rafmacalaba/datause-encoder-data huggingface dataset:
percentage of GDP in developing countries than in developed ones (World Bank 2006, ). To secure steady advances towards poverty alleviation and economic growth in the developing world, suitable risk reduction strategies must be developed and mainstreamed into urban planning and development strategies. Otherwise, years of development and accumulated wealth are repeatedly destroyed and eroded through repeated disasters.

Given the intrinsic high loss potential from natural hazards in urban areas, it comes as a surprise that relatively little is known about global patterns of vulnerability and risk potential of cities. Which cities are likely to be affected by a disaster? Which cities have the highest risk of mortality due to disasters? Which cities are most at risk of economic losses due to natural hazards? And which of the world’s regions will experience the largest increase in urban hazard risk? Efforts to assess urban risks so far have mainly focused on single cities, identifying inner-city hotspots. But a comprehensive ranking of the global cities’ risk to guide priorities in building resilience has been lacking. Building on complementary country level risk assessments, this study creates a disaster risk ranking of large cities in the less developed world. Risk levels of 1,943 cities in 110 countries are evaluated and compared. The five following features characterize the analysis in this paper:

- Risks are assessed for urban agglomerations with more than 100,000 inhabitants.

- For each city, mortality risk and economic risk are calculated by taking into account three components of risks: hazard, exposure, and vulnerability.

- The loss potentials are expressed in relative levels.

- Four major natural hazards, namely earthquakes, cyclones, floods, and slides are considered in this study. Urban risks are identified for each of these hazards separately. In addition, a multihazard index gives a holistic picture of city risk.

- Expected urban risk exposure to earthquakes and cyclones in the year 2050 is determined using a demographic-economic projection model.

4
Table I.3. Robustness Analysis for the Labor Income Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.158**|0.237**|0.180**|0.151**|0.177**|0.162**|0.158**|0.237**|0.180**|0.151**|0.177**|0.162**|
||(0.076)|(0.112)|(0.087)|(0.073)|(0.079)|(0.080)|(0.076)|(0.112)|(0.087)|(0.073)|(0.079)|(0.080)|
|Obs. left|216|146|180|221|145|180|216|146|180|221|145|180|
|Obs. right|439|275|366|591|749|549|439|275|366|591|749|549|
|Mean values (Non-RAMV)|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|0.221**|0.284**|0.313**|0.181*|0.225**|0.162*|0.289**|0.412**|0.430**|0.198*|0.184*|0.122|
||(0.107)|(0.120)|(0.136)|(0.096)|(0.093)|(0.096)|(0.121)|(0.167)|(0.176)|(0.106)|(0.099)|(0.103)|
|Obs. left|255|199|180|239|199|180|255|199|180|239|199|180|
|Obs. right|569|404|366|860|886|939|569|404|366|860|886|939|
|Mean values (Non-RAMV)|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.325**|0.283*|0.330*|0.242*|0.146|0.201|0.398**|0.431*|0.430**|0.319*|0.173|0.291|
||(0.154)|(0.166)|(0.172)|(0.140)|(0.124)|(0.144)|(0.192)|(0.246)|(0.176)|(0.175)|(0.161)|(0.190)|
|Obs. left|239|223|224|239|223|224|239|223|180|239|223|224|
|Obs. right|566|479|486|781|834|776|566|479|366|781|834|776|
|Mean values (Non-RAMV)|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|0.351|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 7. Irrigation potential**

<!-- Start of picture text --> a. Small scale b. Large scale<br>30 90 25 160<br>80 140<br>25<br>20<br>70<br>120<br>20 60<br>100<br>15<br>50<br>15 80<br>40<br>10<br>60<br>10 30<br>40<br>20<br>5<br>5<br>10 20<br>0 0 0 0<br>Area increase (thousand ha) Internal Rate of Return (%) Area increase (thousand ha) Internal Rate of Return (%)<br>Area Increase Area Increase<br>Internal rate of return Internal rate of return<br><!-- End of picture text --> _Source:_ Derived from You and others (2009). Based on 12 percent cutoff estimates, at which the estimated area increase for southern African countries not included in the figures is zero.

# Water supply and sanitation

## **Achievements**

Zimbabwe‘s access to water-utility services is, nominally, among the highest in African low-income countries. Access to piped water is more than three times the rate found in other low-income countries, and Zimbabwe‘s reliance on surface water, at only 7 percent of the population, is correspondingly one of the lowest in Sub-Saharan Africa, below the average for middle-income countries and only one-fifth of comparable low-income countries (table 7).

24
zero otherwise [data on deposit insurance comes from Demirgüç-Kunt, Karacaovali, and Laeven (2005)]. The indicator variable is positive and significant; once we control for per capita income and population density, countries with deposit insurance have more accounts per thousand adults than countries without deposit insurance.<sup>30</sup> Taken as a whole, there is some evidence that macroeconomic stability is associated with a deeper penetration of deposits.

Columns (6) – (9) present the regression results of the impact of infrastructure and political stability on deposit penetration. Deficiencies in infrastructure can drive up costs for financial institutions to supply financial services; may impact business activity, reducing demand for financial services; and (in the case of transport and information infrastructure) may imply higher transaction costs for customers to access services. In fact, countries with higher electricity consumption and more phones per capita have, on average, more accounts per adult. In contrast, while a deficient transportation infrastructure might increase the cost of outreach into more remote areas, the density of roads comes in negatively and not significantly. Although political instability and violence can increase the cost of doing business, this variable is not significantly associated with deeper penetration once per capita income is taken into account. Finally, column (10) considers economies designated “offshore financial centers” by the IMF, whereas we would expect to have greater numbers of accounts per resident, as they often hold accounts for large numbers of non-residents. The sign of the indicator variable, however, is contrary to expectations and not significant, which may reflect the fact that we do not include most of the small islands in the Caribbean or the fact that many offshore centers do not specialize in banking services but instead more advanced financial instruments.<sup>31</sup>

> 30 Our regression analysis uses an indicator variable that takes one if there is a deposit insurance scheme in place in the country, and zero otherwise. This is for simplicity and thus the result should be taken with caution. On one hand, the result does not provide any evidence of causality. On the other hand, there is a well established literature showing that the moral hazard engendered by overly generous deposit insurance schemes leads to greater instability and slower long-run financial development [Demirguc-Kunt, Kane, and Laeven (2008)].

> 31 In unreported regressions, we estimated separately the relationship between deposit penetration and two indices, namely the Creditor Rights Index and the Credit Information Index, controlling for (the logarithm of) per capita income and (the logarithm of) population density. Both indices enter with a positive sign and are statistically significant. In theory, however, these indices should not affect directly the number of deposit accounts, so it is likely that they are simply a proxy for unobserved variation in the development of financial systems or financial reforms.

29
# **Table 7:  Robustness Checks, 2**

||**Control**<br>|**ling for Lagg **<br>|**ed Growth**<br>|**Controlli**<br>|**ng for Lagge **<br>|**d Spending**<br>|**Con**<br>|**trolling for**<br>|**Policy**<br>|
|---|---|---|---|---|---|---|---|---|---|
|_(Dependent variable is Change in Real GDP)_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Sample of Countries|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|
|**_Panel A: OLS Estimates_**||||||||||
|Change in Total Government Spending|0.290***|0.249***|0.267***|0.311***|0.259***|0.284***|0.305***|0.253***|0.267***|
||(0.0381)|(0.0512)|(0.0437)|(0.0363)|(0.0458)|(0.0413)|(0.0390)|(0.0508)|(0.0433)|
|Lagged Change in GDP|0.130***|0.0987***|0.105***|||||||
||(0.0310)|(0.0353)|(0.0352)|||||||
|Lagged Change in Total Government Spending||||0.103***|0.0778***|0.118***||||
|||||(0.0246)|(0.0286)|(0.0304)||||
|Change in CPIA PolicyIndicator|||||||0.0109***|0.00809**|0.00999***|
||||||||(0.00284)|(0.00371)|(0.00336)|
|**_Panel B: 2SLS Estimates_**||||||||||
|Change in Total Government Spending|0.337|0.391**|0.404**|0.053|0.116|0.243|0.286|0.373*|0.441*|
||(0.249)|(0.191)|(0.202)|(0.267)|(0.205)|(0.202)|(0.301)|(0.218)|(0.232)|
|Lagged Change in GDP|0.127***|0.0914**|0.0971**|||||||
||(0.0356)|(0.0361)|(0.0372)|||||||
|Lagged Change in Total Government Spending||||0.247|0.259|0.204||||
|||||(0.169)|(0.158)|(0.166)||||
|Change in CPIA PolicyIndicator|||||||0.0111***|0.00706|0.00856**|
||||||||(0.00362)|(0.00428)|(0.00396)|
|_First-Stage F-Statistic_|11.81|26.66|21.39||||8.7|21.03|16.66|
|_Stock-Yogo Weak Identification Test Statistic_||||2.90|2.93|4.06||||
|_Number of Observations_|2782|1493|1937|2702|1452|1883|2516|1386|1755|
|_Number of Countries_<br>Note: *** (**) (*) denotes significance at the 1 (<br>estimated using pooled country-year data and in<br>by lagged GDP. Panel A reports OLS estimates of<br>lagged growth, (b) controlling for lagged changes<br>(20%) maximal IV size is 3.63 (3.95). The three co<br>countries where disbursements on loans from of|102<br>5) (10) perce<br>clude a full s<br>Equation (1<br>in governm<br>untry sampl<br>ficial credito|60<br>nt level. Het<br>et of country<br>). Panel B re<br>ent spending<br>es correspon<br>rs average at|70<br>eroskedasticity-c<br>and year fixed e<br>ports 2SLS estima<br>, and (c) controll<br>d to the full samp<br>least 10 percent|102<br>onsistent sta<br>ffects. Chan<br>tes of Equati<br>ing for policy<br>le of countri<br>of governme|60<br>ndard errors<br>ges in GDP, g<br>on (1). The t<br>. The Stock-Y<br>es, the samp<br>nt spending|70<br>are clustered at<br>overnment spen<br>hree sets of colu<br>ogo weak identi<br>le of IDA-eligible<br>over the sample|102<br>the country l<br>ding, and dis<br>mns corresp<br>fication test<br>countries, a<br>period.|60<br>evel. All re<br>bursement<br>ond to (a) co<br>critical valu<br>nd the sam|70<br>gressions are<br>s are all scaled<br>ntrolling for<br>es for 25%<br>ple of|

41
_firms, after controlling for the sectoral distribution of exports and for export destinations. The extensive margin explains about two thirds of the variation in exports across countries of different size. The extensive margin is relatively less important in explaining why richer countries export more._

# **4.2 Exporter Dynamics and Country Characteristics**

We next turn to exporter entry, exit and survival. Figure 3 shows how exporter entry, exit, and survival vary with country size and stage of development, using country-year level data averaged for the period 2006-2008. With respect to entry and exit, there is a negative relationship indicating that richer countries and larger countries have lower entry and exit rates. The relationship with entrant survival is less transparent.

Columns (1)-(5) of Table 7 show the results from regressions of exporter entry, exit, firstyear survival of new exporters, net entry, and turnover on log GDP per capita and log GDP based on the country-sector-year level data for years 2004 to 2008, controlling for sector and year fixed effects.<sup>29</sup> It is crucial to account for sector fixed effects as some sectors have much lower turnover than others, as highlighted by the relative importance of sector effects in the ANOVA (Table 3). The results show that controlling for the sectoral composition of trade, stage of development has important effects on exporter dynamics. In particular, richer countries have lower turnover and higher rates of entrant survival, leaving net entry roughly unchanged. The economic magnitudes of the results are sizable—increasing GDP per capita from the value for the country in the 25<sup>th</sup> percentile (Cameroon) to the value for the country in 75<sup>th</sup> percentile (Lebanon) would lead to a

> 29 Turnover is calculated as the sum of the entry rate and exit rate in year _t_ , while net entry is the difference between entry and exit rates in year _t_ .

21
To conclude: the concentration of market income among fiscal units may, or may not, tell us much about the inequality of disposable income among individuals which is ultimately the concept we are interested in. I listed above the caveats that have to go with any use of fiscal data. Piketty mentions some of them (Chapter 8, pp. 440; Chapter 9, pp. 520ff) but does not dwell on it much and later essentially ignores them. But, on a more positive note, the revolution that Piketty and his associates have brought to the field has certainly made everybody much more sensitive to the non-capture of top incomes by household surveys and to the need to combine (nobody yet knows how) household surveys that provide reasonably reliable income estimates for the bulk of the population with fiscal data that are undoubtedly better suited for the very top of the income distribution.<sup>21</sup>

**8. Economic policy recommendations and method.** The policy recommendation that has attracted most attention is Piketty’s breathtaking call for global taxation of capital. It follows directly from his concern with _r_ > _g_ inequality. The only way to reverse it, if _g_ is exogenously given, is to reduce _r_ . Despite its grandiose and perhaps unrealistic nature (Piketty calls it, possibly in a nod to John Rawls, a “useful utopia”) one would be wrong to dismiss the proposal out of hand. Nobody believes that it could be implemented _hic et nunc_ , and neither does Piketty. But it is based on several strong points.

First, the analysis sketched so far (if one accepts it fully) shows the dangers of an inheritancebased system which favors those who do not need to work for their sustenance. This can be modified precisely by a tax on capital. Second, taxes on capital, whether in form of taxes on land or inheritance, have a long history, probably the longest of all taxes, precisely because some forms of capital were difficult to hide. Extending this to include all forms of capital seems logically consistent. Third, technical requirements for such a tax (which in a rudimentary form exists in most advanced economies) are not overwhelming. Housing is already taxed; the market value of different financial instruments is easily ascertainable and the identities of owners known. The problems are, of course, political. The application of such a tax by individual countries, even the most important, like the United States, can easily lead to the outflow of capital. Thus, international collaboration is indispensable. That collaboration is unlikely to be supported by the countries that currently benefit the most from the opacity of financial transactions and offer tax havens to the rich. Moreover, some emerging market economies may be unwilling to subscribe to it too. But a more modest proposal built around the OECD members (or EU and the United States) is, Piketty argues, feasible. He takes the recently passed US legislation (Foreign Account Tax Compliance Act) as one of the first steps that could lead to regional taxation of capital. I will

> 21 For an attempt, see Lakner and Milanovic (2013, forthcoming).

17
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- 47 -

# **Figure 15: Decrease in Long-Run Average Fee over Assets in Mexico**

<!-- Start of picture text -->
1.05<br>1.00<br>0.95<br>0.90<br>0.85<br>0.80<br>0.75<br>0.70<br>0.65<br>0.60<br>Dec-02Jan-03Feb-03Mar-03Apr-03May-03Jun-03Jul-03Aug-03Sep-03Oct-03Nov-03Dec-03Jan-04Feb-04Mar-04Apr-04May-04Jun-04Jul-04Aug-04Sep-04Oct-04Nov-04Dec-04Jan-05Feb-05Mar-05Apr-05<br><!-- End of picture text -->

Source: CONSAR 137. The problems related to the comparability of rates of return are not as severe as those related to the comparability of fees, but there is scope for improvements in this area as well. The HFSA might consider requiring MPFs to calculate true time weighted rate of return similarly to what done by mutual funds. The HFSA may also consider unifying the coverage, operational, and liquidity reserves (or at least the first two) and require MPFs to calculate rates of return on total contributions. Finally, the HFSA may promote the diffusion of Global Performance Investment Standards (GIPS) and require asset managers for MPFs to be GIPS certified. The adoption of GIPS by all asset managers would greatly improve the comparability of performance evaluations across asset managers.<sup>29</sup> 138. Another measure that is frequently used to monitor performance and contain excessive fees, involves the publication of returns on equity and other indicators of profitability of asset managers and other financial institutions. Asset management companies provide a valuable service and are entitled to earn profits in a competitive financial sector. However, asset management companies operating in second pillars sometimes generate much higher returns on equity than other financial institutions such as banks and insurance companies, while taking much lower risks. Disclosing these indicators on a systematic basis would enhance the awareness of regulators, policymakers, and the public at large of the existence of market inefficiencies leading to excessive profits, increasing the pressure for policy corrections.

139. Before embarking on a major effort to improve disclosure and consumer awareness and change key regulations, it would be important to assess whether the low cost funds have the capacity to absorb a larger number of members. Several of these funds indicate that they would be interested in absorbing more members, even in the absence of a profit motive, because a larger membership would reduce their fixed costs. Some managers may also benefit from a larger membership in terms of bonuses and 29 GIPS can be downloaded at <u>http://www.cfainstitute.org/standards/pps/gips.html.</u>
# **3. Methodology**

We estimate three types of welfare distributions for each country: (a) welfare distributions for 2019 and earlier, (b) a 2020 counterfactual welfare distribution (without the influence of the COVID-19 pandemic), and (c) a 2020 observed welfare distribution. In what follows, we outline the methods to estimate each distribution.

## **3.1 Estimating the welfare distribution for 2019 and before**

Survey data are available for 60 countries (covering 59% of the global population) in 2019. For the rest of the countries that do not have survey data, PIP extrapolates the latest distribution to 2019 assuming all households grow with the real per capita growth rate of Household Final Consumption Expenditure or GDP (see Prydz et al. 2019 for details). This assumes that the only information relevant for projecting older surveys to 2019 is national accounts, and that inequality has not changed since the last survey. Mahler et al. (forthcoming) show that both of these assumptions are relatively accurate.<sup>12</sup> This approach is possible for 168 countries with data available in PIP at some point in time.<sup>13</sup> For the countries that do not have any prior household surveys, we assume their 2019 distribution equals the 2019 distribution of the geographic region they belong to, using PIP’s regional definition. For instance, the 2019 distribution we use for Afghanistan is the populationweighted distribution of the rest of the countries in South Asia. This method is applied to 3% of the world population in 2019.

The distributions for the period between 1990 and 2019 are obtained similarly. In addition, adjacent surveys are interpolated for intervening years that do not have a survey estimate.

## **3.2 Estimating the counterfactual 2020 welfare distribution**

To isolate the impact of the pandemic on poverty and inequality, we construct a counterfactual 2020 welfare distribution that approximates what the income distribution in a country would have looked like in the absence of the COVID-19 pandemic. To that end, we use the January 2020 per capita GDP growth forecasts for 2020 to grow each country’s 2019 welfare distributions to 2020. We assume all households grow with the same growth rate within a country, so inequality within a country is held fixed at the 2019 level. Evidence suggests that the pass-through from growth in per capita GDP to growth in the mean consumption in household surveys is less than

> 12 The authors show that more complicated methods that try to predict growth in the survey mean and/or changes to inequality yield only slight increases in predictive accuracy.

> 13 Note that while there are 169 countries with microdata in PIP, there is no national accounts data for the República Bolivariana de Venezuela after 2014. The República Bolivariana de Venezuela is assigned the regional average distribution between 2014 and 2019.

9