The Selection and the Making of Civil Servants: Evidence from China’s College Graduate Civil Services Program

Last registered on July 26, 2021

Pre-Trial

Trial Information

General Information

Title
The Selection and the Making of Civil Servants: Evidence from China’s College Graduate Civil Services Program
RCT ID
AEARCTR-0008003
Initial registration date
July 24, 2021
Last updated
July 26, 2021, 2:01 PM EDT

Locations

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Primary Investigator

Affiliation
Harvard University

Other Primary Investigator(s)

PI Affiliation
University of Chicago

Additional Trial Information

Status
On going
Start date
2021-03-01
End date
2022-12-31
Secondary IDs
Abstract
The quality, incentives, and ideologies of local civil servants in China greatly affect policy implementation and governance efficacy. This project aims to understand what shape the background, preferences, attitudes, and behaviors of civil servants in rural China. We propose to randomly change selection algorithm in half of the job postings of the China’s College Graduate Civil Services Program in Guangdong. We combine the field experiment with surveys and official social security records, and we will separately identify the selection margins (who apply to become local civil servants and which applicants the government selects) and the treatment margin (to what extent civil service experience changes the recruited candidates over time). We provide the first empirical evidence on the treatment effect of civil service experience, and we argue that it is important to incorporate the treatment margin in order to design the optimal selection mechanism.
External Link(s)

Registration Citation

Citation
Wang, Shaoda and David Y. Yang. 2021. "The Selection and the Making of Civil Servants: Evidence from China’s College Graduate Civil Services Program." AEA RCT Registry. July 26. https://doi.org/10.1257/rct.8003-1.0
Experimental Details

Interventions

Intervention(s)
A random half of the job posts will implement the selection algorithm at the status quo, which places heavy weight on candidates’ political obedience. The other random half of the job posts will implement a new algorithm, which maintains the same factors of considerations but places heavier weight on competence.
Intervention Start Date
2022-04-01
Intervention End Date
2022-09-01

Primary Outcomes

Primary Outcomes (end points)
We use both surveys and social security records to examine characteristics of interest along the
selection margins, as well as the outcomes of interests in the treatment margin.
Primary Outcomes (explanation)
- We elicit a wide range of political and economic preferences, attitudes, and beliefs, including various measures on political obedience. For example, alignment with the Chinese Communist Party’s official policy agenda, support of various public policies, performance evaluation and trust of various levels of the government. We elicit Big 5 personality characteristics, as well as fundamental preferences: risk preference, time preference, altruism, and reciprocity using Falk et al. (2016) module. We use lab-in-the-field behavioral games to elicit respondents’ preference for redistribution, following Fisman et al. (2007). The design allows us to separately identify respondents’ tradeoff between equity and efficiency. In addition, we incorporate the “Public Service Motivation Survey” to measure respondents’ broad alignment with the public service career track.
- We elicit various measures on competence. For example, we collect grades in college. We measure respondents’ knowledge on various socioeconomic events and facts, both regarding China as a whole and the local regions specifically.
- We design a visual-based experimental module to elicit how respondents process politically and economically framed information (e.g. local poverty condition), how they interpret new signals and update their beliefs. The potential existence of bias in information acquisition and belief updating allows us to examine patterns such as motivated beliefs.
- We collect administrative information on job performance during the CGCS program. This includes performance evaluations by peers, supervisors, and villagers, as well as objective and aggerate performance measures at the village level.
- We use social security data to trace career trajectories and income of all individuals. This allows to investigate questions such as which civil servants get promoted, who decide to stay in the public sector, and whether those civil servants who return to private sector converge to their counterparts who start off in the private sector.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We partner with the Guangdong local government to randomly alter the selection algorithm during the 2020 recruitment cycle. A random half of the job posts will implement the selection algorithm at the status quo, which places heavy weight on candidates’ political obedience. The other random half of the job posts will implement a new algorithm, which maintains the same factors of considerations but places heavier weight on competence.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Job posting.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
0
Sample size: planned number of observations
According to our simulation based on the application data from the 2017-18 recruitment cycle, the new algorithm would change selection outcomes among 1/3 of the positions. Hence, we will have 600 recruited applicants, and 600 applicants who would have been recruited under the algorithms not implemented in the corresponding posts.
Sample size (or number of clusters) by treatment arms
600 applicants in the treatment group, and 600 applicants in the control group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
The University of Chicago SBS-IRB
IRB Approval Date
2020-01-24
IRB Approval Number
IRB19-1870