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Work Preferences in Rural Health 
Job Posting Among Medical 
Interns in a Lower Middle-Income 
Country— a Discrete Choice 
Experiment 

 
Julius R. Migriño, Jr.1,2,3,* 
 
1San Beda University College of 
Medicine, San Miguel, Manila, 
Philippines;  
2Ateneo School of Medicine and Public 
Health, Pasig City, Philippines; 
3University of the Philippines—Open 
University, Los Baños, Laguna, 
Philippines 
*Corresponding author email: jrmjrmd-
1@yahoo.com 

 
Vol. 9, No. 1 (2020)   |   ISSN 2166-7403 (online)  
DOI 10.5195/cajgh.2020.344   |   http://cajgh.pitt.edu 



 
 
MIGRIÑO 
 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

Abstract 

Introduction: Timely empirical evidence is important in the success of health systems, and such evidence is necessary for informed 
policy making to address inequity in the health workforce. Literature is ripe with incentives that affect recruitment and retention of 
physicians in rural and remote areas, but such data in still lacking in the Philippine setting. Discrete choice experiment is one 
methodology utilized by the World Health Organization which provides both qualitative and quantitative information to aid policy 
makers in health human resource management. 
Methods: The study utilized a discrete choice experiment involving three phases: 1) identification of incentives and levels using 
key informant interviews and focus group discussions, 2) selection of scenarios utilizing an experimental design, and 3) 
administration of survey based on WHO guidelines. Conditional logistic regression, point estimates, and correlational analyses 
were done using Stata. 
Results: There is significant association between type of background and considerations for rural practice among the respondents 
based on Pearson’s correlation (p < 0.01). The respondents put more value into non-wage rural job posting incentives than small 
to modest base salary increases. The high willingness to pay for the presence of supervision, relative location of work areas from 
families, and status of workplace infrastructure/equipment or supplies suggest the importance of workplace conditions to attract 
rural health physicians. Combinations of wage and non-wage incentives may be necessary to provide for the most cost-efficient 
increases in rural job post uptake rates based on post-estimate calculations.  
Conclusion: Philippine medical interns and young doctors value non-wage incentives in considering rural health job postings. 
Rural health job postings with these incentives are predicted to significantly increase recruitment in rural health job posts, 
particularly when combinations of wage and high-impact non-wage incentives are considered. 

Keywords: Global health; Rural job posting; Health human resources; Health systems; Recruitment; Preferences 

Work Preferences in Rural Health Job 
Posting Among Medical Interns in a 
Lower Middle-Income Country— a 
Discrete Choice Experiment 
 
Julius R. Migriño, Jr.1,2,3,* 
 
1San Beda University College of Medicine, 
San Miguel, Manila, Philippines;  
2Ateneo School of Medicine and Public 
Health, Pasig City, Philippines; 
3University of the Philippines—Open 
University, Los Baños, Laguna, Philippines 
*Corresponding author email: jrmjrmd-
1@yahoo.com 
 

Research 

Human Resource for Health (HRH) 
development is vital in the success of a health system, 
and its success depends on empirical evidence for 
informed policy making.1 There is sufficient evidence of 
an overall scarcity of health workers globally,2–5 and this 
is compounded by inequitable distribution of health care 
workers between urban and rural/remote areas of the 
world. In the Philippines, where more than half of the 
population live in rural and remote areas of the country, 
the density of doctors situated in primarily urban areas 
such as in the National Capital Region (NCR) and 
Southern Tagalog are higher compared to some rural and 
remote provinces in Mindanao and Western and Eastern 
Visayas, and many health sector positions in rural and 
isolated areas remain vacant.6,7 This inequity of 



 
 

CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 
 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 
 

This journal is published by the University Library System of the University of Pittsburgh as part  
of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 
Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

distribution and failure to retain health workers in rural 
areas reduces the population’s access to much needed 
health services, resulting in poor health outcomes such as 
higher infant mortality rates (IMR): for instance, Eastern 
Visayas, Western Mindanao, and Autonomous Region of 
Muslim Mindanao have higher IMR (31, 32, 33 per 1,000 
live births, respectively) compared with the national 
average (23 per 1,000 live births).8  

One way to address this issue is for policy-
makers to have access to information on health worker 
preference models. There is limited data on assessments 
of influencing factors or effective strategies to address 
this gap in human health resources,9 including within the 
setting of the Philippines.6,10,11 Currently, the few 
preference studies regarding medical practitioners in the 
Philippines rely on descriptive methods such as cross-
sectional surveys and case studies, and do not take into 
consideration the hierarchy of preferences as well as 
possibilities of trade-off between difference choices. The 
discrete choice experiment (DCE) is one method that 
could be used to quantitatively assess the importance of 
individual factors which influence health worker 
preferences to specific job posting incentives. Data from 
DCEs may aid policy makers in prioritizing rural post 
incentive packages that would prove to be most cost-
effective and have higher uptake potential.  

The study aimed to determine the association of 
different job incentives and the probability of take-up of 
rural health job postings among medical interns and 
recent graduates in the Philippines using a discrete choice 
experiment. The study null hypothesis was that there was 
no significant association between the presence of 
different job incentives and the probability of take-up of 
rural health job postings among medical interns. 

 

Methods 

The study employed a mixed methods discrete 
choice experiment methodology involving three phases:  

Phase 1– Identification of incentives and levels 

Phase 2– Selection of scenarios 

Phase 3– Administration of survey 

The Association of Philippine Medical Colleges 
Foundation, Inc. (APMCFI) is a non-stock, non-profit 
organization that “defines standards and guidelines to 
promote quality medical education in the Philippines” 
and serves as the umbrella organization of all accredited 
medical schools in the country.12 A complete 
enumeration of all the listed medical interns was 
conducted to enable representation of medical interns 
from all regions of the Philippines. All 7,178 medical 
students who participated in the medical internship 
matching system for Academic Years 2016-2017 and 
2017-2018 under APMCFI were included in the 
sampling frame. The inclusive years used in the sampling 
coincide with the time frame of the study. Sample size 
was calculated using OpenEpi to be 365 at 95% 
confidence level, and simple random sampling was done 
to recruit the DCE respondents. 

Phase 1: Identification of incentives and levels 

A literature review was done on incentives to 
attract and retain health workers in the rural setting, based 
on the recommendation by WHO.13 Interviews with key 
informants based on recommendations from the WHO 
report “Increasing access to health workers in remote and 
rural areas through improved retention”14 were done. At 
the end of the key informant interviews (KII), a list of 21 
feasible and relevant job incentives and possible levels 
was identified. 

Two separate focus group discussions (FGD) 
were conducted with five medical interns and five 
recently graduated medical doctors. The participants for 
each FGD were purposively selected based on sex (at 
least one of each sex was represented per FGD group), 
current enrollment status as medical interns (for FGD 1), 
and newly graduated doctors (within 3 years of the 
study); these respondents closely resemble the final study 



 
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This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

respondents. Defining of incentives and recategorization 
of similar incentives were also done during the FGD. The 
respondents were then asked to select a final set of 
incentives and to identify different levels for each that are 
realistic and appropriate in the local context. For 
example, when discussing salary, participants were asked 
what they thought was a fair and realistic salary level for 
the job posting. Each FGD lasted between 1-1.5 hours 
and was facilitated and transcribed by the researcher. At 
the end of the FGD, a final list of seven incentives with 
corresponding levels was generated. 

Phase 2: Selection of scenarios 

Data gathered from Phase 1 was used to 
construct the rural health job postings (“choice sets”). 
The experimental choice set design was generated using 
R version 3.4.2 (2017-09-28), using the package 
AlgDesign and the function optFederov,15 and analyzed 
for orthogonality using PSPP GNU General Public 
License (version 3, 29 June 2007). This produced an 
orthogonal array with level balance, minimal overlap, 
and D-efficiency = 0.926, with minimum collinearity. A 
systematic level change of the original design16 was then 
employed in generating the alternative choice set design 
to ensure a higher efficiency.17,18 At the end of Phase 2, a 
final Google Form survey questionnaire with 13 choice 
sets and an embedded informed consent form was 
constructed. 

Phase 3: Administration of survey 

The link to the Google form questionnaire and 
informed consent was distributed through blind carbon 
copy emails to each survey respondent. Responses were 
collected within a 4-week period. Follow-up emails were 
made after five and ten working days. 

Manual domain analysis was done to the data 
gathered in Phase 1, using the procedure demonstrated by 
Atkinson and Abu El Haj19 using the qualitative data 
analysis software QDA Miner Lite (v2.0.2). The creation 
of scenarios in Phase 2 was based on the data from Phase 
1 and was performed using orthogonal design from R 

software version 3.4.2, which generated an orthogonal 
design for the choice sets that were used in Phase 3.  

Coding of Phase 3 data was done using a 
stacked-format initial data matrix. Univariate analysis 
(measures of central tendencies and percentages) were 
done for the demographic profile using Microsoft Excel. 
Chi square analysis, conditional logistic regression 
analysis and computations for willingness to pay, 
changes in uptake rates, and disaggregation of subgroups 
were done using Stata v.13.0, with the aid of a consultant 
statistician and using the guidelines set by WHO (2012). 
Conditional logistic regression analysis was done using 
the syntax clogit CHOICE wage equipment supervision 
family QoL CPD career const, group(obsid), with wage 
as a continuous variable and the rest as dummy-coded 
variables. The value of CHOICE refers to either 0 (job 
post A) or 1 (job post B) as the respondent’s choice, while 
the term group(obsid) is the paired observation per 
choice set. This function assumed a logit model with the 
probability of choosing job i defined as: 

 

Vi/j = deterministic utility of 
posts i/j 

 

Willingness to pay was calculated as the ratio of 
the value of the coefficient of interest to the negative of 
the cost attribute, 

𝑊𝑇𝑃! =	−	
"#

"!$
"#

""#$%$
	= 	 %!

%"#$%
,  

with n being the incentive of interest.  

Changes in uptake rates (with corresponding 
confidence intervals) were calculated using the nlcom 
command in Stata using the following syntax: 

nlcom(exp(_b[wage]*36000+_b[n])-
exp(_b[wage]*36000))/(exp(_b[wage]*36000)+exp(_b[
wage]*36000+_b[n])), where n = incentive of interest. 



 
 

CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 
 

 

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of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 
Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

Calculations for the disaggregation of subgroups were 
done using the clogit function (as above) but utilizing 
interaction terms. All calculations were done at 95% 
confidence level. 

 

Results 

There was a total of 345 respondents from the 
survey, which represents 4.81% of the total population. 
Table 1 presents a summary of the demographic 
characteristics of the DCE respondents. The respondents 
were distributed across multiple regions of the 
Philippines; however, the majority of the respondents 
(66.38%) came from schools in Luzon, with 48.70% 
coming from the Greater Manila Area. Visayas-based 
respondents made up 24.35%, while those from 

Mindanao accounted for 9.27% of the total participants. 
The mean age of the respondents was 26.42 (±2.26) years 
old, which is the usual age when medical students in the 
Philippines finish their medical school and take up 
medical internship. Most of the respondents were female 
(68.12%), and a significant majority (93.33%) were 
single. The general trend in the family income of the 
respondents belong to middle- and upper-class income 
brackets. Seventy-two percent of the respondents have 
lived mostly in urban environments, while a majority 
(68.12%) at least considered practicing in a rural area 
after graduation, whether as Doctors to the Barrios 
(DTTB), Municipal Health Officers (MHO), or in rural 
private practice. Pearson’s chi square results revealed a 
significant difference (p<0.01) between the type of 
background in relation to considerations of practicing 
medicine in rural areas. 

Variable N(%) 
Age (mean(std. dev.)) 26.42 (2.26) 
Sex  
   Male 110 (31.88) 
   Female 235 (68.12) 
Marital status  
   Single 322 (93.33) 
   Married 23 (6.67) 
Family’s monthly income (in PHP)  
   less than 9,000 8 (2.32) 
   9,000 to less than 17,000 8 (2.32) 
   17,000 to less than 35,000 47 (13.62) 
   35,000 to less than 125,000 148 (42.90) 
   125,000 to less than 185,000 40 (11.59) 
   185,000 and above 94 (27.25) 
Living environment background  
   Rural 96 (27.83) 
   Urban 249 (72.17) 
Location of attended medical school  
   Luzon 229 (66.38) 
   Visayas 84 (24.35) 
   Mindanao 32 (9.27) 
   Greater Metro Manila 168 (48.70) 
Considering working in a rural area soon after graduation?  
   Yes 235 (68.12) 
   No 110 (31.88) 

Table 1. Demographic characteristics of DCE respondents (N=345)



 
 
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This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
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of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

Based on the directly stated preferences for all 
identified job incentives (“Which of the following is the 
MOST IMPORTANT factor in your decision to work in 
a rural area?”, results not shown), career and distance of 
family from work facility was ranked as the most 
important, which is consistent with the results of the 
conditional logit model.  

Main effects conditional logistic regression 
analysis produced coefficients that were consistent with 
what was expected: the respondents preferred job 
postings with higher salaries (“wage”), available 
equipment (“equipment”), presence of supervision 
(“supervision”), locations near their families’ residences 
(“family”), better quality of life amenities (“QoL”), and 
available continuing professional development (CPD; 
“CPD”) and career opportunities (“career”). All seven 
incentives included in the model were significant at 1% 
level.  

Figure 1 shows how much (in Philippine 
currency, PHP) the respondents are willing to trade off 
from their monthly salary to get other incentives in the 
job package. The most significant willingness to pay 
(WTP) are those for “supervision” (PHP46,720.38, 95% 
CI: 26,512.59-66,928.16) and “family” (PHP43,467.51, 
95% CI: 24,919.09-62,015.92), with both incentives 
having wide confidence intervals. WTPcareer came third 
(PHP37,433.40, 95% CI: 27,207.71-47,659.09). As for 
the status of equipment in a rural job post, WTPequipment 
ranks fourth (PHP30,202.40, 95% CI: 25,104.13-
35,300.67), but respondents have less variation with this 
incentive. “QoL” and “CPD” have consistently low WTP 
values (PHP18,281.89, 95% CI: 12,401.89-24,161.89; 
PHP16,850.27, 95% CI: 11,688.33-22,012.22, 
respectively), stating that compared to the other 
incentives, the respondents are not very much concerned 
with the availability of daily living amenities and CPD 
incentives in rural job postings. 

Figure 1. Willingness to pay (WTP) for specific incentives, with 95% confidence intervals

 -

 10,000.00

 20,000.00

 30,000.00

 40,000.00

 50,000.00

 60,000.00

 70,000.00

 80,000.00

equipment supervision family QoL CPD career

W
ill

in
gn

es
s t

o 
pa

y 
(in

 P
H

P)

Incentive



 
 
MIGRIÑO 
 

 
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Central Asian Journal of Global Health 
Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

The relative effectiveness of different policy 
options was forecasted by calculating for differences in 
uptake rates compared with the worst-case (“baseline”) 
job posting scenario: base salary PHP36,000, poor 
equipment, absent supervision, family far from work, 
poor living amenities, CPD unavailable, no avenues for 
career development. Using random utility theory 
discussed in most health-related DCE and econometric 
studies which states that respondents will select the 
option with the highest utility relative to the other 

choices,13,20,21 uptake rates of the baseline job post with a 
change in level of each of the incentives were calculated 
(Figure 2). Among the non-wage incentives, presence of 
supervision was the most valued incentive (uptake 
rate=38.84%, SE 0.040), estimated to produce an impact 
a little above what could be expected from a 228% 
increase in base salary. This is followed by the impact of 
work facilities being near a physician’s family (36.39%, 
SE 0.040), then by provisions for career development 
(31.71%, SE 0.016). 

Note: all uptake rates are significant at 1% level. Baseline job posting incentives include base salary PHP36,000, poor equipment, 
absent supervision, family far from work, poor living amenities, CPD unavailable, and no avenues for career development. 

Figure 2. Uptake rates for selected job incentives compared to baseline, in % (N=345)

13.08%

24.91%

38.30%

25.89%

38.84%36.39%

15.90%

14.68%

31.71%

0%

5%

10%

15%

20%

25%

30%

35%

40%
salary increased to PHP51,000

salary increased to PHP65,000

salary increased to PHP82,000

GOOD
equipment/infrastructure/supply

Supervision/mentoring is
PRESENTFamily lives NEAR work facility

EASY access to daily living
amenities

CPD activities are AVAILABLE

WITH avenues for career
development



 
 
MIGRIÑO 
 

 
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This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

Post-estimation analysis (Figure 3) shows the 
relationship between increases in salary with the 
estimated uptake probabilities: with a 100% increase in 
salary alone, uptake rates may increase by 30%. 
However, a modest (52%) increase in salary plus 
ensuring provisions of avenues for CPD may provide a 
similar increase in uptake rates; this is worth noticing 
since “CPD” has been identified as a low-impact 
incentive in this study. Conversely, providing for a high-
impact incentive such as supervision in the workplace 
may net a perceptible increase (52%) in uptake rates, 

given the same modest (52%) increase in salary. Based 
on all the permutations presented, it can also be observed 
that at a certain level, further increases in wage led to 
decreasing net changes on uptake rates; at a certain level 
of wage increase, combinations of non-wage incentives 
might net higher uptake rates. The “sample posting” 
graph shows the impact of a combination of the three 
high-impact non-wage incentives (“supervision”, 
“family”, and “career”) with respect to changes in base 
salary. 

Note: Sample posting= with “supervision”, “family”, “career” 

Figure 3. Changes in uptake rates with different job incentives as base salary increases

  

0.99, 30%

0.52, 52%

0.52, 30%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

CH
A

N
G

E 
IN

 U
PT

A
K

E 
RA

TE

SALARY INCREASE IN MULTIPLES OF BASE SALARY (IN PHP)

salary increase only with "equipment" with "supervision" with "family"

with "QoL" with "CPD" with "career" sample posting



 
 

CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 
 

 

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Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

Discussion 

The demographic profile of the respondents 
closely resembled the general characteristics of medical 
interns in the Philippines. The greater distribution of the 
respondents to the Greater Manila Area reflect the 
location of most medical schools in the Philippines. Low 
response rates are consistent with electronic survey 
forms;22 however, Johnson et al. states that precision of 
DCE studies “flattens out at around 300 observations”,19 
while Lemiere mentions that DCE studies only need a 
small (~100) effective sample size.23 Due to the nature of 
the survey, it should be noted that the final sample 
population may not be fully representative of the target 
population. Yun and Trumbo have shown that internet-
based surveys tend to favor males of above-average 
socioeconomic backgrounds;22 these finding were not 
consistent with the result of the study. 

Medical students’ and graduates’ considerations 
to practice in rural areas rely in part on their rural 
upbringing: multiple studies have investigated the role of 
rural vs urban backgrounds in a young physician’s 
decision to pursue rural practice.24–27 In fact, a local study 
by Leonardia et al. confirms that a larger cohort of 
DTTBs came from rural backgrounds.28 Similar analysis 
for either sex or marital status as showing associations 
with doctors’ considerations of work opportunities in 
rural/urban areas were not significant. The result for 
directly stated preferences for all identified job incentives 
was consistent with past studies involving both 
doctors9,20,29 and nurses,30 but most studies on career 
involve career advancement after serving a specified time 
in rural practice as opposed to concurrent career 
advancement identified in this study. Additionally, the 
conditional logistic regression analysis results cross-
validate the results of the qualitative phase of the study 
and are also consistent with previous DCE 
studies.9,20,21,30–32 

Presence of supervision has been identified as a 
major factor in both qualitative and quantitative studies 
regarding rural job post preferences,20,30,32 and this can be 

triangulated from comments in Phase 1 of this study. It 
should also be noted that the incentive “family” is not 
routinely seen in other DCE studies; usually, location of 
the work area is relative to the nearest city.20,32 Only one 
DCE study was found to include interaction terms for 
“family”: the study by Smitz et al. in Timor-Leste noted 
significantly higher uptake rates of doctors with rural-
based families compared with those with urban-based 
ones.21 This should be considered in estimating the 
importance of this incentive in policy exercises. There 
also appeared to be a discrepancy with the relative 
importance of supervision: it ranked #5 when 
respondents were asked the directly stated preference 
question, but the DCE model concluded high βsupervision 
(0.82) and WTPsupervision (PHP46,720.38), consistent with 
previous studies9,20 where some degree of supervision is 
among the most significant incentives in rural practice. 
This discrepancy proves the utility of DCE to capture 
preferences not routinely seen in conventional stated 
preference methodologies.23 

Kolstad validates the inclusion of “career”, 
where her study in Tanzania confirms the relative 
importance of professional development early in the 
careers of young physicians;20 however, this study 
highlights the greater relative importance of supervision 
compared with career advancement, which was 
emphasized in another study by Kruk et al. in Ghana.9 
Additionally, this study, a study by Rana et al. in 
Pakistan, and a study by Hanson and Jack in Ethiopia 
confirmed the importance of equipment status as a main 
consideration in rural job posts,29,32 where respondents 
mentioned that poor equipment/infrastructure status and 
availability of supplies gives “a sense of unproductivity” 
which may lead to frustration.29   

The use of DCE enables researchers to 
minimize the major pitfall of other stated preference 
models, which is manipulation of responses due to direct 
or indirect incentives for the respondents.20 Using fixed 
effects models such as was done in this study, including 
that for salary (“wage”), is common practice; however, it 



 
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Volume 9, No. 1 (2020) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.344 | http://cajgh.pitt.edu 

 
 

may lead to implausible mean WTP estimates33 as was 
evident in the unrealistically high mean WTP of most 
incentives. However, in such cases the WTP values 
should be interpreted relative to each other than as 
absolute values to provide a more realistic impact of each 
incentive.31 

Overall, the post-estimation model predicts 
better uptake rates for job postings by including any one 
of the non-wage incentives listed here compared to a 
moderate increase in base salary. The studies by Kruk et 
al.,9 Kolstad20 and Serneels et al.,34 as well as by Smitz et 
al.,21 showed similar results, where uptake rates from 
provision of non-wage incentives such as better 
equipment status or presence of supervision were 
comparable to uptake rates from modest to significantly 
high increases in salary. These studies explained such 
results by the respondents’ high intrinsic motivation, 
relatively higher paying jobs, and/or being at the early 
stages of their professional career.21  

The study also tried to predict the impact of 
increases in wage (base salary) together with specific 
non-wage improvements in rural job postings. Increasing 
base salary or giving monetary incentives have been 
identified as “the most obvious way to induce greater 
labor supply”32 and results from the KIIs and FGDs in 
this study back up this argument (“higher base salary may 
offset the effect of absence of non-wage incentives”); 
however, estimates of increasing base salary by itself 
might not be the most efficient way to increase uptake 
rates, particularly from early-career doctors.  

Due to relatively low levels of financial 
motivation from recent medical graduates in 
consideration of rural practice, the Department of Health 
(DOH) and local government units (LGUs) may focus on 
adding non-wage incentives to current packages to 
produce a significant effect on uptake rates, taking into 
consideration the significant impact of a new doctor’s 
rural upbringing, background, and experience to 
consideration of the opportunity to practice medicine in 
rural communities. Cost estimates of potentially high-

impact non-wage incentives at the LGU level should also 
be conducted to see if such incentives are financially 
feasible. Policy makers such as officers of the DOH-
Health Human Resource Development Bureau  and 
MHOs need to evaluate the cost-effectiveness of 
combinations of incentives from current rural job posts, 
as well as in the design of new rural health job posts. This 
could help increase uptake rates of vacant rural health job 
posts, improve health indices and even help in the 
prioritization of the health budget of an LGU. In the 
selection process for medical students, the study’s 
disaggregated data (results not shown) signify the need 
for rural backgrounds and/or rural community 
experiences to be given higher priority. However, since 
DCE utilizes stated preferences, and few studies have 
been done to determine the relationship of stated 
preference and revealed preference models, it is highly 
recommended that routine monitoring of actual physician 
choices be done to validate the utility of models from 
similar studies using longitudinal cohort studies. 

 

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