rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendices**

## **Appendix I.1: Social Sustainability Index, Incidence and Intensity rates Cross-dimensional Cutoff at 50%**

## **Panel A: Peru**

|**Mi**<sup>**(a)**</sup>|**National**||**Gender**|||||**Ethnici**|**ty**||**Nn**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**etrc**||**Male**|**Female **|**T-test**<sup>**(b)**</sup>|**Quechua**|**T-test**<sup>**(b)**</sup>|**Aymara **|**T-test**<sup>**(b) **</sup>|**Indigenous **|**T-test**<sup>**(b)**</sup>|**o-**<br>**Native**|
|**Incidence rate (H)**|29%|28%|31%|<br>***|43%|<br>***|51%|<br>***|55%|<br>***|25%|
||_(0.07)_|_(0.07)_|_(0.07)_||_(0.07)_||_(0.09)_||_(0.03)_||_(0.05)_|
|**Intensit rate (A)**|55%|55%|55%|<br>ns|56%|<br>***|58%|<br>***|55%|<br>ns|55%|
|**y**|_(0.01)_|_(0.01)_|_(0.01)_|..|_(0.01)_||_(0.01)_||_(0.01)_|..|_(0.01)_|
|**Social Sustainability**|0.16|0.15|0.17|<br>***|0.24|<br>***|0.29|***|0.3|<br>***|0.14|
|**index(SSI)**|_(0.04)_|_(0.04)_|_(0.04)_||_(0.04)_||_(0.05)_||_(0.02)_||_(0.03)_|

(a) For each metric we report the point estimate and its standard errors(in parentheses).

(b) To assess difference between groups we perfomed t-tests. The baseline category for ethnicity is non-native. ***, **, * denote statistically significant differences between groups at 1%, 5% and 10% levels respectively. n.s denotes non-statistical significance.

## **Panel B: South Africa**

|<sup>**(a)**</sup>|**Nil**||**Gender**|||||**Ethnicity**||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**erc**|**aona**||||**ac**|||||||
|||**Male**|**Female **|**T-test**<sup>**(b)**</sup>|<br>**African**|**T-test**<sup>**(b**</sup>|<sup>**) Coloured **</sup>|**T-test**<sup>**(b)**</sup>|**Indian**|**T-test**<sup>**(b)**</sup>|**White**|
|**Incidence rate (H)**|40%|32%|45%|<br>***|46%|<br>***|24%|***|15%|<br>***|7%|
||_(0.02)_|_(0.02)_|_(0.02)_||_(0.02)_||_(0.04)_||_(0.02)_||_(0.02)_|
|**Intensit rate (A)**|61%|60%|61%|<br>ns|61%|<br>***|59%|***|56%|<br>***|59%|
|**y**|_(0)_|_(0.01)_|_(0.01)_|..|_(0.01)_||_(0.01)_||_(0.01)_||_(0.04)_|
|**Social Sustainability**|0.24|0.19|0.27|<br>***|0.28|<br>***|0.14|***|0.08|<br>***|0.04|
|**index(SSI)**|_(0.01)_|_(0.01)_|_(0.01)_||_(0.01)_||_(0.02)_||_(0.01)_||_(0.02)_|

(a) For each metric we report the point estimate and its standard errors(in parentheses).

(b) To assess difference between groups we perfomed t-tests. The baseline category for ethnicity is white. ***, **, * denote statistically significant differences between groups at 1%, 5% and 10% levels respectively. n.s denotes non-statistical significance.

32
|1995<br>Ta<br>Budget bala|1996<br>ble 1.**Pu**<br>nce in pe|1997<br>**blic sect**<br>r cent of|1998<br>**or fiscal o**<br>GDP (a p|1999<br>**utcome**<br>ositive rat|2000<br>**s, 1995-2**<br>io indicate|2001<br>**004**<br>s a surpl|2002<br>us)|2003|2004 3/|
|---|---|---|---|---|---|---|---|---|---|
|Overall balance<br>-7.3|-5.8|-6.1|-7.5|-5.8|-3.6|-3.6|-4.6|-5.2|-4.1|
|Central government 1/<br>-2.4|-2.9|-2.6|-4.9|-2.7|-2.3|-2.1|-0.8|-4.1|-3.3|
|Regional governments<br>-3.6|-2.1|-3.0|-2.0|-3.2|-2.1|-2.0|-3.9|-1.8|-1.5|
|States<br>...|...|...|-1.8|-2.7|-1.8|-1.9|-3.3|-1.5|-1.3|
|Municipalities<br>...|...|...|-0.2|-0.5|-0.3|-0.1|-0.6|-0.3|-0.2|
|Public enterprises<br>-1.3|-0.8|-0.5|-0.5|0.1|0.7|0.6|0.0|0.7|0.7|
|Interest payment<br>7.5|5.8|5.2|7.5|9.0|7.1|7.2|8.5|9.6|8.4|
|Central government 1/<br>2.9|2.9|2.4|5.5|5.0|4.1|3.9|3.1|6.7|5.8|
|Regional governments<br>3.4|2.2|2.3|1.8|3.3|2.6|2.9|4.6|2.7|2.5|
|States<br>...|...|...|1.4|2.8|2.2|2.5|3.9|2.3|2.1|
|Municipalities<br>...|...|...|0.4|0.5|0.4|0.4|0.7|0.4|0.4|
|Public enterprises<br>1.3|0.7|0.5|0.2|0.6|0.3|0.4|0.7|0.2|0.1|
|Primary balance 2/<br>0.3|-0.1|-1.0|0.0|3.2|3.5|3.6|3.9|4.4|4.3|
|Central government 1/<br>0.5|0.0|-0.3|0.6|2.3|1.9|1.8|2.4|2.6|2.6|
|Regional governments<br>-0.2|0.1|-0.7|-0.2|0.3|0.5|0.9|0.8|0.9|0.9|
|States<br>...|...|...|-0.4|0.2|0.4|0.6|0.6|0.8|0.8|
|Municipalities<br>...|...|...|0.2|0.1|0.1|0.3|0.2|0.1|0.1|
|Public enterprises<br>-0.1|-0.1|0.1|-0.4|0.7|1.1|0.9|0.7|0.9|0.8|

Source: Central Bank of Brazil.

1/ Includes the federal government, the social security system and the central bank.

2/ Includes total revenue and expenditure minus interest payments.

3/ Flows accumulated over 12 months to May 2004.

4
- 19 -

**<u>Table 6. IV regression results for market sales share</u>**

||<br>_SHR_||<br>_SHR_||_SHR_||_SHR_||
|---|---|---|---|---|---|---|---|---|
|ln_COSTMORONI_|-0.531|(0.400)|||||-0.778|(1.955)|
|ln_COSTCITY_|||-0.374|(0.211) *|||-0.378|(0.292)|
|ln_MAI_|||||2.089|(1.285) *|-1.654|(6.953)|
|ln_L_|0.242|(0.159)|0.322|(0.181) *|0.138|(0.112)|0.504|(0.556)|
|_SEED_|0.507|(0.247) **|0.720|(0.312) **|0.381|(0.178) **|0.978|(0.814)|
|_FERT_|0.262|(0.159) *|0.283|(0.186)|0.257|(0.149) *|0.310|(0.241)|
|_COOP_|0.367|(0.134) ***|0.421|(0.155) ***|0.340|(0.117) ***|0.447|(0.207)|
|_MKT_|0.973|(0.238) ***|1.114|(0.265) ***|0.901|(0.188) ***|1.311|(0.586)|
|_TRAD_|1.127|(0.726)|0.869|(0.764)|1.128|(0.686) ***|0.750|(0.942)|
|_MALE_|-0.252|(0.183)|-0.510|(0.269) *|-0.207|(0.143)|-0.671|(0.540)|
|_SIZE_|0.102|(0.092)|0.167|(0.119)|0.086|(0.078)|0.229|(0.204)|
|_AGE_|-0.182|(0.198)|-0.475|(0.303)|<sup>-0.105</sup>|<sup>(0.138)</sup>|-0.682|(0.680)|
|_CAR_|-0.054|(0.136)|-0.158|(0.163)|<sup>-0.017</sup>|<sup>(0.109)</sup>|-0.237|(0.322)|
|_MOTR_|-0.010|(0.085)|-0.034|(0.120)|<sup>-0.022</sup>|<sup>(0.089)</sup>|-0.024|(0.154)|
|_BICY_|-0.454|(0.238) *|-0.363|(0.337)|<sup>-0.590</sup>|<sup>(0.233) **</sup>|-0.254|(0.635)|
|Constant|0.779|(0.587)|1.929|(1.003)*|1.145|(0.628)*|1.933|(1.186)|
|Obs.|2587||2587||2587||2587||
|R-squared|0.040||0.048||0.032||0.050||
|Wald chi2|298.1||131.2||241.7||108.9||
|Dummy variables:|||||||||
|Crops|Yes||Yes||Yes||Yes||
|Prefectures|Yes||Yes||Yes||Yes||
|Endogeneity test:|||||||||
|C stat. chi2|1.454||3.6056|**|1.353||3.948||
|Overidentifying restric|tion test:||||||||
|Hansen's J stat. chi2|2.637||0.3487||1.790||0.000||

# **V. CONCLUSION**

34. Agriculture is an important sector in Africa. However, the region’s production system remains small-scale subsistence farming with few advanced inputs used with little produce sold in the market. The paper revisited the traditional transport-agricultural growth nexus. Even if the existing literature is substantially rich, this is still an important question, particularly for small island developing states, such as the Comoros, where the domestic markets are limited, and the transport and transaction costs are often prohibitively high.
**Table 1: Youth Unemployment rate as a dependent variable**

|||||||System G|MM(Two s|tepapproa|ch)|
|---|---|---|---|---|---|---|---|---|---|
||(1)<br>Pooled<br>OLS|(2)<br>Fixed<br>Effects|(3)<br>IV<br>Regression|(4)|(5)|(6)|(7)|(8)|(9)|
|Youth Unemployment(t‐1)|0.913<sup>***</sup>|0.757<sup>***</sup>|0.755<sup>***</sup>|0.813<sup>***</sup>|0.820<sup>***</sup>|0.849<sup>***</sup>|0.850<sup>***</sup>|0.828<sup>***</sup>|0.857<sup>***</sup>|
||(59.93)|(37.90)|(36.63)|(15.56)|(15.75)|(13.75)|(15.73)|(12.31)|(14.09)|
|Corruption Index(t‐1)|0.350|‐0.950|‐9.521|0.318|‐0.296|5.289*|5.460<sup>**</sup>|6.356<sup>**</sup>|6.731<sup>**</sup>|
||(0.84)|(‐0.79)|(‐0.69)|(0.14)|(‐0.15)|(1.75)|(2.25)|(2.07)|(2.30)|
|Education|||||‐|0.185<sup>***</sup>|0.118<sup>**</sup>|0.125<sup>**</sup>|0.0921|
||||||0.0106|||||
||||||(‐0.11)|(3.15)|(2.22)|(2.29)|(1.08)|
|Inflation||||||‐|‐0.0363|‐0.0448|‐0.0656|
|||||||0.0404||||
|||||||(‐0.10)|(‐0.09)|(‐0.14)|(‐0.17)|
|FDI||||||‐0.293*|‐0.279*|‐0.260|‐0.355*|
|||||||(‐1.76)|(‐1.69)|(‐1.30)|(‐1.81)|
|Monetary Aggregate||||||‐2.023*|‐2.797<sup>***</sup>|‐2.692<sup>**</sup>|‐4.701*|
|||||||(‐1.88)|(‐2.60)|(‐2.34)|(‐1.70)|
|Political Opposition|||||||0.0114|0.00761|0.0181*|
||||||||(1.64)|(1.39)|(1.90)|
|Worker’s Rights|||||||0.702<sup>**</sup>|1.159<sup>**</sup>|0.300|
||||||||(2.07)|(2.33)|(0.63)|
|Constant|1.331<sup>***</sup>|4.288<sup>***</sup>|6.713|3.000<sup>***</sup>|3.176<sup>*</sup>|||||
||(5.94)|(8.87)|(1.73)|(4.20)|(2.36)|||||
|Observations|1115|1115|1112|1115|1088|926|914|914|916|
|Hansen Test||||0.87|0.92|0.88|0.90|0.89|0.95|
|AR(0)||||0.00|0.00|0.00|0.00|0.00|0.00|
|AR(1)||||0.54|0.52|0.31|0.38|0.38|0.40|

Notes: Standard errors are in parentheses. All GMM regressions use robust standard errors and treat the lagged corruption measure as endogenous. In the case of two‐step GMM, the Windmeijer (2005) finite sample correction for standard errors is employed. *, ** and *** denote significance at the 10%‐, 5%‐ and 1%‐level, respectively. The row for the Hansen J‐test reports the p‐values for the null hypothesis of instrument validity. The values reported for AR(1) and AR(2) are the p‐values for first and second order autocorrelated disturbances in the first differences equations.

In order to assess the robustness of our results to alternative measures of youth unemployment, we re-estimate in table 2 our two-step system GMM model using the unemployment rate for people with secondary education as a dependent variable. Similar to the main findings above, the results shown in table 2 demonstrate that existing corruption practices has a positive and significant effect on the unemployment rate for this segment of the population. Unemployment rates continue to exhibit a positive and significant persistent effect. The coefficient of the FDI suggests a significant and negative effect on the rate of the secondary level educated job seekers. This effect seems to be consistent with the empirical literature.

Page | 15
22

**Table 11: Staffing Levels of Doctors. 1981-85**

|Year|Establishment|Zambian|Non-Zambian|Vacancies|Vacancy<br>Rate %|
|---|---|---|---|---|---|
|1981|808|62|577|169|21|
|1982|808|79|586|143|18|
|1983|830|92|584|154|19|
|1984|830|88|531|211|25|
|1985|850|103|491|256|30|

Source: Ministry of Health, Manpower Development Office
- 45 -

# Site Slocific Choice of Power Goneration !?echgglc

The previous study is applicable in the general case only when the assumptions hold true at every site. In particular it requires every site to be 'greenfield', that fuel prices are always identical and there are no associated infrastructure costs involved in uslng any of the fuels. More specific analysis will pay attention to these factors, but the results (as opposed to the methodology) cannot then be so easily generalized.

An example of the methodology required to assess fuel substitutability in a specific situation is provided by Pinto and Besant-Jones 119891 for power generation. Although the case they examine is hypothetical it is elaborated in sufficient detail to show how it could be applied in actual cases. The objective is to ascertain the demand curve for gas in three different power systems (i.e. the relation between the price of gas and the amount of gas used and the mix of the generation system). The three systems wave chosen to illustrate realistic situations.

System A is small with a current maximum demaz.4 of 100 MW. The load factor is assumed constant over the 20 year period at 54.5%. The initial power system is entirely thermal with no possibility of future hydro and there is no possibility of importing coal due to the high costs of coal handling plant. The inherited plant mix is five 33 MW steam units fired by residual fuel oil and some diesel units. The steam units are new and not retired over the simulation period, whereas the diesels are retired between 6 and 14 years in the future. The rate of load growth is 7%.

System B has a current maximum demand of 2400 MW with a higher load factor of 69.7%. At present it has a varLed plant mix with hydro/storage, lignite fired steam, gas turbines and diesel. All of the inherited plant is retired over the period. Again load growth is 7% per annum.

System C is a 'greenfield' case with an initial maximum of 800 MW. There is no possible hydro source.

For the thermal plants the following details are used:

- (i) Nameplate rating

- (ii) Sent out rating

- (iLL) Maximum availability

- (lv) Full load heat rate

- (v) Average heat rate
||||luggage, saddlery, harness,<br>footwear|
|---|---|---|---|
|Other Manufacturing|Polyester buttons, tobacco,<br>camping equipment, coffee<br>roasting and grinding,<br>plastic products<br>manufacturing, tannery<br>(sheep, goats), printing,<br>non-metal products (glass,<br>rubber), wire and nails,<br>cement production, PP Bag<br>production, and firms<br>classified as “Others” in<br>the manufacturingsurvey|Other<br>manufacturing<br>and electronics|Other manufacturing**c**|

# **_Constraints_**

The investment climate surveys ask very detailed questions about constraints. These questions overlap considerably, but not perfectly, across the different surveys. To construct constraints measures that are comparable across rural and urban enterprises, we categorised individual constraints into coarser groups of constraints, documented in the Tables below. A drawback to this procedure is that the different constraints categories do not contain an equal number of items. Moreover, due to imperfect overlap, they may not measure exactly the same constructs across rural and urban areas.

# _Most important constraint:_

|**Table B.2: Constructio**<br>|**nof the “Most Important Constraint” V**<br>**“”**|**ariable**<br>**“”**|
|---|---|---|
|**Constraint Category**|**Constituent**<br>**Urban Constraints**|**Constituent**<br>**Rural Constraints**|
|Markets|- Availability of raw materials<br>- Lack of market<br>- Rising of input prices<br>- Shortage of spare parts<br>- Shortage of input<br>- Shortage of capital<br>- Others<br>- Massive inflows of aid food|- Access to inputs<br>- Access to markets (distance and cost))<br>- Difficulty to obtain information on your<br>product’s market<br>- Demand for goods and services produced|
|Finance|- Access to financing<br>(availability/collateral)<br>- Cost of financing (interest rates, fees)<br>- Access to finance due to religious<br>constraints|-(Im)Possibility of borrowing from family,<br>friends or others<br>- (Im)Possibility of borrowing from formal<br>financial institutions<br>- Interest rates<br>- Complicated bank loan procedures (too<br>many forms)<br>-Fear of not being able to pay loan<br>instalments|
|Transport|-Transportation|-Road access<br>-Road Quality<br>-Road Cost<br>-Traffic<br>- Facilities to transportgoods|
|Water|-Shortage of water supply|- Water access<br>- Water Quality<br>- Water cost|
|Government|- Macroeconomic instability|- Corruption|

-46-
Overall, in this scenario, global growth would strengthen notably, to nearly 5 percent in 2021, with advanced economies and EMDEs growing 4.1 percent and 5.8 percent, respectively. Still, world growth in 2022 would be not much stronger than the baseline, with the upside to growth limited by scarring from the exceptionally severe downturn in 2020. By 2022, global and EMDE output would be only 1.9 and 1.8 percent, respectively, above the baseline scenario. Such a robust recovery might be enough to stabilize EMDE debt at its long-term median. Global trade growth would experience a strong recovery, averaging nearly 7 percent over 2021 and 2022.

# 5. Conclusion

The global recovery that lies ahead will depend heavily on controlling the spread of the pandemic—in part a function of vaccine outcomes. In this paper, we proposed an eclectic methodology for translating the projected evolution of the pandemic into global growth outcomes. We first augment a discretized stochastic SIR model with vaccine assumptions to articulate pandemic scenarios for major advanced and EMDE economies. Projected COVID-19 caseloads are then translated into shocks to economic activity. To do so, we exploit the variation across advanced economies in the statistical relationship between COVID-19 cases, the stringency of lockdown measures, and forecast revisions to consumption. We find that both “voluntary” social distancing on the part of households and the imposition of strict lockdowns by governments can weigh heavily on economic activity. Lastly, we leverage a global semi-structural projection model to assess the global economic consequences of various pandemic shocks across major economies.

Our results on the global economic implications of alternative pandemic outcomes are summarized in a set of three scenarios first presented in the January 2021 Global Economic Prospects report (World Bank 2021). In the baseline scenario, a decline in cases, a vaccine rollout that gathers pace in early 2021, and the eventual easing of pandemic control measures underpin a modest rebound. In the downside scenario, persistently higher caseloads, more stringent mandatory social distancing, and slow vaccine development markedly weaken the recovery. In the upside scenario, effective pandemic management, coupled with prompt widespread vaccination, allows activity to recover faster. This scenario analysis exercise underscores the pressing need of authorities to sustain containment efforts in the near-term, while accelerating the rollout of vaccines that promise a durable end to the pandemic.

13
-3-

100%. By 1993, after seven rounds of multilateral trade negotiations, the average tariff had fallen to 5%.' This liberalization process was accompanied bty' hug increases in trade flows. Between 1950 and 1970 international trade grew at a rate of P. percent annually, outpacing the growth of world output. During the 1980s world trade gr:ew at annual rates of between 6 and 9 percent.<sup>3</sup> Trade in manufactures was the most dynamic. In 1965 the proportion of world trade represented by industrial goods was 54 percent; by 1989, it had reached 73 percent.<sup>4</sup>

# GATT Negotiating Rounds

|Round|Dates|Number of<br>Countries|Value of Trade Covered (US$)|
|---|---|---|---|
|Geneva|1947|23|10 billion|
|Annecy|1949|33|n.a.|
|Torquay|1950|34|n.a.|
|Geneva|1956|22|2.5 billion|
|Dillon|1961|45|4.9 billion|
|Kennedy|1962|48|40 billion|
|Tokyo|1973|99|155 billion|
|Uruguay|1986|124|-<br>755 billion|

Source: Quoted in Jagdish Bhagwati. The World Trading System at Risk. Princeton, New Jersey: Princeton University Press. 1991. p. 8 and, GATT. Focus. GATT Newsletter. No.107. Special Issue. May 1994. pp. 1, 8.

Regional trade agreements were accommodated within GAIT, although this actually meant a departure from the MFN principle.<sup>5</sup> They were initially allowed because they could constitute a movement toward achieving freer trade.<sup>6</sup> The creation of regional trade XSebastian Edwards. Trade and Industrial Policy Reform in Latin America." Cambridge, MA: (1994) NBER Working Paper Series. No. 4772. p. 42.

2 World Bank. World Development Report. 1987. Washington, D.C.: Oxford University Press, 1987. p. 14.

3 Diana Brand. "Regional Bloc Formation and World Trade," in Intereconomics. November/December 1992. Vol. 27. No. 6. p. 274.

Tid. p. 277.

> 5 Jagdish Bhagwati. The World Trading System at Risk. Princeton, New Jersey: Princeton University Press, 1991. p. 69.

6 Kenneth W. Dam. The GATT: Law and the Intemational Economic Oreanization. Chicago: University of Chicago Press, 1970. p. 19.
where D is the deficit, B is the stock of domestic d¢bt, H is the monetars base, C is net domestic credit of th- financial system to the public sector, E is the exchange rate, 8* is the stock of foreign public debt, R* is the stc.k of international reserves, p is the rate of inflation, and **e** is the rate of exchange rate devaluation Domestic financing can take the form of government securities, moneN creation or net bank credit to the public sector, while external financing can be an expansion a the forei! public debt or a run-down of foreign exchange r, -ves. Lookir.q at Table 2 we see that the average proportion of the **net** domestic debt has remained around 40X in the period, despite sharp changes in international financial market conditions after 1982. The reasons for that are, first, that Brazil received new money in 1983 and 1984, and, second, that at the margin there was a transfer of foreign debt from the private to the public sector in the period. In I98', the public sector was responsible for 62X of the forcign debt, while in 1987 this proportion increased to 75%. This transfer represented an external source of finance, despite the fact that no new monem was available in 1985 and 1986. An additional reason for the constancs of the shares was the loss of international reserves in 1985/ 1986.

The proportion between money and domestic debt financing, on the other hand, has varied wildl in the period. Using Table 2 and the IGP-DI as the deflator for the domestic debt, the proportion of money financing in total domestic financing was 48%, 42%, 61%, 65%, 196%, and 51% for the period 1982-87. The data show an upward trend in money financing, culminating in the strong domestic debt monetiza 'in of 1986 with the Cruzado Plan. The above proportions, however, conceal the fact that there has been continuous small confiscations of the domestic public debt in the 80's, produced byj rates of monetary correction short of the effective inflation rates. If we take 1981 as a basis, the ORTN had lost about half of its real value by the end of 1987. Both the shift to money financing and the small debt

-8-
2

# **1. Introduction**

Since the publication in 2003 of “Catastrophe and impoverishment in paying for health care” (Wagstaff and van Doorslaer 2003), the measures of catastrophic spending and impoverishment proposed there have become the workhorses of empirical work on financial protection in health. Major international comparative studies include van Doorslaer et al. (2006; 2007) and Xu et al. (2003; 2007). In addition, a large number of country- and region-specific studies have been undertaken, some of which explore trends in financial protection or the effects of reforms on financial protection; for a survey of this literature, see Wagstaff (2010b).

Up until now the catastrophic and impoverishing payment measures have been seen as alternatives, capturing different things: the impoverishment measure capturing whether families fall into poverty through out-of-pocket spending; the catastrophic measure capturing whether out-of-pocket payments exceed some threshold of family income or consumption. As a result, studies often report one or the other. Authors presenting studies that include both inevitably struggle with the question of whether one matters more than the other, especially when the two measures move in different directions over time or country rankings differ depending on which measure is used. The impoverishment measure is often perceived to have two strengths: it speaks directly to the hardship caused by out-ofpocket spending; and it has a threshold (the poverty line) that has a special ethical status having being chosen by policymakers. When asked why catastrophic