Factors that determine system performance

The regression model aims to answer the following question: what are the main determinants of system and WSS service performance in rural areas of Nicaragua?

The main regression model used the calculated CIAS as the dependent variable, while variables excluded from this index (to avoid endogeneity issues) were used as independent variables in the specification:

133 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1𝐶𝐶𝑆𝑆𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝑖𝑖 + 𝛽𝛽3𝑋𝑋𝑖𝑖𝑖𝑖 + 𝛾𝛾 + 𝜀𝜀𝑖𝑖

This model incorporates the CIAS and sub-indices as the dependent (outcome) variables, and a set of independent variables. The specification follows the empirical work of Nkongo (2009) and Mehta & Movik (2014). Among the independent variables, SF corresponds to system flow capacity, SA corresponds to sanitation practices in the community (e.g. handwashing, etc.) and X corresponds to institutional variables related to community i and CAPS k. The term 𝛾𝛾 corresponds to controls of province fixed effects. It is important to add fixed effects controls in the regression so that effects of the independent variables on the outcome variable are not biased by any contextual factor of the region where the community is located. The last term 𝜀𝜀𝑖𝑖 corresponds to an error term. In addition, standard errors were estimated with Huber/White robust estimator. The regressions used the CIAS and sub-indices as dependent variables, leading to 10 different estimation results. The first estimation results used the CIAS index. Further, five infrastructure indices were used to estimate a) autonomy of system management, b) production quality of infrastructure, c) state of infrastructure to protect environmental areas, d) presence of additional water infrastructure in the community, and e) the overall state of infrastructure quality performance. In addition, there were three indices used as the outcome variables measuring: a) access to service, b) continuity of service, and c) seasonally dependent service. Finally, two sub-indices were used as outcome variables for the existence of a) operation and maintenance activities conducted by the provider, and b) active demand of service by the community. The results of 10 models described are presented in Tables 4-8 and 4-9.

The principal model to pay attention to is the one that used the CIAS index as the main outcome variable (model 1). The rest of the models used sub-indices of different attributes of water systems (autonomy of management, secondary facilities, production capacity and demand, and so forth). These sub-indices were also included as dependent variables. The models were specified identically using many controls that were not included in either sub-index or in the CIAS. Other important controls that capture qualitative information from communities and systems are not collected as part of the SIASAR data, such as the types of maintenance performed, and education level of individuals in charge of performing water systems maintenance. Since the CIAS is indexed between 0 and 1 (1 being the highest community ranking in all

134 dimensions), the coefficients represent the percent change contribution to the index.

The results of model (1) show that those systems with legalized CAPS improve their CIAS ranking by 4%. The contribution of technical assistance is even higher: the provision of technical assistance significantly increases the CIAS coefficient by 7.4%.

Proper water management in the community and reported systematic handwashing contribute to improve the CIAS by 6.2% and 6.8%, respectively. The higher the poverty rate in the community, the less likely this community is to have an improvement in the index (by 9.4%). The model fit indicators show that this model has an explanatory power of 46% and a statistically significant fit of the independent variables in explaining the outcome variable (Model test=9.5, P>F=0.000). For model (2) the main sub-index used referred to the community’s autonomy in keeping and sustaining the water supply infrastructure. In this case, the most important determinants have rather small effects.

(1) (2) (3) (4) (5)

Main Factors ^CIAS

(index)

Systematic Handwashing 0.068*** 0.014 0.003 0.001 0.027

s.e. (0.005) (0.015) (0.013) (0.013) (0.023)

Poverty (%) -0.094*** -0.376*** -0.042*** 0.214** -0.2473**

s.e (0.023) (0.069) (0.006) (0.101) (0.110)

R-squared 0.46 0.10 0.07 0.24 0.22

Model Test (Wald/F) 9.5 8.7 4.4 6.6 14.1

Table 4-9 First-tier of Models on the Determinants of Water Service Categories

.*** p<0.01, ** p<0.05, * p<0.1 Note: Fixed effect (F.E.) robust standard errors estimated. Standard errors in parentheses. Based on SIASAR 2015 data.

The rest of the models (Table 4-10) used the exact same specification as in the previous estimates (Table 4-9). The sub-indices used as dependent or outcome variables in Table 4-10 are the following: a) water accessibility (model (6)), b) continuity of service (model (7)), c) water service available in (dry) seasons (model (8)), e) provider’s O&M delivery (model (9)), and f) water demand levels (model (10)).

For water accessibility, the determinants showed rather small effects. Only TA and

135 systematic handwashing showed positive and statistically significant effects on the outcome variable, but with small coefficients of 2.5% and 1.6%, respectively.

Nevertheless, the explanatory power of model (6) is the highest. The small and insignificant effects may be driven by relatively small variation between independent attributes and the outcome sub-index. For model (7) the factors associated with water service available in the community are the legal status of CAPS and presence of systematic handwashing. Poverty levels in the municipality are negatively correlated with water service availability. Model (8) shows two factors determining water provision throughout different seasons. The first relates to systems flow. Higher system flow increases water service availability by 4%. This may be related to the technology in place, since higher system flows are associated with more complex water systems that allow extracting and pumping water from remote locations. Poverty is strongly and negatively correlated with service provision throughout the year. For model (9) TA has the strongest effect on determining the provider’s ability to deliver O&M to rural water systems. TA on average increases the number of providers with O&M activities by 10.3%. The legal status of CAPS is also related to increasing providers with O&M activities. Finally, model (10) shows a very strong effect of system flow and legal status of CAPS in delivering water service according to the demand.

(6) (7) (8) (9) (10)

Main Factors Sub-index of Water

Proper Water Management -0.012* -0.001 0.010 0.0134 -0.009

s.e. (0.007) (0.006) (0.014) (0.008) (0.053)

Systematic Handwashing 0.016** 0.083*** 0.042 0.017* -0.017

s.e. (0.007) (0.0035) (0.016) (0.009) (0.058)

Poverty (%) -0.177*** -0.029* -0.131* -0.024 -0.841***

s.e (0.034) (0.014) (0.076) (0.046) (0.279)

R-squared 0.46 0.10 0.07 0.24 0.22

Model Test (Wald/F) 9.5 8.7 4.4 6.6 14.1

Table 4-10 Second tier of Models on the Determinants of Water Service Categories

*** p<0.01, ** p<0.05, * p<0.1 Note: Fixed effect (F.E.) robust standard errors estimated. Standard errors in parentheses. Based on SIASAR 2015 data. S.e.= standard error.

136 4.8.3. Determinants of Well-Performing Systems: Survival Functions

In order to describe which factors may determine higher shifts in the quality and sustainability of water systems, survival functions were used to estimate the probability of the services and operation of water systems failing over time. By using the system as a unit of analysis, ’survival’⁴² functions of WSS systems can be calculated. Usually a set of systems can present two states in terms of sustainability: failure (shifting from a high classification to a low one) or success (moving up or maintaining a high-quality classification over time). SIASAR collects data on the dates of construction and the dates of O&M visits, so it is possible to construct survival functions to assess the probability ratio of success and failure of systems over time. Survival functions are probability models that are fed with three indicators: i) the age of the system, ii) the failure indicator (which takes value of 1 when systems change their classification status from A or B to either C or D, and iii) the identification of the unit of analysis. The CIAS was used in order to have the highest number of observations available to perform the survival function analysis.