1# RECORD 91b7dd63d779b9bfc5961da2cf5599ed5160c16d2# TITLE: Stratification and Public Utility Services in Colombia: Subsidies to Households or Distortion of Housing Prices?3# YEAR: 2006.0 VENUE: nan DOI: 10.32468/be.4224# AUTHORS: C. Medina; L. Morales5# PAGES: 46 CANDIDATE TABLE PAGES (1-based): [19, 29, 32]6# ABSTRACT: Domiciliary public utility services in Colombia have a cross subsidy system which charges subsidized rates to the households who live in houses located in strata associated to low wealth levels, and taxed rates to the better off. We assesses the hypothesis that the flow of subsidies that potentially come from a particular house, are discounted by housing market agents so that most of them are transferred to the prices of the houses that generate the subsidies. By estimating a hedonic prices model applying a regression discontinuity approach, we find that the increment in house value estimated because of subsidies is similar in magnitude to the present value of the flow of subsidies. Likely effects are found on the rent amount. We conclude that subsidies to the poor population through public spending in domiciliary public utility services in Colombia is being achieved, if anything, in a very limited way. Most of the financial effort on this subject ends up distorting housing relative prices according to socioeconomic strata, with an annual cost of up to 0.7% of GDP in supposed gross subsidies to domiciliary public utility services.789===== PAGE 1 (score 1, numeric-paren 0, table-words 0, var-words 1) =====1011Stratification and Public Utility Services 12in Colombia:Subsidies to Households 13or Distortion of Housing Prices?14T15here is ample consensus about the desirability of subsidizing the con-16sumption of public utility services because of the positive externalities17that result when they are supplied and the high public costs generated by18their absence. On that basis, most Latin American countries subsidize the19supply of services and constantly try to improve their targeting systems while20minimizing social losses associated with the subsidy schemes.21States in the region that allocate subsidies for residential public utility ser-22vices (servicios públicos domiciliarios, or SPD) have always found the target-23ing and coverage of subsidies a source of controversy, debate, and criticism.124Among the ways used in the region to reach households with SPD subsidies25are cross subsidy schemes, subsidies to supplying utilities, cash transfers, and26so forth. Because of the variety of alternatives and the socioeconomic and cul-27tural diversity of the countries of the region, no regional consensus exists on28how subsidies should be targeted.29To the problem of lack of technical consensus can be added the difficulty30of reforming the targeting systems derived from the complex political econ-31omy of subsidy policies in the region. That difficulty has become even greater324133C A R L O S M E D I N A34L E O N A R D O M O R A L E S35kOpinions expressed in this paper are those of the authors and do not necessarily reflect the36views of Banco de la República (Colombian Central Bank) or of the members of its board of37directors. We thank Raquel Bernal, Carmen Pagés, and Maximo Torero for detailed comments38on successive drafts, Carlos Esteban Posada and participants in the Economía and Banco de la39República seminars for helpful comments, and Laura Angarita and Lina Marcela Cardona for40assistance.411. The Colombian case is a clear example. Among recent studies that have assessed Colom-42bia’s current system and formulated proposals to improve it are Fernández (2004), Meléndez43(2004), and INECON (2006). The Colombian government has also taken some steps toward sys-44tem reform with proposals such as one by the Departamento Nacional de Planeación (DNP 2005).454647===== PAGE 19 (score 20, numeric-paren 0, table-words 2, var-words 13) =====4849subsidies (for electricity, gas, and piped water and sewerage) for only 8,27750households. On the other hand, the DACD information allows us to obtain51the real estate appraisal values for 8,879 households; when merged with the52information for the ECV2003 households, these give a total of 5,759 house-53holds with complete information.54It can be inferred from appendix A-4 that the sample with complete infor-55mation is not a random sample of the households in Bogotá. In particular, it56includes a lower proportion of households in strata 1 and 2 and a higher pro-57portion in strata 3, 4, and 5. It also includes houses with higher real estate58appraisals per square meter, houses with smaller lots and built areas, and a59larger proportion of houses than of other housing units. It includes houses60with more bedrooms and bathrooms and a higher probability of having gas61and telephone service, a garage, and a terrace—in general, better amenities.62Table 5 shows housing prices and utility subsidy amounts by stratum.3363These data reveal the need to control in our empirical exercise for character-64istics on which the socioeconomic strata are based to minimize the possibil-65ity of obtaining biased coefficients.66Carlos Medina and Leonardo Morales67596833. The price per square meter is defined as the house price divided by the average of square69meters of terrain and square meters of built area.70T A B L E 3 . Households, by Electricity Consumption, Bogotá, 200371Stratum72Kilowatt-hours73174275376477578679Total80Number of houses by electricity consumption in each stratum81Less than or equal to 2008275,10883381,01084456,7018587,8098626,9818712,000881,039,60989Greater than 2009021,48891154,03692264,3669395,3759450,3279544,96496630,55597Total9896,59799535,047100721,070101183,18810277,31210356,9701041,670,18105Percentage of houses by electricity consumption in each stratum106Less than or equal to 20010777.7510871.2110963.3411047.9311134.9011221.0611362.25114Greater than 20011522.2411628.7911736.6611852.0611965.1012078.9312137.75122Total123100124100125100126100127100128100129100.00130Percentage of houses by electricity consumption per stratum across all strata131Less than or equal to 2001327.2213336.6513443.931358.451362.601371.15138100139Greater than 2001403.4114124.4314241.9314315.131447.981457.13146100147Total1485.7814932.0415043.1715110.971524.631533.41154100155Source: ECV2003 (see table 2).156157158===== PAGE 20 (score 11, numeric-paren 0, table-words 3, var-words 1) =====15916060161E C O N O M I A , Spring 2007162T A B L E 5 . House Prices per Square Meter and Subsidies in Bogotá, 163by Socioeconomic Stratum, 2003a164Subsidies165Number of 166Housing price per 167Water and 168Stratum169observations170square meter171Housing price172Energy173sewerage174Piped gas175117622217785,19417820,500,00017914,65818033,0311812,44118221831,531184129,12818528,000,00018612,04418726,7001882,17618931902,462191199,55819248,400,0001934,85919413,08319501964197738198396,53919976,800,00020002018,37220202035204202205510,514206113,000,000207−12,968208−22,385209−1,9112106211133212723,551213151,000,000214−15,046215−47,480216−1,675217Total2185,288219225,47022051,200,0002215,53922214,368223602224Sources: Housing price per square meter: DACD; Subsidies: ECV2003 (see table 2).225a. Housing prices and subsidies are in Colombian pesos. Negative figures represent contributions.226T A B L E 4 . Households, by Piped Water Consumption, Bogotá, 2003227Stratum228Cubic meters229123022313232423352346235Total236Number of houses by piped water consumption in each stratum237Less than or equal to 2023852,955239352,725240515,297241123,50124251,14524327,4402441,123,06124520 less than 4024617,63624799,118248126,26824943,39325018,65725111,749252316,822253Greater than 402545592555,97425618,1262576,2212582,9782597,03426040,893261Total26271,150263457,817264659,691265173,11526672,78026746,2232681,480,776269Percentage of houses by piped water consumption in each stratum270Less than or equal to 2027174.4327277.0427378.1127471.3427570.2727659.3627775.8427820 less than 4027924.7928021.6528119.1428225.0728325.6328425.4228521.40286Greater than 402870.792881.302892.752903.592914.0929215.222932.76294Total295100296100297100298100299100300100301100302Percentage of houses by piped water consumption per stratum across all strata303Less than or equal to 203044.7230531.4130645.8830711.003084.553092.4431010031120 less than 403125.5731331.2931439.8531513.703165.893173.71318100319Greater than 403201.3732114.6132244.3332315.213247.2832517.20326100327Total3284.8032930.9233044.5533111.693324.913333.12334100335Source: ECV2003 (see table 2).336337338===== PAGE 29 (score 10, numeric-paren 0, table-words 3, var-words 2) =====339340When the NPV is compared with Δvaluation using a 10 percent annual real341interest rate, we find that both magnitudes are similar, which implies that the342SPD subsidies are transferred almost entirely to housing prices.343Finally, table 10 illustrates not only how the net subsidy becomes in fact a344tax but also how it is distributed by income decile, for both EE and AA. Only345when the EE subsidy is discounted at a 10 percent annual real interest rate is346a positive subsidy for the poorest households found. However, that is the pop-347ulation that is expected to have a higher opportunity cost of money, and so it348also would be expected to be more likely to discount the flow of subsidies at349higher rates. There are important reductions in AA subsidies due to housing350capitalization in the case of AA, in which only the poorest 20 percent of the351population end up receiving a somewhat relevant amount.352In short, the estimates obtained allow us to conclude that SPD subsidies are353almost entirely transferred to the value of the house that receives them, without354generating an apparent net benefit and distorting housing prices.355Carlos Medina and Leonardo Morales35669357T A B L E 9 . Comparison of the Net Present Value of Subsidies with Their Incidence 358on Housing Pricesa359Table of average results for annual discount rate of 10 percent360NPV361Δvaluation362Δvaluation per NPV363Due to change 364Due to change 365Variable366Elasticity367Subsidy368Contribution369in subsidy370in contribution371Subsidy372Contribution373Energy (EE)3740.02973751,016,810376−1,811,4393771,320,2983784,014,4193791.30380−2.22381Water and 3820.02953832,147,633384−4,216,3913851,455,4033863,808,2563870.68388−0.90389sewerage (AA)390EE + AA3913,164,444392−6,027,8303932,775,7013947,822,6753950.88396−1.30397Total subsidies3980.05053993,083,605400−6,139,0504012,497,1084026,520,7894030.81404−1.06405Table of average results for annual discount rate of 15 percent406Energy (EE)4070.0297408692,125409−1,233,0154101,320,2984114,014,4194121.91413−3.26414Water and 4150.02954161,461,857417−2,870,0244181,455,4034193,808,2564201.00421−1.33422sewerage (AA)423EE + AA4242,153,982425−4,103,0394262,775,7014277,822,6754281.29429−1.91430Total subsidies4310.05054322,098,956433−4,178,7454342,497,1084356,520,7894361.19437−1.56438Source: Authors’ calculations.439NPV = net present value.440a. Net present values of subsidies and contributions, as well as changes in valuations, are in Colombian pesos of 2003. Results are obtained441with the sample located at an average 250 meters (m) from the border (500m between each house and to the closest one from another stra-442tum on the other side of the boundary), and correcting for measurement error. The change in house valuation, “Δvaluation,” is generated by443a 100 percent change in subsidy.444445446===== PAGE 30 (score 5, numeric-paren 0, table-words 1, var-words 2) =====44744870449E C O N O M I A , Spring 2007450T A B L E 1 0 . Distribution of DPS Subsidies Net of Their Incidence on House Value 451in Bogotá, 2003a452Energy (EE)453Discount rate = 0.10454Discount rate = 0.15455Subsidy as 456Net subsidy 457Net subsidy 458Monthly amount 459percentage 460Amount of 461as percentage 462Amount of 463as percentage 464Decile465of subsidies466of income467net subsidy468of income469net subsidy470of income4711472381,543,0744735.54747,018,1744750.1476−168,676,493477−2.44782479488,009,6564802.248116,445,3454820.1483−204,771,828484−0.94853486545,908,5264871.7488−30,098,973489−0.1490−300,311,872491−0.94924493617,593,4424941.349524,199,2774960.1497−254,169,879498−0.64995500674,633,7075011.0502−6,097,110503−0.01504−325,437,100505−0.55066507629,887,0295080.7509−58,899,267510−0.1511−382,018,229512−0.45137514612,863,3695150.5516−155,701,525517−0.1518−516,245,621519−0.45208521591,676,2425220.3523−280,949,748524−0.2525−690,310,420526−0.45279528463,921,2845290.2530−286,651,201531−0.1532−638,754,762533−0.353410535224,639,5825360.1537−231,103,448538−0.1539−444,898,606540−0.1541Total5425,230,675,9115430.43544−1,001,838,476545−0.08546−3,925,594,811547−0.32548Water and sewerage (AA)5491550959,510,15555113.8552543,167,7675537.8554347,856,1285555.055625571,171,353,7365585.4559682,522,5625603.1561453,205,0785622.156335641,323,456,2385654.1566716,325,4155672.2568431,512,3075691.357045711,449,931,6615723.1573795,147,4525741.7575487,979,1045761.157755781,598,213,7565792.4580874,442,7365811.3582534,911,6015830.858465851,558,287,6895861.8587786,617,7795880.9589424,616,9105900.559175921,530,139,8105931.3594610,165,3335950.5596178,592,7875970.259885991,494,818,6406000.8601428,601,9776020.2603−71,574,625604−0.0460596061,356,714,4586070.5608237,980,8986090.1610−286,832,010611−0.161210613874,734,5396140.2615−189,031,175616−0.05617−688,057,709618−0.2619Total62013,317,160,6846211.096225,485,940,7446230.456241,812,209,5716250.15626Source: Authors’ calculations.627a. Results are obtained with the sample located at an average 250 meters (m) from the border (500m between each house and to the628closest one from another stratum on the other side of the boundary), and correcting for measurement error.629b. Amounts of subsidies are expressed in Colombian pesos.630Results for Rents631The ECV2003 asks households that pay rent to report their monthly payment.632In addition, it asks those who live in their own house for the amount of rent633that they believe the house would generate if it were rented. Using as the634dependent variable the logarithm of the rents reported in both instances, we635repeat the previous exercise. The results show a positive relation between EE636and AA subsidies and the logarithm of the rent paid by households.637638639===== PAGE 32 (score 27, numeric-paren 0, table-words 5, var-words 3) =====640641T A B L E 1 1 . House Price Model, with Additional Controlsa6421,000m643800m644700m645600m646500m647Variable648Coefficient649t650Coefficient651t652Coefficient653t654Coefficient655t656Coefficient657t658Subsidy of energy6592.41E-066601.566613.02E-066621.776633.61E-066641.956653.78E-066661.946674.01E-066681.86669Subsidy of piped water and sewerage6702.68E-066712.836722.92E-066732.846742.79E-066752.556762.80E-066771.606782.89E-066791.63680Subsidy of piped gas6815.58E-076820.08683−5.82E-06684−0.77685−4.51E-06686−0.56687−6.73E-06688−0.77689−7.36E-06690−0.75691Subsidy of energy squared6927.12E-116931.696947.20E-116951.566963.40E-116970.716982.14E-116990.357001.67E-127010.02702Subsidy of piped water and 703−1.47E-11704−0.97705−2.95E-11706−1.70707−1.76E-11708−0.98709−1.06E-11710−0.32711−4.77E-12712−0.14713sewerage squared714Subsidy of piped gas squared7154.13E-107160.45717−1.12E-10718−0.11719−4.37E-10720−0.45721−5.23E-10722−0.40723−1.53E-10724−0.11725Ln(τ)726−0.024727−1.79728−0.038729−2.74730−0.038731−2.68732−0.030733−2.07734−0.036735−2.33736Subsidy of energy7372.33E-067381.507392.99E-067401.757413.46E-067421.887433.59E-067441.847453.72E-067461.72747Subsidy of piped water and sewerage7482.69E-067492.837502.90E-067512.817522.76E-067532.517542.80E-067551.597562.89E-067571.61758Subsidy of piped gas7593.52E-077600.05761−5.78E-06762−0.76763−4.26E-06764−0.53765−6.45E-06766−0.74767−7.36E-06768−0.76769Subsidy of energy squared7706.96E-117711.657727.14E-117731.557743.15E-117750.667761.86E-117770.30778−2.72E-12779−0.04780Subsidy of piped water and 781−1.47E-11782−0.97783−2.99E-11784−1.72785−1.85E-11786−1.04787−1.26E-11788−0.38789−7.12E-12790−0.21791sewerage squared792Subsidy of piped gas squared7934.09E-107940.44795−9.03E-11796−0.09797−3.83E-10798−0.40799−4.65E-10800−0.35801−1.09E-10802−0.07803Ln(τ)804−0.025805−1.80806−0.0381807−2.7808−0.0375809−2.68810−0.030811−2.10812−0.037813−2.38814SR815−0.037816−1.47817−0.032818−1.20819−0.049820−1.68821−0.042822−1.62823−0.051824−1.75825Source: Authors’ calculations.826m = meters; SR = subsidized regime (see text).827a. The dependent variable is the logarithm of the house appraisal. Results are the equivalent to those reported in table 8 once Ln(τ) (the effective property tax rate) and SR are included. T statistics are estimated on828the basis of robust standard errors.829830831===== PAGE 33 (score 0, numeric-paren 0, table-words 0, var-words 0) =====832833In addition, the inclusion of the SR has negligible effects on the relevant 834coefficients.45835Other Potential Biases836Although we think that our results make a good case for the capitalization of837public utility subsidies on house prices, our methodology is not free of caveats.838First, there are several issues common to hedonic regressions that might be839generating bias in our estimates, such as the presence of substantial heterogene-840ity across households, spatial correlation, and so forth. Second, although the841evidence provided supports to a large extent the validity of the RD assumptions842listed earlier (namely subsidies change discontinuously at the boundaries and843observable and unobservable characteristics of houses change continuously at844the boundaries), in the case of the second assumption, whether unobservable as845well as observable characteristics change smoothly around the boundaries846would still need to be proven. In addition, the third and fourth assumptions (the847effect of public utility subsidies on house prices is continuous at the boundaries848and the amount of subsidies is independent of its effect on house prices at the849boundaries) imply more demanding requirements in our case.850The second assumption would not be satisfied if, for example, differences851in preferences across races led individuals from different races to segregate852themselves. Since people would value the network that each neighborhood853offers differently, that could be an example of an unobservable characteris-854tic that we would not be controlling for that could change discontinuously855around the boundaries. In general, since people can estimate in advance the856benefits that they could receive from locating on either side of a boundary, one857could argue that some sort of sorting around boundaries on unobservable char-858acteristics, such as households’ preferences, should be taking place in prac-859tice. As long as that sorting becomes a characteristic that affects house prices860in a discontinuous way around boundaries, the mechanics of the sorting would861imply a violation of the fourth assumption, which in this context would come862along with a violation of the second assumption and, in some cases, the third.86345. Intuitively, however, one could expect the inclusion of the SR to have a positive864effect on house prices in the future, as the recently implemented “New Sisbén” score is con-865structed so that households in lower strata have lower scores and so are more likely to be eli-866gible to benefit from SR, thus increasing the probability that a household will be a beneficiary867if it moves just across the border between strata (in particular when the household moves868from strata 1 or 2 to stratum 3 or higher or vice versa) and affecting house prices much as869public utility subsidies do.870Carlos Medina and Leonardo Morales87173872