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# Gender Inequality after Divorce: The Flip Side of Marital Specialization Evidence from a French Administrative Database

Carole Bonnet, Bertrand Garbinti, Anne Solaz

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Carole Bonnet, Bertrand Garbinti, Anne Solaz. Gender Inequality after Divorce: The Flip Side of Marital Specialization Evidence from a French Administrative Database. 2016. hal-05417159

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Direction des Études et Synthèses Économiques

G 2016 / 03

Gender Inequality after Divorce:
The Flip Side of Marital Specialization
Evidence from a French Administrative Database

Carole BONNET, Bertrand GARBINTI et Anne SOLAZ

Document de travail

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Institut National de la Statistique et des Études Économiques
INSTITUT NATIONAL DE LA STATISTIQUE ET DES ÉTUDES ÉCONOMIQUES

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

G 2016 / 03

Gender Inequality after Divorce:
The Flip Side of Marital Specialization
Evidence from a French Administrative Database

Carole BONNET*, Bertrand GARBINTI** et Anne SOLAZ*

MARS 2016

The authors are grateful to participants of the ESPE and EALE-SOLE Conferences, to seminar participants at the Economic Studies Department at Insee, at the Applied Economics Seminar at Paris School of Economics and at the Internal Seminar LMI-LEPP at Crest. They are also thankful to Thomas DERDYON, Xavier D'HAULTFOEUILLE, Lucie GONZALEZ, Malik KOUBI, Stéfan LOLLIVIER, Thomas PIKETTY and Corinne PROST for their helpful comments and suggestions. They are also grateful to Jérôme ACCARDO and Cédric HOUDRÉ for having facilitated the data access.

<footnote>* Ined</footnote>

<footnote>** Insee - Dese - Département des Études Économiques - Division « Redistribution et Politiques Sociales »
Timbre G210 - 15, bd Gabriel Péri - BP 100 - 92244 MALAKOFF CEDEX
Crest</footnote>
# Inégalités économiques entre hommes et femmes
après le divorce : le revers de la spécialisation conjugale

## Résumé

Les conséquences économiques d'un divorce peuvent être très différentes pour les deux ex-
époux et de nombreuses études étrangères mettent en évidence une baisse importante du
niveau de vie des femmes et une stabilité voire une hausse de celui des hommes. Pour
éclairer cette question dans le contexte français, nous utilisons les données fiscales de
l'impôt sur le revenu et de la taxe d'habitation relatifs aux couples qui ont divorcé ou rompu
leur PACS en 2009. En les comparant avec des couples aux caractéristiques identiques
avant séparation et qui, eux, sont restés mariés, nous montrons que le divorce est à l'origine
d'une perte de niveau de vie moyenne de 19 % pour les femmes, nettement supérieure à
celle des hommes (2,5 %). Nos résultats montrent que ce n'est pas le nombre d'enfants qui
a l'effet le plus important sur ces variations mais que ce sont les différences de revenus et
d'activité entre époux avant la séparation, celles-ci pouvant résulter des choix de
spécialisation du couple pendant le mariage. Les transferts publics jouent un rôle important,
de même que les transitions sur le marché du travail. Ainsi, on observe un retour massif sur
le marché du travail des ex-époux qui étaient sans activité un an avant le divorce,
principalement des femmes. L'existence de transferts publics et les ajustements
professionnels atténuent fortement sans toutefois compenser totalement la différence de
pertes de niveaux de vie entre les hommes et les femmes.

**Mots-clés** : Divorce, niveaux de vie, inégalités homme femme, pension alimentaire, famille monoparentale, offre de travail

## Gender Inequality after Divorce:
The Flip Side of Marital Specialization
Evidence from a French Administrative Database

## Abstract

If divorce has large consequences on both ex-spouses, its economic impact may be very different for each partner. It is well-documented that women generally experience a large decrease of their living standards after union dissolution, whereas men's living standards are often presented as stable or increase. Thanks to a very rich administrative dataset on French couples who broke up their marriage in 2009, matched with identical still married spouses, we interestingly show that both women and men support a loss in average, but still of larger magnitude for women (19%) than for men (2.5%). Results show that the number of children only plays a minor role in the explanations of the large women's impoverishment. The share of couple's resources each spouse provides before divorce, resulting mainly from marital specialization, is the main driver. Child support payments, public transfers and the massive labor market reentry of inactive women mitigate but do not cancel post-divorce gender inequalities.

**Keywords:** Divorce, living standards, gender inequalities, child support payment, alimony, lone parents, labor supply

**Classification JEL:** J12, J16, K36, I38
# 1 Introduction

Divorce has large consequences on both ex-spouses, but its economic impact may be very different for each partner. It is well-documented that women generally experience a large decrease of their living standards, with a higher risk of entering poverty after union dissolution, whereas men's living standards are often presented as stable or higher on average. By raising both poverty and gender inequality, divorce carries important welfare issues. These post-divorce gender inequalities are often explained by mothers being more likely the custodial parents. Having their children at home most of the time, the custodial parents generally bear more expenses and have less opportunity to return to or to remain on the labor market on a full-time basis. However, living arrangements for children raised by divorced or separated parents have changed dramatically over last decades. In many countries, a growing share of separated parents adopts more equal arrangements such as shared custody. Could we expect this trend of a more equal share of children costs to reduce gender inequalities in living standards?

To answer this, we need to look deeper into the components of living standards of divorcees and how they change following divorce. Several factors play a significant role and might interact with the custodial parent status. Four main components can be distinguished. The first one is composed of earnings and replacement income. These labor market resources are related to both past and current situations. The marital specialization could have created or increased an unbalanced sharing of labor market incomes between partners. Women who withdrew from the labor market or reduced significantly their working hours during marital life earn less than men at the moment of divorce. This marital specialization effect is possibly amplified by additional gender wage discriminations. After divorce, women can decide to reenter the labor market or increase their working hours.

Secondly, public transfers (such as housing allowances, minimum welfare, lone parent allowance, family allowance...), that mostly benefit to poorest divorcees, can reduce the income loss shock due to divorce, and thus the risk of poverty. The third element is
private transfers between former spouses. Generally given by the non-custodial parent to the custodial one, the child support participates to balance the child(ren) expenses between the two parents. A spousal alimony could also partly compensate the unbalance sharing of incomes after divorce, which is due to different marital investments decided during the union.

Finally, the size of the household could also play a role. The number and age of children living in the household after the divorce and the time they spend with each parent could affect the living standards.

Why women are poorer than men after divorce? Which component of living standard plays the bigger role in the variation observed? What is divorce responsible for? Answering these questions is difficult because of the interrelationships of these four determinants in such a way that it is challenging to know which one might be the main responsible of women's poverty after divorce. Another difficulty arises as, generally, not all of them are simultaneously observed in data sources. More specifically there is a lack of data on private transfers between former partners. A last issue is to assess a causal effect of divorce, which requires to go beyond the classical but sometimes misleading before-after comparison of living standards. Thanks to a very rich administrative dataset on 92,000 couples who split from marriage or civil partnership in 2009, this article measures and analyses the variations in living standards for both spouses, the role of the different components and more specifically the labor market behavior. We focus on individuals who do not repartner or cohabit the year following divorce. By matching these divorcees to still married spouses who are identical to them on a large range of characteristics, and using a difference-in-differences approach, we are also able to further assess a causal effect of divorce on living standards and on labor market behavior of men and women after divorce.

Our findings confirm that French women still support larger losses on average than men after divorce (respectively -19% compared to -2.5%). Contrary to the common belief, they also suggest that the custodial status plays only a minor role in the explanations of
the large women's impoverishment. The before-after divorce living standard variations
are similar whatever the number of children. The higher losses of large families are com-
pensated by higher public transfers and welfare benefits in France. On the contrary, the
differences in earnings between partners while married, partly due to the marital spe-
cialization process, are by far the major determinant of gender inequalities after divorce,
only partially compensated by an increase in labor force participation.

2 Previous studies

The literature on the economic consequences of divorce is quite extensive. To sum it
up in one sentence, it emphasizes the gendered economic consequences of union dissolu-
tion, showing generally a worsening of women's living standards after separation, whereas
those of men remain stable or increase.

The first studies on the variation of living standards following divorce were done in the US.
According to Duncan and Hoffman (1985), on the “Panel Study of Income Dynamics”,
women experience a decrease in their adjusted income by about 25% following divorce
and men experience an increase by about 3%. Peterson (1996) concludes that women
experience a decrease by 27% of their adjusted income while men experience an increase
by 10%. But he highlights that for ten percent of women (and 9% of men), the decrease
reaches 73% or more, emphasizing the necessity to look not only at the median or average
variation but to consider also its distribution. Other assessments on American data agree
upon a significant decrease in the living standard of women following divorce (table A.1).
However, the magnitude of the variation in living standard they report differ. This ranges
from 10% (Hoffman (1977) and Duncan and Hoffman (1985)) to 36% (Bianchi, Subaiya,
and Kahn (1999)). Holden and Smock (1991) or McKeever and Wolfinger (2001) surveys
report the same order of magnitude (from 10% to 40%).

Outside US, works on Canadian and European data come to the same range of conclusion.
Regarding France, on the European Community Household Panel (ECHP) and the 1994-
2000 period, Uunk (2004) reports one of the highest variation among European countries
with a 30% decrease in women's adjusted median income following divorce. Considering all couple dissolutions, not only divorces, and on more recent data (2003-2005, French part of SILC), Jauneau and Raynaud (2009) also report comparable figures with a median decrease in living standard following a separation of 31% for women remained alone after the separation.

For men, the interest in the economic consequences of divorce came later and the results so far are more ambiguous. The question is more about the sign of the variation than on the magnitude of the effect (table A.1). Most studies found a significant improvement in living standard following divorce (Finnie (1993), Bianchi, Subaiya, and Kahn (1999), Smock (1993) and Smock (1994) who found particularly large variations ranging from +47 to +93%). Some studies conclude rather to a stability (Jarvis and Jenkins (1999), Poortman (2000), Kalmijn and Alessie (2008)) whereas few of them find a deterioration (Burkhauser, Duncan, Hauser, and Berntsen (1991), McManus and DiPrete (2001), Jauneau and Raynaud (2009)).

The difference in these results might come from factors of different nature.

First, of course both period and institutional aspects can participate to explain country-specific results. Welfare systems are more or less generous towards lone parents and public transfers differ from one country to another. This is the point highlighted by Uunk (2004) who focuses on the differences in European welfare systems. He reports that the economic consequences of divorce are lower in countries where social welfare and public childcare are the most important.

Furthermore, both the child alimony decisions and their recovery rate are very country-specific and might drive differences in private transfers. Regarding the definition of living standards, the role of private - in particular spousal and child support payments, and public transfers in balancing the negative consequences of divorce for women (or conversely in making the situation worse for men) has been rarely explored. There are only a few studies that compare income variations before and after taking private and public trans-
fers into account. On German and American data, Burkhauser, Duncan, Hauser, and Berntsen (1991) show that following divorce, once private transfers included, women's living standards in the US decrease by 37% without public transfers and by 24% with them, while in Germany the decrease reaches 44% including or not public transfers. On Dutch data, Poortman (2000) shows that the decrease in the living standard of women excluding private and public transfers is about 65% and 31% if these transfers are included (for men respectively -18% and +4%). Using data from the 1994-2000 ECHP survey, Uunk (2004) compares the short-term economic consequences of divorce for women across 14 Member States of the European Union. He shows that women in the European Union differ in the income changes they experience at divorce. The income decline is less important in countries with a high level of public childcare provision and higher social welfare payments, especially for lone parents. To our knowledge, the article from McManus and DiPrete (2001), focused on men, is the only one distinguishing the role of public and private transfers. They compare three income measures: total income before taxes and government transfers; household income including taxes and public transfers; household income after support payments and including taxes and public transfers and subtracting support payments. White men who contributed less than 60% to the former couple's household income experience an important living standard decline following separation (from 16% to 43% depending on the male share of pre-disruption income). The effects of taxes and transfers reduce their losses (the decrease then equals 12 to 34%). But this mitigation of the decrease by public transfers is offset by the negative effects of support payments. White men who contributed 80% or more to pre-disruption income -best representing the traditional male breadwinner model, experience a significant increase in their living standard (by about 22%). Including taxes and transfers reduces this gain by around a quarter. Finally deducting support payments from their income reduces their gain in standard living following separation to a statistically non-significant 1 percent.

Measurements of living standards might be also a crucial issue that might contribute to explain the heterogeneity of results even within the same country (and sometimes
with the same database). The magnitude of the effects might depend on the definition
of living standards. The sources of income included (private and public transfers), the
choice of the equivalence scale, of the indicator used (variation in the mean/median liv-
ing standard, median/mean variation in the living standards, see table A.1) or on the
sub-population studied (for example Black/White in the US) differ according to the stud-
ies. Of course, for an event such as divorce, the difficulty to have the two partners in
the survey sample and the small size of the sample may lead to inference issues (table A.2).

The reliability of the before-after estimator is also of concern. These estimators are
now well-known for being sensitive to the economic trend and to selection bias. Two
recent works from Bedard and Deschênes (2005) and Ananat and Michaels (2008) have
used intrumental variables to compute a causal effect of divorce. To deal with the prob-
lem of selection into divorce and separation, they use the sex of the first child as an
instrument. The instrumentation stems from the following result: when the first-born is
a girl, the couple is more likely to divorce. Bedard and Deschênes (2005) find a positive
gain for women at separation. With the same dataset, Ananat and Michaels (2008) show
evidence of a strong decrease in standards of living for women at the bottom of the dis-
tribution and a strong increase for those at the top. However, only a local interpretation
can be made of these findings. The results hold for the “compliers” ie for the couples who
break up when the first-born is a girl and who don’t if it is a boy. Even though internal
validity seems acquired, we face here the usual external validity problem. Another kind
of method has been considered by Ongaro, Mazzuco, and Meggiolaro (2008) on Italian
data and Aassve, Betti, Mazzuco, and Mencarini (2007a) on European data. They design
a “control group” composed of couples who don’t divorce and who have similar charac-
teristics as the divorced couples before the separation. Though neither of these studies
computes variation in living standards, they both conclude to a deterioration in women’s
economic situation.
As mentioned above, the magnitude of the effects might depend on the definition of living standards. In particular, up to now, spousal and child support payments have received relatively scant attention in the economic and demographic literature, doubtless for lack of suitable data. As rare studies conclude to, these private transfers may however represent a significant amount of household post-divorce income. Ignoring them could lead to an overstatement of the income decline of women, and symmetrically to an overstatement of income increase for men (Kalmijn and Alessie (2008)). Besides, when information on support payments is available in surveys, the custodial parent (in most cases the mother) more often declares the amount received than the non-custodial parent (the father) does. This could possibly lead to an unequal picture (Jarvis and Jenkins (1999)) when comparing men and women living standards.

Another explanation of the differences in the results regarding economic consequences of divorce in the literature could be the size of the samples used. Indeed, most studies rely on panel data and the relatively low occurrence of the event “divorce” or “separation” between two yearly waves of a panel survey induces a small sample size (Table A.2). Besides, attrition is often important in survey panel data, because of the likelihood to move after the separation. These moves may not be random since the decision to move or not after a divorce might be linked to financial constraints.

New administrative data, recently available in France, enable us to overcome these three main difficulties. We use the exhaustive database of French income tax returns and local residence tax returns. Data coming from tax returns are supposed to be more reliable than data reported in surveys, in particular because part of them are directly filed by employers for example. Besides, the data set gives information about the paid amount for child and spouse support. These transfers being tax deductible, the incentive to report them on the tax return form is pretty high.
For the year 2009, from this income tax returns database of French residents, we ex-
tract a population composed of the divorcees and of partners who broke a Pacs<fnref n="1" /> (the
French civil union contract). We restrict our sample to the couples who do not have
formed union in 2008 neither in 2009 in order to focus on couples who have lived together
for at least one year.<fnref n="2" /> Once excluded tax returns with missing data, we obtain a sample
composed of 126,250 couples who were married (in 2008) and of 9,800 who were linked
by a Pacs (in 2008)<fnref n="3" />, for which we recover at least one of the former partner the year
following the divorce. This sample size has to be compared with the comparable official
statistics regarding the number of divorces and ends of Pacs in France in 2009 (table
B.1). Our sample covers at least 95% of the total of divorces and more than 55% of the
ends of Pacs which took place in 2009 in France. We then match data before divorce
(2008) with data one year after divorce (2010), which leads to a little loss of individuals
(7.2%). Consequently we compute weights to take into account a potential differential
attrition bias (see appendix B for more details on the construction of the dataset and the
computation of weights). This big sample size is one of the significant advantages of our
database.

We then exclude from our sample individuals who either (re)marry, (re)pacs or cohabit
(with at least another adult in 2010) the year after the break up. They represent roughly
30% of separated individuals. This choice has been made to observe only individual
ressources, not biased from those that would bring a potential new partner in case of
repartnering. Of course, it is also for the sake of simplicity, because the observable situ-
ations we observe with cohabitation after divorce covers very different situations. People
could live in the same dwelling with either a new partner, a relative such as a parent, or
a friend, and no information about income pooling. It is then very difficult to estimate
living standards in case of new cohabitation. Finally, as we are interested in labor supply
after divorce, we focus on individuals aged between 20 and 55 years old. The upper age

<footnote n="1">¹For another reason than marriage or death of one partner.</footnote>

<footnote n="2">²Another reason is the difficulty to disentangle tax returns when several events occur the same year.</footnote>

<footnote n="3">³Partner linked by a Pacs, the French civil partnership, have to fill a joint tax return as married couples do.</footnote>
limit has been set to 55 because in France, pre-retirement and retirement for some spe-
cific occupations can start at that age. Our final sample includes 56,500 men and 64,600
women who experienced either a divorce or a Pacs break-up in 2008 and for whom we
get information from the income tax and the local tax residence returns in 2008 and 2010.

These income tax return do not collect data on family and welfare benefits as they are not
taxable, but the rich information available on income, family and housing situation al-
lows to calculate them, assuming that divorcees ask for their welfare benefits (see below).
We compute them using information on family composition (number, age of household
children, child(ren) custody arrangements) also available on the income tax file and in-
formation about the dwelling that is reported in the local tax returns. We compute four
types of family benefits (allocations familiales, allocation de rentrée scolaire, complément
familial, prestation d'accueil du jeune enfant (Paje)), housing allowances (allocation lo-
gement) for renters, and minimum income benefit (RMI in 2008 and RSA in 2010). Due
to the lack of information<fnref n="4" />, we do not compute Family Support Allowance (Allocation de
Soutien Familial). We assume here a 100 % take-up of these social and welfare benefits.
We know that non take-up can be important, especially for minimum income benefit
(RMI/RSA), inducing an overestimation of some of these benefits<fnref n="5" /> and also then of the
living standards. However, the situation of divorce generally involves that people are
more likely to meet and thus receive advices from the lawyers, the social worker or family
mediators and to be informed about available public benefits especially when they have
children. For this reason, we tend to believe that the take-up rate is probably high for
this population. These benefits are especially important for single families.

Of course, and it is an intrinsic limit of such administrative datasets, no other information
than those needed by the fiscal administration is available. We do not know anything

<footnote n="4">⁴The entitlement of this benefit is linked to the non-payment of child support. In our data, when no child support is received, it is not possible to distinguish between a non-payment or no child support decided in the divorce judgment.</footnote>

<footnote n="5">⁵An important feature for the short-term consequences of separation is that welfare benefits could be a bit higher than the years after : the minimum income benefit is increased the year following separation and means-tested condition is assessed on the income of two years ago. That means for example that child support received the year following divorce is not included in the means-tested conditions but will be two years after.</footnote>
about the education level for instance.

# 4 Framework & methods

## 4.1 Measuring the effects of public and private transfers on standards of living

To compare standards of living before and after divorce, we use three definitions of living standards including components step by step to assess their possible balancing effects. First we calculate living standards before any transfer, then living standards with private transfers only and, lastly, total living standards (ie with all transfers).

All living standards correspond to incomes divided by the number of consumption units<fnref n="6" /> to take into account the economies of scale resulting from the household size (see below). For the living standard before any transfer, the relevant income is the gross income. It includes labor market and replacement incomes (such as wage, self-employment income, unemployment benefits or public and private pensions<fnref n="7" />) and asset/capital incomes subject to tax. In the tax returns, these incomes are pre-tax and pre-transfer incomes.

To compute living standard with private transfers, we add to gross income the child support payments reported by both receiver (generally the custodial parent) and payer (generally the non-custodial parent). The receivers are required to report these transfers as income. For givers, these payments are deducted from taxable income, which gives a strong incentive to report them.

At last, to compute total living standard, we calculate disposal income. It corresponds to the sum of gross incomes, public transfers (net of taxes) and private transfers.

<footnote n="6">⁶As a benchmark, we use the “OECD modified equivalence scale”. We also test our results with other equivalence scales.</footnote>

<footnote n="7">⁷very rare given the age range of the population we study.</footnote>
## 4.2 The loss of economies of scale is not the whole story: the role of income sharing

Family composition changes a lot after a marital disruption and depends on which spouse is awarded child(ren) custody. In most cases, mothers are more likely to be the custodial parent, even if the percentage of shared custody has been recently increasing. Then to obtain comparable living standards for men and women, household composition (and more particularly whether post-divorce households include or not children) is taken into account using the “OCDE- modified equivalence scale”. This scale assigns a value of 1 to the household head, 0.5 to each additional adult member or child aged 14 and over and 0.3 to each younger child. In case of shared custody, the child(ren) weights are simply divided between both parents. Other equivalence scales such as the square root household size scale have also been tested to check the sensitivity of our results to this choice.

Furthermore, divorce also involves a loss of economies of scale. Some common costs such as housing were shared before divorce and are not any more afterwards. While a lot of attention has been paid to this loss of economies of scale due to the change in family composition, less attention has been paid to the role of pre-divorce incomes and, more specifically, to the share of earnings provided by each spouse. It turns out to be a main determinant of the changes in living standard, however.

To better understand its role, let us present a simple theoretical framework in which we consider a couple with N children less than 14 years old and earnings R, the living standard of each married partner is R/(1.5+0.3N) according to the OECD modified equivalence scale. After divorce, living standard for a custodial mother will be (1-α)R/(1+0.3N) where α stands for the share of income earned by the husband before the divorce.<fnref n="8" /> The living standard for the non-custodial parent will consequently be αR. We can then decompose the variation of living standards in the two following components: the change

<footnote n="8">⁸For the sake of simplicity, we assume here that there is no change in earnings before and after divorce, letting aside any variation in incomes or social benefits.</footnote>
in living standard due to the end of family live, and the change of custodial status (from non-custodial to custodial).

$$ \underbrace{\frac{(1-\alpha)R - \frac{R}{1.5+0.3N}}{\frac{R}{1.5+0.3N}}} = \underbrace{\frac{(1-\alpha)R - (1-\alpha)R}{\frac{R}{1.5+0.3N}}} + \underbrace{\frac{(1-\alpha)R - \frac{R}{1.5+0.3N}}{\frac{R}{1.5+0.3N}}} \quad (1) $$

From couple with children

From non-custodial

From couple with children

To custodial parent

To custodial parent

To non-custodial parent

(rescaled)

For a fixed share of income provided by the husband (hereafter called “income sharing”), the total variation in living standard appears to be little influenced by the number of children (see also figure 1). However, the comparison of the total variation in living standard with three different income sharings $\alpha$ shows the crucial importance of this parameter. While a woman who earns as much as her husband before the divorce ($\alpha=0.5$) will face a decrease from -31% with one child to -39% with 4 children, a woman who earns less than 20% of the total income before divorce will face a decrease from -72% with one child to -75% with 4 children. On the contrary, if she was the first provider of income in the couple, the variation will go from 11% with one child to -2% with 4 children.

It is quite clear from this simple theoretical example that the magnitude of living standard variation due to family size is really weaker than the one due to the income sharing.

The flat gradient for the number of children appears to be due to the fact that both large effects of being alone (from couple with children to non-custodial parent) and of being a custodial parent (from non-custodial parent to custodial parent) compensate each other.

This decomposition, though not taking into account variations nor in incomes neither in public and private transfers, highlights the fact that income sharing is a key parameter of any analysis of variation in living standard, relatively to family composition.
<figure><img src="image_5.png" /><figcaption>Figure 1: Decomposition of variations in living standards (LS) according to the number of children for several man's share of earnings before divorce</figcaption></figure>

## 4.3 The “pure” effect of divorce on living standards: a matching approach

The comparison of living standard before and after divorce describes the changes for divorcees but does not guaranty that these changes are due to the divorce. They might have
occurred anyway for this population. In recent studies, a topic of interest is to go further
and assess a proper effect of divorce on living standards. This question raised because
divorcees may be a selected population (in terms of income, marital specialization, age,
number of children, ...) and thus its specific characteristics have to be taken into account
in order to evaluate the real effect or the "causal" effect of separation on living standard.
This is the usual problem of selection, widely detailed in the public policy evaluation
literature.

Why could we face this problem? For instance if people tend to divorce more when they
are on a negative trend of income<sup>9</sup>, then we could observe a decrease in the living stan-
dard and a part of the observed decrease would not be due to the divorce but would be
the consequence of this negative trend. Another example is age: if divorce occurs more
often for young people and if, compared to the rest of the population, young people face
more difficulties on the job market, we could conclude that divorce leads to dramatically
decrease living standard when those decreases are partly reflecting the difficulties young
people encountered on the job market.

To deal with these possible selection problems, we use a matching approach. Initially
developed by Rosenbaum and Rubin (1983), this method is now widely used in the pub-
lic policy evaluation literature. Two recent studies used it on the field of divorce (Ongaro,
Mazzuco, and Meggiolaro (2008), Aassve, Betti, Mazzuco, and Mencarini (2007b)). The
idea of matching is the following: once taken into account a set of characteristics, the
event studied (here: divorce) becomes randomly distributed among the populations whose
characteristics are identical. In this population, some people will divorce (the “treated
group” as called in the public policy evaluation literature) and some won’t (the “control
group”). This assumption is called the Conditional Independence Assumption (CIA).
Several matching methods exist and share all the same purpose: to create a control group
(from a given population of “non treated” people) as close as possible to the treated one.
As a matching method, we chose a “nearest-neighbor” approach.<sup>10</sup> It consists in selecting

<footnote n="9"><sup>9</sup>Which is a plausible assumption since the effect of unemployment is generally negative for instance.</footnote>

<footnote n="10"><sup>10</sup>Without replacement.</footnote>
in the whole population of married people who don't divorce in 2009 couples with the same characteristics (“twins”) as the divorce couples.<fnref n="11" />

The CIA assumption is of course more credible when treated and control groups are similar on an important range of characteristics but it remains a strong assumption because unobservable heterogeneity can remain. If unobservable characteristics are linked with the probability of divorcing, then estimators will be biased.<fnref n="12" /> To overcome this issue, we use a difference in differences method applied to a matching method (DID-Matching approach). This two-step method has been widespread since Heckman, Ichimura, and Todd (1997) proposed it in order to relax the CIA assumption. This consists of computing for each group (treated and control) the difference between the outcomes in 2010 and 2008 and then subtracting these differences. In this situation, if unobservable characteristics are time-invariant, they are ruled out and the estimator is not biased anymore. More precisely, we estimate

$$ln(Y_{i,t}) = X_{i,0}\beta_0 + 1_{\{t \ge 1\}} * X_{i,0}\gamma + T_{i,t} * X_{i,0}\beta + a_i + e_{i,t} \quad (2)$$

where $Y_{i,t}$ stands for the living standard in period $t$ for individual $i$, $t = 0, 1$, $t$ the period (0 for the year before divorce, 1 for the year following divorce), $X_{i,0}$ for the couple characteristics the year before divorce (number of children, share of income provided by man, quintile of income), $T_{i,t}$ the treatment status (equal to 1 in period 1 for the “treated” group - the divorcees, and 0 otherwise), $a_i$ is a fixed effect corresponding to unobserved time-constant characteristics and $e_{i,t}$ is the idiosyncratic error term. In the DiD set up, thanks to the first difference, the unobserved characteristics are ruled out:

$$ln(Y_{i,1}) - ln(Y_{i,0}) = X_{i,0}\gamma + T_{i,1} * X_{i,0}\beta + (e_{i,1} - e_{i,0}) \quad (3)$$

<footnote n="11">¹¹To evaluate the difference between still-married couples and divorced one, we use a propensity score approach by computing for each couple a probability to divorce. Then, each divorced couple is matched with the married couple whose propensity score is the closest of his own. Propensity score is evaluated with a logit model.</footnote>

<footnote n="12">¹²Since the treated and the control groups may then differ for these characteristics not taken into account.</footnote>
It highlights the fact that if divorcees and married people differ in unobserved characteristics, it would not bias our result as long as those differences are constant between the two periods.

To assess mean changes in living standards (our main specification) we don't estimate directly equation (3) with an ordinary least squares regression. Had we run an OLS regression, we would have interpreted $exp(\hat{\beta}_{OLS}) - 1$ as the effect of the characteristics $X_{i,0}$ on the change in living standard. But it is an approximation.<fnref n="13" /> To avoid this approximation, we run a non-linear regression model:

$$ \frac{Y_{i,1}}{Y_{i,0}} = exp[X_{i,0}\gamma + T_{i,1} * X_{i,0}\beta + (e_{i,1} - e_{i,0})] \quad (4) $$

In this context, $\beta$ corresponds to the vector of proportional changes.

To estimate median changes in living standard (presented in the robustness checks section E), we use the log-specification since for each quantile $\tau: Q_{\tau}(\ln(X)) = \ln(Q_{\tau}(X))$, and so this specification does not suffer from the same approximation problem.

The more characteristics are identical between the treated and the control groups, the more credible the assumption is that they have the same propensity to divorce. Since we are able to choose a twin in the whole population of married (thanks to exhaustive fiscal data), we can find a neighbor similar on a lot of characteristics available in the database.

Regarding professional situations, we use the previous trends in income and the different kind of incomes each partner earns (wages, overtime work, unemployment benefits, pensions, self-employment profits, ...) that also enable us to take into account potential differences in behavior between occupations. We control for the residential area, ownership status and type of home. Regarding family characteristics, the date of marriage is not available. We use the age of both partners and the number and age of children, these are expected to be a good proxy for the marriage duration. We make the divorcees and their still-married 'twins' similar on demographic characteristics (age of man and woman, age of their child(ren), number of children), housing situation (homeownership, kind of

<footnote n="13">¹³Since by Jensen's Inequality, $exp(E(\ln(Y_1/Y_0))) \neq E(Y_1/Y_0)$.</footnote>
home: house or apartment) and living area (Paris, Parisian area "Ile de France", and elsewhere). They are also matched on similar economic and professional characteristics such as all incomes separately for man and woman (labor income, unemployment benefits, pensions, self-employment incomes), <fnref n="14" /> whether they were unemployed for a long period (more than one year), previous man's and woman's earnings two and three years before.

Since we intend to assess changes in living standard for different subsamples, we follow the recommendation by Dehejia (2005) and match each subsamples separately in order to ensure our matching process is as accurate as possible. Our subsamples are defined by combining gender (man and woman), number of children (0, 1, 2, 3 or more) and share of individual earnings<fnref n="15" /> provided by men relatively to total earnings of the couple, before divorce (3 shares: more than 60%, between 40 and 60% and less than 40%). So we match the divorced couples from each of these 24 subsamples with married couples who do not divorce in 2009.

In practice, this stratification offers two main advantages. First, it leads (by construction) to a perfect matching on the modality of the share of man, of the number of children for each gender, two determinants of our analysis. Second, since the empirical specification of the propensity may change for each subsample, it is then less parametric and takes better into account the heterogeneity of the behaviors than a global propensity score that would have been computed at a more aggregated level.

In order to assess the quality of the matching, we check the overlapping assumption and the balance of covariates. Some results and graphs presented in the appendix show that the overlapping assumption is very well verified and several methods (differences in means, standardized differences and comparison of distribution) prove that the covariates are well balanced. Full results on the matching quality are available on request.

Eventually, to compute standard errors and knowing that bootstrap is not an available

<footnote n="14">¹⁴Self-employment incomes are also detailed between profits from commercial and non-commercial occupations and farm profits.</footnote>

<footnote n="15">¹⁵We then take into account labor and replacement incomes but not capital incomes since it is not possible to individualize them from the tax return.</footnote>
option in our DID-matching situation, we use the subsampling method (cf. for instance Politis, Romano, and Wolf (1999), Romano and Shaikh (2012)).

# 5 Sample description of 2009 divorcees

Divorced women are younger than men because of the traditional age gap between spouses, two years on average (figure 2). For women, the riskier ages range from 40 to 50 years old.

<figure><img src="image_6.png" /><figcaption>Figure 2: Age distribution of divorced women and men in 2009</figcaption></figure>

Source: French exhaustive income-tax returns database, 2009

We also observe seasonal effects for divorce (figure 3). This seasonality is well-known for marriages (or forming a new civil partnership). It results from both a preference for marrying in summer rather than in winter, and also from some fiscal incentives<fnref n="16" /> for couples to marry at the middle of the calendar year (June and July) rather than at the beginning or at the end of the year (see Leturcq (2012) for instance for “Pacs couples”). Indeed, up to 2011, people could make three different tax returns the year they get married or divorced (one tax return for the income corresponding to their couple’s life and two distinct tax returns for the period they were single/divorced). If their income is quite regular over the year, it is optimal, regarding minimizing tax paid, to marry/divorce around the middle of the year. It is very interesting to note that fiscal incentives are also at

<footnote n="16">¹⁶These fiscal incentives exist in 2009 but have been suppressed in 2011.</footnote>
play for divorce. More divorces are also observed in January. Divorcees might be allowed by the fiscal administration to fill separate tax returns when the divorce procedure has begun but the court has not yet granted the judgment of divorce. In this case, the date of divorce is by default set to “the first of January” by the fiscal administration.

<figure><img src="image_7.png" /><figcaption>Figure 3: Seasonality of divorce: frequency by month, year 2009</figcaption></figure>

Source: French exhaustive income-tax returns database, 2009

As shown in table 1, 29% of divorcees were childless couples, 26% have one child, 31% two children and 13% a larger family. Concerning the child's place of residence, the mother is the custodial parent in 73% of cases, the father in 15% of cases and around 10% of divorced parents chose equally shared custody.

For custodian mothers, the median monthly amount of alimony paid by the father to the mother is 150 € per child. The first quartile is 100 € and the third quartile states at 250 €. It represents a significant share of mother's total income, roughly 13%.
<table><caption>Table 1: Households composition: number of children and child custody</caption><thead><tr><th></th><th>Frequency (%)</th></tr></thead><tbody><tr><td><strong>Number of children</strong></td><td></td></tr><tr><td>&lt; 18 y.o. one year before divorce</td><td></td></tr><tr><td>0</td><td>29</td></tr><tr><td>1</td><td>26</td></tr><tr><td>2</td><td>31</td></tr><tr><td>3</td><td>11</td></tr><tr><td>4</td><td>2</td></tr><tr><td>5+</td><td>0.5</td></tr><tr><td><strong>Child(ren) residence</strong></td><td></td></tr><tr><td>(at child level)</td><td></td></tr><tr><td>Primary the mother</td><td>76</td></tr><tr><td>Primary the father</td><td>9</td></tr><tr><td>Equally shared</td><td>15</td></tr></tbody></table>

# 6 Results

## 6.1 Changes in living standard by gender and number of children

To assess how divorce is related to changes in living standards, we compute the mean percentage change in living standard: $(Y_{i,t+1} - Y_{i,t-1})/Y_{i,t-1}$, where $Y_{i,t-1}$ is the living standard of individual $i$ in $t-1$, the year before divorce, divorce occurs in $t$, and $t+1$ stands for the year after divorce. We will also use the word variations in the rest of the article to qualify these percentage changes in living standards.

Figures 4 and 5 describe the distributions of living standards variations after divorce respectively for women and men, considering the three income measures we defined previously. As noted above in the literature review, different indicators could lead to different results in the magnitude of the variations. For example, we could have used the percentage change of the mean in living standard $(\bar{Y}_{t+1} - \bar{Y}_{t-1})/\bar{Y}_{T-1}$. This choice would have led to less negative variations for women and to less positive ones for men. In these figures, the black curve is the variation of living standards before any transfers. For women, the mass point at -100% (figure 4) reflects that a significant proportion of women do not earn any labor or capital income incomes at all after divorce (mainly those who opted
for a traditional model of specialization and were housewives during marriage). As ex-
pected, the addition of private transfers shifts the distribution to the right (dotted curve).
Custodial mothers receive a child support payment that increases their living standards.
Public transfers play an even bigger role in reducing the negative effects of divorce on
living standards (grey curve). Having custody of the children, few resources and being
single result in women receiving family and welfare benefits (especially minimum income
and housing allowances). After adding public transfers, no woman has a null income
anymore and the dispersion in the living standards changes has narrowed. However, 77%
of women have a loss of their living standards with the mode of the distribution at -20%.

<figure><img src="image_8.png" /><figcaption>Figure 4: Pre and post-government transfers variations in living standards for women</figcaption></figure>

Lecture: *Changes in living standards*

Source: Divorced women sample from French exhaustive income-tax returns database, 2009

For men, the story is different. First of all, usually, as they are net payers of child support, the inclusion of private transfers involves a reduction of their income. The distribution of variations of living standards is shifted to the left (dotted curve, figure 5). Public transfers and more specifically tax income lead to another shift on the left: when not the custodial parents (the more frequent post-divorce arrangement is the primary custody to the mother), they do not benefit from any family benefits and from the tax deduction associated with the care of the children. So all in all, adding private and public transfers
<figure><img src="image_9.png" /><figcaption>Figure 5: Pre and post-government transfers variations in living standards for men</figcaption></figure>

Lecture: *Changes in living standards*

Source: Divorced men sample from French exhaustive income-tax returns database, 2009

Lecture: On the X-axis, 0 indicates that these individuals experience no variation in their living standards. -1 means a loss of all the income (100% of loss)

mitigates the negative economic consequences of divorce for women while for men, it reduces their living standards variations. More than half of them (53%) face a loss in their living standards.

Looking at women and men distributions on the same graph is another way to highlight the effects of these transfers. Considering the first income measurement without any transfers, we observe that the distributions of changes in living standards for men and women do not share a large common support (figure 6). Women's distribution is skewed to the left (meaning large living standards losses) while the men's curve is more symmetrically distributed around 0, reflecting almost as many losses than gains. Women seem to support the largest part of the loss in economies of scale. Once both public and private transfers are taken into account, the two distributions are getting closer and are "tightened" around the mode. Transfers seem to offset large variations (either negative or positive ones). The proportion of women experiencing an increase in their living standards after divorce is still clearly much lower than the men's one. The gender gap
remains but is definitively less pronounced (figure 7).

<figure><img src="image_10.png" /><figcaption>Figure 6: Distribution of before-after living standard variations, according to gender - transfers excluded</figcaption></figure>

Source: Divorcees sample from French exhaustive income-tax returns database, 2009

Women experience on average a 35% decrease in their living standards when measured without any transfers, while men experience a 24% increase. Taking into account private transfers moderates the gender gap: the loss for women is -29% and the gain for men reduces to 15%. Finally after including public transfers and taxes, the mean loss of women reaches 14.5% and the changes in men's living standard of men is rather low: +3.5% (figure 8). For men, our results are close to previous results for France.<fnref n="17" /> Our result for women is a less pronounced loss than the one found by Uunk (2004) and Jauneau and Raynaud (2009) (that were respectively - 32% and -31%). They also go in the same direction as those found in other developed countries. In France as elsewhere, women seem to support the main losses after divorce.

So far, we compared living standards for divorced people before and after divorce. But to assess the proper effect of divorce we should compute the difference between the living

<footnote n="17">¹⁷On more limited samples, Jauneau and Raynaud (2009) found a change of -6% whereas we find, after matching, that men's living standard decrease by 3%.</footnote>
<figure><img src="image_11.png" /><figcaption>Figure 7: Distribution of before-after living standard variations, according to gender - transfers included</figcaption></figure>

Source: Divorcees sample from French exhaustive income-tax returns database, 2009

Lecture: On the X-axis, 0 indicates that these individuals experience no variation in their living standards. -1 means a loss of all the income (100% of loss)

standards before divorce and the living standard the divorcees would have reached if they had remained married over the period. We do so by comparing their situation with still married couples with similar characteristics (cf. section 4.3). The situation for both women and men worsens. The loss in living standards for women decreases from -14.5% to -19%. It means that if divorced women had remained married, their income would have increased in the following two years and not just remained stable as it is assumed when computing the before-after estimator. For men, compared to the before after estimator, it leads to a sign change. Comparing divorced men with their still married counterparts, their living standards variation from positive becomes negative and states about -2.5%. In other words, during the divorce period, the still married men encountered an increase in their living standards that divorcees did not.

For women, the loss with private transfers (but before public transfers) is sharply rising with the number of children (figure 9). When public transfers are added, the picture changes. Public transfers offset the increasing loss with family size for women and changes in their total living standards do not depend on the number of children anymore.
<figure><img src="image_12.png" /><figcaption>Figure 8: Pre and post-public transfers variations in living standards, by gender</figcaption></figure>

Source: Divorced women and men samples from French exhaustive income-tax returns database, 2009

For men, the picture is reversed. Before any transfers, an increasing gradient in gain appears with the number of children (figure 10). This increase is largely mechanical. Men are less likely to have children custody. So, after divorce (if we do not take into account private transfers), they do not share their income anymore. Remind that living standards already take into account the current household composition, and whether the children stay with the mother or father after divorce.

Private transfers reduce notably the gain. Public transfers play an even bigger role through two channels. The mothers receives family benefits as soon as she has child custody. Divorced men are not going to benefit anymore from the tax deduction for children (quite important for three children and over) called “quotient familial”. Indeed, after divorce, men have generally to pay more taxes since they do not have any dependent children at home anymore.

The conclusion about the effect of the number of children for women remains when compared with married “twins”: the loss in living standards is more pronounced but does not vary much with the number of children. For men with 1 or 2 children, what appears as an increase in living standards turns out to be a significant decrease (table D.1, column (8)). Eventually, men appear to experience losses in living standard except
when they have 3 children or more. But even in that case, the before-after gain of 12% is divided by 2.

<figure><img src="image_13.png" /><figcaption>Figure 9: Pre and post-public transfers variations in living standards for women, by number of children</figcaption></figure>

Source: Divorced women sample from French exhaustive income-tax returns database, 2009

<figure><img src="image_14.png" /><figcaption>Figure 10: Pre and post-public transfers variations in living standards for men, by number of children</figcaption></figure>

Source: Divorced men sample from French exhaustive income-tax returns database, 2009
<figure><img src="image_15.png" /><figcaption>Figure 11: Pre and post-public transfers variations in living standards for women, by share of earnings provided by man before separation</figcaption></figure>

Source: Divorced women sample from French exhaustive income-tax returns database, 2009

<figure><img src="image_16.png" /><figcaption>Figure 12: Pre and post-public transfers variations in living standards for men, by share of earnings provided by man before separation</figcaption></figure>

Source: Divorced men sample from French exhaustive income-tax returns database, 2009
## 6.2 The first order effect of marital specialization

Up to now, the literature has little focused on the share of income provided by each partner before the separation. To our knowledge, the sole exception is McManus and DiPrete (2001) who focus exclusively on men and show that, when formerly second earner in the couple, they face a loss in their living standards after divorce. Here, we look both at women and men to assess the importance of the marital specialization. Of course, the whole difference in the share of income provided by each ex-partner does not result from marital specialization. The difference may pre-exist to the union, linked to differences in the level of education for example or to the age difference between partners. Furthermore, gender wage discrimination on the labor market can also explain gender wage gap between spouses: for the same education level or experience in the labor market, we could also observe that women earn less than men (Meurs and Pontieux (2015)). But an important part of the difference (or the widening of an existing difference) results from marital specialization: one partner (generally the woman) is investing less in the labor market and more in domestic work, especially in children's education (Meurs and Pontieux (2015)). In the family economics literature, specialization is seen as a way to increase the gains from marriage. But what is supposed to be an efficient allocation of time when married turns out to have different impacts when the couple dissolves. To assess more precisely the economic consequences of marital specialization and, more broadly, of gender inequality in partner's earnings, we distinguish three types of couples: the "traditional" one where the man is the first income provider (providing more than 60%), the "egalitarian" one where man and woman provide roughly the same amount of income, and those, much more scarce, where woman is the first provider of income.

When women and men earn quite similar incomes, they both lose in living standards on average 13.6% for men and 16.3% for women. It means that the loss in economics of scale due to the end of common life is borne almost equally between the partners. The comparison with the control group enlarges the losses but they remain of similar magnitude for men and women. When men or women are the first income providers, they
generally experience a gain in their living standards (figures 11 & 12). This gain appears
to be of similar magnitude before the comparison with the control group. When women
provided more than 60% of the earnings before divorce, they experience an increase of
18% of their living standards. For men, this is a 17% increase. Once taken into account
of the control group, the gender differences are more pronounced with a gain almost
null (1%) for women, and of 10% for men For the second earner, the loss is also much
more pronounced for women (-21%) then for men (-8%). The larger losses or smaller
gains for women in case of unequal income sharing, might be related to their situation
on the labour market. The labour-market earnings and career advancement perspectives
of women who earn more than her husband spouse are probably lower than those of men
who earn more than his spouse, because of a possible glass ceiling for instance.

When compared to the evolution experienced by the still married “twins”, the gain for
women decreases from 18% to +1%. There is also a fall for men but of less importance
and they end up with a 10% increase (instead of the 17% reported above). Whatever the
type of couple, the comparison with the control group leads to more pronounced loss in
living standards. The most striking is for second earner men: the difference between the
before-after estimator and the causal effect of divorce is 12 percentage points, leading to a
decrease of -20%. We will see further that the labor market behavior plays an important
role in those variations (section 6.3).

It should be kept in mind that those shares are not uniformly distributed among the
couples (table 2). Only in a minority of divorced households man was not the first
income provider (13%), whereas the proportion of households with a traditional division
of tasks (where man earned more than 60 % of couple's income) is 53%.

Notice that the number of children and the degree of specialization are related. The
higher the number of children, the more likely is the man to earn relatively more than
the woman (table 3). Moreover, the general level of wealth of previous household is also
important and we introduce the quintile of the household income before separation. The
regression in table 4 disentangles the effects of each of these three elements. It reinforces
<table><caption>Table 2: Share of man's income in total labor income of the couple before separation</caption><thead><tr><th>Man's share</th><th>&gt;60%</th><th>40-60%</th><th>&lt;40%</th></tr></thead><tbody><tr><th>% of couples</th><td>53</td><td>34</td><td>13</td></tr><tr><th>Mean of man's share</th><td>81%</td><td>51.5%</td><td>21%</td></tr></tbody><thead><tr><th>Man's share</th><th>&gt; 80%</th><th>60 - 80%</th><th>40 - 60%</th><th>20 - 40%</th><th>&lt; 20%</th></tr></thead><tbody><tr><th>% of couples</th><td>25</td><td>28</td><td>34</td><td>7.5</td><td>5.5</td></tr><tr><th>Mean of man's share</th><td>95%</td><td>68.5%</td><td>51.5%</td><td>33%</td><td>4.5%</td></tr></tbody></table>

Sample: separated couples with non-nul total labor income.

<table><caption>Table 3: Share of earnings provided by men according to the number of children</caption><thead><tr><th>Number of children</th><th>0</th><th>1</th><th>2</th><th>≥ 3</th></tr></thead><tbody><tr><th>Mean of man's share</th><td>59%</td><td>61%</td><td>63%</td><td>71%</td></tr></tbody></table>

Lecture: In 2 children couples, the men earned in average 63% of the total income before divorce.

the previous results on the important role of specialization (table D.2).

Once controlled for the share of income provided by the man and for the quintile of income before separation, the increase in living standards due to children is of low magnitude and similar for women and men (respectively 3.2 and 2.6 for couples with 2 children relatively to one child, 0.9 and 0.2 when 3 children or more relatively to one child). Comparing to the control group does not lead to alter the conclusion about the magnitude of the low effect of the number of children. Meanwhile, the variation associated with marital specialization is strikingly high. For women, a 20 percentage points decrease in the share of income provided by the man is associated with a variation of living standards by 14 to 20 percents higher. For men, the magnitude of the effect of specialization is also huge. Compared to their still-married “twins”, men providing less than 40% of the earnings before divorce experience a loss from -16.9 to -27.6 percents with respect to those who provided between 60 and 80%.

## 6.3 Divorce & labor supply

Previous analysis relied on pre-divorce characteristics. However, a separation may increase the economic incentives to (re)enter the labor market or to work more hours in order to compensate the income loss due to this splitting. After divorce, labor market
<table><caption>Table 4: Regression estimates of living standard variation</caption><thead><tr><th rowspan="2"></th><th colspan="4">Women</th><th colspan="4">Men</th></tr><tr><th>Before<br>transfers<br>(1)</th><th>With<br>private<br>transfers<br>(2)</th><th>Total<br>living<br>standard<br>(3)</th><th>With<br>control<br>group<br>(4)</th><th>Before<br>transfers<br>(5)</th><th>With<br>private<br>transfers<br>(6)</th><th>Total<br>living<br>standard<br>(7)</th><th>With<br>control<br>group<br>(8)</th></tr></thead><tbody><tr><td colspan="9"><strong>Number of children</strong></td></tr><tr><td>0</td><td>4,8***</td><td>1,1<sup>(ns)</sup></td><td>-8***</td><td>-4,9***</td><td>-13,7***</td><td>-10,8***</td><td>-8***</td><td>-6***</td></tr><tr><td>1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2</td><td>-0,5<sup>(ns)</sup></td><td>2,3***</td><td>3,2***</td><td>3,3***</td><td>12,3***</td><td>8,8***</td><td>2,6***</td><td>2***</td></tr><tr><td>3 or more</td><td>-7,2***</td><td>-3,1***</td><td>0,9**</td><td>4,4***</td><td>29***</td><td>22,7***</td><td>0,2<sup>(ns)</sup></td><td>2,6***</td></tr><tr><td colspan="9"><strong>Share of income provided by man before separation</strong></td></tr><tr><td>&lt; 20%</td><td>136,3***</td><td>126,4***</td><td>47,2***</td><td>23,9***</td><td>-21,9***</td><td>-21***</td><td>-3,2**</td><td>-16,9***</td></tr><tr><td>20-40%</td><td>79,7***</td><td>67,4***</td><td>33,3***</td><td>29,2***</td><td>-34,9***</td><td>-33,2***</td><td>-24,8***</td><td>-27,6***</td></tr><tr><td>40-60%</td><td>35,7***</td><td>28,3***</td><td>13,6***</td><td>15,2***</td><td>-21,9***</td><td>-20,3***</td><td>-17,9***</td><td>-17,3***</td></tr><tr><td>60-80%</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>&gt; 80%</td><td>-36,1***</td><td>-24,5***</td><td>-5,2***</td><td>-6***</td><td>25,7***</td><td>22***</td><td>14,8***</td><td>12***</td></tr><tr><td colspan="9"><strong>Quintile of income before separation</strong></td></tr><tr><td>1<sup>st</sup></td><td>21,6***</td><td>16***</td><td>45,7***</td><td>32,5***</td><td>19,3***</td><td>23,7***</td><td>18***</td><td>4,6***</td></tr><tr><td>2<sup>nd</sup></td><td>1,5***</td><td>0,6<sup>(ns)</sup></td><td>10,5***</td><td>10,1***</td><td>3,9***</td><td>4,9***</td><td>4***</td><td>3***</td></tr><tr><td>3<sup>rd</sup></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4<sup>th</sup></td><td>0,5<sup>(ns)</sup></td><td>0,8*</td><td>-4,6***</td><td>-5,5***</td><td>-0,5<sup>(ns)</sup></td><td>-1,2***</td><td>0,2<sup>(ns)</sup></td><td>-1**</td></tr><tr><td>5<sup>th</sup></td><td>-5,6***</td><td>-4,1***</td><td>-14,4***</td><td>-14,3***</td><td>-1,8***</td><td>-3,2***</td><td>0<sup>(ns)</sup></td><td>0,5<sup>(ns)</sup></td></tr><tr><td>Cons.</td><td>-45,9***</td><td>-40,7***</td><td>-26,2***</td><td>-28,7***</td><td>16,3***</td><td>9,5***</td><td>4,2***</td><td>1,8***</td></tr></tbody></table>

Note: Standard errors are computed by subsampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: divorced women in the reference category face a 26.2% decrease in their living standard after divorce compared to the control group (column (4)). When men brought more than 80% of couple resources, women face a change in their living standard 6% lower than the women from couples where men provided between 60 and 80% (the reference category).

Source: Divorced women and men samples from French exhaustive income-tax returns database, 2009.

involvement may thus change. To assess what part of the observed variations is due to recent changes in labor supply, we look at men and women variations in labor market participation at the intensive and extensive margins.

53% of women who were inactive<fnref n="18" /> the year previous the divorce (re)enter the labor market the year following divorce. It is 29 percentage points higher than the percentage of women with identical characteristics who stayed married (table D.3), results comparable to Bonnet, Solaz, and Algava (2010) showing, on a previous and smaller survey sample, that around 40% of divorced housewives entered the labor market after the sep-

<footnote n="18">¹⁸We define inactivity as receiving none labor nor replacement income or receiving less than one month of minimum wage. For collinearity reasons, we do not enter the share of income provided by man before separation because 99% of these women provided less than 20% of the earnings.</footnote>
aration in France. Obviously, the motherhood status and the size of the family play
as constraints: women with 3 children or more have a lower probability to enter labor
market while those with no child have a higher one. Women who belonged to poorest
households are also less likely to reenter the labor force after divorce compared to those
in the highest quintiles of income (column (2) in table 5), probably because of a lower
human capital.<fnref n="19" />

For women previously working, there is no huge effect of divorce on the probability of being inactive after divorce. While the simple before-after estimator would conclude to a higher probability of being inactive (column (3) in table 5)), the causal effect of divorce turns out to be in the opposite direction: active women are less likely to become inactive after divorce (column (4) in table 5). However those effects are of little magnitude. The probability of withdrawing from the labor market for the divorcees is 3% lower than those of their married counterparts. In other words, divorce maintains more women on the labor market than marriage. The effect of the other factors are small. The labor market attachment of divorcees is only little reduced for women with 2 children or more relatively to one-child mothers. It is also the case for childless woman probably because their inactivity might be linked to other reasons than family ones (health problems for instance) or because their children left home and they have been housewives for such a long time that it led to a depreciation of their human capital. The most notable fact is for women who provided less than 20% of the couple earnings and who are less likely (-5.1 percentage points) to become inactive after divorce. In other words, the divorce may change the reservation wage and thus avoids leaving the workforce because of a too low relative income.

When remaining in the labor market, divorced women can also modify their labor sup-
ply behavior, for example shifting their work schedule from part-time to full-time. We
analyze the extensive margin of labor supply using the variations in labor income for
previously working divorcees. The working hours are not available. A change is thus not
necessarily an increase in working hours but may also correspond to a per hour wage

<footnote n="19">¹⁹Unfortunately, our administrative database does not include education or labor market experience but belonging to a lowest quintile is probably associated with lower human capital.</footnote>
increase over the period around divorce. At the extensive margin, divorce appears to have a positive effect on labor income for women<sup>20</sup> (column (6) in table 5). The highest effect is for women who had a positive earning but provided less than 20% of the couple earnings: compared to women who provided between 60 to 80% they increase their labor market earnings by 27.8%. The more children women have, the less likely they are to increase their labor supply, though the effect is not huge (from -2.4% to 1.6%).

For men, as expected since they are scarcely the custodial parents, whatever the outcome considered in table 6, the effect of the number of children turns out to be either non significant or of very low magnitude. As observed for women, men who were previously inactive<sup>21</sup> are more likely to enter or re-enter the labor market though the probability is less than twice the one of women. Not being in the labor force is less frequent for males than for women and linked to different reasons. Men who are not in the labor force have generally specific characteristics and are different from women who are not working. Women often withdrew from the labor market for family reasons while this phenomenon is for men more linked to health problems or long-term unemployment. This might explain the lower probability of return on the labor market of men. This could also explain that the probability for working men to become inactive is not altered by other factors, except for those who where providing less than 20% of the couple earnings who are significantly less likely to become inactive (column (4) table 6)).

At the extensive margin, we find again a huge effect for the second provider of income (+19.7%, column (6) table 6). For those who were the first providers of income, while the before-after estimator show no effect, we find a low but significant negative causal impact of divorce, worsened for men who were previously in the poorest couples. It could mean that divorce reduces wages of divorcees relatively to their married counterparts. The underlying mechanisms might be they are less promoted at this moment of the career during which, because of this dramatic change in their personal life, divorcees might be

<footnote n="20"><sup>20</sup>Change in labor and UI income are computed for women who were previously considered as active (cf. previous footnote for definition of activity).</footnote>

<footnote n="21"><sup>21</sup>As for women, for collinearity reasons, we do not enter the share of income provided by man before separation because 95% of these men provided less than 20% of the couple's earnings.</footnote>
less involved in work and a higher probability of unemployment after divorce, associated to reduced earnings. Indeed, men who divorce turn out to have a probability of experiencing unemployment 40% higher than similar married men. Those who had a non-zero income and no unemployment benefit before divorce are 8.4% to have unemployment benefits after divorce while still married men with identical characteristics are 6% (table D.4).

<table><caption>Table 5: Changes in women's labor supply</caption><thead><tr><th rowspan="3"></th><th colspan="4">Women</th><th colspan="2">Change in labor and UI income</th></tr><tr><th colspan="2">Increase in participation</th><th colspan="2">Decrease in participation</th><th rowspan="2">Divorcees<br>(5)</th><th rowspan="2">Divorcees<br>(6)</th></tr><tr><th>Divorcees<br>(1)</th><th>Divorcees<br>with ctrl gp<br>(2)</th><th>Divorcees<br>(3)</th><th>Divorcees<br>with ctrl gp<br>(4)</th></tr></thead><tbody><tr><td colspan="7"><strong>Number of children</strong></td></tr><tr><td>0</td><td>4**</td><td>7,4***</td><td>-0,9***</td><td>1,3***</td><td>3,6***</td><td>1,6***</td></tr><tr><td>1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2</td><td>7,6***</td><td>0,4<sup>(ns)</sup></td><td>0<sup>(ns)</sup></td><td>1,1***</td><td>2,5***</td><td>-2,4***</td></tr><tr><td>3 or more</td><td>0,4<sup>(ns)</sup></td><td>-4,8***</td><td>1,7***</td><td>1,2***</td><td>1,8<sup>(ns)</sup></td><td>-2,4***</td></tr><tr><td colspan="7"><strong>Share of income provided by man before separation</strong></td></tr><tr><td>&lt; 20%</td><td></td><td></td><td>-3,7***</td><td>-0,3***</td><td>-14,7***</td><td>-4,7***</td></tr><tr><td>20-40%</td><td></td><td></td><td>-3,2***</td><td>0,9***</td><td>-14,3***</td><td>-7,2***</td></tr><tr><td>40-60%</td><td></td><td></td><td>-1,8***</td><td>1,1***</td><td>-11,7***</td><td>-5,9***</td></tr><tr><td>60-80%</td><td></td><td></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>&gt; 80%</td><td></td><td></td><td>5,3***</td><td>-5,1***</td><td>87,1***</td><td>27,8***</td></tr><tr><td colspan="7"><strong>Quintile of income before separation</strong></td></tr><tr><td>1<sup>st</sup></td><td>-12,6***</td><td>-7,2***</td><td>9,8***</td><td>-0,9***</td><td>18***</td><td>3,2***</td></tr><tr><td>2<sup>nd</sup></td><td>0,2<sup>(ns)</sup></td><td>-2,8***</td><td>3,1***</td><td>-0,5***</td><td>3,8***</td><td>1,6***</td></tr><tr><td>3<sup>rd</sup></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4<sup>th</sup></td><td>1,2<sup>(ns)</sup></td><td>-1,4<sup>(ns)</sup></td><td>-0,7***</td><td>0,6***</td><td>-1,8***</td><td>-1,3***</td></tr><tr><td>5<sup>th</sup></td><td>-5,2**</td><td>4,8***</td><td>-1,4***</td><td>0,8***</td><td>-7,1***</td><td>-1***</td></tr><tr><td>Cons.</td><td>57,4***</td><td>33,4***</td><td>2,7***</td><td>-2,8***</td><td>17,1***</td><td>11,3***</td></tr></tbody></table>

Note: columns (1) and (2): Women who had a null income in 2008. columns (3) to (6): Women who had a non null income in 2008. Columns (1) to (4): Linear probability model, columns (5) and (6): Model 4.

Standard errors are computed by subsampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: the probability of (re)entering the labor market after divorce for mothers of 3 children or more who had no income before divorce is 4.8 percentage points lower than the probability of mothers with one child (column (2)). The labor force exit for mothers of 2 children is 1.1 percentage points higher than the probability of one child mothers (column (4)). After divorce, childless women increase their earnings by 1.6% compared to one child mothers.

Source: Divorced women sample from French exhaustive income-tax returns database, 2009.
<table><caption>Table 6: Changes in men's labor supply</caption><thead><tr><th rowspan="2"></th><th colspan="2">Increase in participation</th><th colspan="2">Decrease in participation</th><th colspan="2">Change in labor and UI income</th></tr><tr><th>Divorcees<br>(1)</th><th>Divorcees<br>with ctrl gp<br>(2)</th><th>Divorcees<br>(3)</th><th>Divorcees<br>with ctrl gp<br>(4)</th><th>Divorcees<br>(5)</th><th>Divorcees<br>with ctrl gp<br>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Number of children</strong></td></tr><tr><td>0</td><td>-5,6<sup>(ns)</sup></td><td>-4,1<sup>(ns)</sup></td><td>0,4<sup>(ns)</sup></td><td>0,6***</td><td>1<sup>(ns)</sup></td><td>-0,1<sup>(ns)</sup></td></tr><tr><td>1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2</td><td>1,7<sup>(ns)</sup></td><td>3,6<sup>(ns)</sup></td><td>-0,1<sup>(ns)</sup></td><td>0,1***</td><td>0,2<sup>(ns)</sup></td><td>-0,1<sup>(ns)</sup></td></tr><tr><td>3 or more</td><td>-3,6<sup>(ns)</sup></td><td>0,6<sup>(ns)</sup></td><td>-0,2<sup>(ns)</sup></td><td>0,02*</td><td>-1,2*</td><td>-1,1***</td></tr><tr><td colspan="7"><strong>Share of income provided by man before separation</strong></td></tr><tr><td>&lt; 20%</td><td></td><td></td><td>10,4***</td><td>-6,5***</td><td>114,6***</td><td>19,7***</td></tr><tr><td>20-40%</td><td></td><td></td><td>4,1***</td><td>-0,6***</td><td>28***</td><td>4,6***</td></tr><tr><td>40-60%</td><td></td><td></td><td>0,7***</td><td>0,2***</td><td>2,8***</td><td>0<sup>(ns)</sup></td></tr><tr><td>60-80%</td><td></td><td></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>&gt; 80%</td><td></td><td></td><td>-0,1<sup>(ns)</sup></td><td>0,03*</td><td>-6,4***</td><td>-2,3***</td></tr><tr><td colspan="7"><strong>Quintile of income before separation</strong></td></tr><tr><td>1<sup>st</sup></td><td>18,1***</td><td>0,2<sup>(ns)</sup></td><td>5,5***</td><td>-0,1*</td><td>11,5***</td><td>-2,7***</td></tr><tr><td>2<sup>nd</sup></td><td>13,3**</td><td>1,2<sup>(ns)</sup></td><td>0,8***</td><td>-0,1<sup>(ns)</sup></td><td>2,8***</td><td>-0,4**</td></tr><tr><td>3<sup>rd</sup></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4<sup>th</sup></td><td>2,6<sup>(ns)</sup></td><td>-0,2<sup>(ns)</sup></td><td>-0,3*</td><td>-0,2***</td><td>-0,2<sup>(ns)</sup></td><td>0,4**</td></tr><tr><td>5<sup>th</sup></td><td>-0,5<sup>(ns)</sup></td><td>3,6<sup>(ns)</sup></td><td>-0,2*</td><td>-0,2***</td><td>-1,1**</td><td>0,1<sup>(ns)</sup></td></tr><tr><td>Cons.</td><td>42,1***</td><td>13,9***</td><td>0,5***</td><td>-0,1<sup>(ns)</sup></td><td>0<sup>(ns)</sup></td><td>-1,7***</td></tr></tbody></table>

Note: columns (1) and (2): Men who had a null income in 2008. columns (3) to (6): Men who had a non null income in 2008. Columns (1) to (4): Linear probability model, columns (5) and (6): Model 4.

Standard errors are computed by subsampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: the probability of (re)entering the labor market after divorce for men with no child who had no income before divorce is 4.1 percentage points lower (not significant) than the probability of men with one child (column (2)). The labor force exit for fathers of 2 children is 0.1 percentage points higher than the probability of one child fathers (column (4)). After divorce, men with 3 children or more decrease their earnings by 1.1% compared to one child fathers.

Source: Divorced men sample from French exhaustive income-tax returns database, 2009.

## 6.4 Robustness checks

The choice of an equivalence scale is a current topic of debate. In the case of lone parent families in particular, it raises two main questions. First of all, the usually “OECD-modified scale” does not consider the specific expenditures of a single parent family. It considers indeed that the economies of scale in a lone-parent family with one child under 14 years old are bigger than in a childless couple (number of consumption units equals 1.3 vs 1.5). Yet it could not be the case. For example, living with one child means in
general having two bedrooms instead of one when living in a childless couple. The other
equivalence scale commonly used is the square root of the household size. In that case the
equivalence scale is higher for the first child and for single families (√2 instead of 1.3).
In France, we observe some other practices, the administration adding 0.2 to the total
number of consumption units they compute as soon as they deal with single families. We
introduce this variant qualified as “Lone parent variant”.

The second critic that could be addressed to the usual equivalence scale is to ignore the
expenditures the non-custodial parent could also bear if (s)he is used to welcoming their
child. We propose here a variant by giving a fraction of consumption unit to the non-
custodial parent. Very few studies compute this extra expenditures for non-custodial
parents (Henman and Mitchell (2001), Martin and Périvier (2015)). We assume this
fraction reflects the time spent at each parent’s home. When shared custody is not
chosen, we suppose that the non-custodial parent sees his child once every two week ends
and half of the school holidays. This corresponds roughly to 25% of the time. So, in
this variant we give a quarter of the “OECD-modified scale” consumption unit to the
non-custodial parent (for example, for a child under 14 years old, it means 0.25 × 0.3).

The square root equivalence scale assumes that the economies of scales are larger. For
example, a couple will need 41%<fnref n="22" /> of extra income to maintain this living standard while
with the OECD modified scale, it needs 50%<fnref n="23" />. As divorce means the loss of economies
of scale, the decrease in living standard is all the larger as these economies are supposed
to be higher. The variation of living standards is more negative for women and for men
(column (2) in table 7 and in table 8). In the lone parent variant, we assume that having
the child custody is more costly than in the “OECD modified scale”. The negative con-
sequences of divorce are reinforced for women (column 3 in table 7). It also plays a role
in the same direction but the effect is lower, men not often being the custodial parent.
Finally, in the non-custodial parent variant, we assume that the non custodial parent gets
a fraction of consumption unit for the child(ren) not living with them. In this variant,
the effect is more important for men, being more in this position (column (4) in table 8)

<footnote n="22">²²It comes from $\sqrt{2} \approx 1.41$.</footnote>

<footnote n="23">²³it corresponds to 1 + 0.5.</footnote>
<table><caption>Table 7: Regression estimates of living standard variation for different equivalence scales, for women</caption><thead><tr><th rowspan="2"></th><th colspan="4">Divorcees</th><th colspan="4">Women<br/>Divorcees with<br/>control group</th></tr><tr><th>OECD<br/>(1)</th><th>Square root<br/>variant<br/>(2)</th><th>Loneparent<br/>variant<br/>(3)</th><th>Non custodial<br/>parent variant<br/>(4)</th><th>OECD<br/>(5)</th><th>Square root<br/>variant<br/>(6)</th><th>Loneparent<br/>variant<br/>(7)</th><th>Non custodial<br/>parent variant<br/>(8)</th></tr></thead><tbody><tr><td colspan="9"><strong>Number of children</strong></td></tr><tr><td>0</td><td>-8***</td><td>-3,9***</td><td>2***</td><td>-7***</td><td>-4,9***</td><td>-0,6***</td><td>5,5***</td><td>-3,9***</td></tr><tr><td>1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2</td><td>3,2***</td><td>2,5***</td><td>3,4***</td><td>3,5***</td><td>3,3***</td><td>1,8***</td><td>3,6***</td><td>3,7***</td></tr><tr><td>3 or more</td><td>0,9**</td><td>0,5<sup>(ns)</sup></td><td>3***</td><td>1,2***</td><td>4,4***</td><td>3***</td><td>6,5***</td><td>4,7***</td></tr><tr><td colspan="9"><strong>Share of income provided by man before separation</strong></td></tr><tr><td>&lt; 20%</td><td>47,2***</td><td>47,8***</td><td>47,7***</td><td>47,1***</td><td>23,9***</td><td>24,9***</td><td>24,4***</td><td>23,9***</td></tr><tr><td>20-40%</td><td>33,3***</td><td>33,5***</td><td>33,3***</td><td>33,7***</td><td>29,2***</td><td>29,5***</td><td>29,2***</td><td>29,6***</td></tr><tr><td>40-60%</td><td>13,6***</td><td>13,6***</td><td>13,3***</td><td>13,9***</td><td>15,2***</td><td>15,2***</td><td>14,9***</td><td>15,6***</td></tr><tr><td>60-80%</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>&gt; 80%</td><td>-5,2***</td><td>-5,2***</td><td>-4,8***</td><td>-5,3***</td><td>-6***</td><td>-6***</td><td>-5,7***</td><td>-6,2***</td></tr><tr><td colspan="9"><strong>Quintile of income before separation</strong></td></tr><tr><td>1<sup>st</sup></td><td>45,7***</td><td>45,7***</td><td>45,2***</td><td>46,2***</td><td>32,5***</td><td>32,2***</td><td>32***</td><td>32,9***</td></tr><tr><td>2<sup>nd</sup></td><td>10,5***</td><td>10,5***</td><td>10,4***</td><td>10,5***</td><td>10,1***</td><td>9,9***</td><td>10***</td><td>10,1***</td></tr><tr><td>3<sup>rd</sup></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4<sup>th</sup></td><td>-4,6***</td><td>-4,6***</td><td>-4,4***</td><td>-4,7***</td><td>-5,5***</td><td>-5,4***</td><td>-5,3***</td><td>-5,6***</td></tr><tr><td>5<sup>th</sup></td><td>-14,4***</td><td>-14,7***</td><td>-14,1***</td><td>-14,7***</td><td>-14,3***</td><td>-14,5***</td><td>-14***</td><td>-14,5***</td></tr><tr><td>Cons.</td><td>-26,2***</td><td>-33,6***</td><td>-34,2***</td><td>-27,2***</td><td>-28,7***</td><td>-35,7***</td><td>-36,4***</td><td>-29,6***</td></tr></tbody></table>

Note: Standard errors are computed by subsampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: The living standard of women with 1 child, whose husbands provided between 60 and 80% of couple earnings before divorce and who were in couple from the 3rd quintile of income (reference category), decreases by -28.7%, compared to the control group, when OECD equivalence scale is used (column (5)), by -35.7% when computed with the square root equivalence scale (column (6)), by -36.4% when computed according to the “loneparent variant”(column (7)) and by -29.6% when computed according to the “non custodial parent variant” (column (8)) (cf. main text for explanations about these variants).

Source: Divorced women sample from French exhaustive income-tax returns database, 2009.

and thus the situation of men is worsened.

Even if we exclude the 0.5% of the bottom of the distribution of living standard variation, big variations could have an influence on the means. We then run median regressions to test the sensitivity of the effects to the extreme value. The results are largely the same (table E.1 and table E.2). One exception, the coefficients for the income share <20% and 20-40% are lower for men in the median regression, suggesting that huge positive variations occur in that population sub-group. This is not surprising considering the variation in labor supply we previously observed.
<table><caption>Table 8: Regression estimates of living standard variation for different equivalence scales, for men</caption><thead><tr><th rowspan="2"></th><th colspan="4">Divorcees</th><th colspan="4">Men</th></tr><tr><th>OECD<br>(1)</th><th>Square root<br>variant<br>(2)</th><th>Loneparent<br>variant<br>(3)</th><th>Non custodial<br>parent variant<br>(4)</th><th>OECD<br>(5)</th><th>Square root<br>variant<br>(6)</th><th>Loneparent<br>variant<br>(7)</th><th>Non custodial<br>parent variant<br>(8)</th></tr></thead><tbody><tr><td colspan="9"><strong>Number of children</strong></td></tr><tr><td>0</td><td>-8***</td><td>-6,1***</td><td>-4,5***</td><td>-2,9***</td><td>-6***</td><td>-3,7***</td><td>-2,3***</td><td>-0,7***</td></tr><tr><td>1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2</td><td>2,6***</td><td>0,5<sup>(ns)</sup></td><td>2***</td><td>-1,2***</td><td>2***</td><td>-1***</td><td>1,4***</td><td>-1,8***</td></tr><tr><td>3 or more</td><td>0,2<sup>(ns)</sup></td><td>-6***</td><td>0,1<sup>(ns)</sup></td><td>-8,5***</td><td>2,6***</td><td>-4,8***</td><td>2,5***</td><td>-6,3***</td></tr><tr><td colspan="9"><strong>Share of income provided by man before separation</strong></td></tr><tr><td>&lt; 20%</td><td>-3,2**</td><td>-3,7***</td><td>-2,5*</td><td>-4,1***</td><td>-16,9***</td><td>-17,2***</td><td>-16,4***</td><td>-17,8***</td></tr><tr><td>20-40%</td><td>-24,8***</td><td>-25***</td><td>-24,3***</td><td>-25,4***</td><td>-27,6***</td><td>-27,6***</td><td>-27,1***</td><td>-28,1***</td></tr><tr><td>40-60%</td><td>-17,9***</td><td>-18***</td><td>-18***</td><td>-17,9***</td><td>-17,3***</td><td>-17,3***</td><td>-17,4***</td><td>-17,2***</td></tr><tr><td>60-80%</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>&gt; 80%</td><td>14,8***</td><td>15,3***</td><td>15,3***</td><td>14,4***</td><td>12***</td><td>12,5***</td><td>12,5***</td><td>11,6***</td></tr><tr><td colspan="9"><strong>Quintile of income before separation</strong></td></tr><tr><td>1<sup>st</sup></td><td>18***</td><td>18,6***</td><td>18,8***</td><td>17,1***</td><td>4,6***</td><td>5***</td><td>5,4***</td><td>3,9***</td></tr><tr><td>2<sup>nd</sup></td><td>4***</td><td>4,2***</td><td>4,3***</td><td>3,5***</td><td>3***</td><td>3,2***</td><td>3,4***</td><td>2,6***</td></tr><tr><td>3<sup>rd</sup></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4<sup>th</sup></td><td>0,2<sup>(ns)</sup></td><td>-0,2<sup>(ns)</sup></td><td>-0,2<sup>(ns)</sup></td><td>0,3<sup>(ns)</sup></td><td>-1***</td><td>-1,3***</td><td>-1,3***</td><td>-0,8***</td></tr><tr><td>5<sup>th</sup></td><td>0<sup>(ns)</sup></td><td>-0,7*</td><td>-0,5<sup>(ns)</sup></td><td>0,6<sup>(ns)</sup></td><td>0,5***</td><td>-0,3**</td><td>0<sup>(ns)</sup></td><td>1***</td></tr><tr><td>Cons.</td><td>4,2***</td><td>-3,9***</td><td>-0,3<sup>(ns)</sup></td><td>-1,3***</td><td>1,8***</td><td>-6,1***</td><td>-2,6***</td><td>-3,6***</td></tr></tbody></table>

Note: Standard errors are computed by subsampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: The living standard of men with 1 child, who provided between 60 and 80% of the couple earnings before divorce and who were in couple from the 3rd quintile of income, increases by +1.8% when the OECD equivalence scale is used (column (5)), decreases by -6.1% when computed with the square root equivalence scale (column (6)), by -2.6% when computed according to the “loneparent variant”(column (7)) and by -3.6% when computed according to the “non custodial parent variant” (column (8)) (cf. main text for explanations about these variants).

Source: Divorced men sample from French exhaustive income-tax returns database, 2009.

# 7 Conclusion & Discussion

If “the formation of a couple is often efficient from the pure economic perspective” (Brown- ing, Chiappori, and Weiss (2014)) because marital gains arises from the presence of public consumption and marital specialization process, couple breakdown has opposite consequences, revealing the flip side of marital specialization.

The loss of economies of scale following the divorce is well-known. Some costs, for ex- ample dwelling and child education expenses, shared between partners during marriage have to be borne separately after the divorce. Previous empirical works showed that this loss of economies of scale involves a decrease in living standards that is mainly supported by women. This gender disparity is often explained by the greater cost of child custody women more often have.

Taking advantage of a huge sample size of couples observed one year before and one year
after their divorce from a French fiscal database, we are able to evaluate a causal effect
of divorce and to distinguish different components of living standards. In line with the
literature, we confirm in this article that women experience a decrease in total living
standards after divorce, by roughly 19% on average whereas for men living standards
remain globally stable (with a decrease of 2.5%). If family composition and child cus-
tody play a role in women's loss, we also show that economic consequences of divorce
are highly more dependent on the share of couple' resources each spouse provides before
divorce resulting mainly from marital specialization choices. Note that the gender gap in
earnings observed at the moment of divorce may come also from pre-marital differences
in spouses' wages, from different career advancement from marriage that we are not able
to control for. This focus on income share leads to some interesting results. We especially
highlight huge heterogeneity in living standard variations for men. When, in most cases,
men are the main earnings providers (before divorce), they experience a gain in their
living standards. But when they are the secondary earner, they also experience losses
as most women in the same situation, and the loss is all the more important as their
earnings represent a small fraction of the before-divorce earnings of the couple. Rather
than a gender inequality or a custodial parent story, the losses after divorce have to be
mainly related to the marital specialization choices and the wage gap between partners
at the time of the divorce.

In this article, we also investigate the role of public and private transfers. We document
the fact that public transfers (family and welfare benefits) partially offset the negative
economic consequences of divorce, especially for the poor and large families. Some child-
less women (and to a lesser extent also childless men) could thus experience larger losses
in living standards than mothers with a large family <sup>24</sup>, helped by generous French family
policies. Private transfers - i.e. child alimony - mitigate the decrease (the increase) in the
living standard of women (men) after divorce by about 7 percentage points. Finally our
results on labor supply after divorce show that for women working before divorce, there

<footnote n="24"><sup>24</sup>this result is also found by Manting and Bouman (2006) on Dutch Data.</footnote>
is no huge effect of divorce on the probability of being inactive after divorce whereas for housewives, we observe a quite massive return on the labor market, constrained however by the family size, and the household poverty (capturing probably indirectly low human capital). For men, no much change is observed for working status, except an increase in labor market incomes for those who were in second earner provider position.

Some limitations to our study have to be mentioned. In spite of their reliability and huge sample size, the administrative data we used only concern the effect of divorce, that is to say legal separation of marital unions (divorces and civil partnerships) while unmarried couples, a quite common marital status in France, are set aside. However, with the widespread of cohabitation, these unmarried couples are more similar to married ones in sociodemographic characteristics, once controlled for income sharing, number of children and household income as we did. Tach and Eads (2015), analysing US dissolution trends, recently found that the economic consequences of cohabitation dissolution and divorce became similar over time. This result is partly driven by the extension of lone parent-hood allowances (reserved to lone mothers) to divorced mothers. However, US unmarried couples still differ from unmarried European. In a more comparable context on Dutch data, Manting and Bouman (2006) find that non-married women short-term losses are less important than married women losses but also point that this result is not driven by the marital status “per se” but by compositional differences: unmarried couples have less children and are more likely to have equal earnings before divorce for instance. As we control for these characteristics, we would have probably reached the same conclusions if we could have had included unmarried couples in the analysis.

A second limitation is the restriction of our sample to divorcees who live alone the year following divorce, that is to say, who are not yet repartnered. Previous studies showed that repartnering may be a way to overcome financial difficulties after divorce especially for poor women (Dewilde and Uunk (2008)). Manting and Bouman (2006) show that gender disparities tend to disappear after five years in case of repartnering. A last limitation is that we are here able to identify only short-term effects, one year after the divorce,
probably the worse period since many studies show that there is a catch-up effect up to
the level of pre-divorce living standards due to repartnering and labor market behavior
adjustements. But still, the foregone incomes relatively to comparable couples who re-
mained married are not taken into account.

But, on the other way, poor recently divorced women are not the most suitable on the
marriage market and can encounter difficulties, so that the direction of the selection ef-
fect is undetermined. Lastly, our measure of private transfers only takes into account
child support whereas spousal payment can also constitute a big financial resource (and
charge). But as spousal payments are more often paid as a lump sum (and not as an
annuity), it was difficult to add them to post-divorce living standards. We should have
chosen an arbitrary payment period to smooth this one-shot payment. Spousal supports
only concern one out of eight divorces (Roumiguières (2004)), in particular older couples
than those observed in this study, a long marriage duration being a main determinant of
the grant of this spousal payments. However, it remains possible that the living standards
losses of women and the stability of men are over-estimated by not taking into account
such transfers. However the huge effect we observed makes us confident that inclusion of
spousal support, if possible, could not have balanced the inequalities created by marital
specialization.

Lastly, one frequent criticism addressed to living standards analyses is that the OECD
unit consumption scale (or any other scale), even intensively used in research, is arbitrary
and may not reflect the real child cost, for instance for a lone parent. This choice of an
equivalence scale could then drive the results regarding changes in living standards. It
is not the case. We first theoretically showed that family size or the custodial status
is far from being the whole story. Secondly, we replicate our analysis using different
equivalence scales and all our conclusions remain. The income sharing before divorce is a
stronger determinant than family structure captured by the number of consumption units.
We document an increase in labor force participation for second earners and especially for women. This tends to support the idea that marital specialization process is the main responsible for the gender inequality in living standards after divorce. In spite of this, couples continue to do so because it is a short-term efficient solution or they are probably not prepared to the risk of divorce. In terms of public policy, our study also shows that divorce constitutes a public cost because of large public transfers to single parents and poor people. However, for dual earners or when divorcees reenter the labor market, this cost is largely reduced.
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# A Tables for the literature review

<table><caption>Table A.1: Living standards variations (before-after divorce) (%)</caption><thead><tr><th rowspan="2">Article</th><th colspan="2">Unadjusted household income</th><th colspan="2">Adjusted household income</th><th rowspan="2">Type of variation</th><th rowspan="2">Type income adjustment</th></tr><tr><th>Women</th><th>Men</th><th>Women</th><th>Men</th></tr></thead><tbody><tr><td colspan="7" style="text-align:center; font-weight:bold;">American data</td></tr><tr><td>Hoffman (1977)</td><td>-29</td><td>-19</td><td>-7</td><td>+17</td><td>Change in mean</td><td>(a)</td></tr><tr><td>Corcoran (1979)</td><td>-44</td><td></td><td>-18</td><td></td><td>Mean of changes</td><td>(a)</td></tr><tr><td>Duncan and Hoffman (1985)</td><td>-28 (W)<br/>-46(B)</td><td>-9 (W)<br/>+2(B)</td><td>-11 (W)<br/>-29 (B)</td><td>+10 (W)<br/>+40 (B)</td><td>Mean of changes</td><td>(b)</td></tr><tr><td>Burkhauser & al. (1991)</td><td></td><td></td><td>-24</td><td>-6</td><td>Median of changes</td><td>(c)</td></tr><tr><td>Smock (1993)</td><td>-16 to -43 (W)<br/>-51 to -45 (B)</td><td>-8 to +7 (W)<br/>-29 to -13 (B)</td><td>-22 to -21 (W)<br/>-44 to -35 (B)</td><td>+61 to +93 (W)<br/>+47 to +80 (B)</td><td>Median of changes</td><td>(d)</td></tr><tr><td>Smock (1994)</td><td></td><td></td><td>-20(B)</td><td>+61(W)</td><td>Mean of changes</td><td>(d)</td></tr><tr><td>Peterson (1996)</td><td></td><td></td><td>-27</td><td>+10</td><td>Changes in mean</td><td>(e)</td></tr><tr><td>Bianchi & al. (1999)</td><td></td><td></td><td>-36</td><td>+28</td><td>Median of changes</td><td>(c)</td></tr><tr><td>McKeever and Wolfinger (2001)</td><td>-45</td><td></td><td>-14</td><td></td><td>Median of changes</td><td>(d)</td></tr><tr><td>McManus and DiPrete (2001)</td><td></td><td>-42 to -40 (B)<br/>-58 à -41 (N)</td><td></td><td>-20 to -14 (B)<br/>-12 to -11 (B)<br/>+29 to +34 (B)<br/>-27 to -3(ns) (N)<br/>-30 to -3(ns) (N)<br/>+30(ns) to +68 (N)</td><td>Mean of changes</td><td>(b)<br/>(f)<br/>(d)<br/>(b)<br/>(f)<br/>(d)</td></tr><tr><td>Bedard and Deschênes (2005)</td><td></td><td></td><td>Increase</td><td></td><td>Mean of changes (Instrumentation for all ever divorced women, Local effect)</td><td>(j)</td></tr><tr><td>Ananat and Michaels (2008)</td><td></td><td></td><td>Increase and decrease</td><td></td><td>Quantile treatment effect (Instrumentation for all ever divorced women. Local effect)</td><td>(c), (j)</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">Canadian data</td></tr><tr><td>Finnie (1993)</td><td>-30 (-49)</td><td>-11 (-25)</td><td>-14 (-33)</td><td>+32 (+11)</td><td>Mean (Median) of changes</td><td>(c)</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">UK Data</td></tr><tr><td>Jarvis and Jenkins (1999)</td><td></td><td></td><td>-18</td><td>2 (ns)</td><td>Median of changes</td><td>(h)</td></tr><tr><td>Uunk (2004)</td><td></td><td></td><td>-36</td><td></td><td>Changes in median</td><td>(i)</td></tr><tr><td>Andreß & al. (2006)</td><td></td><td></td><td>-28</td><td>1</td><td>Median of changes</td><td>(k)</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">Dutch data</td></tr><tr><td>Poortman (2000)</td><td>-46</td><td>-31</td><td>-31</td><td>+4</td><td>Changes in mean</td><td>(g)</td></tr><tr><td>Uunk (2004)</td><td></td><td></td><td>-19</td><td></td><td>Changes in median</td><td>(i)</td></tr><tr><td>Kalmijn and Alessie (2008)</td><td></td><td></td><td>-38</td><td>ns</td><td>Mean of changes</td><td>(i)</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">German data</td></tr><tr><td>Burkhauser et al. (1991)</td><td></td><td></td><td>-44</td><td>-7</td><td>Median of changes</td><td>(c)</td></tr><tr><td>Uunk (2004)</td><td></td><td></td><td>-25</td><td></td><td>Changes in median</td><td></td></tr><tr><td>Andreß & al. (2006)</td><td></td><td></td><td>-33</td><td>-2</td><td>Median of changes</td><td>(k)</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">Norwegian data</td></tr><tr><td>Bratberg and Tjøtta (2002)</td><td>-4 (C)<br/>-32 (NC)</td><td>+36 (C)<br/>-1 (NC)</td><td></td><td></td><td>Median of changes</td><td></td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">Italian data</td></tr><tr><td>Andreß & al. (2006)</td><td></td><td></td><td>-32</td><td>+5</td><td>Median of changes</td><td>(k)</td></tr><tr><td>Uunk (2004)</td><td></td><td></td><td>-3</td><td></td><td>Changes in median</td><td>(i)</td></tr><tr><td>Ongaro & al. (1991)</td><td>-23</td><td>+25</td><td></td><td></td><td>Mean of changes (compared to control group)</td><td>(i)</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">French data</td></tr><tr><td>Uunk (2004)</td><td></td><td></td><td>-32</td><td></td><td>Changes in median</td><td>(i)</td></tr><tr><td>Jauneau and Raynaud (2009)</td><td></td><td></td><td>-30</td><td>-6</td><td>Median of changes</td><td>(i)</td></tr><tr><td colspan="7" style="text-align:center; font-weight:bold;">European data</td></tr><tr><td>Uunk (2004)</td><td></td><td></td><td>-36 to -3 (according to country)</td><td></td><td>Changes in median</td><td>(i)</td></tr></tbody></table>

Results for non-repartencred women after their divorce.

For US data: (B): estimation for Black people, (W) for White. For Bratberg and Tjøtta (2002): (C): Estimation for custodial parents, (NC) non-custodial.

Method of adjustment: (a): This measure of needs uses the Department of Agriculture's "Low-Cost Food Budget"; (b): This measure of needs uses "official US Department poverty standard"; (c): Division by a poverty threshold (depending on the family size); (d): "per capita" (division by the number of people in the household); (e): This measure of needs uses the Bureau of Labor Statistics' Lower Standard Budget; (f): ELES "Expended Linear Extension System" by Merz, Garner, Smeeding, Falk, and Johnson (1994); (g): Schiepers' equivalence scale (1993); (h): McClements "Before Housing Costs" equivalence scale; (i): modified OECD equivalence scale; (j): Census Bureau equivalence scale: (nb adults + 0.7 nb children)<sup>b,7</sup>; (k): square root equivalence scale.

Remark: See table A.3 for a detail of equivalence scales used in the studies.
<table><caption>Table A.2: Sample sizes</caption><thead><tr><th>Article</th><th>Data</th><th>Number of observations</th></tr></thead><tbody><tr><td>Hoffman (1977)</td><td>PSID</td><td>182 Women & 110 Men</td></tr><tr><td>Corcoran (1979)</td><td>PSID</td><td>56 Women</td></tr><tr><td>Duncan and Hoffman (1985)</td><td>PSID</td><td>349 Women & 250 Men</td></tr><tr><td>Poortman (2000)</td><td>SEP</td><td>359</td></tr><tr><td>Peterson (1996)</td><td>PSID</td><td>161</td></tr><tr><td>Burkhauser and al. (1991)</td><td>PSID<br>GSEP</td><td>301 Women & 239 Men<br>56 Women & 45 Men</td></tr><tr><td>Finnie (1993)</td><td>LAD</td><td>2125 Women & 2375 Men</td></tr><tr><td>Smock (1993)</td><td>NLSY, NLSYW<br>& NLSYM</td><td>Women : 133 to 430<br>Men : 67 to 312</td></tr><tr><td>Smock (1994)</td><td>NLSY<br>& NLSYM</td><td>Women : 84 to 258<br>Men : 43 to 167</td></tr><tr><td>Jarvis and Jenkins (1999)</td><td>BHPS</td><td>148 Women & 105 Men</td></tr><tr><td>Bianchi, Subaiya, and Kahn (1999)</td><td>SIPP</td><td>199 couples</td></tr><tr><td>McKeever and Wolfinger (2001)</td><td>NSFH</td><td>472 Women</td></tr><tr><td>Bratberg and Tjøtta (2002)</td><td>KIRUT</td><td>Women : 538 to 2038<br>Men : entre 600 et 1881</td></tr><tr><td>Uunk (2004)</td><td>ECHP</td><td>29 to 157</td></tr></tbody></table>

SEP : Dutch Socioeconomic Panel, PSID : Panel Study of Income Dynamics, GSEP: German Socio-Economic Panel

NLSY : National Longitudinal Surveys of Youth, NLSYW : National Longitudinal Surveys of Young Women

NLSYM : National Longitudinal Surveys of Young Men

KIRUT : base longitudinale norvégienne

NSFH : National Survey of Family and Households

LAD : Longitudinal Administrative Database

BHPS : British Household Panel Survey

ECHP : European Community Household Panel

SIPP : Survey of Income and Program Participation

<table><caption>Table A.3: Some equivalence scales used</caption><thead><tr><th>Échelle</th><th>1<sup>st</sup> pers.</th><th>2<sup>nd</sup> pers.</th><th>3<sup>rd</sup> pers.</th><th>4<sup>th</sup> pers.</th></tr></thead><tbody><tr><td>"Per capital"</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Square root</td><td>1</td><td>√2</td><td>√3</td><td>2</td></tr><tr><td>OECDE (original)</td><td>1</td><td>0.7</td><td>0.5</td><td>0.5</td></tr><tr><td>OCDE (modified)</td><td>1</td><td>0.7</td><td>0.5 (if + 14 y.o)<br>0.3 (if - 14 y.o)</td><td>0.5 (if + 14 y.o)<br>0.3 (if - 14 y.o)</td></tr><tr><td>Buhmann (1988)</td><td></td><td></td><td></td><td></td></tr><tr><td>For θ = 0.5 (classical case)</td><td>1</td><td>0.41</td><td>0.32</td><td>0.27</td></tr><tr><td>For θ = 0.25</td><td>1</td><td>0.19</td><td>0.13</td><td>0.10</td></tr><tr><td>For θ = 0.75</td><td>1</td><td>0.68</td><td>0.60</td><td>0.55</td></tr><tr><td>McClements "Before Housing Costs"</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>0.61</td><td>0.39 (partner)<br>0.46 (non-partner adult)<br>0.32 (children + 14 y.o)<br>0.20 (children - 14 y.o)</td><td>0.42 (non-partner adult)<br>0.32 (children + 14 y.o)<br>0.20 (children - 14 y.o)</td><td>0.36 (non-partner adult)<br>0.32 (children + 14 y.o)<br>0.20 (children - 14 y.o)</td></tr><tr><td>Schiepers (1993)</td><td></td><td></td><td></td><td></td></tr><tr><td>1 adult & all children ≤ 5 y.o</td><td>1</td><td>0.3</td><td>0.24</td><td>0.19</td></tr><tr><td>1 adult & older child from 6 to 11 y.o</td><td>1</td><td>0.32</td><td>0.22</td><td>0.20</td></tr><tr><td>1 adult & older child from 12 to 18 y.o</td><td>1</td><td>0.35</td><td>0.24</td><td>0.20</td></tr><tr><td>2 adults & all children ≤</td><td>1</td><td>0.38</td><td>0.28</td><td>0.16</td></tr><tr><td>2 adults & older child from 6 to 11 y.o</td><td>1</td><td>0.38</td><td>0.31</td><td>0.16</td></tr><tr><td>2 adults & older child from 12 to 18 y.o</td><td>1</td><td>0.38</td><td>0.34</td><td>0.17</td></tr></tbody></table>
# B Creation of dataset and computation of weights

## B.1 Dataset

In 2009, the official statistics report 130,601 divorces in France. 164 divorces are for marriages that last less than one year, 1,700 for ones that last one year and 3,926 two years. So, the number of divorces in 2009 for people who marry before 2008 ranges then between 124,807 and 128,733. Our sample size belongs to this interval. We thus have information about 2008 earnings for at least 98% of the divorced couples who didn't married neither in 2008 nor in 2009. For "pacsed" people, the number of dissolutions reported in official statistics is 17,186 without any clue about the duration of the contract that has been disrupted. In our sample, we found 9,760 pacsed-couples. Then we have information about 2008 earnings for at least 57% of the couples who broke their Pacs in 2009 and probably more if we take into account that we kept only those who do sign their contract before 2008 (whereas the denominator include all Pacs dissolution whatever the year of pacs formation) We call the global sample with information about 2008 earnings for all ex-married and ex-pacsed couples we recovered : the "sample 1" (cf. table B.1).

In order to have information about the dwelling in 2008 we then match our data with the local residence tax ("taxe d'habitation"). This leads to a little loss and we get 122,939 formerly married couples and 9,442 formerly pacsed couples. We thus have information about dwelling (homeownership, house or flat, ...) for at least 95% of divorcees and 55% for those who were pacsed. We call this sample with all ex-married and ex-pacsed couples for which we have information about 2008 earnings and 2008 local residence tax the "sample 2".

From that point we then need to match with tax return for the 2010 earnings (one year after divorce) and the local residence tax for the same year.
<table><caption>Table B.1: Information about dataset creation</caption><thead><tr><th></th><th colspan="2">Official statistics</th><th>Sample 1</th><th>Sample 2</th><th>Sample 3</th><th>Sample 4</th></tr></thead><tbody><tr><td>Divorced %</td><td>All 130,601</td><td>Married before 2008 124,807 to 128,733</td><td>126,250 At least 98%</td><td>122,939 At least 95%</td><td>113,794</td><td>91,732</td></tr><tr><td>End of "Pacs" %</td><td>17,186</td><td>?</td><td>9,760 At least 57%</td><td>9,442 At least 55%</td><td>9,007 (7,932 heterosexual)</td><td>5,557</td></tr><tr><th colspan="7">Available information according to sample</th></tr><tr><td>Income tax 2008</td><td></td><td></td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Local residence tax 2008</td><td></td><td></td><td></td><td>X</td><td>X</td><td>X</td></tr><tr><td>Income tax 2010</td><td></td><td></td><td></td><td></td><td>X</td><td>X</td></tr><tr><td>Local residence 2010</td><td></td><td></td><td></td><td></td><td>X</td><td>X</td></tr><tr><td>Selection</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Married or pacsed before 2008</td><td></td><td></td><td>X</td><td>X</td><td>X</td><td>X</td></tr><tr><td>Heterosexual</td><td></td><td></td><td></td><td></td><td></td><td>X</td></tr><tr><td>Not cohabitating with other adult 2010</td><td></td><td></td><td></td><td></td><td></td><td>X</td></tr></tbody></table>

"Pacs" is the French civil union contract.

## B.2 Weights and attrition correction

The fact that we are using all the available information from the fiscal databases for the year 2010 leads to lose some individuals who do not appear in these databases the year after their conjugal separation. After matching with the 2010 datasets (tax return & local tax residence) we get 113,794 ex-married (ie a loss of 7.5%) and 9,007 ex-pacsed couples (loss of 4.5%) that we call "sample 3". For 74% of these couples, we recovered the two partners.

In some cases, we do not recover the man's tax return, in other cases the woman's one. To take into account a possible attrition bias, we compute weights such that the weighted recovered population of men and women is representative of all the divorces and Pacs dissolutions that occurred in 2009 that we had formerly recovered (ie representative of our sample 1).

To do so, we stratify between couples who were married and those who were "Pacsed" in 2008. Weights are then calibrated for men and women separately in order to keep 2008 couples for which only one spouse was recovered. Since our main approach is to compare women's living standards on one hand and men's ones on the other hand, this
approach appears as the most relevant one. Weights are computed using the “MACRO
CALMAR” developed and freely provided by the French National Stastitics and Studies
Institute (Insee). The margins used are those from the sample 1 for the margins related
with the 2008 earnings and from the sample 2 for information related with dwellings.

Since we study gender gap and alimonies, we finally exclude same sex couples and then
get 7,932 ex-pacsed-couples.

The last exclusion is the one of individuals who either (re)marry, (re)pacs or cohabit
(with at least another adult) the year after the break up in 2010. This choice has been
made for the sake of simplicity, because cohabitation after divorce could be with a new
partner, a relative or a friend. Finally, as we are interested in labor supply after divorce,
we focus on individuals aged between 20 and 55 years old. The upper age limit has been
set to 55 because in France, withdrawals from the labor market may begin at an early
ages.

Finally in sample 4, we have 97,289 couples: 91,732 ex-married couples and 5,557 ex pacsed -couples for which we recover at least one partner who didn't marry / pacs / cohabit in 2010. It represents 132,094 individuals who were formerly married and 8,039 formerly pacsed.
# C Assessing the quality of matching

## C.1 Overlapping assumption

The first one concerns the “overlapping assumption”. This assumption states that for each observation in the treated group, one observation with similar characteristics can be found, in order to be matched with. This assumption is generally verified presenting graph of the propensity score matching for treated and for the group where the “control group” is extracted. Since we extract our control group from a quasi-exhaustive dataset of married couples, the probability to find a “twin” for each of our divorced couples is high. It turns out that for each of our 24 subsamples of our divorced couples, we have no problem finding a nearest-neighbor. We present in graphs C.1 and C.2 the propensity score matching by number of chidren in the subsample of men whose share in total income was above 60% (before divorce). As we can notice, the propensity score matching for the divorced couples is “overlapped” by the one of the non-divorced ones and then it is possible to find a nearest-neighbor for each divorced couple without been compelled to reduce the range of the estimation to a subsample of them. It is also noticeable that the propensity score of the nearest-neighbor chosen (the “control group”) perfectly fit the divorced one. This is not so surprising since the number of married couples used to find a nearest-neighbor is above 10 million which represents more than 100 times the number of divorced couples to match with: the probability to find a close neighbor is then high. We have similar results whatever the subsample of matching is.
<figure><img src="image_17.png" /><figcaption>Figure C.1: Propensity score matching (men, man's share in total income > 60%, 0 child)</figcaption></figure>

Source: French exhaustive income-tax returns database, 2009

<figure><img src="image_18.png" /><figcaption>Figure C.2: Propensity score matching by number of children (men sample, man's share in total income > 60%)</figcaption></figure>

Source: French exhaustive income-tax returns database, 2009
## C.2 Balance of covariates

The second point of concern is to know if the matching leads to balance properly the covariate in the sample. Our purpose is to balance numerous characteristics of the couple in 2008 (that is before the divorced couples split): age of each ex-partner, regions of living, homeownership, married (vs pacs), house (vs apartment), ..., number and age of child(ren), number of dependent persons, and a wide range of earnings for both men and women (labor income, unemployment benefits, pensions, self-employment incomes (details in profits from commercial and non-commercial occupations and farm profits), earnings from previous year and for 2 years before. To assess the balancing, we provide in tables X1 to X3 the several means for the treated (the divorced couples) and the control group. All differences in mean have been tested with a t-test and turn out to be non significant to a 10% threshold. In the kind of subsample we have (with a large number of observations) the t-test is reputed to be generally too demanding because the large number of observations leads to conclude that little differences in means are significant. Another method of testing the equality between the means is to compute "standardized differences". They are presented in appendix, tables X4 to X6. Standardized differences turn out to be very small, always besides 0.1 and it then confirms our former statement about differences in means.

As often, means are not enough. It is also of concern to know if the matching lead to similar distributions of covariates in the treated and the control group.

We show in graphs Y1 that proceeding by matching greatly improve the fitting of the distribution: after matching the distributions for treated and control group are so similar that they are often impossible to distinguish separately. Off course, this last point could be due to a "scale effect" and to check the similarity of the distribution, it is probably better to just compare the 2 distributions of treated and control group without taking into account the distribution of the non-treated-non-control group whose introduction in the graph tend to dramatically change the scale. We provide in graphs Y2 to Y5 evidences that the difference between the covariates distribution are definitively small and that those distribution are often impossible to disentangle. Those graphics concern
all the income quartiles of the subsample for men whose share in total income was higher
than 60%. Findings for the other subsamples are similar and none subsample present
distinctive feature.

<figure><img src="image_19.png" /><figcaption>Figure C.4: Man Labor Income 1 year before divorce (men sample, man's share in total income > 60%)</figcaption></figure>

<figure><img src="image_20.png" /></figure>

Source: French exhaustive income-tax returns database, 2009
<figure><img src="image_21.png" /><figcaption>Figure C.5: Woman Labor Income 1 year before divorce (men sample, man's share in total income > 60%)</figcaption></figure>

Source: French exhaustive income-tax returns database, 2009

<figure><img src="image_22.png" /><figcaption>Figure C.6: Total declared income 1 year before divorce (men sample, man's share in total income > 60%)</figcaption></figure>

Source: French exhaustive income-tax returns database, 2009
<figure><img src="image_23.png" /><figcaption>Figure C.7: Man Labor Income 2 years before divorce (men sample, man's share in total income > 60%)</figcaption></figure>

Source: French exhaustive income-tax returns database, 2009

<figure><img src="image_24.png" /><figcaption>Figure C.8: Woman Labor Income 2 years before divorce (men sample, man's share in total income > 60%)</figcaption></figure>

Source: French exhaustive income-tax returns database, 2009
<table><caption>Table C.1: Means of covariates and standardized differences (Men sample, Share : > 60%, 0 & 1 child)</caption><thead><tr><th rowspan="2"></th><th colspan="3">0 child</th><th colspan="3">1 child</th></tr><tr><th>Divorcees</th><th>Control group</th><th>Standardized differences</th><th>Divorcees</th><th>Control group</th><th>Standardized differences</th></tr></thead><tbody><tr><td>Share of man earnings in total earnings</td><td>0,8</td><td>0,8</td><td>-0,002</td><td>0,8</td><td>0,8</td><td>0,002</td></tr><tr><td>married</td><td>0,9</td><td>0,9</td><td>-0,002</td><td>0,9</td><td>0,9</td><td>0,006</td></tr><tr><td>Paris</td><td>0,1</td><td>0,0</td><td>0,026</td><td>0,0</td><td>0,0</td><td>-0,003</td></tr><tr><td>Ile-de-France (excepted Paris)</td><td>0,2</td><td>0,2</td><td>-0,017</td><td>0,1</td><td>0,1</td><td>-0,025</td></tr><tr><td>Age (men)</td><td>41,8</td><td>41,6</td><td>0,015</td><td>42,1</td><td>41,9</td><td>0,019</td></tr><tr><td>Age (women)</td><td>39,4</td><td>39,3</td><td>0,011</td><td>39,2</td><td>39,2</td><td>0,008</td></tr><tr><td>Total Declared Income (1 year before divorce)</td><td>41222,5</td><td>41306,1</td><td>-0,002</td><td>45926,8</td><td>45141,8</td><td>0,017</td></tr><tr><td>Men Labor Income (1 year before divorce)</td><td>25408,7</td><td>25639,5</td><td>-0,008</td><td>27971,3</td><td>27635,1</td><td>0,011</td></tr><tr><td>Women Labor Income (1 year before divorce)</td><td>7247,9</td><td>7280,7</td><td>-0,004</td><td>8288,2</td><td>8167,6</td><td>0,014</td></tr><tr><td>Homeowner</td><td>0,4</td><td>0,4</td><td>-0,002</td><td>0,5</td><td>0,5</td><td>0,020</td></tr><tr><td>Living in a House</td><td>0,5</td><td>0,5</td><td>0,000</td><td>0,6</td><td>0,6</td><td>0,008</td></tr><tr><td>At least one 1 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,0</td><td>0,0</td><td>0,010</td></tr><tr><td>At least one 2 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,1</td><td>0,1</td><td>-0,001</td></tr><tr><td>At least one 3 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,1</td><td>0,1</td><td>-0,002</td></tr><tr><td>At least one 4 to 6 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,2</td><td>0,2</td><td>0,005</td></tr><tr><td>At least one 7 to 9 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,1</td><td>0,1</td><td>0,006</td></tr><tr><td>At least one 10 to 14 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,2</td><td>0,2</td><td>0,014</td></tr><tr><td>At least one 15 to 17 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,2</td><td>0,2</td><td>-0,016</td></tr><tr><td>At least one 18 to 25 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,2</td><td>0,2</td><td>-0,012</td></tr><tr><td>At least one more than 26 y.o. child</td><td>0,0</td><td>0,0</td><td>0,000</td><td>0,0</td><td>0,0</td><td>-0,007</td></tr><tr><td>Women Earnings (2 years before divorce)</td><td>6422,5</td><td>6410,1</td><td>0,001</td><td>7630,4</td><td>7693,9</td><td>-0,007</td></tr><tr><td>Men Earnings (2 years before divorce)</td><td>20918,0</td><td>21449,1</td><td>-0,021</td><td>24753,3</td><td>24537,2</td><td>0,007</td></tr><tr><td>Women Earnings (3 years before divorce)</td><td>5066,8</td><td>5166,4</td><td>-0,013</td><td>6530,2</td><td>6713,1</td><td>-0,022</td></tr><tr><td>Men Earnings (3 years before divorce)</td><td>18589,1</td><td>19116,1</td><td>-0,024</td><td>22226,2</td><td>21944,8</td><td>0,011</td></tr><tr><td>Tax payed</td><td>2749,7</td><td>2781,1</td><td>-0,003</td><td>2472,1</td><td>2277,0</td><td>0,020</td></tr><tr><td>Number of children</td><td>0,0</td><td>0,0</td><td>0,000</td><td>1,0</td><td>1,0</td><td>0,000</td></tr><tr><td>Long term unemployment (Men)</td><td>0,0</td><td>0,0</td><td>0,014</td><td>0,0</td><td>0,0</td><td>-0,004</td></tr><tr><td>Overtime work (Men)</td><td>618,0</td><td>656,1</td><td>-0,026</td><td>674,3</td><td>698,1</td><td>-0,016</td></tr><tr><td>Pensions (Men)</td><td>548,4</td><td>554,4</td><td>-0,002</td><td>406,3</td><td>395,5</td><td>0,004</td></tr><tr><td>Unemployment and pre-retirement benefit (Men)</td><td>779,1</td><td>799,5</td><td>-0,006</td><td>736,2</td><td>736,7</td><td>0,000</td></tr><tr><td>Long term unemployment (Women)</td><td>0,0</td><td>0,0</td><td>0,020</td><td>0,0</td><td>0,0</td><td>0,002</td></tr><tr><td>Overtime work (Women)</td><td>115,2</td><td>108,2</td><td>0,016</td><td>109,1</td><td>104,7</td><td>0,011</td></tr><tr><td>Pensions (Women)</td><td>383,9</td><td>382,4</td><td>0,001</td><td>227,3</td><td>199,7</td><td>0,018</td></tr><tr><td>Unemployment and pre-retirement benefit (Women)</td><td>734,7</td><td>770,4</td><td>-0,016</td><td>783,6</td><td>786,7</td><td>-0,001</td></tr><tr><td>Self-employment earnings</td><td>3277,1</td><td>3449,2</td><td>-0,009</td><td>3935,8</td><td>4034,9</td><td>-0,005</td></tr><tr><td>Profits from non-commercial occupations (Men)</td><td>1738,9</td><td>1763,9</td><td>-0,002</td><td>1912,6</td><td>2208,9</td><td>-0,017</td></tr><tr><td>Profits from non-commercial occupations (Women)</td><td>166,5</td><td>168,8</td><td>-0,001</td><td>218,0</td><td>236,3</td><td>-0,006</td></tr><tr><td>Profits from commercial occupations (Men)</td><td>1293,1</td><td>1384,0</td><td>-0,010</td><td>1745,8</td><td>1570,8</td><td>0,017</td></tr><tr><td>Profits from commercial occupations (Women)</td><td>56,8</td><td>67,5</td><td>-0,011</td><td>148,7</td><td>166,4</td><td>-0,008</td></tr><tr><td>Farm profits (Men)</td><td>188,0</td><td>262,9</td><td>-0,024</td><td>246,6</td><td>240,9</td><td>0,002</td></tr><tr><td>Farm profits (Women)</td><td>1,7</td><td>4,3</td><td>-0,009</td><td>20,5</td><td>14,9</td><td>0,010</td></tr><tr><td>No activity (Men)</td><td>0,0</td><td>0,0</td><td>-0,003</td><td>0,0</td><td>0,0</td><td>0,002</td></tr><tr><td>No activity (Women)</td><td>0,3</td><td>0,3</td><td>-0,010</td><td>0,3</td><td>0,3</td><td>0,005</td></tr><tr><td>Standard of living before divorce</td><td>24658,7</td><td>24704,8</td><td>-0,002</td><td>22630,7</td><td>22331,9</td><td>0,015</td></tr><tr><td>UC OECD</td><td>1,5</td><td>1,5</td><td>0,000</td><td>1,9</td><td>1,9</td><td>-0,021</td></tr><tr><td>UC square root</td><td>1,4</td><td>1,4</td><td>0,000</td><td>1,7</td><td>1,7</td><td>0,010</td></tr></tbody></table>
<table><caption>Table C.2: Means of covariates and standardized differences (Men sample, Share : > 60%, 2 & 3 children or more)</caption><thead><tr><th rowspan="2"></th><th colspan="3">2 children</th><th colspan="3">3 children or more</th></tr><tr><th>Divorcees</th><th>Control group</th><th>Standardized differences</th><th>Divorcees</th><th>Control group</th><th>Standardized differences</th></tr></thead><tbody><tr><td>Share of man earnings in total earnings</td><td>0,8</td><td>0,8</td><td>-0,003</td><td>0,9</td><td>0,9</td><td>0,006</td></tr><tr><td>married</td><td>1,0</td><td>1,0</td><td>-0,001</td><td>1,0</td><td>1,0</td><td>0,010</td></tr><tr><td>Paris</td><td>0,0</td><td>0,0</td><td>0,003</td><td>0,0</td><td>0,0</td><td>0,005</td></tr><tr><td>Ile-de-France (excepted Paris)</td><td>0,1</td><td>0,1</td><td>-0,003</td><td>0,1</td><td>0,1</td><td>-0,001</td></tr><tr><td>Age (men)</td><td>41,6</td><td>41,6</td><td>0,008</td><td>41,9</td><td>41,8</td><td>0,022</td></tr><tr><td>Age (women)</td><td>38,9</td><td>38,9</td><td>0,002</td><td>39,0</td><td>38,9</td><td>0,008</td></tr><tr><td>Total Declared Income (1 year before divorce)</td><td>49636,4</td><td>49298,4</td><td>0,007</td><td>41524,3</td><td>40952,1</td><td>0,012</td></tr><tr><td>Men Labor Income (1 year before divorce)</td><td>30322,9</td><td>30286,3</td><td>0,001</td><td>26434,7</td><td>26249,5</td><td>0,006</td></tr><tr><td>Women Labor Income (1 year before divorce)</td><td>8961,9</td><td>8781,4</td><td>0,019</td><td>5629,5</td><td>5574,0</td><td>0,007</td></tr><tr><td>Homeowner</td><td>0,6</td><td>0,6</td><td>0,009</td><td>0,5</td><td>0,5</td><td>0,003</td></tr><tr><td>Living in a House</td><td>0,7</td><td>0,7</td><td>0,003</td><td>0,7</td><td>0,7</td><td>0,006</td></tr><tr><td>At least one 1 y.o. child</td><td>0,0</td><td>0,0</td><td>-0,001</td><td>0,1</td><td>0,1</td><td>-0,007</td></tr><tr><td>At least one 2 y.o. child</td><td>0,1</td><td>0,1</td><td>-0,009</td><td>0,1</td><td>0,1</td><td>0,008</td></tr><tr><td>At least one 3 y.o. child</td><td>0,1</td><td>0,1</td><td>0,002</td><td>0,1</td><td>0,1</td><td>-0,019</td></tr><tr><td>At least one 4 to 6 y.o. child</td><td>0,3</td><td>0,3</td><td>0,007</td><td>0,4</td><td>0,4</td><td>-0,015</td></tr><tr><td>At least one 7 to 9 y.o. child</td><td>0,4</td><td>0,4</td><td>-0,013</td><td>0,5</td><td>0,5</td><td>-0,009</td></tr><tr><td>At least one 10 to 14 y.o. child</td><td>0,5</td><td>0,5</td><td>-0,003</td><td>0,7</td><td>0,7</td><td>0,004</td></tr><tr><td>At least one 15 to 17 y.o. child</td><td>0,3</td><td>0,3</td><td>-0,004</td><td>0,4</td><td>0,4</td><td>0,007</td></tr><tr><td>At least one 18 to 25 y.o. child</td><td>0,2</td><td>0,2</td><td>0,003</td><td>0,3</td><td>0,3</td><td>0,017</td></tr><tr><td>At least one more than 26 y.o. child</td><td>0,0</td><td>0,0</td><td>0,011</td><td>0,0</td><td>0,0</td><td>0,027</td></tr><tr><td>Women Earnings (2 years before divorce)</td><td>8388,1</td><td>8310,3</td><td>0,008</td><td>5015,2</td><td>4925,6</td><td>0,011</td></tr><tr><td>Men Earnings (2 years before divorce)</td><td>27684,4</td><td>27676,3</td><td>0,000</td><td>24441,9</td><td>24437,4</td><td>0,000</td></tr><tr><td>Women Earnings (3 years before divorce)</td><td>7616,0</td><td>7605,3</td><td>0,001</td><td>4445,3</td><td>4252,9</td><td>0,024</td></tr><tr><td>Men Earnings (3 years before divorce)</td><td>25200,4</td><td>25284,5</td><td>-0,003</td><td>22247,9</td><td>22353,8</td><td>-0,004</td></tr><tr><td>Tax payed</td><td>2407,1</td><td>2302,6</td><td>0,009</td><td>1333,0</td><td>1215,6</td><td>0,012</td></tr><tr><td>Number of children</td><td>2,0</td><td>2,0</td><td>0,000</td><td>3,0</td><td>3,0</td><td>0,000</td></tr><tr><td>Long term unemployment (Men)</td><td>0,0</td><td>0,0</td><td>0,004</td><td>0,0</td><td>0,0</td><td>0,004</td></tr><tr><td>Overtime work (Men)</td><td>667,5</td><td>660,7</td><td>0,005</td><td>654,8</td><td>650,1</td><td>0,004</td></tr><tr><td>Pensions (Men)</td><td>302,3</td><td>276,3</td><td>0,011</td><td>305,1</td><td>269,9</td><td>0,018</td></tr><tr><td>Unemployment and pre-retirement benefit (Men)</td><td>609,3</td><td>575,0</td><td>0,011</td><td>765,8</td><td>752,0</td><td>0,005</td></tr><tr><td>Long term unemployment (Women)</td><td>0,0</td><td>0,0</td><td>0,017</td><td>0,0</td><td>0,0</td><td>-0,001</td></tr><tr><td>Overtime work (Women)</td><td>106,0</td><td>101,9</td><td>0,010</td><td>75,8</td><td>73,2</td><td>0,007</td></tr><tr><td>Pensions (Women)</td><td>119,5</td><td>120,2</td><td>-0,001</td><td>160,0</td><td>183,3</td><td>-0,018</td></tr><tr><td>Unemployment and pre-retirement benefit (Women)</td><td>667,2</td><td>694,7</td><td>-0,013</td><td>531,4</td><td>538,0</td><td>-0,004</td></tr><tr><td>Self-employment earnings</td><td>4725,4</td><td>4759,8</td><td>-0,001</td><td>4419,1</td><td>4334,8</td><td>0,004</td></tr><tr><td>Profits from non-commercial occupations (Men)</td><td>2564,9</td><td>2689,8</td><td>-0,005</td><td>2621,2</td><td>2652,6</td><td>-0,001</td></tr><tr><td>Profits from non-commercial occupations (Women)</td><td>239,0</td><td>260,9</td><td>-0,007</td><td>238,6</td><td>281,9</td><td>-0,012</td></tr><tr><td>Profits from commercial occupations (Men)</td><td>1837,0</td><td>1794,3</td><td>0,004</td><td>1515,7</td><td>1335,8</td><td>0,019</td></tr><tr><td>Profits from commercial occupations (Women)</td><td>175,2</td><td>174,4</td><td>0,000</td><td>74,7</td><td>92,4</td><td>-0,015</td></tr><tr><td>Farm profits (Men)</td><td>310,6</td><td>262,8</td><td>0,014</td><td>267,8</td><td>334,4</td><td>-0,016</td></tr><tr><td>Farm profits (Women)</td><td>13,6</td><td>15,2</td><td>-0,003</td><td>19,5</td><td>28,8</td><td>-0,011</td></tr><tr><td>No activity (Men)</td><td>0,0</td><td>0,0</td><td>-0,023</td><td>0,0</td><td>0,0</td><td>0,000</td></tr><tr><td>No activity (Women)</td><td>0,3</td><td>0,3</td><td>-0,018</td><td>0,4</td><td>0,4</td><td>0,003</td></tr><tr><td>Standard of living before divorce</td><td>21769,8</td><td>21677,9</td><td>0,005</td><td>17650,6</td><td>17505,5</td><td>0,011</td></tr><tr><td>UC OECD</td><td>2,2</td><td>2,2</td><td>-0,005</td><td>2,7</td><td>2,7</td><td>0,008</td></tr><tr><td>UC square root</td><td>2,0</td><td>2,0</td><td>0,000</td><td>2,3</td><td>2,3</td><td>-0,002</td></tr></tbody></table>
# D Other tables of results

Variations of living standards, by number of children and by share of earnings provided by man before separation

<table><caption>Table D.1: Variations of living standards, by number of children</caption><thead><tr><th rowspan="2"></th><th colspan="4">Women</th><th colspan="4">Men</th></tr><tr><th>Before<br>transfers<br>(1)</th><th>With<br>private<br>transfers<br>(2)</th><th>Total<br>living<br>standard<br>(3)</th><th>With<br>control<br>group<br>(4)</th><th>Before<br>transfers<br>(5)</th><th>With<br>private<br>transfers<br>(6)</th><th>Total<br>living<br>standard<br>(7)</th><th>With<br>control<br>group<br>(8)</th></tr></thead><tbody><tr><th>All</th><td>-35.1***</td><td>-29.4***</td><td>-14.4***</td><td>-18.8***</td><td>24.2***</td><td>15.4***</td><td>3.5***</td><td>-2.4***</td></tr><tr><th>0</th><td>-26.9***</td><td>-25***</td><td>-18.6***</td><td>-21.5***</td><td>-1,9***</td><td>-3,7***</td><td>-5,2***</td><td>-9.8***</td></tr><tr><th>1</th><td>-32.7***</td><td>-28***</td><td>-13.5***</td><td>-18.9***</td><td>12.4***</td><td>6.5***</td><td>2,1***</td><td>-4,1***</td></tr><tr><th>2</th><td>-36.2***</td><td>-29.1***</td><td>-13.9***</td><td>-18.7***</td><td>28.1***</td><td>17.1***</td><td>5,5***</td><td>-1,0***</td></tr><tr><th>≥ 3</th><td>-45***</td><td>-36.6***</td><td>-12***</td><td>-15.7***</td><td>64.6***</td><td>47.5***</td><td>11.9***</td><td>5,6***</td></tr></tbody></table>

*Lecture: Means of changes.*

<table><caption>Table D.2: Variations of living standards, by share of earnings provided by man before separation</caption><thead><tr><th rowspan="2"></th><th colspan="4">Women</th><th colspan="4">Men</th></tr><tr><th>Before<br>transfers<br>(1)</th><th>With<br>private<br>transfers<br>(2)</th><th>Total<br>living<br>standard<br>(3)</th><th>With<br>control<br>group<br>(4)</th><th>Before<br>transfers<br>(5)</th><th>With<br>private<br>transfers<br>(6)</th><th>Total<br>living<br>standard<br>(7)</th><th>With<br>control<br>group<br>(8)</th></tr></thead><tbody><tr><th>All</th><td>-35.1***</td><td>-29.4***</td><td>-14.4***</td><td>-18.8***</td><td>24.2***</td><td>15.4***</td><td>3.5***</td><td>-2.4***</td></tr><tr><th>&lt;40 %</th><td>18.9***</td><td>21.5***</td><td>18***</td><td>1.4***</td><td>-9***</td><td>-12.6***</td><td>-8.2***</td><td>-20.4***</td></tr><tr><th>40-60 %</th><td>-26.1***</td><td>-23***</td><td>-16.3***</td><td>-17.6***</td><td>-3.6***</td><td>-8.6***</td><td>-13.6***</td><td>-15.5***</td></tr><tr><th>&gt;60 %</th><td>-53.8***</td><td>-45.7***</td><td>-21***</td><td>-24.9***</td><td>49.9***</td><td>37.3***</td><td>17.4***</td><td>10.1***</td></tr></tbody></table>

*Lecture: Means of changes.*
Increase in participation & unemployment benefits

<table><caption>Table D.3: Increase in participation for women & men</caption><thead><tr><th rowspan="2"></th><th colspan="2">Women</th><th colspan="2">Men</th></tr><tr><th>Divorcees<br>(1)</th><th>Divorcees<br>with ctrl gp<br>(2)</th><th>Divorcees<br>(3)</th><th>Divorcees<br>with ctrl gp<br>(4)</th></tr></thead><tbody><tr><th>Treated</th><td></td><td>28.8***</td><td></td><td>14.5***</td></tr><tr><th>Cons.</th><td>52.8***</td><td>24.0***</td><td>54.7***</td><td>40.2***</td></tr></tbody></table>

Sample: individuals who had no income or a yearly income lower than the minimum mensual wage before divorce. They are considered to have increase their participation when,after divorce,their yearly income is higher than the minimum mensual wage.

Lecture: Linear probability models.

<table><caption>Table D.4: Presence of unemployment benefits after divorce when none before, women & men</caption><thead><tr><th rowspan="2"></th><th colspan="2">Women</th><th colspan="2">Men</th></tr><tr><th>Divorcees<br>(1)</th><th>Divorcees<br>with ctrl gp<br>(2)</th><th>Divorcees<br>(3)</th><th>Divorcees<br>with ctrl gp<br>(4)</th></tr></thead><tbody><tr><th>Treated</th><td></td><td>3.0***</td><td></td><td>2.4***</td></tr><tr><th>Cons.</th><td>9.7***</td><td>6.7***</td><td>8.4***</td><td>6.0***</td></tr></tbody></table>

Sample: individuals who had no unemployment benefits and a non-zero individual income before divorce.

Lecture: Linear probability models.
# E Tables of quantile regressions

<table><caption>Table E.1: Median regressions : Changes in living standards and changes in labor and UI income for women</caption><thead><tr><th rowspan="3"></th><th colspan="4">Women</th></tr><tr><th colspan="2">Changes in living standards</th><th colspan="2">Changes in labor and UI income</th></tr><tr><th>Divorcees (1)</th><th>Divorcees with ctrl gp (2)</th><th>Divorcees (3)</th><th>Divorcees with ctrl gp (4)</th></tr></thead><tbody><tr><th colspan="5">Number of children</th></tr><tr><td>0</td><td>-8,4***</td><td>-6,8***</td><td>-0,2*</td><td>0,3***</td></tr><tr><td>1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2</td><td>5***</td><td>4,4***</td><td>1,2***</td><td>-0,4***</td></tr><tr><td>3 or more</td><td>4,5***</td><td>6,3***</td><td>1,2***</td><td>-0,5***</td></tr><tr><th colspan="5">Share of income provided by man before separation</th></tr><tr><td>&lt; 20%</td><td>47,7***</td><td>32,6***</td><td>-5,8***</td><td>-4,5***</td></tr><tr><td>20-40%</td><td>36,9***</td><td>34,8***</td><td>-4,2***</td><td>-3,1***</td></tr><tr><td>40-60%</td><td>16,4***</td><td>16,8***</td><td>-2,7***</td><td>-2,3***</td></tr><tr><td>60-80%</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>&gt; 80%</td><td>-9,3***</td><td>-9,8***</td><td>31,5***</td><td>31,9***</td></tr><tr><th colspan="5">Quintile of income before separation</th></tr><tr><td>1<sup>st</sup></td><td>40,8***</td><td>34***</td><td>6,1***</td><td>3,9***</td></tr><tr><td>2<sup>nd</sup></td><td>9***</td><td>9,1***</td><td>1,1***</td><td>1,1***</td></tr><tr><td>3<sup>rd</sup></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4<sup>th</sup></td><td>-4,9***</td><td>-5,5***</td><td>-0,5***</td><td>0<sup>(ns)</sup></td></tr><tr><td>5<sup>th</sup></td><td>-13,6***</td><td>-13,7***</td><td>-1,6***</td><td>-0,7***</td></tr><tr><td>Cons.</td><td>-28,8***</td><td>-30,2***</td><td>7,6***</td><td>4,1***</td></tr></tbody></table>

Note: Median regressions. Standard errors are computed by sub-sampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: The living standard of women with 1 child, whose husbands provided between 60 and 80% of couple earnings before divorce and who were in couple from the 3rd quintile of income (the reference category), decreases by -30.2% compared to control group (column (2)). Their labor and UI income increases by +4.1%.

Source: Divorced women sample from French exhaustive income-tax returns database, 2009.
<table><caption>Table E.2: Median regressions: Changes in living standards and changes in labor and UI income for men</caption><thead><tr><th rowspan="3"></th><th colspan="4">Men</th></tr><tr><th colspan="2">Changes in living standards</th><th colspan="2">Changes in labor and UI income</th></tr><tr><th>Divorcees<br>(1)</th><th>Divorcees<br>with ctrl gp<br>(2)</th><th>Divorcees<br>(3)</th><th>Divorcees<br>with ctrl gp<br>(4)</th></tr></thead><tbody><tr><th colspan="5" style="text-align: left;">Number of children</th></tr><tr><td>0</td><td>-7,3***</td><td>-6,3***</td><td>0,2<sup>(ns)</sup></td><td>0,5***</td></tr><tr><td>1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2</td><td>2,3***</td><td>1,3***</td><td>0<sup>(ns)</sup></td><td>-0,1***</td></tr><tr><td>3 or more</td><td>0,2<sup>(ns)</sup></td><td>1***</td><td>-0,4***</td><td>-0,7***</td></tr><tr><th colspan="5" style="text-align: left;">Share of income provided by man before separation</th></tr><tr><td>&lt; 20%</td><td>-12,5***</td><td>-20,2***</td><td>73,7***</td><td>12,2***</td></tr><tr><td>20-40%</td><td>-31,6***</td><td>-32,5***</td><td>6,2***</td><td>2,1***</td></tr><tr><td>40-60%</td><td>-17,6***</td><td>-17,4***</td><td>1,4***</td><td>0,3***</td></tr><tr><td>60-80%</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>&gt; 80%</td><td>16,9***</td><td>15***</td><td>-1,8***</td><td>-0,9***</td></tr><tr><th colspan="5" style="text-align: left;">Quintile of income before separation</th></tr><tr><td>1<sup>st</sup></td><td>12,8***</td><td>6,2***</td><td>2,6***</td><td>-0,1*</td></tr><tr><td>2<sup>nd</sup></td><td>3,1***</td><td>2,6***</td><td>0,9***</td><td>0,2***</td></tr><tr><td>3<sup>rd</sup></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4<sup>th</sup></td><td>0,2<sup>(ns)</sup></td><td>-0,9***</td><td>0<sup>(ns)</sup></td><td>0,3***</td></tr><tr><td>5<sup>th</sup></td><td>0,3<sup>(ns)</sup></td><td>0,4***</td><td>-0,5***</td><td>0,3***</td></tr><tr><td>Cons.</td><td>3***</td><td>1,8***</td><td>3,4***</td><td>-0,9***</td></tr></tbody></table>

Note: Median regressions. Standard errors are computed by sub-sampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: The living standard of men with 1 child, who provided between 60 and 80% of the couple earnings before divorce and who were in couple from the 3rd quintile of income (the reference category), increases by +1.8% compared to control group (column (2)). Their labor and UI income decreases by -0.9%.

Source: Divorced men sample from French exhaustive income-tax returns database, 2009.

<table><caption>Table E.3: Median regressions: Variations of living standards, by number of children</caption><thead><tr><th rowspan="3"></th><th colspan="4">Women</th><th colspan="4">Men</th></tr><tr><th>Before<br>transfers</th><th>With<br>private<br>transfers</th><th>Total<br>living<br>standard</th><th>With<br>control<br>group</th><th>Before<br>transfers</th><th>With<br>private<br>transfers</th><th>Total<br>living<br>standard</th><th>With<br>control<br>group</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>All</td><td>-37,9***</td><td>-32,5***</td><td>-19***</td><td>-21,8***</td><td>11,9***</td><td>5***</td><td>-2,2***</td><td>-5,1***</td></tr><tr><td>0</td><td>-30,6***</td><td>-29,3***</td><td>-24,8***</td><td>-26,5***</td><td>-8,3***</td><td>-9,5***</td><td>-11,6***</td><td>-13,4***</td></tr><tr><td>1</td><td>-35,6***</td><td>-31,3***</td><td>-19,3***</td><td>-22,4***</td><td>5,6***</td><td>0,2<sup>(ns)</sup></td><td>-4,3***</td><td>-6,8***</td></tr><tr><td>2</td><td>-38,6***</td><td>-31,6***</td><td>-17,7***</td><td>-21,2***</td><td>18,9***</td><td>8,7***</td><td>0,1<sup>(ns)</sup></td><td>-3,3***</td></tr><tr><td>≥ 3</td><td>-50***</td><td>-40,6***</td><td>-15,5***</td><td>-17,6***</td><td>51***</td><td>34,9***</td><td>8,1***</td><td>5***</td></tr></tbody></table>

Lecture: Medians of changes.
<table><caption>Table E.4: Median regressions: Variations of living standards, by share of earnings provided by man before separation.</caption><thead><tr><th rowspan="2"></th><th colspan="4">Women</th><th colspan="4">Men</th></tr><tr><th>Before<br>transfers<br>(1)</th><th>With<br>private<br>transfers<br>(2)</th><th>Total<br>living<br>standard<br>(3)</th><th>With<br>control<br>group<br>(4)</th><th>Before<br>transfers<br>(5)</th><th>With<br>private<br>transfers<br>(6)</th><th>Total<br>living<br>standard<br>(7)</th><th>With<br>control<br>group<br>(8)</th></tr></thead><tbody><tr><th>All</th><td>-37,9***</td><td>-32,5***</td><td>-19***</td><td>-21,8***</td><td>11,9***</td><td>5***</td><td>-2,2***</td><td>-5,1***</td></tr><tr><th>&lt; 40 %</th><td>5,6***</td><td>7,4***</td><td>9,1***</td><td>1,6***</td><td>-27,4***</td><td>-30***</td><td>-22,5***</td><td>-27,2***</td></tr><tr><th>40-60 %</th><td>-27,1***</td><td>-23,5***</td><td>-17,8***</td><td>-19,5***</td><td>-7,1***</td><td>-10,6***</td><td>-15,1***</td><td>-16,5***</td></tr><tr><th>&gt; 60 %</th><td>-56,1***</td><td>-48***</td><td>-26***</td><td>-28,9***</td><td>38,1***</td><td>27,2***</td><td>13***</td><td>9,3***</td></tr></tbody></table>

Lecture: Medians of changes.

<table><caption>Table E.5: Median regressions: Changes in living standards for different equivalence scales, for women.</caption><thead><tr><th rowspan="2"></th><th colspan="4">Women</th><th colspan="4">Divorcees with<br>control group</th></tr><tr><th>OECD<br>(1)</th><th>Square root<br>variant<br>(2)</th><th>Loneparent<br>variant<br>(3)</th><th>Non custodial<br>parent variant<br>(4)</th><th>OECD<br>(5)</th><th>Square root<br>variant<br>(6)</th><th>Loneparent<br>variant<br>(7)</th><th>Non custodial<br>parent variant<br>(8)</th></tr></thead><tbody><tr><th colspan="9">Number of children</th></tr><tr><th>0</th><td>-8,4***</td><td>-4,7***</td><td>3,1***</td><td>-7,7***</td><td>-6,8***</td><td>-2,2***</td><td>5***</td><td>-6***</td></tr><tr><th>1</th><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><th>2</th><td>5***</td><td>4,2***</td><td>6,3***</td><td>5,2***</td><td>4,4***</td><td>3***</td><td>5,7***</td><td>4,7***</td></tr><tr><th>3 or more</th><td>4,5***</td><td>4,5***</td><td>8***</td><td>4,7***</td><td>6,3***</td><td>5,1***</td><td>9,9***</td><td>6,5***</td></tr><tr><th colspan="9">Share of income provided by man before separation</th></tr><tr><th>&lt; 20%</th><td>47,7***</td><td>49,1***</td><td>48***</td><td>48,2***</td><td>32,6***</td><td>34,4***</td><td>32,9***</td><td>33***</td></tr><tr><th>20-40%</th><td>36,9***</td><td>37,3***</td><td>36,2***</td><td>37,7***</td><td>34,8***</td><td>35,5***</td><td>34,1***</td><td>35,6***</td></tr><tr><th>40-60%</th><td>16,4***</td><td>16,4***</td><td>15,9***</td><td>16,8***</td><td>16,8***</td><td>16,7***</td><td>16,3***</td><td>17,3***</td></tr><tr><th>60-80%</th><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><th>&gt; 80%</th><td>-9,3***</td><td>-9,2***</td><td>-9***</td><td>-9,3***</td><td>-9,8***</td><td>-9,9***</td><td>-9,6***</td><td>-9,9***</td></tr><tr><th colspan="9">Quintile of income before separation</th></tr><tr><th>1<sup>st</sup></th><td>40,8***</td><td>41***</td><td>41,1***</td><td>41,3***</td><td>34***</td><td>34,7***</td><td>34,3***</td><td>34,5***</td></tr><tr><th>2<sup>nd</sup></th><td>9***</td><td>9,3***</td><td>9,4***</td><td>9,2***</td><td>9,1***</td><td>9,3***</td><td>9,4***</td><td>9,3***</td></tr><tr><th>3<sup>rd</sup></th><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><th>4<sup>th</sup></th><td>-4,9***</td><td>-4,6***</td><td>-4,8***</td><td>-5***</td><td>-5,5***</td><td>-5,3***</td><td>-5,4***</td><td>-5,5***</td></tr><tr><th>5<sup>th</sup></th><td>-13,6***</td><td>-13,3***</td><td>-13,4***</td><td>-13,7***</td><td>-13,7***</td><td>-13,6***</td><td>-13,5***</td><td>-13,8***</td></tr><tr><th>Cons.</th><td>-28,8***</td><td>-35,9***</td><td>-37,3***</td><td>-29,6***</td><td>-30,2***</td><td>-37,6***</td><td>-38,6***</td><td>-31***</td></tr></tbody></table>

Note: Median regressions. Standard errors are computed by subsampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: The living standard of women with 1 child, whose husbands provided between 60 and 80% of couple earnings before divorce and who were in couple from the 3rd quintile of income (the reference category), decreases by -30.2%, compared to the control group, when OECD equivalence scale is used (column (5)), by -37.6% when computed with the square root equivalence scale (column (6)), by -38.6% when computed according to the "loneparent variant"(column (7)) and by -31% when computed according to the "non custodial parent variant" (column (8)) (cf. main text for explanations about these variants).

Source: Divorced women sample from French exhaustive income-tax returns database, 2009.
<table><caption>Table E.6: Median regressions: Changes in living standards for different equivalence scales, for men.</caption><thead><tr><th rowspan="3"></th><th colspan="4">Divorcees</th><th colspan="4">Men</th></tr><tr><th colspan="4">Divorcees with control group</th><th colspan="4">Divorcees with control group</th></tr><tr><th>OECD (1)</th><th>Square root variant (2)</th><th>Loneparent variant (3)</th><th>Non custodial parent variant (4)</th><th>OECD (5)</th><th>Square root variant (6)</th><th>Loneparent variant (7)</th><th>Non custodial parent variant (8)</th></tr></thead><tbody><tr><td colspan="9"><strong>Number of children</strong></td></tr><tr><td>0</td><td>-7,3***</td><td>-5,5***</td><td>-2,5***</td><td>-2,6***</td><td>-6,3***</td><td>-3,5***</td><td>-1,5***</td><td>-1,5***</td></tr><tr><td>1</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>2</td><td>2,3***</td><td>0,1<sup>(ns)</sup></td><td>0,9**</td><td>-0,5*</td><td>1,3***</td><td>-1,5***</td><td>-0,1<sup>(ns)</sup></td><td>-1,5***</td></tr><tr><td>3 or more</td><td>0,2<sup>(ns)</sup></td><td>-5,3***</td><td>0<sup>(ns)</sup></td><td>-7,1***</td><td>1***</td><td>-5,5***</td><td>0,8***</td><td>-6,4***</td></tr><tr><td colspan="9"><strong>Share of income provided by man before separation</strong></td></tr><tr><td>&lt; 20%</td><td>-12,5***</td><td>-12,6***</td><td>-11,9***</td><td>-13,3***</td><td>-20,2***</td><td>-19,6***</td><td>-19,6***</td><td>-20,9***</td></tr><tr><td>20-40%</td><td>-31,6***</td><td>-31,9***</td><td>-30,4***</td><td>-32,6***</td><td>-32,5***</td><td>-32,7***</td><td>-31,4***</td><td>-33,6***</td></tr><tr><td>40-60%</td><td>-17,6***</td><td>-17,6***</td><td>-17,8***</td><td>-17,6***</td><td>-17,4***</td><td>-17,5***</td><td>-17,7***</td><td>-17,5***</td></tr><tr><td>60-80%</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>&gt; 80%</td><td>16,9***</td><td>17,1***</td><td>16,8***</td><td>16,8***</td><td>15***</td><td>15,3***</td><td>14,8***</td><td>14,9***</td></tr><tr><td colspan="9"><strong>Quintile of income before separation</strong></td></tr><tr><td>1<sup>st</sup></td><td>12,8***</td><td>13,1***</td><td>14,5***</td><td>10,8***</td><td>6,2***</td><td>6,9***</td><td>7,8***</td><td>4,3***</td></tr><tr><td>2<sup>nd</sup></td><td>3,1***</td><td>3,4***</td><td>3,7***</td><td>2,6***</td><td>2,6***</td><td>3***</td><td>3,2***</td><td>2,1***</td></tr><tr><td>3<sup>rd</sup></td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td><td>Ref.</td></tr><tr><td>4<sup>th</sup></td><td>0,2<sup>(ns)</sup></td><td>-0,2<sup>(ns)</sup></td><td>-0,6<sup>(ns)</sup></td><td>0,2<sup>(ns)</sup></td><td>-0,9***</td><td>-1,2***</td><td>-1,7***</td><td>-0,9***</td></tr><tr><td>5<sup>th</sup></td><td>0,3<sup>(ns)</sup></td><td>-0,8**</td><td>-0,9**</td><td>0,2<sup>(ns)</sup></td><td>0,4***</td><td>-1***</td><td>-0,8***</td><td>0,3**</td></tr><tr><td>Cons.</td><td>3***</td><td>-4,6***</td><td>-2,1***</td><td>-1,7***</td><td>1,8***</td><td>-6,6***</td><td>-3,3***</td><td>-2,9***</td></tr></tbody></table>

Note: Median regressions. Standard errors are computed by subsampling. *, **, *** denote significance at the 10%, 5%, and 1% levels respectively.

Lecture: The living standard of men with 1 child, who provided between 60 and 80% of the couple earnings before divorce and who were in couple from the 3rd quintile of income (the reference category), increases by +1.8% when the OECD equivalence scale is used (column (5)), decreases by -6.6% when computed with the square root equivalence scale (column (6)), by -3.3% when computed according to the “loneparent variant”(column (7)) and by -2.9% when computed according to the “non custodial parent variant” (column (8)) (cf. main text for explanations about these variants).

Source: Divorced men sample from French exhaustive income-tax returns database, 2009.
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G 9305
P. JAILLARD
Le traité de Maastricht : présentation juridique et historique

G 9306
J.L. BRILLET
Micro-DMS : présentation et propriétés

G 9307
J.L. BRILLET
Micro-DMS - variantes : les tableaux

G 9308
S. JACOBZONE
Les grands réseaux publics français dans une perspective européenne

G 9309
L. BLOCH - B. CŒURE
Profitabilité de l'investissement productif et transmission des chocs financiers

G 9310
J. BOURDIEU - B. COLIN-SEDILLOT
Les théories sur la structure optimale du capital : quelques points de repère

G 9311
J. BOURDIEU - B. COLIN-SEDILLOT
Les décisions de financement des entreprises françaises : une évaluation empirique des théories de la structure optimale du capital

G 9312
L. BLOCH - B. CŒURE
Q de Tobin marginal et transmission des chocs financiers

G 9313
Équipes Amadeus (INSEE), Banque de France, Métric (DP)
Présentation des propriétés des principaux modèles macroéconomiques du Service Public

G 9314
B. CREPON - E. DUGUET
Research & Development, competition and innovation

G 9315
B. DORMONT
Quelle est l'influence du coût du travail sur l'emploi ?

G 9316
D. BLANCHET - C. BROUSSE
Deux études sur l'âge de la retraite

G 9317
D. BLANCHET
Répartition du travail dans une population hétérogène : deux notes

G 9318
D. EYSSARTIER - N. PONTY
AMADEUS - an annual macro-economic model for the medium and long term

G 9319
G. CETTE - Ph. CUNÉO - D. EYSSARTIER - J. GAUTIÉ
Les effets sur l'emploi d'un abaissement du coût du travail des jeunes

G 9401
D. BLANCHET
Les structures par âge importent-elles ?

G 9402
J. GAUTIÉ
Le chômage des jeunes en France : problème de formation ou phénomène de file d'attente ?
Quelques éléments du débat

G 9403
P. QUIRION
Les déchets en France : éléments statistiques et économiques

G 9404
D. LADIRAY - M. GRUN-REHOMME
Lissage par moyennes mobiles - Le problème des extrémités de série

G 9405
V. MAILLARD
Théorie et pratique de la correction des effets de jours ouvrables

G 9406
F. ROSENWALD
La décision d'investir

G 9407
S. JACOBZONE
Les apports de l'économie industrielle pour définir la stratégie économique de l'hôpital public

G 9408
L. BLOCH, J. BOURDIEU, B. COLIN-SEDILLOT, G. LONGUEVILLE
Du défaut de paiement au dépôt de bilan : les banquiers face aux PME en difficulté

G 9409
D. EYSSARTIER, P. MAIRE
Impacts macro-économiques de mesures d'aide au logement - quelques éléments d'évaluation

G 9410
F. ROSENWALD
Suivi conjoncturel de l'investissement

G 9411
C. DEFEUILLEY - Ph. QUIRION
Les déchets d'emballages ménagers : une

analyse économique des politiques française et allemande

G 9412
J. BOURDIEU - B. CŒURÉ - B. COLIN-SEDILLOT
Investissement, incertitude et irréversibilité
Quelques développements récents de la théorie de l'investissement

G 9413
B. DORMONT - M. PAUCHET
L'évaluation de l'élasticité emploi-salaire dépend-elle des structures de qualification ?

G 9414
I. KABLA
Le Choix de breveter une invention

G 9501
J. BOURDIEU - B. CŒURÉ - B. SEDILLOT
Irreversible Investment and Uncertainty: When is there a Value of Waiting?

G 9502
L. BLOCH - B. CŒURÉ
Imperfections du marché du crédit, investissement des entreprises et cycle économique

G 9503
D. GOUX - E. MAURIN
Les transformations de la demande de travail par qualification en France
Une étude sur la période 1970-1993

G 9504
N. GREENAN
Technologie, changement organisationnel, qualifications et emploi : une étude empirique sur l'industrie manufacturière

G 9505
D. GOUX - E. MAURIN
Persistence des hiérarchies sectorielles de salaires: un réexamen sur données françaises

G 9505
D. GOUX - E. MAURIN
Persistence of inter-industry wages differentials: a reexamination on matched worker-firm panel data

G 9506
S. JACOBZONE
Les liens entre RMI et chômage, une mise en perspective
NON PARU - article sorti dans Économie et Prévision n° 122 (1996) - pages 95 à 113

G 9507
G. CETTE - S. MAHFOUZ
Le partage primaire du revenu
Constat descriptif sur longue période

G 9601
Banque de France - CEPREMAP - Direction de la Prévision - Érasme - INSEE - OFCE
Structures et propriétés de cinq modèles macro-économiques français

G 9602
Rapport d'activité de la DESE de l'année 1995

G 9603
J. BOURDIEU - A. DRAZNIEKS
L'octroi de crédit aux PME : une analyse à partir d'informations bancaires

G 9604
A. TOPIOL-BENSAÏD
Les implantations japonaises en France

G 9605
P. GENIER - S. JACOBZONE
Comportements de prévention, consommation d'alcool et tabagie : peut-on parler d'une gestion globale du capital santé ?

G 9606
Une modélisation microéconométrique empirique

G 9607
C. DOZ - F. LENGLART
Factor analysis and unobserved component models: an application to the study of French business surveys

G 9607
N. GREENAN - D. GUELLEC
La théorie coopérative de la firme
<table><tr><td>G 9608</td><td>N. GREENAN - D. GUELLEC<br>Technological innovation and employment<br>reallocation</td><td>G 9714</td><td>F. LEQUILLER<br>Does the French Consumer Price Index Over-<br>state Inflation?</td><td>G 9807</td><td>Bilan des activités de la Direction des Études<br>et Synthèses Économiques - 1997</td><td>Bis</td><td>Une estimation de l'élasticité de l'emploi peu<br>qualifié à son coût</td></tr><tr><td>G 9609</td><td>Ph. COUR - F. RUPPRECHT<br>L'intégration asymétrique au sein du continent<br>américain : un essai de modélisation</td><td>G 9715</td><td>X. BONNET<br>Peut-on mettre en évidence les rigidités à la<br>baisse des salaires nominaux ?<br>Une étude sur quelques grands pays de l'OCDE</td><td>G 9808</td><td>A. MOUROUGANE<br>Can a Conservative Governor Conduct an Ac-<br>comodative Monetary Policy?</td><td>G 9913</td><td>Division « Redistribution et Politiques Sociales »<br>Le modèle de microsimulation dynamique<br>DESTINIE</td></tr><tr><td>G 9610</td><td>S. DUCHENE - G. FORGEOT - A. JACQUOT<br>Analyse des évolutions récentes de la producti-<br>vité apparente du travail</td><td>G 9716</td><td>N. IUNG - F. RUPPRECHT<br>Productivité de la recherche et rendements<br>d'échelle dans le secteur pharmaceutique<br>français</td><td>G 9809</td><td>X. BONNET - E. DUBOIS - L. FAUVET<br>Asymétrie des inflations relatives et menus costs<br>: tests sur l'inflation française</td><td>G 9914</td><td>E. DUGUET<br>Macro-commandes SAS pour l'économie des<br>panels et des variables qualitatives</td></tr><tr><td>G 9611</td><td>X. BONNET - S. MAHFOUZ<br>The influence of different specifications of wages-<br>prices spirals on the measure of the NAIRU: the<br>case of France</td><td>G 9717</td><td>E. DUGUET - I. KABLA<br>Appropriation strategy and the motivations to use<br>the patent system in France - An econometric<br>analysis at the firm level</td><td>G 9810</td><td>E. DUGUET - N. IUNG<br>Sales and Advertising with Spillovers at the firm<br>level: Estimation of a Dynamic Structural Model<br>on Panel Data</td><td>G 9915</td><td>R. DUHAUTOIS<br>Évolution des flux d'emplois en France entre<br>1990 et 1996 : une étude empirique à partir du<br>fichier des bénéfices réels normaux (BRN)</td></tr><tr><td>G 9612</td><td>PH. COUR - E. DUBOIS, S. MAHFOUZ,<br>J. PISANI-FERRY<br>The cost of fiscal retrenchment revisited: how<br>strong is the evidence?</td><td>G 9718</td><td>L.P. PELÉ - P. RALLE<br>Âge de la retraite : les aspects incitatifs du régime<br>général</td><td>G 9811</td><td>J.P. BERTHIER<br>Congestion urbaine : un modèle de trafic de<br>pointe à courbe débit-vitesse et demande<br>élastique</td><td>G 9916</td><td>J.Y. FOURNIER<br>Extraction du cycle des affaires : la méthode de<br>Baxter et King</td></tr><tr><td>G 9613</td><td>A. JACQUOT<br>Les flexions des taux d'activité sont-elles seule-<br>ment conjoncturelles ?</td><td>G 9719</td><td>ZHANG Yingxiang - SONG Xueqing<br>Lexique macroéconomique français-chinois,<br>chinois-français</td><td>G 9812</td><td>C. PRIGENT<br>La part des salaires dans la valeur ajoutée : une<br>approche macroéconomique</td><td>G 9917</td><td>B. CRÉPON - R. DESPLATZ - J. MAIRESSÉ<br>Estimating price cost margins, scale economies<br>and workers' bargaining power at the firm level</td></tr><tr><td>G 9614</td><td>ZHANG Yingxiang - SONG Xueqing<br>Lexique macroéconomique Français-Chinois</td><td>G 9720</td><td>M. HOUDEBINE - J.L. SCHNEIDER<br>Mesurer l'influence de la fiscalité sur la locali-<br>sation des entreprises</td><td>G 9813</td><td>A.Th. AERTS<br>L'évolution de la part des salaires dans la valeur<br>ajoutée en France reflète-t-elle les évolutions<br>individuelles sur la période 1979-1994 ?</td><td>G 9918</td><td>Ch. GIANELLA - Ph. LAGARDE<br>Productivity of hours in the aggregate production<br>function: an evaluation on a panel of French firms<br>from the manufacturing sector</td></tr><tr><td>G 9701</td><td>J.L. SCHNEIDER<br>La taxe professionnelle : éléments de cadrage<br>économique</td><td>G 9721</td><td>A. MOUROUGANE<br>Crédibilité, indépendance et politique monétaire<br>A revenue de la littérature</td><td>G 9814</td><td>B. SALANIÉ<br>Guide pratique des séries non-stationnaires</td><td>G 9919</td><td>S. AUDRIC - P. GIVORD - C. PROST<br>Évolution de l'emploi et des coûts par quali-<br>fication entre 1982 et 1996</td></tr><tr><td>G 9702</td><td>J.L. SCHNEIDER<br>Transition et stabilité politique d'un système<br>redistributif</td><td>G 9722</td><td>P. AUGERAUD - L. BRIOT<br>Les données comptables d'entreprises<br>Le système intermédiaire d'entreprises<br>Passage des données individuelles aux données<br>sectorielles</td><td>G 9901</td><td>S. DUCHÈNE - A. JACQUOT<br>Une croissance plus riche en emplois depuis le<br>début de la décennie ? Une analyse en compa-<br>raison internationale</td><td>G 2000/01</td><td>R. MAHIEU<br>Les déterminants des dépenses de santé : une<br>approche macroéconomique</td></tr><tr><td>G 9703</td><td>D. GOUX - E. MAURIN<br>Train or Pay: Does it Reduce Inequalities to En-<br>courage Firms to Train their Workers?</td><td>G 9723</td><td>P. AUGERAUD - J.E. CHAPRON<br>Using Business Accounts for Compiling National<br>Accounts: the French Experience</td><td>G 9902</td><td>Ch. COLIN<br>Modélisation des carrières dans Destinie</td><td>G 2000/02</td><td>C. ALLARD-PRIGENT - H. GUILMEAU -<br>A. QUINET<br>The real exchange rate as the relative price of<br>nontrables in terms of tradables: theoretical<br>investigation and empirical study on French data</td></tr><tr><td>G 9704</td><td>P. GENIER<br>Deux contributions sur dépendance et équité</td><td>G 9724</td><td>P. AUGERAUD<br>Les comptes d'entreprise par activités - Le pas-<br>sage aux comptes - De la comptabilité<br>d'entreprise à la comptabilité nationale - A<br>paraitre</td><td>G 9903</td><td>Ch. COLIN<br>Évolution de la dispersion des salaires : un essai<br>de prospective par microsimulation</td><td>G 2000/03</td><td>J.-Y. FOURNIER<br>L'approximation du filtre passe-bande proposée<br>par Christiano et Fitzgerald</td></tr><tr><td>G 9705</td><td>E. DUGUET - N. IUNG<br>R & D Investment, Patent Life and Patent Value<br>An Econometric Analysis at the Firm Level</td><td>G 9801</td><td>H. MICHAUDON - C. PRIGENT<br>Présentation du modèle AMADEUS</td><td>G 9904</td><td>B. CREPON - N. IUNG<br>Innovation, emploi et performances</td><td>G 2000/04</td><td>Bilan des activités de la DESE - 1999</td></tr><tr><td>G 9706</td><td>M. HOUDEBINE - A. TOPIOL-BENSAÏD<br>Les entreprises internationales en France : une<br>analyse à partir de données individuelles</td><td>G 9802</td><td>J. ACCARDO<br>Une étude de comptabilité générationnelle<br>pour la France en 1996</td><td>G 9905</td><td>B. CREPON - Ch. GIANELLA<br>Wages inequalities in France 1969-1992<br>An application of quantile regression techniques</td><td>G 2000/05</td><td>B. CREPON - F. ROSENWALD<br>Investissement et contraintes de financement : le<br>poids du cycle<br>Une estimation sur données françaises</td></tr><tr><td>G 9707</td><td>M. HOUDEBINE<br>Polarisation des activités et spécialisation des<br>départements en France</td><td>G 9803</td><td>X. BONNET - S. DUCHÈNE<br>Apports et limites de la modélisation<br>« Real Business Cycles »</td><td>G 9906</td><td>C. BONNET - R. MAHIEU<br>Microsimulation techniques applied to inter-<br>generational transfers - Pensions in a dynamic<br>framework: the case of France</td><td>G 2000/06</td><td>A. FLIPO<br>Les comportements matrimoniaux de fait</td></tr><tr><td>G 9708</td><td>E. DUGUET - N. GREENAN<br>Le biais technologique : une analyse sur données<br>individuelles</td><td>G 9804</td><td>C. BARLET - C. DUGUET -<br>D. ENCAOUA - J. PRADEL<br>The Commercial Success of Innovations<br>An econometric analysis at the firm level in<br>French manufacturing</td><td>G 9907</td><td>F. ROSENWALD<br>L'impact des contraintes financières dans la dé-<br>cision d'investissement</td><td>G 2000/07</td><td>R. MAHIEU - B. SÉDILLOT<br>Microsimulations of the retirement decision: a<br>supply side approach</td></tr><tr><td>G 9709</td><td>J.L. BRILLET<br>Analyzing a small French ECM Model</td><td>G 9805</td><td>P. CAHUC - Ch. GIANELLA -<br>D. GOUX - A. ZILBERBERG<br>Equalizing Wage Differences and Bargaining<br>Power - Evidence form a Panel of French Firms</td><td>G 9908</td><td>Bilan des activités de la DESE - 1998</td><td>G 2000/08</td><td>C. AUDENIS - C. PROST<br>Déficit conjoncturel : une prise en compte des<br>conjonctures passées</td></tr><tr><td>G 9710</td><td>J.L. BRILLET<br>Formalizing the transition process: scenarios for<br>capital accumulation</td><td>G 9806</td><td>J. ACCARDO - M. JLASSI<br>La productivité globale des facteurs entre 1975 et<br>1996</td><td>G 9909</td><td>J.P. ZOYEM<br>Contrat d'insertion et sortie du RMI<br>Évaluation des effets d'une politique sociale</td><td>G 2000/09</td><td>R. MAHIEU - B. SÉDILLOT<br>Équivalent patrimonial de la rente et souscription<br>de retraite complémentaire</td></tr><tr><td>G 9711</td><td>G. FORGEOT - J. GAUTIÉ<br>Insertion professionnelle des jeunes et processus<br>de déclassement</td><td>G 9911</td><td>G. LAROQUE - B. SALANIÉ<br>Une décomposition du non-emploi en France</td><td>G 9910</td><td>Ch. COLIN - FI. LEGROS - R. MAHIEU<br>Bilans contributifs comparés des régimes de<br>retraite du secteur privé et de la fonction publique</td><td>G 2000/10</td><td>R. DUHAUTOIS<br>Ralentissement de l'investissement : petites ou<br>grandes entreprises ? industrie ou tertiaire ?</td></tr><tr><td>G 9712</td><td>E. DUBOIS<br>High Real Interest Rates: the Consequence of a<br>Saving Investment Disequilibrium or of an in-<br>sufficient Credibility of Monetary Authorities?</td><td>G 9912</td><td>B. SALANIÉ<br>Une maquette analytique de long terme du<br>marché du travail</td><td>G 9911</td><td>B. SALANIÉ<br>Une décomposition du non-emploi en France</td><td>G 2000/11</td><td>G. LAROQUE - B. SALANIÉ<br>Temps partiel féminin et incitations financières à<br>l'emploi</td></tr><tr><td>G 9713</td><td>Bilan des activités de la Direction des Études<br>et Synthèses Économiques - 1996</td><td>G 9912</td><td>Ch. GIANELLA<br>Ch. GIANELLA<br>Local unemployment and wages</td><td>G 9912</td><td>Ch. GIANELLA<br>Ch. GIANELLA</td><td>G 2000/12</td><td>Ch. GIANELLA<br>Local unemployment and wages</td></tr></table>
G2000/13 B. CREPON - Th. HECKEL
- Informatisation en France : une évaluation à partir de données individuelles
- Computerization in France: an evaluation based on individual company data

G2001/01 F. LEQUILLER
- La nouvelle économie et la mesure de la croissance du PIB
- The new economy and the measure ment of GDP growth

G2001/02 S. AUDRIC
La reprise de la croissance de l'emploi profite-t-elle aussi aux non-diplômés ?

G2001/03 I. BRAUN-LEMAIRE
Évolution et répartition du surplus de productivité

G2001/04 A. BEAUDU - Th. HECKEL
Le canal du crédit fonctionne-t-il en Europe ? Une étude de l'hétérogénéité des comportements d'investissement à partir de données de bilan agréées

G2001/05 C. AUDENIS - P. BISCOURP - N. FOURCADE - O. LOISEL
Testing the augmented Solow growth model: An empirical reassessment using panel data

G2001/06 R. MAHIEU - B. SÉDILLOT
Départ à la retraite, irréversibilité et incertitude

G2001/07 Bilan des activités de la DESE - 2000

G2001/08 J. Ph. GAUDEMET
Les dispositifs d'acquisition à titre facultatif d'annuités viagères de retraite

G2001/09 B. CRÉPON - Ch. GIANELLA
Fiscalité, coût d'usage du capital et demande de facteurs : une analyse sur données individuelles

G2001/10 B. CRÉPON - R. DESPLATZ
Évaluation des effets des dispositifs d'allégements de charges sociales sur les bas salaires

G2001/11 J.-Y. FOURNIER
Comparaison des salaires des secteurs public et privé

G2001/12 J.-P. BERTHIER - C. JAULENT
R. CONVENEVOLE - S. PISANI
Une méthodologie de comparaison entre consommations intermédiaires de source fiscale et de comptabilité nationale

G2001/13 P. BISCOURP - Ch. GIANELLA
Substitution and complementarity between capital, skilled and less skilled workers: an analysis at the firm level in the French manufacturing industry

G2001/14 I. ROBERT-BOBEE
Modelling demographic behaviours in the French microsimulation model Destinie: An analysis of future change in completed fertility

G2001/15 J.-P. ZOYEM
Diagnostic sur la pauvreté et calendrier de revenus : le cas du "Panel européen des ménages »

G2001/16 J.-Y. FOURNIER - P. GIVORD
La réduction des taux d'activité aux âges extrêmes, une spécificité française ?

G2001/17 C. AUDENIS - P. BISCOURP - N. RIEDINGER
Existe-t-il une asymétrie dans la transmission du prix du brut aux prix des carburants ?

G2002/01 F. MAGNIEN - J.-L. TAVERNIER - D. THESMAR
Les statistiques internationales de PIB par habitant en standard de pouvoir d'achat : une analyse des résultats

G2002/02 Bilan des activités de la DESE - 2001

G2002/03 B. SÉDILLOT - E. WALRAET
La cessation d'activité au sein des couples : y a-t-il interdépendance des choix ?

G2002/04 G. BRILHAULT
- Rétropolation des séries de FBCF et calcul du capital fixe en SEC-95 dans les comptes nationaux français

- Retropolation of the investment series (GFCF) and estimation of fixed capital stocks on the ESA-95 basis for the French balance sheets

G2002/05 P. BISCOURP - B. CRÉPON - T. HECKEL - N. RIEDINGER

How do firms respond to cheaper computers? Microeconometric evidence for France based on a production function approach

G2002/06 C. AUDENIS - J. DERDYON - N. FOURCADE
L'impact des nouvelles technologies de l'information et de la communication sur l'économie française - un bouclage macro-économique

G2002/07 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Évaluation de trois réformes du Régime Général d'assurance vieillesse à l'aide du modèle de microsimulation DESTINIE

G2002/08 J.-P. BERTHIER
Réflexions sur les différentes notions de volume dans les comptes nationaux : comptes aux prix d'une année fixe ou aux prix de l'année précédente, séries chaînées

G2002/09 F. HILD
Les soldes d'opinion résument-ils au mieux les réponses des entreprises aux enquêtes de conjoncture ?

G2002/10 I. ROBERT-BOBÉE
Les comportements démographiques dans le modèle de microsimulation Destinie - Une comparaison des estimations issues des enquêtes Jeunes et Carrières 1997 et Histoire Familiale 1999

G2002/11 J.-P. ZOYEM
La dynamique des bas revenus : une analyse des entrées-sorties de pauvreté

G2002/12 F. HILD
Prévisions d'inflation pour la France

G2002/13 M. LECLAIR
Réduction du temps de travail et tensions sur les facteurs de production

G2002/14 E. WALRAET - A. VINCENT
- Analyse de la redistribution intragénérationnelle dans le système de retraite des salariés du privé - Une approche par microsimulation
- Intragenerational distributional analysis in the french private sector pension scheme - A microsimulation approach

G2002/15 P. CHONE - D. LE BLANC - I. ROBERT-BOBEE
Offre de travail féminine et garde des jeunes enfants

G2002/16 F. MAUREL - S. GREGOIR
Les indices de compétitivité des pays : inter-prétation et limites

G2003/01 N. RIEDINGER - E.HAUVY
Le coût de dépollution atmosphérique pour les entreprises françaises : Une estimation à partir de données individuelles

G2003/02 P. BISCOURP et F. KRAMARZ
Création d'emplois, destruction d'emplois et internationalisation des entreprises industrielles françaises : une analyse sur la période 1986-1992

G2003/03 Bilan des activités de la DESE - 2002

G2003/04 P.-O. BEFFY - J. DERDYON - N. FOURCADE - S. GREGOIR - N. LAÎB - B. MONFORT
Évolutions démographiques et croissance : une projection macro-économique à l'horizon 2020

G2003/05 P. AUBERT
La situation des salariés de plus de cinquante ans dans le secteur privé

G2003/06 P. AUBERT - B. CRÉPON
Age, salaire et productivité
La productivité des salariés décline-t-elle en fin de carrière ?

G2003/07 H. BARON - P.O. BEFFY - N. FOURCADE - R. MAHIEU
Le ralentissement de la productivité du travail au cours des années 1990

G2003/08 P.-O. BEFFY - B. MONFORT
Patrimoine des ménages, dynamique d'allocation et comportement de consommation

G2003/09 P. BISCOURP - N. FOURCADE
Peut-on mettre en évidence l'existence de rigidités à la baisse des salaires à partir de données individuelles ? Le cas de la France à la fin des années 90

G2003/10 M. LECLAIR - P. PETIT
Présence syndicale dans les firmes : quel impact sur les inégalités salariales entre les hommes et les femmes ?

G2003/11 P.-O. BEFFY - X. BONNET - M. DARRACQ-PARIES - B. MONFORT
MZE: a small macro-model for the euro area

G2004/01 P. AUBERT - M. LECLAIR
La compétitivité exprimée dans les enquêtes trimestrielles sur la situation et les perspectives dans l'industrie

G2004/02 M. DUÉE - C. REBILLARD
La dépendance des personnes âgées : une projection à long terme

G2004/03 S. RASPILLER - N. RIEDINGER
Régulation environnementale et choix de localisation des groupes français

G2004/04 A. NABOULET - S. RASPILLER
Les déterminants de la décision d'investir : une approche par les perceptions subjectives des firmes

G2004/05 N. RAGACHE
La déclaration des enfants par les couples non mariés est-elle fiscalement optimale ?

G2004/06 M. DUÉE
L'impact du chômage des parents sur le devenir scolaire des enfants

G2004/07 P. AUBERT - E. CAROLI - M. ROGER
New Technologies, Workplace Organisation and the Age Structure of the Workforce: Firm-Level Evidence

G2004/08 E. DUGUET - C. LELARGE
Les brevets accroissent-ils les incitations privées à innover ? Un examen microéconomique

G2004/09 S. RASPILLER - P. SILLARD
Affiliating versus Subcontracting: the Case of Multinationals

G2004/10 J. BOISSINOT - C. L'ANGEVIN - B. MONFORT
Public Debt Sustainability: Some Results on the French Case

G2004/11 S. ANANIAN - P. AUBERT
Travailleurs âgés, nouvelles technologies et changements organisationnels : un réexamen à partir de l'enquête « REPONSE »

G2004/12 X. BONNET - H. PONCET
Structures de revenus et propensions différentes à consommer - Vers une équation de consommation des ménages plus robuste en prévision pour la France

G2004/13 C. PICART
Évaluer la rentabilité des sociétés non financières

G2004/14 J. BARDAJI - B. SÉDILLOT - E. WALRAET
Les retraites du secteur public : projections à l'horizon 2040 à l'aide du modèle de microsimulation DESTINIE

G2005/01 S. BUFFETEAU - P. GODEFROY
Conditions de départ en retraite selon l'âge de fin d'études : analyse prospective pour les générations 1945 à 1974

G2005/02 C. AFSA - S. BUFFETEAU
L'évolution de l'activité féminine en France : une approche par pseudo-panel

G2005/03 P. AUBERT - P. SILLARD
Délocalisations et réductions d'effectifs dans l'industrie française

G2005/04 M. LECLAIR - S. ROUX
Mesure et utilisation des emplois instables dans les entreprises

G2005/05 C. L'ANGEVIN - S. SERRAVALLE
Performances à l'exportation de la France et de l'Allemagne - Une analyse par secteur et destination géographique

G2005/06 Bilan des activités de la Direction des Études et Synthèses Economiques - 2004

G2005/07 S. RASPILLER
La concurrence fiscale : principaux enseignements de l'analyse économique

G2005/08 C. L'ANGEVIN - N. LAÎB
Éducation et croissance en France et dans un panel de 21 pays de l'OCDE
G2010/09 P.-Y. CABANNES - V. LAPÈGUE - E. POULIQUEN - M. BEFFY - M. GAINI Quelle croissance de moyen terme après la crise ?

G2010/10 I. BUONO - G. LALANNE La réaction des entreprises françaises à la baisse des tarifs douaniers étrangers

G2010/11 R. RATHELOT - P. SILLARD L'apport des méthodes à noyaux pour mesurer la concentration géographique - Application à la concentration des immigrés en France de 1968 à 1999

G2010/12 M. BARATON - M. BEFFY - D. FOUGÈRE Une évaluation de l'effet de la réforme de 2003 sur les départs en retraite - Le cas des enseignants du second degré public

G2010/13 D. BLANCHET - S. BUFFETEAU - E. CRENNER S. LE MINEZ Le modèle de microsimulation Destinie 2: principales caractéristiques et premiers résultats

G2010/14 D. BLANCHET - E. CRENNER Le bloc retraites du modèle Destinie 2: guide de l'utilisateur

G2010/15 M. BARLET - L. CRUSSON - S. DUPUCH - F. PUECH Des services échangés aux services échangeables : une application sur données françaises

G2010/16 M. BEFFY - T. KAMIONKA Public-private wage gaps: is civil-servant human capital sector-specific?

G2010/17 P.-Y. CABANNES - H. ERKEL-ROUSSE - G. LALANNE - O. MONSO - E. POULIQUEN Le modèle Mésange réestimé en base 2000 Tome 2 - Version avec volumes à prix chaînes

G2010/18 R. AEBERHARDT - L. DAVEZIES Conditional Logit with one Binary Covariate: Link between the Static and Dynamic Cases

G2011/01 T. LE BARBANCHON - B. OURLIAC - O. SIMON Les marchés du travail français et américain face aux chocs conjonctuels des années 1986 à 2007 : une modélisation DSGE

G2011/02 C. MARBOT Une évaluation de la réduction d'impôt pour l'emploi de salariés à domicile

G2011/03 L. DAVEZIES Modèles à effets fixes, à effets aléatoires, modèles mixtes ou multi-niveaux: propriétés et mises en œuvre des modélisations de l'hétérogénéité dans le cas de données groupées

G2011/04 M. ROGER - M. WASMER Heterogeneity matters: labour productivity differentiated by age and skills

G2011/05 J.-C. BRICONGNE - J.-M. FOURNIER V. LAPÈGUE - O. MONSO De la crise financière à la crise économique L'impact des perturbations financières de 2007 et 2008 sur la croissance de sept pays industrialisés

G2011/06 P. CHARNOZ - É. COUDIN - M. GAINI Wage inequalities in France 1976-2004: a quantile regression analysis

G2011/07 M. CLERC - M. GAINI - D. BLANCHET Recommendations of the Stiglitz-Sen-Fitoussi Report: A few illustrations

G2011/08 M. BACHELET - M. BEFFY - D. BLANCHET Projeter l'impact des réformes des retraites sur l'activité des 55 ans et plus : une comparaison de trois modèles

G2011/09 C. LOUVOT-RUNAVOT L'évaluation de l'activité dissimulée des entreprises sur la base des contrôles fiscaux et son insertion dans les comptes nationaux

G2011/10 A. SCHREIBER - A. VICARD La tertiairisation de l'économie française et le ralentissement de la productivité entre 1978 et 2008

G2011/11 M.-É. CLERC - O. MONSO - E. POULIQUEN Les inégalités entre générations depuis le baby-boom

G2011/12 C. MARBOT - D. ROY Évaluation de la transformation de la réduction d'impôt en crédit d'impôt pour l'emploi de salariés à domicile en 2007

G2011/13 P. GIVORD - R. RATHELOT - P. SILLARD Place-based tax exemptions and displacement effects: An evaluation of the Zones Franches Urbaines program

G2011/14 X. D'HAULTFOEUILLE - P. GIVORD - X. BOUTIN The Environmental Effect of Green Taxation: the Case of the French "Bonus/Malus"

G2011/15 M. BARLET - M. CLERC - M. GARNEO - V. LAPÈGUE - V. MARCUS La nouvelle version du modèle MZE, modèle macroéconométrique pour la zone euro

G2011/16 R. AEBERHARDT - I. BUONO - H. FADINGER Learning, Incomplete Contracts and Export Dynamics: Theory and Evidence form French Firms

G2011/17 C. KERDRAIN - V. LAPÈGUE Restrictive Fiscal Policies in Europe: What are the Likely Effects?

G2012/01 P. GIVORD - S. QUANTIN - C. TREVIEN A Long-Term Evaluation of the First Generation of the French Urban Enterprise Zones

G2012/02 N. CECI-RENAUD - V. COTTET Politique salariale et performance des entreprises

G2012/03 P. FÉVRIER - L. WILNER Do Consumers Correctly Expect Price Reductions? Testing Dynamic Behavior

G2012/04 M. GAINI - A. LEDUC - A. VICARD School as a shelter? School leaving-age and the business cycle in France

G2012/05 M. GAINI - A. LEDUC - A. VICARD A scarred generation? French evidence on young people entering into a tough labour market

G2012/06 P. AUBERT - M. BACHELET Disparités de montant de pension et redistribution dans le système de retraite français

G2012/07 R. AEBERHARDT - P GIVORD - C. MARBOT Spillover Effect of the Minimum Wage in France: An Unconditional Quantile Regression Approach

G2012/08 A. EIDELMAN - F. LANGUMIER - A. VICARD Prélèvements obligatoires reposant sur les ménages : des canaux redistributifs différents en 1990 et 2010

G2012/09 O. BARGAIN - A. VICARD Le RMI et son successeur le RSA découragent-ils certains jeunes de travailler ? Une analyse sur les jeunes autour de 25 ans

G2012/10 C. MARBOT - D. ROY Projections du coût de l'APA et des caractéristiques de ses bénéficiaires à l'horizon 2040 à l'aide du modèle Destinie

G2012/11 A. MAUROUX Le crédit d'impôt dédié au développement durable : une évaluation économique

G2012/12 V. COTTET - S. QUANTIN - V. RÉGNIER Coût du travail et allègements de charges : une estimation au niveau établissement de 1996 à 2008

G2012/13 X. D'HAULTFOEUILLE - P. FÉVRIER - L. WILNER Demand Estimation in the Presence of Revenue Management

G2012/14 D. BLANCHET - S. LE MINEZ Joint macro/micro evaluations of accrued-to-date pension liabilities: an application to French reforms

G2013/01-F1301 T. DERDYON - A. MONTAUT - P-A PIONNIER Utilisation rétrospective de l'enquête Emploi à une fréquence mensuelle: apport d'une modélisation espace-état

G2013/02-F1302 C. TREVIEN Habiter en HLM: quel avantage monétaire et quel impact sur les conditions de logement ?

G2013/03 A. POISSONNIER Temporal disaggregation of stock variables - The Chow-Lin method extended to dynamic models

G2013/04 P. GIVORD - C. MARBOT Does the cost of child care affect female labor market participation? An evaluation of a French reform of childcare subsidies

G2013/05 G. LAME - M. LEQUIEN - P.-A. PIONNIER Interpretation and limits of sustainability tests in public finance

G2013/06 C. BELLEGO - V. DORTET-BERNADET La participation aux pôles de compétitivité : quelle incidence sur les dépenses de R&D et l'activité des PME et ETI ?

G2013/07 P.-Y. CABANNES - A. MONTAUT - P.-A. PIONNIER Évaluer la productivité globale des facteurs en France : l'apport d'une mesure de la qualité du capital et du travail

G2013/08 R. AEBERHARDT - C. MARBOT Evolution of Instability on the French Labour Market During the Last Thirty Years

G2013/09 J-B. BERNARD - G. CLÉAUD Oil price: the nature of the shocks and the impact on the French economy

G2013/10 G. LAME Was there a « Greenspan Conundrum » in the Euro area?

G2013/11 P. CHONÉ - F. EVAIN - L. WILNER - E. YILMAZ Introducing activity-based payment in the hospital industry: Evidence from French data

G2013/12 C. GRISLAIN-LETRÉMY Natural Disasters: Exposure and Underinsurance

G2013/13 P.-Y. CABANNES - V. COTTET - Y. DUBOIS - C. LELARGE - M. SICSIC French Firms in the Face of the 2008/2009 Crisis

G2013/14 A. POISSONNIER - D. ROY Households Satellite Account for France in 2010. Methodological issues on the assessment of domestic production

G2013/15 G. CLÉAUD - M. LEMOINE - P.-A. PIONNIER Which size and evolution of the government expenditure multiplier in France (1980-2010)?

G2014/01 M. BACHELET - A. LEDUC - A. MARINO Les biographies du modèle Destinie II: rebasage et projection

G2014/02 B. GARBINTI L'achat de la résidence principale et la création d'entreprises sont-ils favorisés par les donations et héritages ?

G2014/03 N. CECI-RENAUD - P. CHARNOZ - M. GAINI Évolution de la volatilité des revenus salariaux du secteur privé en France depuis 1968

G2014/04 P. AUBERT Modalités d'application des réformes des retraites et prévisibilité du montant de pension

G2014/05 C. GRISLAIN-LETRÉMY - A. KATOSSKY The Impact of Hazardous Industrial Facilities on Housing Prices: A Comparison of Parametric and Semiparametric Hedonic Price Models

G2014/06 J.-M. DAUSSIN-BENICHOU - A. MAUROUX Turning the heat up. How sensitive are households to fiscal incentives on energy efficiency investments?

G2014/07 C. LABONNE - G. LAMÉ Credit Growth and Capital Requirements: Binding or Not?

G2014/08 C. GRISLAIN-LETRÉMY et C. TREVIEN The Impact of Housing Subsidies on the Rental Sector: the French Example

G2014/09 M. LEQUIEN et A. MONTAUT Croissance potentielle en France et en zone euro : un tour d'horizon des méthodes d'estimation

G2014/10 B. GARBINTI - P. LAMARCHE Les hauts revenus épargnent-ils davantage ?

G2014/11 D. AUDENAERT - J. BARDAJI - R. LARDEUX - M. ORAND - M. SICSIC Wage Resilience in France since the Great Recession

G2014/12 F. ARNAUD - J. BOUSSARD - A. POISSONNIER - H. SOUAL Computing additive contributions to growth and other issues for chain-linked quarterly aggregates

G2014/13 H. FRAISSE - F. KRAMARZ - C. PROST Labor Disputes and Job Flows