The determinants of corruption at the individual level: evidence from Bosnia-Herzegovina

Figures & data

Table 1. Examples of dependent variables in the studies of individual-level corruption.

Study	Dependent variable
Swamy et al. (2001)	An individual is not corrupt if he/she says that bribery is never justified; otherwise he/she is considered corrupt Mangers (firm-level): ‘How frequently do the officials providing the service require unofficial payments? Please answer on a scale of 1 to 7, where 1 = Never, 2 = 1–20% of the time, 3 = 21–40% of the time, 4 = 41–60% of the time, 5 = 61–80% of the time, 6 = 81–99% of the time, and 7 = Always.’ (p. 35)
Hunt (2004), Hunt (2007a)	‘In some countries, there is a problem of corruption among government or public officials. During 199x, has any government official, for instance a customs officer, a police officer or inspector in your country asked you, or expected you to pay a bribe for his or her services?’ (p. 8.)
Deininger and Mpuga (2004)	The direct indicator measuring if the household was required to pay a bribe in the last six months, and if so the amount that was paid.
Torgler and Valev (2006)	For several statements, respondents were supposed to indicate if corrupt behaviour could be justified.
Razafindrakoto and Roubaud (2007)	Reports of individuals indicating direct confrontation with corrupt officials and the extent to which corruption is perceived as widespread.
Mocan (2008)	‘During [the past year] has any government official, for instance, a customs officer, police officer or inspector in your own country, asked you or expected you to pay a bribe for his services?’ (p. 4)
Hunt and Laszlo (2012)	One adult per household is asked if the official required a bribe or if the respondent felt obliged to bribe.
Hernandez and McGee (2013)	‘The question asked whether the respondent believed that accepting a bribe in the course of one’s duties was justifiable. Responses were measured on a 10-point Likert scale where 1 is never justifiable and 10 is always justifiable.’ (p. 913)
Gutmann et al. (2014)	Corruption perception measures the degree of corruption in twelve types of organisation. Corruption perception represents the mean value of the eight government agency specific perception indicators
Ivlevs and Hinks (2015)	Engagement in bribery is gathered by asking: has ‘… any member of your household [made] an unofficial payment or gift when using these services over the past 12 months?’
Liu and Peng (2015)	‘How much did you or your parents spend on personal connections with the examiners and admissions workers?’ (p. 176)

Source: Authors.

Table 2. Significant predictors of individual-level corruption.

Significant predictor	More likely to engage in bribery, or evidence to the contrary
Age	Individuals who are 20–39 years of age (Mocan, 2008); Individuals who are 30–65 years of age (Torgler & Valev, 2006); Individuals who are 34–45 years of age (Ivlevs & Hinks, 2015); Individuals who are 30–50 years of age reported a higher corruption perception (Gutmann et al., 2014); Younger individuals (Hernandez & McGee, 2013; Hunt, 2004)
Gender	Males (Hernandez & McGee, 2013; Cameron et al., 2009; Mocan, 2008; Razafindrakoto & Roubaud, 2007; Torgler & Valev, 2006; Swamy et al., 2001); Male-headed households (Deininger & Mpuga, 2004); Females reported a higher perception of corruption (Gutmann et al., 2014); No evidence that females engage less in bribery (Liu & Peng, 2015; Ivlevs & Hinks, 2015)
Marital status	Married individuals (Mocan, 2008; Swamy et al., 2001); Single individuals (Torgler & Valev, 2006)
Income	Individuals with a higher income (Mocan, 2008; Ivlevs & Hinks, 2015; Deininger & Mpuga, 2004); The poorest individuals (Islam & Lee, 2016; Razafindrakoto & Roubaud, 2007); Students from middle-income families (Liu & Peng, 2015); Rich individuals are more likely to bribe than poor ones (Hunt & Laszlo, 2012); Richer patients (Hunt, 2007b); Corruption perception falls with an increase in income (Gutmann et al., 2014); No evidence: Economic status and financial satisfaction do not have a statistically significant association with the justifiability of corruption (Torgler & Valev, 2006).
Education	Highly educated individuals (Mocan, 2008; Razafindrakoto & Roubaud, 2007); Individuals with less knowledge about how to report corruption (Deininger & Mpuga, 2004); A less educated head of household, and girls studying at school (Islam & Lee, 2016); No evidence: No statistically significant association of education with the justifiability of corruption (Torgler & Valev, 2006), bribery (Ivlevs & Hinks, 2015), or the perception of corruption (Gutmann et al., 2014)
Large cities	Individuals living in large cities (Mocan, 2008); Individuals living in larger communities (Hunt, 2004); Rural respondents (Deininger & Mpuga, 2004); Individuals in regions with fewer residents of their own age (Hunt, 2004)
Region	The respondent is from a certain region (Torgler & Valev, 2006)
Employment status & occupation	Self-employed or unemployed individuals (Torgler & Valev, 2006); Farmers (Deininger & Mpuga, 2004)
Confidence in public institutions	People with higher confidence in public institutions (Ivlevs & Hinks, 2015); The level of trust between the briber and the corrupt official can, depending on the country, reduce or increase the probability of a bribe being requested (Razafindrakoto & Roubaud, 2007); A high level of trust does not necessarily reduce corruption, but individuals with absolute trust in the accountability of public servants are less likely to bribe (Liu & Peng, 2015)

Source: Authors.

Table 3. Sample characteristics.

		Frequency	Percentage (% of total)
Total	Total sample size	3084	100.00
Gender	Male	1355	43.9
	Female	1729	56.1
Age	18–24	459	14.9
	25–34	582	18.9
	35–44	465	15.1
	45–54	503	16.3
	55+	1075	34.9
Marital status	Single	806	26.1
	Married	1822	59.1
	Divorced, widower or in a cohabiting partnership	456	14.8
The highest level of education completed	Less than secondary education	762	24.7
	Secondary education	1830	59.3
	More than secondary education	492	16.0
Ethnicity	Bosniak	1700	55.1
	Croat	276	9.0
	Serb	1002	32.5
	Other, Did not declare, or Missing	106	3.4
Current occupation	Legislators, senior officials and chief executives	18	0.6
	Scientists, engineers and other professionals	108	3.5
	Technicians and associate professionals	122	4.0
	Clerical support workers	96	3.1
	Service and sales workers	185	6.0
	Skilled agricultural, forestry and fishery workers	10	0.3
	Craft and related trades workers	62	2.0
	Plant and machine operators and assemblers	71	2.3
	Elementary occupations	138	4.5
	Armed forces occupations	14	0.5
	Does not know/Refuses to answer	185	6.0
	Missing	2075	67.3
Monthly net income of household	No income in this month	87	2.8
	1–500 BAM	591	19.2
	501–1000 BAM	670	21.7
	>1000 BAM	388	12.6
	Does not know/Refuses to answer	1348	43.7

Source: Authors.

Table 4. Have you ever given money, gifts, services or similar in exchange for better treatment?.

		Frequency	Percentage (% of total)
	Total number of respondents who gave at least one type of bribe	939	30.5
Gender	Male	454	33.5
	Female	485	28.1
Age	18–24	105	22.9
	25–34	180	30.9
	35–44	152	32.7
	45–54	174	34.6
	55+	328	30.5
Marital status	Single	214	26.6
	Married	585	32.1
	Divorced, widower/Widow or in a cohabiting partnership	140	30.7
The highest level of education completed	Less than secondary education	207	27.2
	Secondary education	598	32.7
	More than secondary education	134	27.2
Ethnicity	Bosniak	488	28.7
	Croat	86	31.2
	Serb	331	33.0
	Other or Did not declare	34	32.1
Current occupation	Legislators, senior officials and chief executives	8	44.4
	Scientists, engineers and other professionals	30	27.8
	Technicians and associate professionals	60	49.2
	Clerical support workers	28	29.2
	Service and sales workers	66	35.7
	Skilled agricultural, forestry and fishery workers	2	20.0
	Craft and related trades workers	23	37.1
	Plant and machine operators and assemblers	25	35.2
	Elementary occupations	48	34.8
	Armed forces occupations	5	35.7
	Does not know/Refuses to answer	43	23.2
	Missing	601	29.0
Monthly net income of household	No income in this month	23	26.4
	1–500 BAM	162	27.4
	501–1000 BAM	215	32.1
	>1000 BAM	157	40.5
	Does not know/Refuses to answer	382	28.3

Source: Authors.

Table 5. Dependent variables.

	Have you ever given money, gifts, services, or similar to any of the following in exchange for better treatment?
	Yes	No	Does not know
Medical personnel	918 (29.8%)	2006 (65.1%)	160^a (5.2%)
Doctors	831 (26.9%)	2124 (68.9%)	129 (4.2%)
Nurses	711 (23.1%)	2228 (72.2%)	145 (4.7%)
Teachers	109 (3.5%)	2818 (91.4%)	157 (5.1%)
Judicial personnel	132 (4.3%)	2777 (90.1%)	175 (5.7%)
Judges/prosecutors	92 (3.0%)	2837 (92.0%)	155 (5.0%)
Court personnel	90 (2.9%)	2842 (92.2%)	152 (4.9%)
Police officers	517 (16.8%)	2416 (78.3%)	151 (4.9%)
Public servants	308 (10.0%)	2584 (83.8%)	192 (6.2%)
Politicians	94 (3.0%)	2859 (92.7%)	131 (4.2%)
Inspectors	123 (4.0%)	2825 (91.6%)	136 (4.4%)
Government employees	82 (2.7%)	2859 (92.7%)	143 (4.6%)
Other public officials	209 (6.8%)	2731 (88.6%)	144 (4.7%)

Source: Authors.

^a The larger number of respondents in the aggregated category than in subcategories is due to missing data: if a respondent indicated that they had never given a bribe to a doctor, but did not answer the question about bribing a nurse, we could not claim that this respondent fell within the two previous categories.

Table 6. Model specifications.

Model	Outcome variable: Have you ever given money, gifts, services, or similar to any of the following in exchange for better treatment?	Explanatory variables
Model 1	Medical personnel (Yes/No) Doctors (Yes/No) Nurses (Yes/No)	Age (years) Gender (=1 if Male) Marital status: Single (ref.), Married, Divorced, Widower (=1 if Yes) Monthly net household income: Low (0–500 BAM), Middle (501–1000 BAM), High (>1000) Level of Education: No education (ref.) Primary education or less Secondary education Post-secondary education University/Further education Region characteristics: Rural settlement & 13 regions (dummies)
Model 2	Teachers (Yes/No) Teaching staff at primary or secondary schools, or at universities (Yes/No)
Model 3	Judicial personnel (Yes/No) Judges/prosecutors (Yes/No) Court personnel (Yes/No)
Model 4	Police officers (Yes/No) Police officers (Yes/No)
Model 5	Public servants (Yes/No) Politicians (Yes/No) Inspectors (Yes/No) Government employees (Yes/No) Other public officials (Yes/No)
Model 6	Corruption Experience

Source: Authors.

Table 7. Overview of results.

		Odds Ratio					Coefficients
		Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	Explanatory variable	Medical personnel	Teachers	Judicial personnel	Police officers	Public servants	Corruption Experience
Age Reference group 18–24	25–34	1.19	0.75	0.64	1.02	1.09	0.05
	35–44	1.33	0.44	1.10	0.95	1.52	0.09
	45–54	1.38	0.67	1.18	1.15	1.34	0.13
	55+	1.33	0.26**	0.97	0.83	1.15	0.02
Gender	Male (=1 if male)	1.01	1.22	1.29	2.46***	1.18	0.19***
Marital status Reference category: Single	Married	0.96	1.00	0.52*	0.73	0.77	−0.11
	Divorced	1.18	1.28	0.96	1.20	1.40	0.13
	Widower	0.87	0.67	0.43	0.38***	0.57	−0.19
Household Income Reference category: Low income (0–500)	Middle income (501–1000)	1.18	0.98	1.09	1.29	1.21	0.07
	High income (>1000)	1.67***	1.03	1.23	2.07***	1.51*	0.33***
Education Reference category: No education (Model 1, 3, 4, 5 and 6) and Primary education (Model 2)	Primary education or less	1.75**	(Ref.)	2.75	3.38**	3.69**	0.15
	Secondary education	1.68**	1.55	3.64	4.35***	6.23***	0.23**
	Post-secondary education	1.45	1.36	4.02	3.26*	7.16***	0.19
	University/Further education	1.52	1.75	3.70	2.75*	7.18***	0.2
Rural	=1 if living in a rural area	0.83	0.55**	0.39***	0.63***	0.57***	−0.16***
Region Reference category: Una-Sana Canton	Tuzla Canton	0.69	0.73	0.61	0.45***	1.02	−0.2*
	Zenica-Doboj Canton	0.91	0.81	2.24	0.38***	1.45	−0.17
	Central Bosnia Canton	1.15	((empty)	0.49	0.38***	0.26**	−0.27*
	Herzegovina-Neretva Canton	1.69	6.78***	16.33***	1.82	8.55***	0.67***
	West Herzegovina Canton	((empty)	3.08	48.88***	1.05	31.37***	2.15***
	Sarajevo Canton	1.00	1.39	1.21	0.46***	2.36**	−0.02
	Livno Canton	0.56	((empty)	((empty)	((empty)	((empty)	−0.46
	Republika Srpska (North)	0.82	0.38*	0.51	0.64*	0.74	−0.14
	Republika Srpska (East)	0.66*	1.15	1.95	0.56**	1.54	−0.02
	Posavina Canton	1.44	((empty)	((empty)	0.98	1.15	0.07
	Bosnian-Podrinje Canton	0.49	((empty)	((empty)	((empty)	((empty)	−0.85**
	Brcko District	((empty)	((empty)	((empty)	((empty)	1.66	−0.77
Constant		0.26	0.08	0.02	0.09	0.02	0.52***
(Pseudo) R^2		2%	11%	17%	12%	12%	7%
N		1662	1363	1619	1663	1642	1,536
Hosmer-Lemeshow chi^2(8)		3.43	5.62	16.68**	6.20	12.29	/
Prob > chi^2		0.91	0.69	0.03	0.62	0.14	/
Mean VIF		2.10	2.11	2.11	2.11	2.13	2.15

Source: Authors.

*** Significant at 0.01 level.

** Significant at 0.05 level.

* Significant at 0.10 level.

Swamy, A., Knack, S., Lee, Y., & Azfar, O. (2001). Gender and corruption. Journal of Development Economics, 64(1), 25–55. doi:10.1016/S0304-3878(00)00123-1

 Web of Science ®Google Scholar

Hunt, J. (2004). Trust and bribery: the role of the quid pro quo and the link with crime. NBER Working Paper 10510. Retrieved from: https://www.nber.org/papers/w10510

 Google Scholar

Hunt, J. (2007a). How corruption hits people when they are down. Journal of Development Economics, 84(2), 574–589. doi:10.1016/j.jdeveco.2007.02.003

 Web of Science ®Google Scholar

Deininger, K., & Mpuga, P. (2004). Does greater accountability improve the quality of delivery of public services? Evidence from Uganda. Policy Research Working Papers - The World Bank, 10.1596/1813-9450-3277

 Google Scholar

Torgler, B., & Valev, N. T. (2006). Corruption and age. Journal of Bioeconomics, 8(2), 133–145. doi:10.1007/s10818-006-9003-0

 Google Scholar

Razafindrakoto, M., & Roubaud, F. (2007). Corruption, institutional discredit, and exclusion of the poor: A poverty trap. Cape Town: Afrobarometer.

 Google Scholar

Mocan, N. (2008). What determines corruption? International evidence from microdata. Economic Inquiry, 46(4), 493–510. doi:10.1111/j.1465-7295.2007.00107.x

 Web of Science ®Google Scholar

Hunt, J., & Laszlo, S. (2012). Is bribery really regressive? Bribery’s costs, benefits, and mechanisms. World Development, 40 (2), 355–372. doi:10.1016/j.worlddev.2011.06.001

 Web of Science ®Google Scholar

Hernandez, T., & McGee, R. W. (2013). Attitudes toward accepting a bribe: A longitudinal study of the USA. Journal of Accounting, Ethics and Public Policy, 14(4), 907–943. Retrieved from SSRN: https://ssrn.com/abstract=2426961

 Google Scholar

Gutmann, J., Padovano, F., & Voigt, S. (2014). Perception vs. experience: Explaining differences in corruption measures using microdata. Paper Presented at the Annual Meeting of the European Public Choice Society. http://dx.doi.org/10.2139/ssrn.2659349

 Google Scholar

Ivlevs, A., & Hinks, T. (2015). Bribing behaviour and sample selection: Evidence from post-socialist countries and Western Europe. Jahrbücher für Nationalökonomie und Statistik, 235(2), 139–167. doi:10.1515/jbnst-2015-0204

 Web of Science ®Google Scholar

Liu, Q., & Peng, Y. (2015). Determinants of willingness to bribe: Micro evidence from the educational sector in China. Jahrbücher für Nationalökonomie und Statistik, 235(2), 168–183. doi:10.1515/jbnst-2015-0205

 Web of Science ®Google Scholar

Cameron, L., Chaudhuri, A., Erkal, N., & Gangadharan, L. (2009). Propensities to engage in and punish corrupt behavior: Experimental evidence from Australia, India, Indonesia and Singapore. Journal of Public Economics, 93(7–8), 843–851. doi:10.1016/j.jpubeco.2009.03.004

 Web of Science ®Google Scholar

Islam, A., & Lee, W. S. (2016). Bureaucratic corruption and income: evidence from the land sector in Bangladesh. The Journal of Development Studies, 52(10), 1499–1516. doi:10.1080/00220388.2016.1146702

 Web of Science ®Google Scholar

Hunt, J. (2007b). Bribery in health care in Peru and Uganda. NBER Working Paper 13034.

 Google Scholar