Decentralization and Pollution Spillovers. from the Re-drawing of County Borders in Brazil.

Transcription

1 Decentralization Pollution Spillovers: Evidence from the Re-drawing of County Borders in Brazil. Molly Lipscomb University of Virginia Mushfiq Mobarak Yale School of Management April 2, 2015

2 Motivation Over one billion lack sufficient clean water 90% of sewage and 70% of industrial wastes are dumped into surface water untreated. 12% of under-5 mortality is attributed to diarrheal diseases (WHO, 2003) In developing countries, surface waters are often used for bathing, cooking, and drinking maintaining clean water is important to improving health.

3 Motivation: Importance of Water resources in Brazil Brazil has 13% of the world s water resources, but water is unevenly distributed across the country. Many stakeholders for Brazilian water resources: Electricity generation Municipal water plants Municipal sewage plants Agricultural uses Mining Industrial uses (Sugarcane refining, paper mills, etc).

4 Decentralization World Bank, UN, FAO have all promoted decentralization in various forms (see, for example, WDR 2004 on service delivery) Increasing push to give direct loans/grants to state or local level governments forces decentralization Brazil underwent a period of decentralization following the fall of the military dictatorship One component of the decentralization was to encourage increased control at the local level and the formation of new counties County splits are effectively de-facto decentralization since a larger number of counties gain control over the same river segment Allows us to observe strategic behavior by jurisdictions

5 Decentralization Literature Economics literature on the relative merits/problems with decentralized management. Oates, Bardhan and Mookherjee; List and Mason; Besley and Coate; numerous others on spillovers, heterogeneity in preferences and costs, race to the bottom, targeting to local needs, corruption. Possible impacts on water quality (Sigman 2002, 2005): May exacerbate spillovers in the absence of coordination. May reallocate resources towards environmental or sanitation spending (increase local government budgets, or make jurisdictions more homogeneous). Strategic use can also be important in terms of water quantity. Groundwater levels can be affected by level of provision of water access (Sekhri, 2011)

6 Water quality management Decentralized provision of a public good with spillovers The flow of rivers creates upstream and downstream jurisdictions, so coordination in management may be important. Many other public goods suffer from similar strategic use/overuse concerns, but the uni-directional flow of rivers makes them an ideal environment for examining strategic spillovers.

7 Research Questions 1 Does water quality degrade across borders due to increases in pollution close to a river s downstream (exit) point in a jurisdiction? (Are there strategic spillovers?) 2 What is the net effect of a decentralization initiative on water quality once the opposing impacts of cross-border spillovers and increased local budgets for clean up are taken into account?

8 Identification Challenges Possibility of omitted variable bias in estimating the relationship between distance to border and pollution levels Frequency of boundary crossings may be correlated with: Geography: pollution attenuation differs across locations depending on slopes, water depth, flow accumulation, elevation, and geography might be more similar across counties in areas where counties are smaller. Other unobserved characteristics of counties such as population heterogeneity and collective action issues non-random placement of monitoring stations Our Solution: use time series on water quality in order to include location fixed effects and climactic seasonal controls. This requires time variation in border crossings and distances to borders

9 Preview of results BOD -3% 12% 2% per km Direction of Water Flow River County boundary

10 372 Water Quality Monitoring Station Pairs Across Brazil

11 County Borders as of 1991

12 Identification Brazil underwent a period of large growth in terms of number of counties: There were 4492 counties as of 1991 and 5807 in 2005 Newly created counties in 1994, 1997, 2001, and 2005 yield 5 county border maps during the sample period: There are multiple water quality observations for station pairs in each regime (during different months within each regime).

13 Identification There are 372 station pairs in our sample, with 5989 observations. Among 191 station pairs there is at least one change in border. These 191 station pairs account for 3252 observations of the 5989 total observations. We include controls for: station pair fixed effects station pair trends water basin-month dummies water basin-year dummies GDP for upstream, downstream, and intermediate counties population for upstream, downstream, and intermediate counties county size for upstream, downstream, and intermediate counties

14 Can counties affect water quality? Counties can pass laws setting local environmental standards that are more strict than federal/state standards and enforce those standards. Counties are in charge of basic sanitation: sewage collection and treatment, garbage collection, and extending sewer networks. Counties can fine, tax or forbid polluting activities. Counties influence environmental licensing. (Ferraz, 2007)

15 Why do county splits occur? Fundo de Participacao dos Municipios increase in availability of government money for smaller municipalities The Federal government changed the law, no longer allowing state governments to allow more splits to increase the amount of fpm municipal splits declined precipitously.

16 County Responses to Water Pollution County Actions to Reduce Pollution Fining Households with Inadequate Sewer Systems 2462 Fining Companies with Inadequate Industrial Waste 1007 Management Systems Monitoring of Potentially Polluting Industrial Activities 596 Taxing Mining Industries 1027 Taxing Automobiles 104 Management of Toxic Waste 483 Trash Collection Program 1654 Recycling Program 1082 Creation of Sewers 1949 Other 564 *Counts are as of There were 5,560 counties in Brazil in Source: IBGE Environmental Census

17 Quatis and Barra Mansa as of 1991 Resende Barra Mansa S1 B S2

18 Quatis and Barra Mansa as of 1997 Resende Quatis S1 A Porto Real B Barra Mansa C S2

19 Advantages of Empirical Strategy Station pair fixed effects helps with possible omitted variable bias (Sigman 2002). Non-random placement of monitoring stations (Sigman 2005) identification only based on borders changing, not the location of the stations. Station-pair set up makes the classification of upstream and downstream stations natural and unambiguous. Can examine spillovers separately from other effects of decentralization.

20 Possible Biases: Are heterogeneous counties more likely to split? Another mechanism by which decentralization affects outcomes. Are counties under strong leadership less likely to split? A fixed effect which may limit wider applicability of results. Are counties increasing in density more likely to split? We control for population density Use a theoretical model to assess the empirical importance of bias.

21 Modeling a River f (x) is the distribution of the population (of mass 1) q x is the consumption and pollution allowance for a person located at point x. For simplicity, we assume there is a 1-1 relationship between consumption and pollution We assume a decay rate of e (t x) of pollution So pollution level at point t from emissions at x is q x e (t x) Social planner trades off utility from consumption against pollution harm caused downstream.

22 Social Planner Problem W = f (x)u(q x ) 1 x q xe (t x) f (t)dt subject to q x q FOC : f (x)u (q x ) = 1 x e (t x)) f (t)dt + λ Solution for log utility, uniform distribution, no split: q x = min( 1 1 e (1 x), q) Pollution Function: P(y) = y 0 q x e (y x) f (x)dx

23 Theoretical Distribution of Water Pollution Allowances Emissions Location on river q(x) under uniform distribution, one un split county q(x) under uniform distribution, two counties (split at 1/2)

24 Theoretical Distribution of Water Pollution Polution Levels Location on river Pollution after a Jurisdictional Split at 0.5 Pollution in a Single Jurisdiction (Prior to a Split)

25 Modeling Endogeneity Greatest concern: splits are most likely to occur in areas with increasing population density. Increasing density has an independent effect on pollution. When we double population in the model, the per-person pollution allowance is halved (since twice as many people are now harmed by the pollution). Optimizing behavior of this form asserts that endogeneity is not a concern. However, suppose that there is an additional per-person unmonitored pollution ɛ x q-type pollution is due to strategic behavior, while ɛ x type pollution is spurious and due to population changes. We have data only on total pollution we can not distinguish between ɛ type pollution and q type pollution.

26 Endogenous County Splitting Counties split in areas with high population density. We will examine the spatial patterns of q type and ɛ type pollution around borders for the following cases and attempt to extract empirical tests of endogeneity: Basic idea of the empirical test: If the new borders are drawn in high-density areas, that has implications for the spatial pattern of ɛ type pollution to the left and right of the borders. f(x) f(x) 0 county split 1 River 0 county split 1 River

27 Pollution functions under different types of unmeasured pollution endogeneity

28 Predictions of the Theoretical Model 1 Pollution increases as the river travels towards the downstream border of the country (positive slope in pollution). 2 Pollution increases at an increasing rate as the river gets closer to the downstream border. 3 Pollution increases as more borders are crossed. 4 Structural break in slope between area just upstream of the border and area just downstream of the border.

29 Table: Upstream and downstream BOD levels around borders Water Quality Near and Away from the Border Obs Mean Upstream Station BOD Within 3 km from downstream border Beyond 3 km from downstream border Downstream Station BOD Within 3 km of upstream border Beyond 3 km of upstream border

30 Specification Station Regression: ln(bod i,t ) = α i + α i θ t + Σγ basin year + β 2 DistSt1toDownstrmBorder(1D) i,t + β 3 U1 i,t + β 5 2D i,t + Σ k X k i,t + ɛ i,t Station fixed effects included in all specifications Station linear trends included in all specifications Basin month and Basin year fixed effects included in all specifications County specific controls include county size, population, and GDP in the station s county. Standard errors clustered by station

31 Station level specification: Expected Signs Expected Signs: Outcome BOD - U1: Distance from upstream border to station 1 County Boundary Station 1-1D: Distance from station 1 to downstream border

32 Predictions 1 and 2: Increase in pollution toward the border at an increasing rate near the border Table: Single Station Regressions Dependent Variable: 100*LogBOD in the upstream station 1D Distance from station *** *** ** to Downstream border (1D) (0.742) (0.770) (0.703) Squared Distance from station *** 0.034*** 0.031*** to Downstream border (1D 2 ) (0.011) (0.011) (0.011) Distance from Upstream border to station 1 (U1) (0.934) (3.171) (2.788) Squared Distance from upstrm border to station 1 (U1 2 ) (0.192) (0.181) Observations 5,989 5,989 5,989 R-squared Number of pair The dependent variable is 100*the log level BOD at the upstream station. All regressions include station pair fixed effects. Standard errors are clustered by the station.

33 Station Pair as the Unit of Observation Pollution at any point is determined by the geographic history of the river: inflows of tributaries, emissions, distance traveled, etc. With individual stations as the unit of observation, a large portion of the determinants of water quality would be in the error term. In the station pair setup, BOD reading is taken at the upstream station gives us the summary measure of the river s history up to that point. We then model the change in water quality from that point to the downstream point, as a function of the characteristics of the area between the points.

34 Specification Station Pair Regression: ln(bod d i,t ) ln(bodu i,t ) = α i + α i θ t + Σγ basin year + β 1 BorderCrossings + β 2 DistSt1toDownstrmBorder(1D) i,t + β 3 DistUpstreamBordertoSt2(U2)+β 4 U1 i,t +β 5 2D i,t +Σ k X k i,t +ɛ i,t Station pair fixed effects included in all specifications Station pair linear trends included in all specifications Basin*month and Basin*year fixed effects included in all specifications County specific controls include county size, population, and GDP in upstream, downstream, and intermediate counties. Standard errors clustered by station pair

35 Station Pair Specification: Expected Signs Expected Signs: Outcome BOD Downstream -BOD Upstream + Station 1 County Boundary U1: Distance from upstream border to station 1 Number Borders Crossed: Station 2-1D: Distance from station 1 to downstream border U2: Distance from upstream border to station 2 County Boundary 2D: Distance from station 2 to downstream border

36 Prediction 3: Increase in pollution for additional borders crossed. Table: Pair Regressions Dependent Variable: 100*[LogBOD in the downstream station- LogBOD in the Upstream station] Number of borders crossed 3.200* 3.595** 3.418** between station 1 and station 2 (1.647) (1.536) (1.494) Distance from station 1 to 1.526* 1.771* to downstream border (1D) (0.885) (1.029) Distance from upstream border to station 2 (U2) (1.965) (2.108) Outside station pair control:distance from upstream border to station 1 (U1) (2.244) (2.244) Outside station pair control: Distance from station 2 to downstream border (2D) (0.522) (0.606) Observations 5,989 5,989 5,989 F-test for slope of pollution function upstream of border=slope down stream of border

37 Prediction 4: Inflection Point in pollution function Dependent Variable: 100*[LogBOD in the downstream station- LogBOD in the Upstream station] split at x km from border x=3km x=15km U2 far from border (Distance from upstream border to station 2* beyond x km of station) (1.957) (1.460) U2 close to border (Distance from upstream border *** to station 2*within x km of station (4.621) (3.509) 1D close to border (Distance from station 1 to downstream border*within x km of station (4.722) (2.118) 1D far from border (Distance from station 1 to 2.125** 1.609* downstream border*beyond x km from station) (1.000) (0.956) 2D Distance from station to downstream border (0.501) (0.799) U1 Distance from upstream border to station 1 (2.197) (2.275) Observations 5,989 5,989 F-stat for slope upstream of border=slope downtream of border (U2close = 1Dclose) Prob F Decentralization and Pollution Spillovers 0.456

38 Mechanisms Alternative measures of pollution which measure predominantly non-point source pollution. We find no impact of movements in county borders on these measures (Turbidity, Total Dissolved Solids, Conductivity).

39 Effects of Borders on the Intensity of Lights Along Rivers Full Sample Points that Excluding ever are City Cen- Downstreaters Downstream Indicator 0.082*** *** (0.031) (0.104) (0.039) Normalized Distance Upstream 0.325*** *** 0.588*** from border (0.079) (0.299) (0.096) Distance Upstream Squared 0.312*** 1.037*** *** (0.069) (0.271) (0.078) Downstream* Distance Upstream *** *** (1.002) (2.323) (1.245) Downstream*Distance *** *** Upstream Squared (9.437) (19.556) (13.811) Observations 7,300, ,288 3,672,371 R-squared Number of pair 456,374 6, ,152

40 Lights in Downstream Areas Predicted Value of Light Density Exit Border Distance to Exit Border

41 Robustness Tests Top Coding Falsification Test Using Pre-Split Periods Controlling for Split Counties

42 Robustness: Station Pair Regressions top 1% dropped top 1% top coded Number of borders crossed 3.522** 2.949** between station 1 and station 2 (1.518) (1.328) Distance from station * to downstream border (1D) (0.898) (0.820) U2 Distance from upstream border * * to station 2 (U2) (1.786) (1.511) Observations 5,989 5,913 R-squared Number of pair F-stat for equality of dist coefficients P value The dependent variable is 100*the log difference in BOD between the downstream station and the upstream station.

43 Robustness: Falsification Test: Pre-split periods Distance from station to downstream border (1D) (1.298) (2.251) Squared Distance from Station to Downstream Border (1D 2 ) (0.016) (0.029) Distance from Upstream Border to the Station (U1) (3.358) (3.007) Squared Distance from Station to Downstream Border (U 2 1) (0.269) Observations 5,989 5,913 R-squared Number of pair The dependent variable is 100*the log BOD at the station.

44 Some back of the envelope cost calculations: What is the real impact of a border change? The creation of new counties over time generates on average one new border per station pair over the sample period. On average, this decreases the distance of a station to the downstream border from 6 to 3 km. Jointly, these two effects increase BOD by about 9% (more close to the border). At the average, this increases the BOD at the average station from 3.5 to 3.9 What do these levels mean? levels below 2 are considered relatively clean for lakes and streams. levels over 4 are not generally considered acceptable for swimming by the US EPA. Strategic pollution gets the average pollution level substantially closer to unacceptable magnitudes.

45 Where is the Coase Theorem? Downstream counties should be induced to make side payments to upstream counties in order to reduce the impact of pollution. In practice we do not see these side payments, and the externalities from pollution are quite large. Transactions Costs. difficulty of contracting between neighboring counties makes contracts across time difficult to enforce coordination issues between multiple downstream counties information asymmetries: difficulty of ascribing fault where there are many upstream neighbors difficulty of enforcement/jurisdictional issues.

46 The Formation of Water Basin Committees in Brazil: Primarily related to water shortages Small-holders effects (especially fishermen) and hydropower plants create strong pull-factors. National push to institute more water basin committees following a national water management plan (1997).

47 Specification ln(bod U ) = α i +Σδ basinmonth +Σγ basinyear +β 1 UpsttoStation(U1)+ β 2 DisttoDownstr(1D)+β 3 Comm+β 4 UpstmComm+β 5 Comm 1D +β 6 UpstreamCommittee U1 + Σλ k X it + α i θ t + ɛ it Station pair fixed effects and linear trends included in all specifications Basin month and Basin year fixed effects included in all specifications County specific controls include county size, population, and GDP. Standard errors clustered by station pair

48 Table: Are Border Effects Mitigated when Basin Committees are Formed? Dependent variable is 100*Log BOD No Basin Committee Water Basin Committee Distance station 1 to *** Downstream Border (0.779) (4.857) Squared distance station *** to downstream border (0.011) (0.465) Distance from Upstream to station 1 (0.194) (0.600) Squared distance from Upstream to station 1 (3.201) (12.294) Observations 5, R-squared Number of pair Standard errors are clustered by station. Station fixed effects included

49 Conclusions There is a large efficiency cost from negative spillovers arising from decentralization. This is despite the fact that the budgets for sanitation and environmental enforcement grew over the period, and counties that split had access to additional funds. These large spillovers make the effects of decentralization negative in border areas. Where is the Coase theorem? Government efforts to combat these externalities and encourage cooperation between counties has led to formation of river basin committees. The committees appear to have met with limited success.