rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3: Complementary measures of PICs socio-geographical population dispersion**

<!-- Start of picture text --> 1,000<br> 500<br> -<br> 4,000<br> 2,000<br> -<br> 400<br> 200<br> -<br>100%<br>0%<br>200%<br>100%<br>0%<br> 20<br> 10<br> -<br> 100<br> 50<br> -<br> 0.00080<br> 0.00040<br> -<br>Fiji Solomon Vanuatu Kiribati FSM Tonga RMI Tuvalu<br>Islands<br>A1 (km)<br>l scale of<br>Geographica population dispersion<br>A2 ('000<br>Size of EEZ square km)<br>Panel A: Geographical separation A3<br>islands<br>Count of inhabited<br>B1 Rural share<br>population (national definition)<br>Panel B: Rurality B2<br>Share of (<10,000 people)<br>population settlements<br>living in small<br>C1<br>adjusted sparsity<br>Dispersion- population (square km per person)<br>C2<br>sparsity<br>Territorial population (square km per person)<br>Panel C: Population sparsity<br>C3<br>island<br>average<br>Inverse of the population per<br><!-- End of picture text --> 13
the “collective model” of the household. The earlier papers analyzed the household consumption allocation as the outcome of Nash bargaining, with threat points given either by what the partners could achieve by leaving the household, or, alternatively, in some later formulations, by the non-cooperative Nash equilibrium with both partners remaining within the household. Chiappori (1988) generalizes these models also by assuming that the household equilibrium is Pareto efficient and without specifying the particular process by which it is reached. His approach is based on the interpretation of the “leisure” variables of the standard household model as pure leisures. As in the papers by Manser and Brown and McElroy and Horney, Chiappori (1988, 92) specifies a model in which each individual has a utility function defined on consumption and pure leisure, and so instead of the individual utility function in (8), he has Household production is omitted entirely. In this crucial respect, particularly from a gender perspective, the model is more specialized than the standard model estimated on aggregate data, as set out in section 3.2. Subsequent studies that adopt the Chiappori model, and therefore omit household production, include Bourguignon et al. (1993), Browning et al. (1994), Fortin and Lacroix (1997), Blundell et al. (1998) and Bourguignon (1999). More recently, Fong and Zang (2001) extend the Chiappori model by distinguishing between two types of leisure, “independent (or private) leisure” and “spousal leisure” (time spent together).<sup>23</sup> The authors label these leisures as “unobservable” even though data on both are available in time use survey files.<sup>24</sup> Thus, the general formulations in both strands employ the assumption that, whatever decision process may in fact determine the household equilibrium allocation, the latter

> 22 This of course does not apply to the Pitt and Rosenzweig (1986) model for the household production of health, though the point can be made that household production needs to be defined more generally than as just health care.

> 23  Hamermesh (1996, 1999) makes a similar distinction within a more general approach that recognizes that the household faces a coordination problem in its time allocation and household production decisions. 24 Time use surveys frequently collect information on “social context” by recording the presence of other persons and their relationship to respondent. Examples include the Canadian 1992 General Social Survey (GSS) – cycle 7 Time Use and 1998 GSS – cycle 12 Time Use, the Australian ABS 1992 and 1997 Time Use Surveys and the UK Office of National Statistics 2000 Time Use Survey.  Thus, a more sophisticated empirical model than one that distinguishes simply between independent and spousal leisure can be estimated on available data.

18
The changes to poverty estimates at the country level and relative rankings between countries caused by going from the $1.25 poverty line and 2005 PPPs to $1.82 (or $1.92) and 2011 PPPs are much smaller than the changes caused by the introduction of the 2005 PPPs in 2008. Figure 6 replicates Figure 3 by comparing country level poverty rates and rankings under two different global poverty lines. Figure 6 depicts this change by comparing the $1.08 poverty line and the 1993 PPPs (used until 2007), with the estimate using the $1.25 line and the 2005 PPPs (introduced in 2008). When comparing these changes to those displayed in Figure 3, it is clear that the revision of global poverty measurement using the 2011 PPPs proposed by this paper causes significantly smaller changes than the revision associated with introducing the 2005 PPPs. The large “shake-up” of country level estimates and relative rankings is also reflected in bivariate estimates. The regression between poverty estimates resulting from the 1993 PPPs and 2005 PPPs, gives a slope coefficient of 1.06 and an R-squared of 0.65. The Spearman's rho coefficient for a rank correlation for poverty measures in 1993 PPPs and 2005 PPPs is only 0.65.

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Second, the link between food and nutrition is strongly mediated by health status. Given the unsanitary conditions in which the extremely-poor live, and the prevalence of diarrhea and other intestinal diseases, making more food available to them may "...simply meet the needs of the worms in their stomachs" (Streeten, 1989b, p. 6)96. Improving health conditions is also essential to reduce infant mortality and, with a lag, fertility. The type of health services delivered is of central importance. Emphasis must be given to preventive medicine and the elimination of parasitic and infectious diseases. Aspe and Beristain (1984, pp. 301-09) show that Mexico has one of the highest age adjusted rates of mortality from enteritis and diarrheic diseases in the world; they also show that in 1975 parasitic and infectious 97 diseases accounted for the largest share of death rates by age group Third, there is substantial evidence that education for the female head of household has a strong positive effect. Rosensweig and Schultz (1982) find in a study of Colombia that women's education reduces child mortality. Wolfe and Behrman (1987) find that in Nicaragua a mother's schooling has a positive effect on a child's nutrition and health. It also appears that more female 98 education reduces fertility . As with health, the type of education is very important. Since, as just seen, the extremely-poor may not be in a position to demand formal education, emphasis must be centered on issues like food 96Preliminary results from the 1988 National Survey of Nutrition show that about 20% of pre-school children had diarrhea in the two weeks prior to the nutrition survey, with about one fourth of those cases being chronic diarrhea (World Bank, 1990a, p. 3).

97They also analyze government's allocation of expenditures in health services and note that "..the main share of the sector's investment is devoted to building enormous hospitals with sophisticated equipment, which do not take care of the problems of the underprivileged," and "..excessive importance and resources have been awarded to curative in relation to preventive medicine, which perpetuates the high infant mortality rate" (op. cit., p. 323-4).

98"Female education above four years, however, bears one of the strongest and most consistent negative relationships to fertility.... (and) is also associated with higher age at marriage, and may well have some intangible effects on a woman's ability to plan and on her taste for non-familial activities" (Birdsall, 1988, p. 514). There is also an effect that works through the opportunity cost of time: "As female education and female wages rise, the differential between female and child wages widens. This in itself tends to reduce fertility, since it means that the family's loss of the mother's income when children are young is not easily and quickly made up by children's work" (Birdsall, op. cit., p. 515).
Dummy variables that indicate the presence of a border, common colony and common language are included to capture proximity, socio-cultural and historical links between the origin and destination locations. All else equal, neighboring countries rely more on ground than on air transport, implying an expected negative effect of a shared international border on the volume of air passenger traffic. On the other hand, sharing a common language and colonial ties increases the similarity between the cultures, norms and institutions of the two locations, inducing more leisure and business travel. Bilateral trade between origin and destination countries is also included because more trade may require more business travel.<sup>24</sup> Other control variables considered in the estimation, and summarized by the three variable vectors, are: the geographic area of countries (to account for population density and growth of extensive margin expansion), membership in free trade agreements and in the World Trade Organization (aviation liberalization may reflect broader trade liberalization efforts), differences in average annual temperatures (a proxy for leisure travel and tourism), differences in time zones (which increase the non-monetary cost of travel and doing business), and the trade share of differentiated goods (which may be especially likely to require face-to-face meetings for contract negotiations and quality inspections). Finally, we include a dummy for whether both countries are democracies in year 2005 (they are more likely to consider signing a liberal agreement, but also more likely to take advantage of the benefits the ASA offers). We include a dummy variable for country pairs inside Europe to correct any bias that might emerge because the EU arrangements were not scored by the WTO and we have simply applied a score of 50 on these routes.

We first estimate the regression equation (1) using country-pair aggregate data on air passenger traffic flows. This exercise informs our understanding of the relationship between policy and air passenger traffic between countries. We estimate country-level regression specifications using ordinary least squares (OLS) methods.

> 24 The baseline regression includes bilateral trade since we think it is an especially important control that is a good proxy for economic links, and because higher levels of trade may increase demand for passenger travel.  Being sensitive to the concern that trade is possibly endogenous to passenger traffic, we have also estimated the models without including bilateral trade and find that the coefficients are quite stable. We report results for the specification in (1) and (3) but the other results are available on request.

14
20

# **_IV. Public Expenditure Composition and Growth_**

In this section we estimate the long-run and short-run impact of government expenditure on Brazilian growth using two related methods. First, we use the singleequation ARDL methodology used in the previous section, and then we use a multipleequation co-integrating VAR approach to examine the relationship among the several variables.

Using data for 1950-2000, the Autoregressive Distributed Lag (ARDL) estimates a long run relationship and an error correction representation between income per capita, private and public capital stocks per capita and three components of government current expenditure (subsidies, social security and assistance transfers and consumption)<sup>10</sup> . The estimation also included tax revenues and public debt as a share of GDP to control for the government’s budget identity and the potential negative effects of the government financing on economic activity. The data for the stocks of private and public capital were obtained from Reis et al (2002) and the flow data, that is income per capita and government current expenditures come from the National Accounts System - IBGE.

Tables 9 and 10 report the long-run coefficients and short-run dynamics estimated with this method.<sup>11</sup> Table 9 shows that, in the long run the elasticity of output with respect to the public capital stock is larger than in that of the private sector. The estimated elasticity seems high when it is compared with estimated values for the US or OECD economies (Sturn and de Haan, 1995; Hurlin, 2001), but similar to existing Brazilian estimates for infrastructure (Cavalcanti, 2004). However, the negative impact of the tax ratio is surprisingly large: an increase of 1 percentage point in the tax ratio lowers GDP per capita by 1 percent.

> 10 It also has the advantage that the lags in each of the regressors are allowed to be different, and the endogeneity problem can be eliminated by appropriate selection of the lag length (Pesaran and Shin, 1999) 11 The tables report results for the different models: Akaika (AIC), Schwarz (SBC), R-Bar Squared (RBSQ) and Hanaan-Quinn (HQ).  The production function was estimated in per capita terms, dividing all the arguments by the economically active population.  There are 8 variables: GDP per capita, private capital stock per capita, public capital stock per capita, government subsidies, government consumption, government social security transfers, tax revenue ratio to GDP, and the public debt ratio to GDP.  The maximum lag was 3.  This produced a total of  262,144 possible combinations:  AIC, SBC and HQC selected an ARDL (1,2,0,1,0,0,0,3) while the RBSC selected a (1,2,1,1,0,1,0,3) model.
Thus, it should address the following elements:

- a. liquidity status

- b. warning triggers

- c. funding sources

- d. actions to take, depending on the severity of the crisis

- e. functions of the Crisis Management Team

- a) The _liquidity status analysis_ should be quantitatively based on the liquidity indicators but it should also qualitative--based on the perception from the market and clients, incorporating contractual and behavioral assumptions.

- b) _Warning triggers_ should alert the bank to liquidity problems and be classified based on severity: minor, severe or very severe. These alerts allow the bank to recognize not only the liquidity risks coming from within, but also those from other counterparts or the market. Thus, the bank can identify the actions it needs to put in place when the risks occur.

- c) _Funding sources._ The bank should define the volume and quality of liquid instruments, the stand-by lines of credit it has in place, and its access to central bank lending programs and other potential liquidity lines. Liquidity risk management should define _liquidity buffers_<sup>60</sup> , cash and other unencumbered highly liquid assets that allow the institution to meet payments in stressed situations over its defined survival period. These liquidity buffers must be regarded as the readily available funding that is not used for ongoing business.

Other potential sources of funding should be diversified and considered for use depending on the circumstances or nature of the shock. Some of them could include<sup>61</sup> : deposit growth, the lengthening of liabilities’ maturities, new issues of short- and longterm instruments, intra-group fund transfers (within defined regulatory maximum exposure limits), the sale of subsidiaries, new capital issues, the sale of repos or highly illiquid assets, and borrowing from the central bank (LOLR).

- d) The _actions_ needed to respond to liquidity problems should be a mix of options that take into account:  the severity of the problem, its origin, the time horizon being used, the bank ́s risk profile, and the costs of implementing the action. Some of the remedial actions that can be implemented include:

- Use of liquidity buffers: this liquidity cushion should enable the bank to weather liquidity stress during its defined survival period, without requiring adjustments to its business model.

- Review of opportunities to increase the size of the liquidity reserve (moving assets to more liquid instruments or term borrowing to invest in liquid assets).

> 60 CEBS, page 12

> 61 Basel, BIS Principles, page 19
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# **Figure 3**

> Recursive OLS estimates of the long-run parameters of equation (1 _5)_

<!-- Start of picture text --> 0.0<br>-0.4<br>-0.8<br>-1.2<br>-1.6 I. _ _ _ _ _ _ _ _ _ _ ------ _ _ _ _ _ _-1.5<br>78 80 82 84 86 88 90 92<br>0.3<br>0.2<br>0.1<br>0.0<br>r -1 . . . . . . . . . . .0.25<br>78 80 ~~~~~~~~~~~~~~0.4 82 84 86 88 90 92<br>1.2 ________________0.05<br>z<br>0.B<br>0.4 ------ ------<br>0.0 1~~~~~~~~~~~~ -0<br>-0.4<br>78 80 82 84 866 88 906 92<br><!-- End of picture text --> <!-- Start of picture text --> 0.1<br>0.0--o<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>78 80 82 84 86 88 90 92<br>0104 __ _,'_"__ime<br>0.00<br>-0.05<br>00 - - -<br>._._._._. .. Pk<br>O0178 80 82 84 86 88 90 92<br>~~~0.04 .... time<br>00<br>0.02<br>0.01<br>-0.01<br>78 80 82 84 86 88 90 9.2<br><!-- End of picture text -->
# 3.1. Indicators of infrastructure quality

This section analyzes the quality of infrastructure services perceived by firms and the role of flood exposure in explaining service disruptions. It aims to answer the following question: what is the impact for firms of being directly exposed to floods compared to the being indirectly impacted? In other words, do firms which are directly exposed to floods have significantly worse outcomes than firms situated outside of flood zones?

While infrastructure disruptions indeed physically occur with increased frequency and intensity in certain hotspots, infrastructure systems are networks that transmit the adverse effects of disruptions across wide areas. The level of localization however varies across different types of infrastructure. For instance, in the case of electricity, a disruption in one location (e.g. a tree falling on transmission lines during a storm) will affect whole segments of the distribution network.

**Electricity outages:** The spatial distribution of electricity outages is assessed using ES data on firm-level power outages. For each firm, the number of hours without electricity in an average month is calculated. In addition, firms are asked to assess what percentage of their sales they are losing due to power outages. It is found that firms in cities with longer outages forego higher sales, on average. Still, some variation exists.

**Insufficient water supply:** While electricity outages are usually felt with the highest immediacy, disrupted water infrastructure similarly affects firms in many cities in our samples. Based on the Enterprise surveys we calculate the share of firms in each city that experienced insufficient water supply in the year before the survey was carried out. These disruptions of firms relying on water as an input for production or the provision of services are likely to impose significant economic costs.

**Transportation disruptions:** Turning to transportation infrastructure, our analysis shows that the firms suffer from significant disruptions in all 13 cities in our sample. The ES relies on a subjective measure to assess transportation infrastructure disruptions experienced by firms. Firms assess transport as an obstacle to their operations on a scale from 0 (“no obstacle”) to 4 (“very severe obstacle”). A comparison of the average level of transport disruptions is pictured in Figure 6.

Figure 6: Average perception of transport as an obstacle to business across cities. Higher values indicate perception as a greater obstacle <!-- Start of picture text --> 2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Abidjan<br>Monrovia Douala<br>Bamako Kigali<br>Free Town Cotonou Accra<br>Mean perception of greater obstacles) Zanzibar Nairobi Kampala<br>transport as an obstacle (higher values indicate Dar es Salaam<br>Addis Ababa<br><!-- End of picture text --> The perception of transport disruptions seems relatively uniform across cities. Infrastructure disruptions affect everyone 10
# Table 3: Abnormal stock returns and the PEPP in euro area and non-euro area countries

The dependent variable is the abnormal return observed on March 19, 2020. Investment grade (Non-investment grade) is a dummy variable indicating a long term, domestic or foreign, issuer credit rating of at least (lower than) BBB-/Baa3 issued by either Moody’s, S&P or Fitch. Leverage is liabilities divided by assets at the end of 2019. Log of assets is the natural logarithm of assets at the end of 2019. ROA is net income before extraordinary items divided by assets at the end of 2019. Book-to-market is the book value of equity divided by the market value of equity at the end of 2019. Bonds/assets is the sum of the principal amounts of outstanding bonds divided by the book value of assets at the last reporting date in 2019. Regressions 1 to 4 (5 to 8) in Panel A include firms that are incorporated (headquartered) in the euro area. Regressions 1 to 4 (5 to 8) in Panel B include firms that are not incorporated (headquartered) in the euro area. Regressions include country fixed effects. Robust standard errors clustered at the country level are in parentheses. *, ** and *** denote significance at 10%, 5%, and 1%, respectively.

|Panel A||Incorporat|ed in euro area|||Headquarter|ed in euro area||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Investment grade|0.00929|0.0124||-0.00475|0.0134*|0.0160*||-0.00142|
||(0.00654)|(0.00769)||(0.00362)|(0.00693)|(0.00835)||(0.00239)|
|Leverage|-0.0151*|-0.0167*|-0.0236**|-0.0200**|-0.0153*|-0.0165*|-0.0236**|-0.0197*|
||(0.00744)|(0.00797)|(0.00948)|(0.00939)|(0.00865)|(0.00926)|(0.00999)|(0.00984)|
|Log assets|0.000735|0.000231|0.00140|-0.00000153|0.000430|0.0000161|0.00144*|-0.000125|
||(0.000784)|(0.000872)|(0.000832)|(0.00116)|(0.000776)|(0.000891)|(0.000777)|(0.00112)|
|ROA|-0.0415**|-0.0399**|-0.0375*|-0.0353|-0.0370**|-0.0357**|-0.0380*|-0.0351|
||(0.0159)|(0.0155)|(0.0208)|(0.0211)|(0.0158)|(0.0156)|(0.0208)|(0.0212)|
|Book-to-market|-0.00360|-0.00364|-0.00845***|-0.00775***|-0.00468**|-0.00458**|-0.00807***|-0.00733***|
||(0.00296)|(0.00286)|(0.00230)|(0.00236)|(0.00206)|(0.00202)|(0.00232)|(0.00240)|
|Non-investment grade||0.0131||0.00757||0.0111||0.00742|
|||(0.00925)||(0.00665)||(0.00879)||(0.00656)|
|Bonds/assets|||-0.0128|-0.0443|||-0.0154|-0.0456*|
||||(0.0312)|(0.0262)|||(0.0305)|(0.0257)|
|Bonds/assets * Investment grade||||0.131***||||0.122***|
|||||(0.0395)||||(0.0362)|
|Observations|911|911|802|802|921|921|802|802|
|Adjusted R-squared|0.032|0.034|0.036|0.045|0.039|0.040|0.035|0.043|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

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