Accesso libero

Does universal long-term care insurance boost female labor force participation? Macro-level evidence

Michihito Ando
Michihito Ando
, 
Masato Furuichi
Masato Furuichi
 e 
Yoshihiro Kaneko
Yoshihiro Kaneko
   | 14 giu 2021
IZA Journal of Labor Policy's Cover Image
INFORMAZIONI SU QUESTO ARTICOLO
Articolo precedente
Articolo Successivo
Cita
Pubblicato online: 14 giu 2021
Pagine: -
Accettato: 04 gen 2021
DOI: https://doi.org/10.2478/izajolp-2021-0004
Parole chiave
© 2021 Michihito Ando et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

SC estimation for in-kind benefits for the elderly (% of GDP).Notes: SC 1 is constructed from the original donor pool, SC 2 is constructed from the donor pool that excludes the country that receives the highest weights in the first SC estimation, and SC 3 is constructed from the donor pool that also excludes the country that receives the highest weights in the second SC estimation. Canada is excluded from the original donor pool due to lack of data. For SC estimation we use the synth command in Stata with the nested and allopt options. See Tables A4 and A5 in Appendix A3 for detailed estimation results.
SC estimation for in-kind benefits for the elderly (% of GDP).Notes: SC 1 is constructed from the original donor pool, SC 2 is constructed from the donor pool that excludes the country that receives the highest weights in the first SC estimation, and SC 3 is constructed from the donor pool that also excludes the country that receives the highest weights in the second SC estimation. Canada is excluded from the original donor pool due to lack of data. For SC estimation we use the synth command in Stata with the nested and allopt options. See Tables A4 and A5 in Appendix A3 for detailed estimation results.

Figure 2

SC estimation of public health expenditure (% of GDP).Notes: SC 1 is constructed from the original donor pool, SC 2 is constructed from a donor pool that excludes the country that receives the highest weights in the first SC estimation, and SC 3 is constructed from a donor pool that also excludes the country that receives the highest weights in the second SC estimation. Norway is excluded from the original donor pool due to a lack of data. For SC estimation, we use the synth command in Stata with the nested and allopt options. See Tables A6 and A7 in Appendix A3 for detailed estimation results.
SC estimation of public health expenditure (% of GDP).Notes: SC 1 is constructed from the original donor pool, SC 2 is constructed from a donor pool that excludes the country that receives the highest weights in the first SC estimation, and SC 3 is constructed from a donor pool that also excludes the country that receives the highest weights in the second SC estimation. Norway is excluded from the original donor pool due to a lack of data. For SC estimation, we use the synth command in Stata with the nested and allopt options. See Tables A6 and A7 in Appendix A3 for detailed estimation results.

Figure 3

SC estimation of female LFP rates by age cohort.Notes: See the note in Figure 1 for a detailed explanation of the graph. Due to data availability, the first year of our sample is 1986. Austria, Ireland, and Switzerland are excluded from the original donor pool due to lack of data. Except for the age cohort (55–59, lower right graph), Finland is also excluded due to lack of data. For SC estimation, we use the synth command in Stata with the nested and allopt options. See Tables A8–A11 in Appendix A3 for detailed estimation results.
SC estimation of female LFP rates by age cohort.Notes: See the note in Figure 1 for a detailed explanation of the graph. Due to data availability, the first year of our sample is 1986. Austria, Ireland, and Switzerland are excluded from the original donor pool due to lack of data. Except for the age cohort (55–59, lower right graph), Finland is also excluded due to lack of data. For SC estimation, we use the synth command in Stata with the nested and allopt options. See Tables A8–A11 in Appendix A3 for detailed estimation results.

Figure 4

SC estimation for the difference in female LFP rates by age cohort.Notes: See the note in Figure 1 for a detailed explanation of the graph. Due to data availability, the first year of our sample is 1986. Austria, Finland, Ireland, New Zealand, and Switzerland are excluded from the original donor pool due to lack of data. For SC estimation, we use the synth command in Stata with the nested and allopt options, but in cases in which there is an optimization error (due to a poor pre-intervention fit), we implement synth without nested and allopt. See Tables A12 and A13 in Appendix A3 for detailed estimation results.
SC estimation for the difference in female LFP rates by age cohort.Notes: See the note in Figure 1 for a detailed explanation of the graph. Due to data availability, the first year of our sample is 1986. Austria, Finland, Ireland, New Zealand, and Switzerland are excluded from the original donor pool due to lack of data. For SC estimation, we use the synth command in Stata with the nested and allopt options, but in cases in which there is an optimization error (due to a poor pre-intervention fit), we implement synth without nested and allopt. See Tables A12 and A13 in Appendix A3 for detailed estimation results.

Figure 5

SC estimation of demeaned outcomes.Notes: See the note and the legend in Figure 1 for detailed explanations of the graph. Due to data availability, the first year of our sample for the outcomes of female LFP rates is 1986. Due to lack of data, Canada is excluded from the donor pool for the outcome of in-kind benefits for the elderly, Norway for the outcome of public health expenditure, and Austria, Ireland, and Switzerland for the outcome of female LFP rates. Note that we use only pre-intervention demeaned outcomes as predictors. For SC estimation, we use the synth command in Stata with the nested and allopt options.
SC estimation of demeaned outcomes.Notes: See the note and the legend in Figure 1 for detailed explanations of the graph. Due to data availability, the first year of our sample for the outcomes of female LFP rates is 1986. Due to lack of data, Canada is excluded from the donor pool for the outcome of in-kind benefits for the elderly, Norway for the outcome of public health expenditure, and Austria, Ireland, and Switzerland for the outcome of female LFP rates. Note that we use only pre-intervention demeaned outcomes as predictors. For SC estimation, we use the synth command in Stata with the nested and allopt options.

Figure 6

Placebo results.Notes: Thick lines are Japan's SC estimates and the other lines are placebo SC estimates. We calculate placebo SC estimates by assigning the “label” of the intervention status to each control unit, using all the other control units as a donor pool. Note that the composition of donor pools (control units) is different depending on the outcome variables due to data constraints. For baseline SC estimates (bold black line), we use the synth command in Stata with the nested and allopt options. For placebo SC estimates (colored line), we implement synth without nested and allopt, because nested and allopt options sometimes result in optimization errors in some placebo trials.
Placebo results.Notes: Thick lines are Japan's SC estimates and the other lines are placebo SC estimates. We calculate placebo SC estimates by assigning the “label” of the intervention status to each control unit, using all the other control units as a donor pool. Note that the composition of donor pools (control units) is different depending on the outcome variables due to data constraints. For baseline SC estimates (bold black line), we use the synth command in Stata with the nested and allopt options. For placebo SC estimates (colored line), we implement synth without nested and allopt, because nested and allopt options sometimes result in optimization errors in some placebo trials.

Figure A1

Placebo results for demeaned outcomes.Notes: Thick lines are Japan's SC estimates and the other lines are placebo SC estimates. We calculate placebo SC estimates by assigning the “label” of the intervention status to each control unit, using all the other control units as a donor pool. Note that the composition of donor pools (control units) is different depending on the outcome variables due to data constraints. For baseline SC estimates (bold black line), we use the synth command in Stata with the nested and allopt options. For placebo SC estimates (colored line), we implement synth without nested and allopt, because nested and allopt options sometimes result in optimization errors in some placebo trials.
Placebo results for demeaned outcomes.Notes: Thick lines are Japan's SC estimates and the other lines are placebo SC estimates. We calculate placebo SC estimates by assigning the “label” of the intervention status to each control unit, using all the other control units as a donor pool. Note that the composition of donor pools (control units) is different depending on the outcome variables due to data constraints. For baseline SC estimates (bold black line), we use the synth command in Stata with the nested and allopt options. For placebo SC estimates (colored line), we implement synth without nested and allopt, because nested and allopt options sometimes result in optimization errors in some placebo trials.

Figure A2

Extended placebo tests for in-kind benefits for the elderly (% of GDP).Notes: The definitions of test statistics 1–4 are based on Eqs. (A.3) and (A.4). In each graph, the X-axis shows the values of test statistics and the Y-axis indicates the cumulative probability density of placebo-based test statistics. The large circle shows the value of a baseline test statistic for Japan and the small circles show the values of leave-one-out test statistics for Japan. Note that the nested and allopt options in the synth command in Stata are not used in all estimations, including baseline and leave-one-out estimations for Japan.
Extended placebo tests for in-kind benefits for the elderly (% of GDP).Notes: The definitions of test statistics 1–4 are based on Eqs. (A.3) and (A.4). In each graph, the X-axis shows the values of test statistics and the Y-axis indicates the cumulative probability density of placebo-based test statistics. The large circle shows the value of a baseline test statistic for Japan and the small circles show the values of leave-one-out test statistics for Japan. Note that the nested and allopt options in the synth command in Stata are not used in all estimations, including baseline and leave-one-out estimations for Japan.

Figure A3

Extended placebo tests for the female LFP rate of ages 50–54.Notes: The definitions of test statistics 1–4 are based on Eqs. (A.3) and (A.4). In each graph, the X-axis shows the values of test statistics and the Y-axis indicates the cumulative probability density of the placebo-based test statistics. The large circle shows the value of a baseline test statistic for Japan and the small circles show the values of leave-one-out test statistics for Japan. Note that the nested and allopt options in the synth command in Stata are not used in all estimations, including baseline and leave-one-out estimations for Japan.
Extended placebo tests for the female LFP rate of ages 50–54.Notes: The definitions of test statistics 1–4 are based on Eqs. (A.3) and (A.4). In each graph, the X-axis shows the values of test statistics and the Y-axis indicates the cumulative probability density of the placebo-based test statistics. The large circle shows the value of a baseline test statistic for Japan and the small circles show the values of leave-one-out test statistics for Japan. Note that the nested and allopt options in the synth command in Stata are not used in all estimations, including baseline and leave-one-out estimations for Japan.

Figure A4

Extended placebo tests for the female LFP rate of ages 55–59.Notes: The definitions of test statistics 1–4 are based on Eqs. (A.3) and (A.4). In each graph, the X-axis shows the values of test statistics and the Y-axis indicates the cumulative probability density of placebo-based test statistics. The large circle shows the value of a baseline test statistic for Japan and small circles show the values of leave-one-out test statistics for Japan. Note that the nested and allopt options in the synth command in Stata is not used in all estimations, including baseline and leave-one-out estimations for Japan.
Extended placebo tests for the female LFP rate of ages 55–59.Notes: The definitions of test statistics 1–4 are based on Eqs. (A.3) and (A.4). In each graph, the X-axis shows the values of test statistics and the Y-axis indicates the cumulative probability density of placebo-based test statistics. The large circle shows the value of a baseline test statistic for Japan and small circles show the values of leave-one-out test statistics for Japan. Note that the nested and allopt options in the synth command in Stata is not used in all estimations, including baseline and leave-one-out estimations for Japan.

Figure A5

Extended placebo tests for in-kind benefits for the elderly per elderly person.Note: see the note in Figure A2 for the estimation procedure.
Extended placebo tests for in-kind benefits for the elderly per elderly person.Note: see the note in Figure A2 for the estimation procedure.

Figure A6

Extended placebo tests for public health expenditure (% of GDP).Note: see the note in Figure A2 for the estimation procedure.
Extended placebo tests for public health expenditure (% of GDP).Note: see the note in Figure A2 for the estimation procedure.

Figure A7

Extended placebo tests for public health expenditure per capita.Note: see the note in Figure A2 for the estimation procedure.
Extended placebo tests for public health expenditure per capita.Note: see the note in Figure A2 for the estimation procedure.

Figure A8

Extended placebo tests for Female labor force participation rate (ages 40–44).Note: see the note in Figure A2 for the estimation procedure.
Extended placebo tests for Female labor force participation rate (ages 40–44).Note: see the note in Figure A2 for the estimation procedure.

Figure A9

Extended placebo tests for female labor force participation rate (ages 45–49).Note: see the note in Figure A2 for the estimation procedure.
Extended placebo tests for female labor force participation rate (ages 45–49).Note: see the note in Figure A2 for the estimation procedure.

Figure A10

Extended placebo tests for female labor force participation rate (ages 50–54).Note: see the note in Figure A2 for the estimation procedure.
Extended placebo tests for female labor force participation rate (ages 50–54).Note: see the note in Figure A2 for the estimation procedure.

Figure A11

SC estimation with limited donor pool countries 1.Notes: See the note and the legend in Figure 1 for detailed explanations of the graph. Due to data availability, the first year of our sample for the outcome of female LFPs is 1986. Countries with relatively high growth in the in-kind benefits for the elderly in the periods 1999–2005 or 1999–2010 (i.e., Austria, Finland, France, Spain, and the United Kingdom) are excluded from the donor pool. In addition, due to lack of data, Canada is excluded from the donor pool for the outcome of in-kind benefit for the elderly, Norway for the outcome of public health expenditure, and Ireland and Switzerland for the outcome of female LFP rates. For SC estimation, we use the synth command in Stata with the nested and allopt options.
SC estimation with limited donor pool countries 1.Notes: See the note and the legend in Figure 1 for detailed explanations of the graph. Due to data availability, the first year of our sample for the outcome of female LFPs is 1986. Countries with relatively high growth in the in-kind benefits for the elderly in the periods 1999–2005 or 1999–2010 (i.e., Austria, Finland, France, Spain, and the United Kingdom) are excluded from the donor pool. In addition, due to lack of data, Canada is excluded from the donor pool for the outcome of in-kind benefit for the elderly, Norway for the outcome of public health expenditure, and Ireland and Switzerland for the outcome of female LFP rates. For SC estimation, we use the synth command in Stata with the nested and allopt options.

Figure A12

SC estimation with limited donor pool countries 2.Notes: See the note and the legend in Figure 1 for detailed explanations of the graph. Due to data availability, the first year of our sample for the outcome of female LFP rates is 1986. Italy, Belgium, and Portugal are excluded from the donor pool due to zero values of in-kind benefits for the elderly in the period 1980–1989 and Australia and Sweden are also dropped from the donor pool because of fluctuating values in in-kind benefits for the elderly in the pre-intervention period. In addition, due to lack of data, Canada is excluded from the donor pool for the outcome of in-kind benefit for the elderly, Norway for the outcome of public health expenditure, and Austria, Ireland, and Switzerland for the outcome of female LFP rates. For SC estimation, we use the synth command in Stata with the nested and allopt options.
SC estimation with limited donor pool countries 2.Notes: See the note and the legend in Figure 1 for detailed explanations of the graph. Due to data availability, the first year of our sample for the outcome of female LFP rates is 1986. Italy, Belgium, and Portugal are excluded from the donor pool due to zero values of in-kind benefits for the elderly in the period 1980–1989 and Australia and Sweden are also dropped from the donor pool because of fluctuating values in in-kind benefits for the elderly in the pre-intervention period. In addition, due to lack of data, Canada is excluded from the donor pool for the outcome of in-kind benefit for the elderly, Norway for the outcome of public health expenditure, and Austria, Ireland, and Switzerland for the outcome of female LFP rates. For SC estimation, we use the synth command in Stata with the nested and allopt options.

Figure A13

In-time placebo SC estimation with a backdated intervention year.Notes: See the note and the legend in Figure 1 for detailed explanations of the graph. Due to data availability, the first year of our sample for the outcome of female LFP is 1986. In this SC estimation, the backdated intervention year is set as 1993 (solid vertical line) and therefore the period 1993–1999 is interpreted as the in-time placebo period. Due to lack of data, Canada is excluded from the donor pool for the outcome of in-kind benefit for the elderly, Norway for the outcome of public health expenditure, and Austria, Ireland, and Switzerland for the outcome of female LFP rates. For SC estimation, we use the synth command in Stata with the nested and allopt options.
In-time placebo SC estimation with a backdated intervention year.Notes: See the note and the legend in Figure 1 for detailed explanations of the graph. Due to data availability, the first year of our sample for the outcome of female LFP is 1986. In this SC estimation, the backdated intervention year is set as 1993 (solid vertical line) and therefore the period 1993–1999 is interpreted as the in-time placebo period. Due to lack of data, Canada is excluded from the donor pool for the outcome of in-kind benefit for the elderly, Norway for the outcome of public health expenditure, and Austria, Ireland, and Switzerland for the outcome of female LFP rates. For SC estimation, we use the synth command in Stata with the nested and allopt options.

Outcome: difference in female LFP rate (ages 55–59 and ages 45–49)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia0.160.473-
Belgium000
Canada000.017
Denmark0.04700
Finland0.150.1980.291
France000
Italy000
NewZealand000.12
Norway000
Portugal0.08500.376
Spain000
Sweden0.0940.3290.196
UnitedKingdom000
UnitedStates0.464--

Outcome: female labor force participation rate (ages 45–49)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia000
Belgium000.23
Canada000
Denmark0.1570.3150
France000
Italy0.378--
NewZealand000
Norway000
Portugal0.00200
Spain00.346-
Sweden0.3270.3380
UnitedKingdom000.77
UnitedStates0.13600

Outcome: in-kind benefits for the elderly (per elderly person)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia00.0430.01
Austria0.591--
Belgium000.549
Denmark00.0270.034
Finland000
France000
Ireland000
Italy00.586-
NewZealand000
Norway0.0080.0030.007
Portugal00.1480.137
Spain0.400.233
Sweden00.0340.028
Switzerland000.001
UnitedKingdom000
UnitedStates00.1590

Outcome: female labor force participation rate (ages 40–44)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia0.07400.754
Belgium000
Canada000
Denmark000
France000
Italy0.3540.581-
NewZealand0.519--
Norway000
Portugal000
Spain000.103
Sweden0.0530.4190
UnitedKingdom000.143
UnitedStates000

Outcome: public health expenditure (per capita)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia0.3030.313-
Austria000
Belgium000
Canada00.0250.227
Denmark0.03100.065
Finland000
France0.0950.090
Ireland000
Italy000
NewZealand00.0260
Portugal0.0020.1890.278
Spain0.323--
Sweden000
Switzerland0.0550.1170.03
UnitedKingdom000.169
UnitedStates0.1920.2390.231

Descriptive statistics

VariableJapanDonor pool countries
Obs.MeanStd. Dev.Min.Max.Obs.MeanStd. Dev.Min.Max.
Outcomes (1980–2013)
Public expenditure on benefits in kind for the elderly (% of GDP)340.610.590.101.745440.570.720.002.86
Public expenditure on benefits in kind for the elderly (per elderly person)341135.71955.26245.762749.345441491.811970.720.008005.41
Total public expenditure on health care (% of GDP)345.410.994.377.685785.431.302.128.72
Total public expenditure on health care (per capita)341839.76578.51971.473031.475782017.54765.16402.754349.87
Female labor force participation rate (age 40–44)3469.632.0064.1173.1151175.5712.8424.1993.54
Female labor force participation rate (age 45–49)3471.533.2464.4476.1451173.2214.7724.2992.73
Female labor force participation rate (age 50–54)3466.814.4058.7675.1351166.3116.6423.7988.54
Female labor force participation rate (age 55–59)3456.874.7549.8666.5052052.2117.9014.4183.41
Predictors other than pre-intervention outcomes (1980–1999)
Per capita real GDP (million US$, PPP, 2005)2022.805.4516.1830.0134022.295.948.2439.61
Child population (%)2018.923.0314.7923.5134020.292.9414.2930.44
Elderly population (%)2012.292.449.1016.7234013.752.089.4117.91
Employment in agriculture (%)207.411.745.1810.423407.524.841.5627.26
Employment in industry (%)2034.080.9731.6635.3334029.444.2821.2740.26
Employment in services (%)2058.512.5854.2463.1534063.037.7636.1274.47
Annual growth rate of per capita real GDP (%)202.833.72−2.539.973402.673.27−9.7211.94
Annual growth rate of population (%)200.440.200.160.803400.530.48−0.733.93
Annual growth rate of child population (%)20−2.350.78−3.64−0.41340−1.081.13−4.151.40
Annual growth rate of elderly population (%)203.220.671.794.033400.851.00−2.493.02

Outcome: difference in female LFP rate (ages 50–54 and ages 45–49)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia000
Belgium000
Canada000
Denmark0.1890.250.345
France000
Italy0.1760.0790.107
NewZealand000
Norway000.129
Portugal0.1910.1630.199
Spain000
Sweden0.232--
UnitedKingdom00.461-
UnitedStates0.2120.0480.22

Outcome: in-kind benefits for the elderly (% of GDP)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia0.0660.0880.076
Austria0.497--
Belgium0.15500.515
Denmark000.023
Finland000
France000
Ireland000
Italy00.489-
NewZealand000
Norway000
Portugal00.1340.349
Spain0.2800
Sweden0.0020.0370.037
Switzerland000
UnitedKingdom00.0830
UnitedStates00.1690

Changes in in-kind benefits for the elderly (% of GDP)

Change in in-kind benefits for the elderly (% of GDP)
Country1999–2005 (% point)Country1999–2010 (% point)
Japan0.713Japan1.096
Netherlands0.243Spain0.469
Spain0.183Finland0.354
United Kingdom0.160Netherlands0.343
Finland0.152France0.261
France0.148Austria0.216
Austria0.108United Kingdom0.158
Australia0.043Denmark0.129
Denmark0.035Switzerland0.081
Portugal0.013Belgium0.079
Switzerland0.010Portugal0.061
Italy0.004Italy0.033
Germany0.002Germany0.007
United States0.000New Zealand−0.001
New Zealand−0.001Ireland−0.009
Belgium−0.016United States−0.010
Sweden−0.067Sweden−0.104
Ireland−0.110Norway−0.192
Norway−0.410Australia−0.444

Outcome: female labor force participation rate (ages 50–54)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia000
Belgium000
Canada000
Denmark0.1890.250.345
France000
Italy0.1760.0790.107
NewZealand000
Norway000.129
Portugal0.1910.1630.199
Spain000
Sweden0.232--
UnitedKingdom00.461-
UnitedStates0.2120.0480.22

Variable definitions and sources

Outcomes (1980–2013)DefinitionSource
Public expenditure on benefits in kind for the elderly (% of GDP)Public expenditure on benefits in kind for the elderly as percentages of GDPOECD Database
Public expenditure on benefits in kind for the elderly (per elderly person)Public expenditure on benefits in kind for the elderly per aged 65 and older, at constant prices (2010) and constant PPPs (2010) in USDOECD Database
Total public expenditure on health care (% of GDP)Total public expenditure on health care, as a percentage of GDPOECD Database
Total public expenditure on health care (per capita)Total public expenditure on health care per head, at constant prices (2010) and constant PPPs (2010) in USDOECD Database
Female labour force participation rate by age cohortRatio of women in the labor force in an age cohort to the female population of the same age cohortOECD Database

Institutional settings of LTCI programs in Germany and Japan, 2008 (Campbell et al., 2010)

GermanyJapan
Policy objectiveSupport family caregiversDecrease burden of family caregivers
Policy designContain spending to within the premium level set by lawIncrease expenditures as services become more available
Organized and managedSickness funds (but LTC is managed separately)LTC insurance section of the municipal government or their coalitions
FinancingPremiumsHalf by premiums, half by taxes
1.95% of income up to a ceiling1/3 of premium revenue from those age 65+, with 6 premium levels based on income
2/3 from those ages 40–64 at 1% of income, up to a ceiling
Regional differencesNo difference in premium levelsFor those age 65+, premiums linked to local spending level
For those ages 40–64, pooled at national level and redistributed; municipalities having low income levels and more residents age 75+ receive more
Population coveredAll agesUnconditional for those age 65+
Limited to age-related diseases for those ages 40–64
Percentage of those age 65+ who are eligible10.50%17%
Percentage of those age 65+ who are receiving benefits10.5% (all of those eligible receive benefits)13.5% (20% of those eligible have not chosen to receive benefits)
Eligibility levelsThree (plus a limited additional hardship level in HCBS)Two for the “preventive care” program, five for regular LTC insurance
Benefit ceilings per month ($ PPP)No copayment or deductible10% copayment (included below)
Cash: $250–$794 plus caregiver pension premiumsServices only
HCBS: $490–$1,730 (hardship: $2,260)HCBS preventive care: $430–$950
Institutional care: $1,200–$1,730HCBS regular program: $1,440–$3,400
Room-and-board costs not covered; low income covered under public assistanceInstitutional care: $1,680–$3,670
One-third of room-and-board costs covered, up to all costs paid by LTC insurance if income level is low
Fee schedule for servicesNegotiated regionally between sickness funds and providersNegotiated nationally, conversion factor for regional cost differences

Outcome: female labor force participation rate (ages 55–59)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia0.160.473-
Belgium000
Canada000.017
Denmark0.04700
Finland0.150.1980.291
France000
Italy000
NewZealand000.12
Norway000
Portugal0.08500.376
Spain000
Sweden0.0940.3290.196
UnitedKingdom000
UnitedStates0.464--

Outcome: public health expenditure (% of GDP)

Weights
CountrySynthetic control 1Synthetic control 2Synthetic control 3
Australia0.2460.278-
Austria000
Belgium000
Canada000
Denmark0.1550.2640.253
Finland000
France0.140.1460.289
Ireland0.050.110.09
Italy000
NewZealand000
Portugal0.0530.0830.23
Spain000
Sweden000
Switzerland000.123
UnitedKingdom0.357--
UnitedStates00.1180.016

LTC systems in OECD countries

Sources of fundsCoverage and benefits
People with a disabilityAged people with a disability / People with an age-related disability
In kindCash and in kindIn kindCash and in kind
Tax revenuesCanadaCzech Republic, Denmark, Finland, Ireland, Norway, Spain, Sweden, UKGreeceSlovak Republic
LTC insurance (Premiums and taxes)Germany, Luxemboug, NetherlandsJapanKorea
MixedHungary, PortugalAustria, Belgium, France, Italy Poland, Slovenia, SiwitzerlandAustraliaMexico, US
eISSN:
2193-9004
Lingua:
Inglese
Frequenza di pubblicazione:
Volume Open
Argomenti della rivista:
Business and Economics, Political Economics, Microeconomics, Macroecomics, Economic Policy, Mathematics and Statistics for Economists, Econometrics
Feed RSS della rivista