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ECON-UA 323: ECONOMIC DEVELOPMENT

Midterm Exam, Fall 2021

This exam has two sections: some shorter questions and two medium-length questions. You     should write your answers in the blue books. You should remember to write your name/netid on the blue books.

There are 70 points on the exam, so you should aim to spend about a minute per point. I am not  trying to trick you – do not spend 20 minutes crafting a perfect answer to a 5 point question.

This is a closed-book & no-talking-with-friends exam. The midterms are a useful way for you to figure out if you understand the material (and for me to figure out how to improve for next year). NYU forces me to curve your final grades. If you cheat, you aren’t cheating me so much as you  are cheating your honest classmates. Please follow the NYU honor code. Write legibly; if we      can’t read your writing then we can’t give you credit.

Section I: Short Questions (31 total points)

Consider the following statements. For the true/false, make sure to explain your answer –if you give a clear & convincing explanation of your choice you will get full credit even if we were    expecting a different answer (and similarly you won’t get any credit if you write the right”     answer but nothing else). Make sure your answers are clearly labeled in the blue book.

1.   The Solow growth model, where output is a function of capital and labor, assumes constant returns to scale for both capital and labor (7 points)

True / False/ Depends

False. The Solow model assumes constant returns to scale overall, and therefore decreasing returns to both capital and labor

2.   In equilibrium, expectations are correct. (7 points)

True / False/ Depends

Depends. For the most part, we like to think that expectations are correct that’s hopefully what you wrote on your problem sets. The idea is that people would notice if their expectations were consistently wrong. But in the last education lecture we saw a setting with incorrect expectations students don’t see all grown- ups, but only the adults nearby. So if the successful adults leave, and success is caused by school going, then the students will have a biased perspective, and there is nothing observable that will change their mind

3.   You have one vector, called y. Write out the R code to produce a vector x equal to the minimum of y and 700. (7 points)

x = pmin(y,700)

There is great debate about whether height is driven by socio-economic status. As you can see in the graph below, the two are positively correlated

4.   Use evidence in the graph to support the argument that an omitted variable – location - is   biasing the observed relationship between socio-economic status and height. Do you think the true causal relationship is steeper or shallower than the observed relationship? Explain. (10 points)

Even within socio-economic status, some places are systematically taller than others. For      instance, parts of Africa are taller than parts of China which is taller than parts of South Asia for the same income. This could be due to genetics. If the genetic causes of height are           correlated with both wealth and height, then this would bias the observed relationship. Since the relationship is positive in both directions – the genetics that lead to height are both          associated with height and wealth – that means the observed relationship is steeper than the  causal one.

Note that you don’t need genetics to cause wealth. It could just be that the genetically tall      people are wealthy for a reason besides wealth. For instance, we’ll see in the second half of  the semester that some parts of the world got “lucky” with their natural endowments in terms of native plants and animals.

 

Part II: Longer questions. 39 total points.

5.   The Maghribi traders relied heavily on an informal agreement among themselves to sustain trade, whereas the Genoese relied more heavily on formal employment contracts.

a.   Explain how the Maghribi traders punished those who cheated, and in particular how they made sure that the punishments would actually be carried out.  (7 points)

Cheaters were punished by never being hired again. This is self-fulfilling. Part of the      reason not to cheat is to maintain the relationship. Once I’m a cheater, there is no            relationship to maintain. I have nothing left to lose,” so if anyone was foolish enough to hire me, there is no reason for me not to cheat. As a result, no one will hire me.

b.   In the long run, explain why it was easier for the Genoese traders to expand to new markets. (7 points)

The Maghribi traders relied on the community for the long-run incentives. Even if a         particular trader wouldn’t hire someone again, someone else in the community was. Plus, everyone in the community was both sometimes a trader and sometimes a shipper. The    Genoese system was much more hierarchical – a family firm could hire someone to go to a new market, and the incentives would be the same as any other contract.

6.   After NAFTA, many factories making clothing for export were built in Mexico.  Textile factories tend to hire young women. There were some regions where lots of factories were built, and others where few were. The figure below plots the following relationship. The x-axis is age when the factories first started being built. The y axis is the schooling of girls in the high exposure regions minus the schooling of girls in the low exposure regions. (So, for instance,

22 years olds went to roughly the same amount of school regardless of location, whereas girls

who were 10 when the factories were built went to roughly 2 years more school in the high exposure places than in the low exposure places).

 

a)  Explain specifically why it would be a bad idea to assign any causality to the raw         correlation of schooling and number of factories across regions, while the graph can be used for measuring causal effects (7 points)

You would expect factory owners to choose the best locations. The direction of the bias is unclear – maybe factory owners like literate employees, so they would locate in educated places (so the direction would too positive because of the positive reverse causality). Or   perhaps they dislike educated employees, pushing the observed relationship down             relative to the true one.

The graph at its heart is a difference-in-differences type of strategy. There are older,         unaffected, cohorts in both the high and low exposure regions (since they are for sure       done with school). If we are willing to believe that the only difference between the high   and low exposure regions is the factories, then the differences in outcomes is driven by     the factories and not the differences in places (which is essentially controlled for by the    older places) [many of you didn’t answer this part of the question. In general, when that   happens I assume that probably you didn’t notice it, which means it’s my fault, so I didn’t take points off for it]

Recall that in class we derived the Mincer equation

s ⋆  =

Where m and b relate to the benefits of school, and r and  the costs. Explain how the Mincer logic can explain

b)  No effect for 20ish year old women (5 points)

Women who are already out of school aren’t affected by the factories (which presumably don’t affect the returns to school)

c)  Decreased attainment for teenage girls (5 points)

The cost of school is increasing for them. Probably not directly – public schools are still free – but by increasing  .  And you can really see the convexity of the graph – 15 year olds are more likely to drop out than 14 year olds, and it peaks at 16 (which is when the factories are probably starting to be willing to hire people)

d)  Increased attainment for young girls. (8 points)

There are a few stories you could tell here. One is that r is increasing – factories do          require staff and managers, raising the returns to school. I prefer a credit constraints type of story there are families who previously couldn’t afford to send their daughters to      school the benefits were larger than the gains, but the benefits were in the future and as you know borrowing to go to school is extremely complicated. When the older daughters have increased income, this relaxes the credit constraint, allowing the younger girls to go to school.