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Home arrow Management arrow Improving Access and Quality of Public Services in Latin America: To Govern and To Serve

Results and Discussion

Table 4.6 sets out our benchmark results for two different definitions of our two outcome variables. The first definition measures the outcome in levels while the second measures it as the change between a base year (2007) and the last year for which we have data (2010). For each specification we tested for endogeneity using the Hausman test. The results indicate that in none of the cases is our PB indicator (PB intensity) endogenous. Interestingly, however, in the case of PB variables related to participation, the results of the tests suggest endogeneity between the water continuity variable and PB participation. This indicates that participation of the people in the PB process and the quality of service are correlated. These two pieces of evidence together suggest that while participation is associated with the quality of water services, this link is severed in the process of turning participation into municipal budget allocations, where both technical and political issues come into play. This interpretation underlines the weaknesses in the PB process. Finally, in the case of coverage we find no evidence of a link because there is very little investment in expansion of coverage, as qualitative evidence overwhelmingly confirms.

Independent

variables

Water coverage

Continuity

%

%

Change

2007-2010

Change

2007-2010

Hours per day

Hours per day

Change

2007-2010

Change

2007-2010

PB intensity, lax

0.0427

0.0316

-0.00491

-0.0126

1.165

1.133

-0.403

-0.723 (0.461)

match

(0.0371)

(0.0394)

(0.0326)

(0.0359)

(1.399)

(1.208)

(0.411)

Urban population

-0.0667

-0.121

0.0771

-0.0216

-3.141

0.0350

0.454

-3.295**

(%>

(0.0966)

(0.0989)

(0.132)

(0.130)

(3.645)

(3.777)

(2.731)

(1.629)

Poverty incidence

-0.0796

-0.0655

0.0828

-0.000585

-13.49***

-7.728*

-2.060

-2.237 (2.024)

(0.107)

(0.120)

(0.116)

(0.143)

(4.132)

(3.978)

(1.831)

Change in

1.07e-09**

1.01e-09*

1.lie—10

-8.77e-ll

3.78e-08**

2.72e-08*

6.27e-10

-5.47e-09

investment budget

(4.85e-10)

(5.78e—10)

(3.66e-10)

(4.10e—10)

(1.51e—08)

(1.43e-08)

(5.79e-09)

(5.43e-09)

execution

% of budget

0.0545

0.0251

-0.0649

-0.127**

-2.886

-0.0845

2.082***

2.182**

financed by

(0.0581)

(0.0550)

(0.0479)

(0.0562)

(2.064)

(2.044)

(0.784)

(0.848)

mining royalties

The municipality

-0.00332

-0.0434

-0.0203

-0.00892

4.674**

3.372*

-0.572

-0.944 (0.925)

is registered on

(0.0512)

(0.0555)

(0.0454)

(0.0509)

(1.983)

(1.999)

(0.806)

the SNIP

Province capital

0.123**

0.0783

0.0649

0.0985

4.961**

4.819**

-1.168

-0.673 (1.221)

district = 1

(0.0526)

(0.0565)

(0.0573)

(0.0677)

(2.006)

(1.961)

(1.063)

Mayor

-0706**

-0.0479

-0.0211

-0.00870

-0.467

-1.383

0.766

0.844 (0.601)

immediately

(0.0353)

(0.0374)

(0.0323)

(0.0336)

(1.245)

(1.185)

(0.615)

re-elected=l

% of votes for the

0.165 (0.150)

0.112

-0.0846

-0.100

-12.32**

-13.39**

-0.203

-1.423 (3.051)

winner

(0.158)

(0.133)

(0.148)

(6.204)

(5.463)

(2.923)

% of voting

3.706***

3.950***

1.582**

1.724**

51.07**

23.03

9.894

29.00***

women over total

(0.748)

(0.838)

(0.669)

(0.728)

(23.38)

(23.22)

(15.50)

(10.90)

voting population

Average quarterly

0.0807**

0.0376

-0.255

0.387 (0.554)

water price

(0.0312)

(0.0344)

(1.073)

% of unbilled

-0.0994

water

(0.122)

Arrears (number

0.00555

of months)

(0.0100)

Number of

-0.000148

districts attended

(0.00165)

by the local water

supplier (WS)

Size of the WS:

0.0800

big (more than

(0.0627)

40,000

connections = 1)

Size of the WS:

-0.0570

SEDAPAL (more

(0.100)

than 1000000

connections = 1)

Constant

-1.062***

-1.234***

-0.859**

(0.361)

(0.434)

(0.341)

Observations

181

175

171

R-squared

0.231

0.294

0.080

0.220*

-7.167

4.170* (2.442)

(0.129)

(5.591)

0.00920

-0.932***

0.180 (0.141)

(0.00721)

(0.301)

-0.000170

0.125**

-0.0152

(0.00200)

(0.0516)

(0.0272)

0.221**

-5.728*

3.068**

(0.0852)

(2.960)

(1.333)

0.106

-1.482

2.855* (1.620)

(0.112)

(3.805)

-1.136***

-2.099

13.51

-4.696

_15 29***

(0.392)

(11.75)

(12.39)

(6.307)

(5.661)

167

184

178

181

176

0.172

0.189

0.340

0.082

0.178

We find no evidence of significant effects of PB on water service coverage either defined by levels or changes. This result holds for any set of control variables used. For our outcome of water service quality continuity, we have contrasting results when measured in levels vis-a-vis when measured in changes. When measured in levels, there is a significant correlation when we control only for demographics. Once we introduce additional control variables, significance vanishes. In the case of our measure in changes, the association is negative, but (weakly) significant in only two specifications, before we introduce most control variables. Altogether, results suggest that there is no systematic relationship between PB and our water service quality measures.

Regarding our control socio-demographic variables, poverty incidence correlates negatively with our two outcomes when measured in levels, but is only significant for continuity. This corresponds to the intuitive notion that poor districts have less coverage and worse water services quality. When measured in changes, the association is generally not significant. Among variables intended to capture management capacity at the municipality level, the percentage of investment budget execution correlates positively with both coverage and continuity measured in levels. This seems plausible as municipalities with greater capacity to execute investments generally may equally be better at executing water investment projects that thus translate in better coverage and continuity. Also, the fact that the municipality is registered in the SNIP correlates positively with levels of water continuity.

The importance of mining royalties for municipal finances shows a weak negative association with changes in coverage in most specifications. However, it is positively associated with changes in continuity. Since this is a key source of funding for water projects, these two findings may suggest a preference for investment in improving quality for those already served rather than expanding service to those without access, who are likely the poorer. This is related to the fact that resources are not large enough to fund significant increases in coverage. As we indicated above, the investment budget for water and sanitation per district is barely 1.3 million soles (about US$450,000), which, even if fully allocated to coverage expansion, could not have a significant impact on access. In addition, our qualitative evidence also indicates that resources are quite limited so as to permit significant coverage expansion. Two quotes from municipality officers illustrate the point quite clearly:

This time it’s our turn to ask for a change of network, because this is older than 35 years, but the money wasn’t enough to even cover three blocks. (Altagracia Bustamante—President of Bellavista’s District Management/Steering Committee)

Water ought to come first, but it’s always spent more on roadwork, because investment in water and sewage is high if you actually want to do something, it’s a lot of money, one of these projects does not go for less than three million. (Mario Ferreyros—Assistant Accounting Manager, Municipality of San Ramon)

A remarkable result highlights the importance of political participation by women. In effect, we find that the percentage of voting women is associated positively with both measures of coverage (strongly) and with continuity (in levels, before we control for arrears and provider features, and in changes). There are good reasons to think that women may be more concerned about water services than men. Water is an indispensable element in the household. Since women are more involved in house work, they carry a disproportionate portion of the burden of obtaining water in the absence of a connection to piped water. In addition, good quality drinking water is associated with children’s health, as unclean water is a major source of diarrhea. Since typically it is women who care after children’s health, it is not surprising that their political participation is associated with better indicators of coverage and water service quality.

Regarding other political variables, we find that whether the mayor is re-elected correlates negatively with coverage. Also, the percentage of votes obtained by the mayor associates negatively with both coverage and continuity measured in levels. Finally, the fact that the district is the capital of the province has a positive and significant effect on water coverage and continuity in levels. This is consistent with the idea that investment in water services is constrained by the amount of available resources as provincial governments command substantially more resources than district governments.

Finally, provider characteristics seem to be associated with outcomes only in a few cases. Water price has a positive and significant, but statistically weak, effect on coverage when the full set of control variables is included. This is plausible as these providers may have better capacity to provide a better service. Much less intuitively, the percentage of unbilled water correlates positively with changes in the two outcomes, but for both only weakly. Measures of provider size generally do not correlate with outcome levels, except for continuity (negatively), but in this case the correlation is weak. Large-sized providers correlate positively with changes for both coverage and continuity.

The qualitative data collected are consistent with these results. Altogether, the perception of local actors, government officers, civil society participants, and service providers is that PB has little to no effect on coverage or quality of water. There are several reasons for this, which may be summed up in a description of PB as a mechanism with serious weaknesses to fulfill its promise as far as water service is concerned. First, institutionally, although PB is backed by several norms, the law mandate lacks “teeth” as the implementing norms (both Reglamento and Instructivo) do not contain precise indicators of results. In effect, the emphasis of these implementing norms is rather in the process. For instance, it does not make it mandatory for mayors to commit a minimum of resources to the PB process, for instance, a percentage of the municipal investment budget. A consequence of this is a lot of variation in how much this amount changes from one district to the next. In this context, the role of the mayor is key, both in the decision of how much to put to public discussion through PB and how much finally gets into the budget. Further, it is the municipality’s technical team that plays the key role in conducting the process. Another consequence is that districts where population organizations are weak, the mayor faces less pressure to commit resources or abide by the decisions made in PB.

The same argument may also apply to interest groups within the same district. Those better positioned to participate may reap more benefits out of the PB process. Regrettably, those populations in marginal or remote areas of the districts do not stand the same chance of participating because of the costs. We find evidence of some municipalities adapting to these circumstances and implementing, for example, itinerant PB workshops to make sure most populations are included. Furthermore, we find that in districts where the mayor supports PB and fosters participation, the PB process gains credibility and the population is more willing and available to participate.

A second reason is that investment resources per district are insufficient to carry out significant expansion of water services or improvement in the quality of services. Investments in public works that improve coverage or quality of water services have a very high cost that municipalities cannot cover within their limited resources. Consequently, the water projects that the municipality undertakes are basically small renovations of water and sewage networks, discarding the expansion projects due to high costs. This also explains why most of the water and sanitation work (and this also applies to roads) are concentrated in urban centers, leaving aside the more marginal urban areas or the remote rural areas, since works are more costly in such areas. As a municipal officer told us:

There are areas that don’t justify [the investment] Why? Because water and sewage are still to be done, the roads are rather narrow, the houses and fences must be aligned, align telephone poles, electricity, a whole number of things. By contrast, other (areas) are more practically situated, they have water and sewage connections, the houses are aligned, everything then is most likely to be implemented in this area, which is, basically totally formalized. (Jose Hipolito Magallanes—Head of Urban Development Management, Municipality of Sunampe)

This implies that, contrary to the provisions of the law, as far as water is concerned PB primarily may benefit an already privileged sector and not the most marginalized and poor. This also calls attention to the importance of other state agencies—regional governments, the Water for All Program—as it is these that are able to invest more and carry out construction projects of greater magnitude and consequently have a greater impact.

Another factor that limits concentration of investment in substantialsized works is the tendency to disperse investment funds in different small- scale projects. In highly fragmented political environments, this may make sense for the mayor as it would allow him to cater to different groups of the population.13

The second specification proposed, which includes our investment variables to test H2 as explained in the Methods section, provides similar results (Table 4.7), suggesting that there is no impact of PB coming through specific investments made in the water sector (at the local level), which is consistent with the argument in the previous paragraph. Table 4.6 sets out regression results from this specification. In addition, we use these results to approach the question of which set of variables (PB, sociodemographic, political, or service provider) contributes most to explain the variance in outcomes. We can measure this by looking at the change in R-squared once we introduce each set of variables. The conclusion is that provider characteristics weigh the most for every variable except level of coverage. For this it is the set of political variables that induces the greatest change in R-squared. The second most important set is political variables.

Independent

variables

Water coverage

Continuity

%

%

Change

Change

Hours per

Hours per

Change

Change

2007-2010

2007-2010

day

day

2007-2010

2007-2010

PB intensity, lax

0.0394

0.0305

-0.0233

-0.0311

1.467

1.533

-0.281

-0.624 (0.481)

match

(0.0391)

(0.0414)

(0.0321)

(0.0373)

(1.355)

(1.174)

(0.412)

PB intensity3 district

2.91e-08

1.37e-08

7.94e-08

7.25e-08

-1.90e-06

— 1.73e—06

-6.07e-07

-6.32e-07

PB water investment

(3.54e-08)

(3.80e-08)

(5.43e-08)

(5.49e-08)

(2.49e-06)

(1.99e-06)

(6.97e-07)

(6.71e-07)

District water

-6.81e-09

-2.78e-09

-2.27e-08

-2.42e-08

5.09e-07

5.02e-07

3.16e—07

3.04e-07

investment from PB

(8.92e-09)

(1.00e-08)

(1.65e—08)

(1.64e-08)

(7.11e-07)

(5.30e-07)

(2.63e-07)

(2.26e-07)

District water

1.95e-09

1.30e-09

-5.90e-10

-1.66e-09

—6.13e—08

8.23e-09

3.05e-08

1.49e-08

investment not from PB

Urban population

(1.62e-09)

(1.25e-09)

(1.21e—09)

(1.15e—09)

(6.41e-08)

(6.95e-08)

(2.92e-08)

(2.73e-08)

-0.0673

-0.120

0.0809

-0.0220

-3.127

0.0870

0.553

-3.201*

(%>

(0.0977)

(0.100)

(0.132)

(0.130)

(3.638)

(3.843)

(2.776)

(1.644)

Poverty incidence

-0.0804

-0.0615

0.0732

-0.0192

-13.33***

-7.425*

-1.785

-1.964 (2.026)

(0.109)

(0.123)

(0.118)

(0.145)

(4.171)

(4.017)

(1.813)

Change in the

9.02e-10**

8.90e-10*

1.89e-10

8.09e-l 1

4.25e-

2.58e-08*

-3.08e-09

-7.98e-09

percentage of

(3.85e-10)

(5.23e-10)

(3.94e-10)

(4.80e-10)

08***

(1.34e-08)

(5.38e-09)

(5.85e-09)

investment budget

(1.10e-08)

execution

% of budget financed

0.0352

0.0132

-0.0701

-0.120**

-2.229

-0.111

1.688**

1.866** (0.904)

by mining royalties

(0.0646)

(0.0616)

(0.0539)

(0.0604)

(2.269)

(2.225)

(0.819)

Province capital

0.115**

0.0743

0.0612

0.100

5.253**

4.930**

-1.302

-0.747 (1.234)

district = 1

(0.0531)

(0.0580)

(0.0595)

(0.0697)

(2.059)

(2.052)

(1.076)

Mayor immediately

-0.0676*

-0.0465

-0.0184

-0.00814

-0.593

-1.430

0.809

0.856 (0.606)

re-elected=l

(0.0360)

(0.0381)

(0.0332)

(0.0346)

(1.259)

(1.189)

(0.618)

% of votes for the

0.168

0.114

-0.0867

-0.109

-12.26*

-13.07**

0.112

-1.193 (3.076)

winner

(0.152)

(0.160)

(0.135)

(0.150)

(6.224)

(5.434)

(2.950)

% of voting women

3.663***

3.908***

1.568**

1.747**

52.31**

22.67

8.350

26.93** (11.20)

over total voting

(0.757)

(0.859)

(0.673)

(0.732)

(23.96)

(24.24)

(15.83)

pop.

Average quarterly

0.0811**

0.0382

-0.247

0.446 (0.551)

water price

(0.0314)

(0.0350)

(1.115)

% of unbilled water

-0.107

0.241[1] [2] *

-7.423

3.727 (2.410)

(0.126)

(0.130)

(5.748)

Arrears (number of

0.00538

0.00973

-0.938***

0.166 (0.140)

months)

(0.0101)

(0.00741)

(0.303)

Number of districts

-0.000133

-0.000460

0.132**

-0.0149

attended by the local WS

(0.00172)

(0.00202)

(0.0526)

(0.0272)

Size of the WS: big

0.0759

0.233***

-5.854**

2.968** (1.326)

(more than 40,000

(0.0633)

(0.0890)

(2.959)

connections = 1) Size of the WS:

-0.0584

0.127

-1.904

2.693 (1.648)

SEDAPAL (more than 1000000 connections = 1)

(0.103)

(0.115)

(3.800)

Constant

-1.036***

_1 208***

-0.848**

-1.152***

-2.943

13.53

-4.085

-14.14**

(0.365)

(0.447)

(0.344)

(0.398)

(11.97)

(12.89)

(6.434)

(5.816)

Observations

181

175

171

167

184

178

181

176

R-squared

0.236

0.296

0.090

0.185

0.194

0.343

0.097

0.190

  • [1] Sources: Robust standard errors in parentheses
  • [2] p<0.1; ** p<0.05; *** p<0.01 aAlso were included the dummy variables “The municipality has an investment planning office,” “The municipality is registered on the SNIP” and “Size ofthe WS: medium”
 
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