Data Analysis Chapter Example

Data Analysis Chapter Example This 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 statistics The 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 therefore 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 areas 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 years 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 done 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 respondents 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 Analysis Now 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 models With 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 variable The 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 variables Below 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 Methodology: We 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 different 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 STATA output gives a value for Ï  (rho) with associated p-value. This Ï  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, Ï . 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 is Empirical results This 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 statistics The 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 therefore 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 areas 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 years 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 done 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 respondents 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 Analysis Now 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 models With 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 variable The 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 variables Below 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 Methodology: We 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 different 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 STATA output gives a value for Ï  (rho) with associated p-value. This Ï  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, Ï . 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 is We assumed that is observed if Where and have correlation Results: The results of our Heckman model are provided in Table (Annex). Using as a dependent variable in Heckman regression, we find and the constant term are significant while is insignificant. We also find positive relationship for and with . Considering the absolute 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 closer 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 interpretability. 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 expected value of the dependent variable while all the predictors are held constant. Interpreting 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 Modern Approach by Woolridge for discussion and derivation). Well explore the relationship between the landownership pattern and the per capita consumption 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 with 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 Table 1: OLS Result lnd_owners~p | Coef. Std. Err. t P>|t| [95% Conf. Interval] -+- lnpce | 58.21023 18.98437 3.07 0.002 20.86622 95.55423 hh_size | 4.660069 6.495749 0.72 0.474 -8.117666 17.4378 _cons | -204.742 97.52465 -2.10 0.037 -396.5819 -12.90203 We present the usual 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 2 From the above OLS table we consider the independent variables are per capita expenditure, education level, during with the community, household size and asset 2008 and the dependent variable is land ownership pattern of the respondents. In this analysis the model is significant in case of asset 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 asset 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. Values 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 without 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) rhosigma Heckman selection model two-step estimates Number of obs = 417 (regression model with sample selection) Censored obs = 80 Uncensored obs = 337 Wald chi2(4) = 9.83 Prob > chi2 = 0.0434 | Coef. Std. Err. z P>|z| [95% Conf. Interval] -+- lnd_owners~p | lnpce | 61.28878 20.67387 2.96 0.003 20.76873 101.8088 hh_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 .3324671 edulevel | .0096151 .025462 0.38 0.706 -.0402896 .0595197 duringwith~y | .0161874 .005286 3.06 0.002 .005827 .0265477 hh_size | .007615 .046654 0.16 0.870 -.0838252 .0990552 asst2008 | -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 -+- mills | lambda | 181.4302 139.4798 1.30 0.193 -91.94525 454.8057 -+- rho | 0.74328 sigma | 244.09453 lambda | 181.43021 139.4798

Dr. James Banks on Multicultural Education Essay -- Education

As we proceed further into the 21st century, multiculturalism becomes more relevant to obtaining a truly global society. Dr. James A. Banks defines the meaning of multicultural education and its potential impact on society when it is truly integrated into American classrooms. In his lecture, Democracy, Diversity and Social Justice: Education in a Global Age, Banks (2006) defines the five dimensions of multicultural education that serve as a guide to school reform when trying to implement multicultural education (Banks 2010). The goal of multicultural education is to encourage students to value their own cultures and the diverse cultures of those around them without politicizing their differences but rather, as Banks passionately explains in his lecture, â€Å"to actualize the ideals stated in the Constitution† (2006) forming â€Å"civil, moral, and just communities.† The first of the five dimensions of multicultural education is content integration. Teachers can identify exemplary people and information from diverse cultures and integrate it in a nontrivial into the curriculum so students can learn the effects of all cultures on the content they are studying. At the beginning of the school year in my Algebra class, I do a brief activity on the history of numbers. The students learn that we currently use the Arabic number system but there were many other number systems that existed in the history of numbers. We explore and try to represent quantities using various number systems such as Roman, Mayan, Chinese, and Egyptian number systems. The students are able to see the contributions made by people of diverse cultures to mathematics. The knowledge construction process, the second dimension of multicultural education, requires t... ... and administrators alike—must unite in a common plan to weave into all aspects of student life the recognition of diverse cultures and social groups. Dr. Banks (2010) explains the latent curriculum being â€Å"defined as the one that no teacher explicitly teaches but that all students learn.† These are the lessons that students remember long after they have left the school system. References Banks, J.A. (2006). Democracy, Diversity and Social Justice: Education in a Global Age. University Faculty Lecturers Podcast. Retrieved May 9, 2012, from http://www.uwtv.org/video/player.aspx?mediaid=1580263790 Banks, J.A., & McGee Banks, C.A. (2010). Multicultural education: Issues and perspectives. (7th ed.) Hoboken, NJ: John Wiley & Sons, Inc. Rothstein-Fisch, C., & Trumbull, E. (2008). Cultures in Harmony. Educational Leadership, 6 (1), 63-66.

A blow A kiss by Tim Winton

What Of what are individuals identities framed of ? Its It is their house , parents or, religion? ,dDoes it define and shape the action, or the ability of individuals to think , reason and give an opinion . Every individual has a different and unique identity. You may think something is terrible while someone else does not even care and yet another person may laugh, why? The answer is simple, everyone has his own identity and personality. Everyone feels, acts, thinks, and dreams differently. In the short story â€Å"A blow A kiss â€Å"by Tim Winton ,the Australian author identifies the quality of father -son relationships and their responses in situations of stress and emergency . Winton uses conventions and techniques such as narrative structure ,character development and narational commentary in the story to communicate a meaning to the readers . The story also represents how fathers are role models in their son's life and how relationships between different fathers and sons differ , as a result of their identities . Winton starts his story in a country. Where Albie and his father are returning from a fishing trip after losing their salmons . †it did not matter †¦. again â€Å"pg 7 And suddenly on the road, they come across a rider who is injured and drunk . The author suggests the idea of a father and sons typical relationship (gone fishing) using conventions such as timing and placement of events within the narrative. When they come across the rider, Albie is left with the rider . as the injuried man spoke â€Å"oh! oh,Dad I am sorry. Was coming back†pg 9 Albie responses to it by saying â€Å"its alright â€Å"and kisses him on his cheek . The moment of realisation is imposed on Albies expressions . Albie is influenced by this father and tries to comfort the rider . Albie identity is similar to his parents . He treats other people the way he is being treated . On the other hand Beacon and his son have different identities. Both of them drunk and are in a dark part of their life . He scolds his half conscious son for being a coward and insults Albie builds up inside as he felt the rider’s emotions when he thought Albie was ilf Beacon and was crying. Albie felt it was unfair the son wept for his father, and as he is in a critical condition, his father shows no emotions or support. The relationship between Albie and his father is extremely different from to the one of Beacon and his son . Albies dad is a good man who helps the injured rider and seek help upon the accident of the rider and then to follow up by finding. Albie is so content with. He thinks so highly of his father, even more so than God. In contrast Beacon,express emotions through violence and verbal abuse, which is unhealthy for a son to be raised by. He is seen as a male who drinks avoids his family life . Winton construction is shaped through his use of conventions and techniques . He uses narational commentary as seen above and has developed characters . Tim Winton suggest us the ideas about the identities in his short story through comparing two different kind of father -son relationships . The resolution of the story â€Å"sorry about the salmon â€Å"pg 11reminds us that the manhood stays there. And that all our identities get – you need to find and use a better word affected bymy the role models in our life .

what lies beneath essays

what lies beneath essays As showed in the title of Edith Whartons Roman Fever, Mrs. Ansley, one of the main characters of the story was driven by a kind of fever-her passion for Mr. Slade, and did something that is completely contrary to her proclaimed image. Still, she stuck to the old tradition and kept silent about the truth. Mrs. Ansley is described as somewhat reserved and quiet, regarded as typical woman of old New York(244)- conservative and prudent (251). However, when love is concerned, she lost her prudence and went without hesitation for the date with her friends fiance. For her, love can be above tradition for a certain degree but not totally, so she cared for that memory (254) with Mr. Slade even though it lasted only for one night and did not demand a marriage. Although Mrs. Slade constantly mentioned Roman fever to remind her of the past memories, she avoided talking more about it by seemly absorbed in her knitting or giving not much reply. In fact, as we found out at the end of the story that it is partly because she felt sorry for (256) Mrs. Slade as she thought that it was she who betrayed their friendship. Unlike Mrs. Slade who had rested upon a seemingly victory and unveiled the forgery in order to defeat her, she did not defend herself most of the time until Mrs. Slades crowing over is hardly tolerable. Though weaker in physical condition-smaller and paler (241) than Mrs. Slade who is an extremely dashing woman (246), Mrs. Ansley is presented mentally healthier and stronger than Mrs. Slade. The sentence that she began to move ahead of Mrs. Slade is not just a narration of the circumstance, but also a triumphant proclamation of her actual victory. ...

War and Peace essays

War and Peace essays War and Peace one of the greatest novels in the world, written by giant of Russian literature count Leo Tolstoy. Leo Tolstoy, the son of Count Nicholas Tolstoy, was born in 1828 at the family estate Yasnaya Polyana about 100 miles south of Moscow. His greatest novel War and Peace was published in 1869, after his work had undergone several changes in conception and he had spent five years of uninterrupted and exceptionally strenuous labour. The epic War and Peace describes the lives of five aristocratic families during the Napoleonic Wars between Russia and France. His subtle psychological insights and realistic details create an entire world from various points of view. Tolstoy summarizes the moral evil of the war in these words: An event took place opposed to human reason and to human nature. Millions of men perpetrated against one another such innumerable crimes, frauds, treacheries, thefts, forgeries, issues of false money, burglaries, incendiarisms, and murders as in whole centuries are not recorded in the annals of all the law courts of the world, but which those who committed them did not at the time regard as being crimes. There are three main screen versions of War and Peace: the 1956 version by King Vidor, 1967 Russian version and another American movie of 1973. The 1956 version is not the best ever done but it was the first significant screening of the novel. The director of this movie is King Vidor, cast: Audrey Hepburn as Natasha Rostova, Henry Fonda as Pierre Bezukhov, and Mel Ferrer as Andrew Bolkoski. The script was done by six writers including Vidor and, in general, follows the plot of the novel. However, a lot of scenes from the novel are omitted and some minor characters are not shown. Thou I think it is a good adaptation, considering the length of the book and the short length the movie actually had to be. Star-studded cast and spectacular battle scenes (directed by Mario Soldati) certainl...

Management Strategy and Decision Making Assignment

Management Strategy and Decision Making - Assignment Example s main focus was made on the increase of returns to the shareholders, the achievement of the Just Group strategy could be partially evaluated based on the dividends payout. Thus, in 2013 the company informed about annual dividends increase up to 38 cents per share. This increase has comprised 2% comparing with the dividends for 2012 (Premier Investment Limited Annual Report 2013). However, taking into consideration the fact that for 2009 it was declared that dividends were 75 cents per share, the company’s performance could be challenged. On the other hand, this performance could demonstrate strong financial position of the company as the global financial crisis might have had more adverse effects on the shareholder’s value. In order to achieve its strategy of increase of returns to the shareholders, the company has identified six strategic focus areas, such as: expansion and growth of the business through the internet up to 10% of sales; reinvigoration of the five key brands; organization-wide cost efficiency program; two phase gross margin expansion; significant growth of Peter Alexander; and significant growth of Smiggle brands (Premier Investment Limited Annual Report 2013). By reviewing the company’s performance results reported in 2013 it is possible to state the there has been made a substantial progress towards achievement of the above listed strategies. For example, the company has reported that its e-commerce activity has grown significantly. To achieve this result, there were launched 1day delivery program (within Australia) and mobile sites for all brands of the company. In terms of the growth margin expansion strategy, the company has also achieved significant results as gross margin has been expanded by 117 basis points in 2013 (Premier Investment Limited Annual Report 2013). Growth of Peter Alexander brand was also significant in FY 2013, as the total sales have exceeded 17%. Taking into consideration that in 2009 this brand comprised 7, 7% of

Hyperinflation in Germany after World War I Essay

Hyperinflation in Germany after World War I - Essay Example Why such a phenomenon happened in Germany, a nation with a long history of political, economic, psychological, social and academic knowledge and experience, shows the destructive power of policy mistakes caused by weakness and incompetence (Solomon 28-30). Understanding the hyperinflation that raged from June 1922 to December 1923 requires a good knowledge of German history. Inflation is only one of the external manifestations of a number of decisions regarding the supply and demand in the markets for goods and currencies that are made in the minds of politicians, economic policy-makers, businessmen and consumers. A gradual inflation rate is acceptable, but when these decision makers make wrong decisions at the same time, the market breaks down. Hyperinflation, like a bodily fever that is a sign of infection or a virus causing destruction within the body, is a sign of sickness in economic markets. Anyone familiar with Germany's political and national history would know why so many wrong decisions were made in the minds of so many Germans and their foreign business and political partners during this period, what led to these mistakes and, more importantly, why. The fusing of the German nation was a process that took centuries beginning with the widely held belief that in the year 9 A.D., Arminius, a prince of the Germanic tribe called the Cherusci, defeated three Roman legions in the Teutoburg Forest. With each conquest, the tribe grew into the Holy Roman Empire that reached its peak during the reign of Charlemagne in the 9th century. After his death in 814 A.D., the empire of Germanic and Romance speaking people then fell apart, breaking up into eastern and western realms according to the law of inheritance (PIO 106-108). This brief detail is important to understand the events directly related to the study of hyperinflation, because the collective aspiration of a formerly glorious nation that spanned from east to west to wherever territories German settlements were found became one of the arguments used by politicians to justify their actions, no matter how mistaken these may be. By defining the German Fatherland this way - territory that belonged to ancient Germanic tribes by conquest, settlement, or inheritance - the dreams and actions of several generations of German peoples were shaped by their ambitious efforts to expand, reclaim, or retain what they think is justly theirs by historical right. Germany in the early 19th century became a confederation of 39 German kingdoms and political alliances with constantly shifting internal boundaries, not including the Germans in Bohemia (present Czech Republic) and Austria. Each kingdom had its own identity and was not willing to surrender it. This division and the political infighting among the different rulers of the kingdom affected the unity of the government and became one of the sparks that ignited hyperinflation in the 1920s. Acting as stimulus that created tensions in the pre-War politics and economy, intellectuals like Karl Stein, Prince Karl August von Hardenberg and Wilhelm von Humboldt called for the abolition of serfdom, freedom of trade, municipal self-administration, equality before the law, and general conscription into the