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Bloody investment: misaligned incentives, money laundering and violence

Trends in Organized Crime volume 25, pages 8–36 (2022)Cite this article

Abstract

Money laundering is not a victimless crime. Under certain circumstances, it may lead to significant criminal violence. We analyze the specific case of money laundering in local economies. Criminal organizations invest dirty money in legal local businesses, which may lead to short-term improvements in the economy that benefit the population. Authorities with access to local information may (purposely) fail to report suspicious economic activities to specialized agencies in charge of money laundering because it is politically and economically convenient. The economic windfall generated from illicit money can eventually attract additional criminal organizations to the community, or may fragment the dominant criminal organization, endogenously increasing violence. The violence generated in no way compensates the previous economic growth. We develop theoretical insights on the conditions under which this mechanism exists, and empirically test its incidence and the magnitude of its effects, using Mexican municipalities as units of analysis.

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Fig. 1

Notes

  1. See https://www.unodc.org/unodc/en/money-laundering/index.html?ref=menuside. Interpol’s definition is similar, see https://www.interpol.int/Crimes/Financial-crime/Money-laundering

  2. See the UNODC assessment’s here: https://www.unodc.org/unodc/en/money-laundering/globalization.html

  3. It is estimated that a quarter of Mexico’s GDP comes from informal activities. See the International Labor Organization’s Notes on Formalization in Mexico: https://www.ilo.org/wcmsp5/groups/public/%2D%2D-ed_emp/documents/meetingdocument/wcms_433784.pdf

  4. See FATF and GAFILAT 2018 for a detailed description on Mexico’s money laundering institutions.

  5. 2014 is the latest year available (https://www.inegi.org.mx/app/saic/).

  6. INEGI census data (https://inegi.org.mx/datos/?init=2).

  7. See, for instance, these news reports: https://www.milenio.com/estados/quintana-roo-paraiso-para-el-lavado-de-dinero, and https://www.lapalabradelcaribe.com/senala-ofac-a-plaza-xaman-ha-center-como-lavado-de-dinero-del-cdjng/58010/

  8. See https://www.washingtonpost.com/archive/politics/1999/02/20/drug-probe-aimed-at-mexican-official/ffcf79c6-84f7-4b04-9faf-9f59028316f2/

  9. This case, however, was dismissed because of procedural misconducts. See https://www.reuters.com/article/us-mexico-mayor/mayor-of-mexico-resort-arrested-for-drug-gang-ties-idUSTRE64P5ST20100526

  10. See: https://www.animalpolitico.com/2017/01/carteles-cancun-playa-del-carmen/

  11. See: https://www.yucatan.com.mx/mexico/quintana-roo/quintana-roo-unico-estado-donde-operan-siete-carteles-dice-coparmex

  12. The complete regression output is in Appendix A.

  13. See the FBI report: https://ucr.fbi.gov/crime-in-the-u.s/2018/crime-in-the-u.s.-2018/tables/table-16

  14. Further details on federal contributions to municipalities can be found here: http://hacienda.gob.mx/ApartadosHaciendaParaTodos/aportaciones/28/pdf/2.1.pdf

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Funding

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Author information

Authors and Affiliations

  1. Instituto Tecnologico Autonomo de Mexico (ITAM), Río Hondo 1, 01080, Mexico City, Mexico

    Vidal Romero

  2. Center for the Study of Security, Intelligence, and Governance, ITAM, Mexico City, Mexico

    Vidal Romero

  3. Latin America and Caribbean Centre, London School of Economics and Political Science (LSE), London, WC2A 2AE, UK

    Vidal Romero

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  1. Vidal Romero
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Correspondence to Vidal Romero.

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Appendices

Appendices

Appendix 1 – Instrumental variables (first stage estimation)

Model A1

Dependent variable: Real per capita Local revenue

Human Development Index

900.2***

(29.55)

Urban

52.23***

(3.449)

Public investment (t-1)

0.0298***

(0.00197)

Constant

−634.1***

(22.55)

N

49,136

Groups

2430

Observations per group:

Min

1

Avg

20.2

Max

27

R-sq

0.102

  1. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

Model A2 – Placebo regression

Dependent variable: Federal contributions

Human Development Index

186.7**

(83.81)

Urban

−380.0***

(39.20)

Public investment (t-1)

0.6506***

(0.02465)

Constant

833.6***

(84.85)

N

38,169

Groups

2439

Observations per group:

Min

1

Avg

15.7

Max

27

R-sq

0.358

  1. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

Appendix 2 – Summary statistics

Table 4 Summary statistics (n = 29,784)
Full size table

Appendix 3 – Robustness tests

  1. a)

    Non-fixed effects

Dependent variable: Homicide rate per 100,000 habitants

 

Model 1

Model C1

Model C2

Model C3

 

With state and year FE

Without state and year FE

Without year FE

Without state fixed effects

Positive economic shock (t)

−6.228***

−2.478

−2.466*

−6.011***

(1.510)

(1.541)

(1.486)

(1.565)

Positive economic shock (t-1)

−1.736

−0.0963

0.0512

−1.757

(1.772)

(1.837)

(1.775)

(1.830)

Positive economic shock (t-2)

2.992

3.505*

3.762*

2.897

(1.953)

(2.029)

(1.986)

(1.995)

Positive economic shock (t-3)

6.550***

6.994***

7.604***

6.150***

(2.032)

(2.085)

(2.066)

(2.051)

Population log

0.815***

0.329**

0.758***

0.379***

(0.137)

(0.142)

(0.140)

(0.140)

Distance to Northern Border

0.00002***

−0.000009***

0.00002***

−0.00001***

(0.000003)

(0.000001)

(0.000003)

(0.000001)

Human Development Index

−73.54***

−59.96***

−51.46***

−81.75***

(3.942)

(3.283)

(3.139)

(4.044)

Indigenous population

−0.0714***

−0.0951***

−0.0460***

−0.127***

(0.009)

(0.008)

(0.009)

(0.009)

Municipal election

−0.202

0.0172

−0.0996

−0.0361

(0.332)

(0.365)

(0.341)

(0.358)

Mountainous terrain

0.0295***

0.0333***

0.0360***

0.0275***

(0.00404)

(0.00365)

(0.00407)

(0.00365)

Constant

47.60***

66.30***

28.88***

82.74***

(3.651)

(3.271)

(3.231)

(3.654)

State fixed effects

Yes

No

Yes

No

Year fixed effects

Yes

No

No

Yes

N

29,784

29,784

29,784

29,784

Groups

2221

2221

2221

2221

Observations per group:

Min

1

1

1

1

Avg

13.4

13.4

13.4

13.4

Max

23

23

23

23

Wald chi2(62)

6486.87

566.30

6351.43

1174.60

Prob > chi2

0.0000

0.0000

0.0000

0.0000

R-sq

0.183

0.030

0.1542

0.0588

  1. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
  1. b)

    Different lagged periods

Dependent variable: Homicide rate per 100,000 habitants

 

Model 1

Model C4

Model C5

Model C6

Model C7

 

3 lags

1 lag

2 lags

4 lags

5 lags

Positive economic shock (t)

−6.228***

−5.457***

−5.786***

−6.597***

−6.011***

(1.510)

(1.492)

(1.493)

(1.545)

(1.603)

Positive economic shock (t-1)

−1.736

−0.900

−1.080

−1.573

−1.512

(1.772)

(1.754)

(1.765)

(1.923)

(1.818)

Positive economic shock (t-2)

2.992

 

3.382*

2.495

1.894

(1.953)

 

(1.940)

(2.143)

(2.233)

Positive economic shock (t-3)

6.550***

  

6.595***

6.094**

(2.032)

  

(2.263)

(2.415)

Positive economic shock (t-4)

   

2.025

0.952

   

(2.432)

(2.627)

Positive economic shock (t-5)

    

3.972

    

(2.854)

Population log

0.815***

0.812***

0.812***

0.739***

0.709***

(0.137)

(0.137)

(0.137)

(0.149)

(0.158)

Distance to Northern Border

0.00002***

0.00002***

0.00002***

0.00002***

0.00002***

(0.000003)

(0.000003)

(0.000003)

(0.000004)

(0.000004)

Human Development Index

−73.54***

−73.02***

−73.20***

−72.76***

−76.02***

(3.942)

(3.937)

(3.944)

(4.576)

(5.126)

Indigenous population

−0.0714***

−0.0714***

−0.0714***

−0.0714***

−0.0797***

(0.00914)

(0.00915)

(0.00914)

(0.0102)

(0.0109)

Municipal election

−0.202

−0.205

−0.202

−0.229

−0.374

(0.332)

(0.332)

(0.332)

(0.363)

(0.385)

Mountainous terrain

Constant

0.0295***

0.0296***

0.0296***

0.0281***

0.0269***

(0.00404)

(0.00404)

(0.00404)

(0.00437)

(0.00468)

47.60***

47.22***

47.36***

46.77***

50.74***

(3.651)

(3.644)

(3.651)

(4.193)

(4.712)

State fixed effects

Yes

Yes

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

Yes

Yes

N

29,784

29,784

29,784

26,575

23,702

Groups

2221

2221

2221

2151

2046

Observations per group:

Min

1

1

1

1

1

Avg

13.4

13.4

13.4

12.4

11.6

Max

23

23

23

22

21

Wald chi2(62)

6486.87

6491.48

6490.28

5742.98

5206.58

Prob > chi2

0.0000

0.0000

0.0000

0.0000

0.0000

R-sq

0.183

0.182

0.182

0.186

0.194

  1. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
  1. c)

    Different shock thresholds

Dependent variable: Homicide rate per 100,000 habitants

 

Model 1

Model C8

Model C9

 

1 SD

2 SD

3 SD

Positive economic shock (t)

−6.228***

−13.46***

−7.048**

(1.510)

(2.725)

(3.130)

Positive economic shock (t-1)

−1.736

−4.863

−0.455

(1.772)

(3.431)

(2.938)

Positive economic shock (t-2)

2.992

4.836

5.040

(1.953)

(3.768)

(3.106)

Positive economic shock (t-3)

6.550***

8.006**

3.823

(2.032)

(3.576)

(3.559)

Population log

0.815***

0.816***

0.806***

(0.137)

(0.137)

(0.137)

Distance to Northern Border

0.00002***

0.00002***

0.00002***

(0.000003)

(0.000003)

(0.000003)

Human Development Index

−73.54***

−75.34***

−71.94***

(3.942)

(3.953)

(3.915)

Indigenous population

−0.0714***

−0.0744***

−0.0689***

(0.00914)

(0.00911)

(0.00915)

Municipal election

−0.202

−0.200

−0.198

(0.332)

(0.332)

(0.332)

Mountainous terrain

0.0295***

0.0292***

0.0301***

(0.00404)

(0.00405)

(0.00405)

Constant

47.60***

49.13***

46.33***

(3.651)

(3.647)

(3.637)

State fixed effects

Yes

Yes

Yes

Year fixed effects

Yes

Yes

Yes

N

29,784

29,784

29,784

Groups

2221

2221

2221

Observations per group:

Min

1

1

1

Avg

13.4

13.4

13.4

Max

23

23

23

Wald chi2(62)

6486.87

6492.63

6525.74

Prob > chi2

0.0000

0.0000

0.0000

R-sq

0.183

0.183

0.182

  1. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01
  1. d)

    Dependent variable: Homicide rate for males 15–39 years

 

Model 1

Model C10

 

DV: Homicide rate general population

DV: Homicide rate males 15–39 years

Positive economic shock (t)

−6.228***

−12.860***

(1.510)

(2.407)

Positive economic shock (t-1)

−1.736

−3.136

(1.772)

(2.595)

Positive economic shock (t-2)

2.992

4.679*

(1.953)

(2.821)

Positive economic shock (t-3)

6.550***

10.64***

(2.032)

(3.053)

Population

0.815***

0.947***

(0.137)

(0.247)

Distance to N. Border

0.00002***

0.00004***

(0.000003)

(0.000005)

Human Dev. Index

−73.54***

−130.3***

(3.942)

(6.682)

Indigenous population

−0.0714***

−0.112***

(0.00914)

(−0.016)

Municipal election

−0.202

−0.456

(0.332)

(0.597)

Mountainous terrain

0.0295***

0.0566***

(0.00404)

(0.00725)

Constant

47.60***

88.12***

(3.651)

(5.994)

State fixed effects

Yes

Yes

Year fixed effects

Yes

Yes

N

29,784

29,585

Groups

2221

2211

Observations per group:

Min

1

1

Avg

13.4

13.4

Max

23

23

Wald chi2(62)

6486.87

5442.41

Prob > chi2

0.0000

0.0000

R-sq

0.183

0.173

  1. Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01

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Romero, V. Bloody investment: misaligned incentives, money laundering and violence. Trends Organ Crim 25, 8–36 (2022). https://doi.org/10.1007/s12117-020-09391-x

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Keywords

  • Money laundering
  • Violence
  • Criminal organizations
  • Corruption
  • Homicide