Conclusion

In an effort to empirically verify the production-inducing and income-generating effects of the BP, this study posited two dif-ferent scenarios for financing the BP—fiscal streamlining and tax financing—and conducted analyses for both.

The fiscal streamlining analysis showed slight decreases in both production-inducing and income-generating effects across 32 industries.

The tax financing scenario displayed slight variations. While the production-inducing effect of the BP under this scenario de-creased in almost all industries when BP payout began, the pro-duction-inducing effect on the food and beverage industry (3) increased marginally. Moreover, the margins of these decreases were smaller than those of the fiscal streamlining scenario.

Likewise, tax financing also led to decreases in the in-come-generating effects of industries when BP payout began, but showed greater margins of decrease than was the case with the fiscal streamlining scenario. This is because the increases in income taxes, coupled with decreases in consumption ex-penditure, would reduce the income-generating effects on households in the tax financing scenario.

There are a number of policy implications to note with re-spect to these findings. Most importantly, as fiscal streamlining and tax financing could have different results with respect to production inducement and income generation, policymakers

will need to choose carefully between the two, depending on which goal they seek to accomplish.

It should be said that reducing government spending on oth-er programs, rathoth-er than raising taxes, would incur greatoth-er op-portunity costs in terms of economic growth. However, the case is reversed with respect to generating income. If the more urgent goal is to increase household revenue, fiscal stream-lining would mean smaller losses than tax financing in terms of opportunity costs.

Ⅳ ^(NHI)

1. Scenarios for analysis

2. NHI expenditure and revenue: current status and outlook

3. Creating a SAM for analysis 4. Analysis results

5. Conclusion

1. Scenarios for analysis

There are two different scenarios underlying our analysis of the economic ripple effects of the NHI. The first envisions the spending on NHI increasing by 10 percent, or KRW 4.3915 lion, from the budget for 2014, which was KRW 43.9155 tril-lion, while the second involves NHI spending increasing by KRW 10.088 trillion, which was the budget for the BP in 2015.

NHI spending includes both insurance benefit payouts and ad-ministrative expenses. Given the nature of the methodology used in this study, however, we assume that any increase in NHI spending would lead to an increase in the revenue of the household expenditure-commodities (“29. Medicine and healthcare”) of our SAM. In addition, we assume that the con-sumption expenditures of working-age (non-elderly) house-holds in other sectors would decrease, while the consumption expenditures of all households in the medicine and healthcare industries would increase.

<Table 17> NHI Spending Scenarios for SAM Analysis revenue of household expenditure-commodities (“29.

Medicine and healthcare”).

Scenario 1

- NHI spending increases by 10 percent from its 2014 level.

- Consumption expenditures of working-age (non-elderly) households in other sectors decrease, while consumption expenditures of all households in the “29. Medicine and healthcare” industries increase.

Scenario 2

- NHI spending increases by KRW 10.0881 trillion, which was the budget for the BP in 2015.

- Consumption expenditures of working-age (non-elderly) households in other sectors decrease, while consumption expenditures of all households in the “29. Medicine and healthcare” industries increase.

Year 2009 2010 2011 2012 2013

NHI revenue

Total (A) 315,004 339,489 387,611 424,737 472,059

Premiums 261,661 284,577 329,221 363,900 390,319

Government subsidies subtotal 46,828 48,561 50,283 53,432 57,994 Fiscal insurance subsidies 36,566 37,930 40,715 43,359 48,007

Fiscal management subsidies 0 0 0 0

-Tobacco allowance 10,262 10,631 9,568 10,073 9,986

Subtotal 6,515 6,351 8,106 7,405 23,746

NHI expenditure

Total (B) 311,892 349,263 372,587 391,520 412,653 Insurance benefits 300,409 337,493 358,302 375,813 396,743 Actual insurance benefits 300,409 337,493 358,302 375,813 396,743 Recuperation benefits 292,285 328,284 347,828 364,123 384,398 Actual recuperation benefits 292,285 328,284 347,828 364,123 384,398

Funeral service expenses 1 0 0 0

-Reimbursed out-of-pocket

expenses 6 2 1 1 1

〔Figure 4〕 NHI Expenditure and BP Projections (until 2050)

Source : KIHASA

3. Creating a SAM for analysis

A. Processing raw micro-data to create a bridge matrix

Our empirical analysis first requires the construction of SAMs according to the given scenarios. In both of our scenarios, we assume that NHI expenditures would increase, owing mostly to

Year 2009 2010 2011 2012 2013

Health promotion expenses 7,088 8,014 8,808 9,585 9,968 Pregnancy and maternal care

expenses 1,029 1,192 1,664 2,104 2,376

Administrative expenses 6,597 6,751 6,112 6,144 6,309

Misc. (total) 4,886 5,019 8,173 9,563 9,601

Business expenses 1,342 1,504 941 988 1,052

Building maintenance expenses 180 190 222 244 266 Other organizations’

contributions 1,646 2,121 1,786 1,896 2,274

Other 1,718 1,205 5,225 6,435 6,009

Source: NHIS, NHI Statistics, for each year.

decreases in the consumption expenditures of non-elderly households in sectors other than the medical and healthcare industries. We also assume that such decreases would be offset by the increases in all households’ consumption expenditures in the medical and healthcare industries. Having assumed that increases and decreases in household consumption ex-penditures would occur according to the sector-by-sector ra-tios of consumption expenditures, we needed to identify the respective ratios of the sectors in the elderly and non-elderly household consumption expenditures of our SAM. We used the raw micro-data of the HS to estimate the ratios of sectors in elderly and non-elderly household consumption expenditures by income decile. As Ko et al. (2014) confirm, this process of identifying household consumption expenditures in relation to the input-output tables is crucial, because there is no way of ascertaining such expenditures directly. See Tables 3-19 and 3-20 below for the ratios of elderly and non-elderly household consumption expenditures across 32 industries.

B. Using the bridge matrix to create micro SAMs

Having estimated the industry-by-industry distribution of the consumption expenditures of elderly and non-elderly households by income decile, we created a 32x20 bridge matrix. By multi-plying these ratios by the household expenditure-commodities revenue (household consumption) control total of our SAM, we

obtain a 32x20 micro SAM for household consumption.

C. Underlying conditions for analysis

1) Increases in NHI expenditures lead to decreases in the ex-penditures of working-age households in other sectors and industries.

We posited no exogenous sources for the 10-percent in-crease in NHI spending, and assumed that such an inin-crease would be possible only by endogenous means, with work-ing-age (non-elderly) households reducing their consumption expenditures in other industries in order to compensate for the increasing cost of the NHI. We estimated the extent to which working-age households’ consumption expenditures in 31 in-dustries, excluding the medical and healthcare inin-dustries, would decrease by multiplying the sector-by-sector ratios of household consumption expenditures by the KRW 4.3915 tril-lion increase in NHI spending. We also estimated the decreases in working-age households’ consumption expenditures by in-come decile and industry by calculating the respective ratios of income deciles and industries in working-age households’ con-sumption expenditures. Adding up these decreases would amount to KRW 4.3915 trillion, which is the 10-percent NHI expenditure by which it would increase.

2) Increases in NHI spending increase all households’ con-sumption expenditures in the medical and healthcare industries.

Having estimated the decreases in working-age households’

consumption expenditures in other industries, we needed to estimate the distribution of increases in all households’ con-sumption expenditures, amounting to 10 percent of the NHI expenditure in 2014, in the medical and healthcare industries.

To this end, we focused on a 1x20 matrix, representing the medical and healthcare industries, in our micro SAM. We then applied the given ratio of the medical and healthcare industries to elderly and non-elderly households’ consumption ex-penditures (Table 3-26).

4. Analysis results

A. Increasing NHI expenditure by KRW 4.3915 trillion

In our first scenario, increasing the NHI expenditure by 10 percent (KRW 4.3915 trillion) from its 2014 level, resulted in a significant increase in the production-inducing effect on the medical and healthcare industries (3.0890 to 3.1627) and mar-ginal decreases in the production-inducing effect on the other 31 industries. As multiple previous studies, including Ko et al.

(2014), confirm, the production-inducing effect on the medical

and healthcare industries is neither large nor trivial, so changes in the production-inducing effect on households and other in-dustries would not be significant. The production-inducing ef-fect tends to be significant with respect to the real estate and leasing industries (24) and wholesale and retail service in-dustries (19), and marginal with respect to public admin-istration and national defense (27) and the mining and quarry-ing products industry (2). This effect on the medical and healthcare industries is somewhere between these extremes.

The decreases in the production-inducing effect on all in-dustries caused the increase in NHI expenditure were far less than those caused by the increases in the BP, mainly because the amounts of money put in and taken out of the matrix under the NHI are smaller than those under the BP and no direct sub-sidies were provided to households. As already confirmed by numerous previous studies, direct input into households rather than industries would have a better income-redistributing ef-fect by generating income rather than inducing production.

Direct input into industries, by contrast, would have a greater production-inducing effect and thereby contribute to econom-ic growth.

Increasing NHI premiums would lead to certain increases in the production-inducing effect on the medical and healthcare industries, but decreases, albeit trivial ones, in the pro-duction-inducing effect on all other industries due to the

de-crease in consumption expenditure (revenue). Absent dede-creases in the consumption expenditure (revenue) of other sectors, such as tax revenue, the overall effects of increasing NHI ex-penditure may manifest in different ways.

<Table 19> Production-Inducing Effect of Increasing NHI Expenditure by 10 Percent

Industry 1 2 3 4 5 6 7 8 9

Before 2.7491 2.7312 3.4341 3.2922 3.4363 1.6541 3.0702 2.8828 3.2655 After 2.7445 2.7277 3.4311 3.2885 3.4330 1.6523 3.0662 2.8797 3.2611 Change (%) -0.17 -0.13 -0.09 -0.11 -0.10 -0.11 -0.13 -0.11 -0.14

Industry 10 11 27 28 29 30 31 32 Average

Before 3.0868 3.0926 2.7776 3.0929 3.0890 3.3388 3.2986 3.0569 3.0028 After 3.0828 3.0818 2.7732 3.0888 3.1627 3.3355 3.2957 3.0538 2.9995 Change (%) -0.13 -0.35 -0.16 -0.13 2.39 -0.10 -0.09 0.10 -0.11

<Table 20> Industry-by-Industry Production-Inducing Effect of Increasing NHI by 10 Percent 1234567891011 11.36430.03990.49420.05700.08740.00800.04550.03210.02950.03540.0345 20.00181.20140.00190.00220.00250.02830.00450.00580.00940.00310.0022 30.24070.06601.51540.07850.07520.01320.05850.05270.04960.05870.0577 40.03170.02720.02991.55220.04220.00580.02890.02560.02300.02820.0261 50.03450.01990.05940.04301.71980.00620.02950.03760.02070.02900.0238 60.05170.07680.04770.04850.05511.23750.15060.08120.07820.05000.0408 70.12530.07950.11700.19780.17110.03681.75840.12150.06580.11890.0974 80.00520.00490.01170.00580.00870.00240.01041.39460.02130.01150.0127 90.01290.02290.01760.02310.02020.00850.03160.04371.95020.32010.1815 100.01610.03720.03540.03330.02520.01680.03040.04280.03791.38660.1341 110.01350.02490.01530.01880.02000.01070.02560.02520.02400.04291.3947 120.03280.04320.03500.03830.04100.01190.03170.03970.03900.04760.1302 130.00360.00370.00370.00380.00410.00180.00430.00460.00420.00510.0138 140.02990.06030.02840.02660.03150.00750.02240.03340.02310.02720.0325 150.01800.02930.03750.16240.04900.00480.03070.03270.03470.04670.0528 160.04910.07880.05920.08350.09970.02980.07300.08240.11490.07960.0599 170.01360.01260.01950.01590.04890.00380.02440.03120.05820.02760.0169 180.00650.00810.00630.00590.00650.00190.00540.00590.00510.00530.0057 190.15660.12390.25860.23400.21610.04510.17040.16240.13100.17010.1782 200.06570.17960.10330.09200.11570.03590.08490.14580.08790.08470.0801

1234567891011 210.05590.06840.06090.06650.07130.01420.05350.05620.05250.05980.0599 220.05520.06100.06760.06860.07260.01890.05450.05970.05390.05950.0609 230.10390.14870.11280.11610.12790.02640.09020.10250.09120.10570.1074 240.08610.11620.09910.11040.10950.02240.07940.08530.07690.09000.0897 250.02360.02730.03690.03820.03810.01570.03860.03520.03910.03530.0408 260.02000.02460.02760.03320.03100.00900.02440.02500.03250.02690.0232 270.00340.00220.00250.00190.00220.00040.00150.00180.00140.00170.0017 280.04180.04770.04270.04500.04800.00990.03520.03760.03610.04330.0424 290.02280.02400.02280.02250.02410.00460.01760.01900.01750.02180.0212 300.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.0000 310.00900.01020.00900.00940.01000.00190.00740.00790.00750.00910.0089 320.04930.05760.05200.05400.05830.01210.04290.04880.04490.05130.0499 Total2.74452.72773.43113.28853.43301.65233.06622.87973.26113.08283.0818

<Table 21> Industry-by-Industry Production-Inducing Effect of Increasing NHI by 10 Percent (Omitted) 272829303132 10.05330.06820.06200.07090.11020.0595 20.00170.00260.00230.00220.00330.0024 30.08960.11530.08940.11940.18900.1073 40.03720.04300.03880.05720.06160.0423 50.02890.04300.03170.05000.03940.0397 60.03870.05480.05470.05180.06260.0505 70.05580.06910.29610.07690.08230.1083 80.00730.00740.00650.00770.00800.0085 90.01750.01640.01860.01840.02040.0277 100.02690.02080.01920.02410.02850.0361 110.01700.01430.01400.01500.01660.0307 120.05110.06540.04960.06920.07440.0899 130.00650.00980.01790.00590.01330.0065 140.04240.04220.03710.04830.04150.0862 150.02190.04180.02420.03220.04040.0381 160.06710.11220.08350.08470.15490.1010 170.01990.02250.03320.04140.03080.0194 180.03140.01280.00830.01790.01460.0083 190.13830.17450.20660.18410.18230.1709 200.06820.07270.06790.09330.08490.0666

272829303132 210.09430.13510.08350.14720.12270.1079 220.10100.11240.07960.17190.11150.1014 230.13670.15250.15240.18480.15260.1481 240.14020.16720.15750.20330.16070.1349 250.03430.03780.03400.05130.03770.0369 260.04020.03690.03010.05920.03750.0438 271.20030.00260.00250.00280.00300.0025 280.06761.28050.06610.08390.06900.0576 290.03340.04031.28920.03770.03820.0284 300.00000.00000.00001.19890.00000.0000 310.01390.01720.01410.01731.21340.0120 320.09050.09790.09190.10650.09031.2803 Total2.77323.08883.16273.33553.29573.0538

B. Increasing NHI expenditure by KRW 10.088 trillion

In the second scenario, in which the NHI expenditure is in-creased by KRW 10.088 trillion, which was the BP budget for 2015, working-age households’ consumption expenditure in all industries except the medical and healthcare industries again decreases, while all households’ consumption expenditure in the medical and healthcare industries increases. Note that the rates of decrease and increase are the same, and that the only difference from the first scenario is the amount by which the overall NHI expenditure increases. The goal of the second sce-nario analysis is to forecast how financing the BP and NHI sep-arately would affect Korea’s economy at large.

Our analysis shows that, in the second scenario, the increase in NHI expenditure causes the production-inducing effect on almost all industries to decline, and at a significant margin in the case of the food and beverage industry (3) (3.4341 to 3.2774). On the contrary, the production-inducing effect on the medical and healthcare industries rises significantly (3.0890 to 3.2319), and the margins of change in the production-inducing effect differ from industry to industry. The margin of decrease in the amount of working-age households’ consumption ex-penditure on social insurance and welfare services, which take up large proportions of working-age households’ consumption expenditure in general, was relatively small.

<Table 22> Production-Inducing Effect of Increasing NHI Expenditure by BP Budget

<Table 23> Comparison of Production-Inducing Effects of the BP and NHI (Increased by Same Amount)

Note that increasing the BP and NHI expenditures by the same amount through fiscal streamlining (i.e., reducing gov-ernment spending on other programs) results in the largest de-creases in the production-inducing effect. Tax financing for the increased BP expenditure and fiscal streamlining for the in-creased NHI expenditure, on the other hand, led to smaller de-creases in the production-inducing effect. In other words,

in-Industry 1 2 3 4 5 6 7 8 9

Before 2.7491 2.7312 3.4341 3.2922 3.4363 1.6541 3.0702 2.8828 3.2655 After 2.6625 2.6815 3.2774 3.1739 3.3646 1.6063 2.9990 2.8424 3.2221 Change (%) -3.15 -1.82 -4.56 -3.59 -2.09 -2.89 -2.32 -1.40 -1.33

Industry 10 11 27 28 29 30 31 32 Average

Before 3.0868 3.0926 2.7776 3.0929 3.0890 3.3388 3.2986 3.0569 3.0028 After 3.0367 3.0265 2.7318 3.0119 3.2319 3.3022 3.2512 2.9867 2.948 Change (%) -1.62 -2.14 -1.65 -2.62 4.63 -1.10 -1.44 -2.30 -1.82

Industry 1 2 3 4 5 6 7 8 9

Before increase 2.7491 2.7312 3.4341 3.2922 3.4363 1.6541 3.0702 2.8828 3.2655 BP (fiscal streamlining) 2.6353 2.6204 3.3537 3.1796 3.3222 1.6000 2.9735 2.7906 3.1634 BP (tax financing) 2.7011 2.6859 3.4376 3.2590 3.4052 1.6400 3.0478 2.8603 3.2425 NHI 2.6625 2.6815 3.2774 3.1739 3.3646 1.6063 2.9990 2.8424 3.2221

Industry 10 11 27 28 29 30 31 32 Average

Before increase 2.7491 2.7312 3.4341 3.2922 3.4363 1.6541 3.0702 2.8828 3.2655 BP (fiscal streamlining) 2.9892 2.9952 2.6037 2.9082 2.9103 3.1426 3.1109 2.9436 2.8844 BP (tax financing) 3.0639 3.0700 2.6688 2.9809 2.9831 3.2211 3.1887 3.0172 2.9565 NHI 3.0367 3.0265 2.7318 3.0119 3.2319 3.3022 3.2512 2.9867 2.948

creasing spending on industries rather than households would be the more efficient way of increasing the production-induc-ing effect. Increasproduction-induc-ing the NHI expenditure calls for decreases in households’ consumption expenditures in all industries except the medical and healthcare industries, and by a relatively greater margin in the social insurance (30) and social welfare (31) service industries. Yet the margins by which the pro-duction-inducing effect decreased due to the increase in NHI spending are relatively small, most likely due to the offsetting effect of the significant increases in households’ consumption expenditures in the medical and healthcare industries (29).

<Table 24> Production-Inducing Effect by Industry When NHI Expenditure Is Increased (by BP Budget of 2015) 1234567891011 11.32350.03920.47210.05510.08570.00780.04450.03160.02920.03490.0339 20.00171.18110.00180.00210.00250.02750.00440.00570.00930.00310.0022 30.23350.06481.44750.07580.07370.01280.05720.05200.04900.05780.0566 40.03080.02670.02851.49810.04140.00570.02830.02530.02280.02780.0256 50.03350.01960.05680.04151.68550.00600.02880.03710.02050.02850.0234 60.05020.07550.04560.04680.05401.20300.14730.08010.07730.04920.0401 70.12160.07810.11180.19090.16770.03571.71990.11990.06500.11720.0957 80.00510.00480.01120.00560.00850.00230.01011.37650.02100.01130.0125 90.01250.02250.01680.02230.01980.00830.03090.04311.92690.31530.1783 100.01560.03660.03380.03220.02470.01630.02980.04230.03751.36580.1317 110.01310.02440.01460.01820.01960.01040.02500.02490.02370.04231.3697 120.03190.04250.03340.03700.04020.01160.03100.03920.03850.04690.1279 130.00350.00360.00350.00360.00410.00180.00420.00450.00410.00500.0135 140.02900.05930.02710.02570.03090.00730.02190.03300.02280.02680.0319 150.01750.02880.03580.15680.04800.00470.03000.03230.03430.04600.0519 160.04760.07750.05660.08060.09770.02900.07140.08130.11350.07840.0588 170.01320.01240.01860.01540.04790.00370.02380.03080.05750.02720.0166 180.00630.00800.00600.00570.00640.00180.00520.00590.00510.00520.0056 190.15190.12180.24700.22590.21180.04380.16670.16030.12940.16760.1750 200.06370.17650.09860.08880.11330.03490.08300.14400.08680.08340.0787

1234567891011 210.05430.06730.05820.06420.06990.01380.05230.05550.05180.05890.0589 220.05360.06000.06460.06620.07110.01830.05330.05890.05330.05860.0598 230.10080.14620.10770.11210.12540.02560.08820.10120.09010.10420.1055 240.08350.11420.09470.10660.10730.02180.07770.08420.07600.08870.0881 250.02290.02680.03520.03690.03730.01530.03780.03470.03860.03470.0400 260.01940.02410.02640.03200.03040.00870.02380.02470.03210.02650.0228 270.00330.00210.00240.00180.00220.00040.00150.00180.00140.00170.0017 280.04060.04690.04080.04340.04710.00960.03440.03710.03560.04270.0417 290.02210.02360.02180.02170.02360.00450.01720.01880.01730.02150.0208 300.00000.00000.00000.00000.00000.00000.00000.00000.00000.00000.0000 310.00880.01000.00860.00910.00980.00190.00720.00780.00750.00900.0087 320.04780.05660.04970.05210.05710.01170.04200.04810.04440.05050.0490 Total2.66252.68153.27743.17393.36461.60632.99902.84243.22213.03673.0265

<Table 25> Production-Inducing Effect by Industry When NHI Expenditure Is Increased (by BP Budget of 2015) (Omitted) 272829303132 10.05250.06650.06330.07020.10880.0582 20.00170.00250.00230.00220.00320.0024 30.08820.11240.09130.11820.18640.1049 40.03660.04190.03970.05670.06070.0414 50.02850.04190.03240.04950.03890.0388 60.03810.05340.05590.05130.06180.0494 70.05500.06740.30260.07620.08120.1059 80.00720.00720.00660.00760.00790.0084 90.01720.01600.01900.01820.02020.0271 100.02650.02030.01960.02390.02810.0353 110.01670.01390.01430.01480.01640.0300 120.05040.06380.05070.06850.07340.0879 130.00640.00950.01830.00590.01310.0063 140.04170.04120.03790.04780.04090.0843 150.02160.04080.02470.03190.03990.0372 160.06610.10940.08530.08380.15280.0988 170.01960.02190.03390.04100.03040.0190 180.03100.01250.00850.01770.01440.0081 190.13630.17010.21120.18230.17990.1672 200.06720.07090.06940.09240.08370.0652

272829303132 210.09280.13170.08530.14570.12110.1056 220.09950.10960.08140.17010.11000.0992 230.13470.14870.15570.18300.15050.1449 240.13810.16300.16100.20130.15860.1320 250.03380.03680.03470.05080.03720.0361 260.03960.03590.03080.05860.03700.0429 271.18240.00250.00250.00280.00290.0024 280.06661.24860.06760.08310.06810.0563 290.03290.03931.31740.03730.03770.0278 300.00000.00000.00001.18690.00000.0000 310.01370.01680.01440.01711.19710.0118 320.08920.09550.09390.10550.08911.2522 Total2.73183.01193.23193.30223.25122.9867

5. Conclusion

In this section, we attempted to conduct an analysis of the possible economic effects of increasing the NHI expenditure, not by positing an exogenous variable, but by assuming endog-enous changes—that increases in the NHI expenditure would be offset by decreases in working-age households’ consumption expenditures in other industries, coupled with increases in all households’ consumption expenditure in the medical and healthcare industries. Our analysis reveals that increasing the NHI expenditure would lead to a slight decrease in the pro-duction-inducing effect on all industries, except for the medi-cal and healthcare industries.

The demographic changes underway in Korea are expected to result in radical increases in the NHI expenditure in the coming years. Moreover, increasing NHI premiums would have a diminishing effect on production across all industries and sectors of the Korean economy, and could possibly lead to de-clining economic growth rates.

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