metadata: authors: 0: author: Iimi,Atsushi
1: author: Rao,Kulwinder Singh
count: Liberia
docna: 0: docna: Transport connectivity and health care
            access : evidence from Liberia
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Transport Services,Inequality,Health Service Management and Delivery
teratopic: Industry,Health, Nutrition and Population,Poverty Reduction,Transport
abstracts: cdata!: Health care access is an important
            policy concern, especially in rural areas. It is especially
            challenging in developing countries, where rural residents
            are poorer and less likely to be insured than those living
            in urban areas. Using the case of Liberia, this paper
            examines the effects of transport connectivity on health
            care access. The Ebola crisis in 2014 and 2015 clearly
            revealed the vulnerability of the country's transport
            and health systems to unexpected external shocks. Paying
            particular attention to the possible challenge of
            endogeneity associated with infrastructure investment, the
            study found that transport connectivity, especially greater
            road density, can increase access to health care, but there
            was no significant effect of road quality. This may be
            because of significantly skewed underlying data. The vast
            majority of roads in Liberia are in poor condition. The
            study also found that the statistical effect of road density
            varies depending on distance from a health facility. The
            effect is particularly significant within a 30- to
            50-kilometer radius. Not only rural accessibility, but also
            broader transport connectivity needs to be developed to
            increase health care access.
geo_reg_key: 517191,1927320
repnme: repnme: Transport connectivity and health care
            access : evidence from Liberia
display_title: Transport connectivity and health
            care access : evidence from Liberia
listing_relative_url: /research/2018/04/29816101/transport-connectivity-health-care-access-evidence-liberia
topicv3: Transport m1326661 135,Poverty m1326687 695,Health Services Delivery m1327294 1305,Population, Aging and Health m1327736 640,Health m1504661 3480
publishtoextweb_dt: 2018-04-18T00:00:00Z
historic_topic: Industry,Health, Nutrition and Population,Poverty Reduction,Transport
no_of_pages: 21
author_keys: 0: author_key: 914707
1: author_key: 1110678
model_extractions: WPS8413 Policy Research Working Paper 8413 Transport Connectivity and Health Care Access Evidence from Liberia _Atsushi Iimi Kulwinder Rao_ Transport and Digital Development Practice April 2018
Policy Research Working Paper 8413

# **Abstract**

Health care access is an important policy concern, especially in rural areas. It is especially challenging in developing countries, where rural residents are poorer and less likely to be insured than those living in urban areas. Using the case of Liberia, this paper examines the effects of transport connectivity on health care access. The Ebola crisis in 2014 and 2015 clearly revealed the vulnerability of the country’s transport and health systems to unexpected external shocks. Paying particular attention to the possible challenge of endogeneity associated with infrastructure investment, the study found that transport connectivity, especially greater road density, can increase access to health care, but there was no significant effect of road quality. This may be because of significantly skewed underlying data. The vast majority of roads in Liberia are in poor condition. The study also found that the statistical effect of road density varies depending on distance from a health facility. The effect is particularly significant within a 30- to 50-kilometer radius. Not only rural accessibility, but also broader transport connectivity needs to be developed to increase health care access.

This paper is a product of the Transport and Digital Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Transport Connectivity and Health Care Access:**

# **Evidence from Liberia**

Atsushi Iimi<sup>¶</sup> , Kulwinder Rao Transport & ICT Global Practice, The World Bank Group Key words: rural roads; health care access; transport connectivity; instrumental variable estimation.

JEL classification: C25, C26, I15, Q42, R40.

> ¶ Corresponding author.
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# **I. Introduction**

Health care access is an important challenge in rural areas, particularly in developing countries. It is generally difficult for people who live in remote areas to access good-quality health services (e.g., Gamm et al. 2003; Institute of Medicine 2005; Bourke et al. 2012). Even in the United States, rural residents of Washington State were less likely to visit the emergency department than their urban counterparts (Lishner et al. 2000). According to Laditka et al. (2009), rural residents are more likely to be hospitalized for ambulatory care– sensitive conditions in the United States. This could be prevented if people had better access to primary care, but because they do not, their condition tends to deteriorate.

Limited health care access in rural areas is attributed to demand and supply constraints. The demand for health services is weak in rural areas, holding everything else constant, because rural residents tend to be poor. Subscription to health insurance is generally lower in rural areas (e.g., Jovanovic et al. 2003). Without insurance, rural U.S. residents are less likely to use emergency medical services (Fan et al. 2011). Remoteness and unavailability of transport services increase costs further for rural residents (e.g., Klemick et al. 2009). Rural dwellers’ travel distance to health facilities is much longer than that of urban dwellers (Gamm et al. 2003). In rural areas, other social and economic facilities also tend to be scattered or nonexistent, which also reduces demand for health care. Nemet and Bailey (2000) show that not only distance, but also people’s activity space, including grocery shops and community centers, determines health care usage.

On the supply side, rural areas generally have fewer doctors and nurses. In the United States, approximately 20 percent of the total population lives in rural areas, but only 9 percent of physicians practice in rural areas (Rosenblatt and Hart 2000). The quality of health care services provided in rural areas may also be low. Rural physicians often see more patients than urban doctors do and therefore may have to compromise the level of services they provide. Bronstein et al. (1997) show that rural health services are cheaper but that fewer episodes include outpatient facility charges and that fewer ancillary services are provided.
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Supply and demand constraints are complex in developing counties. Particularly in SubSaharan Africa, urban–rural inequality is significant in terms of health care access. In Liberia, for instance, the average distance to a functioning health facility is approximately 7 kilometers. Approximately 40 percent of rural residents must spend more than 2 hours to walk to the nearest health facility (Kruk et al. 2010). In Zambia, approximately 75 percent of urban residents, but only 30 percent of rural residents, live within 2 kilometers of the nearest health facility (Zambia Central Statistical Office 2012).

The current paper examines the effect of transport connectivity on health care access in Liberia. Before the Ebola crisis, Liberia was experiencing robust economic growth, at more than 7 percent on average, mainly driven by strong natural resource exports such as iron ore and rubber. The majority of people engage in subsistence farming, with limited market access, and live below the poverty line. The national poverty rate was estimated at 63.8 percent in 2007. The Ebola crisis in 2014 and 2015 revealed vulnerability of the country’s transport and health care systems to unexpected external shocks. The transport network does not seem to have been adequate to manage a rapid pandemic of disease in rural and remote areas. Economic activity is highly concentrated in Monrovia, but more than 2.2 million people, or approximately half of the total population, live in rural areas.

To examine the effect of transport connectivity on health care access, this paper uses newly collected spatial data at the facility level with the potential endogeneity issue associated with infrastructure placement taken into account. Although attribution is weak, the paper shows that road infrastructure is important to meet health care demand in rural Liberia. The following sections are organized as follows: Section II provides an overview of the road network and health facilities in Liberia. Section III describes data and methodologies. Section IV presents main estimation results and discusses policy implications. Section V concludes.
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supervision of trained health personnel. To control for differences between facilities, the number of beds ( _BED_ ) was used as a proxy for each institution’s capacity. A dummy variable for health centers ( _CENTR_ ), as opposed to hospitals, is also included in _X_ . Health centers generally have less capacity than hospitals. In addition, to control for the size effect, population density within a 10-kilometer radius of each facility, calculated using the global population data set, WorldPop ( _POPDEN_ ), was included. In populated areas, it is expected that more people will visit a health facility regardless of transport connectivity.

# **<u>Table 1 Summary Statistics</u>**

|<br>Variable|Abb.|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Number of patients in outpatient|OPD|69|8.76|7.20|0|44.78|
|department (1,000)|||||||
|Number of malaria cases treated (1,000)|MAL|69|1.29|1.14|0|5.15|
|Number of babies delivered (1,000)|DEL|69|0.39|0.45|0|2.61|
|Population density in a 10-km radius of<br>each facility (1,000 per km<sup>2</sup>)|POPDEN|69|0.74|1.19|0.01|3.63|
|Poverty rate (percent)|POV|69|56.15|18.28|31.60|78.90|
|Number of beds (1,000)|BED|69|0.38|1.01|0|8.02|
|Road density within a 10-km radius of<br>each facility (km/100 km<sup>2</sup>)|RDDEN|69|34.66|24.31|5.68|94.75|
|Share of good roads within a 10-km<br>radius (%)|GDRD|69|14.39|13.26|0|63.86|
|Dummy for health center|CENTR|69|0.57|0.50|0|1|
|Instrumental variables|||||||
|Slope of land where facility is located<br>(degrees)|SLOP|66|0.4|0.5|0.0|3.6|
|Elevation of land where facility is<br>located (m)|ELEV|66|143.3|162.2|5.0|547.0|
|Distance to nearest historicport(km)|DIST|69|76.7|67.2|0.4|222.7|

An empirical challenge to estimating Equation (1) is that transport connectivity may be endogenous. For instance, more people may visit a particular hospital because it is better connected, but transport connectivity may be better because the government invests in roads where more people live. The risk of this self-selection bias is generally high when the effect of infrastructure investment is examined (e.g., Donaldson 2010; Mu and van de Walle 2011). Several techniques to address the problem have been discussed in the literature. Panel analysis can eliminate selection bias, as long as unobserved characteristics are time-invariant (Dercon et al. 2008; Khandker et al. 2009; Khandker and Koolwal 2011). Unfortunately, the
- 9 -

current data from Liberia do not allow this, because time-series data on transport connectivity are not available.

The current paper used the instrumental variable technique. Two types of instruments are considered. First, following Dinkelman (2011), geographic conditions are used. Land gradient ( _SLOP_ ) and elevation ( _ELEV_ ) at each location may affect the technical feasibility of developing road infrastructure. Roads are difficult to build in mountainous areas or on steep terrain.

Second, historical events related to infrastructure development are used (e.g., Jedwab and Moradi 2012). In Africa, much current transport infrastructure, such as railways and trunk roads, date back to the colonial era. In Liberia, several seaports had been developed by the mid-19<sup>th</sup> century. It is likely that proximity to these ports, namely, Monrovia, Edina, Bassa Cove (Buchanan), Mississippi Colony (Greenville), and Cape Palmas (Harper), affects development of the road network but is not necessarily directly related to residents’ access to health care. The straight-line distance to the nearest historic port is calculated ( _DIST_ ), and these three variables in logarithm are used as instruments for _RDDEN_ or _GDRD_ . Exogeneity and validity will be examined empirically.

# **IV. Main estimation results**

Ordinary least squares estimation performed for each of the health care access outcomes ( _OPD_ , _MAL_ , and _DEL_ ) shows that road density has a significant effect on all these health care outcomes; with more roads, more people can take advantage of health care services (table 1). The coefficients vary depending of outcome measurements. The number of visiting patients has the largest coefficient. This reflects all types of medical demand.

Contrary to prior expectations, the quality of roads does not seem to influence people’s health care access. This may mean that road development should be given higher priority than road improvement, given the level of road development in Liberia. From an empirical
- 10 -

point of view, it may also be true that the effect of road quality was not detected because the share of roads in good condition is too small, at 14 percent on average.

In addition to transport connectivity, population density affects health care access, with demand for health care access higher where population density is lower, holding everything else constant. There are two possible reasons for this. First, health service demand may be higher in rural or remote areas, although this is unlikely, because our poverty variable was found to be insignificant. In rural areas in Liberia where population density is low, poverty is high. Thus, the negative coefficient of population density is less likely to capture the effect of remoteness, and the demand for health care services does not differ regardless of poverty. The effect is insignificant.

Another possible reason is that the demand for health care services per health facility is lower where population density is high and many health facilities coexist. This is more likely to be the case in Liberia. Hospitals and health centers are concentrated around Monrovia, and as a result, population density has a negative effect on health care access per facility.

The capacity of hospitals affects the number of babies born there. The coefficients of _BED_ are always positive and significant; health facilities with more capacity, measured according to number of beds in our analysis, can attract or absorb more needs for delivery. Not surprisingly, development of the physical capacity of health facilities is important in improving access to health care.
- 11 -

**<u>Table 2 Ordinary Least Squares Results without County Fixed Effects</u>**

||<br>_OPD_||<br>_MAL_|<br>_DEL_||<br>_OPD_||<br>_MAL_|_DEL_||
|---|---|---|---|---|---|---|---|---|---|---|
|_RDDEN_|0.348|***|0.042|**<br>0.013|**||||||
||(0.103)||(0.017)|(0.006)|||||||
|_GDRD_||||||0.002||-0.007|0.001||
|||||||(0.052)||(0.010)|(0.002)||
|_POPDEN_|-7.669|***|-0.888|**<br>-0.210|*|-1.607|*|-0.143|0.010||
||(2.095)||(0.359)|(0.121)||(0.868)||(0.160)|(0.043)||
|_POV_|-0.041||0.010|0.000||-0.100||0.000|-0.002||
||(0.057)||(0.012)|(0.003)||(0.067)||(0.013)|(0.003)||
|_BED_|1.225||-0.053|0.232|***|1.690||0.023|0.247|***|
||(1.229)||(0.140)|(0.053)||(1.488)||(0.171)|(0.062)||
|_CENTR_|-2.201||-0.275|-0.142||-1.174||-0.122|-0.107||
||(1.793)||(0.325)|(0.097)||(1.870)||(0.332)|(0.096)||
|Constant|5.408||0.114|0.125||15.560|***|1.561|*<br>0.471|**|
||(4.090)||(0.875)|(0.222)||(4.784)||(0.901)|(0.216)||
|Observations|69||69|69||69||69|69||
|R-squared|0.248||0.121|0.415||0.108||0.043|0.368||
|F-statistic|3.07||1.40|5.49||1.36||0.63|4.27||
|Number of<br>county|||||||||||
|dummies|0||0|0||0||0|0||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

County-specific fixed effects are incorporated into the model to take into account potential differences between facilities. Because some counties have only one hospital or health center in our data, only eight county-specific dummy variables are used. The poverty variable was omitted because it is a county-specific variable in our data.

The results are not different from the above (table 3). Although road density has an important effect on health care access, road quality measured according to the share of good roads does not seem to matter much. The needs for health services are relatively high where population density is low, because there are fewer hospitals and health centers in such areas. Hospital capacity is an important determinant of the number of babies delivered. With more capacity, more patients can be treated.
- 12 -

**<u>Table 3 Ordinary Least Squares Results with County Fixed Effects</u>**

||<br>_OPD_|<br>_MAL_||<br>_DEL_||<br>_OPD_||<br>_MAL_||_DEL_||
|---|---|---|---|---|---|---|---|---|---|---|---|
|_RDDEN_|0.332|**<br>0.036|**|0.013|*|||||||
||(0.127)|(0.017)||(0.007)||||||||
|_GDRD_||||||0.022||-0.006||0.006|*|
|||||||(0.071)||(0.015)||(0.003)||
|_POPDEN_|-7.207|**<br>-0.723|**|-0.279||-1.438||-0.066||-0.069||
||(3.132)|(0.325)||(0.202)||(1.399)||(0.190)||(0.099)||
|_BED_|1.286|-0.059||0.235|***|1.598||-0.008||0.237|***|
||(1.350)|(0.136)||(0.061)||(1.569)||(0.160)||(0.070)||
|_CENTR_|-2.163|-0.181||-0.124||-1.153||-0.057||-0.093||
||(1.926)|(0.295)||(0.104)||(1.878)||(0.304)||(0.099)||
|Constant|3.158|1.353|***|0.061||9.321|***|2.079|***|0.271|**|
||(2.758)|(0.435)||(0.147)||(1.999)||(0.389)||(0.104)||
|Observations|69|69||69||69||69||69||
|R-squared|0.257|0.278||0.453||0.146||0.229||0.419||
|F-statistic|2.55|1.55||5.03||2.25||1.81||4.18||
|Number of<br>county||||||||||||
|dummies|8|8||8||8||8||8||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

The above results may be biased if the transport variables are endogenous. To examine this, the instrumental variable technique is used. The analysis is focused on the model with road density, which was found to be statistically significant. The results are broadly consistent with the above (table 4). Higher road density results in greater health care access in terms of total visiting patients and babies delivered.
- 13 -

**<u>Table 4 Instrumental Variable Estimation</u>**

||_OPD_|_MAL_|_DEL_||_OPD_|_MAL_|_DEL_||
|---|---|---|---|---|---|---|---|---|
|_RDDEN_|0.564<br>*|-0.025|0.028|*|1.002|*<br>-0.053|-0.010||
||(0.319)|(0.065)|(0.015)||(0.617)|(0.098)|(0.020)||
|_POPDEN_|-11.490<br>**|<br>0.305|-0.481|*|-18.170|*<br>0.862|0.193||
||(5.616)|(1.206)|(0.256)||(10.920)|(1.747)|(0.358)||
|_POV_|0.030|-0.002|0.004||||||
||(0.077)|(0.013)|(0.004)||||||
|_BED_|0.534|-0.005|0.192|***|0.204|-0.019|0.224|***|
||(0.680)|(0.168)|(0.024)||(0.678)|(0.176)|(0.040)||
|_CENTR_|-2.166|-0.156|-0.132||-3.345|0.008|-0.022||
||(1.635)|(0.361)|(0.085)||(2.790)|(0.403)|(0.097)||
|Constant|-3.389|2.145|-0.492||-9.818|3.107|*<br>0.521||
||(10.180)|(1.834)|(0.490)||(11.790)|(1.931)|(0.354)||
|Obs.|66|66|66||66|66|66||
|R-squared|0.091||0.328|||0.007|0.378||
|Wald chi<sup>2</sup>|11.92|2.28|195.89||20.36|23.43|107.99||
|Number of<br>county dummies|0|0|0||8|8|8||
|Exogeneity test<sup>1</sup>|||||||||
|Chi<sup>2</sup>test<br>statistic|1.167|1.327|2.091||3.420|*<br>0.898|1.481||
|P-value|(0.280)|(0.249)|(0.148)||(0.064)|(0.343)|(0.224)||
|Overidentifying re|striction test<sup>1</sup>||||||||
|Chi<sup>2</sup>test<br>statistic|1.354|2.432|3.263||0.668|2.873|1.045||
|P-value|(0.508)|(0.297)|(0.196)||(0.716)|(0.238)|(0.593)||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively. 1 The instrumental variable estimation is performed with _RDDEN_ or _GDRD_ instrumented with three variables in logarithm: _SLOP_ , _ELEV_ , and _DIST_ .

The results indicate that transport connectivity is exogenous. The traditional exogeneity test is performed; the test statistics are found to be lower than the critical value, except in one case—the regression on _OPD_ with county dummy variables. In this case, the exogeneity hypothesis can be rejected at the 10 percent significance level. The coefficient of _RDDEN_ is still positive and significant, confirming the importance of road density. The overidentifying restriction test is always rejected at the conventional significance level, meaning that our instruments are less likely to be correlated with the dependent variables.

One may be concerned about the robustness of the results against our transport variables. Ordinary least squares regression is performed with different thresholds applied for road
- 14 -

density (table 5), and the results are broadly consistent with the above, where a threshold of 10 kilometers was used; there is greater health care access where there are more roads and road density is high, although the statistical significance of the effect of road density decreases with the threshold of the distance from a health facility. The statistical significance in the model of the total number of patients disappears when road density is measured within a 50-kilometer radius. For institutional deliveries, road density has a significant effect only when it is measured within a 30-kilometer distance. The results indicate the importance of broad transport connectivity up to a 30- to 50-kilometer distance from a health facility. The policy implications for Liberia are straightforward—when transport infrastructure development increases health care access, particularly access to high-level health facilities, it is important to develop not only rural accessibility, but also broader transport connectivity. The supporting coverage depends on the type of health care services.
**<u>Table 5 Ordinary Least Squares Estimation with Different Road Density Thresholds</u>**

||<br>_OPD_|<br>_OPD_|<br>_OPD_|<br>_OPD_|<br>_OPD_|<br>_DEL_|<br>_DEL_|_DEL_|_DEL_|_DEL_|
|---|---|---|---|---|---|---|---|---|---|---|
|_RDDEN_10km_|0.348 ***<br>(0.103)|||||0.013 ***<br>(0.006)|||||
|_RDDEN_20km_||0.406 **<br>(0.168)|||||0.022 **<br>(0.010)||||
|_RDDEN_30km_|||0.325 **<br>(0.138)|||||0.014 *<br>(0.007)|||
|_RDDEN_40km_||||0.305 **|||||0.006<br>||
|||||(0.135)|||||(0.008)<br>||
|_RDDEN_50km_|||||0.325 *|||||0.002|
||||||(0.170)|||||(0.010)|
|_POPDEN_|-7.669 ***|-4.131 ***|-1.960 **|-1.475 *|-1.288|<sup>-0.210 **</sup>|-0.128|-0.004|0.014|0.012|
||(2.095)<br>|<sup>(1.446)</sup><br>|<sup>(0.940)</sup>|<sup>(0.908)</sup>|<sup>(0.891)</sup>|<sup>(0.121)</sup><br>|<sup>(0.084)</sup>|(0.047)<br>|<sup>(0.044)</sup><br>|<sup>(0.045)</sup>|
|_POV_|-0.041<br>|<sup>-0.023</sup><br>|<sup>-0.024</sup>|<sup>-0.028</sup>|<sup>-0.025</sup>|0.000|0.002|0.001|-0.001<br>|<sup>-0.002</sup>|
||(0.057)|(0.064)|(0.071)|(0.074)|(0.082)|(0.003)|(0.003)|(0.003)|(0.004)|(0.004)|
|_BED_|1.225|1.527|1.575|1.474|1.445|0.232 ***|<br>0.240 ***|0.244 ***|0.245 ***|<br>0.248 ***|
||(1.229)<br>|<sup>(1.300)</sup><br>|<sup>(1.395)</sup>|<sup>(1.424)</sup>|<sup>(1.447)</sup>|<sup>(0.053)</sup><br>|<sup>(0.052)</sup>|(0.059)<br>|<sup>(0.062)</sup><br>|<sup>(0.063)</sup>|
|_CENTR_|-2.201<br>|<sup>-1.576</sup><br>|<sup>-1.439</sup>|<sup>-1.630</sup>|<sup>-1.706</sup>|<sup>-0.142</sup><br>|<sup>-0.127</sup>|-0.116|-0.113<br>|<sup>-0.106</sup>|
||(1.793)<br>|<sup>(1.776)</sup><br>|<sup>(1.792)</sup>|<sup>(1.795)</sup>|<sup>(1.810)</sup>|<sup>(0.097)</sup><br>|<sup>(0.092)</sup>|(0.093)<br>|<sup>(0.093)</sup><br>|<sup>(0.092)</sup>|
|Constant|5.408|4.746|6.458|7.510|7.473|0.125<br>|<sup>-0.101</sup>|0.107|0.335|0.459|
||(4.090)|(5.322)|(5.707)|(5.798)|(6.551)|(0.222)|(0.223)|(0.252)|(0.323)|(0.378)|
|Observations|69|69|69|69|69|69|69|69|69|69|
|R-squared|0.248|0.211|0.152|0.138|0.133|0.415|0.447|0.388|0.371|0.368|
|F-statistic|3.07|2.01|2.25|2.28|2.14|6.61|5.78|4.97|4.58|4.38|
|Number of|||||||||||
|county dummies|0|0|0|0|0|0|0|0|0|0|

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5% and 1%, respectively.
# **V. Conclusion**

Health care access is an important policy concern, especially in rural areas. This is especially challenging in developing countries, where residents are poorer and less likely to be insured than those in urban areas. In addition, doctors are less available in rural areas. Supporting infrastructure services, such as electricity and transport connectivity, are less favorably compared in rural areas. The international donor community has been making significant efforts to increase rural health care access in many countries.

Using the case of Liberia, this study examined the effect of transport connectivity on health care access. In Liberia, rural health care access is an important challenge. The Ebola crisis in 2014 and 2015 clearly revealed the vulnerability of the country’s transport and health systems to unexpected external shocks. Although most urban residents have good access to health care services, many rural people do not have any hospital or health center within 10 kilometers.

With particular attention paid to the possible problem with endogeneity associated with infrastructure investment, this study also found that transport connectivity, especially greater road density, can increase access to health care, but no significant effect of road quality was found. This may be because of significantly skewed underlying data; the vast majority of roads are in poor condition in Liberia. The study also found that the statistical effect of road density varies depending on distance from a health facility. The effect is particularly significant within a 30- to 50-kilometer radius. This confirms that, to improve health care access, it is important to develop not only rural accessibility, but also transport connectivity over a broader area. The optimal coverage depends on the type of health care services. A 30to 50-kilometer distance is recommended given the current road network and locations of health facilities.