would expect different outcomes of the program on school at-tendance in rural and urban areas. Expected returns to education and the opportunity cost of investment in schooling in different regions are thought to affect the household’s optimization problem differently. The impact of Progresa on school attendance rates is estimated by a logit regression model analyzing household data within the household optimization framework. Mainly, Progresa has a positive impact on children’s school at-tendance. However, it may be concluded that Progresa has no significant effect for older children in rural areas. This result is assumed to be explained by the different conditions poor families face in different regions. If rural households’ optimization problem indeed looks different; this might suggest that the design of conditional cash transfer programs such as Progresa is crucially important depending on the region of implementation.</p> / <p>Syftet med denna uppsats är att analysera det villkora välfärdsprogrammet Progresa och dess effekt på skolnärvaro i mexikanska rurala och urbana områden. Under senare år har villkora välfärdsprogram kommit att vara en av de mest accepterade formerna av bistånd för att minska fattigdom i de flesta länder. Progresa utvecklades utifrån ett ekonomiskt experiment, med en slumpmässig urvalsprocess samt en experiment- och kontrollgrupp. Med anledning av detta är Progresa ett utmärkt program att studera för ekonomisk analys. Skolnärvaron i mexikanska rurala och urbana områden varierar stort och av denna anledning har effekten av Progresa studerats i de båda regionerna. Det finns många anledningar till varför vi bör förvänta oss avvikande utfall. En förklaring kan vara att utbildningens förväntade avkastning och alternativkostnad påverkar hushållens optimeringsproblem olika. Effekten av Progresa på skolnärvaro är beräknad med en logit regressionsmodell där hushållsdata analyseras inom ramen för hushållets optimeringsproblem. Huvudsakligen har Progresa en positiv effekt på barns skolnärvaro. Dock, och vad som bör noteras, är det faktum att Progresa inte har en signifikant påverkan på äldre rurala barns skolnärvaro. Detta resultat antas förklaras av fattiga familjers olika förutsättningar i rurala och urbana områden. I det fall rurala familjers optimeringsproblem skiljer sig från urbana familjers optimeringsproblem, torde detta innebära att strukturen av ett villkorligt biståndsprogram, så som Progresa, är av största vikt och bör anpassas ändamålsenligt.</p>

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<p>The main objective of this study was to identify ways in which transaction costs can be lowered to improve small-scale farmers&rsquo / participation in and returns from agricultural output markets, with specific reference to small-scale mango farmers in Limpopo province. This study hypothesizes that transaction costs are lower in informal spot markets and increase when small-scale farmers sell in more structured markets (formal markets). This study builds on transaction cost economics (TCE) to demonstrate how to overcome transaction cost barriers that small-scale mango farmers face in the agro-food markets. The approach to collect primary information was sequenced in two steps: first, key informant and focus group interviews were conducted and, secondly, a structured survey instrument was administered in two districts of Limpopo. A total of 235 smallscale mango farmers were interviewed. A binary logistic regression model was used to estimate the impact of transaction costs on the likelihood of households&rsquo / participation in formal (=1) and informal (=0) agro-food markets. STATA Version 10 was used to analyse the data. This study found that a larger proportion of male than female farming households reported participation in the formal markets, suggesting deep-seated gender differentiation in market participation. The average age of small farmers participating in formal markets is 52, compared to 44 for those in informal markets, implying that older farmers might have established stronger networks and acquired experience over a longer period. Farmers staying very far from the densely populated towns (more than 50 km) participate less in the formal markets than those staying closer (0 &ndash / 25 km and 26 &ndash / 49 km), which implies that the further they are from the towns, the less the likelihood of farmers selling in the formal markets. Farmers who own storage facilities and a bakkie (transportation means) participate more in formal markets compared to those who do not own these assets, which suggests that these farmers are able to store mangoes, retaining their freshness and subsequently delivering them to various agro-food markets on time. Households that participate in formal markets have high mean values of income and social grants. However, this study found that the likelihood of a household&rsquo / s participation in the markets is less as income and social grants increase. This suggests that households do not invest their financial assets in order to overcome market access barriers. A large proportion of households that own larger pieces of arable land participate in the formal markets, which implies that they are able to produce marketable surplus. Households that have a high mean value (in Rand) of cattle participate more in formal markets than in informal markets. However, this study found that the likelihood of a household&rsquo / s participation in the markets does not change with an increase in the value of its livestock. These findings suggest that households do not sell their cattle in order to overcome market access barriers. Reduced transaction costs for small-scale mango farmers in Limpopo should improve their participation in and returns from the agro-food markets. Policy interventions to support this need to focus on: access to storage and transportation facilities, enforcement of gender equity requirements in existing policies, and better access to information about markets.</p>

This thesis studies the Second-order Least Squares (SLS) estimation method in regression models with and without measurement error. Applications of the methodology in general quasi-likelihood and variance function models, censored models, and linear and generalized linear models are examined and strong consistency and asymptotic normality are established. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is used and its asymptotic properties are studied. Finite sample performances of the estimators in all of the studied models are investigated through simulation studies.

One of the difficult issues in detection theory is to design a robust detector that takes into account the actual distribution of the original data. The most commonly used statistical detection model for blind detection is Gaussian distribution. Specifically, linear correlation is an optimal detection method in the presence of Gaussian distributed features. This has been found to be sub-optimal detection metric when density deviates completely from Gaussian distributions. Hence, we formulate a detection algorithm that enhances detection probability by exploiting the true characterises of the original data. To understand the underlying distribution function of data, we employed the estimation techniques such as parametric model called approximated density ratio logistic regression model and semiparameric estimations. Semiparametric model has the advantages of yielding density ratios as well as individual densities. Both methods are applicable to signals such as watermark embedded in spatial domain and outperform the conventional linear correlation non-Gaussian distributed.