Background
The DCE literature on employment preferences in sub-Saharan Africa
Authors | Country and Sample | Attributes | Attribute levels |
---|---|---|---|
Mangham and Hanson [27] | Malawi; 107 registered nurses | Place of work | City, District town |
Net monthly payment | K30.000, K40.000, K50.000 | ||
Availability of material resources | Usually inadequate supply, Usually inadequate supply | ||
Typical daily workload | Light, Medium, Heavy | ||
Provision of government housing | No gov. housing provided, Basic gov. housing provided, Superior gov. housing provided | ||
Opportunity to upgrade qualifications | After 3 years, After 5 years | ||
Hanson and Jack [13] | Ethiopia; 219 doctors and 642 nurses | Geographical location (place of work) | For doctors: Addis Ababa, Zonal capital. For nurses: City, Rural area |
Net monthly pay | Base is salary at average civil service grade, Others multiples of this. | ||
Government provided housing | None, Basic, Superior | ||
Availability of equipment and drugs | Inadequate, Improved | ||
Time commitment following training | 1 year, 2 years | ||
Permission to hold a second job in the private sector (doctors only) | Permitted, Not permitted | ||
Level of supervision (nurses only) | High, Low | ||
Blaauw et al. [5] Labelled design; presented alternatives described as ‘urban job’ and ‘rural job’ | Kenya, S Africa, Thailand; 300 graduating nurses per country | Facility | Urban, Rural |
Salary | Urban – entry salary; Rural – entry salary +10, +20 and +30% | ||
Training (years of service before study leave) | Varied by country. (e.g. Kenya: No study leave; 1 years study leave after 4 years service) | ||
Housing | Urban – none, basic; Rural – basic, superior | ||
Promotion (years of service before promotion) | Varied by country Kenya: 2 years; 4 years S Africa and Thailand: 1 year; 2 years | ||
Additional benefit | Varied by country. Kenya: Short-term; Permanent contract S Africa: None; Car allowance Thailand: Basic, expanded insurance cover | ||
Workplace culture | Hierarchical, Relational | ||
Kruk et al. [20] | Ghana; 302 fourth year medical students | Salary | Basic; +30; +50%; Twice basic |
Children’s education | No allowance; Allowance | ||
Infrastructure, equipment, supplies | Basic; Advanced | ||
Management style | Unsupportive; Supportive | ||
Years of work before study leave | Study leave after 5 years of service; After 2 years | ||
Housing | None; Basic; Superior | ||
Transportation | Utility car not provided; Provided | ||
Kolstad [19] | Tanzania; 320 clinical officer final year students | Salary and allowances | |
Education opportunities | None; Education opportunity offered after 2; 4; and 6 years | ||
Location | Dar-es-Salaam; Regional HQ; District HQ; .3 h drive from district HQ | ||
Availability of equipment and drugs | Sufficient; Insufficient | ||
Workload | Normal; Heavy | ||
Housing | None; Decent house provided | ||
Infrastructure | No utilities; Utilities and mobile coverage | ||
Ageyi-Baffour et al. [1] | Ghana: 298 third-year midwifery students | Salary | Base, base plus 30% |
Children’s education | No allowance, allowance | ||
Infrastructure, equipment & supplies | Basic, advanced | ||
Management style | Not supportive, supportive | ||
Minimum years of work before study leave | 2, 5 years | ||
Housing | Free basic, free superior | ||
Transportation | No car loan, car loan | ||
Rockers et al., [37] | Uganda: 246 medical students, 132 nursing students, 50 pharmacy students 57 laboratory students | Salary | 4 levels customised for each cadre |
Facility Quality | Basic, advanced | ||
Housing | No housing, free basic housing, housing allowance | ||
Length of commitment | 2, 5 years | ||
Support from manager | Not supportive, supportive | ||
Future tuition | No provision, full tuition fees | ||
Bocoum et al., [6] | Burkina Faso: 315 regional health workers | Regionalised Recruitment strategy | Continue, cancel, commit 5, 10 years |
Motivation allowance | 3 levels from €33.6-€64.1 | ||
Medical coverage | 75% reduction for lab exams. 80% reduction lab and medicines; free medciation and lab exams | ||
Work equipment | Sufficient quality equipment, insufficient, sufficient quantity but poor quality | ||
Housing | Free housing, no housing, 25% increase in housing allowance | ||
Robyn et al. 2015 [36] | Cameroon: 351 medical students, nursing students and health workers | Accessability/connectivity to the city | Poor; good |
Health Facility infrastructure | Lack of; adequate | ||
Lodging | None; good quality housing | ||
Career development | No prefential access to ongoing training; preferential access | ||
Salary | Base; base + 255; base +50%; Base + 75% | ||
Job assignment in an urban area | Uncertain; automatic after 3 years | ||
Honda & Vio [17] | Mozambique: 334 non-physician clinicians, 123 students | Place of work | Rural, Capital city; provincial city |
Monthly salary | Base salary, base plus 50%; base plus 100% | ||
Housing | None; Government housing | ||
Loan for housing or land | Not available; available | ||
Formal Education | None offered; offered after 5 years only | ||
Skills development | No in-service training; regular in-service training | ||
Availability of equipment & Medicine | Inadequate;adequate | ||
Private practice | Part-time allowed; allowed outside hours | ||
Takemura et al. [44] | Kenya: 57 clinical officers | Quality of the Facility | Basic; Advanced |
Education opportunities | 1 year study leave after 2 years; after 5 years | ||
Housing allowance | Insufficent to afford basic; sufficient for superior | ||
Monthly basic salary | 10% additional; 30% additional | ||
Promotion eligibility | In 2 years; in 3 years |
Methods
Ethics statement
Discrete choice experiments
DCE experimental design
Attribute | Possible levels | Variables for analysis | Variable coding |
---|---|---|---|
Location |
Urban
Rural
| location |
0 = rural
1 = urban
|
Net monthly pay |
Base
1.5 × base
2 × base
| pay1 |
0 = base salary
1 = 1.5 × base salary or 2 × base salary
|
pay2 |
0 = base salary or 1.5 v base salary
1 = 2 × base salary
| ||
Housing |
None
Basic
Superior
| house1 |
0 = no housing
1 = basic or superior housing
|
house2 |
0 = no housing or basic housing
1 = superior housing
| ||
Equipment and Drugs |
Inadequate
Improved
| equip |
0 = Inadequate
1 = Improved
|
Continuing Professional Development |
Limited
Improved
| PD |
0 = Limited
1 = Improved
|
Human Resources Management |
Poor
Functioning
| HRM |
0 = Poor
1 = Functioning
|
Sample
Data collection
The mixed logit model
Model fitting
Malawi (N = 602) | Mozambique (n = 569) | Tanzania (N = 801) | ||
---|---|---|---|---|
Frequency (and percentage) | current location | |||
rural* | 276 (45.85%) | 569 (100%) | 637 (79.53%) | |
urban | 326 (54.15%) | 0 (0%) | 164 (20.47%) | |
facility | ||||
health center* | 65 (10.8%) | 378 (66.43%) | 257 (32.08%) | |
hospital | 537 (89.2%) | 190 (33.39%) | 544 (67.92%) | |
missing | 0 (0%) | 1 (0.18%) | 0 (0%) | |
gender | ||||
male* | 203 (33.72%) | 103 (18.1%) | 202 (25.22%) | |
female | 398 (66.11%) | 463 (81.37%) | 589 (73.53%) | |
missing | 1 (0.17%) | 3 (0.53%) | 10 (1.25%) | |
cadre | ||||
basic | 0 (0%) | 149 (26.19%) | 165 (20.6%) | |
mid* | 380 (63.12%) | 331 (58.17%) | 292 (36.45%) | |
high | 215 (35.71%) | 79 (13.88%) | 342 (42.7%) | |
missing | 7 (1.16%) | 10 (1.76%) | 2 (0.25%) | |
Summary | age | |||
min | 21 | 20 | 20 | |
mean | 34.13 | 32.46 | 39.75 | |
max | 73 | 60 | 63 | |
missing | 33 | 24 | 47 |
Tanzania | Malawi | Mozambique | ||
---|---|---|---|---|
Cadre group | High | Registered nurse Registered nurse midwife Registered public health nurse Clinical Officer Assistant Medical Officer General Doctor Doctor Specialist | Registered nurse Registered nurse midwife Clinical Officer Medical Assistant General Doctor Doctor Specialist | Nurse (higher degree) General Doctor |
Mid | Enrolled Nurse Enrolled Nurse Midwife Enrolled public health nurse | Enrolled Nurse Enrolled Nurse Midwife Nurse Midwife Technician | Mid-level nurse Mid-level MCH nurse Nurse midwife Basic level nurse Basic level MCH nurse | |
Basic | MCH Aide Medical Attendant Nursing Assistant | Elementary level nurse Elementary midwife Medical Agent |
Country model | Log likelihood (uncorrelated random coefficients) | Log likelihood (correlated random coefficients) | Likelihood ratio test |
---|---|---|---|
Malawi | −3524.4 | −3439.2 |
X
2 = 170.4, df = 28, p < 0.001 |
Mozambique | −3899 | −3828.8 |
X
2 = 140.51, df = 28, p < 0.001 |
Tanzania | −5642.7 | −5508.4 |
X
2 = 268.45, df = 28, p < 0.001 |
Software
Results
Malawi
Coefficient | Estimate (95% confidence interval) | Z |
p-value |
---|---|---|---|
Fixed | |||
gender*HRM | 0.537 (0.059, 1.015) | 2.2 | 0.028 |
age*PD | −0.03 (−0.05, −0.01) | −2.99 | 0.003 |
current_location* location | 0.506 (0.184, 0.829) | 3.08 | 0.002 |
Random (Mean) | |||
location | −0.653 (−0.927, −0.378) | −4.66 | <0.001 |
pay1 | 2.39 (2.056, 2.723) | 14.03 | <0.001 |
pay2 | 1.78 (1.318, 2.242) | 7.55 | <0.001 |
house1 | 2.507 (2.108, 2.906) | 12.31 | <0.001 |
house2 | 0.67 (0.336, 1.004) | 3.93 | <0.001 |
equip | 2.184 (1.844, 2.524) | 12.59 | <0.001 |
PD | 3.851 (3.058, 4.645) | 9.51 | <0.001 |
HRM | 3.26 (2.662, 3.857) | 10.69 | <0.001 |
Random (Standard deviation) | |||
location | 0.592 (0.242, 0.943) | 3.31 | 0.001 |
pay1 | 1.288 (0.955, 1.62) | 7.59 | <0.001 |
pay2 | 2.276 (1.825, 2.726) | 9.9 | <0.001 |
house1 | 1.456 (1.05, 1.862) | 7.03 | <0.001 |
house2 | 1.767 (1.396, 2.139) | 9.32 | <0.001 |
equip | 1.74 (1.441, 2.038) | 11.43 | <0.001 |
PD | 2.079 (1.706, 2.453) | 10.91 | <0.001 |
HRM | 2.09 (1.687, 2.493) | 10.17 | <0.001 |
Mozambique
Coefficient | Estimate (95% confidence interval) | Z |
p-value |
---|---|---|---|
Fixed | |||
basic*equip | −0.703 (−1.097, −0.309) | −3.5 | <0.001 |
basic*PD | −0.607 (−1.019, −0.194) | −2.88 | 0.004 |
Random (mean) | |||
location | 0.056 (−0.148, 0.261) | 0.54 | 0.589 |
pay1 | 1.097 (0.887, 1.306) | 10.24 | <0.001 |
pay2 | 0.582 (0.191, 0.973) | 2.92 | 0.004 |
house1 | 1.505 (1.199, 1.81) | 9.64 | <0.001 |
house2 | 0.069 (−0.188, 0.326) | 0.53 | 0.599 |
equip | 1.9 (1.616, 2.184) | 13.12 | <0.001 |
PD | 2.305 (2.015, 2.595) | 15.6 | <0.001 |
HRM | 1.979 (1.598, 2.36) | 10.19 | <0.001 |
Random (standard deviation) | |||
location | 0.485 (0.149, 0.822) | 2.83 | 0.005 |
pay1 | 1.055 (0.798, 1.312) | 8.05 | <0.001 |
pay2 | 1.829 (1.444, 2.214) | 9.32 | <0.001 |
house1 | 1.55 (1.197, 1.903) | 8.61 | <0.001 |
house2 | 1.14 (0.81, 1.471) | 6.76 | <0.001 |
equip | 1.434 (1.18, 1.688) | 11.07 | <0.001 |
PD | 1.433 (1.139, 1.728) | 9.53 | <0.001 |
HRM | 1.615 (1.269, 1.961) | 9.16 | <0.001 |
Tanzania
Coefficient | Estimate (with 95% confidence interval) | Z |
p-value |
---|---|---|---|
Fixed | |||
fc*location | 0.457 (0.196, 0.718) | 3.44 | 0.001 |
high_pay1 | 0.388 (0.122, 0.654) | 2.86 | 0.004 |
Random (mean) | |||
location | −0.122 (−0.349, 0.105) | −1.06 | 0.291 |
pay1 | 0.944 (0.4570.731, 1.158) | 8.66 | <0.001 |
pay2 | 0.451 (0.135, 0.766) | 2.8 | 0.005 |
house1 | 1.308 (1.087, 1.529) | 11.59 | <0.001 |
house2 | −0.308 (−0.504, −0.112) | −3.09 | 0.002 |
equip | 1.478 (1.262, 1.694) | 13.41 | <0.001 |
PD | 1.453 (1.253, 1.652) | 14.27 | <0.001 |
HRM | 2.053 (1.736, 2.371) | 12.69 | <0.001 |
Random (Standard deviation) | |||
location | 0.8 (0.579, 1.02) | 7.09 | <0.001 |
pay1 | 0.964 (0.692, 1.236) | 6.94 | <0.001 |
pay2 | 1.166 (0.898, 1.435) | 8.51 | <0.001 |
house1 | 1.363 (1.139, 1.587) | 11.92 | <0.001 |
house2 | 1.495 (1.165, 1.825) | 8.88 | <0.001 |
equip | 1.408 (1.179, 1.637) | 12.05 | <0.001 |
PD | 1.442 (1.237, 1.648) | 13.79 | <0.001 |
HRM | 1.913 (1.63, 2.196) | 13.26 | <0.001 |