Health service availability and health seeking behaviour in resource poor settings: Evidence from Mozambique

Anselmi, Laura; Lagarde, Mylène; Hanson, Kara Article Health service availability and health seeking behaviour in resource poor settings: Evidence from Mozambique Health Economics Review Provided in Cooperation with: Springer Nature Suggested Citation: Anselmi, Laura; Lagarde, Mylène; Hanson, Kara (2015) : Health service availability and health seeking behaviour in resource poor settings: Evidence from Mozambique, Health Economics Review, ISSN 2191-1991, Springer, Heidelberg, Vol. 5, Iss. 26, pp. 1-13, https://doi.org/10.1186/s13561-015-0062-6 This Version is available at: https://hdl.handle.net/10419/150505 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ RESEARCH ARTICLE Open Access Health service availability and health seeking behaviour in resource poor settings: evidence from Mozambique Laura Anselmi 1* , Mylène Lagarde 2 and Kara Hanson 3 Abstract Low-income countries are plagued by a high burden of preventable and curable disease as well as unmet need for healthcare, but detailed microeconomic evidence on the relationship between supply-side factors and service use is limited. Causality has rarely been assessed due to the challenges posed by the endogeneity of health service supply. In this study, using data from Mozambique, we investigate the effect of healthcare service availability, measured as the type of health facilities and their level of staffing and equipment, on the individual decision to seek care. We apply an instrumental variable approach to test for causality in the effect of staff and equipment availability on the decision to seek care and we explore heterogeneous effects based on the distance of households to the closest health facility. We find that living in the proximity of a health facility increases the probability of seeking care. A greater availability of referral health services in the locality has no significant effect on decision to seek care, while greater availability of staff and equipment increases the probability of seeking care when ill. Demand side barriers to health care use exist, but have a smaller impact when health care services are available within one hour walking distance. Keywords: Health seeking behaviour; Demand for health care; Health care availability; Reverse causality; Instrumental variables; Mozambique Introduction Despite the high burden of preventable and curable disease in Low and Middle-Income Countries (LMICs) [1], there is considerable unmet need for health care [2]. Service availability is still limited and numerous barriers to access exist [3], preventing service use especially for the poorer socio-economic groups [4, 5]. Given these premises, exploring the determinants of service utilisation is central to identifying the causes of inequalities in health and health care. Especially where service provision is constrained and unequal across geographic areas, quantifying the causal effect of health care supply on use is key to understand inequity in all dimensions of health care financing and provision. Identifying the separate effects of supply and demand-side determinants of health care use may provide indications to policy makers about the most effective levers to increase access and encourage the use of services when needed. The empirical literature on the determinants of health care use in LMICs has mostly relied on household survey data. Health seeking behaviour has been analysed by estimating the individual probability of seeking care when ill, or the probability of choosing a specific type of provider [6–11]. Due to the limited data on health care services available to the household, most studies have focused on the influence of demand-side factors, including individual demographic and socio-economic characteristics, as well as the indirect cost of using services, proxied by the travel time to the nearest health facility (HF). This body of literature highlights the existence of demand-side barriers to service use such as household geographic remoteness (and therefore difficulties in reaching the providers), low education levels, cultural aspects and poor economic conditions. * Correspondence: [email protected] 1 Manchester Centre For Health Economics, Institute of Population Health, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester M13 9PL, UK Full list of author information is available at the end of the article © 2015 Anselmi et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Anselmi et al. Health Economics Review (2015) 5:26 DOI 10.1186/s13561-015-0062-6 A limited number of quantitative empirical studies have analysed how aspects of access to or quality of health services supply affect the demand for health care [8–11]. The existing studies have so far captured the effects of availability and affordability, two key dimensions of access to health care [12]. Affordability has been proxied by user fees [8, 13], while availability has been measured through structural indicators, such as the number, type and conditions of health infrastructure [9] and the availability in HFs of staff [10], equipment [14] and drugs [9, 11]. Fewer studies have analysed the impact of quality on service use, using measures of technical quality, such as staff adherence to the treatment protocol [10, 15], or patients’perceptions of quality [16]. Although HF characteristics have been interpreted as proxies for structural quality, it could be argued that they are capturing health care availability through the HFs’capacity for service provision. The evidence points to the existence of a positive correlation between the availability and quality of health services and their use [9, 10, 14]. Most of the existing studies assess the correlation between supply and use of health care services but they are not able to determine the causal impact of supply factors on health care use due to potential reverse causality. Indeed more and better resources for service provision are likely to be allocated where use is higher. Although the endogeneity of health care services supply has been acknowledged in the literature [17, 18], the issue has been addressed in only one of the studies looking at the determinants of health care seeking in LMICs. Kumar et al. (2014) [19] used an instrumental variable (IV) approach to estimate the causal impact of the household distance to the HF (interpreted there as a measure of access) on institutional deliveries. In this study, we seek to investigate the effect of health service availability on the decision to use health care when ill. We use household survey and routine HF data from Mozambique and measure health service availability along two dimensions: the type of HF available in the locality where a household lives (i.e. lower-level HFs providing only basic primary care vs. higher-level HFs providing extended primary and secondary care) and their resources in terms of staff and equipment, as a proxy for their capacity for effective provision of care. We consider the number and type of HFs to be fixed and exogenous in the period covered in this study, which would not allow for adjustment of health care supply to utilization patterns. However, since staff and equipment could be allocated to HFs where service use is higher there is an endogeneity problem which we overcome by following Kumar et al. (2014) [19] and using an instrumental variable approach. Specifically, we use the availability of housing for personnel as an instrument for staff and equipment. Finally, we explore heterogeneous effects by carrying out the analysis separately on the sub-samples of individuals living close (less than one hour walking time) and far (more than one hour walking time) from the nearest HF. This study contributes to the existing literature in two ways. First, it adds to the limited evidence on the effect of health care supply on health seeking behaviour in LMIC, and in particular in Mozambique. Second, we test for causality in the relationship between health service availability and use through an instrumental variable approach. The next sections describe the study setting and the data, present the methods and the results and discuss and conclude. Setting and data Country setting Mozambique is a sub-Saharan country with a population of 26 million. After independence from Portugal in 1975 and a subsequent civil war which lasted until 1992, the country has experienced peace, sustained economic growth over 6 % per annum and improvements in socio-economic indicators. For example, between 1997 and 2007, the GDP rose from 236 to 454 USD per-capita, the share of population living below the poverty line felt from 69 % to 55 % and the primary school completion rate increased from 22 % to 77 % (MPD, 2010). Health care provision progressively expanded and health indicators improved. The number of health facilities increased from 1,210 in 2004 to 1,392 in 2011 and the number of doctors from 424 in 2000 to 1106 in 2010. Infant and under-five mortality reduced from 106 to 64 per thousand live-births and from 158 to 97 per thousand livebirths between 1996 and 2011 [20]. However, life expectancy is still low (49 years), and similarly to other LMICs, the burden of disease is still high and predominantly constituted by preventable and curable diseases, most notably HIV/AIDS, malaria and respiratory diseases (IHME, 2013), which are also among the major causes of under-five mortality [21]. Inequalities in health and health care use still exist, and appear to be related to household socioeconomic indicators (mainly wealth and education) and inequalities in health care availability across provinces and districts [22–25]. Health care is mostly publicly funded and provided. Central, provincial and district levels constitute the backbone of the top-down hierarchical health sector organization. Specialised care is managed at provincial level and provided by central or provincial hospitals, while primary and secondary care are managed at district level and provided by district hospitals, health centres and clinics. Clinics provide only basic primary care, health centres provide inpatient and general medicine consultation, while district hospitals provide also small surgery. At least one health centre is available in most districts particularly where a district hospital is not. During the time Anselmi et al. Health Economics Review (2015) 5:26 Page 2 of 13 period covered by the data used in this analysis private care was limited to a few clinics, mainly concentrated in the capital [23]. Inequalities in the supply of health services in Mozambique are a reflection of disparities across provinces, districts and localities in the number and type of HF, as well as of human, financial and physical resources, such as drugs, consumables and equipment [22, 25, 26]. National directives from the MoH set the classification of HFs as well as, for each type of HF, norms for the minimum staff and equipment requirements based on the minimum inputs required to deliver the health care that a HF ought to provide to the population in the catchment area [27]. To improve recruitment and retention of professional health care workers outside the capital city Maputo, houses should be made available to mid- and high-level staff next to the HFs where they are working [28]. Despite these existing norms, lack of adequate equipment, staff and housing options are widespread across HFs in the country, and much more prevalent in some provinces and districts than others [25, 26]. User fees in public HFs are low: in local currency, Mozambican Meticais (MZM) 2 and MZM 1 for outpatient consultation in urban and rural areas, MZM 5 for all drug prescriptions and MZM 10 for inpatient care (equivalent to USD 0.07, USD 0.04, USD 0.16 and USD 0.32 respectively) and exemptions cover the large majority of the population [23]. Although higher fees are applied to prevent unreferred access to district and provincial hospitals (known as “bypassing”), anecdotal evidence of unreferred cased in district hospitals and provincial hospitals exist. Indeed, when individuals have access and can afford user fees they may decide to go directly to hospitals which offer a wider range of services and have extended working hours [23]. Beyond the limited direct costs implied by user fees, the indirect cost implied by distance from the HF and low levels of education and income limit the use of health care services [6, 7]. Provincially and temporally limited evidence suggests that the presence of trained health personnel in HF may influence institutional deliveries but not the use of outpatient care [14]. Data In this study, we use data on health care utilisation and on individual, household and community characteristics from the 2008/2009 household budget survey [29]. The sample consists of 10,831 households and 51,188 individual observations (9,362 households and 45,356 individuals in 847 communities excluding Maputo City) and is representative at provincial and urban and rural areas level. Data were collected through household and community questionnaires administered in the 599 rural communities. As in similar surveys, information on health care seeking (decision to seek care and choice of provider) is available only for individuals who reported illness in the past two weeks. Following the approach adopted in most of the existing literature, we restrict the sample of the analysis to those individuals who reported illness in the recall period. Measures of household (real) consumption per capita, spatially and temporally adjusted, were calculated by the Ministry of Planning and Development (MPD) for the third national poverty assessment, based on the Household Budget Survey 2008/ 2009 data [30, 31]. Adult equivalence scales were also provided by the MPD [32]. Data on HFs are derived from the National Health Information System [33] as provided by the Ministry of Health (MoH) in June 2012. A complete list of existing HFs is available for 2009, with information on staffing, equipment and housing for personnel. We verified the existence of each HF and its location based on a census of HFs undertaken in 2007 [34] and resolved mismatches through consultation with the relevant provincial or district directorates of health. Since routine data collected at local level may be biased and resource availability may be understated in less resourced HFs, to minimize inconsistency and bias, we cross-checked information on availability of staff and equipment across all available years (2008, 2009, 2010 and 2011). When a large discrepancy was found, the 2009 value was substituted with the average across the four years, to avoid using data that reflect availability in an exceptional period rather than a typical one. If the discrepancy was found for one year only, the exceptional year was excluded from the calculation if the average. A total number of 1,261 HFs providing primary and secondary care constituted the database in 2009. Data for the HF located in four districts (Mecula, Ibo, Tambara, Massingira), where less than one percent of the national population live, were not available and the districts were excluded from the analysis. Information about the recommended minimum service coverage, staffing, equipment and availability of staff housing for each type of HF were extracted from official documents [24, 27, 28] and used to calculate for each item the ratio of the actual availability and the minimum set by norms. The norms used as a benchmark for the items included in the HF routine data are the following (reported for clinics, health centre and district hospitals respectively): health workers with basic level health training: 6, 13, 39; medium level health training: 1, 9, 29; high level health training: 0, 1, 9; and availability of an autoclave: 1, 1,1; bike:2,2,2;motorbike:0,1,9;andhousesforstaff:0,1,9. Since the Household Budget Survey does not provide information on the specific HF visited by individuals, we merged household survey and HF data at the locality level. In 2009, excluding Maputo City, the country was organised in 10 provinces, 142 district administrations and 1272 localities. Districts comprise between 1 and 22 Anselmi et al. Health Economics Review (2015) 5:26 Page 3 of 13 localities, which cover a population between 250 and 50,000 people, except for some urban localities which cover up to 150,000 people. The organization of the public health sector referral system and the limited presence of alternative care providers led us to focus on the decision to visit a public HF providing outpatient care when ill. We assume that individuals visit the closest HF. Since most HF catchment areas fall within the administrative boundaries of the locality of residence, we assume that the closest HF is within the locality of residence of the household. Because of their unusual pattern of health service provision and peculiar demographic and socio-economic characteristics compared with the rest of the country, we excluded Maputo City and Matola from the analysis Methods Estimating the probability of seeking care The economic analysis of health care use has so far been rooted in a random utility model framework [35, 36], where individuals maximize their utility according to preferences over health and the consumption of other goods, conditional to their budget constraint, which incorporates individual income and the prices of consumables. The individual utility function can therefore be written as: Ui¼Hi;Zi ðÞ ð1Þ where H i is health status, which depends on the decision to seek care, and Z i is the bundle of other goods consumed by individual i. Individuals derive indirect utility from health care through the improvement of their health status and they choose from the affordable combinations of health care and other consumables the one that maximizes their utility: U i¼max US1 i;US0 i  ð2Þ where US1 iand US0 iare the utility levels associated with using health care or not. Since both H j and Z j depend on a set of individual, household and community characteristics, including health care use, using a latent variable approach, we defined the observed decision to seek care as a function of the observed determinants of health care demand and supply: y icl ¼US1 i−US0 i¼α1þβ1Xicl þβ2Dcl þβ3HLFlþβ4HRElþεicl Sicl ¼1if y icl≥0 0if y icl <0 ð3Þ where y icl is the unobserved difference between the utility from seeking (US1 i) and not seeking care (US0 i), and S icl is a dummy taking value 1 if the individual i,in community cand locality l, is better off when seeking care from a public provider, and 0 vice versa. Due to the lack of additional information we assume that each episode of health care seeking is an initial contact and we do not consider follow-up visits. HLF l and HRE l , capture the supply-side characteristics in locality l, where we assume that the HF closest to the household is located. HLF l is the proportion of higher level HFs (health centre and district hospital) out of all HFs in locality land accounts for the type of service which is accessible. HRE l is an index of HF staffing and equipment, to account for technical quality of the service provided. HRE l is measured as the ratio of available to minimum staff and equipment required by norms averaged across the following six dimensions: basic, medium and high level trained health cadres hired by the government, functional motorbike, car and autoclave. To capture the complementarities between human resources and equipment inputs in service delivery, we attributed the same weight to all dimensions. For localities with more than one HF, we calculated HRE l by averaging across HFs and based on the assumption that individuals would visit the closest HF in the same district we imputed HLF l and HRE l using the equivalent average figures at district level for localities without HF. User fees are not included here as supply-side determinant of service use since they are minimal and we do not expect variability across HFs. D cl includes a set of dummies for the time required to walk between the community and the closest HF. Three dummies are defined according to the following thresholds: 0–59 minutes, 60–119 minutes and 120 minutes or more, which we used as the reference category. Distance from the closest HF was set to 0–59 minutes for households in urban areas to which the community questionnaire did not apply but where HFs are more concentrated. Distance from the HF, as well as transport availability and employment conditions, defined below, capture the indirect costs of using health care. X icl is a vector of additional individual, household and community characteristics: –Gender and age to account for specific health care needs; –Two non-mutually exclusive dummies for selfdefined employment to account for the monetary and time opportunity cost of taking time off to visit a HF: permanently employed (versus seasonally or occasionally) and non-remunerated housekeeping worker. –The highest level of education attained among household members, measured by years of schooling, as a proxy of social status, was preferred to the commonly used level of education of the head of household who in the specific context often had Anselmi et al. Health Economics Review (2015) 5:26 Page 4 of 13 limited schooling opportunities due to civil war disruptions; –The household adult equivalent consumption per capita, log-transformed to allow for nonlinear effects, to capture the economic condition; –The average number of household members per room, to capture the availability of assets which is not captured by and may diverge from consumption measures of economic status [37]; –The availability of a latrine in the house, to account for household access to sanitation; –The availability of public transport reaching the community, to account for geographic remoteness and ease of travelling to, and accessing, a HF; –The month of the interview, corresponding to the month of the reported illness, to account for disease seasonality. From the empirical specification shown in (3) we estimated the probability of seeking care, using a probit model: Pr Sicl ¼1jXicl;Dcl;HLFl;HREl ðÞð4Þ ¼Φα1þβ1Xicl þβ2Dcl þβ3HLFlþβ4HREl  We corrected for clustering at the locality level, the lower administrative level which incorporates villages with similar characteristics in terms of health care and other public service provision, and we included dummies for province fixed effects. The analysis was carried out using Stata 13. Using an instrumental variable to test for causality in the effect of health services availability We use an instrumental variable (IV) approach to test for causality in the relationship between health care availability and the decision to seek care. The availability at the HF of staff houses in good physical condition was selected as an instrument for the availability of staff and equipment. Since it has no effect on the individual decision to seek care other than through the availability of staff and equipment, for which is it is a good predictor, the availability of staff houses satisfies both the relevance and external validity conditions [38]. Indeed, staff housing is an important non-financial benefit for the retention of human resources in rural areas [39]. In Mozambique, according to a recent study, after salary, the availability of housing is the most important incentive for health workers to accept a placement outside of the capital [40]. Housing for personnel is therefore likely to increase health care personnel in the HF, and in turn equipment and other resources, such as drugs [41], which likely depend on the presence of HF staff. Furthermore, since 2007, districts have been given autonomy to build clinics and staff housing, and started at the same time to receive decentralised financial resources from the Ministry of Finance for small local initiatives, for which the prioritization criteria were not fixed. Decisions on allocations would ultimately depend on the quality of the investment proposal. New houses for personnel were frequently built and old houses refurbished, given the relatively small investment required and the potential gain from attracting extra health staff [23]. Because of the decentralization of responsibilities and financial resources , the distribution of staff housing does not depend exclusively on provincial or national health administrations (MISAU, 2012c) and is therefore not potentially correlated with the patterns of service use. We estimated an IV probit model which includes a set of two equations: the first estimates the probability of seeking care, as previously described in equation (3), and the second predicts HF staff and as a function of housing for personnel as well as of the other independent variables: y icl ¼α1þβ1Xicl þβ2Dcl þβ3HLFlþβ4HRElþεicl HREl¼γ0þγ1Xicl þγ2Dcl þγ3HLFlþγ4HSlþvicl Sicl ¼1if y icl≥0 0if y icl <0  As in (3), S icl is a dummy taking value 1 if the individual i, seeks care from any public provider and 0 vice versa, HLF l and HRE l capture supply-side characteristics, D cl is the set of dummies capturing the distance from the closest HF and X icl is a vector including other demand-side characteristics and controls, as in (3). HS l is the ratio of available staff housing to the minimum set by norms for each type of HF. In localities where more than one HF is available, we average housing availability across HFs and in localities where no HF is available, we inpute the average across all district HFs. After estimating the model, we checked for the endogeneity of HRElby looking at the Wald test of exogeneity, which in the case of a single endogenous variable reduces to testing the null hypothesis of no correlation (rho) between the error terms of the first and second equations, εicl and vicl . A significant rho and a Wald test rejecting the null hypothesis of exogeneity would suggest endogeneity of HF staff and equipment [42]. A significant coefficient associated with HS l (γ4) and an F statistic of the first-stage OLS regression greater than 10 would suggest that HS l is a non-weak instrument [42]. (5) Anselmi et al. Health Economics Review (2015) 5:26 Page 5 of 13 Exploring heterogeneity in the effect of health care availability To explore the heterogeneity in the effect of health care availability, we estimated the effect of staff and equipment availability on the decision to seek care separately for the two-subsamples of households according to their proximity to a HF. Households are considered close to and far from a HF if it can be reached in less than one hour by foot and vice-versa. Results Individuals reporting an illness during the two weeks preceding the interview represent 13 % of the whole survey sample. Of these, 61 % sought care from a public HF, while only 3 % sought care from other providers (see Table 1 for descriptive statistics). 92 % of all individuals and 97 % of those residing within one hour of the closest HF live in a locality with at least one HF. Around 80 % of the HFs are clinics and they have 77 % and 60 % of the minimum staff and equipment and staff housing set by norms. Greater variability is observed across localities, rather than across districts, and the mean household characteristics appear to be an average between the 60 % of the population living within one hour walking distance from a HF and the remaining 40 %. Table 2 presents the results from the probit and IV probit models estimated on the whole sample. The probit model (Column 1) shows that living in proximity of a HF increases the probability of seeking care when ill, while neither the type of HF available in the locality nor their staff and equipment affect the decision to seek care. Among the demand side characteristics, as expected, having better education, income and assets, being permanently employed, living in a house with latrine and in a community reached by public transport, all increase the probability of seeking care when ill. Interestingly, being employed in unpaid housekeeping work and residing in urban area reduce the probability of seeking Table 1 Descriptive statistics for individuals ill in the two weeks prior to the interview, Mozambique 2009 Variable Mean SD Min Max Service utilisation Number of visits to HF (previous month) 0.75 0.83 0.00 15.00 Seeking care from a public HF 0.61 0.49 0.00 1.00 Seeking care from other providers 0.03 0.18 0.00 1.00 Demand-side characteristics Woman 0.58 0.49 0.00 1.00 Age 24 22 0 99 Highest level education in household (years schooling 5.44 3.23 0.00 18.00 Employed in permanent work 0.44 0.50 0.00 1.00 Employed in non remunerated housekeeping work 0.20 0.40 0.00 1.00 Household adult equivalent consumption per-capita (MZM per day) 33 33 1 921 Number of household members per room 1.98 1.23 0.03 10.00 Latrine in the house 0.58 0.49 0.00 1.00 Transport reaching the community 0.63 0.48 0.00 1.00 Urban 0.37 0.48 0.00 1.00 Supply-side characteristics 1 hour time distance from closest HF 0.60 0.49 0.00 1.00 2 hours time distance from closest HF 0.08 0.28 0.00 1.00 More than 2 hours time distance from closest HF 0.31 0.46 0.00 1.00 HF in locality of residence 0.92 0.28 0.00 1.00 Percentage of HCs and DHs among HFs in locality 0.23 0.24 0.00 1.00 Percentage of HC s and DHs among HFs in district 0.18 0.11 0.33 1.00 HF staff and equipment index (locality average) 0.50 0.26 0.00 2.13 HF staff and equipment index (district average) 0.47 0.18 0.19 1.25 HF housing availability index (locality average) 0.60 0.89 0.00 10.00 HF housing availability index (district average) 0.60 0.62 0.00 3.38 Number of observations: 6,034 HC: health centre, DH: district hospital Anselmi et al. Health Economics Review (2015) 5:26 Page 6 of 13 care from a public HF, which may reflect time constraints and greater availability of alternative providers. The IV probit model (Columns 2 and 3) rules out the hypothesis of reverse causality bias in the probit estimates. Indeed the significance of the coefficient associated with the ratio of available to minimum number of houses in the first stage of the IV probit and the F-test (F = 18.7) confirm that it is a not a weak instrument. The Wald test (p= 0.397) result suggests that HF staff and equipment is not endogenous in the first place and the probit estimates are more efficient and should be preferred to the IV estimates [42]. The coefficient associated with HF staff and equipment in the probit estimates can be given causal interpretation. Even if we were to follow stricter criteria suggesting that the difference between the probit and IV probit coefficient associated with the availability of staff and equipment could per se be a sign of endogeneity [42] and therefore prefer the IV probit coefficient, we would still find a non-significant effect but a larger coefficient. Table 3 shows the results of the probit models estimated on two sub-samples of individuals, living within one or more than one hour from the closest HF. Three findings emerge from the probit estimates on the subsample of individuals living close to a HF (Column 1). First, as before, the type of services provided in the locality has no significant effect on the decision to seek care. However, the availability of staff and equipment in local HFs has a positive and significant effect on the decision to seek care, with a marginal effect of 0.075. The latter corresponds to an increase of 0.00075 for each extra percentage point of the ratio of available to minimum HF staff and equipment. Since on average HF currently have only 50 % of the staff and equipment set by norms, reaching the minimum set Table 2 Determinants of the decision to seek care when ill, Mozambique 2009 Probit IV-Probit 2 nd stage 1 st stage Supply-side characteristics Percentage of HCs and DHs among HFs in locality 0.042 0.033 −0.118 (0.038) (0.040) (0.077) HF staff and equipment index (locality average) 0.043 0.122 (0.029) (0.083) HF availability of housing (locality average) 0.098*** (0.023) HF time distance: < 1 hour 0.184*** 0.182*** 0.016 (0.026) (0.026) (0.019) HF time distance: 1–2 hour 0.053 0.050 0.034 (0.033) (0.034) (0.028) Demand-side characteristics Woman 0.011 0.011 0.000 (0.014) (0.014) (0.005) Age −0.005*** −0.005*** 0.000 (0.001) (0.001) (0.001) Age squared 0.000*** 0.000*** 0.000 (0.000) (0.000) (0.000) Higher level education attained in household 0.012*** 0.012*** 0.005* (0.003) (0.003) (0.003) Employed in permanent work 0.061*** 0.063*** −0.015 (0.020) (0.020) (0.013) Employed in housekeeping work −0.046** −0.046*** 0.002 (0.020) (0.019) (0.009) Log household consumption per-capita 0.032*** 0.031*** 0.014* (0.010) (0.010) (0.008) Number of household members per room −0.014** −0.014** −0.001 (0.006) (0.006) (0.004) Latrine in the house 0.029* 0.030* −0.007 (0.017) (0.017) (0.014) Transport reaching the community 0.069*** 0.068*** 0.016 (0.025) (0.025) (0.018) Urban −0.052** −0.060** 0.138*** (0.025) (0.025) (0.039) Constant Yes Yes Yes Province dummies Yes Yes Yes Month dummies Yes Yes Yes Table 2 Determinants of the decision to seek care when ill, Mozambique 2009 (Continued) Observations 6,026 6,026 Pseudo R-Squared 0.090 Log pseudolikelihood −3676.406 −2916.161 Rho −0.054 Standard error (0.056) Wald test of exogeneity (a) 0.333 p-value 0.397 F-test first stage (a) 18.697*** Average marginal effects reported 1 st and 2 nd Stage refer respectively to the reduced-form and structural equations Standard errors corrected for intra-cluster correlation at locality level in parentheses (a) Adjusted for clusters (N= 452) HC health Centre, DH district Hospital ***p< 0.01, **p< 0.05, *p< 0.1 Anselmi et al. Health Economics Review (2015) 5:26 Page 7 of 13 by norms, on its own would increase the probability of seeking care for those who live near a HF by 0.04. The probit results for the population living further away from HFs (Column 4) show that neither the type of services available in the locality, nor the availability of staff and equipment have a significant effect on the decision to seek care. Unlike in the analysis performed on the whole sample, employment in housekeeping work, the availability of a latrine in the house and of public transport reaching the community have no significant effect for those living faraway from a HF. The non-significant effect of transport availability in the sub-sample considered here suggests that if the HF is further away the opportunity cost to reach it may be so high that travelling to it would not be considered an option. Robustness checks We carried out five additional analyses to test the robustness of our results to the assumptions made and the methods chosen. The marginal effects associated with the two measures of availability of health services used throughout this paper, obtained by re-estimating all models for each robustness check, are summarized in Tables 4 and 5. First, since in settings with a number of different providers individuals may not automatically seek care from the closer one, but rather seek higher quality of care [43], we allow for individuals to seek care from a HF in their district rather than locality of residence and used district measures of health care availability. The coefficient associated with the availability of staff and equipment in these models is still significant and larger. However, the Wald test indicates endogeneity and housing for personnel is a weak instrument in this model, indicating that caution should be taken in inferring a causal relationship at district level. The level of service availability in a district has a positive and significant effect on care seeking. Second, since in provincial capitals health care provision is notoriously more heterogeneous and housing allowances may be given to staff instead of providing accommodation, we excluded the provincial capital districts from the sample. The marginal effect of district average HF staff and equipment on the decision to seek care for individuals living in proximity of a HF is still positive, and even larger, and the proportion of HFs offering a higher level of services has a positive and significant effect for the whole sample, as well as for those living in proximity of a HF. In this specification the Wald test confirms exogeneity of the availability of staff and equipment. Third, we used an alternative HF staff and equipment measure where we weight each of the six dimension using factor scores obtained from a principal component analysis (PCA). PCA is a multivariate statistical technique Table 3 Effect of supply-side characteristics on healthcare seeking according to distance from the closest health facility, Mozambique 2009 Hhold lives within 1 hour from HF Hhold lives more than 1 hour from HF Probit Probit Supply-side characteristics Percentage of clinics among HFs in locality −0.025 −0.077 (0.050) (0.063) HF Staff and Equipment index (locality average) 0.075*** −0.060 (0.029) (0.061) HF time distance: 1–2 hour 0.054 (0.036) Demand-side characteristics Woman 0.026 −0.014 (0.017) (0.020) Age −0.004*** −0.007*** (0.001) (0.002) Age squared 0.000** 0.000 (0.000) (0.000) Highest level education in hhold (years schooling) 0.009*** 0.018*** (0.003) (0.005) Employed in permanent work 0.045** 0.085** (0.021) (0.037) Employed in housekeeping work −0.046* −0.038 (0.028) (0.027) Log hhold consumption per-capita 0.028** 0.047** (0.011) (0.020) Number of hhold members per room 0.006 −0.039*** (0.010) (0.008) Latrine in the house 0.034 0.017 (0.024) (0.026) Transport reaching the community 0.106*** 0.052 (0.037) (0.032) Urban −0.060** (0.025) Constant Yes Yes Province dummies Yes Yes Month dummies Yes Yes Observations 3,597 2,429 Pseudo R-squared 0.040 0.070 Log pseudolikelihood −2,079.49 −1,557.05 Average marginal effects reported Standard errors corrected for intra-cluster correlation at locality level in parentheses Number of clusters: 1 hour walking distance: 234, more than 1 hour walking distance: 281 HC health Centre, DH district Hospital ***p< 0.01, **p< 0.05, *p< 0.1 Anselmi et al. Health Economics Review (2015) 5:26 Page 8 of 13