Assignment #9

In Chapter 7 of Poor Economics, Duflo and Banerjee talk about the economic rationale behind the wide-spread microcredit business in developing countries, especially in India and provide analysis of its current situation. The authors argue that despite of the problems associated with the program, it is an outstanding achievement that has reached a large number of people. The next step of the program should be focused on the financing of medium-scale enterprises. To reach the conclusion, the authors employ a number of statistics to illustrate their point.

When they describe the situation where households do not borrow from a proper lending institution even though interest rates from such a source is so much lower, they quote “In the survey we conducted in rural India, about two-thirds of the poor had a loan. Of these, 23 percent were from a relative, 18 percent from a moneylender, 37 percent from a shopkeeper, and only 6.4 percent from a formal source.” They also write, “Interest rates vary across sectors and countries, but the bottom line is always the same: yearly interest rates in the 40 to 200 percent range are the norm.” These astonishing numbers clearly illustrate their point that poor people tend to borrow from the people they know at excessively high rates rather than from legitimate institution at lower rates. Following the discussion, the authors introduce the idea of multiplier effect to elaborate on the difficulty with collecting interests and loans. When talking about the limits of microcredit, they quote the incident where the father has to urgently borrow 179 dollars without knowing when to start his repayment to indicate the lack of flexibility of the program. The authors quote that “India has a priority sector regulation, which constrains banks to lending 40 percent of their portfolios to the priority sector, consisting of agriculture, microfinance and small and medium enterprises, which can include quite large firms.” This statement was used to show the increasing attention focused on financing medium-scale companies. The chapter is very illustrative in the way that it provides a detailed analysis of the microcredit sector in India. I am really surprised to see the triple digit interest rates charged to poor people and their reluctance to borrow at lower rates because they tend to borrow from familiar people.

Also, the way institutions prevent default through signing contracts with a group of people is quite innovative. However, I do have a question remaining. I think this method works in India rural area because in Indian culture, it is very inappropriate to be the person hat drags the group back. However, in some culture, people tend to be more selfish and do not care much about the negative impact on the collective good. Therefore, how can microcredit work in these developing countries? Personally, I do believe in the power of microcredit but definitely it can improve in many ways.


Assignment #8

The following article from ABC News illustrates the Fed’s focus on interest rates as an important measure to curb unemployment. ( In the article, it is said that The Fed wants to lower near-zero interest rates, citing an “elevated” unemployment rate and “strains in global financial markets.” Scott Brown, chief economist with Raymond James explained, “The idea is that you want to encourage more economic activity,” Brown said. “Having low interest rates, consumers are more likely to be able to borrow, take risks and to make car and home purchases.” This is exactly what I had in mind when I determined my topic. His point view reinforced my logic explaining the relationship between interest rate and unemployment. However, I do realize that my models have been insufficient in terms of the number of independent variables included. Unemployment rate is a very informative indicator of the current situation of the economy. Therefore, there are many factors that have an effect on unemployment rate. I may have to include variables such as nominal GDP, inflation rate and others. Most importantly, the article indicates that businesses tend to perform in an unusual way compared with their operation in normal period. Thus, I am going to include a dummy variable that takes value of 0 when the year is not included in a recession and takes value of 1 when the year is included in a recession. In addition, I will include the data of 30-year fix-rate mortgage because it is a good indicator of the housing market. Houses are definitely the priority concern of workers during economic recessions because a lot of workers have to relocate to find jobs to refinance their houses or start working after resting for a while. Thus, the housing market should also have an impact on unemployment. The article is quite helpful to me because it reinforces my position and convinces me to add more variables to my model.

Assignmenet 7

Gunsel and Soytas discussed the relationship between oil price, interest rate and unemployment. They employ quantitative methodology and concluded that there is a negative relationship between interest rate and unemployment. My prediction in the paper was that there is a positive relationship and the effect is more obvious in small businesses.

This paper really helps me to clear the doubts I have had regarding my preliminary regression results. I did not include enough variables in my model. In their model, the authors included oil price as an indicator of economic conditions. I will definitely try to add more variables such as inflation rates to my own model to make it more accurate.


I. Introduction

                Unemployment has been an enormous problem in the current US economy. The Fed has been actively trying to lower the unemployment rate through monetary policy. The most important tool of monetary policy is the manipulation of interest rates. By controlling the interest rates, the Fed adjusts how expensive it is for various businesses to borrow  money to expand their operation. During economic downturn, the Fed would lower the interest rates to stimulate the growth of economy and vise versa during economic boom. The natural consequence of business expansion is the hiring of additional workers. Therefore, through the intermediary of banks and businesses, the Fed can affect unemployment rate through adjusting interest rates. However, businesses of different size will adjust their operation differently to a change in interest rates. I would like to investigate how a change in interest rates will affect unemployment across different sized companies.

II. Literature Review

                Dogrul and soytas use the example of Turkey , a fast expanding emerging market, to analyze the relationship between interest rates and unemployment rate.  The journal is really interesting because it is very similar to what I am going to do, providing an example of my research about the relationship in the USA.

                Boianovsky and Presley talk about the relationship between natural rate of unemployment and interest rates. Their work stipulated that the work of the Fed can only affect the unemployment to a certain extent. Business cycles and structural changes are more influential in terms of determining unemployment.

                The third article talks about the relationship between interest rates and unemployment rate in the long. It reaches the same conclusion with the second article that changes in interest rates can only affect the unemployment rate in the short run. Other factors, such as business cycles will be more significant in the long run.

III. Data

                My data of unemployment across industries comes from the following website. The report classify businesses that have more than 499 workers on their payrolls as large businesses, between 50(included) and 499(included) as medium businesses and less than or equal to 49 as small businesses. The data of interest rate can be found at One problem that I ran into was that if a business grew from 480 to 530, should the 50 increase be attributed to Medium business or Large business. The data provider specifies that the increase will be split in half for each industry.

IV. Modeling

                In my research, interest rates is the only independent variable. I will run three regressions of various sized industries’ unemployment rates on the interest rates and compare results with each other.

 V. Evidence

I have found some articles and empirical evidence to support my hypothesis and will include them in this section.

Vi. Conclusion

I will summarize the findings of my regressions in this section.


H. Günsel Doğrul; Ugur Soytas.  Relationship between oil prices, interest rate, and unemployment: Evidence from an emerging market. Energy Economics. 32(6):1523-1528

Mauro Boianovsky; John R. Presley. The Robertson connection between the natural rates of interest and unemployment. Structural Change and Economic Dynamics. 20(2):136-150

Berentsen, Aleksander; Menzio, Guido; Wright, Randall; Inflation and Unemployment in the Long Run, American Economic Review, February 2011, 101(1): 371-98

Assignment #5

In Chapter 4 of Poor Economics, the author talks about various factors that have resulted in the miserable school attendance rate and unsatisfactory performance of students who are attending school. I found an article It is also concerned about the poor secondary education attendance rate. Both the book and the article believe that reforms must take place to ensure the equal opportunity for students, but they differ in the roots of the problem.


The book contributes the cause of the low secondary attendance rate to the students’ disinclination towards school. At the core of the demand wallahs’ view is the idea that education is just another form of investment: People invest in education, as they invest in anything else, to make more money.  Therefore, people may easily conclude that poor parents would most likely stop their children from attending school since going to school is equal to forfeiting income that could have been made during that time. However, the author subverted the easy conclusion with data from Conditional Cash Transfer program. The program stipulates that a family will get a cash transfer if they let their children go to secondary education. The results show that secondary education attendance rate rises from 67 to 75 for girls and from 73 to 77 for boys. As a result, when parents are given compensation, even relatively small, they are willing to send their children to schools. It turns out that children’s reluctance for schools plays a more important role in the poor attendance rate than family does. The example of Karnataka is the perfect illustration of the point. All her six children could get access to education like her eldest does, but some of them simply hate school and choose not to attend school. In addition, the author mentions that 95 percent of Indian children now have a school within half a mile. Therefore, the lack of resources is not sufficient to explain the poor performance.

In contrast, the article that I found proposes that the lack of resources is the main cause of the poor attendance rate. Two thirds of African children are effectively locked out of secondary school, according to a new UN report which cites secondary education as one of the next great development challenges facing many of the world’s poorest countries. Globally, there were 531 million secondary students in 2009, compared to 196 million in 1970. Much of this growth took place in large and populous countries that started with relatively low levels of secondary education. The rapid growth in secondary education enrollment has squeezed the chance for youngsters.

I think both stories are credible, especially with all the stats involved. Different regions have their unique circumstances and that’s why the stories yield different conclusions.  

Assignment 4

In Chapter 3 of Freakonomics, the author talks about why many drug dealers, who mostly appear to live fancy life styles, are still living with their mothers. The thesis of the chapter is that most of the drug dealers are low level members of gangs and earn very little money, being forced to live with their families while high-level members earn so much money that they can afford to live lavish lives. However, the number of youngsters that want to join the drug business has been steadily increasing because this is the only career path many of them could imagine.

The first interesting data talks about the amount of money that J.T makes. “At 8500 per moth, J.T.’s annual salary was about 100000-tax-free, of course, and not including the various off-the-books money he pocketed. This really supports the author’s point that high-level drug members make lots of money and have created the fancy image of their life styles. I do believe this stats, because it is very compatible with the image of drug dealers in movies.

The second data talks about how much foot soldiers make. Foot soldiers make only 3.3 dollars an hour, less than the minimum wage. The contrast between 3.3 dollars an hour of a foot soldier and more than 100000 for a gang leader clearly illustrates the hierarchy in gangs.  This number is surprisingly low given the working conditions of the job. The place where the data is described is really critical, since it leads to the discussion why so many young people want to join the business even with such a low wage.

The third relevant data is the chance of being killed as a member of J.T’s gang. The 1-in-4 rate is much higher than the 1-in-200 rate for timber cutter, the most dangerous job considered by Bureau of Labor Statistics.  This number highlights the dangerous nature of gangster and further questions the phenomenon that youngsters would like to get into the business.

The fourth interesting data is that there were more than 1300 gangs in Chicago in 1920s. This historic data shows the deep root of gang culture. The omnipresent phenomenon of gangs has made a deep impression on the young generation of communities. They start to consider joining gangs as the only career pathway at an early age and therefore would take the job with miserable pay in the hope of climbing up the ladder.

Assignment 3

In my research paper, I will discuss the effect of change in interest rates by the FED on the number of jobs created in different- sized businesses. We divide businesses into three different categories, small businesses, medium businesses and large businesses, based on the number of workers on the payroll. Businesses that have more than 499 workers on their payrolls are classified as large businesses, between 50(included) and 499(included) as medium businesses and less than or equal to 49 as small businesses. As we know, when the FED lowers the interest rates, the commercial banks would lower the interest rates of business loans that they offer. Consequently, companies that would like to expand their businesses would be able to get a loan at a cheaper price, motivating them to expand their business. Hiring more staff is a natural consequence of business expansion. However, the number of the newly employed varies from business to business. For example, opening a new Chinese restaurant in Gettysburg will create far fewer jobs than opening a car assembly line at a Ford factory. I am motivated by this common phenomenon in life. I will closely examine the relationship between changes in interest rates and the number of jobs created by those changes. The data of monthly created jobs in different- sized businesses can be found at The data of interest rate can be accessed at My hypothesis is that for a given change in interest rates, the number of jobs created in the business has a negative relationship with the size of the business. Namely, there would be more jobs created in small- businesses than in large- businesses for a change in interest rate.