The Hidden Cost of Violent Confmict: Sorting into Local Labor - - PowerPoint PPT Presentation

▶
the hidden cost of violent confmict sorting into local
SMART_READER_LITE
LIVE PREVIEW

The Hidden Cost of Violent Confmict: Sorting into Local Labor - - PowerPoint PPT Presentation

Jun 20, 2023 436 likes •707 views

The Hidden Cost of Violent Confmict: Sorting into Local Labor Markets A Field Experiment in Colombia May 4, 2019 1 IHS, Vienna 2 University of Gttingen 3 DICE, University of Dsseldorf 1 / 25 Kerstin Grosch 1 Marcela Ibanez 2 Gerhard Riener 3

slide-1
SLIDE 1

The Hidden Cost of Violent Confmict: Sorting into Local Labor Markets A Field Experiment in Colombia

Kerstin Grosch 1 Marcela Ibanez 2 Gerhard Riener 3 May 4, 2019

1IHS, Vienna 2University of Göttingen 3DICE, University of Düsseldorf

1 / 25

slide-2
SLIDE 2

Motivation

Confmicts have devastating consequences for development and growth.

  • What is the impact of confmict on labor markets?
  • Do individuals sort out of confmict affected areas?
  • Who sorts out?
  • Are there gender differences in sorting?
  • Do confmict affected areas lose the more qualifjed

individuals?

  • How do previous experiences of confmict and violence

shape sorting?

  • Can higher wages reduce the sorting effects?

2 / 25

slide-3
SLIDE 3

Contribution

Most research considers impact of internal displacement.

  • Displaced population higher probability unemployed or

self-employed (Bozzoli, Brück, Wald, 2013; Kondylis, 2010)

  • Negative shock on wages and employment (Calderon and

Ibañez, 2009)

  • Children are more likely to enter labor market (Rodriquez

and Sanchez, 2012) Our contribution

  • First causal evidence sorting into life risk jobs using

experimental approach

  • Asses the cost of confmict in terms of loss of talent

3 / 25

slide-4
SLIDE 4

Local context: Colombia

4 / 25

slide-5
SLIDE 5

Regional heterogeneity

5 / 25

slide-6
SLIDE 6

Experimental Design

Research assistants to work in two research projects: Santander or Putumayo.

  • Stage 1: Statement of interest (2206 job-seekers)
  • Stage 2: Randomization (797)
  • Stage 3: Application (383 applicants)
  • Stage 4: Hiring (3)

6 / 25

slide-7
SLIDE 7

Experimental Design

  • 1. Job Type
  • Low Risk: Garzon, Huila and San Gil, Santander (homicides

17.11 per 100.000)

  • High Risk: Puerto Asis, Putumayo and Vista Hermosa, Meta

(homicides 63.48 per 100.000)

  • 2. Salary for High Risk Job
  • Base: 1.5m COP

500€

  • High: 1.8m COP

600€

7 / 25

slide-8
SLIDE 8

Regional variation

8 / 25

slide-9
SLIDE 9

Results: Descriptive Statistics

  • The proportion of female job seekers is 56%
  • The average job seeker is 28-years-old.
  • About 10% of job seekers hold a master’s degree.
  • On average, participants have three years of relevant job

experience.

  • A large share, nearly 90%, is medically insured.
  • Roughly 50% are vaccinated against tetanus and

hepatitis B.

  • Good balance across treatments

9 / 25

slide-10
SLIDE 10

Results: Sorting into labor markets

Figure 1: Application Rates Base vs Risky low wage p=0.001; Base vs Risky high wage p = 0.057 (Fisher exact)

10 / 25

slide-11
SLIDE 11

Research Question

Are women less likely to apply to risky jobs?

  • Women are more risk averse than men (Eckel, Grossman,

2008).

  • This will be refmected in all aspects of their decision

making.

  • Magnitude of gender differences is relatively small

(Filippin and Crosetto, 2016) What are the consequences on sorting in labor markets?

11 / 25

slide-12
SLIDE 12

Results: Gender effect

12 / 25

slide-13
SLIDE 13

Research Question

Is sorting associated with loss of talents? Life-risk might deter job seekers with higher opportunity cost, i.e. higher qualifjcation). Completedi = α + β1Ti + β2Zi + β3Ti × Zi + ǫi Where T indicates our treatments and Z the qualifjcation/outside option of the candidate

13 / 25

slide-14
SLIDE 14

Results: Qualifjcation level

  • We use data from the World Bank STEPS survey, 2014 to

estimate Mincer equation.

  • Two step Heckman (1979) sample selection model.
  • Out-of-sample prediction on job seekers in the study.
  • Explanatory variables: experience, area of study, and level
  • f studies
  • Exclusion restriction: experience squared
  • 1. Probability of being employed
  • 2. Wage Equation: Gronau’s (1974) model

14 / 25

slide-15
SLIDE 15

Results: Estimated Expected Earning

15 / 25

slide-16
SLIDE 16

Estimation strategy: Quality and early life risk exposure

  • Linear probability model, dependent variable: completion
  • f application process
  • Explanatory variables: Job treatment interacted with

gender, predicted earnings and index of risk exposure (Homicides at city of mother when average job seeker aged 6)

  • Department fjxed effects
  • Clustered standard errors on the current city level

16 / 25

slide-17
SLIDE 17

Results: Marginal effects Risk Job base salary

17 / 25

slide-18
SLIDE 18

Results: Marginal effects Risk Job high salary

18 / 25

slide-19
SLIDE 19

Results: Exposure to violence and application to Risky Job base salary

  • What are the long term consequences of crime?
  • Does exposure to violence affects sorting?

19 / 25

slide-20
SLIDE 20

Results: Exposure to violence and application to Risky Job base salary

20 / 25

slide-21
SLIDE 21

Results: Exposure to violence and application to Risky Job base salary

21 / 25

slide-22
SLIDE 22

Results: Exposure to violence and application to Risky Job high salary

22 / 25

slide-23
SLIDE 23

Conclusions

  • We fjnd that jobs with a high fatality risk discourage job

seekers from applying.

  • A higher wage can compensate for the risk exposure and

can stimulate application rates.

  • We do not detect any gender difference in application

rates for the risky jobs.

  • Negative sorting out of low and very high qualifjed

individuals.

  • Negative sorting out of low risk participants

23 / 25

slide-24
SLIDE 24

Results: Qualifjcations

(1) Risky: Low salary

  • 0.139**

(0.059) Risky: High salary

  • 0.092*

(0.048) Female

  • 0.115***

(0.042) Predicted wage (centered)

  • 0.030

(0.021) Vulnerability (centered) 0.008 (0.016) Risky: Low salary × Female 0.024 (0.057) Risky: High salary × Female 0.044 (0.091) Risky: Low salary × Predicted wage (centered) 0.015 (0.022) Risky: High salary × Predicted wage (centered) 0.022 (0.027) Risky: Low salary × Vulnerability (centered)

  • 0.023

(0.019) Risky: High salary × Vulnerability (centered)

  • 0.007

(0.030) Bogota

  • 0.392

(0.368) Civil state: Single=1

  • 0.807***

(0.090) Civil state: Married=1

  • 0.643***

(0.034) Civil state: Divorced=1

  • 0.744***

(0.052) Civil state: Partnership=1

  • 0.643***

(0.218) Civil state: Separated=1

  • 0.804***

(0.093) Civil state: Single Mother=1

  • 0.675***

(0.210) Constant 1.486*** (0.369) Departments yes Master yes Age yes Experience yes Obs. 1097

  • Diff. risky jobs F-stat

1.003 (p-value) 0.319

24 / 25

slide-25
SLIDE 25

Randomization table

(1) (2) (3) (4) (5) Base Risky low wage Risky high wage p-value N Master 0.104 0.100 0.117 0.752 1105 (0.016) (0.016) (0.017) Experience 3.076 2.970 2.664 0.534 1105 (0.235) (0.200) (0.352) Age 27.512 27.699 27.388 0.799 1105 (0.342) (0.318) (0.333) Bogota 0.362 0.366 0.363 0.995 1105 (0.025) (0.025) (0.025) (0.022) Civil state: Single 0.022 0.027 0.014 0.431 1105 (0.008) (0.008) (0.006) Civil state: Married 0.853 0.824 0.859 0.370 1105 (0.019) (0.020) (0.018) Civil state: Divorced 0.084 0.070 0.068 0.651 1105 (0.015) (0.013) (0.013) Civil state: Partnership 0.008 0.008 0.005 0.880 1105 (0.005) (0.005) (0.004) Civil state: Separated 0.025 0.054 0.049 0.105 1105 (0.008) (0.012) (0.011) Civil state: Single Mother 0.008 0.011 0.005 0.716 1105 (0.005) (0.005) (0.004) (0.000) (0.004) (0.000) Homicides 2010: Current city 0.380 0.362 0.356 0.361 1086 (0.013) (0.012) (0.012) Homicides 1990: City father 1.034 1.118 1.056 0.712 771 (0.071) (0.075) (0.078) Homicides 1990: City mother 1.098 1.116 1.129 0.953 889 (0.067) (0.063) (0.079) N 367 369 369

25 / 25