rafmacalaba/datause-encoder-data huggingface dataset:
|Improved sanitation|0.0450***|0.0452***|0.0451***|
|---|---|---|---|
||(0.00911)|(0.00911)|(0.00911)|
|Improved water|-0.00446|-0.00388|-0.00379|
||(0.0257)|(0.0258)|(0.0256)|
|Water treatment|0.0671***|0.0674***|0.0673***|
||(0.0204)|(0.0204)|(0.0204)|
|Arsenic (WHO)|0.00347|||
||(0.0103)|||
|Arsenic (BD)||-0.00280<br>(0.0142)||
|Arsenic - medium (11-50 ppb)|||0.00720<br>(0.0130)|
|Arsenic - high (51+ ppb)|||-0.00147<br>(0.0143)|
|Observations|7,979|7,979|7,979|
|Standard errors in parentheses||||
|*** p<0.01, ** p<0.05, * p<0.1||||

**Table 5. Correlates of child learning including three-specifications of arsenic exposure (N=7,968 children aged 3-5 years)**

|VARIABLES|(1)|(2)|(3)|
|---|---|---|---|
||WHO|Bangladesh|Risk Levels|
|Child ever breastfed|0.0463***|0.0454***|0.0467***|
||(0.0158)|(0.0157)|(0.0158)|
|Child is female|-0.00433|-0.00443|-0.00438|
||(0.00678)|(0.00678)|(0.00678)|
|Child's age (log)|0.127***|0.126***|0.126***|
||(0.0192)|(0.0191)|(0.0192)|
|Mother's education (primary/some secondary)|0.0203***|0.0208***|0.0203***|
||(0.00771)|(0.00778)|(0.00773)|
|Mother's education (secondary or higher)|0.0396***|0.0415***|0.0396***|
||(0.0123)|(0.0123)|(0.0123)|
|Urban|-0.00645|-0.00676|-0.00688|
||(0.0103)|(0.0104)|(0.0103)|
|Household size (log)|-0.0360***|-0.0362***|-0.0356***|
||(0.00957)|(0.00960)|(0.00960)|
|Quintile 2|-0.0300***|-0.0302***|-0.0299***|
||(0.00971)|(0.00972)|(0.00973)|
|Quintile 3|-0.0260**|-0.0260**|-0.0258**|
||(0.0102)|(0.0102)|(0.0102)|
|Quintile 4|-0.0122|-0.0124|-0.0120|
||(0.0106)|(0.0105)|(0.0106)|
|Quintile 5|-0.00177|-0.00350|-0.00194|
||(0.0130)|(0.0131)|(0.0130)|
|Improved sanitation|0.0140*|0.0133*|0.0140*|

24
government balance sheet (central government, central bank, SOEs) and calculates net financial positions in domestic and foreign currency (see box 2).

# **Box 2.**

# **Reporting on net financial position of the public sector in Chile. Implications for sovereign ALM**

The Ministry of Finance of Chile publishes on a quarterly basis a public debt statistical bulletin (Report on Public Debt Statistics<sup>_21_</sup> ) that presents simplified balance sheets of the central government and of the central bank, with details on the public‐sector debt (including guarantees, for example on students’ loans, as well as transfers to SOEs). These balance sheets distinguish assets and liabilities in local currency and in USD, presenting a net financial position in both denominations. For example, as of September 2017, both the central government and the central bank had a net credit (long) position in USD and a net debit (short) position in local currency.<sup>22</sup> Even though there is no formal coordination between the MOF and the Central Bank to implement a sovereign ALM strategy (e.g. composition of assets and liabilities to seek natural hedges), both institutions hold frequent discussions on recent developments in financial markets. Within the MOF, a committee composed of representatives from the Budget department, Treasury and the DMO, ensures coordination in the implementation of the debt strategy and the management of the assets of the government funds.

The ALM approach is currently used in Chile to support the decision on the currency composition of the assets of the Fiscal Stability Fund ( _Fondo de Estabilización Económica y Social_ – FEES), the revenues of which are generated by headline fiscal balances (in excess of 0.2 percent of GDP), and of the liabilities of the central government: ‐ the assets of the FEES are invested in securities denominated in currencies with low or negative correlation with the price of the copper (such as JPY); ‐ the strategy of the central government is to issue in local currency or in currencies with a strong positive correlation to the price of copper (such as USD).

Based on financial studies, currencies were identified, and theoretical ranges were determined for the currency composition of assets and liabilities. These results are balanced with other considerations (financial returns, market supply/demand, etc.) to fix the operational objectives (asset allocation of the FEES and medium‐term debt strategy).

> 21 Available at http://www.hacienda.cl/english/public‐debt‐office/statistics/public‐debt.html. Also available <u>are</u> statistical series in Excel format.

> 22 The net position of the central government essentially includes the sovereign debt and the financial assets of the government funds (FEES, _Fondo de Reserva de Pensiones_ , etc.).

19
are constructed based on accumulating and depreciating past investments using the perpetual inventory method.

-  Trade Data. Data on international trade ows come from the United Nations Statistical Division (UNSD) Commodity Trade Statistics Database (COMTRADE). We complement the international trade ows data with data on domestic trade ows from Anderson et al. (2020), which we use both for the estimation and for the counterfactual analysis. Anderson et al. (2020) construct domestic trade ows at the aggregate level in two steps. First, they use the ratio between aggregate manufacturing in gross values and total exports of manufacturing goods to construct a multiplier at the country-time level. (Data on gross manufacturing production, which came from the United Nations' IndStat database.) Then, they use this multiplier along with data on aggregate exports to project the values for domestic sales. Availability of data on domestic trade ows predetermined the time coverage of our estimating sample.

-  Other Data. Finally, in the estimation analysis we employ the following additional covariates as control variables. We control for the presence of bilateral investment treaties with an indicator variable _BITij,t_ , which comes from the UNCTAD 's data on international investment agreements (IIAs), which can be found at http://investmentpolicyhub.unctad.org/IIA. Data on sanctions come from the Global Sanctions Database (GSDB), cf. Felbermayr et al. (2020) and Kirilakha et al. (2021) (http://www.rug.nl/research/ggdc/data/pwt/). We use the GSDB to include and indicator variable ( _SANCTij,t_ ) for the presence of sanctions in our estimations. Finally, data on WTO membership, captured by an indicator variable _WTOij,t_ in our analysis, come from the Dynamic Gravity Dataset (DGD) of the U.S. International Trade Commission, cf. Gurevich and Herman (2018) (http://www.rug.nl/research/ggdc/data/ pwt/).

19
5 by such migration, and involves<sup>numbers in excess of any available estimate of</sup> the number of Mexican migrants<sup>currently in the US (Garcia y Griego (1989)).</sup> We therefore focus on internal<sup>migration, and assume that migration</sup> flows keep the ratio of per-capita<sup>utility differentials between landless</sup> rural workers (the most likely<sup>migrants) and urban workers (the most likely</sup> target group) constant. We use<sup>utility differentials rather than wage</sup> differences (as in the Harris-Todaro<sup>m4odel)</sup> because urban transfers like<sup>the</sup> tortivale program also affect migration<sup>choices. We capture allsucheffects</sup> by focusing on total utility.<sup>With LrU the stock of migrants from rural to</sup> urban areas, U. and Uu per capita<sup>utility of a worker in the rural and urban</sup> areas, respectively, and the superscripz<sup>0 an initial equilibrium, we get:</sup> _Llu = kf (U/UU) / (L/U,") ] - k ; k> O 1n2 (1 )_ where vi is the elasticity of migration<sup>to urban-rural utility differentials.</sup> Keeping utility differentials constant<sup>is achieved by setting v very high.</sup> We distinguish physical (the actual<sup>physical hectares of land allocated</sup> to a particular crop) from effective<sup>land (the amount actually usable). The</sup> relationship between them is: where - denotes effective land; the subscript<sup>jrefers to the four</sup> agricultural goods. Equation (2)<sup>is intended to capture incentives for crop</sup> rotation and other practices that<sup>preclude allocating all land to a single</sup> crop. Irrigated land is assumed<sup>to be better than rain-fed in that 01,02 j,</sup> so that diminishing returns set<sup>in more slowly than in rain-fed lands. Hence,</sup> for the same price change, the supply<sup>response in irrigated lands is stronger.</sup> Agricultural production functions<sup>exhibit constant returns to scale to</sup> labor and effective land; thus<sup>value added in maize, m, in rain-fed lands is:</sup>
Kingdom and the US. Interestingly, Vietnam also performs relatively well compared to the rest of the group. It enjoys higher immunization coverage and higher proportion of births attended by skilled health personnel than the Philippines and Indonesia, although not for antenatal care. As expected, Cambodia and Lao PDR lag behind others countries in its health service coverage.

**Table 1: Healthcare services coverage**

|**Country**<br> <br>|**Immunizatio**<br>**1**<br>**Measles**<br>**2003**|**n coverage (**<br>**-year-olds**<sup>**a**</sup><br>**DTP3**<br>**2003**|**%) among**<br>**HepB3**<br>**2003**|**Anten**<br>**cove**<br>**(%)**|**atal care**<br>**rage**<sup>**b**</sup><br>**year**|**Births atten**<br>**health p**<br>**(%)**|**ded by skilled**<br>**ersonnel**<sup>**b**</sup><br>**Year**|
|---|---|---|---|---|---|---|---|
|Cambodia|65|69|0|44|2000|32|2000|
|Indonesia|72|70|75|97|2003|66|2003|
|Lao PDR|42|50|50|44|2001|19|2001|
|Malaysia|92|96|95|...|...|97|2001|
|Philippines|80|79|40|94|2003|60|2003|
|Singapore|88|92|92|...|...|100|1998|
|Thailand|94|96|95|...|...|99|2002|
|Vietnam|93|99|78|70|2002|85|2002|
|_United Kingdom_<sup>c</sup>|_80_|_91_|_0_|_..._|_..._|_99_|_1998_|
|_United States_<sup>c</sup>|_93_|_96_|_92_|_..._|_..._|_99_|_1997_|

<u>Notes:</u> ...Data not available or not applicable a. World Health Organization, Department of Immunization Vaccines and Biologicals, Vaccine Assessment and Monitoring Team. (http//www.who.int/vaccines-surveillance, accessed on 16 April 2005) b. The World Health Report 2005: make every mother and child count. Geneva, World Health Organization, 2005.

(http://www.who.int/whr/2005/en/index.html) c. The figures for the UK and USA have been included for purposes of comparison.

A similar picture emerges when comparing the number of hospital beds across the seven ASEAN countries studied (Table 2). The Thais, Malaysians and Singaporeans enjoy the highest number of beds per population. Vietnam also performs relatively well. Despite this, there are signs that the healthcare infrastructure in Vietnam is not being used efficiently. Thang (2005) describes heavy reliance on hospitals for primary treatments and high inpatient admission rates relative to other countries in the region, indicating over-utilization of hospital beds. The number of hospital beds per population in Cambodia and Indonesia are respectively one-third and one-fourth that of Thailand.

In Thailand, Indonesia, Vietnam, Cambodia and Laos, state-funded primary healthcare centers (HCs) are the main providers of basic healthcare services to the majority of the people.<sup>3</sup> Thailand enjoys the highest number of HCs per population in this group. HCs in Cambodia and Laos are reported to be ill-equipped and ill-staffed due to insufficient funding. Poor access to

> 3 The services typically include preventive, ambulatory, inpatient services and referral services.

4
|**OLS**<br>**INSTITUTIONAL**<br>**TOOLS INDEX**|Performance-based<br>payments**Q81_V**|Employees’ training<br>**Q83_V**<br>Publication of annual<br>report**Q88_V_A**<br>Publication of<br>institutional strategy<br>**Q90_V_A**<br>Structure of posts and<br>salaries**Q78_V**<br>Website**Q91_V**<br>Users’ claims<br>registration**Q92_V**<br>Regulatory quality<br>standards**Q93_V**<br>Public consultations<br>**Q63_V**|
|---|---|---|
|**nce Indexes**<br>**TO**<br>**REGULATORY**<br>**TOOLS INDEX**|Benchmarking<br>**Q74_V**|Regulatory<br>instruments**Q73_V**<br>Consumers’ rights<br>regulations**Q85_V**|
|**Regulatory Governa**<br>**ARENCY**<br>**INSTITUTIONAL**<br>**TRANSPARENCY**<br>**INDEX**|Agency’s proc. for<br>rules’ elab.**Q60_V**|Agency’s publication<br>of its decisions<br>**Q67_DES_V**<br>Collective or single<br>decision-making<br>**Q70_V**<br>Quarantine rules for<br>directors**Q71_V**<br>Agency’s reporting<br>instrument**Q84_V**<br>Publication of<br>institutional strategy<br>**Q90_V_A**<br>Public exam. for<br>employees**Q82_V**<br>Publication of<br>agency’s audit<br>accounts**Q95_V_A**<br>Publication of career<br>posts**Q80_V**<br>Board’s meetings<br>records**Q94_V**<br>Publication of annual<br>report**Q88_V_A**<br>Norms of ethics<br>**Q96_V**|
|**regated Electricity**<br>**TRANSP**<br>**SOCIAL**<br>**TRANSPARENCY**<br>**INDEX**|Stakeholders’<br>participation (law)<br>**Q62_V_A**|Agency’s publication<br>of its decisions<br>**Q67_DES_V**<br>Publication of basic<br>data**Q59_V**<br>The agency perform<br>pub. consultations<br>**Q63_V**<br>Consultations with<br>advisory committees<br>**Q68_V**<br>Website**Q91_V**<br>Users’ claims<br>registration**Q92_V**|
|**DIX 3 (cont.): Agg**<br>**REGULATORY**<br>**AUTONOMY**<br>**INDEX**|Regulation of the<br>sector**Q25_V**|Regulatory powers<br>**Q30_V**<br>Resp. on particular<br>issues**Q31_V**<br>Enforcement<br>powers**Q33_V**|
|**APPEN**<br>**AUTONOMY**<br>**MANAGERIAL**<br>**AUTONOMY**<br>**INDEX**|Organiz. of<br>agency’s structure<br>**Q42_V**|Employment<br>policies**Q43_V**<br>Agency’s decisions<br>on personnel<br>**Q45_V**<br>Financial<br>autonomy **Q47_V**<br>Structure of posts<br>and salaries<br>**Q78_V**<br>Performance-<br>based payments<br>**Q81_V**|
|**POLITICAL**<br>**AUTONOMY**<br>**INDEX**|Instructions to the<br>agency**Q24_V**|Legal status<br>**Q21_V**<br>Agency’s<br>intervention<br>**Q23_V_A**<br>Separation of roles<br>**Q26_V**<br>Interference by the<br>Minister**Q32_V**<br>Directors’<br>selection**Q34_V**<br>Renewability of<br>directors’ mandate<br>**Q38_V**<br>Directors not<br>completed their<br>terms**Q39_V**<br>Reasons directors<br>leave**Q41_V**<br>Budget’s<br>percentages<br>**Q46_V_P**|
infant mortality rate (IMR), and under-five mortality (U5MR). On health interventions, we included child and maternal health services. Two of the child health services indicators included in this study are child immunization indicators: measles vaccination and full immunization. The other three are maternal health services, namely prevalence of modern contraceptive use by married women, four or more antenatal care visits from a skilled professional (ANC4+), and delivery assistance from a skilled birth attendant (SBA).

# _2.2 Data sources and variable construction_

We used four demographic and health surveys (DHS) 2000, 2005, 2011 and 2014.<sup>5</sup> The surveys implemented in different periods follow standard and comparable questionnaires. The data provide nationally representative information on the health outcomes we selected for this study. The surveys also allow generating estimates for rural and urban areas and regions. In addition, the timeline of the surveys in general overlaps the larger part of the MDGs period.

The DHS data are organized in different files. Household level characteristics, such as wealth and housing conditions (e.g., water and sanitation facilities), are from the household data. Quintiles and other transformed variables, such as bottom 40% and top 60% wealth categories, are computed from the household wealth index that is

> 5 The 2014 survey did not cover immunizations. The analysis for   immunization indicators (full immunization and measles immunization) is based on the first three surveys.

7
are designed and financed by private stakeholders with limited focus on individual premises. This has been experienced in many parts of the city where individual housing societies have increased the heights of their plots, thus flooding the road or nearby premises. As there is no regulation on the measures undertaken by the private stakeholders, resilience in these areas to future risks may be compromised.

# 4.3.3 Relocation as an option

As flooding in Mumbai is recurrent and the poor households bear the brunt of the impacts every year, relocation to a flood‐free area might be one of the solutions, especially for those who reside in chronic flood spots. Therefore, we asked our sample of 200 households specific questions about relocation as a possible option and factors they would consider important while accepting relocation to a flood‐free area.

As seen in Table 11, half of the BPL households have considered moving out the flood‐prone area after the experience during July 2005 floods. However, the BPL as well as poor households have not moved out for a number of reasons. For the BPL families, main consideration is the existing strong social network, which is often the source of support while living in informal settlements with little access to amenities and public utilities. These families feel that they will not be comfortable in new locations where such networks are not present. Another important consideration is that such families do not have enough financial resources to move to a better location. For the other poor and non‐ poor households, the decision not to move out of the flood‐prone areas is proximity to the work place and strong social networks.

Table 11: Reasons cited by households for not moving out of flood‐prone areas

||**BPL**|**Poor**|**Non‐poor**|
|---|---|---|---|
|**Have you considered**<br>|**moving out**<br>|||
|Yes|50.0|24.8|23.3|
|No|50.0|75.2|76.7|
|**Reasons for not mo**<br>|**ving out**<br>|||
|Not enough financial resources|35.0|34.4|27.9|
|Can't avail of loans for another house|15.0|18.9|16.3|
|Work place is nearer|25.0|45.9|48.8|
|School/college is nearby|10.0|4.1|7.0|
|Access to public transportation is good|20.0|19.7|16.3|
|Do not feel comfortable about new location|45.0|42.6|44.2|
|Transportation will be costly|10.0|2.5|2.3|
|Water, electricity or maintenance will be<br>costly|10.0|2.5|0.0|
|Have strong social network|45.0|47.5|41.9|

Source: Calculations based on the data obtained from 200 households 24
banking crisis is characterized by signs of distress in the banking system (e.g. bank runs, large losses, bank liquidations) and large-scale policy interventions to support distressed financial institutions. Laeven and Valencia (2013) also make a distinction, that we will exploit, between borderline (two policy interventions) and systemic cases (at least three policy interventions). In our sample, the 23 crisis-affected countries are: Austria, Belgium, Denmark, France, Germany, Greece, Hungary, Iceland, Ireland, Kazakhstan, Latvia, Luxembourg, Netherlands, Nigeria, Portugal, Russia, Slovenia, Spain, Sweden , Switzerland, Ukraine, United Kingdom, United States.

Following Laeven and Valencia (2013), we create an alternative measure of banking crisis which is based on the evolutions of real GDP growth and nominal private credit growth. This alternative banking crisis dummy variable takes the value of one if, during the period 2008-2010 and in a given year, a country has experienced both negative real GDP growth and a slowdown in nominal private credit growth. With this alternative methodology, 49 countries are identified as having suffered from a banking crisis.

Some descriptive statistics can be found in Table 4 in Appendix B.

# **3.5 Estimation method**

It is common in the FDI literature to model the conditional mean of ln(FDI) instead of the conditional mean of FDI. One fundamental problem with using log-linear models is that observations for which the FDI value is equal to zero are dropped from the sample.<sup>24</sup> This truncation issue does not arise when the conditional mean of FDI is modeled directly using an exponential function, as we have done in equations (1) or (2). Consistent estimation of the conditional mean parameters can be

> 2448% of the observations in our sample take the value of zero. They correspond to cases where no FDI in a given sector of a given country is recorded in the FDI database that we use.

12
**Table 4: Impact of the crisis on** **_consumption_ poverty and inequality**

|**_Poverty/inequality measure_**|**Actual**|**Counterfactual**<br>**(all shocks zero)**|**Impact**<br>**(%)**|**(without L-**<br>**shock)**|**Counterfactu**<br>**(without E-**<br>**shock)**|**al**<br>**(without LE-**<br>**shock)**|
|---|---|---|---|---|---|---|
|Mean consumption|26482|27859|-4.9|26837|27004|26982|
|(per capita per year)|_450_|_468_||_454_|_460_|_454_|
|||||[25.8]|**[37.9]**|[36.3|
|Headcount index**(%)**|31.7|29.1|8.7|31.2|30.5|30.7|
||_0.47_|_0.46_||_0.47_|_0.46_|_0.47_|
|||||**[17.0]**|**[46.5]**|**[36.5]**|
|Poverty gap index (%)|9.43|8.47|11.3|9.32|8.91|9.10|
||_0.20_|_0.19_||_0.20_|_0.19_|_0.20_|
|||||**[11.5]**|**[54.1]**|**(34.4]**|
|Squared poverty gap index**(%)**|3.93|3.48|13.0|3.89|3.67|3.78|
||_0.11_|_0.Io_||_0.11_|_0.11_|_0.11_|
|||||**[9.51**|**[57.01**|**(33.5]**|
|Theil's T-index|0.513|0.512|0.1|0.513|0.513|0.511|
||_0.057_|_0,056_||_0.057_|_0.058_|_0.056_|
|Variance of logs|0.628|0.633|-0.9|0.635|0.624|0.631|
||_0.011_|_0.011_||_0.011_|_0.011_|_0.0o_|
|Generalized entropy measure (e=2)|2.02|1.97|2.4|1.99|2.05|1.98|
||_0.68_|_0.67_||_0.67_|_0.71_|_0.66_|
|Generalized entropy measure (e=3)|75.0|69.7|7.5|72.1|76.4|71.0|
||_48.4_|_44.4_||_46.5_|_48.7_|_45.8_|

_Note:_ Figures in _italics_ are the standard errors corrected for sample design effect. The figures in square brackets give the relative contribution of the three shocks to the total impact (not calculated for the inequality measures for which these contributions are not additive. _Source:_ Calculated from 1998 APIS data.

31