functionality. In fact, among water points that are one-year old, hydrogeological factors such as groundwater productivity, groundwater storage and depth to groundwater have the greatest effect among other factors on their functionality.

# **4. Data and Methodology**

# _(a) Water Point Mapping Data_

This study uses the WPM data compiled in November 2016)<sup>15</sup> . The analysis made use of data on 40,917 water points out of a total of 83,295. This final figure was derived after we 1) restricted our sample to those between 1 and 20 years old; 2) removed other water schemes, defined as a water point with multiple taps; 3) identified other additional duplicates; and 4) dropped water points with incomplete information<sup>16</sup> . Information pertaining to the settlement type, types of pump, types of promoters and water source is found in the WPM dataset, in addition to the functionality status of the water points. Following Andres et al., (2018) and Fisher (2015) a number of hydrological information were combined with the WPM dataset which were obtained from the British Geological Survey (BGS) (MacDonald et al., 2012). The hydrological database included groundwater productivity<sup>17</sup> , groundwater storage<sup>18</sup> and depth to groundwater<sup>19</sup> . Each data were presented in the form of a map with a 5km resolution grid and they were geospatially matched to each water point available in the WPM dataset where possible. The water points were matched such that only about one percent of water points in the dataset did not have at least one of those measurements.

# _(b) Methodology and Empirical Strategy_

First, <mark>Locally Weighted Scatterplot Smoothing</mark> (“Lowess Smoothing” of running-line least squares) is employed to estimate a locally weighted non-parametric regression of ‘non- _functionality’_ (0,1) on age in years (1<age<15) for the water points in our data. Lowess Smoothing Curves across the different subgroups of the water point characteristics such as the types of pumps and their management type were also estimated to analyze the association between age and the probability of failure.

> 15 The distribution of water points is shown in the Map of Tanzania (figure 17).

> 16 As Verplanke and Georgiadou (2017) highlighted, errors in the dataset such as the double counting of water points are common within the WPM data.

> 17 <mark>The groundwater productivity map indicates the borehole yields that can reasonably be expected in different hydrogeological units (MacDonald, 2012). Definitions can be found at:</mark> <u><mark>http://www.bgs.ac.uk/research/groundwater/international/africangroundwater/mapsDownload.html</mark></u>

> 18 <mark>Groundwater storage was estimated by combining the saturated aquifer thickness and effective porosity of aquifers across Africa. For each aquifer flow/storage type an effective porosity range was assigned based on a series of studies across Africa and surrogates in other parts of the world (MacDonald et al., 2012).</mark> 19 Depth to groundwater was modelled using an empirical rules-based approach, where depth to groundwater was assigned according to rainfall and aquifer type, as well as proximity to rivers <mark>(MacDonald et al., 2012)</mark> .

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