Personalities and Public Sector Performance: Experimental Evidence from Pakistan Michael Callen 1 Saad Gulzar 2 Ali Hasanain 3 Yasir Khan 4 Arman Rezaee 5 1 Harvard Kennedy School of Government 2 New York University 3 Lahore University of Management Sciences; University of Oxford 4 International Growth Center 5 University of California, San Diego September 23, 2014
Partners and Collaborators ◮ Zubair Bhatti, World Bank ◮ Farasat Iqbal, Punjab Health Sector Reforms Program ◮ Asim Fayaz, World Bank/Technology for People Initiative ◮ International Growth Center (IGC)
Motivation ◮ We focus on a common and intractable service delivery issue in the developing world—absence Chaudhury et al. 2006 ◮ We report results from two experiments targeting health worker absence in Punjab, Pakistan
Rural health clinics in Punjab
Paper overview ◮ Question 1: Are personality measures associated with health worker performance (under status quo incentives)? ◮ Question 2: Do personality measures predict who will respond to changes in incentives? ◮ Question 3: Do personality measures predict who will act on information?
Why intrinsic motivation? ◮ Governments are composed of people
Why intrinsic motivation? ◮ Governments are composed of people ◮ There is evidence that personalities predict performance in the US, primarily in the private sector (Heckman 2011)
Why intrinsic motivation? ◮ Governments are composed of people ◮ There is evidence that personalities predict performance in the US, primarily in the private sector (Heckman 2011) ◮ Several possible benefits: 1. Diagnostics and insights into bureaucratic decision-making 2. Profile of applicants responds to adjustable features of the position (Dal B´ o, Finan, & Rossi 2013) 3. Traits are malleable (Almund et al. 2011)
This project 1. Experiment 1—implemented a smartphone monitoring system in Punjab 2. Experiment 2—made absence data salient to senior health officials
This project 1. Experiment 1—implemented a smartphone monitoring system in Punjab 2. Experiment 2—made absence data salient to senior health officials 3. Measured performance—doctor attendance, health inspections, and collusion between doctors and inspectors
This project 1. Experiment 1—implemented a smartphone monitoring system in Punjab 2. Experiment 2—made absence data salient to senior health officials 3. Measured performance—doctor attendance, health inspections, and collusion between doctors and inspectors 4. Measured personality traits for: ◮ A large, representative sample of doctors in Punjab ◮ The universe of health inspectors in Punjab ◮ The universe of senior health officials in Punjab
Preview of findings 1. Personality traits positively predict doctor attendance and negatively predict doctor-inspector collusion 2. Personality traits strongly predict health inspector responses to monitoring intervention ◮ 1SD higher health inspector Big5 index ⇒ 35pp differential increase in inspections 3. Personality traits strongly predict which senior officials act on reports of doctor absence ◮ 1SD higher senior health official Big5 index ⇒ 40pp reduction in doctor absence following underperforming facility flag
Outline I. Introduction II. Conceptual framework III. Monitoring the Monitors IV. Research design V. Results Question 1: Does personality predict status-quo performance? Question 2: Does personality predict response to changes in incentives? Question 3: Does personality predict who will act on information? V. Conclusion
The effects of increased monitoring on shirking Simple model f ( θ ) Induced to work Always shirk Always work θ M 2 θ M 1 MU work θ (or MU leisure )
The effects of increased monitoring on shirking Simple model f ( θ ) Induced to work Always shirk Always work θ M 2 θ M 1 MU work θ (or MU leisure ) Question: Can personality measures give us θ (or a proxy for θ )? Context matters
Outline I. Introduction II. Conceptual framework III. Monitoring the Monitors IV. Research design V. Results Question 1: Does personality predict status-quo performance? Question 2: Does personality predict response to changes in incentives? Question 3: Does personality predict who will act on information? V. Conclusion
Punjab Department of Health Health Secretary Senior health officials (EDOs) (1 per district) Health inspectors (DDOs) (1 per subdistrict) Doctors (MOs) (1 per health clinic)
Same data, new interface
Smartphones for health inspectors
Online dashboard—summary stats
Online dashboard—visit logs
Increased cost of shirking—GPS, timestamps
Increased cost of shirking—pictures
Outline I. Introduction II. Conceptual framework III. Monitoring the Monitors IV. Research design V. Results Question 1: Does personality predict status-quo performance? Question 2: Does personality predict response to changes in incentives? Question 3: Does personality predict who will act on information? V. Conclusion
District-level randomization
Rural clinic sample
Personality measures—Big 5 Personality Index ◮ Five dimensions: 1. Agreeableness 2. Conscientiousness 3. Emotional stability 4. Extroversion 5. Openness ◮ Example statements: ◮ I like to be amongst lots of people. ◮ I don’t want to waste time day-dreaming. ◮ I try to be polite to everyone I meet. ◮ I keep all my things clean and tidy.
Personality measures—Perry Public Service Motivation ◮ Six dimensions: 1. Attraction to policymaking 2. Civic duty 3. Commitment to policymaking 4. Compassion 5. Self-sacrifice 6. Social justice ◮ Example statements: ◮ Politics is a bad word. ◮ The attitude of an elected official is just as important as his/her competency. ◮ The words ‘work’, ‘honor’ and ‘country’ evoke strong emotions in the bottom of my heart.
Outline I. Introduction II. Conceptual framework III. Monitoring the Monitors IV. Research design V. Results Question 1: Does personality predict status-quo performance? Question 2: Does personality predict response to changes in incentives? Question 3: Does personality predict who will act on information? V. Conclusion
Personality and doctor attendance Doctor Attendance (=1) Big 5 index Agreeableness Conscientiousness Extroversion Emotional stability Doctor Personality Openness PSM index Attraction Civic duty Commitment Compassion Self-sacrifice Social justice -.05 0 .05 .1 .15 Standardized Regression Coefficient Doctor summary stats Results tables
Doctor personality and doctor-inspector collusion Doctor-Inspector Collusion (=1) Big 5 index Agreeableness Conscientiousness Extroversion Emotional stability Doctor Personality Openness PSM index Attraction Civic duty Commitment Compassion Self-sacrifice Social justice -.2 -.15 -.1 -.05 0 .05 Standardized Regression Coefficient Results tables
Outline I. Introduction II. Conceptual framework III. Monitoring the Monitors IV. Research design V. Results Question 1: Does personality predict status-quo performance? Question 2: Does personality predict response to changes in incentives? Question 3: Does personality predict who will act on information? V. Conclusion
Differential LATEs by inspector personality Health inspection in last two months (=1) (1) (2) (3) (4) (5) (6) (7) Monitoring (=1) 0.178 -0.006 0.010 0.003 0.030 -0.033 0.023 (0.154) (0.114) (0.109) (0.115) (0.124) (0.118) (0.129) Monitoring x Big5 index 0.351** (0.133) Monitoring x Agreeableness 0.170* (0.094) Monitoring x Conscientiousness 0.186* (0.102) Monitoring x Extroversion 0.116 (0.098) Monitoring x Emotional stability 0.210** (0.083) Monitoring x Openness 0.195 (0.126) Mean of dependent variable 0.642 0.656 0.656 0.656 0.656 0.656 0.656 # Observations 1331 1145 1145 1145 1145 1145 1145 # Clinics 644 547 547 547 547 547 547 R-Squared 0.048 0.069 0.069 0.062 0.053 0.064 0.063 Notes : * p < 0 . 1, ** p < 0 . 05, *** p < 0 . 01. Standard errors clustered at the clinic level reported in parentheses. All regressions include Tehsil (Tehsil) and survey wave fixed effects and are conditional on a doctor being posted at the clinic. Column (1) reports average treatment effects on treatment and control district clinics. Columns (2) - (7) are limited to clinics in Tehsils for which health inspector personality data is available. All personality traits are normalized. Inspector summary stats Balance PSM results
Non-parametric differential LATEs by inspector personality .8 Health inspection in the last two months (=1) .6 .4 .2 0 -.2 0 .2 .4 .6 .8 1 Baseline Inspector Big5 percentile Control Treatment Difference 95% CI Non-parametric results by trait
Outline I. Introduction II. Conceptual framework III. Monitoring the Monitors IV. Research design V. Results Question 1: Does personality predict status-quo performance? Question 2: Does personality predict response to changes in incentives? Question 3: Does personality predict who will act on information? V. Conclusion
Experiment 2—making absence salient
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