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Government Incentives for PrivateOwnership of Public Goods: Theory
and Evidence from Belgium∗
Gani Aldashev†, François Libois‡, Joaquín Morales Belpaire§& Astrid Similon¶
May 14, 2013
Keywords: Nonprofits ; Subsidies to Public Goods ; NGOs; NaturalReserves ; Wallon Region
JEL codes: L31, H41, N5, L22, Q26
∗We thank the Nature Direction of the Department of Nature and Forest of the Walloonadministration for allowing us to collect data in their archives. For useful discussions,we are grateful to Jean-Marie Baland, Jean-Philippe Platteau, Vincenzo Verardi, ourcolleagues from the CRED as well as participants to the “NGO" Workshop at the LSE.†Department of Economics and CRED, University of Namur and ECARES, Université
Libre de Bruxelles. Email: [email protected]‡Department of Economics and CRED, University of Namur.
Email: [email protected]§Department of Economics and CRED, University of Namur.
Email: [email protected]¶Department of Law and CRED, University of Namur.
Email: [email protected]
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Abstract
We study the effect of a subsidy to land purchases by NGOs inviews of creating natural reserves in the Wallon region of Belgium. Asimple theoretical model finds that while the subsidy makes prices soarin the short run, the effect is not persisting in time and prices declinein the long run. We suggest that this happens because the subsidyreleases resources for the NGOs that allow them to exert more effortnegotiating prices and stimulating supply, effectively driving pricesdown. We test this prediction empirically benefiting from first-handcollected notarial data and we take advantage of the structural breakcreated by the introduction of the subsidy. Using the MS-estimationmethod (Maronna and Yohai, 2000) that is robust to outliers, weprovide a methodological contribution in the analysis of markets withquasi-donations. The method is relevant for markets of goods withpatrimonial value because some sellers are willing (or are persuaded)to donate their assets free of charge for the common good.
1 IntroductionNon-profits been playing an important and ever increasing role in moderneconomies, both in developing and developed countries. For instance, 67per cent and 42 per cent of the inpatient hospitals in the United Statesand Germany, respectively, are non-profits. 100 per cent of orchestra andopera theatres in the United Kingdom and Japan are non-profit organizations(Bilodeau and Steinberg, 2006). In the OECD countries, on average, 7.5 percent of economically active population is employed in the non-profit sector,and for some countries (Belgium, Netherlands, Canada, U.K., Ireland) thisshare exceeds 10 per cent (Salamon, 2010). The main activity of nonprofitsconsists of providing public goods in such diverse sectors as education, health,environment, social protection, arts and culture, and human rights. Thosewho consider this tendency as positive argue that the non-profit or voluntarysector reduces problems of under-provision of public goods attributable tomarket and government failures. The opponents, instead, raise the concern
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that while the non-profit sector can fill gaps created by inadequate provisionby the public sector, it may also provoke the disengagement of the stateof its prerogatives. Thus, the key issue is whether non-profit provision (andownership of key assets used in provision) of public goods is socially desirable.Besley and Ghatak (2001) provide the first answer to this question: theyargue that the ownership of such assets should be given to the party that hasthe highest valuation of the public good, and this regardless whose investmentincreases the value most.
However, this seminal normative analysis does not provide much guid-ance about how the transfer of ownership of public goods to non-profits (incase these are the parties with the highest valuation) should be organized.This is a key policy question, given that governments dispose of a rich set oftools that can affect the non-profits’ provision of public goods and ownershipof assets involved. These tools include lump-sum grants, matching grants(grants disbursed by the government in proportion to the funds collected bynon-profits from private donors), tax exemptions, subsidies for acquisitionof assets from private holders, etc. Addressing this issue requires, first ofall, understanding - theoretically and empirically - the effects of such poli-cies on the behaviour of nonprofits and the outcomes on the asset markets.Economists’ current knowledge in this area is extremely limited, in partic-ular, at the industry or market level. Moreover, the insights from standardmicroeconomic analysis of markets (e.g. that a subsidy for the acquisitionof assets is likely to increase the price of these assets) provide only limitedanswers, given that the subsidy might also have repercussions on on thecompetition between non-profits and on the allocation of time and humanresources by non-profits. Even such simple instrument as a direct grant mayinduce non-profits to reallocate their time resources and thus have strongunexpected effects on input acquisition by non-profits, as has been shown byAndreoni and Payne (2003, 2011) in their analysis of fundraising crowding-out of charities in the United States.
In this paper, we study the effect of public subsidies for the acquisitionof inputs used in the production of public goods, on the behaviour of non-profits and the outcomes on the market for inputs. To do so, we first develop asimple model of a market for inputs populated by competing non-profits, andderive the testable predictions about the effect of lump-sum and ad valoremsubsidies on the price and quantity of inputs exchanged, both in the short-and the long-run, as well as on the market structure on the buyers’ side.Given that the market is not centralized and finding each new asset requires
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some search (prospecting) costs, we show that the short-run response ofmarket outcomes differs fundamentally from the long-run ones; in particular,the long-run effect on the market price is smaller than the short-run increase.
Next, we test these predictions empirically using a novel dataset thatwe constructed using the universe of notarial purchasing acts in the Wal-loon region in Belgium. This dataset includes all the land plots bought byenvironmental conservation non-profits in Wallonia between 1950 and 1994.Importantly, it allows us to study the effect of the policy reform undertakenin 1984 when the ad valorem subsidy to land acquisition by non-profits hasbeen introduced by the Regional government. We find that, in accordancewith the predictions of our model, the subsidy creates a short-run spikein the (detrended) price of land plots, and that the price start to declineover time. We argue that this happens because while the subsidy inducesthe non-profits to reallocate their time/human resources from fundraisingto prospecting and negotiation activities, which endogenously increases thesupply of land plots. The resulting effect is that in the long-run prices differvery little from the pre-reform prices, but that quantities exchanged increasedramatically. In addition, the data also show that a non-profit which giveslarger weight to the quantity of land plots managed (rather than to the qual-ity of management) gains disproportionately from the input subsidy, in factallowing such non-profits capture most of the market, reducing the share ofmore management-oriented organisations.
Our paper also makes a methodological contribution for the empiricalanalysis of markets for assets with a public-good component. Several mar-kets in which non-profits represent the buyer side fall into this category; forinstance, the markets for art, labour markets for jobs in the non-profit sector,etc. In such markets, the motivation of actors on the supply side often con-sists of a mix of profit-oriented and altruistic elements, and thus such actorsare often willing to sell the assets at reduced price or donate them free ofcharge. Data from such markets thus usually contain numerous observationsthat are considered as outliers. We show that the use of an appropriate es-timator (the MS-estimator, developed by Maronna and Yohai, 2000) robustto outliers, one can accurately estimate the trends in market outcomes.
The structure of the rest of the paper is as follows. Section 2 presentsour theoretical model of a market for assets and derives several testablepredictions. Section 3 describes the context from which our data is collected,reviews the history of natural reserves in the Walloon region and describes ourdata. Section 4 presents our identification strategy, the descriptive statistics
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and the results of our regression analysis. Section 5 concludes.
2 Basic ModelIn this section, we construct a simple decision-theoretic model of a nonprofitorganization, whose mission is to develop environmental natural reserves. Wewill derive several testable predictions which will guide our empirical analysisin section 4.
2.1 Partial EquilibriumSuppose that there are N nonprofits indexed i. For a given market price p,the optimization problem is:
Maxfi,ni,xi,qi
qαii x
1−αii s.t.
1 = fi + ni + xi (Time constraint)(1− σ)pqi = µfi + s (Non distribution constraint)
qi = φni (Technology of search)
(1)
Where qi is the quantity of plots purchased and xi is the time devotedto management efforts. Management efforts echoes better quality plots,therefore the nonprofit values quantity and quality trough this simple Cobb-Douglas utility function. Parameter αi denotes the individual nonprofit’spreferences for land quantity relative to quality of managed plots. Besides ofmanagement activities, a unit endowment of time can be used for fundraisingactivities fi and prospecting activities ni, that is time devoted to searchingplots and negotiating with the potential sellers. Next to the time constraint,the non-distribution constraint states that expenditures in land purchased,with σ ∈ [0, 1] being the ad valorem subsidy over the market price p, areequal to the funds raised by the agency with the simple linear technology µfi,where µ parametrises fundraising technology, and some lump-sum transfer sprovided by the state. Finally, the technology of search parametrizes in φhow efficient are prospecting efforts ni in “revealing” valuable land.
Rearranging the constraints the objective function of the nonprofit canbe rewritten as
Maxni
(niφ)αi
[1− ni
(1 + (1− σ)pφ
µ
)+ s
µ
]1−αi
(2)
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which yields the following optimal amount of time devoted to prospectingactivities:
n∗i = αiµ+ s
µ+ (1− σ)pφ. (3)
Using this we can compute the optimal amount of time devoted to man-agement of land, that is
x∗i = 1− αi + s
(1µ− αiµ+ (1− σ)pφ
). (4)
Finally, the optimal level of fundraising activities is:
f ∗i = αi
(1− µ
µ+ (1− σ)pφ
)− s
µ(5)
Comparative statics reveal that an increase in price of land and in search-ing efficiency decreases searching activities to the benefit of managementand fundraising activities. Increasing the ad valorem subsidy σ will havethe inverse effect, reducing fundraising and management activities in favourof prospecting activities. The effect of the lump-sum subsidy s will alsopush time allocation towards more prospecting and reduce the burden offundrainsing, but opposite to the proportional subsidy it will encourage moremanagement efforts due to a positive income effect.
As expected, nonprofits favouring quantity over quality will prospect moreand perform less management activities, but they will also fund-rise moreaggressively. Combining this with the effect of the subsidy, consider thesecond order derivatives in Lemma 1.
Lemma 1 Effect of the subsidies across heterogeneous preferencesCross derivatives of subsidies and preferences over prospecting efforts aresigned as follows:
d2n∗idσdαi
> 0 and d2n∗idsdαi
> 0 (6)
These conditions state that both subsidies will increase prospecting effortsand this effect will be more pronounced for NGOs that favour quantity ofland over management.
Finally, improvements in the fundraising technology will decrease time de-voted to management and fundraising, effectively releasing time for prospect-ing activities but only when the lump-sum transfer is sufficiently high. If this
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lump-sum transfer tends to zero, better fundraising technology will increasefundraising and decrease prospecting times, leaving management activitiesunchanged.
From here, the individual demand from any nonprofit for plots is :
q∗i = αiφµ+ s
µ+ (1− σ)pφ. (7)
Which evolves in the same direction than the prospecting activities, ex-cept on the prospecting technology φ which will decrease searching effortsbut increase purchased land.
2.2 Market EquilibriumAssume the N NGOs are symmetric in technology and time endowments butheterogeneous in preferences αi. Denote α the average value of αi. Thenaggregate demand for land is
QD(p) = ΣN0 q∗i = αN
µ+ s
µ+ (1− σ)pφ. (8)
Let us turn now to the supply side of the market. We assume that themarket is not centralized so that owners of land plots need to be persuadedto sell them. This depends on total prospecting time
∫N0 n∗i . Suppose a
continuum of size 1 of sellers: each seller has an emotional attachment oropportunity cost θi ∼ U [0, 1] such that she will sell the plot if θi < p+ ΣN
0 n∗i .
The probability that a given seller sells is thus Pr(θi ≤ p+ΣN0 n∗i ) = p+ΣN
0 n∗i
1. Notice that because prospecting by any nonprofit affects the willingnessto sell of the owners, prospecting is a public good. This has an importantimpact because if there are many nonprofits, each will individually free-ride
1The assumption that prospecting efforts have a negative impact on the sellers’ reser-vation price can be grounded on Stigler and Becker (1977) theory of Stability of Tastes.Suppose that agents derive pleasure from ownership of their land plot and from the exis-tence of a network of natural reserves. Assume also that these goods are substitutes andthat the marginal utility of natural reserves increases with accumulated environmentalawareness. By investing on environmental awareness, the nonprofit can increase the user’smarginal utility of the reserve relative to the marginal utility of ownership, effectivelydriving reservation prices down. Preferences of the seller are not modified: decreasing thereservation price for land is a rational decision stemming from better returns from thenatural reserves.
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on the prospecting efforts of others, reducing stimulation of supply. We canwrite the market supply of plots as
QS(p, n∗) = p+ ΣN0 n∗i (9)
.Notice that with this simple supply function, the marginal effect of one
additional unit of prospecting ni increases supply by one unit of land. Fromthe nonprofit perspective, technology of search is such that an additional unitof prospecting makes it discover φ units of land. Therefore we need to assumethat φ > 1, that is, one hour of prospecting discovers more land than whatpersuasion leads to supply. Some land is also sold by pecuniary reasons.From here, taking into account the soliciting efforts of the nonprofits, theaggregate supply of land becomes
QS(p) = p+ αNµ+ s
µ+ (1− σ)pφ. (10)
Remark that the market price of land has a non-monotonic effect onsupply. This occurs because while agents are willing to sell at higher prices,this implies also less soliciting efforts from NGOs, reducing willingness to sellfrom land-owners. A depiction of the shape of the supply and demand curvesis provided in Figures 1 and 2.
Lemma 2 Shape of the Supply CurveThere is a threshold price p below which the supply curve is downward-slopingin prices. It becomes upward sloping after this threshold price.
Proof is provided in appendix A1.Intuitively, the atypical shape of the supply curve is explained by the
prospecting efforts of the NGOs. If the price of land is very low, agents supplyplots mostly because of the large persuasion efforts exerted by the NGOs andnot by pecuniary reasons. As prices increase NGOs exert less soliciting effortsbut the monetary advantage outweighs the persuasion arguments. At somepoint the pecuniary motives overtake the effect of decreasing soliciting effortsmaking supply behave as usual.
At market clearing conditions, that is when Q∗D(p) = QS(p), we cancompute the equilibrium price, given by:
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p∗ =
√µ2 + 4α(1− σ)(φ2 − φ)(µ+ s)N − µ
2(1− σ)φ (11)
Proof of this computation is found in the Appendix A2.It is easy to see that the long run price positively depends the lump sum
subsidy s and the proportional subsidy σ.From this equilibrium price we derive two related propositions concerning
(1) predicted changes in prices and quantity over time following the intro-duction of the subsidy and (2) effects of the subsidy on market shares andeffects and the interaction of the market structure with the subsidy. Thesetwo propositions concern observable variables and thus will be tested in theempirical section of this paper. Proposition (3) considers comparative staticson variables that are not included on the data and therefore are not testable.
Proposition 1 Predictions related to short run and long run ef-fects
Given the market clearing price in (11):
1. Keeping the NGOs’ time allocation constant in the short run, introduc-ing a subsidy σ immediately causes a demand shock, increasing marketprices.
2. In the long run, the subsidy increases prospecting efforts, stimulatingsupply and progressively driving prices down.
3. The new long run price is higher than before the shock and lower thanimmediately after the shock. Therefore we should expect in the longrun a small increase in prices but a very large effect on exchangedquantities.
Proof of the previous statements is provided in Appendix A2. Considerin Figure 1 the effects of the introduction of the subsidy:
Insert Figure 1 here
In the short run, if each NGO’s time allocation is unmodified (for organ-isational reasons for example), introducing a subsidy increases demand buthas not yet an effect in shifting supply. The price climbs from p to p′. In thelong run NGOs re-optimize n∗ which unambiguously increases by equation
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(3), on its turn increasing the supply of “dormant" suppliers. We then gofrom p′ to p′′. We should expect unambiguously that p < p′ and p′ > p′′. Weshould also expect Q < Q′ < Q′′. Let us now turn to predictions related tothe structure of the market.
Proposition 2 Predictions related to market structureGiven the market clearing price in (11) and Lemma 1:
1. The long run price positively depends on the number of NGO’s N . Inthe case of an oligopoly, the long run price should approach to the pre-subsidy price.
2. By lemma 1, the subsidy should make NGOs prospect more aggressivelyand purchase more if they prefer quantity over management (αi high).Therefore we expect that quantity-oriented agencies capture a largerpart of the market after introducing the subsidy.
Given that prospecting is seen as a public good, intense competition willlead to less prospecting, therefore less stimulation of supply and in the long-run higher prices compared to a monopolistic case, as seen in figure 2. Withfew actors, the case that concerns us, prospecting will be sufficient to drivethe long-run price closer to the initial price, as in figure 1.
Insert Figure 2 here
We predict therefore that in our context (1) the long-run price approachespre-subsidy levels and (2) NGOs declaring more interest in purchasing land,as evidenced in the mission statements available on their websites, will buya larger share of the land. Finally, proposition 3 looks at comparative staticsfor non-observables. Although not testable with the available data, thisproposition has important policy implications.
Proposition 3 Non-testable Comparative StaticsGiven the market clearing price in (11) and Lemma 1:
1. Technical improvements in searching technology φ leads to a lower pricein the long run.
2. Technical improvements in fundraising µ will increase the price if theproportional subsidy is high respective to the lump sum subsidy.
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3. For all i, increasing proportional subsidy σ increases both fundraisingfi and prospecting activities ni, effectively reducing the managementactivities xi. As a consequence, because more prospecting also leads tomore land purchases, the management efforts per unit of land (xi/qi)decrease.
4. Increasing the lump-sum subsidy releases time from fund-rising whileincreasing the price of land. While it does not induce excessive landpurchases, it releases time for management activities and the increaseof price deters further land purchases. It follows that managementefforts per unit of land (xi/qi) increase.
Proof of this statement is provided in Appendix A4.The model predicts that improvements in searching technologies drives
down the prices on the long-run. This can be an alternative explanationfor the slow decrease in prices found in the empirical part of this paper. Itmay be that prospectors received better training financed by the subsidyover the years or benefited from experience in more resourceful nonprofit.This, however, is not inconsistent with the main argument that the subsidyreleased resources for more prospecting. It shifts the argument from more tobetter prospecting, but it yields the equivalent effect of stimulating supplytrough non-price mechanisms.
Better technologies in fundraising have the interesting feature of reducingthe need of soliciting funds relocating efforts towards prospecting. This drivesthe price down, but only works if the budget constraint is sufficiently relaxedby the lump sum subsidy. Without the lump sum subsidy, nonprofit will useimprovements in fundraising technologies to gather more funds because theystill need to gather the non-reimbursed part of the purchase, and thereforewill devote less time stimulating supply. If it is desirable that nonprofit getlow prices and don’t spend much efforts fundraising, changes in fundraisingtechnologies (soliciting trough the Internet, hiring fund solicitors motivatedby environmental arguments, improvements in marketing techniques) shouldbe accompanied by the flat transfer. Finally, the effects of the different typesof subsidy have interesting implications. Because the flat subsidy relaxesthe budget constraint, it allows nonprofit to dispose freely of their income,reducing the burden of fundrising and directing efforts both to managementand prospecting. Interestingly, because the flat subsidy drives prices up,purchasing land is not that attractive so that management per unit of land
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increases. The proportional subsidy, however, by rewarding land purchaseswithout relaxing the budget constraint makes the nonprofit put more effortsimultaneously in prospecting activities and into fundraising. This happensbecause benefiting from the proportional subsidy requires to collect the non-reimbursed part of the price trough fundraising (nonprofit need cash to getthe subsidy due to its matching nature), leaving little time for managing ac-tivities. In that sense, the model suggests that the proportional tax allows fora rapid expansion of protected areas, at the detriment of the management ofthe latter. A lump sump subsidy would have the effect of a slower expansionalbeit better managed.
We do not derive the optimal subsidy policy because estimating the op-timal management over land ratio falls beyond the scope of our model. Suchan estimation would require to elaborate an environmental evaluation ofthe plots observed, which is not possible with the data presently available.Nonetheless, future studies evaluating the optimal management to surfaceratio can benefit from the tools we suggest for reaching such objectives.
In the rest of the paper, we support and test the predictions described inpropositions 1 and 2 using self-collected data on land plots acquisitions byenvironmental nonprofits in the Walloon region of Belgium.
3 Natural Reserves in WalloniaIn this section we quickly review the context of private versus public own-ership of natural reserves, and we describe the nature of the data that wecollected for this study.
3.1 ContextIf the first American National Park, the Yellowstone National Park, was es-tablished by the U.S. Congress in 1872, nature conservation started later onin Europe and was much more grassroots driven. In the United Kingdom andin France, private societies were created in, respectively 1895 and 1901, withthe aim to protect landscapes, scenic beauty and major sites (Lansley, 1996;Lepart and Marty, 2006). The National Trust in the UK almost immediatelystarted to acquire land and to buy buildings. It created nature reserves andprogressively became the main field actor of nature conservancy policy inthe UK. With more than 2 million members, the National Trust has dis-
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charged the UK government from a significant part of its duties while it wasgranted substantial fiscal advantages by the same government. In France,the “Société pour la Protection des Paysages et de lÉsthétique de la France”,the oldest French association took another strategy and heavily lobbied thegovernment. The first laws on environmental protection were passed in thebeginning of the 19th century while the first national park was establishedin 1913 by the French government. Today, the role of private foundationsrelated to the protection and creation of natural reserves remains an impor-tant phenomenon. The most notable example is provided by the AmericanNGO the Nature Conservancy. With more than 1 million members, the Na-ture Conservancy manages over 8000 km. of rivers and 50 million hectaresof land (roughly the surface of Spain) over 30 countries (www.nature.org).
The interesting feature of Belgium and especially of the Walloon Regioncomes from the fact that we can observe both private and public regimes ofprotection. Originally, a nonprofit initiated the creation of natural parks inWallonia by creating the first one in 1943. Then, the environmental non-profits continued their role of public good providers by either contractinglong-term leases or purchasing other plots of land in the aim of protectingand managing them. At that time, nonprofits used their own budget to doso. This budget was mainly constituted by private contributions, product ofsmall scale commercial activities and fundraising activities.
The State only showed its interest in the natural park creation around1957 and made its first acquisition of land in 1972. However, since natureconservation has become a regional matter in 1980, the Walloon Government(Southern part of Belgium) has implemented its own action plan and policiestowards legally registered nonprofits. Among them, a regional law that waspassed in 1986 with retroactive effect until the 1st of January 1985, causeda real structural break. Indeed, it allowed for two kinds of subsidies: (1) asubsidy for land acquisition, i.e. a 50% refund on land that was purchasedafter 1984, and (2) a subsidy for management, i.e. for usual management a50% subsidy of effective expenses or a flat rate of around 100e/ha/year, andfor exceptional management a 100% subsidy. To be eligible for subsidies, theland plot should be awarded the status of “Réserve Naturelle Agrée” (RNA)by the Walloon Government following advices of a specific regional council.Notice first that this council, composed of scientists, nonprofits representa-tives and environment administration members, is the only structure able togive this status, provided that the environmental value of the land is highenough. Moreover the same council has a similar role in the creation process
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of “Réserve Naturelle Domaniale” (RND), the public counterpart of RNAs.As a consequence, natural reserves are all created on land with a relatively
high potential environmental value. With substantial positive externalitiesand few alternative production allocations, such land is primarily intendedto protect natural habitats and endangered species. In Wallonia this landmarket is currently shared by two main actors: the Walloon Government andone of the ten legally approved environmental nonprofits.
Therefore we observe an intermediate system between the French pub-lic approach and the British charity approach allowing us to improve ourunderstanding of the process of public versus private good provision.
3.2 DataThe data collected comes from archives of the administration in charge ofnature conservation (DGO3) in the Walloon Region. This administrationchannels payments of subsidies to environmental nonprofits for both the ac-quisition and the management of some of their “private” natural reserves(RNA). The documents on which our study is based consist in copies ofdeeds relating land acquisitions. These documents have to be provided tothe DGO3 by nonprofits willing to get acquisition or management subsidies.
Even though the acquisition subsidy only concerns acquisitions after 1984,nonprofits have very high incentives to apply for a management subsidy forland plots that were bought before 1985. Provided that the land plot hasa sufficiently high environmental value, conditions to fulfil in order to getthe management subsidy are quite loose. It means that our data coverage,ranging from 1943 to 2010, represents the full universe of “private” natural re-serves. However, in this paper, we restrict our analysis to the period between1950 and March 1994 for two main reasons: (1) there is no price deflatoravailable for the observations before 1950 and (2), the first “Life project" waslaunched in Wallonia in April 1994. By introducing its “Life program”, theEuropean Union allowed the nonprofits to receive another 50% subventionfor their land acquisition. In the end, since both regional and European sub-ventions can be cumulative, nonprofits can purchase land at negligible cost.The Life program creates a second structural break. Therefore, it would beinvestigated in a further work.
The entire data set was self-coded by the co-authors based on notarialdeeds. Thus, there is no issue of misreporting. From the deeds, we couldextract information about the transaction, i.e. date and prices, parties at
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stake, and about land plots themselves, i.e. land characteristics, cadastralnumber, land size, localization.
4 Empirical AnalysisIn this section we justify our identification strategy, provide the descriptivestatistics and perform the regression analysis testing the predictions madeby the model in section 2.
4.1 Identification strategyOur identification strategy uses the discrete change of policy towards non-profits introduced in 1986 by the order of the Walloon government describedabove. It has drastically modified the institutional setting faced by nonprof-its. We want to compare the response of nonprofits to this policy change,both on the short and the long run, compared to what they were doing justbefore the policy break.
We argue that this identification strategy is valid because the changewas unexpected, unanticipated and not simultaneous with any other majorchange related to the environmental policy. Actually, the order was passedby a Christian-Democrat-Liberal government. It’s the sole government inthe history of the Walloon region where the Socialist party was not in power.For the first time, the minister of environment was a liberal after a socialistera. Moreover, one of the influential members of this liberal cabinet was closeto a big conservancy NGO. But, in the Belgian context, it is already hardto predict the absence of socialists in a government and even more difficultfor outsiders to infer the composition of the government and the identity ofministers’ cabinet members long before the elections. Moreover, the orderhas not been widely discussed, nor at the parliament, nor in the media. Theenvironment was not a hot issue in this period of tensions between the Frenchand the Flemish speaking communities2 .
The timing of elections and of the order implementation also limitedpotential anticipations. The regional government was in place in December1985, after the election of October 1985. Since the order allows for refundingof land acquisition down to January, 1st 1985, it is very hard to believe that
2We could not find any newspaper article dealing about this topic in the archive of“L’avenir” press group, one of the main actors of the Walloon regional press.
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acquisitions in 1984 and before have been influenced by the order passed inJuly 1986. It means that our identification strategy can only downward biasour estimates since it is plausible that many transactions finalized in 1985and even at the beginning of 1986 had not yet been influenced by the newlegislation.
4.2 Descriptive statisticsThe dataset we use consists of 957 plots acquired by nonprofits between 1950and 1994. Due to missing information about the purchasing price of 12 plots,our effective sample size is 945. As expected, the first acquisitions were notnumerous. Systematic acquisition started in the 70’s with a real boom inboth their number and surface in the 90’s. We have 126 plots purchased bynonprofits before the subvention was introduced and 814 thereafter.
Insert figure 3 here
Not only the area purchased increased over time by rising from 119 habefore the reform to 852 ha after it, but the number of nonprofits did aswell. Three nonprofits were buying land before 1985. This number doubledup after the reform. Despite the fact that they became more numerous, wenote a real change in terms of land market structure. Before the reform,2 nonprofits had large market shares, with one around 60% and anotherat 37%, leaving a residual market share to the third actor. In the periodafter the introduction of the subvention, one nonprofit occupies a prominentposition on the market, with a share, in terms of area purchased, as large as93%. Therefore, as depicted in figure 4, it means that on the regional landmarket, this nonprofit can be seen as a de facto monopsonist.
Insert figure 4 here
Table 1 presents separate descriptive statistics of our main variables be-fore and after the subsidy was introduced. We also restrict our sample tothe 4 years before and the 4 years after the introduction to pick observationswhich are potentially highly affected by the structural change in incentivesfaced by nonprofit and less affected by non-observables. Especially, it avoidsthe high spike in the number of plots purchased that we can observe in thebeginning of the nineties. Prices are systematically different before and af-ter the reform, but only on a short span window. The average size of land
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purchased slightly increases between both periods, but this difference is notsignificant, with the average plot area remaining close to one hectare. Around40% of plots were owned by multiple sellers. This remains stable across bothperiods. The main change between before and after the introduction of thesubvention is a potential change in the type of land purchased by nonprofits.They buy more woodland and pasture lands after the reform and decreasethe amount of wastelands purchased. The change in the quality of land pur-chased is however quite small if we restrict our comparison within a 8 yearswindow around the implementation of the subvention mechanism. It shouldbe noticed that the differences are not significant any more in this restrictedwindow because differences are indeed smaller and not because difference-in-mean tests are less accurate in smaller sample.
Insert table 1 here
Figure 5 shows that nonprofits have increased the spread of their activitiesafter 1985. Nonprofits remain active around their core area in the Easternpart of Wallonia but have started to buy plots in other sub-regions. Thisspread has however been relatively mild just after the introduction of thesubsidy, as shown in figure 6 which depicts land plots purchased in the 4years before and after 1985. This is particularly important since we arguethat the land market can be considered as relatively homogeneous aroundthe introduction of the new policy.
Insert figure 5 here
Insert figure 6 here
4.3 Regression analysisOur analysis relies on the discontinuity around the policy break. It meansthat, from an econometrical point of view, we want to estimate the relation-ship:
Lnpricei = α0 + α1Subi + α2Y eari + α3Subi ∗ Y eari + Xβ + εi (12)
where the logarithm of the price of each purchased plot i is a function ofSub, an indicator variable equals to one if the land acquisition was subsidized
17
and zero otherwise. Y ear denotes the effective date of acquisition; X is avector of control variables, β a vector of parameters and ε an idiosyncraticcomponent. The interpretation of the main coefficients is presented in figure7. α1 denotes the average short term change in percentage of the price of landrelated to the change in policy (when the year of the change is references asY ear = 0). Since the dependant variable is expressed in logarithm and Sub isa dummy variable, the exact predicted average change between the subsidizedand non subsidized acquisitions is given by the expression eα−1. α2 controlsfor the overall trend in prices while α3 allows for a trend differential betweenthe period before the subsidy and the period after. In a linear specification,the time to go back to the trend after the break is given by the ratio betweenα1 and α3, provided that they have opposite signs.
Insert figure 7 here
Table 2 reports estimations of equation 12) by Ordinary Least Squares.Estimations of α1 are systematically positive, quite large but very imprecise.It results in estimates which are never significantly different from 0 in any ofthe specifications. However it is worth noting that estimates are relativelystable if we control for the number of sellers, for land quality and for the factthat nonprofits start buying in provinces where the land prices are higher.The coefficient drops in specification (5), which is estimated on a sub-sampleof our data covering the 4 years before and after 1985. This specificationlacks estimation power but is interesting in the sense that we compare amore homogeneous sample before and after. The main lesson at this stageis that all signs are consistent across specifications. This is also the casefor most of point estimates. Specification (6) is run on the whole sampleand also controls for the identity of the buyer. Keeping in mind that only2 NGOs are buying land before and after 1985, it cleans up the coefficientfrom a potentially different behaviour of new entrants on the market. Sincethe estimated coefficient is larger, it suggests that new entrants would buy,on average, at lower prices than incumbents.
If we find some evidence in favour of an increase in prices after the in-troduction of the new policy, it also comes out that prices would come backto the trend after some time as suggested by the negative sign of α3, thecoefficient of the interaction term between the time trend and the subsidydummy. Once more this coefficient is never statistically different from 0.
Nevertheless, this set of estimations is not satisfactory, especially if we fo-cus on the coefficient of the logarithm of the area. First of all, this coefficient
18
is relatively unstable despite the fact that land market prices do depend onthe area and that discussions with notaries clearly indicate that most trans-actions are based on a price per hectare basis multiplied by the area. Fromour estimations, we should conclude that a one percent increase of plot areawould increase prices by only 0.7%, or even less. Actually, the problem ofthis specific market is that assets producing externalities are traded betweenprivate agents and non-profit organizations, with a large effort of non-profitto bargain over acquisition prices. We collected anecdotal evidence relat-ing that some landowners are just happy to give their land for free, or al-most for free, provided that the nonprofit manages the land, maintains thelandscape or protects endangered animals. These benefits are non-rival andnon-excludable, which means that the seller is almost sure that he will enjoythose environmental services without paying management costs. Likewise,nonprofits are sometimes inclined to pay relatively high prices for small plotsbecause some plots have network externalities allowing the extension of agreen mesh between sites. These transactions do not fit in a classical mar-ket model between for-profit agents. Somehow, we have to take that intoaccount.
From an econometrical point of view, these transactions will appear asvertical outliers for small plots with high prices and bad leverage points forlarge plots at low prices. The presence of vertical outliers and bad leveragepoints downward biases coefficients, increases standard errors and prevent usfrom reading the R2. In specification (7), we estimate our model includinga dummy variable flagging the 10 most obvious quasi-donations, i.e. landplots “sold” for a single and symbolic monetary unit. 5 of them are madebefore 1985 and 5 after. The price-area elasticity is largely affected jumpingfrom 0.65 to 0.77, with its t-stat doubling. The subsidy coefficient dropsby one point. The R2 now reaches 70%. It means that only one percent ofobservations has a major influence on our results. It would not be a criticalissue if we could flag all rebates and exceptionally high prices, but this is notpossible by hand and without discretionary decisions.
One way of getting around this problem is to use an estimator robust tooutliers and able to tackle for the presence of dummy variables among ex-planatory variables. We opt for the MS-estimator proposed by Maronna andYohai (2000) and developed by Verardi and Croux (2009). This allows ro-bust and efficient estimations in the presence of outliers in a multidimensionalsetting. The loss function is a Tukey Biweight function where the marginalweight of a residual tends to 0 as the residual becomes large. It means that
19
all observations have some weight in the regression but that this weight isnot exploding when the observation lies far away from the regression line,and actually far away from the core group of observations .
Insert table 3
Table 3 reports estimates of the same specifications in table 2 but esti-mated with this method robust to outliers. First, it is important to emphasizethat all signs of the main variables of interest and of the control variables areunchanged. The first striking change between both sets of estimates is thestability of the “log area” coefficient. It is now very close to one, which isnothing else that what is expected. The introduction of the subsidy seems tohave increased prices in the short run by around 50%. Therefore, for a plotvalued 100e, the introduction of the subsidy increased the price to 150e, ofwhich 75e where effectively paid. At the short run, the subsidy representsthus a 25e saving for the nonprofit. After that, the price steadily drops untilit reaches the pre-subsidy price 10 years after the introduction of the subsidy.Because our sample is limited to these 10 following years, it is impossible forus to know if the trend would have continued until subsidized prices becomeactually lower than than non-subsidized prices.
The introduction of our dummy variable flagging the quasi-donationsleaves estimates practically unchanged. Of course the coefficient associatedto this variable is large, predicting a drop in the prices of these plots by morethan 99% but all other coefficients remain unaffected. This indicates that ourstrategy is valid and that the results of our regressions depict “normal mar-ket conditions” without being affected too much by non neoclassic marketbehaviour.
Results from this estimation are consistent with the theoretical frameworkdeveloped in section 2. All points of proposition 1 are satisfied, in the sensethat we observe a spike of prices just after the introduction of the subsidy,followed by a progressive decline towards the initial price, as depicted infigure 1. However, even if the effect of the subsidy vanishes on the marketprice, the number of transactions has dramatically increased at the end ofthe period as evidenced in figure 3. This is fully consistent with a positiveshock of demand followed with a long-run activation of supply.
Let us turn now to testable proposition 2. We predicted that a marketwith few actors would make the subsidy have a small effect on the long-runprice. We do not dispose of a counter-factual exploring what would hap-pen if the market was more competitive, however it is very hard to think of
20
instances where subsidies have the long-run effect of decreasing prices. Weclaim than the de facto monopsonistic position of the nonprofit has allowedit to efficiently encourage owners to sell of donate their land, avoiding freeriding-issues related to multiple prospectors. This creates exceptional con-ditions in which the long-run impact of the ad valorem subsidy to such amonopsony has low impact on prices and a very high impact on quantitiesexchanged. However, we claim that the change in the market structure isendogenous to the introduced policy. Consistent with the second point ofproposition 2, the change in the market structure is expected when there aredifferences among NGOs in the prioritization criteria between quantity andquality, parameter αi in our equation. These differences are made clear whenwe look at the mission statements of the two main nonprofits:
[The association] has the objective of creating, participate inthe creation, to manage and to participate into the managementof natural reserves [...] and more generally any structure, privateor public, whichever its form, that contributes to preserve nature- A&G.
[The association] is devoted to preserve and manage threat-ened natural habitats. To this end, the association develops astrategy of purchasing or renting lands with remarkable biologi-cal interest, mainly in Wallonia and in Brussels.- RNOB
While the first nonprofit seems to be more open to collaboration andemphasizes management efforts, the second makes clear that prioritizationis put on purchasing land. The market share of each nonprofit responds tothe subsidy as predicted by proposition 2; the subsidy makes the quality-prioritizing nonprofit capture almost all land plots, as is displayed in figure8.
Insert figure 8 here
The introduction of the subsidy has had a limited impact price-wise, buta very important effect on exchanged quantities. Policy-wise this seems apositive result because large extents of land have been purchased withouta large price pass-trough effect to the sellers. A caveat on this happy re-sult should nonetheless be mentioned. Because the less aggressive buyers aredriven out of the market, predominant space is left to groups that prioritize
21
purchases over management. A negative effect of the subsidy is that man-agement efforts per unit of land are predicted to decrease. The ad valoremsubsidy seems an efficient policy tool only if the number of natural reserves,and not their ecosystemic value, is the policy objective.
5 ConclusionWhat are the broader implications of our model and empirical findings? Theframework of analysis that we have developed above applies to a large set ofcontexts with the provision of a public good being delegated to a nonprofitorganization. These are the contexts in which a certain asset or input poten-tially can generate substantial externalities or has a fundamental public goodnature, but is initially held by a private party that does not value these ex-ternalities. At the same time, there exist agents (or organizations founded bysuch agents) which are intrinsically motivated and would like to internalizethese externalities or release the public good potential of the asset. Usually,however, these agents are credit-constrained (or, more generally, cannot eas-ily monetize their intrinsic motivation; so their willingness-to-pay does notfully reflect their motivation). Thus, they can either raise funds throughsolicitations from other agents, or the government can assist in transferringthe ownership of the asset to these motivated agents. Finally, locating suchassets imply a positive search cost.
Examples of such settings (beyond environmental conservation discussedabove) are numerous. Most private non-profit organizations devoted to artis-tic or cultural heritage conservation face the same problem: artworks are of-ten held by individuals that do not fully value their public good nature, andsuch objects are traded in a competitive market. Government can subsidizethe acquisition by non-profit museums of such art pieces, but finding thepieces that best suit the collection of the non-profit museum requires effort.
Another example is the non-governmental organizations whose missionis to combat environmental pollution by, for example, properly recyclingpolluting second-hand appliances. Sometimes, there exists markets for theseobjects, and, again, their purchase by the nonprofits can be subsidized bythe government. If certain appliances are more polluting than others, findingthem can require substantial effort.
In addition, our empirical analysis has an important methodological im-plication. In applied problems of evaluating the effects of government subsi-
22
dies on the market outcomes in this kind of settings, a non-negligible fractionof data points exhibit very low prices. This occurs because some of the ini-tial owners of the assets can also be intrinsically motivated, and these ownerswould sell these assets to nonprofits for a price that does not correctly re-flect the market value of the assets (a nominal price). In that case, failingto treat such outliers properly might induce the researcher to underestimatethe effect of the subsidy on the market price and to overestimate the effecton the quantity. Our analysis above highlights this pitfall and illustrates theappropriate method for treating these outliers.
23
ReferencesAndreoni, J. and Payne, A. A. (2003). Do government grants to privatecharities crowd out giving or fund-raising? American Economic Review,93(3):792–812.
Andreoni, J. and Payne, A. A. (2011). Is crowding out due entirely tofundraising? evidence from a panel of charities. Journal of Public Eco-nomics, 95(5-6):334–343.
Besley, T. and Ghatak, M. (2001). Government versus private ownership ofpublic goods. The Quarterly Journal of Economics, 116(4):1343–1372.
Bilodeau, M. and Steinberg, R. (2006). Donative nonprofit organizations, vol-ume 1 of Handbook on the Economics of Giving, Reciprocity and Altruism,chapter 19, pages 1271–1333. Elsevier.
Lansley, J. (1996). Membership participation and ideology in large voluntaryorganisations: the case of the national trust. Voluntas, 7:3:221–240.
Lepart, J. and Marty, P. (2006). From natural reservations to the biodiversityterritories. the case of france. Annales de Géographie, 651:485–507.
Maronna, R. and Yohai, V. (2000). Robust regression with both continuousand categorical predictors. Journal of Statistical Planning and Inference,89(1):197–214.
Salamon, L. M. (2010). Putting the civil society sector on the economic mapof the world. Annals of Public and Cooperative Economics, 81(2):167–210.
Stigler, G. J. and Becker, G. S. (1977). De gustibus non est disputandum.American Economic Review, 67(2):76–90.
Verardi, V. and Croux, C. (2009). Robust regression in stata. Stata Journal,9(3):439–453.
24
Appendix AA1. Proof of Lemma 2.
Proof 1 Take the aggregate supply function given by equation (6). The firstderivative of this function with respect to price is
d
dpQS(p) = 1− (1− σ)φ(µ+ s)
2((1− σ)φp+ µ)2
The root of this expression in p is
p =
√√√√N(µ+ s)2(1− σ) −
µ
(1− σ)φ
The second order derivative of the supply curve is
d2
dp2QS(p) = (1− σ)2φ2(µ+ s)((1− σ)φp+ µ)3 > 0
Therefore QS(p) is U-shaped.
A2. Computation of the Equilibrium Price.At market clearing conditions we have
αNφ · µ+ s
µ+ (1− σ)pφ = p+ αN · µ+ s
µ+ (1− σ)pφ
which is a quadratic function in p. Its roots are:
p1 =
√µ2 + 4αN(1− σ)φ(µ+ s)(φ2 − φ)− µ
2(1− σ)φ
p2 =−√µ2 + 4αN(1− σ)φ(µ+ s)(φ2 − φ)− µ
2(1− σ)φ
We can without ambiguity exclude the negative price solution.A3. Proof of Proposition 1
25
Proof 2 1. The non-distribution constraint can be written in the shortrun as
qi = µf + s
(1− σ)pShort-run demand thus increases with the subsidy.
2. At the long run, prospecting effort n∗i (σ) increases in σ by equation(3). Aggregate supply is given by QS(p) = p + Nn∗(σ). Therefore ifprospecting increases in σ so does supply.
3. This results from the two previous statements is illustrated in figure 1.
A4. Proof of Proposition 3
Proof 3 At the long-run equilibrium, we have
p∗ = (φ− 1)ΣN0 n∗i (σ, p, s, φ, µ)
By implicit function theorem we can, for any variable z ∈ {σ, s, φ, µ}, write
dp(z)∗dz
= ∂ΣN0 ni(z)∗/∂z
1/N(1− φ)− ∂n∗/∂p∗ (13)
Because the denominator of this expression is always positive, we have thatp(z)∗ varies in the same direction than the prospecting efforts ΣN
0 ni(z)∗ hold-ing p∗ constant. We conclude that
1.dp(φ)∗dφ
< 0
2.dp(µ)∗dµ
> 0 if (1− σ)p∗φ > s
3. The market clearing price can be written as p∗ = ΣN0 (φ − 1)n∗i and
the searching technology gives q∗i = φn∗i . We can thus rewrite the non-distribution constraint as
fi = (φ− 1)φσ(ΣN0 n∗i )n∗i − s
µ;∀i (14)
26
Therefore, because n∗i (σ) increases in σ, we have that so does fi(σ).Therefore x(σ) has to be decreasing in the subsidy. Notice that becauseprospecting also increases the quantity of land purchased, we have
d
dσ
xi(σ)qi(σ) < 0 ;∀i
4. Return to the optimisation problem of any individual NGO given in (1).We can associate to this problem the following Lagrangian equation:
Maxfi,ni,xi,qi
qαii x
1−αi + λ
(1− (1− σ)pqi − s
µ+ qiφ
+ xi
)
It yields the following optimality condition:
xiqi
= 1− αiαi
(1− σµ
p+ 1φ
)
By equation (11) we have dp(s)/ds > 0, therefore
d
ds
xi(s)qi(s)
> 0 ;∀i (15)
27
Table 1: Descriptive Statistics for NGO’s AcquisitionsBefore After Before_4 After_4mean/se mean/se mean/se mean/se
price in Euro 2524.17 3496.48 1324.70 ** 4323.26(9465.07) (12878.38) (1228.58) (7351.08)
area in ha 0.92 1.04 0.46 1.74(2.89) (4.42) (0.55) (7.47)
price/ha 3608.16 5015.06 3582.93 4475.48(2965.07) (18991.85) (1852.13) (6639.55)
number of sellers 1.82 1.79 1.73 1.68(1.46) (1.47) (1.15) (1.20)
=1 if mult. owners 0.43 0.38 0.43 0.38(0.50) (0.49) (0.50) (0.49)
=1 if wasteland 0.32 *** 0.06 0.19 0.15(0.47) (0.24) (0.40) (0.36)
=1 if woodlands 0.01 *** 0.16 0.00 ** 0.13(0.09) (0.37) (0.00) (0.34)
=1 if wetland 0.02 * 0.05 0.03 * 0.00(0.12) (0.22) (0.16) (0.00)
=1 if pasture land 0.53 ** 0.66 0.59 0.61(0.50) (0.47) (0.50) (0.49)
=1 if pond 0.00 0.02 0.00 0.05(0.00) (0.13) (0.00) (0.21)
=1 if cultivable land 0.12 0.12 0.03 * 0.14(0.32) (0.33) (0.16) (0.35)
Observations 129 816 37 107*, **, *** denote significant differences between beforeand after means respectively at 10%, 5% and 1%
28
Table2:
OLS
Regressionof
thelogarit
hmof
prices
(1)
(2)
(3)
(4)
(5)
(6)
(7)
logof
price
logof
price
logof
price
logof
price
logof
price
logof
price
logof
price
=1ifsubsidized
purcha
se-0.214
-0.206
-0.216
-0.215
0.0755
-0.0912
-0.0576
year,1
jan1
985=
00.0452
0.0452
0.0473
0.0462
0.0365
0.0392
0.00683
[0.0338]
[0.0337]
[0.0335]
[0.0331]
[0.126]
[0.0317]
[0.0130]
year
*sub
-0.0425
-0.0437
-0.0444
-0.0498
-0.0174
-0.0543
-0.0137
[0.0415]
[0.0413]
[0.0415]
[0.0404]
[0.148]
[0.0385]
[0.0223]
logof
area
0.728∗∗∗
0.725∗∗∗
0.681∗∗∗
0.649∗∗∗
0.635∗∗∗
0.649∗∗∗
0.772∗∗∗
[0.0843]
[0.0847]
[0.0883]
[0.0858]
[0.189]
[0.0883]
[0.0533]
numbe
rof
sellers
0.0384
0.0289
0.0288
0.0696
0.0329
0.00598
[0.0241]
[0.0236]
[0.0235]
[0.0600]
[0.0241]
[0.0182]
prov
_new
0.452∗∗∗
1.507∗∗∗
0.331∗
0.422∗∗∗
[0.151]
[0.476]
[0.187]
[0.131]
symbo
l-8.048∗∗∗
[0.723]
CONTRO
LSLa
ndqu
ality
nono
YES
YES
YES
YES
YES
buyers
identit
yno
nono
nono
YES
YES
restric
tedsample
nono
nono
YES
nono
Observatio
ns945
945
945
945
144
945
945
AdjustedR
20.334
0.335
0.348
0.359
0.432
0.369
0.713
Stan
dard
erro
rsin
brac
kets
,cl
uste
red
atth
em
unic
ipal
ity
leve
l;
∗p
<0.
10,
∗∗
p<
0.05
,∗
∗∗
p<
0.01
29
Table3:
MSRegressionof
thelogarit
hmof
prices
(1)
(2)
(3)
(4)
(5)
(6)
(7)
logof
price
logof
price
logof
price
logof
price
logof
price
logof
price
logof
price
=1ifsubsidized
purcha
se0.326∗∗∗
0.320∗∗∗
0.333∗∗∗
0.335∗∗∗
-0.312
0.410∗∗∗
0.429∗∗∗
[0.0995]
[0.0993]
[0.107]
[0.108]
[0.402]
[0.107]
[0.108]
year,1
jan1
985=
0-0.00209
-0.00185
-0.00265
-0.00296
0.151
-0.00695
-0.00784
[0.00645]
[0.00648]
[0.00727]
[0.00734]
[0.110]
[0.00697]
[0.00702]
year
*sub
-0.0463∗∗∗
-0.0463∗∗∗
-0.0431∗∗∗
-0.0452∗∗∗
-0.123
-0.0455∗∗∗
-0.0451∗∗∗
[0.0102]
[0.0100]
[0.0112]
[0.0111]
[0.0957]
[0.0111]
[0.0113]
logof
area
1.062∗∗∗
1.062∗∗∗
1.053∗∗∗
1.037∗∗∗
0.926∗∗∗
1.037∗∗∗
1.037∗∗∗
[0.0136]
[0.0148]
[0.0153]
[0.0155]
[0.103]
[0.0163]
[0.0168]
numbe
rof
sellers
-0.0244∗∗
-0.0274∗∗
-0.0264∗∗
-0.0546∗∗
-0.0282∗∗
-0.0287∗∗
[0.0119]
[0.0138]
[0.0127]
[0.0229]
[0.0133]
[0.0134]
prov
_new
0.186∗∗∗
0.981
0.147∗∗∗
0.154∗∗∗
[0.0380]
[3.277]
[0.0396]
[0.0403]
symbo
l-9.242∗∗∗
[0.303]
CONTRO
LSLa
ndqu
ality
nono
YES
YES
YES
YES
YES
buyers
identit
yno
nono
nono
YES
YES
restric
tedsample
nono
nono
YES
nono
Observatio
ns945
945
945
945
144
945
945
Rob
ust
Stan
dard
erro
rsin
brac
kets
;∗
p<
0.10
,∗
∗p
<0.
05,
∗∗
∗p
<0.
01
30
Table4:
OLS
Regressionof
thelogarit
hmof
prices,w
ithou
tou
tliers
(1)
(2)
(3)
(4)
(5)
(6)
logof
price
logof
price
logof
price
logof
price
logof
price
logof
price
=1ifsubsidized
purcha
se0.256∗∗
0.259∗∗
0.227∗∗
0.236∗∗
-0.292
0.248∗∗
[0.118]
[0.112]
[0.104]
[0.104]
[0.217]
[0.109]
year,1
jan1
985=
0-0.00312
-0.00268
-0.00286
-0.00313
0.154∗∗∗
-0.00326
[0.00825]
[0.00815]
[0.00835]
[0.00851]
[0.0506]
[0.00881]
year
*sub
-0.0339∗∗
-0.0362∗∗
-0.0278∗
-0.0321∗∗
-0.161∗∗
-0.0327∗∗
[0.0142]
[0.0146]
[0.0147]
[0.0142]
[0.0689]
[0.0143]
logof
area
1.058∗∗∗
1.057∗∗∗
1.045∗∗∗
1.029∗∗∗
0.905∗∗∗
1.028∗∗∗
[0.0171]
[0.0180]
[0.0167]
[0.0132]
[0.0329]
[0.0130]
numbe
rof
sellers
-0.0202∗∗
-0.0139
-0.0149
-0.0381∗
-0.0145
[0.00778]
[0.00920]
[0.00902]
[0.0198]
[0.00903]
prov
_new
0.213∗∗
0.981∗∗∗
0.168∗
[0.0905]
[0.128]
[0.0871]
CONTRO
LSLa
ndqu
ality
nono
YES
YES
YES
YES
buyers
identit
yno
nono
nono
YES
restric
tedsample
nono
nono
YES
noObservatio
ns849
849
841
841
120
843
AdjustedR
20.919
0.919
0.923
0.927
0.936
0.929
Stan
dard
erro
rsin
brac
kets
,cl
uste
red
atth
em
unic
ipal
ity
leve
l
∗p
<0.
10,
∗∗
p<
0.05
,∗
∗∗
p<
0.01
31
Table5:
MSRegressionwith
diffe
rent
breaks
(1)
(2)
(3)
(4)
(5)
(6)
(7)
logof
price
logof
price
logof
price
logof
price
logof
price
logof
price
logof
price
placeb
obreak
0.347∗∗
0.586∗∗∗
0.434∗∗∗
0.322∗∗∗
0.181∗∗∗
-0.0929∗∗∗
0.120
[0.157]
[0.0833]
[0.107]
[0.0836]
[0.0661]
[0.0334]
[0.0786]
placeb
odif.
trend
-0.0167
-0.0389∗∗∗
-0.0377∗∗∗
-0.0556∗∗∗
-0.0732∗∗∗
-0.0664∗∗∗
-0.177∗∗∗
[0.0176]
[0.00948]
[0.0109]
[0.0103]
[0.0105]
[0.0130]
[0.0488]
year,=
0at
break
-0.00812
-0.0112∗
-0.00825
-0.00232
0.00216
0.00765∗∗∗
0.0007
[0.0147]
[0.00673]
[0.00809]
[0.00537]
[0.00387]
[0.00167]
[0.00195]
logof
area
1.041∗∗∗
1.032∗∗∗
1.034∗∗∗
1.041∗∗∗
1.043∗∗∗
1.042∗∗∗
1.042∗∗∗
[0.0149]
[0.0159]
[0.0156]
[0.0150]
[0.0144]
[0.0144]
[0.0142]
numbe
rof
sellers
-0.0232∗∗
-0.0251∗∗
-0.0257∗∗
-0.0251∗∗
-0.0267∗∗
-0.0261∗∗
-0.0259∗∗
[0.0118]
[0.0127]
[0.0125]
[0.0119]
[0.0116]
[0.0115]
[0.0116]
prov
_new
0.187∗∗∗
0.187∗∗∗
0.188∗∗∗
0.191∗∗∗
0.187∗∗∗
0.185∗∗∗
0.166∗∗∗
[0.0392]
[0.0377]
[0.0380]
[0.0373]
[0.0375]
[0.0369]
[0.0381]
CONTRO
LSLa
ndqu
ality
YES
YES
YES
YES
YES
YES
YES
buyers
identit
yno
nono
nono
nono
year
ofthebreak
1980
1982
1984
1986
1988
1990
1992
Observatio
ns945
945
945
945
945
945
945
Rob
ust
stan
dard
erro
rsin
brac
kets
∗p
<0.
10,
∗∗
p<
0.05
,∗
∗∗
p<
0.01
32
Figure 1: Effect of the subsidy in the monopsonist case
6
-
p
Q
p’
p,p”
Q”Q Q’
Figure 2: Effect of the subsidy in the competitive case
6
-
p
Q
p’
p
p”
Q”Q Q’
33
Figure 3: Number of land acquisitions by NGOs
Figure 4: Land market share by NGO, before and after the reform
34
Figure 5: Location of acquisitions before and after the introduction of thesubsidy
Figure 6: Location of acquisitions 4 years before and after the introductionof the subsidy
35
Figure 7: Interpretation of regression main parameters
Figure 8: NGO’s Market share during the studied period
36