rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4. Instruments To Mitigate Poverty from Loss of Earnings Ability in Old Age Variables from the** PRIESO

|**Pooling**_(to)_<br>Saving(s)|Prevention (r)||
|---|---|---|
|Formal<br>o<br>MPG<br>o<br>CONDEN|*<br>YEDU||
|**Public**<br>o<br>CONDEN<br>o<br>CONT98<br>*.<br>CONT99<br>(dependantvar)|*<br>HOSP||
|**Formal**<br>**o**<br>Toofew<br>o<br>COLATT<br>**Private**<br>observations<br>o<br>FINASST<br>*<br>LRENT|o<br>YEDU<br>*<br>WRKASST<br>*HOSP|-|
|**Informal**<br>*****<br>GIVES<br>o<br>Toofew<br>o<br>GETS<br>observations<br>o<br>SHYKIDS<br>•<br>SHKIDS|o<br>SMOKER(not<br>preventing)<br>f<br>Toofew<br>~~~~~~observations|**<br>*|
|*<br>SHOLDM|||
|*<br>SHOLDW|||
|*<br>RESFAM|||
|*<br>EXKIDS|||
|*<br>LEDKIDS<br>*<br>FAMEX|||

Probability of the bad state "p": AGE, SLIFEX Demonstration effect " ':I LJUB, LPASIS 44
Bangladesh, the risk of arsenic contamination is primarily dependent on geography, where households living in some areas are more susceptible to arsenic contamination than others (UNICEF, 2015). Interestingly, exposure to arsenic does not seem to be dependent on one’s socioeconomic status, despite available mitigating mechanisms including well-switching, regular testing, filtration, rain water harvesting, and installation of deeper tubewells (Rice, 2010). However, it would be expected that those without access to mitigation mechanisms are most likely those individuals from households with lower socio-economic status. The latest data from the 2012-2013 Multiple Indicator Cluster Survey (MICS) show that households across wealth quintiles have similar exposure levels to arsenic (UNICEF, 2015). Therefore, it is warranted that high-risk geographical areas of arsenic are instead prioritized for mitigation efforts.

In addition to prioritizing high-risk geographical areas, arsenic mitigation efforts should also be prioritized for expectant mothers and children. In recent years, the scientific community and policy makers have placed a strong emphasis on maternal and childhood interventions during the “first 1,000 days,” or simply the time period between a woman’s pregnancy and her child’s second birthday. Advocates for better nutrition have particularly embraced the “first 1,000 days” targeting strategy, claiming that investment in nutrition during this period is pivotal, as scientific evidence shows that the first years of a child’s life build the biological architecture for healthy brain development, growth, and strong immunity (Miller et al., 2015). Investment in reducing environmental toxicities such as exposure to arsenic in the early years of child development may also be an essential intervention for improving long-term health, cognition, and productivity. Yet interventions for arsenic mitigation are not commonly targeted to expectant mothers and young children. For example, supporting water quality testing and installation of safe water sources in community health or conditional cash transfer programs could be good opportunities for targeting expectant mothers and children. Most studies observe cognitive impairment from arsenic exposure in school-aged and adolescent children; however, this study particularly sees this relationship in children as young as three years old. The study group was limited to children aged 3-5 due to the availability of data on cognitive development, but it is plausible that arsenic is damaging brain architecture at even younger ages. It is imperative that exposure to arsenic is eliminated at early ages when the brain is most vulnerable.

Based on the results already discussed in the paper, one last recommendation is for the Government of Bangladesh to revise its national water quality standards. Currently, the national drinking water standard of arsenic concentration in Bangladesh is greater than 50 ppb. However, since 15
Table 3: Impact of IOp on investment behavior depends on Earnings Rank

|**Dep var:**Binary = 1 if subject|(1)<br>chooses to invest|(2)|(3)|
|---|---|---|---|
||All<br>With|out rank feedback|With rank feedback|
|Pre-investment earnings rank|0.0337|-0.0161|0.0528|
||[0.618]|[0.912]|[0.488]|
|IOp treatment|0.427|-0.0206|0.612|
||[0.362]|[0.983]|[0.249]|
|IOp X Pre-investment rank|-0.0827|-0.0288|-0.104|
||[0.284]|[0.861]|[0.231]|
|Constant|1.066***|1.564*|0.859*|
||[0.00630]|[0.0609]|[0.0536]|
|Observations|499|164|335|
|Pseudo R-squared|0.006|0.004|0.014|

**Dep var:** Portion of earnings invested, conditional on being _>_ 0

||All<br>W|ithout rank feedback|With rank feedback|
|---|---|---|---|
|Pre-investment earnings rank|0.512|0.838|0.361|
||-[0.345]|[0.471]|[0.550]|
|IOp treatment|9.419**|4.188|11.00**|
||[0.0182]|[0.612]|[0.0171]|
|IOp X Pre-investment rank|-1.384**|-0.307|-1.771**|
||[0.0449]|[0.841]|[0.0195]|
|Constant|19.19***|16.23***|20.71***|
||[0.000]|[0.006]|[8.80e-10]|
|Observations|385|130|255|
|R-squared|0.019|0.014|0.043|

Note: Results are from logit and OLS regressions, respectively, with robust standard errors. Columns 2 and 3 are partitioned by whether subjects receive feedback on their actual investment rank (prior to investment). IOp treatment is a dummy equal to 1 if Round 3 investment funds are determined by childhood zip code, and 0 for the control group. Pre-investment rank is subjects’ actual earnings rank based on the total value of the Round 3 investment funds. It is on a scale of 1-10, where higher values indicate higher earnings. Robust p-values are in brackets. Significance also indicated using stars, for reader convenience: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 23
# **Table A - 7 :  Traders' Perception of the Evolution of Difficulties in Dealing with Customs officials in Their Countries**

**(Today compared to 3 years ago)**

# **(% of total responses)**

||**Difficulties in Dealing with**<br>**Customs - Decreased**|**Difficulties in**<br>**Rem**|**Dealing with Customs -**<br>**ained the same**|
|---|---|---|---|
|**Egypt**|75|Jordan|72 (remained same)|
|**Syria**|54|Lebanon|82 (remained same)|
|**Tunisia**|84|Morocco|70 (remained same)|
|**UAE**|87|Saudi Arabia|74 (remained same)|
|||Yemen|60 (remained same)|

**Table A - 8 :  Traders' Responses about "Informal" Payments to Customs Officials**

||**Is it common for trading that a**||**Range of**<br>**(C**|**"informal"**<br>**ompanies'**|**payments i**<br>**responses in**|**n % of impor**<br>**percent)**|**t value**||
|---|---|---|---|---|---|---|---|---|
||**C "Ifl"**||**0**|||||**D'**|
|**Country**|**ompany pays norma**<br>**Payments to Customs Ofiicials?**<br>**(Average)**<br>**(1)**|**0%**<br>**(2)**|**- No more**<br>**than 1%**|**2 - 9%**|**10 - 17%**|**18 - 25%**|**In-Kind**|**ont**<br>**Know**|
|**Egypt**|Mostly|7.7|42.3|42.3|11.5|3.9||0.0|
|**Jordan**|Seldom|93.9|97|-|-|-||3.0|
|**Lebanon**|Frequently|10.3|48.3|17.2|6.9|-|13.8|13.8|
|**Morocco**|Sometimes|18.2|36.4|36.4|1|-||18.2|
|**Saudi Arabia**|Never|95.0|95|-|-|-||5.0|
|**Syria**|Always|5.3|60.3|26.3|-|-||13.2|
|**Tunisia**|Sometimes|21.3|55.3|2.2|-|-||42.6|
|**UAE**|Sometimes|38.5|38.5|7.7|-|-||53.8|
|**Yemen**|Frequently|6.5|74.2|22.6|3.2|-||0.0|
|**Average (2008 Survey)**|**Sometimes**|**30.6**|**64.1**|**15.7**|**2.8**|**-**|**1.6**|**16.1**|
|**Average (2001 Survey)**|||**56.3**|**18.6**|**3.6**|**0.5**||**20**|

- (1)   Companies were asked to rank their answers using one of the following six point scale: always, mostly, frequently, sometimes, seldom, never; the reported results represent averages using Tukey's hinges method (see footnote in Table A-1).

- (2)   The reported precents in this column were not reported in the 2001 survey.

26
developing countries have increased market shares in international tourism. SubSaharan Africa, in particular, has experienced very strong growth in tourism within the last two decades – increasing its market share of global arrivals from 1.5% in 1970 to 4.5% by 2003.<sup>37</sup> Despite a strong tourism asset base and its geographic proximity to South Africa, one of the world’s top destinations, Mozambique still trails behind all its neighbors except for Malawi. Despite quite an impressive annual growth rate of 13% (1999-2003), the average number of tourists per 100 inhabitants, at 2 for Mozambique is half of that of Africa’s average, and well below the world average of 11 per 100 inhabitants. Mozambique’s poor performance reflects problems with the country’s overall image, product variety and quality of tourists’ experiences. Realizing this potential depends substantially on the ability of all players in the Mozambique tourism value chain – from air service providers, to frontier agencies (e.g. for immigration and visas), hotels, tourism service providers, to other suppliers and government officials – to create and deliver highquality tourism experiences that can transform the country into a “must see” destination in Africa.

<!-- Start of picture text --> Figure 15: Price Ranges of Tours for Competing Destinations in the Portuguese<br>Market<br>5500<br>5000<br>4500<br>4000<br>3500<br>3000 High<br>2500 Low<br>2000<br>1500<br>1000<br>500<br>0<br>Egypt BrazilCape Verde TunisiaSeychelles Mauritius KenyaTanzaniaMozambique<br>Euros<br><!-- End of picture text --> <u>Source: FIAS & OECD, (2006), “The Tourism Sector in Mozambique: A Value Chain Analysis,”</u> World Bank Group.

Figure 15 illustrates Mozambique’s shortcomings. On the one hand, the country cannot compete with Brazil, Egypt, Cape Verde and Tunisia in the “low budget” market segment. And on the other hand, for “higher budget” travelers, it still cannot charge the premiums Mauritius and the Seychelles can charge.

The requirements in turning Mozambique into a regional star for tourism are extremely high. First of all, the country needs to address its visa regulations. Many countries in its region don’t require visas at all from EU citizens (Mauritius,

> 37 FIAS and OECD (2006), “The Tourism Sector in Mozambique: A Value Chain Analysis,” _World Bank Group_ .

23
_36 How Fwzi Has Chin se Indsany Grown?_

# TABLE 12: ENERGY DATA FOR STATE SEcTOR INDEPENDENT UNITS, 40 BRANCHES, 1980 AND 1985 (contiustion)

B. Analysis and Consistency Check: Energy Data for 40 branches of State Industry

||% rise<br>in energy<br>productivity|Deriv|ed GVIO|Published OVIO|%diff.|
|---|---|---|---|---|---|
||<br>1980/15|Yi|Yuan|(Yi Yuan)|1985|
|Branch||1980|198S|1980<br>1985||
|1|12.45|125.16|156.78|1S6.19|-0.38|
|2|_15.66_|131.08|160.42|161.14<br>|0.4S|
|3|-1.23|8.20|10.92|10.86|-O.S1|
|4|26.79|20.00|7.75|27.79|0.14|
|5|8.18|10.44|12.44|11.99|-3.61|
|6|-8.82|16.21|15.73|15.75|0.11|
|7|-17.31|0.00|0.00|0.02|77300.00|
|8|-18.47|38.4|36.75|36.95|_0.55_|
|9|3.32|10.89|16.62|16.58|-0.26|
|10|-4.33|334.84|484.85|483.00|-0.38|
|11|24.50|55.14|98.68|96.83|-1.87|
|12|-4.65|87.80|158.14|156.90|-0.78|
|13|240.00|1.47|2S.00|23.82|-4.72|
|14|5,52|553.49|757.06|754.70|-0.31|
|1S|4.76|22.73|30.95|31.71|2.45|
|16|5.06|22.46|26.97|26.74|-0.54|
|17|-17.58|20.22|24.18|24.73|2.29|
|18|26.00|4.76|6.67|6.55|-1.75|
|19|13.01|64.50|91.53|90.30|-1.35|
|20|17.91|34.18|53.73|54.25|0.97|
|2 1|26.47|12.79|19.12|19.31|1.01|
|22|45.98|4.72|10.34|10.09|-2.46|
|23|12.92|191.08|265.01|263.85|-0.44|
|<br>24|21.52|165.43|207.86|207.50|-0.17|
|<br>25|<br>10.59|<br>13.00|15.81|15.85|0.24|
|26|31.20|322.40|450.73|449.98|-0.17|
|<br>27|48.13|59.70|120.15|119.55|-0.S0|
|28|79.81|33.06|89.07|90.12|1.17|
|<br>29|19.02|73.06|105.98|106.12|0.13|
|<br>30|15.3^|24.68|43.07|43.63|1.31|
|31|10.86|122.71|185.83|185.62|-0.11|
|32|1620|29916|391.01|391.90|0.23|
|<br>33|.<br>12.07|.<br>119.23|<br>162.50|<br>163.94|0.89|
|<br>34|<br>22.83|<br>50.19|<br>70.32|69.52|-1.14|
|<br>35|<br>50.95|<br>369.09|639.52|636.38|-0.49|
|36|5120|147.41|262.05|261.30|-0.29|
|<br>37|.<br>50.41|<br>104.86|<br>182.11|181.43|-0.38|
|38|16400|60.61|198.00|196.43|-0.79|
|<br>39|.<br>43.82|<br>29.69|<br>48.31|48.42|.22|
|40|-14.60|3.42|_5.11_|9.03|76.73|
|sum|27.47|3768.28|5667.07|5656.77|-0.18|
countries’ mean incomes. This is a sharp reversal from a situation which existed around the time of the Industrial Revolution when more than half of the (admittedly very rough) estimate of global inequality was due to income differences within nations (see Bourguignon and Morrisson, 2002).<sup>29</sup> Then, in contrast to today, the differences between countries’ mean incomes were relatively small. For example in 1870, the average (unweighted) GDI per capita of the ten richest countries was 6 times greater than the average (unweighted) GDI per capita of the ten poorest countries. In 2002, the ratio was 42 to 1.<sup>30</sup> While income inequality between countries is the largest component of global inequality, overlaps between countries’ distributions (that is, some people from a poor country being better off than some people from a rich country) are non-negligible either.<sup>31</sup> We illustrate this in Figure 1 which plots the position of each 5 percent (ventile) of different countries’ distributions in the global distribution. Consider the line for France. We calculate the mean income (in international dollars) of each French ventile from the lowest (first) to the highest—arrayed on the horizontal axis—and then find their positions in global income distribution. As can be seen, the poorest 5 percent of Frenchmen have a mean income which places them at the 72<sup>nd</sup> percentile of world income distribution; the richest 5 percent have an income which places them in the top percentile of the world. Hence French income distributions spans the range between the 72<sup>nd</sup> and 100<sup>th</sup> percentile in the world. Consider now rural Indonesia in the bottom of the figure. Here the span is from the 4<sup>th</sup> percentile in the world to the 56<sup>th</sup> percentile. The 29 This is an estimate based on the Theil decomposition between inequality due to the differences in incomes between six country groups, namely (Africa), (Asia), (Japan, Korea and Taiwan), (Latin America), (Eastern Europe), and (Western Europe and its offshoots) and inequality within country groups. Since there are no data on income distributions for most countries in the world prior to 1950, Bourguignon and Morrison use estimates for a few select countries to “impart” the same distributions to other countries in the group. Their between-component accounts for some 30 percent of global inequality. Obviously, if they had data on all countries’ distributions, the between component would have been larger. However it is unlikely to have exceeded one-half of global inequality.

30 Both calculated from Maddison (2004) data.

> 31 Note that in a world of large between-county income differences, and very small within-national inequalities, there would be no overlap at all, and 100% of global inequality would have been “caused” by between-national differences.

17
usage. The results are summarized in Table 4 and Figure 10. The intercept represents the estimated usage for a country where there is 100 percent access.<sup>33</sup> At that point, checking account usage is projected at 78 percent,<sup>34</sup> and holdings of bonds at 7 percent. Projected usage at 50 percent access is also shown in Table 4. Checking and deposit accounts and their associated cards are in the first rank for usage. Life assurance comes next, followed at some distance by the various loans and more complex investment instruments. Checkbook, overdraft and occupational pensions have much higher variance of usage, little of which can be explained by variations in overall access: instead these should be seen as system-specific rather than generic products.

# **5.  Concluding remarks**

Financial asset holdings by households are highly concentrated: in the US one-half of households own about 97 percent of financial assets. The savings they represent are pooled and transformed by financial intermediaries and markets, enhancing their risk and return. Although firms are also providers of funds to intermediaries and markets, the savings of prosperous households ultimately represent a large fraction of the resources employed by the financial sector in its provision of risk-reduction services and investable funds. As such, they are a key ingredient in the growth-promoting process of financial intermediation.

Data on the financial asset holdings of households in developing countries are very sparse indeed. The quality of data for the handful of countries for which systematic surveys have been conducted is suspect, with indications that asset holdings of the highest income groups are systematically understated.

> 33 When a country reaches 100 percent access, usage of products may continue to grow – attempts to deal with this by adding a term in per capita GDP does not prove to yield precisely estimated coefficients on this rather small dataset.

> 34 Checking accounts and deposit accounts appear to be substitutes in the sense that inclusion of the percentage using deposit accounts results in a much improved fit; the estimated coefficient on deposit account usage is –0.52.  No other substitutes were found in the data.

22
# **4.1 Crash Rate**

The estimates in the previous two sections demonstrate a decrease in the number of crashes during the curfew hours, but the decrease is offset by spillovers during the hours right before the curfew begins. Especially for the curfew period, these results still do not speak to the externality of congestion since the decreases seen may be proportional to the decreases in vehicles. Focusing on the probability of a crash during curfew hours, we find that the curfew led to a significant increase in the crash rate despite the decrease in crash numbers (Table 3). We also find a large and significant increase in the probability of a crash during pre-curfew hours. This result is not surprising as the pre-curfew hours experienced a decrease in congestion (indicating a decrease in cars on the road), and an increase in crashes. Therefore, the curfew not only led to a spillover of crashes to pre-curfew hours, but the curfew also led to an additional externality of an increase in the probability of being in a crash during both curfew hours and the hours right before the curfew. There is no change in the crash rate during the daytime hours (Column 3).

**Table 3:** Impact of Mobility Reducing Policies on Crash Rates

||Curfew Hours<br>(20:00-5:00)<br>(1)|Pre-Curfew Hours<br>(17:00-20:00)<br>(2)|Daytime<br>(5:00-17:00)<br>(3)|
|---|---|---|---|
|Curfew Period (Post)|5.630<br>(3.734)|-0.770<br>(0.676)|0.388<br>(1.193)|
|_I_2020 (2020)|-0.242<br>(0.584)|-0.172<br>(0.201)|0.133<br>(0.386)|
|Curfew Period (Post) x 2020|1.787**<br>(0.798)|0.962***<br>(0.306)|0.203<br>(0.389)|
|Constant|27.947<br>(89.915)|4.884<br>(15.592)|-42.219<br>(47.028)|
|Observations<br>|293|293|293|
|R<sup>2</sup>|0.224|0.246|0.140|

_Notes_ . Bootstrap standard errors are reported in parentheses and are estimated after performing 200 replications. *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include fixed effects for week and day of the week, and controls for weather (temperature, precipitation and two measures of wind).

# **4.2 Spillover Mechanisms**

The increase in crashes in the pre-curfew hours suggests that there could be a few things happening. First, as the number of vehicles on the road decreases and congestion decreases, there is an opportunity for vehicles to driver faster, which may increase the likelihood of a crash. During the pre-curfew hours, people may also drive even faster and more aggressively than they typically would under the same density circumstances because the time cost is higher because of the risk of punishment for being caught on the street past curfew. We explore this by looking at how speed changes in relation to the curfew during different times of day (see Appendix Figure A.2 for a comparison of speed 13
Policy Research Working Paper 7581

# **Abstract**

Reducing child undernutrition is a key social policy objective of the Ethiopian government. Despite substantial reduction over the past decade and a half, child undernutrition is still high. With 48 percent of children stunted, underweight, or wasted, undernutrition remains an important child health challenge. The existing literature highlights that the targeting of efforts to reduce undernutrition in Ethiopia is inefficient, in part because of the lack of data and updated information. This paper remedies some of this shortfall by estimating levels of stunting and underweight in each woreda for 2014. The estimates are small area estimations based on the 2014 Demographic and Health Survey and the latest population census. It is shown that small area estimations are powerful predictors of undernutrition, even controlling for household characteristics, such as wealth and education, and hence a valuable targeting metric. The results show large variations in share of children undernourished in each region, more than between regions. The results also show that the locations with larger challenges depend on the chosen undernutrition statistic, as the share, number, and concentration of undernourished children point to vastly different locations. There is limited correlation between the shares of children underweight and stunted across woredas, indicating that different locations face different challenges.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at aambel@worldbank.org or tpavesohnesen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
microfinance (see, for example, Ahlin et al. 2011, McIntosh and Wydick 2005, Hermes et al. 2011).

For example, Cull et al. (2014) use MIX Market data (see below) to show that the supporting institutional framework in terms of accounting transparency, client protection, the quality of credit bureaus, and the feasibility of financial transactions through agents are all positively associated with microfinance penetration rates. Effective client protection (as rated by expert observers) is most strongly associated with the scale of microfinance in a country.

Regulation and supervision also affect the trajectory of microfinance development, though impact varies across institutional types. For example, empirical evidence from crosscountry data suggests that commercially oriented microfinance institutions bear the costs of active prudential supervision by making larger loans and lending less to women, both signs of curtailed outreach (Cull et al. 2011). In contrast, outreach indicators for microfinance institutions that rely more heavily on non-commercial sources of funding such as donations (typically chartered as non-governmental organizations) are not affected by prudential supervision. However, the costs of compliance are reflected in their significantly lower profitability when actively supervised.

Similar patterns are reflected for measures of the quality of microfinance regulation and supervision as rated by expert observers. Specifically, better developed regulatory and supervisory regimes are associated with less outreach to poorer clients (as reflected in larger average loan sizes) but stronger financial performance for commercially oriented microfinance institutions (Cull et al. 2014). The outreach and financial performance of less commercially oriented institutions (NGOs) are not associated with broad measures of the quality of regulation and supervision. However, NGOs tend to make smaller loans and lend more to women in 11