<figure><img src="image_1.png" /></figure>

HAL
open science

# Turning the heat up. How sensitive are households to fiscal incentives on energy efficiency investments?

Jeanne-Marie Daussin-Benichou, Amélie Mauroux

► To cite this version:

Jeanne-Marie Daussin-Benichou, Amélie Mauroux. Turning the heat up. How sensitive are households to fiscal incentives on energy efficiency investments?. 2014. hal-05451128

HAL Id: hal-05451128

https://insee.hal.science/hal-05451128v1

Preprint submitted on 9 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Direction des Études et Synthèses Économiques

G 2014 / 06

Turning the heat up.

How sensitive are households to fiscal incentives
on energy efficiency investments?

Jeanne-Marie DAUSSIN-BENICHOU et Amélie MAUROUX

Document de travail
INSTITUT NATIONAL DE LA STATISTIQUE ET DES ÉTUDES ÉCONOMIQUES

Série des documents de travail
de la Direction des Études et Synthèses Économiques

G 2014 / 06

Turning the heat up.
How sensitive are households to fiscal incentives
on energy efficiency investments?

Jeanne-Marie DAUSSIN-BENICHOU* et Amélie MAUROUX**

JUILLET 2014

Les auteures remercient José BARDAJI, Éric DUBOIS, Pierre-Alain PIONNIER, Corinne PROST et Bertrand VILLENEUVE pour leurs précieux conseils, les participants du séminaire du département des Études Économiques de l'Insee (Paris, 2013), en particulier Pauline GIVORD pour sa discussion, les participants à la conférence de l'Association française de sciences économiques (Aix-en-Provence, 2013) et de la conférence annuelle de l'European Association of Environmental and Resource Economists (Toulouse, 2013), les participants des séminaires des laboratoires de microéconomie et d'économie industrielle du Crest, les participants des séminaires de l'université Paris Dauphine, en particulier Kevin BEAUBRUN DIANT pour sa discussion, Élise COUDIN, Maxime TO, Aurélien POISSONNIER, Pauline CHARNOZ et Pierre-Yves CABANNES pour leur aide, Christine PINEL pour la recherche et la préparation de séries fines de prix, Laurent AUZET pour ses nombreuses explications pour l'appariement des fichiers fiscaux ainsi que Martine DELANGRE pour son aide pour l'utilisation des données.

<footnote>* Insee - Département des Études Économiques - Division « Études macroéconomiques » Timbre G220 - 15, bd Gabriel Péri - BP 100 - 92244 MALAKOFF CEDEX</footnote>

<footnote>** Dauphine, CREST-LEI. Amélie Mauroux travaillait au Département des Études Économiques quand l'étude a débuté.</footnote>
Turning the heat up.

How sensitive are households to fiscal incentives
on energy efficiency investments?

Abstract

This article studies the sensitivity of French households to fiscal incentives, focusing on the French tax credit on home energy efficient renovations. We estimate the ajustment of the households' average expenditures after an unexpected increase in the tax credit rate (intensive margin). This evaluation complements Mauroux's (2012) results on the number of additional beneficiaries (extensive margin). In 2006, a reform was restricted to new owners of pre-1977 dwellings, allowing us to develop an original difference-in-differences model. It is combined with a Tobit model and censored quantile regressions and estimated on exhaustive fiscal data. In reaction to this tax credit increase, households increased their housing improvement expenditures. This effect appears to be highly heterogeneous depending on the level of expenditures and households characteristics. On average the final net expenditures would have stayed constant. The multiplier of this program is assessed at 1.5, due to the extensive margin.

Keywords: tax credit, energy efficiency investments, sustainable development, public policy evaluation, censored quantile regressions, difference-in-differences estimates.

Dépenses de rénovation résidentielle et crédit d'impôt
développement durable

Résumé

Cet article étudie la sensibilité des ménages français aux incitations fiscales en s'intéressant au crédit d'impôt développement durable. Nous estimons l'ajustement des dépenses d'amélioration de l'efficacité énergétique du logement suite à une hausse du taux de crédit d'impôt (marge intensive). Cette évaluation vient compléter les résultats de Mauroux (2012) sur le nombre additionnel de bénéficiaires (marge extensive). En 2006, une réforme, qui ciblait les nouveaux propriétaires de logements achevés avant 1977, permet de développer un modèle original en différence-de-différences. Il est couplé à un modèle Tobit et à des régressions quantiles censurées et estimé sur données fiscales exhaustives. Suite à la hausse de taux, les ménages ont accru leurs dépenses d'amélioration de l'efficacité énergétique. Cet effet serait très hétérogène selon les montants dépensés et les caractéristiques des ménages. En moyenne, le reste à charge final des ménages resterait inchangé. Le multiplicateur budgétaire associé à cette mesure serait de 1,5 du fait de la marge extensive.

Mots-clés : crédit d'impôt, développement durable, évaluation des politiques publiques, estimation en différence de différences, matching, régression quantile censurée, Tobit

Classification JEL : H31, H23, D12
# 1 Introduction and motivation

In energy efficiency policies, price-based instruments (tax credit, subsidies...) may seem more attractive than regulatory tools (norms, standards...) to achieve environmental goals. Indeed new standards will take fewer years to diffuse, especially when equipment replacement rates are low like in the automobile sector (15% per year) or the housing sector (1% per year). But these policies may come at a high budgetary cost, especially if they are *ex ante* ill-designed. On the French bonus-malus scheme introduced for car purchases in December 2007, Durrmeyer et al. (2011) found that the bonus had an impact on car sales three times larger than the impact that could be *ex ante* expected based on the price elasticity they estimate on past car sales. As a result, this policy cost 250 million Euros only in the first year even though it was designed to be *ex ante* cost-neutral. A better understanding of the responsiveness of households to price-based instruments is crucial, especially in a context of fiscal constraints.

Are French households sensitive to environmental fiscal incentives? In this paper we study another emblematic French environmental fiscal policy, the "Crédit d'impôt Développement Durable" (CIDD), a tax credit on housing energy efficiency improvements and renewable energy investments. It was created in 2005 in order to encourage households to improve the energy efficiency of their dwellings and to install renewable energy equipments in their primary residences. It allows households to deduct a 15% to 50% of these expenditures from their income taxes. It was a major success: between 2005 and 2010, 6.2 million households used it at least once, so almost one primary residence in four was renovated or modernized in only six years (Mauroux et al. 2010; Marcus et al. 2012). The total budgetary cost was about 12 billion euros from 2005 to 2010. In 2010, the budgetary cost represented 1.9 billion euros, namely 0.1% of French GDP. In this paper we try to assess the sensitiveness of the French households to this fiscal incentive by measuring the impact of an increase in this tax credit on their energy efficiency improvement investment.

The theoretical impact is straightforward: a tax credit is equivalent to a reduction in the marginal cost of energy efficiency investment. This can trigger the decision to retrofit by turning profitable investments which were previously not profitable (extensive margin). It may also incentive households who had already decide to retrofit to increase their expenditures (intensive margin). Nevertheless, empirical evidence on the incentive impact of energy efficiency tax credit is rather mixed. The first *ex post* evaluations of the impact of the Energy Tax Act suggested that an increase in the fiscal incentive had a low (if not negative) impact on both the probability to retrofit (Wash, 1989) and the amount invested (Dubin and Henson, 1988). The Energy Tax Act was a federal credit available in the US from 1978 to 1985, which allowed taxpayers to reduce their income tax by 15% of amount spent for eligible conservation equipment (up to $2,000). Those studies rely on
geographical variation in the reduction rates to identify the impact of the fiscal incentive, which can be sensitive to fixed effect and endogeneity. Using panel and controlling for fixed effects, Hasset and Metcalf (1995) found results more consistent with intuition and theory: a 10 percentage point change in the tax price for energy investment would lead to a 24 percent increase in the probability of making an investment. Recent findings on a similar policy in Italy tend to question the incentive effect of tax credits (Alberini et al., 2013). The Italian tax credit rate also allowed homeowners to deduct up to 55% of their expenses from their income taxes. The Italian fiscal incentive is quite massive, in particular compared to the 15% American tax credit. Alberini et al. (2013) found that door and window replacements became significantly more frequent after the implementation of the tax credit in 2007, especially in colder climates regions of Italy, but not the heating system replacement rate. This result leads them to the conclusion that free riding must have been pervasive. Nevertheless, their data source, the Italian Consumer Expenditure Survey, does not guarantee that the households actually claimed nor benefited from the tax credit for which they were eligible. Thus, it is not clear whether the absence of effect is due to free-riding or to a low use of the tax credit (ineffective measure).

In France, the tax credit rates range between 10% and 50% and it brings us back to our question of the effectiveness of the fiscal instruments to encourage households to undertake energy efficiency investments. Mauroux (2012) showed that a marginal tax credit increase targeted on new homeowners of pre-1977 dwellings had a positive and significant impact on the number of tax credit claimed for energy efficient investments (positive intensive margin). Based on a survey data from 2001 to 2011, Nauleau (2014) studied the impact of the creation of the CIDD in 2005 on the thermal renovation activity (retrofitting rate/probability of retrofitting investments) and on the average expenditures of the households who retrofit (including non CIDD items). Her results suggest that the CIDD has no impact before 2007 both neither on the probability to retrofit nor on the average amount spent on retrofitting. Starting from 2007, the CIDD has a significant and increasing impact on the amount by which the households that invest in energy efficiency increased. This suggests that there was a latency period during which households did not adjust their behavior to the new fiscal incentive. There are more and more pieces of evidence that fiscal policies may have a signal effect in addition to the price effect, as showed by Durrmeyer et al. (2011). Indeed, the estimated impact of the implementation of bonus on energy efficient cars cannot be explained only by a price effect. They show that the bonus itself had a specific impact five times higher than the price elasticity. Koomey (2002) warns that price-based instruments may have a two-fold effect: a "*direct price effect*" and an "*announcement effect*". This second effect is the impact of a rebate that is independent of the size of the rebate. Koomey also cites changes in marketing strategies of the people selling the product as they may use the existence of the new tax credit. It can also come from the credibility conferred to certain goods by the regulator through a labeling effect. This effect may be even stronger in the case of energy efficiency
equipments because of the complexity of the technical characteristics, of the retrofitting options,... The eligibility to a bonus or a tax credit can act as an implicit label on the performance of the equipment. At the implementation, both the price and behavioral effects are mixed. A "before-after" approach as in Nauleau (2014) does not disentangle those two effects. In this paper we propose an empirical strategy to identify the price effect of the CIDD.

From 2006 to 2009, households who had purchased in the past 2 years a dwelling constructed prior to 1977 could claim a 40% tax credit rate on their energy efficiency expenditures (higher efficient boilers, insulation materials, etc) instead of a 25% tax credit rate for the others. The reform was announced at the end of 2005 so it was not anticipated by the households. We take advantage of this quasi-natural experiment to identify the sensitivity of energy efficiency expenses to the level on the fiscal incentive (intensive margin). We develop a triple differences (DDD) strategy as in Mauroux (2012) to control for fixed effects for each of the eligibility criteria: housing unit built before 1977, purchased in the past 2 years. Mauroux (2012) only estimated the extensive margin of the 2006 reform. Our estimation of the intensive margin allows us to now provide an evaluation of the total incentive impact of the 2006 reform.

We had access to fiscal files from 2005 to 2008 ; they contain the tax credit claims and informations on both households and dwellings. The use of fiscal data guarantees exhaustiveness and a high level of reliability on the energy efficiency expenditures. To guarantee homogeneity in housing units characteristics and households renovation behaviour, we restrict to observations "close" to the two eligibility thresholds: housing units whose construction was completed between 1969 and 1988 and households who purchased it in the past 5 years.

Each year only a small fraction of the French households fill a tax credit claim form. To control the potential censoring bias we estimate a Tobit-DDD model. We combine it with a matching method to control for the potential structural differences between the treatment and control group. To gain some insights on the heterogeneity of the sensitivity of investments to the fiscal incentive and overcome the statistical limits of the Tobit model (Maddala and Nelson 1975, Goldberger 1980, Arabmazar and Schmidt 1981, 1982), we also run censored quantile regressions (Powell, 1986) on various clusters of the population.

Our results suggest that households did adjust upward their energy efficiency expenditures after the tax credit rate increase, confirming that they are price sensitive. On average, in 2006, expenditures were 1% to 28% higher than if the tax credit rate had remained 25%, 23% to 47% in 2007 and 27% to 41% in 2008. Censored quantile regressions confirm those results but provide strong evidence that the impact is highly heterogeneous across quantiles and households. Price sensitivity seems to be lower at the top of the
distribution of expenditures and stronger for relatively well-off middle age couples with
children living in Ile de France. Computing the expenditures net of the tax credit refund
(in constant euros), our results suggest that in 2007 and 2008 the average final cost was
equal or slightly higher than the average final cost in case of a 25 percent tax credit.
As shown in Mauroux (2012) a majority of households would have undertaken energy
efficiency investments but they adjusted their investments choices to match their home
renovation budget. They either installed a more efficient version of the same equipment
- quality effect - or installed more equipments - quantity effect. Finally, reconciling re-
sults on the extensive margin (Mauroux, 2012) and our results on the intensive margin,
we estimate that, in 2007 and 2008, on average one euro of budgetary cost caused by
the 2006 reform generated 1.5 euro of private investment. This greater than one ratio
is mainly explained by the increased in the number of households investing in energy
efficiency expenditures.

The article is structured as follow: the second part presents the tax credit on residen-
tial energy efficiency, the data and some stylized fact. In the third part, we investigate the
determinants of energy efficiency expenditures by homeowners with a censoring model,
before estimating in the fourth part the sensitivity of these expenditures to the level of
the tax credit rate. The results are reported in the last part.

2 The tax credit on residential energy efficiency

2.1 The program

Households can deduct from their income taxes from 15 to 50 percent of their expendi-
tures on energy conservation or renewable energy equipments in their primary home. If
the tax credit exceeds the tax liability of the household then the household is refunded
the difference. It is in particular the case for households not paying income taxes.

The tax credit is calculated on the price of equipments and materials costs (net of
taxes), labor cost not included. The eligible items are selected according to energy
efficiency criteria which are regularly updated. They fall into two main categories:

* energy efficiency and energy conservation investments: thermal insulation materials (insulating walls and ceiling, thermal windows, shutters and doors), clock thermostats, high efficiency boiler (low temperature, condensing);

* equipments using a renewable source of energy: photovoltaic, solar water heat, heat pumps, wood heating or other biomass heating, geothermal energy.

The tax credit rate ranges from 15% to 50% depending on the installed item, on the
home construction year and the equipment installation year. There is a five-year ceiling
on the total amount of expenditures taken into account to calculate the tax credit for the
household and the home. The ceiling depends on the household composition (8,000 euros
for a single person, 16,000 euros for a couple, plus 400 euros per dependent). The tax
credit is calculated on the price excluding taxes, so it can be claimed in addition to
the 5.5% VAT rate on housing repair services and products. If the household receives
local or other national subsidies for the equipment purchase (regional council, department
councils, Housing National Agency), the tax credit is calculated on the total expenditures
net of the other public subsidies.

## 2.2 Data

We use exhaustive fiscal files from 2006 to 2009. Tax payers fill a tax credit claim and report their total energy efficiency expenditures on their income tax returns. French households fill tax files in year N+1 for income earned in year N so we follow French households home renovation investments undertaken between 2005 and 2008. The use of fiscal data guarantees exhaustiveness and a high level of reliability on the energy efficiency expenditures.

The first drawback of using this fiscal data source is that we do not observe the total amount spent by households in energy efficiency improvement expenditures, nor on home renovation. Households only report the share spent on items eligible to the tax credit that were installed by a professional, not including labor cost.<fnref n="1" /> Our study only covers the CIDD investments and not the entire scope of thermal home repair investments. We know the expenditures only if the household knows the existence of the tax credit. Nevertheless, surveys on housing renovation show that this information quickly spread as 53% of the households knew this tax credit the first year of its existence, 63% in 2006, 74% in 2007 and 78% in 2008 (survey "Maîtrise de l'énergie" on all tenants and homeowners, Ademe).

The second drawback is that we have no detailed information on the installed equip-
ments because households are only asked to give their total expenditures by tax credit
rate (15%, 25%, 40% or 50%). Nevertheless, between 2005 and 2008, tax credit rates on
energy conservation equipments were different from tax credit rates on renewable so we
are able to discriminate between those two main types of expenditures.

We use income tax return files and merge them with the local residence tax files to match households with their dwellings. We observe between 2005 and 2008 each housing unit (year of construction, number of rooms, size, apartment or private house, year of household moving in), its occupants (homeowner or tenant, size, age, total fiscal income), and on the total amount of energy efficiency investments spent on CIDD equipments and

<footnote n="1">¹Labor cost was only included in 2009 for wall insulation interventions.</footnote>
installed by a professional. The main characteristics of the French fiscal households are stable between 2005 and 2008 (see table 14 in annex A).

Between 2005 and 2008 the number of households filling a tax credit claim more than doubled, reaching 1.6 million in 2008 (table 1). 70% of households declared energy conservation investments and 30% renewable energy equipment expenditures. Households seldom declare more than once so each year one million households use this tax credit for the first time. On average, reported expenditures rose from 3,700 euros in 2005 to 5,125 euros in 2008. Total expenditures almost tripled in four years (3.6 billions in 2005, 8 billion euros in 2008). In total, between 2005 and 2008, 4.1 million households invested at least once in energy conservation and/or renewable energies, declaring in total 23.1 billion euros, for a total budgetary cost of 7.8 billion euros. By the end of 2008 one primary residence in 16 had been renovated.

<table><caption>Table 1: Evolution of tax credit claims between 2005 and 2008 (in current euros)</caption><thead><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>Total</th></tr></thead><tbody><tr><td>Fiscal households (in thousands)</td><td>25,785</td><td>26,080</td><td>26,400</td><td>26,687</td><td></td></tr><tr><td>Households claiming a tax credit (in thousands)</td><td>984</td><td>1,267</td><td>1,336</td><td>1,569</td><td>5,156</td></tr><tr><td>Energy efficiency</td><td>767</td><td>901</td><td>947</td><td>1,064</td><td>3,679</td></tr><tr><td>Renewable energy</td><td>217</td><td>365</td><td>389</td><td>505</td><td>1,477</td></tr><tr><td>Declaration rate</td><td>3.8%</td><td>4.9%</td><td>5.1%</td><td>5.9%</td><td></td></tr><tr><td>Energy efficiency</td><td>3.0%</td><td>3.4%</td><td>3.6%</td><td>4.0%</td><td>13.8%</td></tr><tr><td>Renewable energy</td><td>0.8%</td><td>1.%</td><td>1.5%</td><td>1.9%</td><td>5.5%</td></tr><tr><td>Households claiming a tax credit for the first time (in thousands)</td><td>959</td><td>1,046</td><td>993</td><td>1,082</td><td>4,080</td></tr><tr><td>Total expenditures (in million euros)</td><td>3,632</td><td>5,390</td><td>6,044</td><td>8,039</td><td>23,106</td></tr><tr><td>Energy efficiency</td><td>2,771</td><td>3,439</td><td>3,684</td><td>4,319</td><td>14,212</td></tr><tr><td>Renewable energy</td><td>862</td><td>1,952</td><td>2,360</td><td>3,720</td><td>8,894</td></tr><tr><td>Average expenditures (in euros)</td><td>3,691</td><td>4,254</td><td>4,524</td><td>5,124</td><td>4,481</td></tr><tr><td>Energy efficiency</td><td>3,612</td><td>3,814</td><td>3,892</td><td>4,059</td><td>4,613</td></tr><tr><td>Renewable energy</td><td>3,959</td><td>5,336</td><td>6,060</td><td>7,365</td><td>6,350</td></tr><tr><td>Budgetary cost (in billion euros)</td><td>1</td><td>1.9</td><td>2.1</td><td>2.8</td><td>7.8</td></tr><tr><td>Average refund (in euros)</td><td>1,002</td><td>1,493</td><td>1,577</td><td>1,774</td><td>1,820</td></tr><tr><td>Average refund rate</td><td>27.0%</td><td>33.8%</td><td>33.3%</td><td>32.4%</td><td>32.4%</td></tr></tbody></table>

Note: the year refers to the date of investments. Households declare their housing renovation expenditures in May of the following year in the income tax return file.

Source: fiscal data from 2006 to 2009, authors' own calculation.
## 2.3 Energy efficiency tax credit claims

Not surprisingly, this tax credit is mainly used by homeowners: in 2008, 6.3% of the homeowners of private houses and 4.9% of apartments filled a tax credit claim whereas 1% of tenants did (table 2). Nevertheless, when tenants do undertake home renovation, they tend to spend more than owners. Energy efficiency investments and tax credit use is strongly correlated with fiscal income: in 2008, 6.5% of the households among the 10% more affluent filled a claim whereas less than 1% did among the 10% less affluent households. More than a third of the households filling a tax credit claim belong to the two top deciles and 5% to the two bottom deciles of fiscal income (table 15 in annex A). On average, expenditures of households belonging to the most affluent 10% are 10% higher than expenditures of the less affluent 10% (table 16 in annex A). Nonetheless, once taken into account the size of the dwelling, average expenditures in energy conservation investments are almost similar across decile (42 euros per square meter).

Home renovation behaviors highly depend on the composition and age of the house-
holds too. Couples tend to fill a tax credit claim twice more often than single person
households (table 17 in annex A). Tax credit use sharply increases with age until 75
then decreases. Expenditures also increase with age but start decreasing sooner, after 50.
Differences between households by living standards and age can partly be explained by
unequal access to property. Only a third of the households belonging to the less affluent
own their primary residence whereas 80% of the most affluent do.

Not surprisingly, the older the dwelling, the more frequent the renovation works and
the higher the expenditures (top figure 1, table 15 in annex A). The fraction of tax payers
filling a claim is roughly constant for dwellings constructed prior to the 1970s, peaks for
for dwellings constructed in 1980 and then slowly decreases with construction year, as
well as energy efficiency expenditures (top figure 1). This might result from a combi-
nation of two effects: more stringent thermal norms on residential construction in 1982,
1988, 2000 and 2005 and less old equipments.

It is in large dwellings, located in rural towns, in regions with a mild climate or in
dwellings constructed prior to the 1980s that the use of the tax credit and the energy
efficiency expenditures are the highest (table 2).
<table><caption>Table 2: In 2008, who filled energy conservation tax credit claim and how much did they spend?</caption><thead><tr><th></th><th>Tax credit claim filled<br>(in %)</th><th>Expenditures (among declarant)<br>(in current euros)</th><th>(in 2005 euros)</th></tr></thead><tbody><tr><td>Average</td><td>3.6</td><td>3,869</td><td>3,650</td></tr><tr><td colspan="4"><strong>Household's characteristics</strong></td></tr><tr><td><em>Status and housing unit</em></td><td></td><td></td><td></td></tr><tr><td>Tenant of an apartment</td><td>0.2</td><td>4,170</td><td>3,934</td></tr><tr><td>Tenant of a private house</td><td>0.9</td><td>4,479</td><td>4,225</td></tr><tr><td>Homeowner of an apartment</td><td>4.9</td><td>3,225</td><td>3,042</td></tr><tr><td>Homeowner of a private house</td><td>6.3</td><td>3,978</td><td>3,753</td></tr><tr><td><em>Age</em></td><td></td><td></td><td></td></tr><tr><td>Less than 30 year old</td><td>1.4</td><td>3,528</td><td>3,328</td></tr><tr><td>30 to 39 year-old</td><td>3.3</td><td>3,885</td><td>3,665</td></tr><tr><td>40 to 49 year-old</td><td>3.4</td><td>4,169</td><td>3,933</td></tr><tr><td>50 to 59 year-old</td><td>4.3</td><td>4,052</td><td>3,823</td></tr><tr><td>60 to 74 year-old</td><td>5.1</td><td>3,803</td><td>3,588</td></tr><tr><td>75 year-old and more</td><td>2.9</td><td>3,294</td><td>3,108</td></tr><tr><td><em>Household's composition</em></td><td></td><td></td><td></td></tr><tr><td>Single persons</td><td>2.2</td><td>3,349</td><td>3,159</td></tr><tr><td>Couples with no child enfant</td><td>5.7</td><td>3,928</td><td>3,706</td></tr><tr><td>Single persons with children</td><td>2.4</td><td>3,876</td><td>3,657</td></tr><tr><td>Couples with children</td><td>4.8</td><td>4,233</td><td>3,993</td></tr><tr><td>Others</td><td>3.2</td><td>4,522</td><td>4,266</td></tr><tr><td><em>Fiscal Standards of living (*)</em></td><td></td><td></td><td></td></tr><tr><td>1<sup>st</sup> decile</td><td>0.6</td><td>4,250</td><td>4,009</td></tr><tr><td>2<sup>nd</sup> decile</td><td>1.3</td><td>3,757</td><td>3,544</td></tr><tr><td>3<sup>rd</sup> decile</td><td>2.0</td><td>3,627</td><td>3,422</td></tr><tr><td>4<sup>th</sup> decile</td><td>2.6</td><td>3,508</td><td>3,309</td></tr><tr><td>5<sup>th</sup> decile</td><td>3.3</td><td>3,446</td><td>3,251</td></tr><tr><td>6<sup>th</sup> decile</td><td>4.0</td><td>3,472</td><td>3,275</td></tr><tr><td>7<sup>th</sup> decile</td><td>4.6</td><td>3,570</td><td>3,368</td></tr><tr><td>8<sup>th</sup> decile</td><td>5.4</td><td>3,707</td><td>3,497</td></tr><tr><td>9<sup>th</sup> decile</td><td>6.1</td><td>3,926</td><td>3,704</td></tr><tr><td>10<sup>th</sup> decile</td><td>6.5</td><td>4,826</td><td>4,553</td></tr><tr><td colspan="4"><strong>Dwelling's characteristics</strong></td></tr><tr><td><em>Size</em></td><td></td><td></td><td></td></tr><tr><td>Smaller than 30 m<sup>2</sup></td><td>0.4</td><td>2,705</td><td>2,552</td></tr><tr><td>30 to 59 m<sup>2</sup></td><td>1.3</td><td>2,993</td><td>2,824</td></tr><tr><td>60 to 89 m<sup>2</sup></td><td>3.3</td><td>3,490</td><td>3,293</td></tr><tr><td>90 to 119 m<sup>2</sup></td><td>5.3</td><td>3,880</td><td>3,660</td></tr><tr><td>Greater than 119 m<sup>2</sup></td><td>6.2</td><td>4,586</td><td>4,326</td></tr><tr><td><em>Housing unit construction year</em></td><td></td><td></td><td></td></tr><tr><td>Before 1948</td><td>4.0</td><td>3,837</td><td>3,620</td></tr><tr><td>1949-1975</td><td>4.5</td><td>3,899</td><td>3,679</td></tr><tr><td>1976-1982</td><td>5.6</td><td>4,094</td><td>3,862</td></tr><tr><td>1983-1989</td><td>3.8</td><td>3,877</td><td>3,657</td></tr><tr><td>1990-2000</td><td>1.6</td><td>3,455</td><td>3,259</td></tr><tr><td>2001-2005</td><td>0.9</td><td>3,213</td><td>3,031</td></tr><tr><td>after 2005</td><td>1.3</td><td>3,881</td><td>3,661</td></tr><tr><td><em>Town size</em></td><td></td><td></td><td></td></tr><tr><td>Rural town</td><td>4.6</td><td>4,031</td><td>3,803</td></tr><tr><td>Less than 20,000 inhabitants</td><td>4.1</td><td>3,902</td><td>3,681</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td>3.8</td><td>3,749</td><td>3,537</td></tr><tr><td>100,000 inhabitants and more</td><td>3.6</td><td>3,669</td><td>3,461</td></tr><tr><td>Paris</td><td>3.0</td><td>4,067</td><td>3,837</td></tr><tr><td><em>Thermal regulation climate zones</em></td><td></td><td></td><td></td></tr><tr><td>H1 (ex. Paris)</td><td>3.6</td><td>3,964</td><td>3,739</td></tr><tr><td>H2 (ex. Nantes)</td><td>10.2</td><td>3,715</td><td>3,505</td></tr><tr><td>H3 (ex. Marseille)</td><td>2.8</td><td>3,837</td><td>3,619</td></tr></tbody></table>

<footnote>(*) Fiscal standards of living are defined as the net fiscal income of the households divided by the number of consumption units in the household (1 for the first adult, 0.4 for each child under 14 and 0.5 for additional adults or children above 14). Source: fiscal data for 2009, authors' own calculation.</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1: Use of the tax credit (left axis) and expenditures of the claimants (right axis), by construction year (top) and years spent in the dwelling (bottom)</figcaption></figure>

Source: fiscal data from 2006 to 2009, authors’ own calculation.
# 3 Determinants of housing energy efficiency investments

## 3.1 Households' demand for energy investments

Few households undertake energy efficiency investments each year. If the decision to install a more efficient equipment is the result of an economic trade-off between cost and expected profit, we can interpret it as the optimal solution of the household's consumption program. Dubin and Henson (1988) developed a model of residential energy conservation behavior that predicts which households will be more likely to weatherize their homes and how much they will invest. Using the household production framework, they assume households derive utility from consumption and housing comfort, defined as the indoor temperature. Comfort is produced from purchased energy inputs and using a technology that depends on climate and on the thermal integrity of the housing unit. To improve comfort, the household can either increase her use of energy or improve the integrity of her house, by undertaking energy efficiency investments. Households face a tradeoff between comfort and other goods. The consumer's decision problem consists in allocating her income optimally among energy inputs, energy efficiency measures and other goods to achieve maximum utility. Solving this program, Dubin and Henson find that positive expenditures are interior solutions and zeros correspond to corner solutions characterized by the Kuhn and Tucker conditions:

$$w^* > 0 \Leftrightarrow (1 - \tau)c'(0) < -p_e \frac{\partial H}{\partial w}(t, 0) \quad (1)$$

$$(1 - \tau)c'(w^*) = -p_e \frac{\partial H}{\partial w}(t, w^*) \quad (2)$$

where $w$ is the amount of energy efficiency investments, $\tau$ is the tax credit rate, $c(w)$ is the energy efficiency cost function ($c'(w) < 0$), $p_e$ is the unit price of energy, $H(t, w; R, t_0)$ is the quantity of energy needed to reach an indoor temperature of $t$ when the outdoor temperature is $t_0$ and the thermal resistance of the dwelling is $R$ (heating "production function", $\partial H/\partial w < 0$).

The first condition can be interpreted as a condition for positive energy efficiency investments: the household invests in energy efficiency (interior solution) if and only if the marginal benefit (equal here to the decrease in the marginal cost of heating) is greater than the threshold value $(1 - \tau)c'(0)$. The second equation gives the amount spent given energy prices, income, outdoor temperature and the tax credit rate. A consumer undertaking an improvement in thermal integrity does so up to the point where the marginal cost of improvement equals the present value of the marginal reduction in energy costs (Dubin and Henson, 1988). This specification is consistent with the additional assumptions of myopia - also called energy efficiency gap (Allcott and Greenstone, 2012) - and of imperfect access to credit.
The statistical counterpart is a censoring model:

$$y = \max\{0, X\beta + u\} \qquad (3)$$

where $y$ is the variable of interest, $X$ the covariates and $u$ the residual. Ordinary least square regressions either on the entire sample or on the subsample of positive outcomes ($y_i > 0$) give inconsistent estimates of the parameters because they rely on the assumption that $y$ is linear in $X$ when it is only partly linear (Wooldridge, 2002). When $y > 0$, $E[y|X]$ cannot be linear in $X$ unless the range of $X$ is limited and without further restrictions OLS predicted values can be negative for many combinations of $X$ and $\beta$.

The most frequently used model to correct for the censoring bias is the Tobit model. The latent variable $y^*$ is linear in $x$ but we only observe the positive part of its distribution$^2$, under the assumption that the residuals are normal and homoscedastic ($u|X(0, \sigma^2)$) it provides consistent estimates of $\beta$.

$$ \begin{aligned} y_i^* &= X_i\beta + u_i, & u|X(0, \sigma^2) \\ y_i &= \max\{0, y_i^*\} \end{aligned} \qquad (4) $$

Using the properties of the normal distribution, the conditional expectation of $E[y|X, y > 0]$ when $y$ follows Tobit model is equal to:

$$ E[y|X, y > 0] = X\beta + \sigma\lambda \left(\frac{X\beta}{\sigma}\right) \qquad (5) $$

where $\lambda(x) = \phi(x)/\Phi(x)$ is the inverse Mills ratio.

Here we only observe expenditures if the households do fill a claim so we do not directly observe energy efficiency investments. Some households installed CIDD eligible items but did not fill a tax credit claim because they were not aware of the tax credit, did not understand it... If we think that the two decisions (investing and filling a claim) are driven by different motivations, thus a selection model would be more appropriate than a censoring model. Unfortunately since we only observe households who actually filled a claim form, we do not separately observe the two decisions and we cannot deal with the selection problem.

<footnote>$^2$In corner solution, the latent variable interpretation is less relevant but the statistical models are the same.</footnote>
## 3.2 Estimation

We estimate a Tobit model for homeowners. They represent more than 95% of the households who use the tax credit so it is likely that tenants' expenditures obey to a different housing improvement model. Tenants only benefit from the energy savings during the time spent in the dwelling whereas from the homeowner point of view, renovation expenditures and energy efficiency improvements are a way to maintain or increase the value of their capital. For computational reasons, we run for each year a Tobit regression on a sub-sample of the population.<fnref n="3" /> Results of Tobit estimations on the expenditures in euro, on the log of expenditures<fnref n="4" /> and on expenditures per square meters are reported in table 3.

Results are consistent with intuition. All else equal, expenditures increase with fiscal standards of living of the households, the size of the dwelling and if it is a house. Middle age households (between 40 and 49 years) spent more on energy efficiency and energy saving than younger and older households. Households living in rural towns report higher energy efficiency expenditures than households living in Paris. Renovation expenditures in dwellings constructed before 1948 or after 1983 are lower than in dwelling constructed between 1949 and 1982. More surprisingly homes located in the Mediterranean part of France (Thermal regulation climate zone H3) tend to benefit from higher energy efficiency expenditures than homes located in mild and continental regions. Without the CIDD, energy efficiency investments were probably less profitable in the area because of warmer climate than in the other regions. As a result, it is probable that, all else equal, homes locates in the Central and Northern parts of France were already better insulated and equipped with more energy efficient devices then the Southern part of France.

<footnote n="3">³We draw a 1/1,000 subsample of the households who report zero expenditures and then weight them. The subsample is exhaustive for the tax credit claimants.</footnote>

<footnote n="4">⁴We use log dep+1. Given the nature and the level of the expenditures, results should not be sensitive to this shift.</footnote>
<table><caption>Table 3: Determinants of the energy efficiency expenditures</caption><thead><tr><th></th><th>in log euro</th><th>in euro</th><th>in euro per m<sup>2</sup></th></tr></thead><tbody><tr><td colspan="4"><strong>Household's characteristics</strong></td></tr><tr><td><em>Age</em></td><td></td><td></td><td></td></tr><tr><td>Less than 30 year old</td><td>-0.06 ***</td><td>-714 ***</td><td>4.0 ***</td></tr><tr><td>30 to 39 year-old</td><td>-0.04 ***</td><td>775 ***</td><td>10.2 ***</td></tr><tr><td>40 to 59 year-old</td><td>ref</td><td>ref</td><td>ref</td></tr><tr><td>60 to 74 year-old</td><td>0.01 ***</td><td>-625 ***</td><td>-3.3 ***</td></tr><tr><td>40 to 74 year-old</td><td>-0.04 ***</td><td>-727 ***</td><td>-4.8 ***</td></tr><tr><td>75 year-old and more</td><td>-0.11 ***</td><td>-3,682 ***</td><td>-31.5 ***</td></tr><tr><td><em>Household's composition</em></td><td></td><td></td><td></td></tr><tr><td>Single persons</td><td>-0.04 ***</td><td>-1,658 ***</td><td>-13.6 ***</td></tr><tr><td>Couples with no child</td><td>-0.02 ***</td><td>-2,363 ***</td><td>-10.9 ***</td></tr><tr><td>Single persons with children</td><td>0.02 ***</td><td>-89 **</td><td>6.1 ***</td></tr><tr><td>Couples with children</td><td>ref</td><td>ref</td><td>ref</td></tr><tr><td><em>Years spent in the dwelling</em></td><td>0.0003 ***</td><td>-8 ***</td><td>-0.3 ***</td></tr><tr><td><em>Fiscal Standards of living (*)</em></td><td>0.10 ***</td><td>0.012 ***</td><td>0.0001 ***</td></tr><tr><td colspan="4"><strong>Dwelling's characteristics</strong></td></tr><tr><td>Private house</td><td>0.28 ***</td><td>2,357 ***</td><td>13.5 ***</td></tr><tr><td>Size (in m<sup>2</sup>)</td><td>0.21 ***</td><td>5 ***</td><td>0.03 ***</td></tr><tr><td><em>Construction year</em></td><td></td><td></td><td></td></tr><tr><td>Before 1948</td><td>-0.13 ***</td><td>-1,688 ***</td><td>-14.8 ***</td></tr><tr><td>1949-1975</td><td>-0.03 ***</td><td>-210 ***</td><td>3.7 ***</td></tr><tr><td>1976-1982</td><td>ref</td><td>ref</td><td>ref</td></tr><tr><td>1983-1989</td><td>-0.11 ***</td><td>-2,425 ***</td><td>-25.1 ***</td></tr><tr><td>1990-2000</td><td>-0.28 ***</td><td>-6,270 ***</td><td>-54.8 ***</td></tr><tr><td>2001-2005</td><td>-0.42 ***</td><td>-8,651 ***</td><td>-93.1 ***</td></tr><tr><td>after 2005</td><td>-0.11 ***</td><td>-2,891 ***</td><td>-58.2 ***</td></tr><tr><td><em>Town size</em></td><td></td><td></td><td></td></tr><tr><td>Rural town</td><td>0.05 ***</td><td>-2,003 ***</td><td>-4.4 ***</td></tr><tr><td>Less than 20,000 inhabitants</td><td>0.05 ***</td><td>-1,538 ***</td><td>2.7 ***</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td>0.02 ***</td><td>-1,114 ***</td><td>6.3 ***</td></tr><tr><td>100,000 inhabitants and more</td><td>0.00</td><td>-822 ***</td><td>5.5 ***</td></tr><tr><td>Paris</td><td>ref</td><td>ref</td><td>ref</td></tr><tr><td colspan="4"><strong>Thermal regulation climate zones</strong></td></tr><tr><td>H1 (ex. Paris)</td><td>-0.06 ***</td><td>-2,046 ***</td><td>1.8 ***</td></tr><tr><td>H2 (ex. Nantes)</td><td>-0.10 ***</td><td>-1,564 ***</td><td>5.1 ***</td></tr><tr><td>H3 (ex. Marseille)</td><td>ref</td><td>ref</td><td>ref</td></tr><tr><td><em>Year of the housing improvement</em></td><td></td><td></td><td></td></tr><tr><td>2005</td><td>ref</td><td>ref</td><td>ref</td></tr><tr><td>2006</td><td>0.04 ***</td><td>702 ***</td><td>7.1 ***</td></tr><tr><td>2007</td><td>0.03 ***</td><td>1,046 ***</td><td>10.3 ***</td></tr><tr><td>2008</td><td>0.06 ***</td><td>1,801 ***</td><td>17.0 ***</td></tr><tr><td>Eligible to the 40% rate</td><td>0.24 ***</td><td>3,571 ***</td><td>30.1 ***</td></tr><tr><td>Constant</td><td>5.59 ***</td><td>-16,531 ***</td><td>-182.4 ***</td></tr><tr><td>Sigma</td><td>1.06 ***</td><td>11,405 ***</td><td>106.5 ***</td></tr><tr><td>Log likelihood</td><td>-4,152,690</td><td>-2,161,803</td><td>-1,393,169</td></tr><tr><td>Number of observations</td><td>2,804,852</td><td>2,971,445</td><td>2,966,739</td></tr></tbody></table>

<footnote>(*) Fiscal standards of living are defined as the net fiscal income of the households divided by the number of consumption units in the household.</footnote>

<footnote>Note: * coefficient significant at the 10% level, ** at the 5% level and *** at the 1% level.</footnote>

<footnote>Source: fiscal data from 2006 to 2009, authors' own calculation.</footnote>
# 4 Sensitivity of energy efficiency expenditures to the tax credit rate

We now turn to the estimation of the sensitivity of French households to environmental fiscal incentives on energy efficiency investments. The tax credit rates were changed many times, upward in 2006 then mainly downward since 2009. We use variations in the tax credit rate as a natural experiment to identify the incentive impact of the green tax credit on the French households’ energy efficiency investments. To control for the potential censoring issue, we adapt Tobit and Censored quantile regressions to a difference-in-differences set-up.

## 4.1 Preliminary discussion

Let $w_i(\tau)$ be the optimal amount household $i$ invests in energy efficiency improvement when the tax credit rate is equal to $\tau$. A tax credit rate increase from $\tau$ to $\tau'$ ($\tau' > \tau$) is equivalent to a decrease in the marginal cost of energy efficiency improvement. This may trigger the decision to retrofit by turning profitable investments which were previously not profitable (extensive margin). It may also incentive households who had already decided to retrofit to increase their expenses (intensive margin). At the individual level, there are three categories of households:

* those who invest but who would have invested even if the rate were $\tau$:

$$ 
\begin{array}{l}
w_i(\tau) > 0 \\
w_i(\tau') > 0
\end{array}
\qquad (6) $$

* those who invest but would have not invested if the rate were $\tau$:

$$ 
\begin{array}{l}
w_i(\tau) = 0 \\
w_i(\tau') > 0
\end{array}
\qquad (7) $$

* those who do not invest even when the rate is equal to $\tau'$:

$$ w_i(\tau) = w_i(\tau') = 0 \qquad (8) $$

Let $N_\tau$ be the number of households filling a claim form when the tax credit rate is equal to $\tau$, $W_\tau$ be the total amount of expenditures declared when the rate is equal to $\tau$ and $\bar{w}_\tau$ the average expenditures. At the aggregate level, a tax credit rate increase has two impacts. First, by turning profitable investments which were not it increases the number of households investing ($N_{\tau'} > N_\tau$). Second, households who would have invested anyway may adjust upward their expenditures ($w_i(\tau') > w_i(\tau)$).

$$ W_{\tau'} - W_{\tau} = \underbrace{(N_{\tau'} - N_{\tau})}_{\text{Increase in the number of claims}} \bar{w}(\tau') + \underbrace{N_{\tau}}_{\substack{\text{nb of claims without incentive increase} \\ \text{Increase on average expenditures}}} \underbrace{(\bar{w}(\tau') - \bar{w}(\tau))}_{(9)} $$
The global impact of a tax credit rate increase on total expenditures is a combination of those two effects, commonly referred to as the extensive and intensive margins in labor economics (see for example Rogerson and Wallenius 2007, Blundell et al. 2011) or international trade literature (see for example Buono and Lalanne, 2012). The extensive margin is usually defined as the evolution of discrete factors (number of workers, number of exporting firms) whereas the intensive margin refers to the evolution of continuous factors (number of worked hours per person, average exportations per firm).

In the case of the CIDD, at the individual level the extensive margin corresponds to the decision whether to undertake thermal renovation (individual probability to invest). Its aggregate counterpart is simply the evolution of the number of tax credit claims. As for the intensive margin, at the micro level it corresponds to the decision of how much more to spend on thermal renovation. A straightforward macro counterpart could be the average amount spent on thermal renovation by households undertaking thermal renovation. Nevertheless, with this definition of a macro intensive margin, the variations of the mean expenditures capture the variations of investment both by the households would have invested even without the tax credit rate increase ($w_i(\tau') - w_i(\tau) > 0$) and by households who would have not invested ($w_i(\tau') - 0$). As a consequence the micro-elasticity of investment to tax credit rate will differ from the macro-elasticity, as showed by Rogerson and Wallenius (2007) on the labor market. A "strict" definition of the intensive margin would be the variation of the average amount spent by the households who would have invested even without the tax credit rate increase.

We are not able to identify in the data the households who would have retrofitted even at a lower tax credit rate ($w_i(\tau) > 0$). As a consequence we cannot estimate this strict intensive margin and will only estimate the impact of the tax credit rate increase on the overall expenditures ("gross intensive margin"). In this paper we will refer to "intensive margin" as the increase on the average expenditures of the households reporting energy efficiency investments:

$$ \delta_{Int} = \bar{w}(40) - \bar{w}(25) \qquad (10) $$

where $\bar{w}(25)$ is the average expenditures we would have observed in the absence of the reform. We will not be able to interpret the results at the individual level ("household i spent x additional euros thanks to the reform") but only at a macro level ("on average, the expenditures were x euros higher"). From a budgetary perspective, $W_{40} - W_{25}$ gives a clue to estimate the efficiency of this policy.

After presenting the identification strategy, we estimate the impact of a tax credit rate increase on the average expenditures (intensive margin) applying difference-in-differences models to censoring models.
## 4.2 Identification strategy

### 4.2.1 Definition of the treatment

We use the same identification strategy as in Mauroux (2012): in 2006 the tax credit rate on energy efficiency investments was raised from 25% to 40% but the 40% rate was restricted to households who had purchased in the past 2 years a home constructed prior to 1977 as shown in table 4. Each year only one homeowner in ten meets the two eligibility criteria (table 5) so only a fraction of the households benefited from this tax credit rate increase. The 40% tax credit rate was suppressed in 2010.

<table><caption>Table 4: Tax credit rate on energy efficiency expenditures from 2006 to 2009</caption><thead><tr><th></th><th colspan="2">Construction completed</th></tr><tr><th></th><th>before 1977</th><th>1977 or after</th></tr></thead><tbody><tr><td>Dwelling purchased in the past 2 years</td><td><strong>40%</strong></td><td>25%</td></tr><tr><td>Dwelling purchased 3 years ago or more</td><td>25%</td><td>25%</td></tr></tbody></table>

Source: "Bulletins officiels" n°147 September 1 2005 and n°83 May 18 2006.

<table><caption>Table 5: Households meeting the 40%-rate eligibility criteria</caption><thead><tr><th rowspan="2"></th><th rowspan="2">in thousands</th><th colspan="2">Eligible households</th></tr><tr><th>share of households</th><th>share of homeowners</th></tr></thead><tbody><tr><td>2005</td><td>1.53</td><td>5.9%</td><td>10.3%</td></tr><tr><td>2006</td><td>1.52</td><td>5.8%</td><td>10.1%</td></tr><tr><td>2007</td><td>1.50</td><td>5.7%</td><td>9.8%</td></tr><tr><td>2008</td><td>1.44</td><td>5.4%</td><td>9.3%</td></tr></tbody></table>

Source: fiscal data from 2006 to 2009, authors' own calculation.

We define the treatment as being eligible to the 2006 credit tax rate increase on energy efficiency expenditures, that is to say being able to claim a 40% instead of a 25% tax credit. The estimated effect will be interpreted as the impact of a marginal 15 percentage points increase in an already existing tax credit on the flow of housing energy efficient improvement investments. It does not correspond to the impact of the introduction of a new 15% tax credit on energy conservation expenditures.
<table><caption>Table 6: Maximum tax refund according to household composition and tax credit rate</caption><thead><tr><th></th><th>ceiling</th><th>maximum refund for a 25% rate</th><th>maximum refund for a 40% rate</th><th>difference</th></tr></thead><tbody><tr><td>Single persons</td><td>8,000</td><td>2,000</td><td>3,200</td><td>1,200</td></tr><tr><td>Couples without children</td><td>16,000</td><td>4,000</td><td>6,400</td><td>2,400</td></tr><tr><td>Couples with child</td><td>16,400</td><td>4,100</td><td>6,560</td><td>2,460</td></tr><tr><td>Couples with two children</td><td>16,800</td><td>4,200</td><td>6,720</td><td>2,520</td></tr></tbody></table>

Let $C_1$ be a dummy variable taking the value 1 if the household satisfies the first criterion: "the dwelling was purchased in the past 2 years". Let $C_2$ be a dummy variable taking the value 1 if the dwelling satisfies the second criterion: "construction was completed before 1977". Let $E_{it}$ be a dummy variable taking the value 1 if the household is eligible to the 40% tax rate in year $t$. Then:

$$E_{it} = C_1 \times C_2 \qquad (11)$$

The 2006 tax credit rate increase was announced by the end of 2005, so it was not anticipated by the households and can be seen as a natural experiment. We are also pretty confident that households did not massively purchase homes built before 1977 rather than in 1977 or after to get a 40% tax credit rate instead of a 25% and did not self selected themselves into the treatment group. The figure 2 represents the distribution of dwellings bought between 2005 and 2008 according to their construction year. If the raise in the tax credit rate had influenced the home buyers' choice, we should observe after 2005 a relative increase in the proportion of pre-1977 dwellings and a relative decrease in the proportion of post-1976 dwelling but we do not. Self-selection into the treatment group assumes that households can perfectly compare a set of homes, across all characteristics and choose the one that perfectly matches their preferences. In practice, housing supply is constraint and this arbitrage is not possible. Even if it were, the marginal benefit from buying a pre-1977 rather, all else being equal, than a post-1976 dwelling appears to be too small compared to housing prices to systematically be crucial in the decision of buying a house. The additional refund a household could get is limited to 15% of its ceiling. For a couple without children whose ceiling is 16,000 euros the maximal benefit is 2,400 euros. For a couple with two children with a ceiling of 16,800 euros the maximal benefit is 2,540 euros, it is of 1,200 for a single person (table 6).

Based on this exogenous increase in the tax credit rate only for a small number of households, it is possible to build a counter-factual of the behavior of the eligible households without the 2006 reform by observing the behavior of similar households not meeting the eligibility criteria, and estimate the impact of the 15 percentage points increase in the tax credit rate.

Our data set includes all French dwellings and households but the treatment effect
<figure><img src="image_4.png" /><figcaption>Figure 2: Housing stock purchased between 2005 and 2008 by construction year</figcaption></figure>

Source: fiscal data from 2006 to 2009, authors' own calculation.

may be different across dwellings and households. For instance, retrofitting investment returns are likely to be higher in home built before the introduction of thermal norms on residential construction in 1974 than in homes constructed in the 2000s. To ensure a relative homogeneity across housing units in the data set, we restrict to dwellings constructed a few years before and after the eligibility threshold, taking thermal norms on construction years as boundaries (1969 and 1988). At first thermal regulation was not too restrictive so it is likely that the selected dwellings have relatively similar insulation properties. To insure comparability in the home renovation behaviors, we also restrict the data set to households who have recently purchased their home. Indeed, it is in the first two years after purchase that the share of households filling a tax credit claim is the largest. It then drops and starts increasing again only after 15 years. We do not want to capture the effect of the replacements of equipments during this second cycle of housing investments so we restrict to households who purchased their dwellings in the past 5 years. Our estimation dataset contains between 1.1 and 1.2 million households per year (table 7) and 24% of them are in the treatment group.

<table><caption>Table 7: Homeowners for 5 years or less of a dwelling constructed between 1969 and 1988</caption><thead><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th></tr></thead><tbody><tr><th>Eligible</th><td>287,262</td><td>283,315</td><td>278,591</td><td>266,442</td></tr><tr><th>Non eligible</th><td>924,280</td><td>910,026</td><td>889,362</td><td>866,321</td></tr><tr><th>Total</th><td>1,220,047</td><td>1,202,798</td><td>1,178,007</td><td>1,141,628</td></tr></tbody></table>

Source: fiscal data from 2006 to 2009, authors' own calculation.
4.2.2 Expenditures of the treated and the non treated households

Households meeting the two criteria filled more often tax credit claim forms. When they did, they reported higher expenditures than households that did not meet the eligibility criteria (tables 8 and 9). After 2005, their housing energy efficiency improvement expenditures seem to have risen slightly faster than the expenditures of the rest of the households. We observe a 282 euros increase (+6.8%) between 2005 and 2006 while in the meantime non treated households' expenditures increased by 223 euros (+6.4%). The differences in expenditures evolution suggest that the tax credit rate increase may have had a small but positive impact on the expenditures of the eligible households (+59 euros in 2006, +143 euros in 2007 and +71 euros in 2008).

We may be concerned by the fact that part of these expenditures increase is in reality
due to a price increase, and thus may not correspond to an increase in the demand for
housing improvement. Indeed the index price on housing improvement (IPEA, SOeS) rose
by 1.6% between 2005 and 2006, by 3.1% between 2005 and 2007 and by 6% between
2005 and 2008. Using this price index to control for inflation in the housing improvement
sector, we estimate the density of the expenditures for the treated and the non treated in
2005 euros in 2006, 2007 and 2008 (figure 3). In 2005 euros, the difference of evolution of
the expenditures of the treated and non treated is divided by half, and becomes negative
in 2008. If we narrow the dataset to a window around the eligibility thresholds, the
difference-in-differences again suggest that the tax credit rate increase had a positive
impact on the expenditures of the eligible households. The difference-in-differences on
the log of the expenditures suggest that after the tax credit raise, the eligible households
slightly increased their expenditures (+ 2% in 2006, + 7% in 2007 and + 5% in 2008). We
now need to check if this small treatment effect is not due to selection effects and if it is
significatively different from zero when controlling for the characteristics of the households
and their dwellings and for the censoring bias. For all our following estimations, we use
the index price on housing improvement to control inflation in the housing repair sector.
<table><caption>Table 8: Tax credit claim filling rate and reported expenditures between 2005 and 2008, by eligibility status</caption><thead><tr><th colspan="9">All homeowners</th></tr><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>Δ 2006-2005</th><th>Δ 2007-2005</th><th>Δ 2008-2005</th></tr></thead><tbody><tr><td colspan="8"><em>Use of the tax credit (in %)</em></td></tr><tr><td>Eligible</td><td>8.1</td><td>10.0</td><td>10.7</td><td>11.4</td><td>+ 1.9</td><td>+ 2.6</td><td>+ 3.3</td></tr><tr><td>Non eligible</td><td>4.0</td><td>4.7</td><td>4.9</td><td>5.5</td><td>+ 0.7</td><td>+ 0.9</td><td>+ 1.5</td></tr><tr><td>Δ Eligible - Non eligible</td><td>+ 4.1</td><td>+ 5.4</td><td>+ 5.8</td><td>+ 5.9</td><td>1.3</td><td>1.7</td><td>1.8</td></tr><tr><td colspan="8"><em>Expenditures (in current euros)</em></td></tr><tr><td>Eligible</td><td>4,153</td><td>4,435</td><td>4,609</td><td>4,753</td><td>+ 282</td><td>+ 456</td><td>+ 600</td></tr><tr><td>Non eligible</td><td>3,462</td><td>3,685</td><td>3,776</td><td>3,991</td><td>+ 223</td><td>+ 314</td><td>+ 530</td></tr><tr><td>Δ Eligible - Non eligible</td><td>+ 691</td><td>+ 750</td><td>+ 834</td><td>+ 762</td><td>59</td><td>143</td><td>71</td></tr><tr><td colspan="8"><em>Expenditures (in 2005 euros)</em></td></tr><tr><td>Eligible</td><td>4,153</td><td>4,256</td><td>4,236</td><td>4,184</td><td>+ 104</td><td>+ 84</td><td>+ 31</td></tr><tr><td>Non eligible</td><td>3,462</td><td>3,536</td><td>3,470</td><td>3,514</td><td>+ 74</td><td>+ 8</td><td>+ 52</td></tr><tr><td>Δ Eligible - Non eligible</td><td>+ 691</td><td>+ 720</td><td>+ 766</td><td>+ 671</td><td>29</td><td>75</td><td>-21</td></tr><tr><td>Difference-in-differences of the log (/2005)</td><td></td><td></td><td></td><td></td><td>0.00</td><td>0.02</td><td>-0.01</td></tr></tbody></table>

<table><thead><tr><th colspan="9">Homeowners for 5 years or less of a dwelling is constructed between 1969 and 1988</th></tr><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>Δ 2006-2005</th><th>Δ 2007-2005</th><th>Δ 2008-2005</th></tr></thead><tbody><tr><td colspan="8"><em>Use of the tax credit (in %)</em></td></tr><tr><td>Eligible</td><td>8.6</td><td>10.7</td><td>11.5</td><td>12.4</td><td>+ 2.0</td><td>+ 2.9</td><td>+ 3.7</td></tr><tr><td>Non eligible</td><td>5.9</td><td>6.9</td><td>7.2</td><td>8.0</td><td>+ 1.0</td><td>+ 1.3</td><td>+ 2.1</td></tr><tr><td>Δ Eligible - Non eligible</td><td>+ 2.8</td><td>+ 3.8</td><td>+ 4.3</td><td>+ 4.4</td><td>1.1</td><td>1.6</td><td>1.6</td></tr><tr><td colspan="8"><em>Expenditures (in current euros)</em></td></tr><tr><td>Eligible</td><td>4,134</td><td>4,543</td><td>4,956</td><td>5,043</td><td>+ 409</td><td>+ 822</td><td>+ 909</td></tr><tr><td>Non eligible</td><td>3,618</td><td>3,880</td><td>4,041</td><td>4,200</td><td>+ 262</td><td>+ 423</td><td>+ 581</td></tr><tr><td>Δ Eligible - Non eligible</td><td>+ 516</td><td>+ 662</td><td>+ 914</td><td>+ 843</td><td>147</td><td>399</td><td>328</td></tr><tr><td colspan="8"><em>Expenditures (in 2005 euros)</em></td></tr><tr><td>Eligible</td><td>4,134</td><td>4,360</td><td>4,555</td><td>4,439</td><td>+ 225</td><td>+ 421</td><td>+ 305</td></tr><tr><td>Non eligible</td><td>3,618</td><td>3,724</td><td>3,714</td><td>3,697</td><td>+ 106</td><td>+ 96</td><td>+ 79</td></tr><tr><td>Δ Eligible - Non eligible</td><td>+ 516</td><td>+ 636</td><td>+ 841</td><td>+ 742</td><td>120</td><td>325</td><td>227</td></tr><tr><td>Difference-in-differences of the log (/2005)</td><td></td><td></td><td></td><td></td><td>0.02</td><td>0.07</td><td>0.05</td></tr></tbody></table>

Source: fiscal data from 2006 to 2009, authors' own calculation.
<figure><img src="image_5.png" /><figcaption>Figure 3: Density of energy efficiency expenditures (in 2005 euros)</figcaption></figure>

Source: fiscal data from 2006 to 2009, authors' own calculation.

<table><caption>Table 9: Quantiles of energy efficiency expenditures (in euros)</caption><thead><tr><th rowspan="2">Year</th><th colspan="10">Quantiles (in euros)</th></tr><tr><th>88</th><th>89</th><th>90</th><th>91</th><th>92</th><th>93</th><th>94</th><th>95</th><th>96</th><th>97</th><th>98</th><th>99</th></tr></thead><tbody><tr><th colspan="12">A - Eligible households (C<sub>1</sub> × C<sub>2</sub>)</th></tr><tr><th>2005</th><td>0</td><td>0</td><td>0</td><td>0</td><td>353</td><td>1,018</td><td>1,733</td><td>2,555</td><td>3,462</td><td>4,500</td><td>5,949</td><td>8,332</td></tr><tr><th>2006</th><td>0</td><td>0</td><td>397</td><td>1,035</td><td>1,669</td><td>2,360</td><td>3,088</td><td>3,858</td><td>4,620</td><td>5,648</td><td>7,137</td><td>9,753</td></tr><tr><th>2007</th><td>0</td><td>344</td><td>977</td><td>1,584</td><td>2,236</td><td>2,916</td><td>3,600</td><td>4,320</td><td>5,180</td><td>6,264</td><td>7,799</td><td>10,394</td></tr><tr><th>2008</th><td>259</td><td>895</td><td>1,500</td><td>2,126</td><td>2,750</td><td>3,382</td><td>4,005</td><td>4,700</td><td>5,567</td><td>6,665</td><td>8,194</td><td>11,008</td></tr><tr><th colspan="12">B - Non eligible households</th></tr><tr><th>2005</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>690</td><td>1,582</td><td>2,679</td><td>4,030</td><td>6,161</td></tr><tr><th>2006</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>673</td><td>1,475</td><td>2,400</td><td>3,487</td><td>4,778</td><td>7,000</td></tr><tr><th>2007</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>190</td><td>970</td><td>1,755</td><td>2,677</td><td>3,757</td><td>5,080</td><td>7,457</td></tr><tr><th>2008</th><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>791</td><td>1,537</td><td>2,343</td><td>3,248</td><td>4,258</td><td>5,610</td><td>7,971</td></tr></tbody></table>

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling that was constructed between 1969 and 1988, authors' own calculation.
<figure><img src="image_6.png" /><figcaption>Figure 4: Expenditures conditional on the dwelling construction year (left) and on the years spent in the dwelling (right)</figcaption></figure>

Homeowners of a dwelling built before January 1st 1977 (left) and homeowners of their dwelling for 2 years or less (right)
Source: fiscal data from 2006 to 2009, authors' own calculation.

4.2.3 Definition of the control groups

Even before the reform, households meeting the eligibility criteria reported higher expenditures than non eligible households. To control for the selection effect of the eligibility criteria, we plot how expenditures vary according to one criterion, controlling for the other one. Figure 4 on the left plots the evolution of the expenditures of homeowners of relatively old dwellings ($C_2 = 1$) according to the number of years spent in it. Expenditures of the non eligible households (3 years or more in the dwelling) are pretty similar before and after 2006 but there is a clear upward shift in 2006 in the expenditures of the eligible households (2 years or less). Figure 4 on the right plots the new homeowners' expenditures ($C_1 = 1$) according to the year of construction of the dwelling. Expenditures of non eligible households (constructed after 1976) do not seem to differ much before and after the reform. After 2005 we observe a slight shift upward of the curves representing the eligible households' expenditures (dwellings constructed before 1977). Nevertheless this conditional effect is not as clear cut as the one we observe on left figure for the other eligibility criterion.

Households forming the control group are to be selected among the non-eligible households. In the difference-in-differences model, the control group is composed of all the non-treated households. The double differences estimator compares the evolution of the expenditures of the treated with the evolution of the expenditures of the non treated. The treatment effect on the treated is identified under the two following assumptions:

* the treatment and control groups are stable across periods (constant group fixed effect);
* the treatment and control groups are equally affected by cyclical shocks (common trend).

The common trend assumption means that in the absence of the treatment, the outcome of interest would have evolved the same way in the treatment and control groups. It is a strong assumption here because it states that in the absence of the reform, the energy efficiency investments of households who purchased in the past 2 years a relatively old dwelling would have evolved exactly the same way as the energy efficiency investments of households who purchased a more recent dwelling or households who purchased a dwelling three years ago or more. On the contrary, it is likely that the energy efficiency equipments and the structural shell of older dwellings has poorer thermal properties than more recent dwellings so the former may need more capital intensive energy efficiency investments then the later. Moreover, if there are specific time trends for households meeting just $C_1$ and for households meeting just $C_2$, then the double differences estimate will be biased.

To correct for this potential bias and control for the specific effect of each eligibility criterion on energy efficiency investments, we extend the difference-in-differences setting and following Mauroux (2012), we divide the control group into three groups, corresponding to each one of the three 25% cells in table 4:

* households satisfying $C_1$ (duration since purchase) but not $C_2$ (dwelling construction year),

* households satisfying $C_2$ criterion but not $C_1$,

* households satisfying none of the two criteria.

We call the corresponding estimator a difference-in-difference-in-differences estimator (DDD). In a linear model, it can be estimated with the following equation:

$$y_{it} = \alpha + \beta_1 T + \beta_2 C_1 + \beta_3 C_2 + \beta_4 C_1 C_2 + \beta_5 T C_1 + \beta_6 T C_2 + \delta_T T C_1 C_2 + u_{it} \quad (12)$$

where $y_{it}$ is the energy efficiency expenditures of household $i$ at date $t$, $T$ is a dummy of the treatment year $T$ and $u_{it}$ is an error term. The treatment effect on the treated year $T$ is given by $\delta_T$.

This DDD estimator will be identified under additional assumptions. It is based upon the assumption that the specific effect of the eligibility criteria $C_1$ and $C_2$ are additive, so that all the group and time effects are linear and additive. What is more, the DDD estimator relaxes the strict common trend assumption and so additional assumptions are needed: it is assumed that each control sub-group is stable across periods and that there are specific additional shocks common for households meeting each criterion. This assumption means that there are common time effects for all households who respectively
purchased a house in the past 2 years (e.g. credit constraints....) and for all households who own a pre-1977 home (e.g. marketing operation or add campaigns targeted to old homes, additional public subsidies....). If those assumptions are verified, the fixed effects for each criterion and time cross effects guarantee that we control for energy efficiency investments behavior specific to each sub groups and for exogenous shocks affecting them.

The DDD estimator can be written as the sum of the classical double difference estimator and two correction terms:

$$
\begin{align}
\hat{\delta}_{DDD} &= [(\bar{y}_{C1=1,C2=1,T} - \bar{y}_{C1=1,C2=1,t_0}) - (\bar{y}_{C1=1,C2=0,T} - \bar{y}_{C1=1,C2=0,t_0})] \nonumber \\
&\quad -[(\bar{y}_{C1=0,C2=1,T} - \bar{y}_{C1=0,C2=1,t_0}) - (\bar{y}_{C1=0,C2=0,T} - \bar{y}_{C1=0,C2=0,t_0})] \\
\hat{\delta}_{DDD} &= \hat{\delta}_{DD} - K_{C1} - K_{C2} \nonumber \\
&= [(\bar{y}_{11}^T - \bar{y}_{11}^{t_0}) - (\bar{y}_{\neq 11}^T - \bar{y}_{\neq 11}^{t_0})] \nonumber \\
&\quad - \frac{n_{01}+n_{00}}{n_{\neq 11}} [(\bar{y}_{10}^T - \bar{y}_{10}^{t_0}) - (\bar{y}_{00}^T - \bar{y}_{00}^{t_0})] - \frac{n_{10}+n_{00}}{n_{\neq 11}} [(\bar{y}_{01}^T - \bar{y}_{01}^{t_0}) - (\bar{y}_{00}^T - \bar{y}_{00}^{t_0})] \tag{13}
\end{align}
$$

where $\bar{y}_{d_1, d_2}^t$ states for the average expenditures of households satisfying $C_1 = d_1$ and $C_2 = d_2$ at time $t$, and $n_{d_1, d_2}$ for the number of households satisfying $C_1 = d_1$ and $C_2 = d_2$. $T$ is a treatment year and $t_0$ the year before treatment.

If $K_{C_1}$ and $K_{C_2}$ are null, then the double differences and the triple differences estimates are equal. Mauroux (2012) showed that $K_{C_1}$ is positive and significatively different from zero when assessing the probability of using the tax credit. In that case, the double differences over-estimates the treatment effect on the probability of using the tax credit.

As already stated before we face a corner data issue, we only observe the CIDD expenditures $y_i$ of households who had a positive optimal investment level $y_i^*$ (profitability condition verified).

$$
\begin{align}
y_{it}^* &= \alpha + \beta_1 T + \beta_2 C_1 + \beta_3 C_2 + \beta_4 C_1 C_2 + \beta_5 T C_1 + \beta_6 T C_2 + \gamma_T T C_1 C_2 + \theta X + u_{it} \nonumber \\
y_{it} &= \max\{0, y_{it}^*\} \tag{14}
\end{align}
$$

The difference-in-differences estimators are based on the assumption that in the absence of treatment the outcome of the treated and control groups would have evolved the same way (common trend assumption) and on the assumption of additive separability of the error term conditional on the observables. These assumptions become particularly unrealistic when the outcome of interest is not a continuous variable, as it is the case for censored data (Blundell and Costa Dias 2009). Nevertheless not controlling for censoring could lead to negative predicted expenditures and biased estimates. We will thus make the additional assumption that the difference-in-differences identification conditions are verified by the latent variable $y^*$ and not the observed variable as in Ai and Norton (2003) and Puhani (2012).
### 4.2.4 Controlling for structural differences between treated and non-treated households

Homeowners meeting the two eligibility criteria live more often in apartments, in relatively smaller dwellings and are more often located in Paris than non treated homeowners (table 10). To quantify the structural differences between treated and non treated households, we follow Imbens and Wooldridge's (2008) suggestion to compute a normalized difference on averages for each covariate, by treatment status. It is equal to the difference on averages, scaled by the square root of the sum of the variances. It is thus a scale-free measure of the difference in distributions:

$$ \Delta_X = \frac{\bar{X}_1 - \bar{X}_0}{\sqrt{S_1^2 + S_0^2}} \qquad (15) $$

where $S_j^2 = \sum_{i:T_i=j}(X_i - \bar{X}_j)^2(N_j - 1)$ is the sample variance of $X_i$ in the group j and $\bar{X}_j$ the sample mean in the group j, with $j \in \{0, 1\}$.

Imbens and Wooldridge (2008) suggest as a rule of thumb that with a normalized difference exceeding one quarter, linear regression methods tend to be sensitive to the specification. It is the case for households' composition and age, and for the dwelling type and size, so we may be worried our estimation will be sensitive to the specification and may need to control for structural differences between the treated and the non treated.

To compose a control group similar to the treatment group and control for differences in the distribution of covariates between the two groups we adapt the method of matching combined with difference-in-differences introduced by Heckman et al. (1998). The matching estimator compares at a given date the treated households' probability of declaring conservation investments to that of similar non-treated households. Under common support and "conditional independence assumption" (CIA), matching allows controlling for disequilibrium in the distribution of characteristics of the two groups. The common support condition imposes that there exist both treated households and non treated households for all values of the observable characteristics. If it is not the case, it is not possible to find a non-treated counterpart for each treated household. The CIA states that given observable characteristics and without treatment, the energy efficiency investment level is independent of the fact of being part or not of the treatment group. Nevertheless, Heckman et al. (1998) show that there is still a selection bias on the unobservable variables. They propose to combine matching and difference-in-differences. Under the assumption that, conditionally on the observable characteristics, selection bias on unobservable variables are the same on average at the different periods of the program, this estimator gives an unbiased estimate of the impact of the treatment on the treated.

We choose matching by cluster (Marbot and Roy 2011, Mauroux 2012). The difference-in-differences model will be estimated within cells defined according to covariates values. The overall effect is then the weighted average of the cell estimator, the weight being the
<table><caption>Table 10: Characteristics of the eligible and non eligible households in the estimation sample in 2005 (%)</caption><thead><tr><th></th><th>Eligible</th><th>Non eligible</th><th>Normalized difference</th></tr></thead><tbody><tr><td><strong>Age</strong></td><td></td><td></td><td></td></tr><tr><td>Less than 30 year old</td><td>9.4</td><td>4.5</td><td>0.51</td></tr><tr><td>30 to 39 year-old</td><td>23.5</td><td>21.7</td><td>0.07</td></tr><tr><td>40 to 49 year-old</td><td>16.6</td><td>21.6</td><td>-0.23</td></tr><tr><td>50 to 59 year-old</td><td>15.9</td><td>22.7</td><td>-0.31</td></tr><tr><td>60 to 74 year-old</td><td>23.1</td><td>20.4</td><td>0.11</td></tr><tr><td>75 year-old and more</td><td>11.6</td><td>9.1</td><td>0.19</td></tr><tr><td><strong>Household's composition</strong></td><td></td><td></td><td></td></tr><tr><td>Single persons</td><td>23.0</td><td>29.4</td><td>-0.24</td></tr><tr><td>Couples with no child</td><td>20.5</td><td>23.5</td><td>-0.12</td></tr><tr><td>Single persons with children</td><td>13.0</td><td>13.4</td><td>-0.02</td></tr><tr><td>Couples with children</td><td>42.8</td><td>33.1</td><td>0.30</td></tr><tr><td>Others</td><td>0.7</td><td>0.6</td><td>0.03</td></tr><tr><td><strong>Fiscal Standards of living (*)</strong></td><td></td><td></td><td></td></tr><tr><td>1<sup>st</sup> decile</td><td>5.4</td><td>4.3</td><td>0.18</td></tr><tr><td>2<sup>nd</sup> decile</td><td>6.3</td><td>5.8</td><td>0.07</td></tr><tr><td>3<sup>rd</sup> decile</td><td>7.3</td><td>6.9</td><td>0.04</td></tr><tr><td>4<sup>th</sup> decile</td><td>8.1</td><td>7.9</td><td>0.02</td></tr><tr><td>5<sup>th</sup> decile</td><td>9.2</td><td>9.2</td><td>0.00</td></tr><tr><td>6<sup>th</sup> decile</td><td>10.6</td><td>10.7</td><td>-0.01</td></tr><tr><td>7<sup>th</sup> decile</td><td>12.0</td><td>12.2</td><td>-0.01</td></tr><tr><td>8<sup>th</sup> decile</td><td>13.1</td><td>13.6</td><td>-0.03</td></tr><tr><td>9<sup>th</sup> decile</td><td>14.3</td><td>14.8</td><td>-0.03</td></tr><tr><td>10<sup>th</sup> decile</td><td>14.2</td><td>14.6</td><td>-0.02</td></tr><tr><td><strong>Dwelling's characteristics</strong></td><td></td><td></td><td></td></tr><tr><td><strong>Housing unit</strong></td><td></td><td></td><td></td></tr><tr><td>Apartment</td><td>36.3</td><td>22.2</td><td>0.49</td></tr><tr><td>Private house</td><td>63.7</td><td>77.8</td><td>-0.49</td></tr><tr><td><strong>Size</strong></td><td></td><td></td><td></td></tr><tr><td>Smaller than 30 m<sup>2</sup></td><td>1.3</td><td>1.1</td><td>0.13</td></tr><tr><td>30 to 59 m<sup>2</sup></td><td>10.8</td><td>7.3</td><td>0.29</td></tr><tr><td>60 to 89 m<sup>2</sup></td><td>43.0</td><td>35.8</td><td>0.21</td></tr><tr><td>90 to 119 m<sup>2</sup></td><td>29.8</td><td>36.1</td><td>-0.20</td></tr><tr><td>Greater than 119 m<sup>2</sup></td><td>15.2</td><td>19.7</td><td>-0.22</td></tr><tr><td><strong>Town size</strong></td><td></td><td></td><td></td></tr><tr><td>Rural town</td><td>23.3</td><td>30.1</td><td>-0.25</td></tr><tr><td>Less than 20,000 inhabitants</td><td>18.4</td><td>20.5</td><td>-0.10</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td>13.2</td><td>12.6</td><td>0.04</td></tr><tr><td>100,000 inhabitants and more</td><td>28.1</td><td>24.6</td><td>0.13</td></tr><tr><td>Paris</td><td>16.9</td><td>12.1</td><td>0.28</td></tr><tr><td><strong>Thermal regulation climate zones</strong></td><td></td><td></td><td></td></tr><tr><td>H1 (ex. Paris)</td><td>56.9</td><td>52.4</td><td>0.13</td></tr><tr><td>H2 (ex. Nantes)</td><td>28.7</td><td>30.6</td><td>-0.06</td></tr><tr><td>H3 (ex. Marseille)</td><td>11.9</td><td>14.4</td><td>-0.15</td></tr></tbody></table>

Source: fiscal data from 2006, authors' own calculation.

<footnote>(*) fiscal standards of living are defined as the net fiscal income of the households divided by the number of consumption units in the household.</footnote>
number of treated households the year of treatment in each cell. Standard deviations are computed by bootstrap.<fnref n="5" /> This matching method is similar to a "closest neighbors" matching since within each cell treated and non-treated households share exactly the same characteristics.

The estimator of the impact of the treatment on the treated is the following:

$$ \delta_{T,C} = \frac{1}{N_{11,T}} \sum_{j=1}^{N_C} N_{11,T/j} \hat{\delta}_{T,j} \quad (16) $$

where $\hat{\delta}_{T,j}$ is the treatment effect estimate in cell $j$, $N_{11,T}$ is the number of treated households at time $T$, $N_{11,T/j}$ is the number of treated households at time $T$ in cell $j$ and $N_C$ is the total number of cells.

The set of covariates is limited and we face a trade-off between controlling for the structural differences between treated and non-treated on the one hand, and the goodness of fit of the censoring estimation on the other hand (see infra). To construct the cells we use in priority the variables for which the adjusted mean differences are the largest (table 10): age of the head of the household, housing unit type (house or apartment) and the household composition. We define three specifications, from 4 to 40 cells (table 11). To respect the common support condition we exclude cells that do not include both eligible and non eligible households.

<table><caption>Table 11: Definition of cells</caption><thead><tr><th></th><th>Age</th><th>Housing unit type</th><th>Household's composition</th><th>Theoretical</th><th>Existing</th><th>Kept (*)</th><th>Size(**)</th></tr></thead><tbody><tr><td>Specification A</td><td>4</td><td></td><td></td><td>4</td><td>4</td><td>4</td><td>286,826</td></tr><tr><td>Specification B</td><td>4</td><td>2</td><td></td><td>8</td><td>8</td><td>8</td><td>143,413</td></tr><tr><td>Specification C</td><td>4</td><td>2</td><td>5</td><td>40</td><td>40</td><td>40</td><td>28,683</td></tr></tbody></table>

Note: Variables used to constitute the cells are the following:

* *Housing unit type:* equals one if the dwelling is a private house, zero if it is an apartment

* *Age:* age of the head of the household in four modalities (20-29 year old, 30-49 year old, 50-74 year old, 75 year old and more)

* *Household's composition:* composition of the household depending on the marital status and the presence of children (single persons, couples, single persons with children, couples with children, others).

<footnote>(*) Cells containing both treated and non treated households.</footnote>

<footnote>(**) Average number of households per cell and per year.</footnote>

<footnote n="5">⁵We draw with replacement M samples of size N from the data, we compute the triple differences estimates for each bootstrap sample, then we compute the 2.5ᵉ et 97.5ᵉ quantiles of those M-bootstrap estimate distribution.</footnote>
## 4.3 Estimation strategy

The economic value we are interested in is the difference of potential outcome of the treatment group with treatment (Y¹) and without treatment (Y⁰):

$$ \delta_T = E[Y^1|T=1, C_1C_2=1] - E[Y^0|T=1, C_1C_2=1] \quad (17) $$

### 4.3.1 Censoring model (1): Tobit

The Tobit model is the most frequently used model to control for censoring. Because of non linearity, the following "difference-in-difference-in-differences" Tobit model does not give an estimate the treatment effect (Puhani 2012, Ai and Norton 2003, see annex D for detailed explanations).

$$ \begin{aligned} y_{it}^* &= \alpha + \beta_1 T + \beta_2 C_1 + \beta_3 C_2 + \beta_4 C_1 C_2 + \beta_5 T C_1 + \beta_6 T C_2 + \gamma_T T C_1 C_2 + \theta X + u_{it} \\ y_{it} &= \max\{0, y_{it}^*\} \\ u|X &\sim N(0, \sigma^2) \end{aligned} \quad (18) $$

Indeed, in the DDD Tobit model, the difference of potential outcome of the treatment group with and without treatment is equal to:

$$ \delta_T = \gamma_T + \sigma \left[ \lambda \left( \frac{\alpha + \beta_1 + \beta_2 + \beta_3 + \beta_4 + \beta_5 + \gamma_T + \theta X}{\sigma} \right) - \lambda \left( \frac{\alpha + \beta_1 + \beta_2 + \beta_3 + \beta_4 + \beta_5 + \gamma_T + \theta X}{\sigma} \right) \right] \quad (19) $$

where $\lambda(x)$ is the inverse Mills ratio. It is important to notice that the interaction term coefficient $\gamma_T$ is no longer equal to the treatment effect and gives a biased estimate of $\delta_T$. However, it has the same sign as $\delta_T$ and given that $\lambda(.)$ is strictly monotonic $\delta_T$ is equal to zero if and only if $\gamma_T$ is equal to zero too. So if the coefficient is not significant, the treatment effect will not be significant.

The Mills ratio $\lambda(x)$ is a known strictly monotonous parametric function so it is possible to compute the bias and then back out the treatment effect. First we estimate the "difference-in-difference-in-differences" Tobit model (equation 18) and get estimates for $\alpha$, $\beta_1$ to $\beta_6$ and $\gamma_T$. We then compute the treatment effect taking the average value of the inverse Mills ratios to get the average treatment effect on the treated:

$$ \hat{\delta}_T = \hat{\gamma}_T + \hat{\sigma} \left[ \bar{\lambda} \left( \frac{\hat{\alpha} + \sum_{i=1}^6 \hat{\beta}_i + \hat{\gamma}_T + \hat{\theta} X}{\hat{\sigma}} \right) - \bar{\lambda} \left( \frac{\hat{\alpha} + \sum_{i=1}^6 \hat{\beta}_i + \hat{\theta} X}{\hat{\sigma}} \right) \right] \quad (20) $$

The Tobit model is widely used to correct for censoring but suffer from limits both from a statistical and economical point of view. The estimation heavily relies on the assumptions that residuals are normal and homoscedastic. As a consequence the results will be sensitive to misspecifications and the Tobit estimator will not be consistent if the residuals are non normal (Goldberger 1980, Arabmazar and Schmidt, 1982) and if they
are heteroscedastic (Maddala and Nelson, 1975, Arabmazar and Schmidt, 1981).

From an economic point of view, the Tobit model only estimates a mean effect. When the marginal effects differ at different points of the distribution as compared to the conditional mean, the Tobit estimates may then provide inaccurate results. In the case of energy efficiency investment, it is likely that the impact of a tax credit rise on expenditures differs across classes of investments and renovation works. Indeed the lowest investments correspond to light renovation works (heating regulation devices, thermal insulation material, ...) whereas the highest investments correspond to heavy renovation works (boiler, windows replacement, combination of works,...). Due to differences in the characteristics of the equipments installed and the non divisibility of the equipment demand (one cannot increase its retrofitting demand by half a boiler for instance), the price elasticity of demand for energy efficiency improvements may not be constant across levels of investment. A solution to the statistical critics of the Tobit model is to use non parametric censoring models as they are robust to the potential heteroscedasticity of the residuals.

### 4.3.2 Censoring model (2): Censored quantile regressions

Powel (1986) showed that under some fairly weak regularity conditions, the censored quantile regression (CQR) estimator is a consistent estimator, regardless of the distribution of the error term and even in the presence of heteroscedasticity. Another desirable feature of quantile regressions is that they estimate marginal effects at different point in the distribution and thus provide information on the heterogeneity of energy conservation behavior.

By the equivariance to monotone transformation property of quantile regressions (Powel, 1986), in the presence of censoring the quantile regression model rewrites as follows:

$$Q_Y(\tau) = \max(0, X'\beta(\tau)) \qquad (21)$$

where $Q_Y(\tau)$ is the $\tau^{th}$ quantile of Y's distribution.

Contrary to uncensored quantile regressions, in the CQR model, the constraints are no longer linear: for some values of X, the $\tau^{th}$ quantile may be below the censoring point and the real value is unobserved. The semi-linearity turns the estimation by standard linear optimization techniques uneasy.

Powell suggested the following censored quantile regression estimator of $\beta(\tau)$ as the solution to the following problem:

$$\min_{\beta \in R} \sum_{i=1}^{n} \rho_{\tau} \{\max(0, Y_i - X_i'\beta)\} \qquad (22)$$
where $\rho_{\tau}(x) = (\tau - 1(x \le 0))x$ is the "check" function (Koenker, 2005). This estimator is known as the Censored Least Absolute Deviation (CLAD) and is asymptotic normal. Nevertheless, it is biased at finite distance (Paarsch 1984, Moon 1989) and the objective function is not globally convex so it leads to poor convergence rate and to computational difficulties (Buchinsky 1994, Fitzenberger 1997). Other methods based on iterative procedures were developed to overcome these issues (Buchinsky and Hahn 1998, Khan and Powell 2001, Chernozhukov and Hong 2002).

As in Athey and Imbens (2006), we extend the difference-in-differences model to the quantile models. The principle is straightforward: difference-in-differences estimates apply to each quantile rather than to the mean. So individuals are compared across groups and time according to their quantile. To get the counter-factual value, the difference is added over time in the control group at quantile $\tau$ to the $Q_{Y|E=1,t=t_0}(\tau)$ quantile in the treatment group.

Conditionally on the date T and group G, for each quantile, the Quantile difference-in-differences model proposed by Athey and Imbens is the following:

$$Q_{Y|E,t}(\tau) = \alpha(\tau) + \beta_T(\tau)T + \beta_E(\tau)C_1C_2 + \delta(\tau)C_1C_2T \quad (23)$$

It is assumed to be homogeneous and constant across households of this quantile.

Our control group is composed of three sub-groups so we estimate our triple differences model at each quantile instead of the mean. We assume that, conditionally on T, $C_1$ and $C_2$, each quantile is:

$$Q_{Y_{C_1,C_2,t}^N}(\tau) = \max\{0, \alpha(\tau) + \beta_1(\tau)C_1 + \beta_2(\tau)C_2 + \beta_3(\tau)C_1C_2 \\ + \beta_4(\tau)C_1T + \beta_5(\tau)C_2T + \delta_T(\tau)C_1C_2T\} \quad (24)$$

The treatment effect on the expenditures of the treated for the quantile $\tau$ of the distribution of Y is given by $\delta_T(\tau)$.

The identification conditions are (Athey and Imbens, 2006):

* the distribution of unobserved variables is identical in all sub-groups of the population;

* group and time effects are additive;

* the treatment is rank-preserving.

As in the Tobit model, we assume two first assumptions are verified by the latent quantile.

The rank preserving assumption imposes that the rank of each household in the distribution is independent of its treatment status: even if they had not been treated, the
households' rank in the distribution of expenditures would be the same. It is a strong assumption because it implies that the relative value of the potential outcome of a given individual is the same if the individual is in the treatment group or in the control group. If the assumption that the treatment is rank preserving holds, then the results can be interpreted at the individual level: a positive estimate at the lowest quantile means that small energy efficiency investors who are eligible to 40% tax credit rate have increased their expenditures after the treatment, all else being equal.

On the contrary, if the "treatment rank preserving" assumption is not verified, the results can only be interpreted in terms of changes in the whole distribution of expenditures. A positive estimate at the lower quantiles means that average expenditures reported at the lowest quantiles by treated households have increased after the tax credit rate increase. The estimated effect corresponds to shifts in the whole distribution. In other words, eligible households who undertake the lowest investments for a 40% tax credit rate invest more than households who undertake the lowest investments for a 25% tax credit rate. It cannot be interpreted as the fact that households who used to undertake small investments before the reform (lower quantiles without treatment) now invest more after treatment than otherwise.

The rank preserving assumption cannot be tested. We assume it is verified but leave the reader free to reject this assumption and interpret the results accordingly.

# 5 Results

## 5.1 Treatment effect

Results from both censoring models indicate that eligible households did adjust upward their energy efficiency expenditures after the 15 percentage point tax credit rate increase and that this effect is increasing with time. As a remainder we control for inflation in the housing repair sector so our results are net of most of the price effect. The increase in energy efficiency can be interpreted as both a quantity effect (increase in the number of items) and a quality effect (more efficient item).

The results of the Tobit DDD are reported in table 12, panel A. They are fairly robust to matching specifications: the conditional mean of the energy efficiency expenditures of the treated was 7 to 14% higher in 2006 to what would have been observed if the tax credit rate had not changed, 28% to 33% in 2007 and 23% to 34% in 2008 (columns 1 and 2). As a robustness check, covariates used to define cells of the other specifications are added in the Tobit equation. Results of the augmented equations give slightly larger estimates (columns 3 to 4). The difference-in-difference (DD) Tobit estimates are larger than the DDD Tobit estimates (table 12, panel B): not controlling for the impact of the
two eligibility criteria on the average expenditures would have led to overestimate the treatment effect on the treated. The ordinary least-square DDD estimations (table 12, panel C) are downward biased and not significant: not correcting for censoring would have led to underestimate the treatment effect on the treated. The selection bias seems to be small: the Tobit DDD without matching only slightly over-estimates the treatment effect on the treated, especially in 2006. As a robustness check we estimate the impact of the tax credit rate increase on the expenditures of new homeowners of dwellings built between 1977 and 1982. In this group no household benefited from the 40% tax credit rate so no impact should be observed. The estimated effect is close to null in 2006 and 2007 and lower than the estimated effect of the real treatment in 2008 (table 21 in annex C). These results confirm that the effect we estimated can be attributed to the tax credit rate increase and not to another factor.

Due to computational limits we were not able to include covariates in the censored quantile regressions (CQR), nor to estimate the matching model. Still, to gain some insights on the heterogeneity of the treatment effect depending on the level of investment, we run the DDD censored quantile regressions with no covariate (results in figure 5 and annex B, tables 18, 19, 20, panel A). Then to partially control for structural effects, we run separate CQR with no covariate on various clusters (results in figure 6 to 8 and annex B, tables 18, 19, 20, panel B). As a reminder due to heavy censoring it is not possible to robustly estimate coefficients for quantiles smaller than the censoring point (96th for the whole estimation sample for example). The CQR results on the whole estimation sample confirm the previous findings: the 2006 tax credit rate increase had a positive and significant impact on the distribution of the expenditures reported by the eligible households, and this effect is increasing with time. The order of magnitude of the treatment effect is consistent with the Tobit results with no covariate (column 1 in table 12, panel A). Nevertheless the CQR estimates provide strong evidence of a high heterogeneity in the treatment effect across quantile. If the treatment effect were homogenous, we would observe a translation to the right of the entire distribution of expenditures and the estimated coefficients would be equal for all quantiles. On the contrary we observe that the bottom of the distribution adjusted more to the tax credit rate increase than the top of the distribution: in 2006, the 40% tax credit rate implementation led to a 17% increase in expenditures at the 96th quantile, a 10% increase at the 97th but only a 6% increase at the highest quantiles. In 2007 and 2008 the impacts were almost as twice as important at each percentile but still decreasing at the highest percentiles.

The same regularity in the results holds when the CQR are estimated on clusters of the population: when significant, the treatment effect is positive, decreasing at the larger quantiles and increasing with time. Nevertheless, the results greatly differ across dwelling types, households' age, composition, fiscal income and dwelling location clusters. The tax credit rate increase had no significant impact on the energy efficiency investments
when CQR are estimated on the apartment cluster, whereas it always had a positive
and significant impact when estimated on the house cluster. On the house cluster, the
smallest investments were highly sensitive to the tax credit rate increase. The estimated
treatment effect on the expenditures of the treated is close to 100% at the lowest positive
quantile (+ 73% in 2006, + 100% in 2007 and + 108% in 2008 at the 95th quantile of
expenditures). At the largest quantiles, the treatment effect is more moderate and lower
than 25%. The tax credit rate increase had no impact on the distribution of expenditures
reported when estimated on the youngest households cluster (20 to 30 year-old), on the
eldest households cluster (75 year old and more) nor on the more modest households (1st
and 2nd deciles of fiscal revenue). On the contrary, the treatment effect is strong and
significant when estimated on the 40 to 75 year old households cluster, and starting in
2007 on the distribution of expenditures reported by households in the second to fourth
quintile of fiscal revenue. The results on the various clusters based on households' com-
position give insights on the impact of the ceiling level (8,000 for a single person, 16,000
for a couple, plus 400 euros per child). The treatment effect is significant and high at the
lowest quantiles for the households with the highest ceilings (couples). On the contrary
we observe almost no impact of a highest tax credit rate on the distribution of energy
efficiency investments of single persons with no child. Single persons tend to live more
often in apartments and are more frequently below 30 or above 75 year-old so this result
may also be driven by composition effects.

To control for composition effect, we select clusters of the most representative house-
hold types among tax credit claim filers, depending on their composition location, their
fiscal standard of living, the type of the housing unit (apartment or house) and its lo-
cation. In 2006, the treatment effect is not significant when estimated on the clusters
of rural and urban households living in houses by household's composition, except for
the relatively well-off 30 to 49 year-old couples with children and living in Paris or Île
de France (in figure 9 and annex B, tables 18, 19, 20, panel C and D). After the tax
credit increase, the 93th quantile of the expenditures of the treated was 56% higher than
it would have been without the tax credit increase, and the 98th 24% higher. It is worth
noting that this is the only cluster on household's composition for which the treatment
is significant each year and for which the estimated treatment effect is the largest (close
to 1 at the lowest quantiles above the censoring point).
<table><caption>Table 12: Treatment effect on treated expenditures - censored Tobit</caption><thead><tr><th rowspan="2"></th><th colspan="3">2006</th><th colspan="3">2007</th><th colspan="3">2008</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="11"><strong>Panel A: DDD Tobit</strong></td></tr><tr><td>Specif A</td><td>0,13<br/>[0, 00; 0, 27]</td><td>0,14<br/>[0, 01; 0, 28]</td><td>0,19<br/>[0, 11; 0, 41]</td><td><strong>0,15<br/>[0, 01; 0, 28]</strong></td><td>0,32<br/>[0, 20; 0, 46]</td><td>0,33<br/>[0, 22; 0, 46]</td><td>0,39<br/>[0, 30; 0, 61]</td><td><strong>0,34<br/>[0, 23; 0, 47]</strong></td><td>0,32<br/>[0, 16; 0, 42]</td><td>0,34<br/>[0, 20; 0, 44]</td><td><strong>0,34<br/>[0, 20; 0, 45]</strong></td></tr><tr><td>Specif B</td><td>0,12<br/>[-0, 04; 0, 25]</td><td>0,12<br/>[-0, 05; 0, 27]</td><td>0,12<br/>[0, 07; 0, 38]</td><td></td><td>0,30<br/>[0, 14; 0, 46]</td><td>0,31<br/>[0, 16; 0, 46]</td><td>0,29<br/>[0, 25; 0, 58]</td><td></td><td>0,23<br/>[0, 06; 0, 35]</td><td>0,25<br/>[0, 09; 0, 37]</td><td><strong>0,29<br/>[0, 14; 0, 45]</strong></td></tr><tr><td>Specif C</td><td>0,05<br/>[-0, 41; 0, 18]</td><td><strong>0,07<br/>[-1, 87; 0, 18]</strong></td><td></td><td></td><td>0,28<br/>[0, 02; 0, 43]</td><td><strong>0,31<br/>[0, 09; 0, 43]</strong></td><td></td><td></td><td>0,23<br/>[0, 24; 0, 47]</td><td></td><td><strong>0,27<br/>[0, 27; 0, 48]</strong></td><td></td></tr><tr><td><strong>No matching</strong></td><td>0,18<br/>[0, 08; 0, 29]</td><td>0,19<br/>[0, 09; 0, 29]</td><td>0,69<br/>[0, 08; 0, 74]</td><td>0,20<br/>[0, 09; 0, 30]</td><td>0,34<br/>[0, 23; 0, 45]</td><td>0,35<br/>[0, 24; 0, 47]</td><td>0,38<br/>[0, 25; 0, 74]</td><td>0,36<br/>[0, 25; 0, 48]</td><td>0,37<br/>[0, 24; 0, 47]</td><td>0,38<br/>[0, 27; 0, 49]</td><td>0,38<br/>[0, 27; 0, 48]</td><td>0,39<br/>[0, 27; 0, 49]</td></tr><tr><td colspan="11"><strong>Panel B: DD Tobit</strong></td></tr><tr><td>Specif A</td><td>0,19<br/>[0, 14; 0, 25]</td><td>0,19<br/>[0, 14; 0, 25]</td><td>0,19<br/>[0, 14; 0, 25]</td><td>0,19<br/>[0, 14; 0, 25]</td><td>0,28<br/>[0, 23; 0, 33]</td><td>0,28<br/>[0, 23; 0, 33]</td><td>0,29<br/>[0, 24; 0, 34]</td><td>0,29<br/>[0, 24; 0, 34]</td><td>0,20<br/>[0, 13; 0, 24]</td><td>0,22<br/>[0, 34]</td><td>0,34<br/>[0, 23]</td><td></td></tr><tr><td>Specif B</td><td>0,26<br/>[0, 21; 0, 32]</td><td>0,26<br/>[0, 20; 0, 31]</td><td>0,19<br/>[0, 20; 0, 31]</td><td></td><td>0,37<br/>[0, 32; 0, 43]</td><td>0,37<br/>[0, 32; 0, 42]</td><td>0,29<br/>[0, 32; 0, 42]</td><td></td><td>0,31<br/>[0, 24; 0, 35]</td><td>0,32<br/>[0, 25; 0, 36]</td><td>0,23<br/>[0, 26; 0, 39]</td><td></td></tr><tr><td>Specif C</td><td>0,27<br/>[0, 19; 0, 33]</td><td>0,27<br/>[0, 06; 0, 33]</td><td></td><td></td><td>0,39<br/>[0, 30; 0, 44]</td><td>0,39<br/>[0, 22; 0, 44]</td><td></td><td></td><td>0,34<br/>[0, 26; 0, 39]</td><td>0,35<br/>[0, 21; 0, 39]</td><td></td><td></td></tr><tr><td><strong>No matching</strong></td><td>0,21<br/>[0, 16; 0, 28]</td><td>0,21<br/>[0, 16; 0, 28]</td><td>0,21<br/>[0, 16; 0, 28]</td><td>0,54<br/>[0, 17; 0, 28]</td><td>0,30<br/>[0, 26; 0, 36]</td><td>0,31<br/>[0, 26; 0, 36]</td><td>0,30<br/>[0, 26; 0, 36]</td><td>0,31<br/>[0, 27; 0, 37]</td><td>0,23<br/>[0, 16; 0, 27]</td><td>0,24<br/>[0, 24]</td><td>0,24<br/>[0, 23]</td><td></td></tr><tr><td colspan="11"><strong>Panel C: DDD OLS</strong></td></tr><tr><td>Specif A</td><td>0,03<br/>[-0, 06; 0, 11]</td><td>0,03<br/>[-0, 05; 0, 12]</td><td>0,04<br/>[-0, 05; 0, 12]</td><td><strong>0,04<br/>[-0, 05; 0, 12]</strong></td><td>0,07<br/>[-0, 02; 0, 16]</td><td>0,07<br/>[-0, 01; 0, 16]</td><td>0,08<br/>[-0, 01; 0, 17]</td><td><strong>0,08<br/>[-0, 01; 0, 17]</strong></td><td>0,07<br/>[-0, 02; 0, 15]</td><td>0,07<br/>[-0, 02; 0, 15]</td><td>0,07<br/>[-0, 01; 0, 16]</td><td><strong>0,07<br/>[-0, 01; 0, 16]</strong></td></tr><tr><td>Specif B</td><td>0,03<br/>[-0, 10; 0, 16]</td><td>0,03<br/>[-0, 10; 0, 16]</td><td><strong>0,03<br/>[-0, 10; 0, 16]</strong></td><td></td><td>0,08<br/>[-0, 05; 0, 21]</td><td>0,09<br/>[-0, 05; 0, 22]</td><td><strong>0,09<br/>[-0, 05; 0, 22]</strong></td><td></td><td>0,07<br/>[-0, 06; 0, 21]</td><td>0,07<br/>[-0, 06; 0, 21]</td><td>0,07<br/>[-0, 05; 0, 21]</td><td><strong>0,07<br/>[-0, 05; 0, 21]</strong></td></tr><tr><td>Specif C</td><td>0,03<br/>[-0, 22; 0, 28]</td><td><strong>0,03<br/>[-0, 22; 0, 28]</strong></td><td></td><td></td><td>0,08<br/>[-0, 17; 0, 33]</td><td><strong>0,08<br/>[-0, 17; 0, 34]</strong></td><td></td><td></td><td>0,08<br/>[-0, 18; 0, 33]</td><td><strong>0,08<br/>[-0, 17; 0, 33]</strong></td><td></td><td></td></tr><tr><td><strong>No matching</strong></td><td>0,03<br/>[-0, 02; 0, 07]</td><td>0,03<br/>[-0, 01; 0, 08]</td><td>0,04<br/>[-0, 01; 0, 08]</td><td><strong>0,04<br/>[-0, 01; 0, 08]</strong></td><td>0,08<br/>[0, 04; 0, 12]</td><td>0,09<br/>[0, 04; 0, 13]</td><td>0,09<br/>[0, 05; 0, 13]</td><td>0,09<br/>[0, 05; 0, 13]</td><td>0,07<br/>[0, 03; 0, 11]</td><td>0,07<br/>[0, 03; 0, 11]</td><td>0,07<br/>[0, 03; 0, 12]</td><td>0,07<br/>[0, 04; 0, 12]</td></tr><tr><td><strong>Controls</strong></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td colspan="11"><strong>Augmented regression:</strong></td></tr><tr><td>House</td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td></td><td>x</td><td></td></tr><tr><td>Household's composition</td><td></td><td>x</td><td></td><td>x</td><td></td><td>x</td><td></td><td></td><td>x</td><td></td><td>x</td><td></td></tr></tbody></table>

Note: 95% Confidence intervals are reported in brackets below the coefficient. They are calculated by bootstrap (100 replications to be done)

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982.

Authors' own calculation.
<figure><img src="image_7.png" /><figcaption>Figure 5: Results of the DDD censored quantile regression</figcaption></figure>

Note: 95% confidence intervals are reported in dashed lines. They are calculated by bootstrap (500 replications).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982, author's calculations.

Estimation sample: households who purchased in the past 5 years a dwelling constructed between 1969 and 1988.

<figure><img src="image_8.png" /><figcaption>Figure 6: Results of the DDD censored quantile regression on the cluster of private houses</figcaption></figure>

Note: 95% confidence intervals are reported in dashed lines. They are calculated by bootstrap (500 replications).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982, author's calculations.

Estimation sample: households who purchased in the past 5 years a private house constructed between 1969 and 1988.
<figure><img src="image_9.png" /><figcaption>Figure 7: Results of the DDD censored quantile regression on the cluster of couples with children</figcaption></figure>

Note: 95% confidence intervals are reported in dashed lines. They are calculated by bootstrap (500 replications).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982, author's calculations.

Estimation sample: couples with children who purchased in the past 5 years a dwelling constructed between 1969 and 1988.

<figure><img src="image_10.png" /><figcaption>Figure 8: Results of the DDD censored quantile regression on the cluster of households belonging to the highest quintile of fiscal standard of living</figcaption></figure>

Note: 95% confidence intervals are reported in dashed lines. They are calculated by bootstrap (500 replications).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982, author's calculations.

Estimation sample: households in the 20th richest who purchased in the past 5 years a dwelling constructed between 1969 and 1988.
<figure><img src="image_11.png" /><figcaption>Figure 9: Results of the DDD censored quantile regression on the cluster of 30 to 49 year-old couples with children, living in Paris or Île de France, in the 2 top quintiles of fiscal standard of living</figcaption></figure>

Note: 95% confidence intervals are reported in dashed lines. They are calculated by bootstrap (500 replications).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982, author's calculations.

Estimation sample: households in the 20th richest who purchased in the past 5 years a dwelling constructed between 1969 and 1988.
## 5.2 Discussion

Mauroux (2012) suggests that a majority of households would have undertaken home renovation updates even if the tax credit rate were 25% but our results show that they adjusted their investment choices and increased their expenditures. They either installed more equipments (quantity effect) or chose even more efficient ones (quality effect).

Like Nauleau (2013) we observe a small latency in the impact of the tax credit rate increase. According to the Open database, the maturation period of a renovation project lasts more than 6 months. As a consequence the impact of a larger fiscal incentive may also take time to translate into actions and effective retrofit investments. This pattern is also consistent with the increase in the knowledge of the existence of the tax credit. In 2005, only 53% of the households were aware of it existence but they were 63% in 2006, 74% in 2007 and 78% en 2008 (survey "Maîtrise de l'énergie" Ademe-TNS-SOFRES 2008, on all households, including tenant). Moreover, the rapid increase in the number of households undertaking CIDD works fostered the diffusion of this information through word of mouth, the better understanding of its conditions and learning effects. Lastly, since the mid-2000 the survey "Maîtrise de l'énergie" reports a growing sensitivity of households to financial incentives (Ademe-TNS-SOFRES, 2008. The share of households stating that they undertake retrofitting investment to reduce their energy bill doubled between 2002 and 2008. It became the first reason to retrofit, before the historically more cited reason, improving indoor comfort).

The heterogeneity in the treatment effect, and in particular the smaller impact at the highest percentile of the distribution, may be caused by the existence of the ceiling. Only the first 8,000 euros reported by a single person and the first 16,000 euros reported by a couple (plus 400 euros per child) enter the refund calculation. When total expenditures are larger than this cap the tax credit is equivalent to a lump sum transfer. The larger the expenditures, the less sensitive to the level of tax credit rate the household should be. This theoretical results is confirmed by our estimation: at the 98th and the 99th quantiles, the estimated treatment effects are rather low (between 6 and 18%) and systematically lower than the estimated value for the first quantile above the censoring point. Moreover due to the nature of the expenditures (insulation material, double-glazing windows, boilers, ...), when the level of investment is already high it is not always possible to increase the quantity nor the quality of the equipments installed.

### 5.2.1 Impact on the final cost for households

We calculate *AFC* the average final cost for treated household as the average expenditures reported on the tax credit claim net of the tax credit refund :

$$AFC(\tau) = (1 - \tau)\bar{w}_{\tau} \qquad (25)$$
The difference on the average final cost with and without treatment is equal to:

$$ 
\begin{aligned}
\Delta AFC = AFC(40) - AFC(25) &= (1 - 0.40)\bar{w}_{40} - (1 - 0.25)\bar{w}_{25} \quad (26) \\
&= (1 - 0.40)(1 + \hat{\delta})\bar{w}_{25} - (1 - 0.25)\bar{w}_{25} \\
&= \underbrace{(1 - 0.40)\hat{\delta})\bar{w}_{25}}_{\text{net additional cost}} - \underbrace{(0.40 - 0.25)\bar{w}_{25}}_{\text{windfall profit}}
\end{aligned}
$$

where $\bar{w}_{\tau}$ is the average expenditures of the treated for a tax credit rate equal to $\tau$ and $\hat{\delta}$ is the treatment effect on the treated.

When the estimated treatment effect on the treatment is null (meaning that the households did not increase their expenses, $\hat{\delta} = 0$), their final cost decreases and they benefit from windfall profits ($\Delta AFC = -0.15\bar{w}_{25} < 0$).

When the estimated increase in expenditures is equal to 25%, the net additional cost is equal to the windfall profits and on average the households final expenses are unchanged.<fnref n="6" /> Households adjust upward their expenditures but only to match the same final cost they would have spent if the tax credit rate had been 25%. In this case the reform is neutral with respect to the households renovation budget but leads to additional investments and, in the end, improves the final energy efficiency of the dwellings of the treated. When $\hat{\delta}$ is greater than 25%, the reform led to an increase on the average thermal renovation budget because the final cost is higher than what treated households would have spent if the tax credit rate had been unchanged at 25%.

Our results suggest a great variety of profiles. In 2006, the estimated treatment effect on the conditional mean expenditures is lower than 25%. Nevertheless, it is larger in 2007 and 2008, meaning that the budget spent by the treated households on thermal renovation, net of the tax credit, is equal to or slightly larger to what it would have been if the tax credit rate had remained 25%. This is consistent with the idea that households are price sensitive but face budgetary constraints. Even if just after the purchase of a new dwelling the time horizon for returns on energy efficiency investments is the longest, some households may have reached their maximal financial or debt capacity. Even if the investments would be profitable, they may not be able to increase their final budget dedicated to home renovation, even after the increase of the subsidy rate.

Looking at the CQR results, coefficients at the lowest quantile of expenditures appear to be large and almost always greater than 25%. It suggests that on average the final cost of the lower investments increased greatly after the reform. As a remainder we estimate only a gross intensive margin (increase on the average expenditures) and not a strict intensive margin (increase in expenditures of households who would have invested

<footnote n="6">⁶If the energy efficiency were all caped by the ceiling, this threshold would be 15%.</footnote>
even at the 25% rate). At the lowest quantile the estimated treatment effect may capture the impact of the reform on the extensive margin: the expenditures of households who would not have invested at the 25% rate go from 0 to $w(40)$ so their adjustment ratio $w(40)/w(25)$ is theoretically infinite. The CQR results reflect this non-linearity in the expenditures.

On the contrary, when significant the 98$^{th}$ and 99$^{th}$ quantiles are always lower than 25%. Again this may be due to the nature of the expenditure and to the ceiling effect: when total expenditures exceed the ceiling, the tax credit is equivalent to a lump-sum transfer and the incentive effect is diluted.

### 5.2.2 Multiplicative effect

Reconciling the results of Mauroux (2012) on the extensive margin and our results on the gross intensive margins, we estimate a multiplicative effect of the public expenditures on the private expenses. We define it as the ratio between the total increase in private energy efficiency improvement investment $\Delta PI$ by the treated and the consequential increase in the budgetary cost $\Delta BC$:

$$ 
\begin{aligned}
M &= \frac{\Delta PI}{\Delta BC} \\
&= \frac{\sum_{i=1}^{N_{40}} w_i(40) - \sum_{i=1}^{N_{25}} w_i(25)}{0.40 \sum_{i=1}^{N_{40}} w_i(40) - 0.25 \sum_{i=1}^{N_{25}} w_i(25)} \\
&= \frac{N_{40}\bar{w}(40) - N_{25}\bar{w}(25)}{0.40N_{40}\bar{w}(40) - 0.25N_{25}\bar{w}(25)}
\end{aligned} 
\quad (27) $$

where $N_τ$ is the number of treated households who undertake home repairs and $\bar{w}(τ)$ is the average expenditures when the tax credit rate is equal to $τ$. $N_{40}$ and $\bar{w}(40)$ are observed in the data, $N_{40} - N_{25}$ is the extensive margin estimated by Mauroux (2012) and $\bar{w}(40) - \bar{w}(25)$ is the gross intensive margin we estimate. $N_{25}$ and $\bar{w}(25)$ are not observed because they correspond to an hypothetical situation but they can be recovered using the definition of the treatment effect:

$$ N_{40} = (1 + \delta_{Ext}) N_{25} \quad (28) $$

$$ \bar{w}(40) = (1 + \delta_{Int}) \bar{w}(25) \quad (29) $$

Calculations based on the Tobit estimations are provided in table 13 for the three matching specifications. It is not possible to conclude on the magnitude of the reform multiplier in 2006 but, in 2007 and 2008, each euro of budgetary expenditures generated 1.5 euro of private investment. Based on the results of simulations by ADEME on the total private investment in home renovation with and without CIDD in 2008 (Report of the comité d’évaluation des dépenses fiscales et des niches sociales, 2011), we compute
the budgetary multiplier of the whole CIDD. In 2008, a euro of budgetary expenditures generated 2.2 euros of private investment. The leverage effect of the 2006 reform we estimated is lower so the 15 point increase in the tax credit appears to be less efficient than the overall policy. It is not surprising given that it was only a marginal increase in the tax credit.

Our estimates of a greater than one budgetary multiplier is not inconsistent with our previous finding of a constant average final cost because of the impact of the reform on the extensive margin. The aggregate final cost for households is equal to the difference between the total private investment and the total budgetary expenditures and as shown is the equation 30 even if the average final cost is constant, the increase in total private investments is greater than the increase in the budgetary cost if $N_{40} > N_{25}$:

$$ 
\begin{align*}
\Delta PI - \Delta BC &= \sum_{N_{40}} AFC(40) - \sum_{N_{25}} AFC(25) \tag{30} \\
&= N_{40}AFC(40) - N_{25}AFC(25) \\
&= N_{40}(AFC(40) - AFC(25)) + (N_{40} - N_{25})AFC(25)
\end{align*}
$$
<table><caption>Table 13: Impact of the 2006 reform on private investment and public finance</caption><thead><tr><th rowspan="2"></th><th rowspan="2">2005</th><th colspan="3">2006</th><th colspan="3">2007</th><th colspan="3">2008</th></tr><tr><th>Lower bound Mean value Upper bound</th><th></th><th></th><th>Lower bound Mean value Upper bound</th><th></th><th></th><th>Lower bound Mean value Upper bound</th><th></th><th></th></tr></thead><tbody><tr><td colspan="10"><strong>(1) Extensive margin</strong></td></tr><tr><td>(A) Impact on the tax credit claim filling rate. (δ<sub>Ext</sub>)</td><td></td><td>0.52</td><td>0.79</td><td>1</td><td>1.2</td><td>1.48</td><td>1.73</td><td>1.09</td><td>1.49</td><td>1.72</td></tr><tr><td>(B) Number of treated households in the population</td><td>1,530,000</td><td>1,520,000</td><td>1,500,000</td><td>1,440,000</td><td>1,500,000</td><td>1,500,000</td><td>1,500,000</td><td>1,440,000</td><td>1,440,000</td><td>1,440,000</td></tr><tr><td>(C) Number of treated households in the evaluation (N<sub>E</sub>)</td><td>287,262</td><td>283,315</td><td>283,315</td><td>283,315</td><td>278,591</td><td>278,591</td><td>278,591</td><td>266,442</td><td>266,442</td><td>266,442</td></tr><tr><td>(D) Number of tax credit claim fillers among the treated (N<sub>40</sub>)</td><td>24,705</td><td>30,315</td><td>30,315</td><td>30,315</td><td>32,038</td><td>32,038</td><td>32,038</td><td>33,039</td><td>33,039</td><td>33,039</td></tr><tr><td>(E) Additional tax credit claim fillers among the treated (N<sub>40</sub> - N<sub>25</sub>)</td><td></td><td>1,473</td><td>2,238</td><td>2,833</td><td>3,343</td><td>4,123</td><td>4,820</td><td>2,904</td><td>3,970</td><td>4,583</td></tr><tr><td colspan="10"><strong>(2) Gross intensive margin</strong></td></tr><tr><td>(F) Impact on the average expenditures (in %) (δ<sub>Int</sub>)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(G) Average expenditures of the treated (w<sub>40</sub>)</td><td>4,134</td><td>4,543</td><td>4,543</td><td>4,543</td><td>4,956</td><td>4,956</td><td>4,956</td><td>27 *</td><td>34</td><td>41 *</td></tr><tr><td>(H) Average additional investment. in euros (w&#773;<sub>40</sub> - w&#773;<sub>25</sub>)</td><td></td><td>45</td><td>593</td><td>994</td><td>927</td><td>1,257</td><td>1,585</td><td>5,043</td><td>5,043</td><td>5,043</td></tr><tr><td>(I) Impact on private investment (in million euros) (W<sub>40</sub> - W<sub>25</sub>)</td><td></td><td>8</td><td>27</td><td>40</td><td>43</td><td>56</td><td>67</td><td>1,066</td><td>1,280</td><td>1,471</td></tr><tr><td colspan="10"><strong>(3) Average final cost</strong></td></tr><tr><td>(J) ΔAFC in %</td><td></td><td>-19.2</td><td>-8</td><td>2.4</td><td>-1.6</td><td>7.2</td><td>17.6</td><td>1.4</td><td>7.2</td><td>13.0</td></tr><tr><td>(K) ΔAFC in euros</td><td></td><td>-648</td><td>-237</td><td>64</td><td>-48</td><td>200</td><td>445</td><td>43</td><td>203</td><td>347</td></tr><tr><td colspan="10"><strong>(4) Budgetary impact</strong></td></tr><tr><td>(L) Total budgetary cost of the tax credit (in million euros)</td><td>900</td><td>1,900</td><td>1,900</td><td>1,900</td><td>1,900</td><td>1,900</td><td>1,900</td><td>1,900</td><td>1,900</td><td>1,900</td></tr><tr><td>(M) budgetary cost of the tax credit of the treated, in million euros (BC(40))</td><td></td><td>55.1</td><td>55.1</td><td>55.1</td><td>63.5</td><td>63.5</td><td>63.5</td><td>66.6</td><td>66.6</td><td>66.6</td></tr><tr><td>(N) Impact of the reform on budgetary cost (BC(40) - BC(25))</td><td></td><td>22.7</td><td>27.4</td><td>30.7</td><td>34.6</td><td>37.7</td><td>40.6</td><td>36.7</td><td>39.3</td><td>41.2</td></tr><tr><td>(O) Multiplicative effect (M)</td><td></td><td>0.3</td><td>1.0</td><td>1.8</td><td>1.1</td><td>1.5</td><td>1.9</td><td>1.1</td><td>1.5</td><td>1.8</td></tr></tbody></table>

Sources:

(A) Mauroux (2012), matching specification C (dwelling type, age in 4 modalities, climate zone in 3 modalities, town size in 5 modalities, fiscal standard of living in 8 modalities, household's composition in 4 modalities).

(B), (C), (D), (G), (L): fiscal data from 2005 to 2008

(E) Calculation: (A)/100 * (C) (because by definition N<sub>40</sub>/N<sub>E</sub> = N<sub>25</sub>/N<sub>E</sub> + δ<sub>Ext</sub>)

(F) Results of the study: DDD Tobit for the matching specific A, augmented regression (table 12, panel A, column 4).

(H) Calculation: (E) - (E)/(1 + (F))

(I) Calculation: (D) * (G) - (D - E) * (G - H)

(J) Calculation: (1 - 0.4)/(1 - 0.25)(1 + (F)) - 1

(K) Calculation: (1 - 0.4) * (G) - (1 - 0.25)(G - H)

(M) Calculation: 0.4 * (I)

(N) Calculation: (M) - 0.25 * (D - E) * (G - H)

(O) Calculation: (I)/(N)
# 6 Conclusion

In this paper we study the sensitivity of residential energy efficiency investments to the level of fiscal incentives, here the tax credit dedicated to sustainable development. Our results suggest that French households did adjust upward their housing improvement expenditures after an increase of the fiscal incentive, even if many of them would have undertaken home repairs anyway. Indeed, the 2006 tax credit rate increase had a positive and significant impact on the distribution of the expenditures reported by the eligible households, and this effect is increasing with time. The DDD Tobit estimations suggest that the average final private cost would have been the same with or without the 2006 reform. As we control for inflation in the housing repair sector, households either installed a more efficient version of the same equipment, or installed more equipment than what they would have done for a 25% tax credit rate. This result comforts the intuition that households face strong budgetary constraints for their home renovation. On several clusters of the populations Censored Quantile Regressions suggest that there is a high heterogeneity in the treatment effect across the distribution of expenditures and across categories of households.
References

Ademe - TNS-SOFRES (2008), "Enquête Maîtrise de l'Énergie".

Ai C. and Norton E. C. (2003), "Interaction terms in logit and probit models", Economics Letters n. 80, pp. 123-129.

Alberini A., Bigano A. and Boeri M. (2013), "Looking for Free-riding: Energy Efficiency Incentives and Italian Homeowners", Nota di Lavoro n. 24.2013, Fondazione Eni Enrico Mattei.

Allcott H. and Greenstone M. (2012), "Is There an Energy Efficiency Gap?", Journal of Economic Perspectives, vol 26(1), pp. 3-28.

Arabmazar A. and P. Schmidt (1981), "Further evidence on the robustness of the Tobit estimator to heteroscedasticity," Journal of Econometrics, 253-258.

Arabmazar A. and P. Schmidt (1982), "An Investigation of the Robustness of the Tobit Estimator to Non-normality," Econometrica, 50, 1055-1063.

Athey S. and Imbens G. (2006), "Identification and inference in non linear difference-in-differences models", Econometrica.

Blundell R. and Costa Dias M. (2009), "Alternative Approaches to Evaluation in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 44, n. 3.

Blundell R., Bozio A. and Laroque G., (2011) "Extensive and Intensive Margins of Labor Supply: Working Hours in the US, UK and France", Institute for fiscal studies, IFS Working Paper 01/11.

Buchinsky M. (1994), "Changes in the U.S. Wage Structure 1963-1984: Application of Quantile Regression." Econometrica, vol. 62, pp. 405-458.

Buchinsky M. and Hahn J. (1998), "An alternative estimator for the censored regression model", Econometrica, 66, pp. 405-458.

Buono I. and Lalanne G. (2012), "The Effect of the Uruguay Round on the Intensive and Extensive Margins of Trade", Journal of International Economics, Elsevier, vol. 86(2), pp. 269-283.
Chernozhukov V. and Hong H. (2002), "Three-step censored quantile regression and extramarital affairs", Journal of the American statistical association, vol. 97, n. 459.

Clerc M.-E., Marcus V. and Mauroux A. (2010), "Le recours au crédit d'impôt en faveur du développement durable. Une résidence principale sur sept rénovée entre 2005 et 2008", Insee Première, n. 1316.

Dubin J. A., Henson S. E. (1988), "The distributional effects of the federal energy tax act". Resources and Energy, vol. 10.

Durrmeyer I., d'Haultfoeuille X. and Février P. (2011), "Le coût du Bonus|Malus écologique: Que pouvait-on prévoir?", La Revue Économique.

Fitzenberger B. (1997), "Computational Aspects of Censored Quantile Regression", in *Proceedings of the 3rd International Conference on Statistical Data Analysis Based on the L1-Norm and Related Methods*, ed. Y. dodge, Hayward, CA: IMS, pp. 171-186.

Goldberger A. S. (1980), "Abnormal Selection Bias", SSRI Discussion Paper 8006, University of Wisconsin, Madison.

Hasset K. and Metcalf G. (1995), "Energy tax credit and residential conservation investment: Evidence from panel data", Journal of Public Economics, vol. 57.

Heckman J., Ichimura H., Smith J. and Todd P. (1998), "Characterizing selection bias using experimental data", Econometrica, vol. 66, n. 5.

Inspection Générale des Finances, (2011), "Rapport du comité d'évaluation des dépenses fiscales et des niches sociales : Annexe D, Synthèse de l'évaluation du crédit d'impôt développement durable".

Imbens G. W. and Wooldridge J. (2008), "Recent Developments in the Econometrics of Program Evaluation", NBER Working Paper, 14251.

Khan S. and Powell J. L. (2001), "Two-step estimation of semiparametric censored regression models", Journal of Econometrics, 100, 319-355.

Koenker R. (2005), Quantile Regression, Econometric Society Monograph Series, Cambridge University Press.

Koomey J. K. (2002), "Avoiding 'The Big Mistake' in forecasting technology adoption", Technological Forecasting and Social Change, n. 69, pp. 511-518.
Maddala G. S. and F. D. Nelson (1975) "Specification Errors in Limited Dependent Variable Models," NBER Working Paper No. 96.

Marbot C. and Roy D. (2011), "Évaluation de la transformation de la réduction d'impôt en crédit d'impôt pour l'emploi de salariés à domicile en 2007". Document de travail, Insee, n. G2011-12.

Marcus V., Meilhac C. and Penot-Antoniou L. (2012), "Le crédit d'impôt développement durable: 1,4 million de bénéficiaires en 2010, très majoritairement propriétaires de maisons individuelles", Commissariat Général au Développement Durable, Le Point sur n. 147.

Mauroux A. (2012), "Le crédit d'impôt développement durable. Une évaluation économétrique", Document de Travail, Insee, n. G2012-11.

Moon C.-G. (1989), "A Monte Carlo comparison of semiparametric Tobit estimators", Journal of Applied Econometrics, n. 4, pp. 361-382.

Nauleau M.-L. (2014), "Free-ridership in Tax Credits For Home Insulation in France: an econometric Assessment Using Panel Data", Fondazione Eni Enrico Mattei, Nota di Lavoro 26.2014.

Paarsch H.J. (1984), "A Monte Carlo comparison of estimators for censored regression models", Journal of Econometrics, n. 24, pp. 197-213.

Powell J. L. (1986), "Censored regression quantiles", Journal of Econometrics, 32, 143-155.

Puhani P. A. (2012), "The treatment effect, the cross difference, and the interaction term in nonlinear "difference-in-differences" models", Economics Letters, vol 115, issue 1, pp. 85-87.

Rogerson R., Wallenius J., (2007) "Micro and macro elasticities in a life cycle model with taxes", NBER WP 1307.

SOeS (2013), "Indice des prix des travaux d'entretien-amélioration de logement à la fin du deuxième trimestre 2013", Commissariat Général au Développement Durable, Chiffres et statistiques n. 450.

Walsh M. J. (1989), "Energy tax credit and housing improvement. Energy Economics", n. 11.
Wooldridge J. M. (2002), "Econometric Analysis of Cross Section and Panel Data", The
MIT Press.
A Additional statistics
<table><caption>Table 14: Main characteristics of French households (in %)</caption><thead><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th></tr></thead><tbody><tr><td colspan="5"><strong>Household's characteristics</strong></td></tr><tr><td colspan="5"><em>Status and housing unit</em></td></tr><tr><td>Tenant of an apartment</td><td>31.4</td><td>31.3</td><td>31.1</td><td>31.0</td></tr><tr><td>Tenant of a private house</td><td>11.2</td><td>11.2</td><td>11.2</td><td>11.1</td></tr><tr><td>Homeowner of an apartment</td><td>12.2</td><td>12.3</td><td>12.4</td><td>12.4</td></tr><tr><td>Homeowner of a private house</td><td>45.2</td><td>45.2</td><td>45.3</td><td>45.4</td></tr><tr><td colspan="5"><em>Age</em></td></tr><tr><td>Less than 30 year old</td><td>9.8</td><td>9.7</td><td>9.7</td><td>9.7</td></tr><tr><td>30 to 39 year-old</td><td>18.0</td><td>17.6</td><td>17.3</td><td>17.1</td></tr><tr><td>40 to 49 year-old</td><td>19.6</td><td>19.6</td><td>19.4</td><td>19.4</td></tr><tr><td>50 to 59 year-old</td><td>19.4</td><td>19.3</td><td>19.1</td><td>18.9</td></tr><tr><td>60 to 74 year-old</td><td>19.3</td><td>19.5</td><td>19.9</td><td>20.3</td></tr><tr><td>75 year-old and more</td><td>14.0</td><td>14.2</td><td>14.5</td><td>14.6</td></tr><tr><td colspan="5"><em>Household's composition</em></td></tr><tr><td>Single persons</td><td>40.1</td><td>40.3</td><td>40.6</td><td>40.8</td></tr><tr><td>Couples with no child enfant</td><td>24.2</td><td>24.1</td><td>24.1</td><td>24.1</td></tr><tr><td>Single persons with children</td><td>13.0</td><td>13.2</td><td>13.2</td><td>13.3</td></tr><tr><td>Couples with children</td><td>22.0</td><td>21.6</td><td>21.2</td><td>21.0</td></tr><tr><td>Others</td><td>0.7</td><td>0.7</td><td>0.8</td><td>0.8</td></tr><tr><td colspan="5"><strong>Dwelling's characteristics</strong></td></tr><tr><td colspan="5"><em>Size</em></td></tr><tr><td>Smaller than 30 m²</td><td>3.7</td><td>3.7</td><td>3.7</td><td>3.6</td></tr><tr><td>30 to 59 m²</td><td>21.0</td><td>20.9</td><td>20.8</td><td>20.7</td></tr><tr><td>60 to 89 m²</td><td>38.1</td><td>37.9</td><td>37.7</td><td>37.5</td></tr><tr><td>90 to 119 m²</td><td>22.5</td><td>22.5</td><td>22.6</td><td>22.6</td></tr><tr><td>Greater than 119 m²</td><td>14.7</td><td>15.0</td><td>15.3</td><td>15.6</td></tr><tr><td colspan="5"><em>Housing unit construction year</em></td></tr><tr><td>Before 1948</td><td>30.7</td><td>32.3</td><td>31.8</td><td>31.3</td></tr><tr><td>1949-1975</td><td>28.7</td><td>30.1</td><td>29.5</td><td>29.0</td></tr><tr><td>1976-1982</td><td>10.2</td><td>10.7</td><td>10.5</td><td>10.4</td></tr><tr><td>1983-1989</td><td>7.6</td><td>8.0</td><td>7.8</td><td>7.7</td></tr><tr><td>1990-2000</td><td>11.3</td><td>11.9</td><td>11.7</td><td>11.6</td></tr><tr><td>2001-2005</td><td>5.3</td><td>6.0</td><td>6.2</td><td>6.2</td></tr><tr><td>after 2005</td><td>0.0</td><td>1.0</td><td>2.4</td><td>3.8</td></tr><tr><td colspan="5"><em>Town size</em></td></tr><tr><td>Rural town</td><td>25.6</td><td>25.5</td><td>25.6</td><td>25.7</td></tr><tr><td>Less than 20,000 inhabitants</td><td>18.2</td><td>18.3</td><td>18.3</td><td>18.3</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td>13.9</td><td>13.9</td><td>13.9</td><td>13.9</td></tr><tr><td>100,000 inhabitants and more</td><td>28.1</td><td>28.1</td><td>28.1</td><td>28.0</td></tr><tr><td>Paris</td><td>14.2</td><td>14.2</td><td>14.1</td><td>14.0</td></tr><tr><td colspan="5"><em>Thermal regulation climate zone</em></td></tr><tr><td>H1 (ex. Paris)</td><td>58.3</td><td>58.3</td><td>58.1</td><td>58.0</td></tr><tr><td>H2 (ex. Nantes)</td><td>28.6</td><td>28.6</td><td>28.7</td><td>28.7</td></tr><tr><td>H3 (ex. Marseille)</td><td>11.1</td><td>11.1</td><td>11.2</td><td>11.2</td></tr></tbody></table>

<footnote>(*) Fiscal standards of living are defined as the net fiscal income of the households divided by the number of consumption units in the household.</footnote>

<footnote>Source: fiscal data from 2006 to 2009, authors' own calculation.</footnote>
<table><caption>Table 15: Main characteristics of French households filling a tax credit claim (in %)</caption><thead><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th></tr></thead><tbody><tr><td colspan="5"><strong>Household's characteristics</strong></td></tr><tr><td><em>Status and housing unit</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Tenant of an apartment</td><td>1.7</td><td>1.7</td><td>1.7</td><td>1.5</td></tr><tr><td>Tenant of a private house</td><td>2.7</td><td>2.8</td><td>2.8</td><td>2.6</td></tr><tr><td>Homeowner of an apartment</td><td>18.3</td><td>17.2</td><td>17.2</td><td>16.7</td></tr><tr><td>Homeowner of a private house</td><td>77.3</td><td>78.3</td><td>78.3</td><td>79.1</td></tr><tr><td><em>Age</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 30 year old</td><td>3.7</td><td>4.0</td><td>4.1</td><td>3.8</td></tr><tr><td>30 to 39 year-old</td><td>17.0</td><td>17.0</td><td>16.7</td><td>15.6</td></tr><tr><td>40 to 49 year-old</td><td>19.7</td><td>19.3</td><td>19.0</td><td>18.3</td></tr><tr><td>50 to 59 year-old</td><td>24.2</td><td>23.0</td><td>22.4</td><td>22.3</td></tr><tr><td>60 to 74 year-old</td><td>25.8</td><td>26.4</td><td>26.9</td><td>28.4</td></tr><tr><td>75 year-old and more</td><td>9.7</td><td>10.4</td><td>10.9</td><td>11.7</td></tr><tr><td><em>Household's composition</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Single persons</td><td>23.9</td><td>24.1</td><td>24.7</td><td>24.8</td></tr><tr><td>Couples with no child enfant</td><td>37.3</td><td>36.9</td><td>36.8</td><td>37.9</td></tr><tr><td>Single persons with children</td><td>8.7</td><td>9.1</td><td>9.2</td><td>8.8</td></tr><tr><td>Couples with children</td><td>29.5</td><td>29.1</td><td>28.7</td><td>27.8</td></tr><tr><td>Others</td><td>0.6</td><td>0.7</td><td>0.7</td><td>0.7</td></tr><tr><td><em>Fiscal Standards of living (*)</em></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sup>st</sup> decile</td><td>1.1</td><td>1.4</td><td>1.6</td><td>1.6</td></tr><tr><td>2<sup>nd</sup> decile</td><td>2.7</td><td>3.2</td><td>3.5</td><td>3.5</td></tr><tr><td>3<sup>rd</sup> decile</td><td>4.5</td><td>5.1</td><td>5.4</td><td>5.6</td></tr><tr><td>4<sup>th</sup> decile</td><td>6.6</td><td>6.9</td><td>7.2</td><td>7.2</td></tr><tr><td>5<sup>th</sup> decile</td><td>8.9</td><td>9.0</td><td>9.0</td><td>8.9</td></tr><tr><td>6<sup>th</sup> decile</td><td>11.3</td><td>11.1</td><td>10.9</td><td>10.9</td></tr><tr><td>7<sup>th</sup> decile</td><td>13.5</td><td>13.1</td><td>12.9</td><td>12.8</td></tr><tr><td>8<sup>th</sup> decile</td><td>15.8</td><td>15.1</td><td>14.9</td><td>14.8</td></tr><tr><td>9<sup>th</sup> decile</td><td>17.8</td><td>17.2</td><td>16.9</td><td>16.9</td></tr><tr><td>10<sup>th</sup> decile</td><td>18.1</td><td>18.0</td><td>17.8</td><td>17.8</td></tr><tr><td colspan="5"><strong>Dwelling's characteristics</strong></td></tr><tr><td><em>Size</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Smaller than 30 m<sup>2</sup></td><td>0.4</td><td>0.4</td><td>0.4</td><td>0.4</td></tr><tr><td>30 to 59 m<sup>2</sup></td><td>7.9</td><td>7.7</td><td>7.6</td><td>7.4</td></tr><tr><td>60 to 89 m<sup>2</sup></td><td>34.9</td><td>34.2</td><td>34.0</td><td>33.4</td></tr><tr><td>90 to 119 m<sup>2</sup></td><td>32.3</td><td>32.4</td><td>32.3</td><td>32.6</td></tr><tr><td>Greater than 119 m<sup>2</sup></td><td>24.5</td><td>25.4</td><td>25.7</td><td>26.1</td></tr><tr><td><em>Housing unit construction year</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Before 1948</td><td>33.6</td><td>34.2</td><td>33.8</td><td>33.6</td></tr><tr><td>1949-1975</td><td>37.0</td><td>36.3</td><td>35.4</td><td>35.0</td></tr><tr><td>1976-1982</td><td>15.9</td><td>15.5</td><td>15.4</td><td>15.7</td></tr><tr><td>1983-1989</td><td>7.3</td><td>7.2</td><td>7.5</td><td>7.9</td></tr><tr><td>1990-2000</td><td>4.8</td><td>4.9</td><td>5.0</td><td>5.1</td></tr><tr><td>2001-2005</td><td>1.5</td><td>1.4</td><td>1.6</td><td>1.5</td></tr><tr><td>after 2005</td><td>0.0</td><td>0.6</td><td>1.2</td><td>1.3</td></tr><tr><td><em>Town size</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Rural town</td><td>28.8</td><td>29.1</td><td>29.6</td><td>30.1</td></tr><tr><td>Less than 20,000 inhabitants</td><td>19.3</td><td>19.6</td><td>19.4</td><td>19.5</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td>13.5</td><td>13.6</td><td>13.7</td><td>13.7</td></tr><tr><td>100,000 inhabitants and more</td><td>26.3</td><td>26.4</td><td>26.4</td><td>25.8</td></tr><tr><td>Paris</td><td>12.1</td><td>11.2</td><td>10.9</td><td>10.8</td></tr><tr><td><em>Thermal regulation climate zone</em></td><td></td><td></td><td></td><td></td></tr><tr><td>H1 (ex. Paris)</td><td>57.9</td><td>57.7</td><td>57.4</td><td>57.7</td></tr><tr><td>H2 (ex. Nantes)</td><td>31.9</td><td>32.5</td><td>33.2</td><td>33.4</td></tr><tr><td>H3 (ex. Marseille)</td><td>10.1</td><td>9.7</td><td>9.3</td><td>8.8</td></tr></tbody></table>

<footnote>(*) Fiscal standards of living are defined as the net fiscal income of the households divided by the number of consumption units in the household.</footnote>

<footnote>Source: fiscal data from 2006 to 2009, authors' own calculation.</footnote>
<table><caption>Table 16: Expenditures on energy efficiency of tax credit claimants (in current euros)</caption><thead><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th></tr></thead><tbody><tr><td>Average</td><td>3,598</td><td>3,761</td><td>3,747</td><td>3,869</td></tr><tr><td colspan="5"><strong>Household's characteristics</strong></td></tr><tr><td><em>Status and housing unit</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Tenant of an apartment</td><td>3,644</td><td>3,917</td><td>3,992</td><td>4,170</td></tr><tr><td>Tenant of a private house</td><td>4,095</td><td>4,747</td><td>4,326</td><td>4,479</td></tr><tr><td>Homeowner of an apartment</td><td>2,828</td><td>3,032</td><td>3,129</td><td>3,225</td></tr><tr><td>Homeowner of a private house</td><td>3,761</td><td>3,883</td><td>3,856</td><td>3,978</td></tr><tr><td><em>Age</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 30 year old</td><td>3,275</td><td>3,428</td><td>3,502</td><td>3,528</td></tr><tr><td>30 to 39 year-old</td><td>3,734</td><td>3,857</td><td>3,836</td><td>3,885</td></tr><tr><td>40 to 49 year-old</td><td>3,790</td><td>3,989</td><td>4,009</td><td>4,169</td></tr><tr><td>50 to 59 year-old</td><td>3,779</td><td>3,975</td><td>3,943</td><td>4,052</td></tr><tr><td>60 to 74 year-old</td><td>3,472</td><td>3,653</td><td>3,634</td><td>3,803</td></tr><tr><td>75 year-old and more</td><td>2,971</td><td>3,109</td><td>3,123</td><td>3,294</td></tr><tr><td><em>Household's composition</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Single persons</td><td>3,153</td><td>3,233</td><td>3,262</td><td>3,349</td></tr><tr><td>Couples with no child enfant</td><td>3,624</td><td>3,790</td><td>3,757</td><td>3,928</td></tr><tr><td>Single persons with children</td><td>3,581</td><td>3,812</td><td>3,782</td><td>3,876</td></tr><tr><td>Couples with children</td><td>3,917</td><td>4,131</td><td>4,125</td><td>4,233</td></tr><tr><td>Others</td><td>4,268</td><td>4,427</td><td>4,419</td><td>4,522</td></tr><tr><td><em>Fiscal Standards of living (*)</em></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sup>st</sup> decile</td><td>4,052</td><td>4,273</td><td>4,202</td><td>4,250</td></tr><tr><td>2<sup>nd</sup> decile</td><td>3,606</td><td>3,768</td><td>3,722</td><td>3,757</td></tr><tr><td>3<sup>rd</sup> decile</td><td>3,431</td><td>3,515</td><td>3,495</td><td>3,627</td></tr><tr><td>4<sup>th</sup> decile</td><td>3,448</td><td>3,458</td><td>3,432</td><td>3,508</td></tr><tr><td>5<sup>th</sup> decile</td><td>3,236</td><td>3,391</td><td>3,379</td><td>3,446</td></tr><tr><td>6<sup>th</sup> decile</td><td>3,255</td><td>3,434</td><td>3,419</td><td>3,472</td></tr><tr><td>7<sup>th</sup> decile</td><td>3,347</td><td>3,490</td><td>3,468</td><td>3,570</td></tr><tr><td>8<sup>th</sup> decile</td><td>3,484</td><td>3,589</td><td>3,583</td><td>3,707</td></tr><tr><td>9<sup>th</sup> decile</td><td>3,663</td><td>3,786</td><td>3,835</td><td>3,926</td></tr><tr><td>10<sup>th</sup> decile</td><td>4,278</td><td>4,611</td><td>4,558</td><td>4,826</td></tr><tr><td colspan="5"><strong>Dwelling's characteristics</strong></td></tr><tr><td><em>Size</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Smaller than 30 m<sup>2</sup></td><td>2,595</td><td>2,575</td><td>2,737</td><td>2,705</td></tr><tr><td>30 to 59 m<sup>2</sup></td><td>2,810</td><td>2,919</td><td>2,939</td><td>2,993</td></tr><tr><td>60 to 89 m<sup>2</sup></td><td>3,297</td><td>3,410</td><td>3,421</td><td>3,490</td></tr><tr><td>90 to 119 m<sup>2</sup></td><td>3,645</td><td>3,778</td><td>3,774</td><td>3,880</td></tr><tr><td>Greater than 119 m<sup>2</sup></td><td>4,216</td><td>4,467</td><td>4,382</td><td>4,586</td></tr><tr><td><em>Housing unit construction year</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Before 1948</td><td>3,635</td><td>3,769</td><td>3,707</td><td>3,837</td></tr><tr><td>1949-1975</td><td>3,608</td><td>3,801</td><td>3,803</td><td>3,899</td></tr><tr><td>1976-1982</td><td>3,819</td><td>3,956</td><td>3,970</td><td>4,094</td></tr><tr><td>1983-1989</td><td>3,488</td><td>3,641</td><td>3,722</td><td>3,877</td></tr><tr><td>1990-2000</td><td>3,026</td><td>3,213</td><td>3,283</td><td>3,455</td></tr><tr><td>2001-2005</td><td>3,364</td><td>3,062</td><td>3,129</td><td>3,213</td></tr><tr><td>after 2005</td><td></td><td>4,975</td><td>3,938</td><td>3,881</td></tr><tr><td><em>Town size</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Rural town</td><td>3,763</td><td>3,903</td><td>3,865</td><td>4,031</td></tr><tr><td>Less than 20,000 inhabitants</td><td>3,722</td><td>3,908</td><td>3,805</td><td>3,902</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td>3,520</td><td>3,667</td><td>3,685</td><td>3,749</td></tr><tr><td>100,000 inhabitants and more</td><td>3,425</td><td>3,574</td><td>3,578</td><td>3,669</td></tr><tr><td>Paris</td><td>3,476</td><td>3,762</td><td>3,896</td><td>4,067</td></tr><tr><td><em>Thermal regulation climate zone</em></td><td></td><td></td><td></td><td></td></tr><tr><td>H1 (ex. Paris)</td><td>3,608</td><td>3,805</td><td>3,813</td><td>3,964</td></tr><tr><td>H2 (ex. Nantes)</td><td>3,630</td><td>3,710</td><td>3,647</td><td>3,715</td></tr><tr><td>H3 (ex. Marseille)</td><td>3,440</td><td>3,668</td><td>3,700</td><td>3,837</td></tr></tbody></table>

<footnote>(*) Fiscal standards of living are defined as the net fiscal income of the households divided by the number of consumption units in the household.</footnote>

<footnote>Source: fiscal data from 2006 to 2009, authors' own calculation.</footnote>
<table><caption>Table 17: Share of households filling a tax credit claim (in %)</caption><thead><tr><th></th><th>2005</th><th>2006</th><th>2007</th><th>2008</th></tr></thead><tbody><tr><td>Average</td><td>2.7</td><td>3.1</td><td>3.3</td><td>3.6</td></tr><tr><td colspan="5"><strong>Household's characteristics</strong></td></tr><tr><td><em>Status and housing unit</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Tenant of an apartment</td><td>0.1</td><td>0.2</td><td>0.2</td><td>0.2</td></tr><tr><td>Tenant of a private house</td><td>0.6</td><td>0.8</td><td>0.8</td><td>0.9</td></tr><tr><td>Homeowner of an apartment</td><td>4.0</td><td>4.4</td><td>4.5</td><td>4.9</td></tr><tr><td>Homeowner of a private house</td><td>4.6</td><td>5.4</td><td>5.7</td><td>6.3</td></tr><tr><td><em>Age</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Less than 30 year old</td><td>1.0</td><td>1.1</td><td>1.4</td><td>1.4</td></tr><tr><td>30 to 39 year-old</td><td>2.5</td><td>3.3</td><td>3.2</td><td>3.3</td></tr><tr><td>40 to 49 year-old</td><td>2.7</td><td>3.3</td><td>3.2</td><td>3.4</td></tr><tr><td>50 to 59 year-old</td><td>3.3</td><td>3.3</td><td>3.9</td><td>4.3</td></tr><tr><td>60 to 74 year-old</td><td>3.6</td><td>4.4</td><td>4.4</td><td>5.1</td></tr><tr><td>75 year-old and more</td><td>1.8</td><td>2.2</td><td>2.5</td><td>2.9</td></tr><tr><td><em>Household's composition</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Single persons</td><td>1.6</td><td>1.9</td><td>2.0</td><td>2.2</td></tr><tr><td>Couples with no child enfant</td><td>4.1</td><td>4.8</td><td>5.0</td><td>5.7</td></tr><tr><td>Single persons with children</td><td>1.8</td><td>2.2</td><td>2.3</td><td>2.4</td></tr><tr><td>Couples with children</td><td>3.6</td><td>4.2</td><td>4.4</td><td>4.8</td></tr><tr><td>Others</td><td>2.3</td><td>2.8</td><td>2.9</td><td>3.2</td></tr><tr><td><em>Fiscal Standards of living (*)</em></td><td></td><td></td><td></td><td></td></tr><tr><td>1<sup>st</sup> decile</td><td>0.3</td><td>0.4</td><td>0.5</td><td>0.6</td></tr><tr><td>2<sup>nd</sup> decile</td><td>0.7</td><td>1.0</td><td>1.1</td><td>1.3</td></tr><tr><td>3<sup>rd</sup> decile</td><td>1.2</td><td>1.6</td><td>1.8</td><td>2.0</td></tr><tr><td>4<sup>th</sup> decile</td><td>1.7</td><td>2.2</td><td>2.4</td><td>2.6</td></tr><tr><td>5<sup>th</sup> decile</td><td>2.4</td><td>2.8</td><td>2.9</td><td>3.3</td></tr><tr><td>6<sup>th</sup> decile</td><td>3.0</td><td>3.5</td><td>3.6</td><td>4.0</td></tr><tr><td>7<sup>th</sup> decile</td><td>3.6</td><td>4.1</td><td>4.2</td><td>4.6</td></tr><tr><td>8<sup>th</sup> decile</td><td>4.2</td><td>4.7</td><td>4.9</td><td>5.4</td></tr><tr><td>9<sup>th</sup> decile</td><td>4.7</td><td>5.4</td><td>5.6</td><td>6.1</td></tr><tr><td>10<sup>th</sup> decile</td><td>4.8</td><td>5.6</td><td>5.8</td><td>6.5</td></tr><tr><td colspan="5"><strong>Dwelling's characteristics</strong></td></tr><tr><td><em>Size</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Smaller than 30 m<sup>2</sup></td><td>0.3</td><td>0.4</td><td>0.4</td><td>0.4</td></tr><tr><td>30 to 59 m<sup>2</sup></td><td>1.0</td><td>1.2</td><td>1.2</td><td>1.3</td></tr><tr><td>60 to 89 m<sup>2</sup></td><td>2.5</td><td>2.8</td><td>3.0</td><td>3.3</td></tr><tr><td>90 to 119 m<sup>2</sup></td><td>3.9</td><td>4.5</td><td>4.7</td><td>5.3</td></tr><tr><td>Greater than 119 m<sup>2</sup></td><td>4.5</td><td>5.3</td><td>5.6</td><td>6.2</td></tr><tr><td><em>Housing unit construction year</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Before 1948</td><td>2.8</td><td>3.4</td><td>3.5</td><td>4.0</td></tr><tr><td>1949-1975</td><td>3.3</td><td>3.8</td><td>4.0</td><td>4.5</td></tr><tr><td>1976-1982</td><td>4.0</td><td>4.6</td><td>4.9</td><td>5.6</td></tr><tr><td>1983-1989</td><td>2.4</td><td>2.9</td><td>3.2</td><td>3.8</td></tr><tr><td>1990-2000</td><td>1.1</td><td>1.3</td><td>1.4</td><td>1.6</td></tr><tr><td>2001-2005</td><td>0.7</td><td>0.8</td><td>0.9</td><td>0.9</td></tr><tr><td>after 2005</td><td>0.0</td><td>1.8</td><td>1.7</td><td>1.3</td></tr><tr><td><em>Town size</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Rural town</td><td>3.3</td><td>3.9</td><td>4.1</td><td>4.6</td></tr><tr><td>Less than 20,000 inhabitants</td><td>3.1</td><td>3.6</td><td>3.7</td><td>4.1</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td>2.8</td><td>3.3</td><td>3.5</td><td>3.8</td></tr><tr><td>100,000 inhabitants and more</td><td>2.7</td><td>3.2</td><td>3.3</td><td>3.6</td></tr><tr><td>Paris</td><td>2.5</td><td>2.7</td><td>2.7</td><td>3.0</td></tr><tr><td><em>Thermal regulation climate zone</em></td><td></td><td></td><td></td><td></td></tr><tr><td>H1 (ex. Paris)</td><td>2.6</td><td>3.1</td><td>3.2</td><td>3.6</td></tr><tr><td>H2 (ex. Nantes)</td><td>3.0</td><td>3.6</td><td>3.8</td><td>4.2</td></tr><tr><td>H3 (ex. Marseille)</td><td>2.4</td><td>2.7</td><td>2.7</td><td>2.8</td></tr></tbody></table>

<footnote>(*) Fiscal standards of living are defined as the net fiscal income of the households divided by the number of consumption units in the household.</footnote>

<footnote>Source: fiscal data from 2006 to 2009, authors' own calculation.</footnote>
B Censored quantile regressions results
<table><caption>Table 18: Treatment effect on treated expenditures 2006 - Quantile regressions on various clusters</caption><thead><tr><th>2005-2006 Quantiles</th><th>93</th><th>94</th><th>95</th><th>96</th><th>97</th><th>98</th><th>99</th></tr></thead><tbody><tr><td colspan="8"><strong>Panel A: no covariates. estimation on the cluster of:</strong></td></tr><tr><td>Homeowners (whole sample)</td><td></td><td></td><td></td><td><strong>0.17</strong><br>[0.08; 0.26]</td><td><strong>0.09</strong><br>[0.03; 0.16]</td><td><strong>0.06</strong><br>[0.01; 0.10]</td><td><strong>0.06</strong><br>[0.01; 0.11]</td></tr><tr><td colspan="8"><strong>Panel B: no covariates. estimation on the cluster of:</strong></td></tr><tr><td>Apartments</td><td></td><td></td><td></td><td></td><td>-0.06<br>[-0.73; 0.52]</td><td>0.00<br>[-0.17; 0.16]</td><td>0.01<br>[-0.11; 0.12]</td></tr><tr><td>Private houses</td><td></td><td></td><td></td><td><strong>0.73</strong><br>[0.46; 0.99]</td><td><strong>0.15</strong><br>[0.07; 0.24]</td><td><strong>0.12</strong><br>[0.05; 0.18]</td><td><strong>0.03</strong><br>[-0.03; 0.08]</td><td><strong>0.08</strong><br>[0.03; 0.13]</td></tr><tr><td>Less than 30 years old</td><td></td><td></td><td></td><td></td><td>0.05<br>[-0.59; 0.62]</td><td>0.01<br>[-0.40; 0.44]</td><td>0.01<br>[-0.29; 0.29]</td><td>-0.02<br>[-0.25; 0.24]</td></tr><tr><td>30 to 49 year-old</td><td></td><td></td><td></td><td><strong>0.36</strong><br>[0.18; 0.58]</td><td><strong>0.15</strong><br>[0.05; 0.25]</td><td><strong>0.14</strong><br>[0.05; 0.21]</td><td>0.04<br>[-0.03; 0.11]</td><td><strong>0.10</strong><br>[0.03; 0.16]</td></tr><tr><td>50 to 74 year-old</td><td></td><td></td><td></td><td></td><td><strong>0.24</strong><br>[0.08; 0.48]</td><td>0.10<br>[-0.01; 0.20]</td><td>0.07<br>[-0.01; 0.15]</td><td><strong>0.09</strong><br>[0.01; 0.16]</td></tr><tr><td>75 year-old and more</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.02<br>[-0.65; 1.02]</td><td>-0.20<br>[-0.49; 0.12]</td></tr><tr><td>Single persons</td><td></td><td></td><td></td><td></td><td></td><td>0.18<br>[-0.09; 0.50]</td><td>0.03<br>[-0.12; 0.17]</td><td>0.00<br>[-0.10; 0.09]</td></tr><tr><td>Couples with no child</td><td></td><td></td><td></td><td></td><td>0.09<br>[-0.25; 0.50]</td><td>0.04<br>[-0.19; 0.23]</td><td>0.05<br>[-0.06; 0.18]</td><td>0.04<br>[-0.11; 0.18]</td></tr><tr><td>Single persons with children</td><td></td><td></td><td></td><td><strong>0.82</strong><br>[0.31; 1.51]</td><td>0.05<br>[-0.11; 0.22]</td><td>0.05<br>[-0.07; 0.16]</td><td>0.01<br>[-0.08; 0.12]</td><td>0.09<br>[0.00; 0.18]</td></tr><tr><td>Couples with children</td><td></td><td></td><td></td><td><strong>1.35</strong><br>[0.92; 2.24]</td><td><strong>0.39</strong><br>[0.21; 0.55]</td><td><strong>0.23</strong><br>[0.13; 0.34]</td><td><strong>0.13</strong><br>[0.06; 0.23]</td><td><strong>0.12</strong><br>[0.03; 0.19]</td><td><strong>0.09</strong><br>[0.03; 0.18]</td></tr><tr><td>Rural towns</td><td></td><td></td><td></td><td></td><td><strong>0.27</strong><br>[0.05; 0.47]</td><td><strong>0.24</strong><br>[0.10; 0.37]</td><td><strong>0.12</strong><br>[0.02; 0.21]</td><td>0.06<br>[-0.02; 0.15]</td></tr><tr><td>Less than 20,000 inhabitants</td><td></td><td></td><td></td><td>0.50<br>[-0.03; 0.99]</td><td>0.00<br>[-0.21; 0.21]</td><td>-0.01<br>[-0.15; 0.12]</td><td>-0.03<br>[-0.15; 0.09]</td><td>0.06<br>[-0.05; 0.17]</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td></td><td></td><td></td><td></td><td>-0.01<br>[-0.26; 0.24]</td><td>-0.03<br>[-0.23; 0.15]</td><td>-0.08<br>[-0.20; 0.08]</td><td>0.01<br>[-0.15; 0.16]</td></tr><tr><td>100,000 inhabitants and more</td><td></td><td></td><td></td><td></td><td><strong>0.72</strong><br>[0.39; 1.23]</td><td><strong>0.27</strong><br>[0.11; 0.42]</td><td><strong>0.19</strong><br>[0.07; 0.31]</td><td><strong>0.16</strong><br>[0.07; 0.26]</td><td><strong>0.17</strong><br>[0.07; 0.27]</td></tr><tr><td>Paris and Île-de-France</td><td></td><td></td><td></td><td></td><td></td><td>0.12<br>[-0.38; 0.56]</td><td>0.02<br>[-0.21; 0.24]</td><td>0.04<br>[-0.12; 0.20]</td><td>-0.02<br>[-0.14; 0.14]</td></tr><tr><td>1<sup>st</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.09<br>[-0.19; 0.39]</td></tr><tr><td>2<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.09<br>[-0.10; 0.29]</td><td>0.08<br>[-0.06; 0.21]</td></tr><tr><td>3<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.33</strong><br>[0.04; 0.58]</td><td>0.14<br>[-0.01; 0.30]</td><td><strong>0.14</strong><br>[0.03; 0.26]</td><td>0.06<br>[-0.05; 0.16]</td></tr><tr><td>4<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td>0.17<br>[-0.06; 0.44]</td><td>0.05<br>[-0.09; 0.18]</td><td>0.03<br>[-0.06; 0.14]</td><td>-0.01<br>[-0.09; 0.07]</td><td>0.01<br>[-0.08; 0.11]</td></tr><tr><td>5<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td><strong>0.21</strong><br>[0.01; 0.39]</td><td>0.10<br>[-0.01; 0.22]</td><td><strong>0.09</strong><br>[0.01; 0.19]</td><td>0.03<br>[-0.04; 0.11]</td><td>0.06<br>[-0.02; 0.13]</td></tr><tr><td colspan="8"><strong>Panel C: no covariates. estimation on the cluster of private houses in until 20,000 inhabitants' towns and:</strong></td></tr><tr><td>Couples with no child<br>+ 50 to 74 year-old + Q4-Q5</td><td></td><td></td><td></td><td>0.33<br>[-0.02; 0.72]</td><td>0.18<br>[-0.06; 0.42]</td><td>0.19<br>[-0.04; 0.34]</td><td>0.14<br>[-0.05; 0.31]</td><td>0.14<br>[-0.03; 0.34]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q1-Q2</td><td></td><td></td><td></td><td></td><td></td><td>0.28<br>[-0.21; 0.93]</td><td>0.14<br>[-0.19; 0.45]</td><td>0.09<br>[-0.18; 0.38]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q3</td><td></td><td></td><td></td><td>0.41<br>[-0.10; 0.93]</td><td>0.29<br>[-0.06; 0.66]</td><td>0.10<br>[-0.17; 0.44]</td><td>0.17<br>[-0.12; 0.47]</td><td>0.09<br>[-0.19; 0.37]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q4-Q5</td><td>0.25<br>[-0.09; 0.70]</td><td>0.18<br>[-0.13; 0.51]</td><td>0.10<br>[-0.13; 0.32]</td><td></td><td>0.02<br>[-0.19; 0.20]</td><td>-0.04<br>[-0.22; 0.16]</td><td>0.06<br>[-0.14; 0.20]</td><td>0.00<br>[-0.18; 0.21]</td></tr><tr><td colspan="8"><strong>Panel D: no covariates. estimation on the cluster of private houses in Paris and Île de France and:</strong></td></tr><tr><td>Couples with no child<br>+ 50 to 74 year-old + Q4-Q5</td><td></td><td></td><td></td><td></td><td>-0.06<br>[-0.54; 0.34]</td><td>0.01<br>[-0.36; 0.32]</td><td>-0.04<br>[-0.31; 0.27]</td><td>0.02<br>[-0.25; 0.32]</td><td>0.02<br>[-0.34; 0.32]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q1-Q2</td><td></td><td></td><td></td><td></td><td></td><td>-0.08<br>[-0.76; 0.41]</td><td>-0.02<br>[-0.54; 0.46]</td><td>0.22<br>[-0.20; 0.66]</td><td></td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q4-Q5</td><td></td><td></td><td></td><td><strong>0.56</strong><br>[0.23; 1.21]</td><td><strong>0.42</strong><br>[0.19; 0.78]</td><td><strong>0.37</strong><br>[0.17; 0.57]</td><td><strong>0.22</strong><br>[0.05; 0.41]</td><td><strong>0.20</strong><br>[0.01; 0.37]</td><td><strong>0.24</strong><br>[0.05; 0.44]</td><td>0.14<br>[-0.07; 0.34]</td></tr></tbody></table>

Note: 95% Confidence intervals are reported in brackets below the coefficient. They are calculated by bootstrap (500 replications).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982.
<table><caption>Table 19: Treatment effect on treated expenditures 2007 - Quantile regressions on various clusters</caption><thead><tr><th>2005-2007 Quantiles</th><th>93</th><th>94</th><th>95</th><th>96</th><th>97</th><th>98</th><th>99</th></tr></thead><tbody><tr><td colspan="8"><strong>Panel A: no covariates. estimation on the cluster of:</strong></td></tr><tr><td>Homeowners (whole sample)</td><td></td><td></td><td></td><td><strong>0.32</strong><br>[0.23; 0.43]</td><td><strong>0.20</strong><br>[0.14; 0.27]</td><td><strong>0.12</strong><br>[0.07; 0.17]</td><td><strong>0.10</strong><br>[0.05; 0.15]</td></tr><tr><td colspan="8"><strong>Panel B: no covariates. estimation on the cluster of:</strong></td></tr><tr><td>Apartments</td><td></td><td></td><td></td><td></td><td>0.18<br>[-0.26; 0.73]</td><td>0.07<br>[-0.10; 0.24]</td><td>0.04<br>[-0.09; 0.14]</td></tr><tr><td>Private houses</td><td></td><td></td><td></td><td><strong>0.95</strong><br>[0.68; 1.22]</td><td><strong>0.33</strong><br>[0.22; 0.40]</td><td><strong>0.22</strong><br>[0.16; 0.29]</td><td><strong>0.10</strong><br>[0.04; 0.15]</td><td><strong>0.12</strong><br>[0.07; 0.17]</td></tr><tr><td>Less than 30 years old</td><td></td><td></td><td></td><td></td><td>-0.01<br>[-0.56; 0.54]</td><td>-0.03<br>[-0.41; 0.38]</td><td>-0.06<br>[-0.40; 0.23]</td><td>-0.03<br>[-0.27; 0.20]</td></tr><tr><td>30 to 49 year-old</td><td></td><td></td><td></td><td><strong>0.56</strong><br>[0.37; 0.79]</td><td><strong>0.29</strong><br>[0.19; 0.39]</td><td><strong>0.20</strong><br>[0.13; 0.28]</td><td><strong>0.11</strong><br>[0.03; 0.17]</td><td><strong>0.16</strong><br>[0.09; 0.23]</td></tr><tr><td>50 to 74 year-old</td><td></td><td></td><td></td><td></td><td><strong>0.41</strong><br>[0.24; 0.61]</td><td><strong>0.24</strong><br>[0.14; 0.35]</td><td><strong>0.14</strong><br>[0.05; 0.21]</td><td><strong>0.10</strong><br>[0.03; 0.18]</td></tr><tr><td>75 year-old and more</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.17<br>[-0.37; 1.20]</td><td>-0.06<br>[-0.33; 0.25]</td></tr><tr><td>Single persons</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.31</strong><br>[0.11; 0.58]</td><td>0.10<br>[-0.03; 0.24]</td><td>0.04<br>[-0.05; 0.15]</td></tr><tr><td>Couples with no child</td><td></td><td></td><td></td><td></td><td><strong>0.42</strong><br>[0.10; 0.78]</td><td>0.21<br>[-0.01; 0.39]</td><td><strong>0.15</strong><br>[0.02; 0.27]</td><td>0.07<br>[-0.05; 0.19]</td></tr><tr><td>Single persons with children</td><td></td><td></td><td></td><td><strong>1.06</strong><br>[0.59; 1.74]</td><td><strong>0.21</strong><br>[0.05; 0.39]</td><td><strong>0.17</strong><br>[0.04; 0.28]</td><td>0.07<br>[-0.04; 0.17]</td><td><strong>0.13</strong><br>[0.04; 0.22]</td></tr><tr><td>Couples with children</td><td></td><td></td><td></td><td><strong>1.53</strong><br>[1.05; 2.37]</td><td><strong>0.51</strong><br>[0.37; 0.65]</td><td><strong>0.31</strong><br>[0.20; 0.42]</td><td><strong>0.18</strong><br>[0.10; 0.27]</td><td><strong>0.14</strong><br>[0.07; 0.22]</td><td><strong>0.13</strong><br>[0.05; 0.21]</td></tr><tr><td>Rural towns</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.46</strong><br>[0.24; 0.68]</td><td><strong>0.38</strong><br>[0.25; 0.51]</td><td><strong>0.17</strong><br>[0.06; 0.26]</td><td><strong>0.14</strong><br>[0.05; 0.22]</td></tr><tr><td>Less than 20,000 inhabitants</td><td></td><td></td><td></td><td><strong>0.82</strong><br>[0.31; 1.25]</td><td><strong>0.23</strong><br>[0.02; 0.42]</td><td>0.12<br>[-0.02; 0.25]</td><td>0.04<br>[-0.07; 0.14]</td><td>0.09<br>[-0.01; 0.21]</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td></td><td></td><td></td><td></td><td>0.11<br>[-0.12; 0.34]</td><td>0.05<br>[-0.13; 0.24]</td><td>-0.01<br>[-0.15; 0.13]</td><td>0.12<br>[-0.02; 0.26]</td></tr><tr><td>100,000 inhabitants and more</td><td></td><td></td><td></td><td><strong>0.81</strong><br>[0.45; 1.26]</td><td><strong>0.29</strong><br>[0.14; 0.45]</td><td><strong>0.23</strong><br>[0.13; 0.36]</td><td><strong>0.17</strong><br>[0.07; 0.25]</td><td><strong>0.17</strong><br>[0.08; 0.26]</td></tr><tr><td>Paris and Île-de-France</td><td></td><td></td><td></td><td></td><td>0.32<br>[-0.13; 0.69]</td><td>0.15<br>[-0.06; 0.34]</td><td>0.09<br>[-0.07; 0.27]</td><td>0.09<br>[-0.07; 0.24]</td></tr><tr><td>1<sup>st</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.20<br>[-0.11; 0.45]</td><td></td></tr><tr><td>2<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.20<br>[-0.01; 0.41]</td><td><strong>0.18</strong><br>[0.01; 0.30]</td></tr><tr><td>3<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td><strong>0.43</strong><br>[0.16; 0.71]</td><td><strong>0.21</strong><br>[0.06; 0.38]</td><td><strong>0.18</strong><br>[0.07; 0.30]</td><td><strong>0.13</strong><br>[0.02; 0.24]</td></tr><tr><td>4<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td><strong>0.35</strong><br>[0.13; 0.57]</td><td><strong>0.17</strong><br>[0.04; 0.30]</td><td><strong>0.14</strong><br>[0.04; 0.25]</td><td>0.02<br>[-0.08; 0.11]</td><td>0.04<br>[-0.04; 0.14]</td></tr><tr><td>5<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td><strong>0.41</strong><br>[0.22; 0.58]</td><td><strong>0.23</strong><br>[0.12; 0.34]</td><td><strong>0.19</strong><br>[0.10; 0.28]</td><td><strong>0.11</strong><br>[0.03; 0.20]</td><td><strong>0.14</strong><br>[0.05; 0.20]</td></tr><tr><td colspan="8"><strong>Panel C: no covariates. estimation on the cluster of private houses in until 20,000 inhabitants' towns and:</strong></td></tr><tr><td>Couples with no child<br>+ 50 to 74 year-old + Q4-Q5</td><td></td><td></td><td></td><td><strong>0.78</strong><br>[0.41; 1.14]</td><td><strong>0.50</strong><br>[0.31; 0.73]</td><td><strong>0.34</strong><br>[0.11; 0.49]</td><td><strong>0.26</strong><br>[0.08; 0.43]</td><td><strong>0.25</strong><br>[0.08; 0.40]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q1-Q2</td><td></td><td></td><td></td><td></td><td></td><td>0.33<br>[-0.16; 0.91]</td><td>0.11<br>[-0.19; 0.42]</td><td>0.14<br>[-0.19; 0.43]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q3</td><td></td><td></td><td></td><td><strong>0.71</strong><br>[0.34; 1.23]</td><td><strong>0.52</strong><br>[0.22; 0.86]</td><td><strong>0.36</strong><br>[0.12; 0.65]</td><td><strong>0.37</strong><br>[0.02; 0.63]</td><td><strong>0.36</strong><br>[0.10; 0.55]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q4-Q5</td><td>0.24<br>[-0.14; 0.67]</td><td>0.08<br>[-0.18; 0.43]</td><td>0.00<br>[-0.23; 0.27]</td><td></td><td>-0.06<br>[-0.26; 0.14]</td><td>-0.09<br>[-0.25; 0.11]</td><td>0.03<br>[-0.14; 0.20]</td><td>-0.02<br>[-0.20; 0.20]</td></tr><tr><td colspan="8"><strong>Panel D: no covariates. estimation on the cluster of private houses in Paris and Île de France and:</strong></td></tr><tr><td>Couples with no child<br>+ 50 to 74 year-old + Q4-Q5</td><td></td><td></td><td></td><td>0.32<br>[-0.32; 1.47]</td><td>0.18<br>[-0.22; 0.60]</td><td>0.27<br>[-0.12; 0.61]</td><td>0.08<br>[-0.19; 0.40]</td><td>-0.07<br>[-0.29; 0.33]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q1-Q2</td><td></td><td></td><td></td><td></td><td></td><td>-0.10<br>[-0.70; 0.58]</td><td><strong>0.19</strong><br>[0.39; 0.55]</td><td>0.11<br>[-0.37; 0.56]</td></tr><tr><td>Couples with children<br>+ 30 to 49 year-old + Q4-Q5</td><td></td><td></td><td></td><td><strong>0.82</strong><br>[0.46; 1.46]</td><td><strong>0.60</strong><br>[0.37; 0.89]</td><td><strong>0.50</strong><br>[0.33; 0.70]</td><td><strong>0.35</strong><br>[0.19; 0.55]</td><td><strong>0.29</strong><br>[0.13; 0.48]</td><td><strong>0.38</strong><br>[0.21; 0.57]</td><td><strong>0.23</strong><br>[0.06; 0.40]</td></tr></tbody></table>

Note: 95% Confidence intervals are reported in brackets below the coefficient. They are calculated by bootstrap (500 replications).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982.
<table><caption>Table 20: Treatment effect on treated expenditures 2008 - Quantile regressions on various clusters</caption><thead><tr><th>2005-2008 Quantiles</th><th>93</th><th>94</th><th>95</th><th>96</th><th>97</th><th>98</th><th>99</th></tr></thead><tbody><tr><td colspan="8"><strong>Panel A: no covariates. estimation on the cluster of:</strong></td></tr><tr><td>Homeowners (whole sample)</td><td></td><td></td><td></td><td><strong>0.38</strong></td><td><strong>0.25</strong></td><td><strong>0.13</strong></td><td><strong>0.13</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.28; 0.49]</td><td>[0.19; 0.31]</td><td>[0.08; 0.17]</td><td>[0.09; 0.18]</td></tr><tr><td colspan="8"><strong>Panel B: no covariates. estimation on the cluster of:</strong></td></tr><tr><td>Apartments</td><td></td><td></td><td></td><td></td><td>0.17</td><td>0.06</td><td>0.14</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>[-0.22; 0.68]</td><td>[-0.09; 0.24]</td><td>[0.00; 0.24]</td></tr><tr><td>Private houses</td><td></td><td></td><td></td><td><strong>1.08</strong></td><td><strong>0.39</strong></td><td><strong>0.24</strong></td><td><strong>0.11</strong></td><td><strong>0.14</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.82; 1.35]</td><td>[0.30; 0.48]</td><td>[0.19; 0.32]</td><td>[0.06; 0.18]</td><td>[0.09; 0.19]</td></tr><tr><td>Less than 30 years old</td><td></td><td></td><td></td><td></td><td>0.09</td><td>0.00</td><td>-0.02</td><td>-0.08</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>[-0.52; 0.68]</td><td>[-0.32; 0.38]</td><td>[-0.35; 0.24]</td><td>[-0.28; 0.14]</td></tr><tr><td>30 to 49 year-old</td><td></td><td></td><td></td><td><strong>0.66</strong></td><td><strong>0.37</strong></td><td><strong>0.27</strong></td><td><strong>0.12</strong></td><td><strong>0.18</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.50; 0.87]</td><td>[0.27; 0.46]</td><td>[0.20; 0.36]</td><td>[0.05; 0.18]</td><td>[0.12; 0.24]</td></tr><tr><td>50 to 74 year-old</td><td></td><td></td><td></td><td><strong>0.51</strong></td><td><strong>0.27</strong></td><td><strong>0.16</strong></td><td><strong>0.13</strong></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.36; 0.71]</td><td>[0.19; 0.38]</td><td>[0.07; 0.22]</td><td>[0.06; 0.22]</td><td></td></tr><tr><td>75 year-old and more</td><td></td><td></td><td></td><td></td><td></td><td>0.12</td><td>0.04</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>[-0.44; 1.30]</td><td>[-0.34; 0.24]</td><td></td></tr><tr><td>Single persons</td><td></td><td></td><td></td><td></td><td><strong>0.31</strong></td><td>0.11</td><td>0.04</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>[0.12; 0.60]</td><td>[-0.02; 0.23]</td><td>[-0.05; 0.12]</td><td></td></tr><tr><td>Couples with no child</td><td></td><td></td><td></td><td><strong>0.56</strong></td><td><strong>0.37</strong></td><td><strong>0.18</strong></td><td><strong>0.13</strong></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.22; 0.94]</td><td>[0.15; 0.54]</td><td>[0.02; 0.27]</td><td>[0.11; 0.24]</td><td></td></tr><tr><td>Single persons with children</td><td></td><td></td><td></td><td><strong>1.15</strong></td><td>0.26</td><td><strong>0.19</strong></td><td>0.11</td><td><strong>0.13</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.63; 1.74]</td><td>[-0.08; 0.44]</td><td>[0.07; 0.31]</td><td>[0.00; 0.21]</td><td>[0.04; 0.23]</td></tr><tr><td>Couples with children</td><td></td><td></td><td></td><td><strong>1.73</strong></td><td><strong>0.63</strong></td><td><strong>0.39</strong></td><td><strong>0.21</strong></td><td><strong>0.18</strong></td><td><strong>0.14</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[1.30; 2.63]</td><td>[0.49; 0.78]</td><td>[0.29; 0.51]</td><td>[0.13; 0.30]</td><td>[0.11; 0.26]</td><td>[0.07; 0.23]</td></tr><tr><td>Rural towns</td><td></td><td></td><td></td><td></td><td><strong>0.52</strong></td><td><strong>0.40</strong></td><td><strong>0.21</strong></td><td><strong>0.17</strong></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>[0.35; 0.72]</td><td>[0.28; 0.53]</td><td>[0.11; 0.31]</td><td>[0.09; 0.28]</td></tr><tr><td>Less than 20,000 inhabitants</td><td></td><td></td><td></td><td><strong>0.84</strong></td><td><strong>0.25</strong></td><td>0.10</td><td>0.06</td><td><strong>0.12</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.38; 1.36]</td><td>[0.06; 0.45]</td><td>[-0.01; 0.23]</td><td>[-0.09; 0.13]</td><td>[0.01; 0.22]</td></tr><tr><td>Between 20,000 and 99,999 inhabitants</td><td></td><td></td><td></td><td><strong>0.23</strong></td><td></td><td>0.14</td><td>0.02</td><td>0.06</td></tr><tr><td></td><td></td><td></td><td></td><td>[0.04; 0.48]</td><td>[-0.02; 0.30]</td><td>[-0.11; 0.17]</td><td>[-0.08; 0.20]</td></tr><tr><td>100,000 inhabitants and more</td><td></td><td></td><td></td><td><strong>0.76</strong></td><td><strong>0.27</strong></td><td><strong>0.24</strong></td><td><strong>0.16</strong></td><td><strong>0.16</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.40; 1.21]</td><td>[0.13; 0.43]</td><td>[0.14; 0.36]</td><td>[0.07; 0.25]</td><td>[0.07; 0.26]</td></tr><tr><td>Paris and Île-de-France</td><td></td><td></td><td></td><td></td><td><strong>0.76</strong></td><td><strong>0.36</strong></td><td><strong>0.16</strong></td><td><strong>0.15</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.38; 1.15]</td><td>[0.18; 0.57]</td><td>[0.01; 0.31]</td><td>[0.02; 0.30]</td></tr><tr><td>1<sup>st</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.22</td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>[-0.05; 0.52]</td><td></td></tr><tr><td>2<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.20</td><td><strong>0.16</strong></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>[0.00; 0.42]</td><td>[0.03; 0.29]</td></tr><tr><td>3<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td></td><td><strong>0.57</strong></td><td><strong>0.33</strong></td><td><strong>0.22</strong></td><td><strong>0.16</strong></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>[0.31; 0.80]</td><td>[0.21; 0.48]</td><td>[0.12; 0.33]</td><td>[0.05; 0.27]</td></tr><tr><td>4<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td><strong>0.47</strong></td><td><strong>0.26</strong></td><td><strong>0.17</strong></td><td>0.03</td><td>0.06</td></tr><tr><td></td><td></td><td></td><td></td><td>[0.24; 0.68]</td><td>[0.16; 0.41]</td><td>[0.08; 0.27]</td><td>[-0.05; 0.11]</td><td>[-0.03; 0.14]</td></tr><tr><td>5<sup>nd</sup> quintile of fiscal standard of living</td><td></td><td></td><td></td><td><strong>0.46</strong></td><td><strong>0.29</strong></td><td><strong>0.19</strong></td><td><strong>0.13</strong></td><td><strong>0.13</strong></td></tr><tr><td></td><td></td><td></td><td></td><td>[0.27; 0.63]</td><td>[0.18; 0.41]</td><td>[0.10; 0.27]</td><td>[0.05; 0.21]</td><td>[0.05; 0.21]</td></tr><tr><td colspan="8"><strong>Panel C: no covariates. estimation on the cluster of private houses in until 20,000 inhabitants' towns and:</strong></td></tr><tr><td>Couples with no child</td><td></td><td></td><td></td><td><strong>0.68</strong></td><td><strong>0.39</strong></td><td><strong>0.28</strong></td><td><strong>0.24</strong></td><td>0.15</td></tr><tr><td>+ 50 to 74 year-old + Q4-Q5</td><td></td><td></td><td></td><td>[0.36; 1.02]</td><td>[0.16; 0.59]</td><td>[0.06; 0.43]</td><td>[0.08; 0.42]</td><td>[-0.02; 0.31]</td></tr><tr><td>Couples with children</td><td></td><td></td><td></td><td></td><td></td><td>0.37</td><td>0.18</td><td><strong>0.31</strong></td></tr><tr><td>+ 30 to 49 year-old + Q1-Q2</td><td></td><td></td><td></td><td></td><td></td><td>[-0.06; 1.00]</td><td>[-0.07; 0.50]</td><td>[0.02; 0.54]</td></tr><tr><td>Couples with children</td><td></td><td></td><td></td><td><strong>0.70</strong></td><td><strong>0.46</strong></td><td>0.24</td><td>0.30</td><td>0.25</td></tr><tr><td>+ 30 to 49 year-old + Q3</td><td></td><td></td><td></td><td>[0.22; 1.14]</td><td>[0.11; 0.82]</td><td>[-0.01; 0.55]</td><td>[-0.02; 0.55]</td><td>[-0.10; 0.49]</td></tr><tr><td>Couples with children</td><td></td><td></td><td></td><td><strong>0.53</strong></td><td><strong>0.39</strong></td><td><strong>0.25</strong></td><td>0.10</td><td>0.03</td></tr><tr><td>+ 30 to 49 year-old + Q4-Q5</td><td></td><td></td><td></td><td>[0.14; 0.90]</td><td>[0.10; 0.73]</td><td>[0.01; 0.50]</td><td>[-0.08; 0.28]</td><td>[-0.09; 0.27]</td><td>[-0.06; 0.32]</td><td>[-0.17; 0.22]</td></tr><tr><td colspan="8"><strong>Panel D: no covariates. estimation on the cluster of private houses in Paris and Île de France and:</strong></td></tr><tr><td>Couples with no child</td><td></td><td></td><td></td><td><strong>0.72</strong></td><td><strong>0.39</strong></td><td>0.25</td><td>0.10</td><td>0.11</td><td>-0.03</td></tr><tr><td>+ 50 to 74 year-old + Q4-Q5</td><td></td><td></td><td></td><td>[0.01; 1.95]</td><td>[0.02; 0.80]</td><td>[-0.09; 0.59]</td><td>[-0.22; 0.44]</td><td>[-0.13; 0.39]</td><td>[-0.36; 0.27]</td></tr><tr><td>Couples with children</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.40</td><td>-0.10</td><td>-0.18</td></tr><tr><td>+ 30 to 49 year-old + Q1-Q2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>[-0.93; 0.26]</td><td>[-0.66; 0.29]</td><td>[-0.60; 0.32]</td></tr><tr><td>Couples with children</td><td></td><td></td><td></td><td><strong>0.97</strong></td><td><strong>0.66</strong></td><td><strong>0.55</strong></td><td><strong>0.36</strong></td><td><strong>0.32</strong></td><td><strong>0.35</strong></td><td>0.08</td></tr><tr><td>+ 30 to 49 year-old + Q4-Q5</td><td></td><td></td><td></td><td>[0.58; 1.54]</td><td>[0.41; 0.94]</td><td>[0.39; 0.74]</td><td>[0.20; 0.55]</td><td>[0.14; 0.50]</td><td>[0.16; 0.52]</td><td>[-0.09; 0.32]</td></tr></tbody></table>

Note: 95% Confidence intervals are reported in brackets below the coefficient. They are calculated by bootstrap (500 replications).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less a dwelling constructed between 1969 and 1982.
## C Placebo test results

<table><caption>Table 21: Robustness check - Placebo DDD</caption><thead><tr><th rowspan="2"></th><th colspan="4">2006</th><th colspan="4">2007</th><th colspan="4">2008</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="14"><strong>DDD TOBIT Placebo</strong></td></tr><tr><td colspan="14">Dwelling constructed between 1977 and 1988</td></tr><tr><td>Specif A</td><td>-0,02<br/>[-0, 22; 0, 13]</td><td>-0,01<br/>[-0, 22; 0, 12]</td><td>0,30<br/>[0, 01; 0, 46]</td><td><strong>0,13<br/>[-0, 23; 0, 13]</strong></td><td>-0,01<br/>[-0, 19; 0, 14]</td><td>-0,02<br/>[-0, 20; 0, 13]</td><td>0,14<br/>[-0, 03; 0, 39]</td><td><strong>0,12<br/>[-0, 22; 0, 14]</strong></td><td>0,18<br/>[-0, 03; 0, 35]</td><td>0,18<br/>[-0, 02; 0, 35]</td><td>0,39<br/>[0, 10; 0, 53]</td><td><strong>0,19<br/>[0, 07; 0, 35]</strong></td></tr><tr><td>Specif B</td><td>-0,03<br/>[-0, 21; 0, 14]</td><td>-0,04<br/>[-0, 21; 0, 13]</td><td><strong>0,25<br/>[0, 03; 0, 38]</strong></td><td></td><td>0,02<br/>[-0, 21; 0, 16]</td><td>0,07<br/>[-0, 22; 0, 15]</td><td><strong>0,20<br/>[-0, 05; 0, 34]</strong></td><td></td><td>0,22<br/>[-0, 05; 0, 32]</td><td>0,15<br/>[-0, 05; 0, 31]</td><td><strong>0,41<br/>[0, 07; 0, 46]</strong></td><td></td></tr><tr><td>Specif C</td><td>-0,10<br/>[-0, 57; -0, 02]</td><td><strong>-0,09<br/>[-4, 80; -0, 03]</strong></td><td></td><td></td><td>-0,01<br/>[-0, 41; 0, 15]</td><td><strong>-0,04<br/>[-0, 76; 0, 10]</strong></td><td></td><td></td><td>0,12<br/>[-0, 40; 0, 28]</td><td><strong>0,10<br/>[-0, 93; 0, 27]</strong></td><td></td><td></td></tr><tr><td><strong>No matching</strong></td><td>-0,08<br/>[-0, 25; 0, 08]</td><td>-0,08<br/>[-0, 25; 0, 07]</td><td>0,52<br/>[-0, 07; 0, 63]</td><td><strong>-0,08<br/>[-0, 07; 0, 50]</strong></td><td>0,02<br/>[-0, 17; 0, 18]</td><td>0,01<br/>[-0, 19; 0, 16]</td><td>0,56<br/>[0, 14; 0, 66]</td><td>0,02<br/>[-0, 18; 0, 17]</td><td>0,15<br/>[-0, 03; 0, 27]</td><td>0,15<br/>[-0, 03; 0, 27]</td><td>0,29<br/>[0, 10; 0, 70]</td><td>0,15<br/>[-0, 03; 0, 27]</td></tr><tr><td><em>Contacts</em></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td>x</td><td></td></tr><tr><td colspan="14"><em>Augmented regression</em></td></tr><tr><td>House</td><td></td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td></td><td>x</td><td></td></tr><tr><td>Household's composition</td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td>x</td><td>x</td><td></td><td>x</td><td></td><td>x</td><td></td></tr></tbody></table>

Note: 95% Confidence intervals are reported in brackets below the coefficient.

They are calculated by bootstrap (100 replications to be done).

Source: fiscal data from 2006 to 2009 on households owning for 5 years or less.

Authors' own calculation.
# D Difference in differences in non linear models

We take the Rubin framework of potential outcomes. Let $Y_t^I$ be the potential investment of the treated if they are treated and $Y_t^0$ if they are not treated, with $Y_t^I = w(40)$ and $Y_t^0 = w(25)$. We assume potential outcomes are linear:

$$Y_t^I = \alpha + \beta_t + \beta_{C1} + \beta_{C2} + \beta_{C1C2} + \delta + \theta X + \epsilon$$

$$Y_t^0 = \alpha + \beta_t + \beta_{C1} + \beta_{C2} + \beta_{C1C2} + \theta X + \epsilon$$

where $\beta_k$ is the fixed effect of the dummy $k$, $X$ is a vector of covariates and $\epsilon$ is an error term.

We observe

$$Y_t = I.Y_t^I + (1-I).Y_t^0$$

where $I = 1$ if the household is treated in $t$. We are interested in the difference of potential outcome of the treatment group with and without treatment:

$$\delta = E[Y^1|T=1, G=1] - E[Y^0|T=1, G=1] \quad (31)$$

where $T = 1$ for periods after the treatment and $G = 1$ if the households is in the treatment group. $TG = I$. In linear model (and under identifying conditions), it can be simply computed as differences in conditional expectations and estimated with ordinary least squares. In non-linear models the treatment effect is not equal to the interaction term coefficient (Ai and Norton 2003, Puhani 2012). Indeed the treatment effect is equal to:

$$\delta = E[Y^1|T=1, G=1] - E[Y^0|T=1, G=1] \\ = \Phi(\alpha + \beta_T + \beta_G + \gamma + X\beta) - \Phi(\alpha + \beta_T + \beta_G + X\beta)$$

where $\Phi(.)$ is the conditional distribution function of the residuals. When $X_k$ is a dummy, the coefficient $\beta_k$ can be calculated as the difference between the conditional expectation taken at $X_k = 1$ and $X_k = 0$. Here the interaction term coefficient is equal to the cross difference of the conditional expectation of $Y$ with respect to $T$ and $G$:

$$\frac{\Delta^2 E[Y|Y, G, X]}{\Delta T \Delta G} = [\Phi(\alpha + \beta + \gamma + X\theta) - \Phi(\beta + X\theta)] - [\Phi(\alpha + X\theta) - \Phi(X\theta)] \\ \neq \delta$$

The treatment effect $\delta$ is zero if and only if $\gamma$ is equal to zero and have the same sign as $\gamma$ ($\Phi(.)$ strictly monotonic).

Puhani (2012) showed that the treatment effect can be computed as the difference between the cross difference of the conditional expectation of the observed outcome $Y$
and the cross difference of the conditional expectation of the counterfactual outcome $Y_0$.
Noting that:

$$ \frac{\Delta^2 E[Y^0|Y, G, X]}{\Delta T \Delta G} = [\Phi(\alpha + \beta + X\theta) - \Phi(\beta + X\theta)] + [\Phi(\alpha + X\theta) - \Phi(X\theta)] $$
He deducts,
$$ \frac{\Delta^2 E[Y|Y, G, X]}{\Delta T \Delta G} - \frac{\Delta^2 E[Y^0|Y, G, X]}{\Delta T \Delta G} = \Phi(\alpha + \beta + \gamma + X\theta) - \Phi(\alpha + \beta + X\theta) $$
$$ = \delta $$

This result holds for to all non linear models with a parametric structure where $\Phi(.)$ is a nonlinear strictly monotonic transformation function.

Following this, the difference in difference Tobit treatment effect is:

$$ \begin{aligned} \delta &= E[Y^1|T=1.G=1] - E[Y^0|T=1.G=1] \\ &= \alpha + \beta_T + \beta_G + \gamma + X\theta + \sigma\lambda \left( \frac{\alpha + \beta_T + \beta_G + \gamma + X\beta}{\sigma} \right) \\ &\quad - \left[ \alpha + \beta_T + \beta_G + \gamma + X\theta + \sigma\lambda \left( \frac{\alpha + \beta_T + \beta_G + \gamma + X\beta}{\sigma} \right) \right] \\ &= \gamma + \sigma \left[ \lambda \left( \frac{\alpha + \beta_T + \beta_G + \gamma + X\beta}{\sigma} \right) - \lambda \left( \frac{\alpha + \beta_T + \beta_G + X\beta}{\sigma} \right) \right] \end{aligned} $$

where $\lambda(x)$ is the inverse Mills ratio. From this we see that $\gamma$ is a biased estimate of the treatment effect.
<table>
  <tr>
    <td>G 9001</td>
    <td>J. FAYOLLE et M. FLEURBAEY<br>Accumulation, profitabilité et endettement des entreprises</td>
    <td></td>
    <td>Macro-economic import functions with imperfect competition - An application to the E.C. Trade</td>
  </tr>
  <tr>
    <td>G 9002</td>
    <td>H. ROUSSE<br>Détection et effets de la multicolinéarité dans les modèles linéaires ordinaires - Un prolongement de la réflexion de BELSLEY, KUH et WELSCH</td>
    <td>G 9203</td>
    <td>I. STAPIC<br>Les échanges internationaux de services de la France dans le cadre des négociations multilatérales du GATT<br>Jun 1992 (1ère version)<br>Novembre 1992 (version finale)</td>
  </tr>
  <tr>
    <td>G 9003</td>
    <td>P. RALLE et J. TOUJAS-BERNATE<br>Indexation des salaires : la rupture de 1983</td>
    <td>G 9204</td>
    <td>P. SEVESTRE<br>L'économie sur données individuelles-temporelles. Une note introductive</td>
  </tr>
  <tr>
    <td>G 9004</td>
    <td>D. GUELLEC et P. RALLE<br>Compétitivité, croissance et innovation de produit</td>
    <td>G 9205</td>
    <td>H. ERKEL-ROUSSE<br>Le commerce extérieur et l'environnement international dans le modèle AMADEUS (réestimation 1992)</td>
  </tr>
  <tr>
    <td>G 9005</td>
    <td>P. RALLE et J. TOUJAS-BERNATE<br>Les conséquences de la désindexation. Analyse dans une maquette prix-salaires</td>
    <td>G 9206</td>
    <td>N. GREENAN et D. GUELLEC<br>Coordination within the firm and endogenous growth</td>
  </tr>
  <tr>
    <td>G 9101</td>
    <td>Équipe AMADEUS<br>Le modèle AMADEUS - Première partie - Présentation générale</td>
    <td>G 9207</td>
    <td>A. MAGNIER et J. TOUJAS-BERNATE<br>Technology and trade: empirical evidences for the major five industrialized countries</td>
  </tr>
  <tr>
    <td>G 9102</td>
    <td>J.L. BRILLET<br>Le modèle AMADEUS - Deuxième partie - Propriétés variantielles</td>
    <td>G 9208</td>
    <td>B. CREPON, E. DUGUET, D. ENCAOUA et P. MOHNEN<br>Cooperative, non cooperative R &amp; D and optimal patent life</td>
  </tr>
  <tr>
    <td>G 9103</td>
    <td>D. GUELLEC et P. RALLE<br>Endogenous growth and product innovation</td>
    <td>G 9209</td>
    <td>B. CREPON et E. DUGUET<br>Research and development, competition and innovation: an application of pseudo maximum likelihood methods to Poisson models with heterogeneity</td>
  </tr>
  <tr>
    <td>G 9104</td>
    <td>H. ROUSSE<br>Le modèle AMADEUS - Troisième partie - Le commerce extérieur et l'environnement international</td>
    <td>G 9301</td>
    <td>J. TOUJAS-BERNATE<br>Commerce international et concurrence impartie : développements récents et implications pour la politique commerciale</td>
  </tr>
  <tr>
    <td>G 9105</td>
    <td>H. ROUSSE<br>Effets de demande et d'offre dans les résultats du commerce extérieur manufacturé de la France au cours des deux dernières décennies</td>
    <td>G 9302</td>
    <td>Ch. CASES<br>Durées de chômage et comportements d'offre de travail : une revue de la littérature</td>
  </tr>
  <tr>
    <td>G 9106</td>
    <td>B. CREPON<br>Innovation, taille et concentration : causalités et dynamiques</td>
    <td>G 9303</td>
    <td>H. ERKEL-ROUSSE<br>Union économique et monétaire : le débat économique</td>
  </tr>
  <tr>
    <td>G 9107</td>
    <td>B. AMABLE et D. GUELLEC<br>Un panorama des théories de la croissance endogène</td>
    <td>G 9304</td>
    <td>N. GREENAN - D. GUELLEC / G. BROUSSAUDIER - L. MIOTTI<br>Innovation organisationnelle, dynamisme technologique et performances des entreprises</td>
  </tr>
  <tr>
    <td>G 9108</td>
    <td>M. GLAUDE et M. MOUTARDIER<br>Une évaluation du coût direct de l'enfant de 1979 à 1989</td>
    <td>G 9305</td>
    <td>P. JAILLARD<br>Le traité de Maastricht : présentation juridique et historique</td>
  </tr>
  <tr>
    <td>G 9109</td>
    <td>P. RALLE et alii<br>France - Allemagne : performances économiques comparées</td>
    <td>G 9306</td>
    <td>J.L. BRILLET<br>Micro-DMS : présentation et propriétés</td>
  </tr>
  <tr>
    <td>G 9110</td>
    <td>J.L. BRILLET<br>Micro-DMS<br>NON PARU</td>
    <td>G 9307</td>
    <td>J.L. BRILLET<br>Micro-DMS - variantes : les tableaux</td>
  </tr>
  <tr>
    <td>G 9111</td>
    <td>A. MAGNIER<br>Effets accélérateur et multiplicateur en France depuis 1970 : quelques résultats empiriques</td>
    <td>G 9308</td>
    <td>S. JACOBZONE<br>Les grands réseaux publics français dans une perspective européenne</td>
  </tr>
  <tr>
    <td>G 9112</td>
    <td>B. CREPON et G. DUREAU<br>Investissement en recherche-développement : analyse de causalités dans un modèle d'accélérateur généralisé</td>
    <td>G 9309</td>
    <td>L. BLOCH - B. CŒURÉ<br>Profitabilité de l'investissement productif et transmission des chocs financiers</td>
  </tr>
  <tr>
    <td>G 9113</td>
    <td>J.L. BRILLET, H. ERKEL-ROUSSE, J. TOUJAS-BERNATE<br>"France-Allemagne Couplées" - Deux économies vues par une maquette macro-économétrique</td>
    <td>G 9400</td>
    <td>L. BLOCH, J. BOURDIEU, G. LONGUEVILLE<br>Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté</td>
  </tr>
  <tr>
    <td>G 9201</td>
    <td>W.J. ADAMS, B. CREPON, D. ENCAOUA<br>Choix technologiques et stratégies de dissuasion d'entrée</td>
    <td>G 9401</td>
    <td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'aide au logement - quelques éléments d'évaluation</td>
  </tr>
  <tr>
    <td>G 9202</td>
    <td>J. OLIVEIRA-MARTINS, J. TOUJAS-BERNATE</td>
    <td>G 9402</td>
    <td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9403</td>
    <td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique de l'hôpital public</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9404</td>
    <td>L. BLOCH, J. BOURDIEU, B. COLIN-SEDILLOT, G. LONGUEVILLE<br>Les déchets d'emballages ménagers : une analyse économique des politiques française et allemande</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9405</td>
    <td>V. MAILLARD<br>Theorie et pratique de la correction des effets de jours ouvrables</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9406</td>
    <td>F. ROSENWALD<br>La décision d'investir</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9407</td>
    <td>S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique de l'hôpital public</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9408</td>
    <td>L. BLOCH, J. BOURDIEU, B. COLIN-SEDILLOT, G. LONGUEVILLE<br>Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9409</td>
    <td>D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'aide au logement - quelques éléments d'évaluation</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9410</td>
    <td>F. ROSENWALD<br>Suivi conjoncturel de l'investissement</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9411</td>
    <td>C. DEFEUILLE - Ph. QUIRION<br>Les déchets d'emballages ménagers : une analyse économique des politiques française et allemande</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9412</td>
    <td>J. BOURDIEU - B. CŒURÉ - B. COLIN-SEDILLOT<br>Investissement, incertitude et irréversibilité<br>Quelques développements récents de la théorie de l'investissement</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9413</td>
    <td>B. DORMONT - M. PAUCHET<br>L'évaluation de l'élasticité emploi-salaire dépend-elle des structures de qualification ?</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9414</td>
    <td>I. KABLA<br>Le Choix de breveter une invention</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9501</td>
    <td>J. BOURDIEU - B. CŒURÉ - B. SEDILLOT<br>Irreversible Investment and Uncertainty: When is there a Value of Waiting?</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9502</td>
    <td>L. BLOCH - B. CŒURÉ<br>Imperfections du marché du crédit, investissement des entreprises et cycle économique</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9503</td>
    <td>D. GOUX - E. MAURIN<br>Les transformations de la demande de travail par qualification en France<br>Une étude sur la période 1970-1993</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9504</td>
    <td>N. GREENAN<br>Technologie, changement organisationnel, qualifications et emploi : une étude empirique sur l'industrie manufacturière</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9505</td>
    <td>D. GOUX - E. MAURIN<br>Persistance des hiérarchies sectorielles de salaires: un réexamen sur données françaises</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9505</td>
    <td>D. GOUX - E. MAURIN<br>Persistance de inter-industry wages differentials: a reexamination on matched worker-firm panel data</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9506</td>
    <td>S. JACOBZONE<br>Les liens entre RMI et chômage, une mise en perspective<br>NON PARU - article sorti dans Économie et Prévision n° 122 (1996) - pages 95 à 113</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9507</td>
    <td>G. CETTE - S. MAHFOUZ<br>Le partage primaire du revenu<br>Constat descriptif sur longue période</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9601</td>
    <td>Banque de France - CEPREMAP - Direction de la Prévision - Érasme - INSEE - OFCE<br>Structures et propriétés de cinq modèles macro-économiques français</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9602</td>
    <td>Rapport d'activité de la DESE de l'année 1995</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9603</td>
    <td>J. BOURDIEU - A. DRAZNIEKS<br>L'octroi de crédit aux PME : une analyse à partir d'informations bancaires</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9604</td>
    <td>A. TOPIOL-BENSAÎD<br>Les implantations japonaises en France</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9605</td>
    <td>P. GENIER - S. JACOBZONE<br>Comportements de prévention, consommation d'alcool et tabagie : peut-on parler d'une gestion globale du capital santé ?<br>Une modélisation microéconométrique empirique</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9606</td>
    <td>C. DOZ - F. LENGLART<br>Factor analysis and unobserved component models: an application to the study of French business surveys</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>G 9607</td>
    <td>N. GREENAN - D. GUELLEC<br>La théorie coopérative de la firme</td>
  </tr>
</table>

françaises : une évaluation empirique des théories de la structure optimale du capital

L. BLOCH - B. CŒURÉ<br>Q de Tobin marginal et transmission des chocs financiers

Équipes Amadeus (INSEE), Banque de France, Métric (DP)<br>Presentation des propriétés des principaux modèles macroéconomiques du Service Public

B. CREPON - E. DUGUET<br>Research & Development, competition and innovation

B. DORMONT<br>Quelle est l'influence du coût du travail sur l'emploi ?

D. BLANCHET - C. BROUSSE<br>Deux études sur l'âge de la retraite

D. BLANCHET<br>Répartition du travail dans une population hétérogène : deux notes

D. EYSSARTIER - N. PONTY<br>AMADEUS - an annual macro-economic model for the medium and long term

G. CETTE - Ph. CUNÉO - D. EYSSARTIER - J. GAUTIÉ<br>Les effets sur l'emploi d'un abaissement du coût du travail des jeunes

D. BLANCHET<br>Les structures par âge importent-elles ?

J. GAUTIÉ<br>Le chômage des jeunes en France : problème de formation ou phénomène de file d'attente ?<br>Quelques éléments du débat

P. QUIRION<br>Les déchets en France : éléments statistiques et économiques

D. LADIRAY - M. GRUN-REHOMME<br>Lissage par moyennes mobiles - Le problème des extrémités de série

V. MAILLARD<br>Theorie et pratique de la correction des effets de jours ouvrables

F. ROSENWALD<br>La décision d'investir

S. JACOBZONE<br>Les apports de l'économie industrielle pour définir la stratégie économique de l'hôpital public

L. BLOCH, J. BOURDIEU,

B. COLIN-SEDILLOT, G. LONGUEVILLE<br>Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté

D. EYSSARTIER, P. MAIRE<br>Impacts macro-économiques de mesures d'aide au logement - quelques éléments d'évaluation

F. ROSENWALD<br>Suivi conjoncturel de l'investissement

C. DEFEUILLE - Ph. QUIRION<br>Les déchets d'emballages ménagers : une analyse économique des politiques française et allemande