Employment preferences of public sector nurses ... - Wiley Online Library

Tropical Medicine and International Health

doi:10.1111/j.1365-3156.2008.02167.x

volume 13 no 12 pp 1433–1441 december 2008

Employment preferences of public sector nurses in Malawi: results from a discrete choice experiment Lindsay J. Mangham and Kara Hanson Health Economics and Financing Programme, London School of Hygiene and Tropical Medicine, UK

Summary

objectives To understand the employment preferences of Malawian public sector registered nurses, and to ascertain whether salary increases significantly affect how nurses regard their employment. methods A discrete choice experiment was used to assess the significance of six job attributes on nurses’ preferences over pairs of job descriptions: net monthly pay, provision of government housing, opportunities to upgrade their qualifications, typical workload, availability of resources and place of work. A multivariate model was used to estimate the extent to which nurses were willing to trade between their monetary benefits, non-monetary benefits, and working conditions, and to determine the relative importance of the job attributes. results Most nurses were willing to trade among attributes, and very few appeared to have preferences that were dominated by a single job attribute. All attributes had a statistically significant influence on nurses’ preferences, and further analysis showed the rate at which they were willing to forego pay increases for other improvements in their employment conditions. Opportunities to upgrade professional qualifications, government housing and the increases in net monthly pay had the greatest impact on nurses’ employment choices. conclusions Salary enhancement can improve the motivation and retention of nurses, as well as improvements of employment conditions, which support existing efforts to address the health worker shortage. keywords discrete choice experiment, stated preference, human resources, nurses, Malawi

Introduction There is a global shortage of health workers, with some of the most serious shortfalls occurring in sub-Saharan Africa. The literature on human resources for health highlights the extent of the crisis, and describes the devastating impact of HIV ⁄ AIDS, labour migration and legacy of underinvestment in health (Martinez & Martineau 2002; Joint Learning Initiative 2004; Lindelow et al. 2005, World Health Organization 2006). Strategies for improving health worker performance have received limited consideration and there is a lack of evidence on how remuneration and employment conditions affect motivation (Chopra et al. 2008; McCoy et al. 2008). There are also methodological challenges to use data about actual working conditions to explore these issues. For example, standardized terms and conditions within the public sector limit the variation between public sector jobs, while centralized deployment constrains individual choice. Therefore, There has been a limited quantitative exploration of the importance of different job characteristics

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for health worker motivation in low-income countries (Chomitz et al. 1997; Penn-Kekana et al. 2005). The human resource shortage in Malawi’s health sector is severe, with fewer than 4000 doctors, nurses and midwives serving a population of approximately 12 million in 2003 (Joint Learning Initiative 2004). With such a low density of professional health workers, the coverage and quality of health services are inevitably constrained (Anand & Barnighausen 2004). In April 2005 the Malawi government with support from international donors introduced a 6-year Emergency Human Resources Programme for the health sector, which comprises a 52% taxable salary increase for health workers, measures to enhance the capacity of training institutions, and the recruitment of additional expatriate volunteer doctors and nurse tutors to fill key posts in the short-term (Palmer 2006). Of the three elements, the salary increase is intended to improve the working conditions for existing staff and the retention of health workers in the public service. Other factors that affect the motivation and retention of health workers, such as availability of drugs, 1433



L. J. Mangham and K. Hanson Employment preferences of Malawian nurses

are undergoing reform as part of the government’s sectorwide programme to improve the health of the population and the provision of health care. The aim of this project was to better understand the employment preferences of public sector registered nurses. In light of the salary enhancement scheme, we wanted to ascertain whether increases in pay significantly affect how nurses value their employment. Moreover, we were interested in the extent to which nurses were willing to trade between monetary benefits, non-monetary benefits, and changes to their employment conditions, and to determine the relative importance of these job attributes. Context Malawi’s health outcomes reflect the extent and severity of poverty. Over the last decade life expectancy has fallen to 41 years, largely due to HIV ⁄ AIDS (World Health Organization 2006). Despite improvements in the infant and under-five mortality rates, child health remains a concern and the maternal mortality rate is extremely high at 984 per 100 000 live births in 2004 (National Statistical Office Malawi 2005). Access to health services in Malawi is good by African standards, with 84% of people living within a 5 km radius of a facility, and government provision of essential health services to all citizens without charge (Ministry of Health Malawi 2004). Yet access to effective health care is limited: front-line health services operate with extremely limited staff, equipment, drugs and other supplies; a study of 617 health facilities in 2003 estimated that only 10% of those facilities were able to deliver the Essential Health Package (Hozumi 2003). The number of newly qualified health workers entering the public service is insufficient. This reflects the capacity of the training institutions, but also the limited appeal of employment in the public (including NGO) health sector. Increasing attrition is a widely reported problem, more and more people seem to opt for voluntary resignation (Ministry of Health Malawi 2004). Especially retaining registered nurses is a problem; many emigrate in search of higher salaries and better working conditions (Ministry of Health Malawi 2004). The Malawi government’s primary strategy for addressing staff shortages rests on the assumption that improving monetary benefits will positively affect health worker motivation and their retention in the public service. The decision to prioritize monetary benefits over other motivating factors suggests that the government believes that the low level of remuneration is the main reason for the human resource shortage. There was however, limited evidence available to support this strategy and routine data 1434

were insufficient to adequately reveal employment preferences. Consolidated government data indicated only the number and distribution of public sector nurses by district and very little is known about those leaving the public service. Moreover, as the Ministry of Health appoints nurses to those health facilities most in need, and personal circumstances are only taken into account when an individual submits a transfer request, the current distribution of nurses is not indicative of their employment preferences. Methods A discrete choice experiment (DCE) was designed to elicit the employment preferences of public sector registered nurses in Malawi. The research was discussed with the Malawian Ministry of Health and ethical clearance was obtained from the National Health Sciences Research Committee. A DCE is a quantitative technique for eliciting preferences. It can be used where inferring preferences from actual behaviour is either invalid or not possible. Respondents state their choice over hypothetical scenarios, which are described by several attributes. The method assumes that the utility (or ‘happiness’) associated with a good or service is made up of the utilities of its composing characteristics, and that individual valuations depend on those characteristics (Lancaster 1966). For this study a DCE was designed that required respondents to state their preference over hypothetical job descriptions, where each job description was described by a bundle of job attributes. Design of the discrete choice experiment Attributes and attribute levels were established using information collected from in-depth interviews with 20 registered nurses working in three geographically different districts and in primary, secondary and tertiary health facilities. Open questions were used to encourage respondents to share their experiences and views on their current working conditions, preferences for different type of jobs, and priority areas for reform. Interviews were conducted primarily in English by social scientists from the University of Malawi, and with the individual’s consent the discussions were recorded and transcribed. Manual content analysis identified several descriptive themes, which encompassed a range of monetary and nonmonetary benefits, aspects of their working environment, organizational structure, management issues, individual responsibilities and professional development. Nine job attributes were short listed for potential inclusion in the DCE based on their importance to nurses and policy





relevance. They were: net monthly pay, provision of government housing, availability of material resources (equipment, drugs and other consumables), typical workload, access to in-service training, opportunities to upgrade professional qualifications, promotion prospects, place of work, and transport. It was necessary, however, to exclude three of these attributes – in-service training, promotion prospects and transport – because of conceptual overlap with others. For example, promotion prospects were closely associated with opportunities to upgrade qualifications. Attribute levels were established for each attribute that reflected the prevailing working conditions. Additional levels were then determined that represented a reasonable improvement from the base level. The attributes and attribute levels are presented in Table 1. The questionnaire took the form of a series of choices between two jobs, with one held constant. For each of the 15 pairs of job descriptions the respondent was asked two questions. First, they were asked the constrained choice of which one of the two jobs shown they considered to be the best job. Second, respondents were asked to take into account their personal circumstances and state whether they would choose ‘Job 1’, ‘Job2’ or ‘Neither Job’. The questionnaire was pre-tested on 21 nurses located in three districts, and minor modifications were made to attribute definitions and levels. An example choice pair is shown in Figure 1. Respondents were randomly allocated to one of four versions of the questionnaire in which the orders of choice sets and attributes were varied to minimize bias Table 1 Attributes and levels used in the discrete choice experiment Attribute

Levels

Place of work

City District Town K30 000 (approximately $240) K40 000 (approximately $320) K50 000 (approximately $400) Usually inadequate supply

Net monthly pay

Availability of material resources (equipment, drugs and other supplies) Typical daily workload

Provision of government housing Opportunity to upgrade qualifications


Usually adequate supply Light: more than enough time to complete duties Medium: enough time to complete duties Heavy: barely enough time to complete duties No government housing provided Basic government housing provided Superior government housing provided After 3 years After 5 years

related to their sequencing. At the end of the questionnaire respondents were also asked about their personal and family characteristics. Sampling and administration of the discrete choice experiment Ministry of Health records indicated that there were 330 registered nurses working for the government in January 2005 and that they were distributed in approximately equal shares across tertiary hospitals in four districts and district hospitals in main towns of the remaining districts. Stratified sampling was applied to the 232 registered nurses at grades I, J and K (which excluded chief nursing officers and more senior grades), aiming to interview 150 of them, 75 from urban and 75 from rural areas. Simple random sampling was used to select districts, with only one district excluded because of its extreme remoteness. In each district we sought to survey all nurses present on the day of visit. Questionnaires were individually administered, providing each respondent with an overview of the research, supplementary documentation and example choice pairs before starting the questionnaire. Written consent was obtained. Data were independently double-entered into a Microsoft Access database, and checked for consistency using EPI Info. All data entry errors were manually corrected. Data Analysis The responses to the DCE were analysed using the survey probit estimator in stata version 9.2 (Stata corp 2005). The correlation among an individual’s responses was adjusted for by specifying the individual as the primary sampling unit. A finite population correction was used as a large proportion of total population were surveyed. The dependent variable was binary and indicated, for a given choice pair, whether the individual chose ‘Job 1’ or ‘Job 2’. The probit model estimated the probability of choosing Job 1, which was assumed to be equivalent to the probability that the utility associated with Job 1 was greater than the utility associated with Job 2. The specified model was a linear expression in which estimated coefficients represent the marginal utility associated with each attribute: Prob½Y ¼ 1jx ¼ b1 place þ b2 pay1ð3040Þ þ b3 pay2ð4050Þ þ b4 resource þ b5 work1ðlightheavyÞ þ b6 work2ðmediumheavyÞ þ b7 house1ðnonebasicÞ þ b8 house2ðbasicsuperÞ þb9 upgradeþeþl where Y was the dependent variable and equalled one if Job 1 was chosen and zero if Job 2 was chosen; x is the set 1435




Choice Set: A Job 1

Job 2

Location:

City

Location:

District town

Net monthly pay:

K40,000

Net monthly pay:

K50,000

Usually Availability of material resources inadequate (equipment, drugs and other supplies)

Availability of Usually adequate material resources (equipment, drugs and other supplies)

Typical workload:

Heavy: Barely enough time to complete duties

Typical workload:

Provision of government housing:

Basic housing provided

Provision of government housing:

No housing provided

Opportunity to upgrade qualifications:

After 5 years

Opportunity to upgrade qualifications:

After 3 years

Medium: Enough time to complete duties

Question 1: In your opinion, of the two jobs described which one do you think is the best job? Job 1

Job 2

Question 2: Taking into account your circumstances, would you choose to take …. Job 1

Job 2

Neither Job

of job attributes, place, pay1(30-40), pay2(40-50), resource, work1(light-heavy), work2(medium-heavy), house1(none-basic), house2(basic-super), and upgrade were dummy variables representing the difference in the attribute levels between Job 1 and Job 2; and corr(, l)=q, which took account of the correlation among individual choices. The internal consistency of an individual’s responses was investigated by including one choice pair in which one job was superior or equal to the other on all attributes. This assumed that a higher level of net monthly pay, better resource availability, a lighter workload, a shorter time before being eligible to upgrade qualifications, and the provision of government housing were all rationally superior and would yield higher utility. Individuals who failed to choose the superior job were thought to have misunderstood the questionnaire or were unable to provide consistent answers for other reasons, though all responses were included in the data analysis (Lancsar & Louviere 2007). Responses were also analysed to investigate whether respondents had ‘dominant preferences’ and consistently chose the alternative with the highest value of a 1436

Figure 1 Example of a choice pair.

particular attribute. This would indicate that they were not willing to trade among attributes (Scott 2002). The theoretical validity of the model was assessed by determining whether the coefficients were of the anticipated sign. The variables were coded such that a positive sign shows that an improvement in the attribute level yields a higher utility. No assumption was made about how differences in place would affect utility, and it was coded such that a positive sign shows a preference for jobs located in a city and a negative one for jobs located in a district town. Average marginal effects of the job attributes were computed by taking the difference in estimated probability of Y with and without the variable, while holding the distribution of other variables at their sample value, and then taking the sample mean of these differences (Christofides et al. 2006). These marginal effects can be quantitatively interpreted as the change in the probability of selecting a job description following a change in a single variable. The relative importance of the differences in the attribute levels was examined by calculating how much each





attribute contributes to the log-likelihood of the model (Lancsar et al. 2007). The contribution of each attribute is obtained by systematically re-estimating the model, omitting each variable one at a time and calculating the difference between the full and the reduced model loglikelihoods (Lancsar et al. 2007). An attribute was considered to be more important the greater its contribution to the log-likelihood of the choice model. Results A total of 107 registered nurses working for the Malawi government completed the questionnaire, 50 (47%) in urban and 57 (53%) in rural areas. Nurses were keen to participate in the research; only three (2.8%) declined to participate. Respondents were predominately female (90%), married (61%) and had children (63%). There was an almost equal distribution between registered nurses who had qualified with a degree (49%) and nurse with a diploma (51%). Around a quarter of respondents (26%) were provided a government house. When asked to describe their current working conditions, 90% of registered nurses said that resources were usually inadequate and 82% described their workload as heavy; 16% reported a medium and just 2% a light workload. The internal consistency of responses was high; only six respondents (5.6%) failed to choose the superior job. We found very little evidence of dominant preferences. Table 2 shows that less than 10% of respondents always selected the job description in which one attribute was consistently higher. For example, 2 of the 107 (1.9%) respondents always thought the best job was the one with the highest pay, and 10 of the 107 (9.3%) respondents would always choose the job with the highest level of pay. The vast majority of respondents were, therefore, willing to trade

Table 2 Analysing dominance: respondents that always state the job with the higher attribute Responses to which is the best job Attribute

Responses to which job they would choose

Number of % of Number of % of respondents respondents respondents respondents

Pay 2 Resources 9 Workload 10 Upgrading 10 Housing 1 Place = city 0 Place = 6 district town


1.9% 8.4% 9.3% 9.3% 0.9% 0.0% 5.6%

10 4 6 7 0 9 3

9.3% 3.7% 5.6% 6.5% 0.0% 8.4% 2.8%

between attributes and very few appear to apply a simple decision rule in which a job was selected on the basis of a single attribute. These results demonstrate that there was a willingness to forego salary increases to obtain improvements to other aspects of their employment conditions. The results of the multivariate regression models for both scenarios are shown in Table 3, with Model 1 presenting the results on what respondents considered the best job and Model 2 the results of what job respondents would choose having taking into account their circumstances. The estimated employment attribute coefficients were all of the anticipated sign, which implies that respondents derived a higher level of utility from the superior attribute level and made rational choices. No assumption was made about the superiority of the attribute levels for place of work, although the negative sign indicated that on average a job in the city was considered inferior to one in a district town. Regression coefficients were statistically significant at the 5% level (with the exception of the difference between the provision of basic and superior government housing in Model 2). Thus all attributes contributed to the decisions made by nurses when stating their preferences between the alternative job descriptions. The marginal effects show the impact on the probability of choosing Job 1 over Job 2 of the change in attribute levels. For example, in Model 1 an increase in material resources from ‘usually inadequate’ to ‘usually adequate’ was associated with a 12.7% increase in the probability of choosing the job with ‘usually adequate’ resources. The relative impacts of the attributes were estimated using partial log-likelihood analysis and are shown in Tables 4 and 5. In both scenarios, shortening the time period before having the opportunity to upgrade was ranked most highly. This was closely followed by the provision of basic government housing (in comparison with none) and the differences in the net monthly pay levels. Although the results for the two models are similar there were some differences in the middle of the distribution, with the place of work attribute having a greater impact on the responses corresponding to what job the respondent would choose having considered their personal circumstances. Finally, we examined whether the nurses’ residence influenced their preferences over the place of work attribute when choosing a job. For choice sets with a difference in the attribute levels for place, we found very few dominant preferences, with 9 of the 107 (8.4%) respondents always choosing the job located in a city and just 3 (2.8%) always choosing the job in a district town. Table 6 shows that urban residents had a slight preference for jobs in a city and rural residents for jobs in a district town, 1437




Table 3 Multivariate regression results (Dependent variable = 1, if job 1 preferred) Responses to which job is considered the best job

Main effects

Responses to which job they would choose

Difference in…

From … to …

Coefficient

Standard error

Marginal effect

Coefficient

Standard error

Marginal effect

Place Pay (1) Pay (2) Resources Workload (1) Workload (2) House (1) House (2) Upgrade qualifications

City to district town K30 000 to K40 000 K40 000 to K50 000 Inadequate to adequate Heavy to light Heavy to medium None to basic Basic to superior After 5 years to after 3 years

)0.281*** 0.673*** 0.711*** 0.354*** 0.487*** 0.331*** 0.674*** 0.200** 0.638***

0.055 0.078 0.103 0.057 0.096 0.052 0.078 0.080 0.072

)0.102 0.236 0.256 0.127 0.175 0.119 0.241 0.072 0.230

)0.349*** 0.504*** 0.795*** 0.190*** 0.303** 0.231*** 0.675*** 0.025 0.695***

0.073 0.084 0.093 0.067 0.119 0.067 0.087 0.084 0.073

)0.123 0.174 0.275 0.066 0.105 0.080 0.236 0.009 0.240

*Significant at 10% level. **Significant at 5% level. ***Significant at 1% level.

Table 4 Partial log-likelihood analysis of ranking of importance of attributes (Model 1) Attribute excluded from the analysis

Difference in…

From … to …

Log Likelihood

None Upgrade qualification House (1) Pay (1) Pay (2) Resources Workload (1) Workload (2) Place House (2)

After 5 years to after 3 years None to basic K30 000 to K40 000 K40 000 to K50 000 Inadequate to adequate Heavy to light Heavy to medium City to district town Basic to superior

)832.822 )870.502 )861.429 )861.043 )852.283 )847.660 )842.413 )841.799 )841.434 )834.758

although there was no statistically significant difference between the two groups. Discussion The results of the discrete choice experiment found that there were relatively few nurses whose preferences appeared to be dominated by a single attribute, and all six attributes had a statistically significant influence on the nurses’ preferences. The nurses were willing to trade between job attributes, and therefore willing to forego pay increases to obtain improvements in their non-monetary benefits or working conditions. The opportunity to upgrade qualifications, provision of basic government 1438

Partial effect

Relative effect

Cumulative

(change in log-likelihood)

(% sum of change in log-likelihood)

(%)

Order of impact

)37.680 )28.606 )28.220 )19.460 )14.837 )9.591 )8.976 )8.611 )1.936

0.239 0.181 0.179 0.123 0.094 0.061 0.057 0.055 0.012

0.239 0.420 0.598 0.722 0.816 0.876 0.933 0.988 1.000

1 2 3 4 5 6 7 8 9

housing (compared with none) and increases in net monthly pay had the greatest impact on the utility associated with a particular job. The research provides an insight into the employment preferences of Malawian registered nurses. An advantage of the DCE methodology is that as a stated preference technique it can be used to elicit preferences where available data on actual choices does not exist or, as in this instance, include a selection bias as only those who are relatively content with their job remain employed in the public service and the prevailing distribution of nurses is assigned by the Ministry of Health. The method also allowed us to isolate the contribution of each job attribute to the overall utility associated with the employment





Table 5 Partial log-likelihood analysis of ranking of importance of attributes (Model 2) Attribute excluded from the analysis

Difference in…

From … to …

Log Likelihood

None Upgrade qualification House (1) Pay (2) Pay (1) Place Workload (2) Resources Workload (1) House (2)

After 5 years to after 3 years None to basic K40 000 to K50 000 K30 000 to K40 000 City to district town Heavy to medium Inadequate to adequate Heavy to light Basic to superior

)567.279 )600.192 )587.498 )586.779 )579.139 )576.388 )570.335 )570.309 )569.812 )567.302

Table 6 Comparing choices over place of work by current residence Residence

Job chosen city

District town

Total

Urban Rural

228 (57%) 205 (45%)

172 (43%) 251 (55%)

400 456

Total

433

423

856

options. This would not have otherwise been possible as in practice some attributes are closely associated. For example, government housing is more likely to be provided in rural districts than in urban settings. Some limitations to the study should be noted. For practical and financial reasons it was necessary for us to limit the research to a single cadre. Registered nurses were chosen because this cadre faced acute shortages and difficulties retaining staff. The comparatively small sample size largely reflected the small numbers of registered nurses working for the Malawi government, although it was a nationally representative sample, and the model generated statistically significant results. The results represent the preferences of those nurses at work on the day of the survey, and some bias is possible if those absent from work have different preferences. The DCE method limits the analysis to a subset of all possible attributes and further research on other attributes would be valuable, such as the impact of management practices on motivation and performance. The DCE design has shortcomings: at the time we began our study, the experimental design was consistent with prevailing guidance (Hensher et al. 2005) and published studies (Scott et al. 2003; Hanson et al. 2005). However, after some more recent publications


Partial effect

Relative effect

Cumulative

(change in log-likelihood)

(% sum of change in log-likelihood)

(%)

Order of impact

)32.913 )20.220 )19.500 )11.860 )9.109 )3.057 )3.030 )2.533 )0.024

0.322 0.198 0.191 0.116 0.089 0.030 0.030 0.025 0.000

0.322 0.520 0.710 0.826 0.915 0.945 0.975 1.000 1.000

1 2 3 4 5 6 7 8 9

(Burgess & Street 2005; Street et al. 2005; Louviere 2006) we are aware that the design we used is inefficient. Efficient designs are both orthogonal and balanced: orthogonal designs ensure the attributes are statistically independent, while a balanced design occurs when each attribute level occurs equally often and this minimizes the variance in the parameter estimates. Acknowledging the design inefficiency, we have restricted our analysis to estimate only the main effects. Nevertheless we believe that our study findings remain valid and relevant for policy makers. Other studies that have used this approach to elicit employment preferences of health workers have also found both monetary and non-monetary job attributes to have a statistically significant impact on how individuals consider their work (Chomitz et al. 1997; Scott 2001; Scott et al. 2003; Ubach et al. 2003; Wordsworth et al. 2004; PennKekana et al. 2005). To our knowledge the study by PennKekana et al. is the only published DCE to consider the employment preferences of African nurses (Penn-Kekana et al. 2005). They found that remuneration had a relatively large impact on job valuation, although good management and a fully equipped health facility were also considered relatively important attributes. A study of Indonesian doctors found access to specialist training and urban locations were important job attributes, although differences in cadres and settings limit comparison (Chomitz et al. 1997). This study was designed in light of the Malawi government’s programme to address the shortage of health workers. We sought to identify the extent to which the employment choices of registered nurses were likely to be influenced by salary increases and also their willingness to forego pay increases for improvements in non-monetary 1439




benefits and other aspects of the working environment. The results suggest that salary enhancements should positively affect how nurses value their employment, and support the argument that improved remuneration will have a positive impact on motivation and encourage the recruitment and retention of registered nurses. Moreover, the statistical significance of all six attributes implies that the Malawi government has a range of interventions available that would improve how registered nurses perceive their employment in public service. The results on the relative importance of the attributes provide evidence on which changes to the employment conditions of nurses are likely to have the greatest impact. In using these findings to inform policy decisions, it would be advisable to undertake supplementary research to obtain a deeper understanding of the expected impact of the alternative strategies as well as their implementation cost. Further research would be useful to examine how employment preferences vary with individual characteristics and the impact of interactions between job attributes. Qualitative research would also be valuable to achieve a richer understanding of the quantitative results and explore the policy implications. In summary, this is one of the few studies to make a quantitative assessment of the relative importance of job attributes for health workers in a developing country context, and provides insights into the employment preferences of registered nurses in the Malawian public service. References Anand S & Barnighausen T (2004) Human resources and health outcomes: Cross-country econometric study. Lancet 364, 1603– 1609. Burgess L & Street DJ (2005) Optimal designs for choice experiments with asymmetric attributes. Journal of Statistical Planning and Inference 134, 288–301. Chomitz KM, Setiadi G, Azwar A, Ismail N & Widiyarti (1997) What Do Doctors Want? Developing Incentives for Doctors to Serve in Indonesia’s Rural and Remote Areas? Policy Research Working Paper 1888. World Bank, Washington DC. Chopra M, Munro S, Lavis JN, Vist G & Bennett S (2008) Effects of policy options for human resources for health: an analysis of systematic reviews. Lancet 371, 668–674. Christofides NJ, Muirhead D, Jewkes RK, Penn-Kekana L & Conco DN (2006) Women’s experiences of and preferences for services after rape in South Africa: interview study. BMJ 332, 209–213. Hanson K, McPake B, Nakamba P & Archard L (2005) Preferences for hospital quality in Zambia: results from a discrete choice experiment. Health Economics 14, 687–701. Hensher DA, Rose JM & Greene WH (2005) Applied Choice Analysis: A Primer. Cambridge University Press, Cambridge.

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Hozumi D (2003) Status of Health Facilities in Malawi. Prepared for the Republic of Malawi and funded by the Japan International Cooperation Agency, Tokyo. Joint Learning Initiative (2004) Human Resources for Health: Overcoming the Crisis Global Health Initiative Harvard University, Washington DC. Lancaster K (1966) A new approach to consumer theory. The Journal of Political Economy 74, 132–157. Lancsar E & Louviere JJ (2007) Deleting ‘irrational’ responses from discrete choice experiments: a case of investigating or imposing preferences? Health Economics 18, 797–812. Lancsar E, Louviere JJ & Flynn TN (2007) Several methods to investigate relative attribute impact in stated preference experiments. Social Science & Medicine 64, 1738–1753. Lindelow M, Serneels P & Lemma T (2005) The Performance of Health Workers in Ethiopia: Results from Qualitative Research. Policy Research Working Paper 3558. World Bank, Washington DC. Louviere JJ (2006) What you don’t know might hurt you: some unresolved issues in the design and analysis of discrete choice experiments. Environmental & Resource Economics 34, 173– 188. Martinez J & Martineau T (2002) Human Resources in the Health Sector: an International Perspective: Issues Paper. DFID Health Systems Resource Centre, London. McCoy D, Bennett S, Witter S et al. (2008) Salaries and incomes of health workers in sub-Saharan Africa. Lancet 371, 675–681. Ministry of Health Malawi (2004) A Joint Programme of Work for a Health Sector Wide Approach (SWAp) 2004-2010. Government of Malawi, Lilongwe. National Statistical Office (NSO) Malawi and ORC Marco (2005) Malawi Demographic and Health Survey 2004. NSO and ORC Macro, Calverton Maryland. Palmer D (2006) Tackling Malawi’s human resource crisis. Reproductive Health Matters 14, 1–13. Penn-Kekana L, Blaauw D, Monareng D & Chege J (2005) Nursing Staff Dynamics and Implications for Maternal Health Provision in Public Health Facilities in the Context of HIV ⁄ AIDS. Frontiers Final Report. Population Council, Washington DC. Scott A (2001) Eliciting GPs’ preferences for pecuniary and nonpecuniary job characteristics. Journal of Health Economics 20, 329–347. Scott A (2002) Identifying and analysing dominant preferences in discrete choice experiments: An application in health care. Journal of Economic Psychology 23, 383–398. Scott A, Watson MS & Ross S (2003) Eliciting preferences of the community for out of hours care provided by general practitioners: a stated preference discrete choice experiment. Social Science & Medicine 56, 803–814. Stata Corp (2005) Stata Statistical Software: Release 9.2. Stata Corp LP, college station, TX. Street DJ, Burgess A & Louviere JJ (2005) Quick and easy choice sets: constructing optimal and nearly optimal stated choice experiments. International Journal of Research in Marketing 22, 459–470.





Ubach C, Scott A, French F, Awramenko M & Needham G (2003) What do hospital consultants value about their jobs? A discrete choice experiment. BMJ 326, 1432. Wordsworth S, Skatun D, Scott A & French F (2004) Preferences for general practice jobs: a survey of principals and

sessional GPs. British Journal of General Practice 54, 740– 746. World Health Organization (2006) World Health Report 2006: Working together for health. World Health Organization, Geneva.

Corresponding Author Lindsay J. Mangham, Health Policy Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. Tel: +44 (0) 20 7297 2148, 7941 776 376; E-mail: [email protected]


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