Alternatives for Financing Social Security in Luxembourg by Resident and Cross-Border Households

Given the rapid evolution of structural socio-demographic determinants in Luxembourg (ageing, migration, cross-border households) and the resulting social needs (pensions, healthcare, dependency), the question of how to finance Luxembourg’s social security system is high on the agenda and could become even more pressing over time.

In it’s baseline scenario, the 2024 Ageing Report from the EU-Economic Policy Committee’s Ageing Working Group is expecting for Luxembourg an increase of age-related expenditure from 17.2% of GDP in 2022 to 27.9% in 2070,mostly due to pensions (+ 8.3% of GDP over the period).¹ This is the biggest increase expected in EU-countries, a first particularity for Luxembourg.

A second specificity is the importance of cross-border commuters in the Luxembourg economy. Those do matter by large, representing more than 40% of total employment (41% in 2017, 43% in 2022), hence significantly participating in the funding of social security, for example through social contributions and personal income taxes.²

Given such a situation, the country’s social players are looking for avenues of reflection for future concrete proposals. In particular, the Chambre des Salariés du Luxembourg(CSL), a key social partner, has initiated a study aimed at launching a debate on the impact of hypothetical changes in social contributions and personal income tax (hereafter the “alternatives”).³ This quest is formulated by highlighting four aspects.

Firstly, the main objective of the study is to examine funding possibilities. We do not consider other relevant issues related to public spending. Furthermore, given the nature of the Luxembourg economy, we should involve both residents and cross-border households.

Secondly, despite this keen interest in funding possibilities, distributive aspects are also at stake. What might be the impact of alternatives on income inequality, poverty, well-being, etc.? However, cross-border households are only partially covered by the microdata at our disposal and are embedded in a broader population that is not exclusively made up of commuters. Consequently, this examination of distributions for non-residents, while possible, can only be partial. The present analysis will therefore be limited mainly to residents in this respect, which is in any case relevant from a political point of view and an important first step towards a more comprehensive view.

Thirdly, the alternatives discussed here are the result of internal debates at CSL, which is responding to the concerns of its main affiliates as part of an ongoing public debate. The changes envisaged are parametric (e.g. thresholds, tax rates and social security contributions). We are therefore not concerned here with non-parametric reforms which, for example, would change the nature of the tax bases considered for calculating social security contributions or other taxes such as those based on consumption.

Finally, the analysis must be as transparent and independent as possible, hence a request to the Luxembourg Institute of Socio-Economic Research (LISER) to carry out a preliminary analysis. Dedicated tools can be found in the public administration in general or in other relevant bodies, but they are often either not very accessible to the social partners, unclear as to the assumptions made or the content, incomplete, or simply considered too much driven from external spheres with concerns differing from CSL’s ones. Whether a reality or an a priori fear, such an interpretation is quite common in public debate, and can be partially addressed by independent analysis and the use of tools that are as simple and interpretable as possible.⁴

This is why we have chosen to build the present analysis on a pair of EUROMOD-type static microsimulation models, one for the resident population, the other targeting cross-border households, the latter population involving an essential innovative extension to previous assessments.⁵ Microsimulation techniques are well suited to generate missing information (e.g. individual taxes and benefits, if unknown from other sources) and analyze the distributional impact of changes in the socio-demographic environment (including the search for winners and losers). Such a technique is particularly relevant when the interactions induced by changes are non-linear, such as those resulting from the application of complex socio-fiscal policies.

More specifically, the EUROMOD platform is also well established, rapidly applicable, considered reliable and simple enough to analyze the background interactions leading to overall results. EUROMOD allows us to take a representative snapshot of a population for a given year and apply several policy rules to this population (calculating social contributions, benefits, income tax, etc. for each household or individual), leading to a distributional view of the “welfare” resulting from this specific social and fiscal policy. If we modify the rules applied, we obtain an alternative distribution, and we can focus both on overall changes (in income and expenditure) and on sub-groups particularly affected, positively or negatively, by this alternative.

The simple “day after” EUROMOD approach is therefore seen here by the CSL as an essential step towards the identification of avenues that could be of interest and explored in greater depth at a later stage when examining global proposals or longer-term perspectives.

We underline that projections of social security expenditure over the coming decades (up to 2070), in a context of major macroeconomic and demographic changes, may substantially differ from results derived from a purely static framework such as EUROMOD. This limitation also applies, even in the short term, when analyzing non-marginal policy alternatives that may generate significant second-order effects, notably on labor market outcomes. However, the present mandate required the use of a simple analytical toolbox, combining transparency with ease of interpretation, in order to support a preliminary assessment. Although modelling instruments accounting for behavioral responses, partial equilibrium or broader macroeconomic interactions do exist in Luxembourg, they are not fully accessible to social partners and provide limited, if any, insight into longer-term higher-order effects.⁶ Moreover, these models rely on additional assumptions that are themselves subject to debate and are often difficult to communicate in the public arena, which constitutes the primary target of the present analysis. The need for immediately accessible results, together with resource constraints, therefore justifies the deliberately restricted analytical scope adopted here, which was considered sufficient for the social partner’s objectives. Within this context, we relied on this “day-after” tool, which indeed proved adequate to support the formulation of concrete reform proposals during recent discussions with the government on pension amendments implemented from 2026 onwards.⁷ What should remain relevant during necessary additional steps to come.

This study is initially based on a previous one carried out by DIW Berlin (Ochmann et al., 2014), yet deviating a bit from it. For example, we simulate cross-border households explicitly. In the DIW report, cross-border households are indirectly considered through proportional summary keys indicating to what extent they may contribute to the “totals”, in case of policy changes, as a proportion of the resident population. Moreover, we build up for the two populations an explicit background information set which may help in understanding outcomes resulting from alternatives (gross income distributions for residents and cross-border households, socio-economic status, share of households through taxable income brackets, etc.).

Finally, although the analysis is specific to Luxembourg, the policy alternatives and methodology considered here may also be relevant for other countries with comparable socio-economic fundamentals, particularly in the context of the EU-wide concern regarding the fiscal implications of population ageing.

The paper is structured as follows. Firstly, we briefly describe the tools (models and data) on which this study is grounded and provide some information on the benchmark “STD” currently in force in Luxembourg, which will serve as a basis for comparisons (Section 2). Then we present the alternatives to be examined (Section 3) and develop the outcomes resulting from these alternatives, addressing the issue of the complexity of the comparisons and attempting to progressively reduce it (Section 4). Finally, we conclude (Section 5).

We introduce some basic building bricks that could be useful for a better understanding of the general outcomes resulting from the alternatives that will be presented in the next section. These elements concern the micro-data and the models specifically developed for this study (Section 2.1.) as well as some information on the benchmark that will be used as a reference when analyzing the effects of these alternatives (Section 2.2.). More details and contextual comments on the models can be found in Liégeois (2025a), some extracts of which being reproduced here in extensor for the sake of self-sufficiency of the present paper.

2.2. An Overview of Outcomes for the Benchmark “STD” (2017)

Table 1 gives an overall view of the benchmark “STD” which is a picture available for Luxembourg in 2017 (income year), as derived from EUROMOD/HFCS-R and EUROMOD/HFCS-XB, and completed with regard the macro adjustment which is dealing with “non-active” XB households who are not covered by HFCS/XB.¹⁴

Let’s just mention here that social security contributions and personal income taxes paid by XBs in Luxembourg are not proportional to the respective populations.¹⁵ The global tax rate on XBs’ income (10.4%), is also much lower than for residents (17.2%). This is explained by background fundamentals which differ between the two populations.

For example and with regard employees, the most frequent “employment status” both for residents (43%) and for active cross-border households (56%), Figure 1 is showing up the distribution of wages (EUROMOD variable “yem”).¹⁶ Those are classified based on the so-called “Minimum Social Wage (MSW) /Salaire Social Minimum” which is an important social parameter in Luxembourg. For example, the monthly base for computation of social contributions is most often limited to 5*MSW: above this upper limit, no supplementary contributions are requested. The MSW represents 1,998.59 EUR per month in 2017. Clearly, XB employees are more often facing lower wage levels than residents.

More largely, Figure 2 is classifying the fiscal households based on fiscal brackets and the main “classes of tax”. The latter can be identified as “1” (roughly “single without dependents and aged < 65”) and “2” (roughly “couples”) and the less frequent “1a”-class (roughly “single with dependents or aged >= 65”). Fiscal households are all persons belonging to the same dwelling and considered as to be taxed jointly on income, taking into account the relevant fiscal rules. “Class-2” households represent 37% of fiscal households in the resident population (54% for active cross-border households), “Class 1” 47% of households for the residents (38% for active cross-border households). Here again, we can see that XB active households are more concentrated around the lowest tax brackets.

The next section in Table 1 is reporting about several “well-being” and inequality indices, all dimensions genuinely embedded in an approach undertaken through microsimulation.

We consider here as an indicator of individual “well-being” the so-called and standard equivalent income which is the ratio between the disposable income of the household (= gross income + social benefits – social contributions – personal income taxes) and a coefficient considering the composition hence needs of that household.¹⁷ The equivalent income is determined at the residence household level -all persons belonging to the same dwelling- and then each member of the household is attributed this household value.

Then, the at-risk-of-poverty rate (hereafter the “poverty rate” or “poverty”) represented here is the share of population below 60% of the median equivalent income, a threshold called the “poverty line”.

Another indicator is telling something about the unequal distribution of well-being throughout the population: the inequality Gini coefficient (hereafter the “Gini”). As summarized in Vergnat et al. (2022), this coefficient is an index with a value between 0 and 1 (0.2993 in Luxembourg in 2017, based on EUROMOD/HFCS-R), increasing if inequalities in equivalent incomes become greater (zero would indicate perfect equality; the same income for all). It is equal to the absolute Gini index (the average absolute difference between incomes, 2,103 EUR/month) divided by twice the average equivalent income (3,513 EUR/month).

Table 1 is dealing with both residents and XBs and outcomes could be broken down based on usual basic typologies for sub-groups of population: for example the gender, the type of household, the decile of equivalent income and the class of age.

We are not commenting the levels of well-being, poverty and Gini resulting from the EUROMOD/HFCS-XB platform for XB active households, shown up in Table 1 as well. This population is partial only and less information is collected, compared to the usual reference involving a whole (resident) population, hence comparisons with residents being more hazardous, as mentioned in the introductory section.¹⁸ At most might those results be of interest when assessing the effect of an alternative within the cross-border population itself, i.e. while comparing alternatives and not populations, what is done in the full reports, including Liégeois (2023a).

Those above-mentioned indicators will serve as references while analyzing the effects of alternative socio-fiscal policies, compared to our benchmark “STD”, in Section 4.

Table 1

Data and EUROMOD platforms			SILC *	HFCS-R	HFCS-XB (Active households & LU-incomes only)
Population covered by the survey, in persons			574,184	535,897	418,997
Taxable Income, before Tax allowances, in millions € / year (from tinty_s in HFCS-R, or tinty_lu_s for Active XB households)			20,808	20,041	7,947
… out of additional amount from XBs not covered by the survey (for subsequent macro adjustment, see Section 2.4)					1,109
Public Revenue (in millions € / year)
	Social contributions		3,914	3,714	2,030
	whose	Employee	1,820	1,693	945
		Self-employed	178	221	27
		Others (Long term care from Social assistance)	3	3	0
		Employers	1,710	1,667	1,059
		Credited (Replacement income, Social assistance, Pensions)	202	130	0
	… out of additional amount from XBs not covered by the survey (for macro adjustment)				62
	Personal Income Tax		3,289	3,439	824
		Implicit Tax Rate on Tax Base (tinty_s – tinta_s, or LU versions if XBs), before Tax Credits		20.9%	14.4%
		Global Tax Rate (after Tax Allowances & Credits) = Income Tax / Taxable Income, on average		17.2%	10.4%
	… out of additional amount from XBs not covered by the survey (for macro adjustment)				115
	Þ Total Public Revenue (before macro adjustment)		7,203	7,153	2,855
Inequalities					If All Incomes
	Gini
		Relative = Abs / (2*Avg)	0.2524	0.2993	0.1940
		Absolute (in € / month)	1,701	2,103	1,088
		Average (in € / month)	3,371	3,513	2,804
	Poverty
		Line (in € / month)	1,790	1,770	1,564
		Rate	11%	13%	2%
		by Type of Residence Household:
		Single (<65)	14%	16%	1%
		Single (65+)	8%	9%	2%
		Single with dependent(s)	31%	18%	0%
		Couple - 0 dep	6%	5%	2%
		Couple - 1-2 dep	11%	18%	2%
		Couple - 3+ dep	14%	21%	2%
"Well-being", as equivalized income (all in € / month), on average					If All Incomes
	All		3,371	3,513	2,804
	1st Decile		1,574	1,472	1,635
	by Type of Residence Household:
		Single (<65)	3,308	3,249	2,828
		Single (65+)	3,215	3,440	2,878
		Single with dependent(s)	2,473	2,661	2,530
		Couple - 0 dep	3,922	4,150	3,194
		Couple - 1-2 dep	3,117	3,292	2,768
		Couple - 3+ dep	2,673	2,826	2,353

1. Source: Liégeois (2025a) Table 2

2. *

   A comparison with official statistics can be found in the EUROMOD country report for Luxembourg (https://euromod-web.jrc.ec.europa.eu/resources/country-reports - Archives - I3.0+ - Annex 2: Validation tables)

Figure 1

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Figure 2

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Departing from “STD”, the standard system of socio-fiscal policies implemented in Luxembourg for the (income) year 2017, we are now introducing a large number of alternatives that modify some parameters, like the maximum thresholds for the computation of social contributions, the tax schedules, etc.).

Tables 2–4 summarize the alternatives discussed in this paper, considering alterations of the social contributions for pensions, health care (in-kind) or long-term care (dependency), as well as modifications in the income tax schedule. For each of them, we provide some information about its contents. 2-letter acronyms have been chosen for those alternatives. The first letter in the code is underlying the policy considered (Pensions / Health / Dependency / Income tax) and the second letter is the order number of the alternative within each category (1,2, … , 9, A, B, … H).

For a better immediate understanding, we can point out that a maximum threshold of 5 times the Minimum Social Wage (Section 2.2.) is often set in Luxembourg, for example as a basis for derivation of contributions if the worker is not a civil servant (labor income exceeding this limit is not considered for social security contributions).

Moreover, four strong yet quite hypothetical alternatives “C1-C4” have been imagined, which purpose is to look for a combination of systems possibly leading to high additional total receipts (Table 5). The objective was to reach approximatively an equivalent of a 5%-GDP gain in terms of total additional receipts.

Such a target is sometimes cited by social partners in relation with -about half of- the expected increase in age-related expenditure over the long term (see the introductory section). This might be reached through a 25% increase for the sum of social contributions and personal income taxes.¹⁹

We underline that several of the policy alternatives considered are non-marginal and may generate significant higher-order or feedback effects. Such a complementary exercise, given the high level of changes, is mainly theoretical (at most seen as a long-term perspective), aiming at raising the question of a feasibility for those tracks and entering the black box in terms of implications. Nevertheless, the transparent “day-after” approach adopted here is considered sufficient to meet the initial objectives of the analysis, as outlined in Section 1.

The 43 alternatives developed in Section 3 may generate a large number of indicators dealing with total social contributions and personal income tax, well-being, inequalities and poverty rates, including by population sub-groups and for both residents and XBs.

Table 1, which is part of a description usually proposed by analysts to policy makers, is giving a flavor of such a complexity for a single policy mix (here the baseline “STD”). A fortiori, comparing a great number of alternatives is a high dimension challenge that we are now trying to meet by proposing a progressive path of analysis.

As a starter and to ensure the legitimacy of our approach in the eyes of the social partners, we go on with usual practices and indicators well-established in the sphere of socio-economic analyses. While proceeding, we keep in mind the main objective of some additional revenue in terms of social contributions and personal income tax. But we complement this with an additional focus on distributional impacts, with a look at the Gini inequality index and the poverty rate, two other classical indicators. Moreover, while the first one is here logically involving both the residents and the XBs, the next two are addressing the residents only, for reasons evoked in the introductory section.

This limitation to a triple of indicators for a comparative analysis is done to make clearer a combination of objectives. However, we show in the Appendix A that those indicators are correlated -hence may also be seen as “related”- to other relevant ones.

We now propose an overall view of alternatives, based on this reduced set of indicators, either separately for each indicator (Section 4.1.) or when combining indicators (Section 4.2.).

4.1. A View on all Alternatives through the 3 Selected Indicators

Figures 3–6 are showing up an overall view of the 3 basic indicators: the total receipts from residents an XBs, inequalities and the level of poverty among the residents as they result from all alternatives, including the benchmark STD.

Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 3 shows that among all alternatives, the combination C3 drives to the highest increase in total receipts for Luxembourg (whatever from residents or XBs): 122% compared to its level through the benchmark STD, I6 being the most depressing design with that respect (97%).^{20 21} compared to its level through the benchmark STD, I6 being the most depressing design with that respect (97%). Out of the alternatives “Cx”, PC (rate from 8% to 12%) is reaching 114%. A majority of the alternatives are leading to higher receipts, compared to STD, yet just a little for many of them. The three dotted horizontal lines are marking the % relevant for STD.

Gains in receipts are often coming from higher social contributions (blue line), which is directly resulting from the attributes of the alternatives retained by the social partner in the present exercise. Personal income taxes (green line) resulting from alternatives lie usually around or below their STD level, rather, which is partly explained by higher social contributions pushing down tax allowances.

The benchmark STD is reporting 57% of total receipts coming from social contributions, hence 43% from personal income taxes. This can go up to 83% for social contributions in C3 (in proportion of the total receipts for STD) or, leaving aside the combined alternatives, 74% for PC, and down to 54% for H6.

Figure 4 is telling more about the share of receipts coming from residents. In STD, the former are funding around 70% of total receipts going to Luxembourg, 79% of personal income taxes and 64% of social contributions, with some variations throughout the alternatives. For example, I6 is leading residents up to 80.4% for the income taxes. Roughly said, the proportions visible for residents -higher than the share of this population (56%, based on Table 1), and still higher for income taxes compared to social contributions - may also be a combined effect of lower earnings for XBs (see Figures 2 and 3) and the progressive nature of the income taxation (: Section 4).

We now turn to a view of inequalities. Figure 5 is showing the Gini coefficient pertaining to residents for all alternatives, ranked from the highest value to the lowest one, as well as its decomposition into the average well-being and the average absolute earnings gap (the Gini coefficient is equal to the latter divided by twice the former). All percentages reported are relative to the values of each determinant in the benchmark STD. Very few alternatives are doing worse than STD for the Gini coefficient (>100%), with PC leading to 100.4%. The best option with that respect is C4, which drives the Gini down to 97.6% of what is observed in STD. Everything being constant by elsewhere, those 2.4% would represent a decrease of about 50 €/month for the average (absolute) earnings gap, to be compared to its value of 2,103 € in STD -a gain that might be considered as rather limited by the social planner, hence maybe not a priority-.

The real story in the background differs indeed a bit: we approximatively face through C4 a 7% reduction in the mean earnings gap (blue curve), which is however partially compensated in terms of inequalities by a 5 % loss in the well-being (green curve), altogether leading to this 2% change in Gini. Out of the combined systems, I3 seems the best option, with a reduction of 1.8% for the Gini.

Figure 6 is now giving a flavor of what happens with the poverty rate among residents. This indicator may go up to 14.7% for I3 (13.2% in STD) and can be reduced a little at 12.6% through C4. Like for the Gini, explaining changes in the poverty rate is often a complex task, this being the result of a combination of (changes in) both the median well-being (hence the poverty line which is here 60% of the median) and the distribution of well-being around this line as modified through alternatives. Figure 6 shows that several configurations can happen: a negative change in both the poverty line and the poverty rate, which seems an intuitive association, like for PA or C1. Or vice versa, as for I5 or I6. But a counter-intuitive increase in poverty combined with a lower poverty line can also be observed, like in PC or C3, which is an indication that the depressing effect of the alternative on the earnings is still more pronounced on lower income deciles than around 60% of the median. In particular, C3 is characterized by a lower Gini index (Figure 5) combined with a higher poverty rate. As complementarily shown in Liégeois (2023a), the upper equivalent income deciles are more adversely affected by this alternative than the lower deciles, leading to a compression of the income distribution and, overall, to a reduction in inequality.

Altogether, Table 6 is summarizing the maximum and minimum (“I_MIN” and “I_MAX”) reached for the three representative indicators retained as well as the alternative leading to such values, together with “I_STD” prevailing for the benchmark in 2017 (STD). The “normalized index” referred to in Table 6 is clarified in Section 4.2..

Table 6

Basic Indicator considered ß	C1-C4 ß	Min		Max		STD
		IMIN *	Alternative	IMAX [**]	Alternative	ISTD	Normalized index iSTD (if Cx included) *
Gini coefficient(residents)	Included	0.2922	C4	0.3004	PC	0.2993	0.138 *
	Excluded	0.2939	I3
Poverty Rate(residents)	Included	12.6%	C4	14.7%	I3	13.2%	0.697
	Excluded	12.6%	PE
Total Receipts for Luxembourg(in millions €/year)	Included	9,849	I6	12,432	C3	10,185	0.130
	Excluded			11,558	PC

1. *

   See Section 4.2 - The Gini normalized value g_STD directly computed from this table = (0.3004 – 0.2993) / (0.3004 - 0.2922) = 0.134 is slightly deviating from the “effective” value 0.138 mentioned in the table, due to rounding effects

4.2. A Look at Alternatives While Combining Indicators

More global and useful lessons can be derived from a combination of these figures. Figures 7 and 8 are illustrating those associations for the couples “Gini residents – Total receipts” and “Poverty Residents – Gini Residents”. We show here a selection of alternatives only, to keep the pictures readable.

Yet PC being the best alternative for total receipts, out of the combined “Cx” systems and as visible from Figure 3, Figure 8 is revealing that this is also at the expense of both an increase in poverty and a higher Gini, compared to STD. If we concentrate initially on poverty rather, PE (the best alternative with that respect, out of “Cx” ones) is also decreasing the Gini (Figures 5 and 8) but with a moderate effect of total receipts only, as also shown through Figures 3 and 7. Taking now into account the third dimension, alternative I3 improves the Gini at best, yet implying a strong increase in poverty (Figure 8) and all this for unchanged total receipts (Figure 3).

Therefore, the natural question that now arises is how to find our way through so many alternatives and indicators, in search of a path that would “best” combine the 3 selected dimensions. Let’s emphasize already alternative PF that seems of interest for all three dimensions (Figures 7 and 8).

Note that summarizing, and ranking up to a certain extent, alternative systems of socio-fiscal policies is neither an easy task nor an ultimate answer. Obviously, an alternative increasing a tax or contribution by 2% will most often drive -unless strong non-linearities due for example to a threshold effect- to results qualitatively close to, yet more pronounced than, another change of the same rate by 1% only. For example, the alternative PC (social contribution rate from 8% to 12% for pensions) will impact indicators in the same way, qualitatively, as P4 (8% to 9%), yet with more pronounced effects, what can be seen for example from Figure 8. This does not imply as such that, when outcomes are considered desirable, the former track is better than the latter that might be anyway preferred for any political economy reason: progressive reform, acceptable thresholds or rates, political agenda, … .

Figure 7

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Figure 8

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Nevertheless, we may desire to complete our detailed overview with some assessment about the overall performance of each alternative and a comparison of all alternatives based on such an overall view designated here as the “global performance index” (hereafter, “GPI”).

A simple GPI might be a composite indicator derived from the specific dimensions previously selected: inequalities among the residents (represented by “G”), poverty rates for the residents (“P”) and total receipts (“R”). A vast literature exists on composite indicators, but it lies outside the scope of the present request from the social partner, as it is not directly suited to exchanges with third parties.²² Nevertheless, the index proposed here may help to clarify internally specific policy advantages or to confirm the relative positioning of alternatives already identified in previous analyses. For these reasons, this type of analytical approach is only briefly mentioned in the present paper.

Practically and taking into account that ranges of values for the basic indicators G, P and R can differ a bit in absolute terms, we may choose to normalize each of them to the interval [0-1]:

${\displaystyle {\displaystyle i_A=\frac{I_A-I_{MIN}}{I_{MAX}-I_{MIN}}ifR,or\frac{I_{MAX}-I_A}{I_{MAX}-I_{MIN}}ifG,P}}$

where:

• A is the alternative system examined (for example STD or P7)

• I is the original value for the basic indicator under consideration here (G, P or R - see remark in the next paragraph)

• i_A is the normalized value of the basic indicator considered for alternative A

• MIN and MAX are the minimum and maximum values reachable for each basic indicator G, P or R throughout all the alternatives (for example, 12.6% to 14.7% for the poverty of residents, with an STD value equal to 13.2% - see Figure 6

The normalized indicator i_A is expected to increase when the outcome is socially considered as “desirable”, hence a specification differing for R, on the one side, G and P on the other side (the normalized indicator for P should be greater if P is lower). The higher the normalized index, the better is the original index, socially speaking: “1” is the maximum/best (resp. “0” the minimum/worst) accessible value among all alternatives. Therefore, a value closer to 0 for i_STD, like total receipts or the Gini coefficient in Table 6, is showing that other alternatives can offer some room for “improvement”, from STD to the best option, compared to the worst one.

We are now properly equipped for combining the basic indicators. At this stage, we can use linear and non-linear approaches. Linear aggregation of indicators assumes full compensability between dimensions, allowing poor performance in one dimension to be offset by good performance in another. When such substitutability is undesirable, non-linear aggregation methods—such as geometric means or non-compensatory indices—provide a more appropriate framework by penalizing unbalanced performance and limiting trade-offs between dimensions.

For sake of illustration here, we consider a linear weighting procedure: a*g + β*p + g*r, where a, β and g are exogenous parameters revealing the social planner’s preference. For example, 0.1*g + 0.2*p + 0.7*g would imply that a stress is put on receipts in the global performance of an alternative. Note that a, β and g are summing up to 1 without loss of generality. Such a linear specification is simple enough and probably sufficient as a first step for a better understanding of the overall procedure.

Appendix B is showing up the first- and second-best alternative systems of policies, as well as the worst one, identified through their global performance index GPI, taking into account several values for the weights a, β and g. For example, a balanced objective with weights 0.3, 0.3 and 0.4 respectively, is leading to PF as the best possible alternative… as evoked earlier.

This paper presents alternative, sometimes merely exploratory, avenues devised by a key social partner for the financing of social security in Luxembourg and their implications in terms of income distribution. We build on an earlier analysis carried out by DIW in 2013-2014 but rewrite its content in order to i) add an in-depth examination of the cross-border population in addition to the resident one, ii) test new combinations of socio-fiscal policy systems and iii) isolate determinants important to the results.

The cross-border population plays a more important role in Luxembourg (and on public finances) than in most other developed countries, and therefore definitely deserves to be included for a more comprehensive analysis. The new light shed on Luxembourg by this paper requires the updating of an older version of a EUROMOD model using HFCS micro-data for the resident population, and the creation of a new model for XBs. The constraints inherent in micro-data prompts us to opt for 2017 (income year) as the analysis year.

Given the significant structural differences between residents and XBs in terms of socio-economic status, gross labor income and taxable income, we show that total receipts from residents are greater than those from XBs, even when controlling for population size. For the same reason, a reform of the socio-fiscal system in Luxembourg might not have the same impact on XBs (facing lower income levels on average) and residents.

By examining the 42 alternatives devised here (in addition to the standard STD, in force in Luxembourg in 2017), some of them are highlighted, based on considerations including the values achieved for a triple of indicators retained for this overall analysis: total receipts (including XBs), the Gini inequality coefficient and the poverty rate (the latter two for the resident population only).

Table 7 summarizes this point and highlights the scope for change in relation to the reference STD, while considering all the alternatives discussed in the study. The final choice of a specific alternative would obviously depend on the desired level for each indicator, the trade-off between possibly competing dimensions and, more generally, on social preferences or political feasibility.

While reviewing the results highlighted in this paper, the reader should bear in mind important aspects that may also merit further examination in the future.

Firstly, the 42 (+1) alternatives selected for this paper are mainly ad hoc (in particular, the changes are parametric) and mainly based on the CSL’s concerns and proposals.

When designing the reforms, other choices could have been made for several parameters (thresholds, rates). To a certain extent, the results would be easily resized if other parameter levels were preferred (such as 8.5% for social contribution rates for pensions, instead of the current 8% in STD and 9% in alternative P4). If we consider that the social and tax system is sufficiently linear (hence smooth enough) and additive (which is clearly a simplifying assumption), turning to 0.5% for a change, rather than 1%, could simply lead at first sight to results half as important (at least for social contributions).

Secondly, the method we have chosen to reduce the high dimensionality of the comparative analysis is also partly ad hoc and crude. It includes both the selection of core indicators from a subset of classical ones, while considering their correlation matrix (Section 4.1. and Appendix A), and the design of a Global Performance Index (Section 4.2.). But this particular approach is conceived here as simple enough and probably sufficient as a first step towards a better overall understanding of the analytical challenges involved.

Finally, several alternatives would lead to “major” changes in socio-fiscal policies. Some of them, such as PC or C3, were mainly designed to provide an initial sense of the implications, assuming that any future increase in pension spending would be financed solely through additional resources while keeping in mind, yet not addressing at first sight, downstream effects. However, such a shift raises two types of questions.

The first question focuses precisely on the possible effects of alternatives that would go beyond the immediate or “day-after” impact, including behavioral responses, partial or general equilibrium effects and longer-term perspectives.

In particular, some alternatives may have substantial effects on labor supply if they alter net wages, rather than merely increasing charges borne by firms. Future research could analyze these behavioral responses, once policymakers have identified preferred alternatives on the basis of this preliminary step, through a discrete-choice modelling approach, similar to that used for Luxembourg by Berger et al. (2011). Moreover, higher gross wages may reduce labor demand, with possible effects on both resident and cross-border workers, thereby raising a partial-equilibrium issue. Finally, given the “lumpy” features of Luxembourg’s labor market—participation decisions, cross-border commuting costs, and fixed working hours—a Random Utility Random Opportunity (RURO) framework could also be considered, insofar as data constraints allow.²³

However, the need for sufficient confidence, on the side of the social partners, in the proposed modeling apparatus leads us to a static platform such as EUROMOD as a starting point. This is a well-established and widely accepted tool, requiring fewer resources for rapid implementation. What’s more, this simplified approach can be seen as an essential step towards identifying avenues that could be of interest and further explored at a later date in terms of expected effects, also considering, on top of behavioral returns during a first period, longer-term perspectives (as in Liégeois and Genevois, 2015; Liégeois, 2025b), whereas less sophisticated behavioral contents for the latter. Accordingly, the present paper can be interpreted as a first step in a broader analytical process, paving the way for more in-depth investigations to be carried out at a later stage in support of the social partners, if desired.

The second question relates to concerns about the feasibility of these reforms in political terms. In this sense, and without even mentioning the possible feedback effects of these alternatives, a lighter reform (more progressive, acceptable thresholds or rates, political agenda, etc.) could be considered preferable although less promising in terms of total revenues or impact on inequalities.

All this could open up tracks for future developments and questioning, if one of the alternatives examined were considered sufficiently ripe for general debate, in line with a specific concern and/or close to a publicly defined (political) agenda. In this respect, it is noteworthy that the 2026 reform in Luxembourg introduces –among other aspects– a 0.5% increase in pension social contributions for both employees and employers, alongside a similar 0.5% government contribution. It therefore lies between P4 and the status quo (STD). This may reflect both a grounded assessment of needs and a cautious political approach. The present analysis may also lead to further work and possible adjustments on related issues, including inequalities and the poverty rate among residents (see Figures 7 and 8).

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Author details

1. Philippe Liégeois

   Luxembourg Institute of Socio-Economic Research/LISER, 4366 Belval Esch-sur-Alzette, Luxembourg

   For correspondence

   Philippe.Liegeois-ext@liser.lu

   "This ORCID iD identifies the author of this article:" 0000-0003-2329-5609