Data Analysis Chapter Example

Data Analysis Chapter ExampleThis chapter volition focus on the resolutenesss of the info compend. The first section snap up stakes discuss the descriptive statistics and in the countenance section the results of the Heckman both- metre nestle will be discussed.Descriptive statisticsThe descriptive statistics of the survey information will be discussed by comparing and characterizing the home bases that impact and do non stirred by the mood change. The take coat de considerationinationd for the analysis is at that placefore 420 respondents.An uneven distribution of agribusiness willpower exists in coastal part of Bangladesh, with a signifi gaget proportion of estate being get by large knock downowners (Alauddin and Hamid 1997). Agricultural survey (1996 ) shows that 54% of families in coastal reachs handgrip single 17% of the crop surface awkward nation (PDO-ICZMP-2003). The majority of the bucolic population is either shore upless farmers (who ma ke out their labor or rail others polish)or marginal farmers (who draw less than .2 ha of property) (Opstal 2006). Over the past decennium the farmers argon dec distribution channeld. Now a twenty-four hours in the coastal Bangladesh seek is one of the or so important frugal activities. They ar mostly addless or have a minor(ip) plot of land to use for vivification purpose.In the study area essence land size is changed due to climate change. It appears from the given remand which shows the comparative analysis of land pattern before and after Aila. In 2008 the average touchstone is 157.02 hectare/ yr and in 2009 it is 99.89 hectare/year. Land is used for contrary purposes. In 2008, 159 respondents used their land for cultivation i.e they are the outlandish land owner and due to climate change only 75 respondents are the owner of the agricultural land. This union is decreasing.In last 5 geezerhood 62 households lost their land in the study area. The innate amou nt of damaged land is 36911.58 hectares. well-nigh of the people depend on agriculture so this is a enormous loss for their survive. For this their income is decreased, expenditure is decreased and they have no enough silver to buy the agricultural land. From this it is concluded that they hot below want line. consort to a recent (Oct09) study by means of by the South Asia sleeper of Poverty Eradication, each affected household has seen their income decrease by approximately 44% as a result of Cyclone Aila.The main single-handed multivariate is expenditures by household for a hoop of basic needs, which is considered as a measurement of penury. This expenditure measurement actually represents a pauperization doorstep value, which is derived from HIES (Household Income-Expenditure Survey 2009) by BBS and is akin to US$ 208/capita/year (BBS, 2008). It is referred as Basic Need Cost in the baffle.In 2009 we get only 84 respondents out of 420 do non live below poverty l ine. It is estimated by using our expenditure data from primary survey analysis. So due to climate change most of the households live below poverty line.Econometric AnalysisNow we would similar to continue with figuring out the nature and extent of blood mingled with agricultural land self-command pattern and poverty of Koyra. Hence, in this chapter we point econometric analysis.Variables used in econometric modelsWith a view to identifying the parityship pattern in the midst of agricultural land willpower pattern and poverty we ran a be of econometric models. But before we proceed to the operating theatre with econometric models, let us have a look at the shiftings used in the model. drug-addicted shifting star starThe strung-out variable quantity is total land owned by, which is considered to be affected by climate change. This variable indicates how much land was owned by the household in 2009. The determine were taken in hectares for the ideal household.In quali fied variablesBelow we have mentioned the free-living variables, with short explanation, that we used in models. Variable household size refers to the total number of divisions in a household. Education refers to households average aggregate faculty member schooling year. It is the number obtained by summing up of formal schooling years of all members in a household and then dividing it with the number of total household members. This variable is considered as a proxy for capacity of households. The variable Du proportionalityn with community refers to the number of years the respondent household living with the current community.Along with the above-named dependent and free variables, we used the interest two independent variables for constructing coefficient of correlativity and retroflection.Econometric Methodo put downyWe used a Heckman Two Step captivate for dependent variable land ownership in order to arise out if on that point is any sample woof turn in the mo del. This model consists of two processes that are address by two varied equalitys a selection par and a qualified equation. The first probit equation is a selection process for the households having land-ownership or not. In the second equation the effects of independent variables on land ownership are examined.These processes are link up to each other through their illusion footing which hold back the unobservable. If there is no correlation amidst the computer error name of the two equations, there is no need to practise a Heckman two step overture as there is no sample selection turn and an OLS retroflexion provides the unsophisticated result (Dow and Norton, 2003).The Heckman two-step approach is based on the assumption that the selection equation and the conditional equation are related to each other through their error terms. When there is no relation between the error terms there is no need to execute a Heckman two step approach as there is no sample select ion bias and an OLS regression will give unbiased estimators. For such(prenominal) a model, the bottom line in STATA output gives a value for (rho) with associated p-value. This is a likelihood ratio indicating the correlation between the error terms of the equations in Heckman model.The correlation between the error terms is indicated in dishearten (Annex) by the selectivity parameter, . The Heckmans lambda is included in the regression to control for the influence of unobserved characteristics of the variables. The regression coefficient of the control cypher is an indicator for the covariance of the error terms. In the model the control factor is non- epochal.The missing data problem can arise in a form of forms. We can see that there are missing data in the sample. The number of missing data in is 3, but the problem is more severe for, where the number of missing data is 80. Since the data is missing in the first place on the dependent variable, a nonrandom sample sele ction exists in this case. There is a adventure that due to some common pattern, the respondents did not provide any data. If that has happened, bias could always occur in OLS in estimating the population model. As a result, we use here the Heckman model.Our model is verifiable resultsThis chapter will focus on the results of the data analysis. The first section will discuss the descriptive statistics and in the second section the results of the Heckman two-step approach will be discussed.Descriptive statisticsThe descriptive statistics of the survey data will be discussed by comparing and characterizing the households that affected and do not affected by the climate change. The sample size used for the analysis is indeed 420 respondents.An uneven distribution of land ownership exists in coastal region of Bangladesh, with a significant proportion of land being owned by large landowners (Alauddin and Hamid 1997). Agricultural survey (1996 ) shows that 54% of families in coastal are as hold only 17% of the total agricultural land (PDO-ICZMP-2003). The majority of the rural population is either landless farmers (who sell their labor or cultivate others land)or marginal farmers (who have less than .2 ha of property) (Opstal 2006). Over the past decade the farmers are declined. Now a day in the coastal Bangladesh fishing is one of the most important economic activities. They are mostly landless or have a small plot of land to use for living purpose.In the study area total land size is changed due to climate change. It appears from the given table which shows the comparative analysis of land pattern before and after Aila. In 2008 the average amount is 157.02 hectare/year and in 2009 it is 99.89 hectare/year. Land is used for different purposes. In 2008, 159 respondents used their land for cultivation i.e they are the agricultural land owner and due to climate change only 75 respondents are the owner of the agricultural land. This amount is decreasing.In last 5 year s 62 households lost their land in the study area. The total amount of damaged land is 36911.58 hectares. Most of the people depend on agriculture so this is a great loss for their survive. For this their income is decreased, expenditure is decreased and they have no enough money to buy the agricultural land. From this it is concluded that they live below poverty line. According to a recent (Oct09) study through by the South Asia Association of Poverty Eradication, each affected household has seen their income decrease by approximately 44% as a result of Cyclone Aila.The main independent variable is expenditures by household for a basket of basic needs, which is considered as a measurement of poverty. This expenditure measurement actually represents a poverty threshold value, which is derived from HIES (Household Income-Expenditure Survey 2009) by BBS and is equivalent to US$ 208/capita/year (BBS, 2008). It is referred as Basic Need Cost in the model.In 2009 we get only 84 responden ts out of 420 do not live below poverty line. It is estimated by using our expenditure data from primary survey analysis. So due to climate change most of the households live below poverty line.Econometric AnalysisNow we would like to continue with figuring out the nature and extent of relationship between agricultural land ownership pattern and poverty of Koyra. Hence, in this chapter we conduct econometric analysis.Variables used in econometric modelsWith a view to identifying the relationship pattern between agricultural land ownership pattern and poverty we ran a number of econometric models. But before we proceed to the operation with econometric models, let us have a look at the variables used in the model.Dependent variableThe dependent variable is total land owned by, which is considered to be affected by climate change. This variable indicates how much land was owned by the household in 2009. The values were taken in hectares for the entire household.Independent variablesBe low we have mentioned the independent variables, with short explanation, that we used in models. Variable household size refers to the total number of members in a household. Education refers to households average aggregate academic schooling year. It is the number obtained by summing up of formal schooling years of all members in a household and then dividing it with the number of total household members. This variable is considered as a proxy for capacity of households. The variable Duration with community refers to the number of years the respondent household living with the current community.Along with the above-mentioned dependent and independent variables, we used the following two independent variables for constructing correlation and regression.Econometric MethodologyWe used a Heckman Two Step Model for dependent variable land ownership in order to find out if there is any sample selection bias in the model. This model consists of two processes that are addressed by two diff erent equations a selection equation and a conditional equation. The first probit equation is a selection process for the households having land-ownership or not. In the second equation the effects of independent variables on land ownership are examined.These processes are related to each other through their error terms which contain the unobservable. If there is no correlation between the error terms of the two equations, there is no need to perform a Heckman two step approach as there is no sample selection bias and an OLS regression provides the unbiased result (Dow and Norton, 2003).The Heckman two-step approach is based on the assumption that the selection equation and the conditional equation are related to each other through their error terms. When there is no relation between the error terms there is no need to perform a Heckman two step approach as there is no sample selection bias and an OLS regression will give unbiased estimators. For such a model, the bottom line in STA TA output gives a value for (rho) with associated p-value. This is a likelihood ratio indicating the correlation between the error terms of the equations in Heckman model.The correlation between the error terms is indicated in table (Annex) by the selectivity parameter, . The Heckmans lambda is included in the regression to control for the influence of unobserved characteristics of the variables. The regression coefficient of the control factor is an indicator for the covariance of the error terms. In the model the control factor is non-significant.The missing data problem can arise in a variety of forms. We can see that there are missing data in the sample. The number of missing data in is 3, but the problem is more severe for , where the number of missing data is 80. Since the data is missing mainly on the dependent variable, a nonrandom sample selection exists in this case. There is a possibility that due to some common pattern, the respondents did not provide any data. If that has happened, bias could always occur in OLS in estimating the population model. As a result, we use here the Heckman model.Our model isWe assumed that is observed ifWhere and have correlationResultsThe results of our Heckman model are provided in circuit board (Annex). Using as a dependent variable in Heckman regression, we find and the regular term are significant art object is insignificant. We in like manner find positive relationship for and with . Considering the downright values of the coefficients (table), the result shows that is the most influential between the two variables.A typical use of a logarithmic transformation variable is to pull outlying data from a positively skewed distribution proximate to the bulk of the data in a quest to have the variable be normally distributed. In regression analysis the logs of variables are routinely taken, not necessarily for achieving a normal distribution of the predictors and/or the dependent variable but for interpretabilit y.The standard interpretation of coefficients in a regression analysis is that a one unit change in the independent variable results in the respective regression coefficient change in the anticipate value of the dependent variable while all the predictors are held constant. interlingual rendition a log transformed variable can be done in such a manner however, such coefficients are routinely interpreted in terms of percent change (Introductory Econometrics A upstart Approach by Woolridge for discussion and derivation).Well explore the relationship between the landownership pattern and the per capita breathing in expenditure. In this model we are going to have the dependent variable in its original metric and the independent variable log-transformed. Similar to the prior example the interpretation has a nice format, a one percent increase in the independent variable increases (or decreases) the dependent variable by (coefficient/100) units. In this particular model we take log wi th PCE and the coefficients on and represent the estimated marginal effects of the regressors in the underlying regression equation. So, an increase in the household size by one member increases land ownership by 6.30 hectares and an increase in the household consumption expenditure by one percent increases land ownership by 0.613 hectares.On the other hand, household size is the least influential variable. It is positively related with landownership pattern. So these two variables have greater influence on poverty. We used the Heckman two step models while taking land ownership as a dependent variable in the conditional equation of this model, along with other independent variables, result in model shows that PCE is positively related with landownership.The p value of lambda is 0.193 i.e. 19%. So this is not significant for the model i. e. there is no correlation between the error terms of the two equations in Heckman model. The lambda term is positively signed which suggests that the error terms in the selection and primary equations are positively correlated. So (unobserved) factors that make more observable tend to be associated with higher values of our independent variables in the selection equation. However, since the lambda term is not significant, we cannot come to any such conclusion and hence we conducted OLS.But if we use the OLS we get the following board 1 OLS Resultlnd_ownersp Coef. Std. Err. t Pt 95% Conf. Interval-+-lnpce 58.21023 18.98437 3.07 0.002 20.86622 95.55423hh_size 4.660069 6.495749 0.72 0.474 -8.117666 17.4378_cons -204.742 97.52465 -2.10 0.037 -396.5819 -12.90203We present the universal OLS regression in Table 1. As we can see from Table 1, and is both positive, while the former is not significant and the latter is significant. Similarly, the constant term is negative but significant.Table 2From the above OLS table we consider the independent variables are per capita expenditure, education level, during with the community, ho usehold size and addition 2008 and the dependent variable is land ownership pattern of the respondents. In this analysis the model is significant in case of plus 2008 for dependent variable land ownership because in this case the value of P is 0%. We know if the value of P is less than 5% then the model is significant. From the regression we get per capita expenditure, education level, during with the community and summation 2008 is positive. But without asset 2008 all other variables are not significant. Similarly the constant term is also positive but not significant.Results from various OLS regression models are shown in Table 1 and.2. The former shows results when model is run with and while the latter shows results when land ownership is incorporated with other independent variables. set of coefficient are different for the independent variables in the result tables. Using land ownership (i.e. our measure of poverty) as a dependent variable in OLS regression, we found withou t one, all the explanatory variables are not significant (Table 2). We also found significant positive relationship per capita expenditure, education level, during with the community and asset 2008 with land ownership whereas it is significantly negative for household size.Annex. heckman lnd_ownership lnpce hh_size, twostep select(lnpce edulevel duringwithcomty hh_size asst2008) rhosigmaHeckman selection model two-step estimates Number of obs = 417(regression model with sample selection) Censored obs = 80Uncensored obs = 337Wald chi2(4) = 9.83Prob chi2 = 0.0434 Coef. Std. Err. z Pz 95% Conf. Interval-+-lnd_ownersp lnpce 61.28878 20.67387 2.96 0.003 20.76873 101.8088hh_size 6.303549 7.203314 0.88 0.382 -7.814687 20.42179_cons -286.9731 123.3481 -2.33 0.020 -528.731 -45.21517-+-select lnpce .0682579 .1348031 0.51 0.613 -.1959514 .3324671edulevel .0096151 .025462 0.38 0.706 -.0402896 .0595197duringwithy .0161874 .005286 3.06 0.002 .005827 .0265477hh_size .007615 .046654 0.16 0 .870 -.0838252 .0990552asst2008 -1.13e-06 7.34e-07 -1.53 0.125 -2.57e-06 3.12e-07_cons -.0686488 .6543009 -0.10 0.916 -1.351055 1.213757-+-mill lambda 181.4302 139.4798 1.30 0.193 -91.94525 454.8057-+-rho 0.74328sigma 244.09453lambda 181.43021 139.4798