1 General Information

The focus of the course is close reading and re-analysis of recent research in the political economy of development, broadly construed. The focus is on well identified research whether based on experimental or observational data. It is intended for students who already have strong analytic skills.

Topics include economic growth, democratization, social cohesion, political accountability, violence and welfare.

Class times

  • 19.05.2023, 9 - 11
  • 16.06.2023, 9 - 3
  • 30.06.2023, 9 - 3
  • 07.07.2023, 9 - 3
  • 14.07.2023, 9 - 3

Typical structure will be:

  • 09:00 - 10:10: reading 1
  • 10:15 - 11:25: reading 2
  • 11:30 - 12:10: reading 3
  • 12:10 - 13:20: break
  • 13:20 - 14:30: reading 4
  • 14:30 - 15:00: general discussion

Location

All classes will be in person at the Institutsgebäude - K12B Universitätsstraße 3b

2 Expectations

2.1 Reading

The reading loads are not especially heavy; typically 4 readings per session. You should read these carefully whether or not you are on the “rep” team for the reading. There is no point coming to the class unprepared. My thoughts on reading and discussanting. See also chapter in declare design book

2.2 Repping

Data should be available for all readings. For each reading a rep team will be assigned who is responsible for replicate results and submitting the results to robustness checks.

See this replication repo for some code fragments to get you started. See here for compiled page

A “rep” team (of up to six students) will be assigned a formal role and prepare oral and written commentary for the reading. You should expect to be on one rep team each session.

Key elements of this are:

  • Be sure you have the data, papers, and all you need at least a week in advance

  • Make sure you can make sense of the data and run a basic replication.

  • When you have a feel of things jot down a brief “pre-replication plan”. What do you plan to look at? What do you expect to find? Post it to Moodle before engaging in reanalysis (honor code)

  • Then there are two ways to expand the analysis;

    • One is to check for robustness. How much do things depend on the particular models or measurements?
    • The second is to go more deeply into the logic of the explanation. This might sometimes require assembling more data, constructing new tests and so on.

  • Generate a presentation that

    • presents the paper in general
    • goes through the results and replication and
    • goes through robustness and extensions
    • does all this in quorto or rmarkdown so that content and code in a single file (great reference: https://quarto.org/docs/presentations/revealjs/)

  • (ideally) contribute your replication to a class package (I will share notes on how to do this)

  • I urge you also to try to use DeclareDesign to formally characterize the research design in abstract terms and assess its properties

    • Note that while we focus a lot on statistical replication and re-analysis there are many sides to a paper. Your presentation should in no way shy from discussing more fundamental conceptual or interpretational issues as appropriate.

2.3 Writing

You will be expected to write up a short research design (10 pages) containing (i) a theoretical argument or motivation, (ii) a proposed empirical test of that argument (iii) a formal design object and (iv) a discussion of policy prescriptions that might result from the argument.

3 The Readings

The readings use a wide range of strategies including experimental strategies and a range of observational strategies including IV and RDD.

Reading Data
1 Macro processes
1.1 Daron Acemoglu, Simon Johnson, and James A. Robinson. The Colonial Origins of Comparative Development: An Empirical Investigation. AER (2001) Data
1.2 James Fearon and David D. Laitin. Ethnicity, insurgency, and civil war. APSR (2003). Data
1.3 Nathan Nunn. The long term effects of Africa’s slave trade QJE (2008) 1, 2
1.4 Daron Acemoglu; Simon Johnson; James A. Robinson; Pierre Yared, Income and Democracy Data
2 Group politics
2.1 Alberto Alesina, Paola Giuliano, and Nathan Nunn. On the Origins of Gender Roles: Women and the Plough QJE (2013). Data
2.2 Raghabendra Chattopadhyay, Esther Duflo Women as Policy Makers: Evidence from a Randomized Policy Experiment in India Econometrica (2004) Data
2.3 Salma Mousa Building Social Cohesion Between Christians and Muslims Science (2020) Data
2.4 Saad Gulzer, Nicholas Haas and Benjamin Pasquale Does Political Affirmative Action Work, and for Whom? Theory and Evidence on India’s Scheduled Areas APSR 2020. Data
3 Institutions and accountability
3.1 Guy Grossman, Kristin G. Michelitch, and Carlo Prato. The Effect of Sustained Transparency on Electoral Accountability AJPS 2023 Data
3.2 Claudio Ferraz and Frederico Finan Electoral Accountability and Corruption: Evidence from the Audits of Local Governments Data
3.3 Pia J Raffler Does political oversight of the bureaucracy increase accountability? Field experimental evidence from a dominant party regime APSR (2022) Data
3.4 Thomas Fujiwara and Leonard Wantchekon Can Informed Public Deliberation Overcome Clientelism? Experimental Evidence from Benin AEJ (2013) Data
4 Aid and interventions
4.1 Nathan Nunn and Nancy Qian U.S. Food Aid and Civil Conflict AER (2014) Data
4.2 Robert Blair, Di Salvatore, Jessica; Smidt, Hannah, UN Peacekeeping and Democratization in Conflict-Affected Countries APSR (2023). Data
4.3 Christopher Blattman; Annan, Jeannie, 2015, Can Employment Reduce Lawlessness and Rebellion? A Field Experiment with High-Risk Men in a Fragile State data
4.4 Karthik Muralidharan, Paul Niehaus, and Sandip Sukhtankar. Building State Capacity: Evidence from Biometric Smartcards in India. AER 2016 https://doi.org/10.1257/aer.20141346. AER (2016) Data

4 Workflow and Tools

Main tools

The main tools that we will employ are:

Other resources

Other readings on long list