Effect of deforestation on access to clean drinking water

Main Results.

Table 1 describes various sources of drinking water in Malawi’s rural areas where 85% of the population lives. SI Appendix, Background explains how the water system is maintained in these areas. In our satellite dataset, the ratio of forest area decreased from 39% to 33.4% or by 14.3% (= 5.6/39) at the national level (SI Appendix, Table S3).

SI Appendix, Table S4 shows the summary statistics of the main variables used in our analysis. The dataset indicates that 38% of households used unsafe water as their source of drinking water in 2000. In 2010, this percentage decreased to 17%. At clusters that are used for the analysis, the ratio of forest area decreased from 27.7% in 2000 to 22.2% in 2010. SI Appendix, Fig. S2 shows a histogram of changes in the ratio of forest area from 2000 to 2010. Although the mean of changes in the ratio of forest area is just 5.5 percentage points, there is a huge variation in the changes in this ratio.

Table 2 presents the results of our two-stage least-squares (2SLS) estimation with the cluster and time fixed effects. Top and middle sections in Table 2 show the first- and second-stage estimation results, respectively. Our instrumental variable is the latitude × time dummy. Bottom section lists the control variables that apply to both the first- and second-stage estimations. All columns include the cluster fixed effect, time fixed effect, log of rainfall, (cluster-level) ratio of forest area in the 1990 × time dummy, and cluster-level average of the floor material dummy in the 2000 × time dummy as control variables. Bottom section lists other control variables that apply to both first- and second-stage estimations. Temperature is the temperature in July and November. Demographic characteristics include the size of each household, year of schooling, and gender of the head of the household. Household wealth includes information on the floor material and bicycle and radio ownership.

The Kleibergen–Paap rank in Table 2 indicates the Kleibergen–Paap rank Wald statistics, and it examines whether our instrumental variable is weak. The Kleibergen–Paap rank Wald statistics show that our instrumental variable is not weak even after the inclusion of many covariates as well as cluster-level initial values × time dummy as control variables. Note that our instrumental variable is the latitude × time dummy, and a cluster in the northern region has lower latitude than a cluster in the southern region because Malawi is in the southern hemisphere. Top section of Table 2 shows that a cluster with higher latitude (southern region) has a time trend of reforestation. Thus, a cluster with lower latitude (northern region) has a time trend of deforestation, which is consistent with the past pattern of deforestation (SI Appendix, Tables S1 and S3) and our prediction. In Table 2, top section, column 1 shows that if a cluster’s location is 1° more north, the forest area decreases further by 3 percentage points over time than other clusters. In Table 2, columns 2–4 show that the inclusion of other control variables does not affect the first-stage results.

Table 2, middle section shows our main results regarding the causal effect of the ratio of forest area on access to clean drinking water. Column 1 shows that a 1-percentage-point increase in the forest ratio increases the probability of access to clean drinking water by 1.06 percentage points, and a 1% increase in the amount of rainfall increases the probability of access to clean drinking water by 0.61 percentage points. Columns 2–4 show that the estimated coefficients of the ratio of forest area are quite stable with the inclusion of these control variables. Including all control variables, Table 2, middle section, column 4 shows that a 1-percentage-point increase in the forest ratio increases the probability of access to clean drinking water by about 0.93 percentage points, and a 1% increase in rainfall increases the probability of access to clean drinking water by 0.57 percentage points. The estimated coefficient of log of rainfall measures the total effect of log of rainfall on access to clean drinking water, including the effect of a change of the quality of water at watersheds. A statistically and economically significant estimated coefficient of log of rainfall suggests that local weather conditions matter for access to clean drinking water.

SI Appendix, Table S6 shows the results of the reduced-form estimation, which displays the estimated coefficient of our instrumental variable (latitude × time dummy). The estimated coefficients are stable with different specifications. SI Appendix, Table S6 shows that a cluster located 1° south has a 2.8 percentage points higher time trend on the probability of accessing clean drinking water than other clusters. On the other hand, the first-stage regression shows that such a cluster has a 3 percentage points higher time trend of the forest area ratio. Thus, a 1-percentage-point higher forest area ratio leads to about 0.93 (= 2.8/3) percentage points higher probability of accessing clean drinking water.

Fig. 2 shows the graphical relationship between the first-stage estimation (Fig. 2A) and the reduced-form estimation (Fig. 2B). In Fig. 2A, the vertical axis measures the adjusted difference of the ratio of forest area, and the horizontal axis is the adjusted latitude. In Fig. 2B, the vertical axis is the adjusted difference of the average of the clean drinking-water access dummy between 2000 and 2010. The adjusted latitude is the residual from the regression of the latitude on the difference of the log of the amount of rainfall. The vertical axes are measured in a similar way.

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Fig. 2.

(A and B) Graphical presentation of the (A) first-stage regression and (B) reduced-form regression, after controlling for the effect of control variables. A and B show positive relationships between the adjusted latitude and adjusted difference of the forest area during the period 2000–2010 and the adjusted latitude and adjusted difference of the cluster-level average access to clean drinking water during the period 2000–2010. Note that slopes of the two fitted lines (0.03 and 0.028) are quite similar, although they look different due to the difference in the range of the vertical axes in A and B. In A and B, the axes range from −0.4 to 0.2 and from −1 to 1, respectively. Also note that higher variance of B is due to the nature of the reduced form. The error term of the reduced form includes the error term of the first-stage equation.

SI Appendix, Table S5 shows the estimation results of the ordinary least-squares estimation. The estimated coefficients in SI Appendix, Table S5 are 1/10th of the estimated coefficients of Table 2, and they are statistically insignificant. The exogeneity test rejects the null hypothesis that the ratio of forest is exogenous. Thus, SI Appendix, Table S5 shows the importance of controlling the endogeneity of the ratio of forest area.

SI Appendix, Tables S7 and S8 examine the sensitivity of our results by controlling the ratio of cropland, cropland intensity, and the ratio of the wetland. SI Appendix, Tables S7 and S8 show that the estimated coefficients of the ratio of forest area increase slightly, and the results of Table 2 are strengthened. SI Appendix, Table S9 examines whether our results are sensitive to the exclusion of the initial cluster-level characteristics. SI Appendix, Table S9 shows that our results are not sensitive to the initial cluster-level characteristics. In SI Appendix, Table S10, we make a circle with a radius of 12.5 km, instead of 7.5 km, at each cluster; calculate the forest area ratio; and conduct a 2SLS estimation. SI Appendix, Table S10 shows that the estimated coefficients are quite similar to the estimated coefficients in Table 2, which demonstrates the robustness of the results in Table 2.

Mechanism.

In this study, as we discussed in the Introduction, we hypothesize that deforestation decreases the capacity of soil infiltration of water, increases soil erosion, and decreases the quality of water at the source. We justify this hypothesis based on the fact that most water systems in rural areas of Malawi are developed and maintained by local communities. Thus, we should expect that when deforestation progresses, the use of piped water will decrease and the use of unprotected wells, rivers, and ponds will increase, because a local piped water system needs high-quality water sources, whereas an unprotected well, river, or pond does not.

Table 3 shows the effect of the ratio of forest area on access to each source of drinking water in the 2SLS estimation. In Table 3, each row shows the estimation result of a 2SLS regression with a different dependent variable. Each dependent variable is a dummy variable indicating the source of drinking water. For example, the row of piped water at public tap presents the estimation result when the dependent variable is a dummy variable indicating whether the household uses piped water at a public tap as the source of drinking water. The column for the estimated coefficient shows the estimated coefficient of the forest area ratio in the 2SLS regression. Table 3 shows when the ratio of the forest area increases by 1 percentage point, access to piped water at public taps increases by 0.93 percentage points, and the use of unprotected wells as the source of drinking water decreases by about 0.59 percentage points. The effects on the use of the river/pond/lake/dam and unprotected springs are negative as the hypothesis predicted, although they are statistically insignificant. Table 3 is consistent with the idea that when the ratio of forest area increases, the quality of the source of a piped water system that connects to a public tap improves; as a result, people can use piped water at a public tap instead of an unprotected well as the source of drinking water.

Discussion.

Studies in forest science and hydrology often indicate that the relationship between land use and the quality of water is complex (4, 7, 27). Thus, readers might argue that many factors that are not included in our model are correlated with both the ratio of forest area and the accessibility to clean drinking water and that our estimated coefficients of the ratio of forest area are biased. However, our model includes the time fixed effect and the cluster fixed effect. Thus, the national-level time trend and the effect of time-invariant cluster-specific variables are already controlled. By applying 2SLS, we also prevent the possibility that the (time-variant) error term, the effect of time-variant variables that are not modeled in Eq. 1, is correlated with the ratio of the forest area and that our estimated coefficients of the ratio of forest area are biased.

Note that the instrumental variable in our 2SLS is the latitude × time dummy and we include the cluster-level initial values × time dummy as control variables. Thus, essentially in our 2SLS estimation, we compare clusters with different latitudes but the same initial characteristics, assuming that those clusters have the same time trend on access to clean drinking water if the rates of deforestation are the same.

However, some might argue that a cluster with higher latitude, that is, a cluster in the southern areas, has a time trend of higher infrastructure development, even after controlling the initial cluster-level characteristics and other covariates. In such a case, our 2SLS estimation might pick up not only the effect of the ratio of forest area but also the fact that a cluster with higher latitude has a time trend of higher infrastructure development.

To determine whether a cluster with higher latitude has a time trend of higher infrastructure development, we conduct a falsification test based on the reduced-form regression. In this test, we pick up a variable that is strongly correlated with development but is not directly affected by the forest ratio. Then, we regress this variable on the instrumental variable and control variables. The estimated coefficient of the instrumental variable showing a significant positive sign indicates that, in Table 2, our instrumental variable picks up the effect of urban development since the ratio of forest area should not affect such a variable. For the first falsification test, we use a dummy variable indicating access to electricity as a dependent variable because the ratio of forest area should not directly affect the use of electricity. For the second and third tests, we use the radio ownership and the quality of the respondent’s home as dependent variables. SI Appendix, Data Sets discusses how we measure the quality of a home. We exclude information on household wealth from the control variables in this falsification test, since the dependent variables are related to wealth.

SI Appendix, Table S11A shows the estimated coefficients of the instrumental variable in our first falsification test. In SI Appendix, Table S11A, the absolute value of the estimated coefficient of the instrumental variable is 1/10th of the estimated coefficient of the effect of the instrumental variable on access to clean drinking water (SI Appendix, Table S6). The sign of the estimated coefficients is the opposite of the signs in SI Appendix, Table S6, and the estimated coefficients are statistically insignificant. Thus, it is unlikely that our instrumental variable is picking up the higher time trend of development in clusters with higher latitude. SI Appendix, Table S11B, which uses the radio ownership as a dependent variable, shows a similar pattern.

SI Appendix, Table S11C shows that the effect of the instrumental variable is again negative and statistically insignificant. SI Appendix, Table S11A–C suggests that it is unlikely that the southern region has a time trend of higher development. We can thus safely conclude that it is unlikely that a positive effect of the forest ratio on access to clean drinking water in our 2SLS estimation is the consequence of the southern region having a higher time trend of development.

In summary, all robustness checks and falsification tests show that our estimated results are robust and consistent. Thus, it is very unlikely that the estimated coefficients of the ratio of forest area in our 2SLS pick up other unobserved effects.