1. (54p) In this exercise, you will calculate and review a fixed effects model and a fixed effects model with time effects. The context and data for the question come from work that one of your instructors did as a statistical consultant to the United Nations in 2015. As an aside, your instructor encountered there several Columbia University graduate students serving as interns, sometimes contributing public health capabilities, and sometimes contributing statistical and database capabilities.
One of the major challenges that the poorest of the poor countries in the world face in working to increase living standards is that couples do not have access to birth control, so they cannot control the initial timing, the spacing, and the quantity of children. The list of problems that results from this situation is long and often obvious, involving lack of per capita resources for education, health care and infrastructure. It also involves the physical and social damage of child marriage and early birthing, of the early aging and death of overtaxed mothers, and the abandonment of women and children.
The United Nations, as well as other world entities (think Melissa French Gates) works with donor countries to put contraceptives within reach of couples in poor countries. Often these couples do not even know that such a thing as contraception is possible. The UN subdivision that handles this work is called the UN Population Fund, and goes by its initials in French, UNFPA. Donor countries want to know if the money going to UNFPA is having an effect.
Data about life in poor countries is hard to acquire, but usually there is an internationally-assisted small survey in these countries every handful of years. UN demography professionals turn these surveys into annual estimates via a somewhat involved Bayesian technique. Thus, we have annual data, albeit somewhat second-hand.
Contraception availability is measured via the contraceptive prevalence rate, CPR, sometimes written as mCPR, with the lower-case “m” indicating “married”, because some countries do not want contraception involving unmarried women to be discussed. The goal is to increase the CPR in poor countries. Additionally, there is an education process to teach couples that contraception is possible and in a perfect world they may even have a choice of methods. This education
process creates “demand” for contraception, which can then be characterized as met or unmet. So in addition to raising the CPR, the UNFPA wants to raise total demand.
CPR and total demand increase slightly over time even in very poor countries as some contraception resources are imported by a country’s elite and perhaps middle class. The idea of international involvement is to increase the rate of change CPR and total demand increases.
The UNFPA must be invited into a country by its government. Some governments instead invite in other international entities, or otherwise align away from UNFPA, thus providing a non- UNFPA-assisted control group that can be referenced. The control group is 57 other poor countries with another, presumably less effective, alignment. In total, the dataset contains 69 countries over the 10 years of 2004 to 2013. A draft working paper from 2015 is available upon request for any student interested. The analysis replicated in this problem set in on pages 24-25.
Recall that Stata is case sensitive (and R is also). The data set is composed of Country, not country, Year, not year, CPR, TD, and UNFPA = 1 if the UNFPA is involved. So there is only one regressor; there are no control variables. The one regressor is binary. The data are arranged in the conventional panel data set way. The first entity is listed for its first time period, then its second … to its last, then the second entity is listed for its first time period, etc. You will replicate the panel data portions of table 2, depicted below. Note that CPR and total demand are measured in percentages in the format of 50 represents 50%, so a number like 0.33 means increasing the rate of change of the dependent variable by 1/3 of 1% faster than it would otherwise be.
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