Price controls that disproportionately target agricultural products are one way a government can exert urban bias on its rural voters. Although Lipton coined the name of the theory of “urban bias” in the 1970s, the decision of where best to allocate resources in a country trying to industrialize has been a question for the past hundred years. The main class conflict in the poor countries of the world today is not about labor and capital.
So the urban classes have been able to win most rounds of the battle with the rural areas.” 24 Artificially low food prices are specifically aimed at promoting the interests of urban consumers who are not involved in the production or cultivation of food. An examination of the theory behind price controls and why they have not been historically successful, along with an introduction to urban bias theory, allows us to draw a few conclusions.
In this section, I will provide a brief history of the politics behind the agricultural sector in Argentina to demonstrate a few things. The junta was more supportive of the landed oligarchy as opposed to the manufacturing industry. The first represented the landlord oligarchy, and the second the industrialists of the country.
41 This means that instead of the urban bias usually seen under Peronist governments, the rural bias created under the military junta continued under Menem. Not only was it out of character for the parties, but it was also one of the last times in history. It is my belief that in Argentina the latter of the two will be more prevalent.
Price controls have been used in many different countries in Latin America since the end of World War II as a way to curb inflation. It is clear that the government will make more efforts to alleviate the problems of citizens living closer to the capital. On the map below we see the distribution of companies across the country in Figure 5.
However, Argentina has proven to be one of the most contentious democratic countries in the world, with the highest frequency of protests and riots in Latin America. I suspect this because of the policies adopted by the last two presidents to appease the strong urban coalition through the implementation of price controls to reduce the price of agricultural commodities, which has therefore led to a shift in the burden of inflation from urban consumers to rural producers, who now get most of the impact of high inflation. The historical analysis of inflation and price controls in Argentina combined with the government's relationship with its urban and rural constituents has provided some insight into whether there has been urban bias in the past decade.
We will return to the theory of urban bias from a business perspective rather than a particular respondent perspective, and also examine the importance of the location of various businesses that have price controls.
Public Opinion
This is done to simplify the process and ensure that all variables used are relevant to the research. The first hypothesis that will be examined in this analysis regarding urban bias is as follows: as the size of the country where the respondent is located increases, the tendency to see inflation as the biggest problem for the country will increase. . From the frequency distribution we can see that the majority of these respondents identified themselves as being from a "cuidad pequeña", which is a small town and accounted for almost half of the responses with 47.6%.
It also shows that the smallest number of respondents came from the metropolitan area, at only 8.3% of the respondents, so this may bias the results towards the smaller cities because there are more respondents from that area. Furthermore, the column labeled Exp(B) identifies the odds ratio between the two variables, and indicates how much the odds of the dependent variable change for each increase in the independent variable. So, since the Exp(B) value is greater than 1, it means that the probability of the dependent variable increases than the independent variable.
Therefore, since the size of the town starts from 0 at the largest city and goes to 5 as a small rural town, this means that as the size of the residence becomes smaller, the probability that the person believes that inflation is the biggest problem that the country faces. increase, which proves my hypothesis to a certain extent. Control variables are also important to alleviate some of the bias present from the amount of respondents in the small city category. The other breakdowns in the following Annexes are those of monthly household income, showing the ranges used and the number of male and female respondents.
Although the significance of the tamano variable changed from .000 to .007 with the addition of the control variables, there is still a statistically significant relationship between the independent and dependent variables. This slight increase shows that even when we control for many other factors that may or may not influence the dependent variable, the size of the city where the respondent lives is still highly significant. Because this number is greater than one, it means that there is a positive relationship between the two variables; as the size of the city decreases, the likelihood increases that the individual will see inflation as the main concern.
The results of each analysis show that the hypothesis considered in this first part of the section—as the size of the place where the respondent is located becomes smaller, the tendency to consider inflation as the biggest problem for the country will increase— was supported because every test performed gave a significant result under the variable. However, I was surprised to discover the role of the variables of employment and monthly household income was not significant. In relation to the first test, although the regression showed that the size of the area lived in was significant in relation to how it affected inflation, there are certain limitations.
Companies
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