Annex I.1
Methodological aspects of the analysis of net transfers
The main specific database used for the study consists of extracts from CBSS (Crossroads Bank for Social Security) database(s). A number of extracts, reflecting social transfers and income from work for different population groups, have been obtained specifically for the public finance part of the study. Other extracts, have been obtained for other parts of the study. In each of these extracts, variables of interest, such as family allowances for instance, are computed as averages for different groups of the population. They are expressed as an average amount per person in the group, in euro per year. Although the source database covers all residents for Belgium, for privacy reasons the extracts do not present data at the individual level – it is not a pure “micro” database. It is only possible to combine the extracts using common variables defining the sub‑groups of the population, such as age and country of birth for instance. Extracts also include demographics data, i.e. number of individuals in each group.
At the most detailed level, population groups are defined by the following 11 variables : Gender, Region, Age group, Nationality, Country of Birth, Country of Birth of the Mother, Country of Birth of the Father, Migration channel, Length of stay, Socio‑economic position (in employment or not), Education.
The extracts from the CBSS database include the following variables for the social transfers : family allowances, unemployment benefits, pensions, social assistance (allocation de remplacement de revenus, allocation d’intégration, aide aux personnes âgées, revenu d’intégration sociale), and sickness benefits (incapacity for work, work‑related accident and occupational diseases). Data for health care costs by age categories from INAMI have also been used.
There is no information on taxes, and only partial information on social contributions in the CBSS extracts.
These items have been estimated based on information on income from work (see next section). The database includes the following incomes : income from self‑employment, income of employees and income of public sector employees.
The next sections provide more detailed information on the methodology used to estimate transfers paid by individuals to the government, i.e. personal income tax, social contributions, and consumption tax.
For the analysis, the different items (transfers paid and received) have been scaled using corresponding statistics from the general governments statistics that are part of the national accounts. In other words, the estimated items, as well as the items directly obtained from the CBSS Database, are used as distribution keys to allocate the statistics to the different groups of the population (see chapter 2 of Part I).
A. Estimation of personal income taxes
Personal income taxes are not available from the BCSS database. They have been estimated using adapted tax functions. Firstly, a fiscal and parafiscal simulation module for 2016 has been used to define average taxes rates for each income level and in the case of 0, 1, 2, 3, 4, or 5 dependent children. Secondly, the tax functions have been adapted to correct for an aggregation bias. As the available information on incomes are averages, it conceals the real distribution of incomes around the average and this is not neutral because the tax functions are not linear (see below for additional information). Thirdly, the average number of dependent children in each group has been estimated based on the amount of family allowance received by the group compared to the theoretical average amount per child. And finally, combining average income, the estimated number of children,
and the corresponding adapted tax functions, it has been possible to estimate the income tax paid per person per year by each sub‑group (5 children was assumed in case there was more than 5 children).
The fiscal and parafiscal simulation module used to construct the tax functions is simplified because it does not account for the many special cases. For example, personal income tax expenditure regimes (deductions related to mortgages for owner occupied dwellings, deductions for second and third pillar pension savings, waiver for research, etc.) are not taken into account. The same applies to the specific expatriate personal income tax scheme for example. Moreover, the database does not include information on company cars and group insurances.
The need for a correction of the initial tax functions to account for the potential aggregation bias can be illustrated by an example. At an average income of 800 euros / month the tax functions imply a zero tax rate. If all individuals in the group have that same income, then the zero rate is correct for the group. However most often the income level of the different individuals in the group will differ to some degree. If a uniform distribution of income around 800 euros / month is assumed for a group, a share of the group will have a higher income and hence a tax rate higher than zero. After averaging all the individual tax rates, a higher average (corrected) tax rate is obtained in this specific case. The opposite can be true at other income levels, depending on the form of the initial tax functions. The adapted tax functions have been constructed assuming a uniform distribution of income around the average plus / minus approximately one standard deviation. The result is a more realistic
“adapted” tax function.
B. Estimation of employer’s social contributions
The employer’s social contributions are not available from the CBSS database. To estimate these a similar approach was used as for the personal income tax estimates. Firstly, a fiscal and parafiscal simulation module for 2016 has been used to define average contribution rates for each income level. Secondly, the tax functions have been adapted to correct for an aggregation bias. And finally, combining average income and the corresponding adapted contribution rate function, it has been possible to estimate the employer’s social contributions paid per person per year by each sub‑group.
The fiscal and parafiscal simulation module used to construct the function is simplified because it does not account for the many special cases. Targeted and general social insurance contribution relief measures are not taken into account for instance.
C. Employee’s social contributions
Employee’s social contributions are directly computed on the basis of the information provided in the BCSS database. It as obtained as the difference between gross taxable income and gross income.
D. Estimation of social contributions of self-employed
For self‑employed there is no information on social contributions in the database. In order to estimate social contributions of self‑employed, a simplified uniform contribution rate is assumed. It is based on macroeconomic data, dividing the total revenue from social contributions by total income of self‑employed.
E. Estimation of consumption taxes
To estimate consumption taxes, i.e. VAT and excise duties, various data sources has to be combined. Three steps are needed. Firstly, the share of indirect taxes in total consumption of a group has to be computed. In order to
do so, Statbel used information from the Households budget survey to provide consumption profiles by group, by VAT rate and for the different categories of excise duties. Next, VAT rates are used to compute the share of indirect taxes in consumption. The same is done for excise duties, using information on the share of excise duties in average prices of the selected items derived from CPI statistics.
Secondly, consumption by group has to be computed based on income data in a broad sense (including transfers received) from the CBSS database. To obtain an average propensity to consume, information on saving rates are needed. Saving rates by age groups have been taken form experimental data from Eurostat. These have been extrapolated from 4 age groups to the age groups categories in the database.
In a last step, the two previous results – i.e. average consumption spending by person and by group, and group specific shares of taxes – are combined to obtain average consumption tax paid by person for the different groups. This has been done by age groups – the same for the different origins. This is clearly a second‑best solution. In fact, consumption and savings are to a much larger extent linked to income levels. However, in this exercise the available data has not made it possible to combine CBSS data and other sources based on income levels, using income deciles for example.
Annex I.2
Decomposition of the differences in net transfers between natives and non-natives
Following Chojnick et al. 2018 (Appendix F.2. Pages 76‑78), it is possible to make a breakdown (along the lines of Benet, 1920) of the difference between the per capita contributions of natives and non‑natives.
The table below shows the results of this breakdown. The largest part of the difference between the average per capita contribution of natives and immigrants, which amounts to € 2.281, is due to differences between natives and immigrants in the population aged 20‑65 (€ 4.222). It is the tax component that weighs most heavily in this difference (€ 5.592), while the demographic component is negative (€ –1.370). The rest of the difference (€ –1.941) stems from dissimilarities between the other individuals (the young and the elderly), which stem almost entirely from their different shares in their population of origin (€ –1.889), while their individual contributions play only a very secondary role (€ –52).
This decomposition therefore confirms the predominant role of differences in net transfers between the working population. If this factor is broken down further, it appears that it is essentially the differences in transfers from individuals to the government, i.e. contributions through taxes and social contributions, that explain the observed difference.
Decomposition of the gap between the per capita contribution of natives and immigrants
Total Difference attributable to Individuals between
20 and 64 years old Other individuals Decomposition of the gap between the per capita
contribution 2 281 4 222 −1 941
Demographic component −3 259 −1 370 −1 889
Fiscal component 5 540 5 592 −52
Decomposition of the fiscal component
Tax component 5 912 5 784 128
Transfer component −372 −192 −181
Sources : NBB calculations.
Annex II.1
The labour market integration of immigrants : Incidence of personal characteristics by origin
Table 1.1
Probit regression of the employment probability by origin
(marginal effect 1 coefficients multiplied by 100 (can be interpreted as a percentage point variation in the probability) and
predicted probabilities in percent, people aged between 20 and 64 years, annual data from 2009 to 2016, year fixed‑effect estimation)
Natives First‑generation
Men Ref 71.2 Ref 53.3 Ref 60.3
Women −4.9 66.3 −9.7 43.6 −6.9 53.3
Region of residence
Brussels Ref 59.7 Ref 42.9 Ref 51.4
Flanders 13.8 73.5 11.6 54.5 11.7 63.1
Wallonia 0.3 60.0 0.0 43.0 2.4 53.8
Age
20‑24 Ref 58.7 Ref 36.9 Ref 45.5
25‑29 21.6 80.3 14.8 51.7 19.2 64.6
30‑34 19.2 77.9 16.6 53.5 17.8 63.3
35‑39 14.9 73.6 17.4 54.3 15.5 61.0
40‑44 12.8 71.5 18.0 54.9 15.2 60.6
45‑49 11.3 70.0 16.3 53.2 14.0 59.5
50‑54 5.2 63.9 9.9 46.8 8.1 53.6
55‑59 −13.2 45.5 −3.1 33.8 −9.4 36.1
60‑64 −37.0 21.7 −17.5 19.4 −28.6 16.8
Level of education
High educated Ref 78.8 Ref 55.3 Ref 70.6
Middle educated −10.9 67.9 −1.4 53.9 −13.1 57.5
Low educated −28.1 50.7 −13.7 41.6 −28.3 42.3
Type of household
Single without children Ref 64.1 Ref 42.8 Ref 51.0
Married with children 14.3 78.4 9.6 52.4 12.4 63.4
Married without children 10.0 74.1 8.4 51.2 15.6 66.6
Unmarried couple with children 13.0 77.1 11.3 54.2 18.5 69.4
Unmarried couple without children 17 81.1 15.6 58.4 22.3 73.3
Single with children 1.9 66.0 2.5 45.3 0.5 51.5
Children living with their parents −12.7 51.4 −4.5 38.3 −4.9 46.1
Other 0.9 65.0 1.6 44.5 5.0 55.9
Near and Middle East −18.7 37.0
North America −4.0 51.7
Oceania and Far East −2.8 53.0
Latin America −4.7 51.0
Sub‑saharan Africa −9.3 46.4
Other Asian countries −1.8 53.9
EU Ref 59.9
Non‑EU −7.1 52.8
Sources : CBSS Datawarehouse, NBB calculations.
Note : given the almost exhaustivity of the database, all coefficients are significant at 99 % so that to simplify the table, we do not put the usual *** and standard errors.