rafmacalaba/datause-encoder-data huggingface dataset:
HIES 2016/17 sample might be prone to larger margins of error than estimated due to the small district-level sample sizes.

In this section, we use a simple test of robustness to assess how the relatively small district level sample sizes in the HIES 2010 might have affected accuracy statistics for the HIES 2016/17. First, we estimate different sets of division-level statistics for urban and rural areas using both a “bottom-up” and then a “top-down” approach. The bottom-up approach, the methodology we have used to produce the accuracy statistics reported in Table 12, constructs stratum-level statistics by aggregating “up” from district-level accuracy statistics. This approach produces aggregated statistics at the division level for urban and rural areas assuming that the sample is selected from each district independently, as is the case for the HIES 2016/17. The top-down approach instead estimates directly all division-level performance statistics for urban and rural areas, which is the lowest level at which the HIES 2010 sample produced accurate poverty statistics. Comparing the top-down approach to division-level aggregated estimates for urban and rural areas, gives a good test of the accuracy of our bottom-up approach as the latter are estimated based on a sample that has been designed to produce reliable estimates at this level.<sup>25</sup> Tables 14, 15, and 16 summarize the main results from this exercise. RSE estimates of mean nominal per capita and total household expenditure, and SE estimates of poverty headcount ratios derived using the bottom-up approach are lower than estimates produced using the topdown approach. Therefore, the bottom-up approach produces more precise estimates in our application than the top-down. Notably, however, even the top-down approach in most cases produces accuracy estimates within the desired levels.<sup>26</sup> These results imply that the performance of the HIES 2016/17 sample achieves the desired level of accuracy for poverty

> 25 For allocation of PSUs across sub-strata, however, we cannot use the bottom-up approach because we cannot fine tune the estimates of the key sampling parameters to allocate PSUs at the sub-strata level. 26 Except for the RSE estimates of nominal per capita expenditure for urban Chittagong and urban Sylhet, 12.5 percent and 11.1 percent, respectively; the RSEs estimates of nominal total household expenditure for urban Chittagong and urban Mymensingh, 13.8 percent and 11.4 percent respectively; and for the SE estimates of the poverty headcount ratios for urban Barisal, urban Mymensingh, urban Khulna, and urban Rangpur, 7.5 percent, 7.2 percent, 5.3 percent, and 5.3 percent respectively.

30
also a (very small) effect in the number of schools. This could be driven by demand due to the migration of students, which may cause fewer school openings in places where outmigration is commonplace. Alternatively, it could also be driven by extortion to private schools (in fact, some businesses have closed in response to DTO gangs requiring private businesses to pay them in exchange for protection).

Table 9: The impact of homicide rates on the supply of schools and teachers

|**Dependent Variable: Logarit **|**hmof the num**|**ber of teacher**|**s (columns 1-5**<br>Total Teachers|**) and of the nu**|**mber of schools**|**(columns 6-10)**||Total Schools|||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Homicide Rate|-0.00640**|-0.000399|-4.16e-06||-0.000466*|0.00330|-0.000552**|-0.000162||-0.000615**|
||(0.00297)|(0.000268)|(0.000277)||(0.000279)|(0.00359)|(0.000227)|(0.000162)||(0.000245)|
|Drug Related Homicide Rate||||-0.000115<br>(0.000116)|||||-6.21e-05<br>(6.69e-05)||
|Public Expenditure|||||2.10e-09***|||||2.73e-09***|
||||||(4.14e-10)|||||(3.76e-10)|
|Constant|5.206***|5.150***|5.139***|5.137***|5.287***|3.792***|3.734***|3.733***|3.732***|3.859***|
||(0.0296)|(0.00216)|(0.00170)|(0.00235)|(0.00478)|(0.0260)|(0.00531)|(0.00116)|(0.00158)|(0.00425)|
|Observations|24,962|24,962|9,807|9,811|23,295|24,957|24,957|9,807|9,811|23,290|
|R-squared|0.000|0.992|0.999|0.998|0.992|0.000|0.993|0.999|0.998|0.993|
|Time period|2000-2010|2000-2010|2007-2010|2007-2010|2000-2010|2000-2010|2000-2010|2007-2010|2007-2010|2000-2010|
|Fixed Effects|No|Yes|Yes|Yes|Yes|No|Yes|Yes|Yes|Yes|
|Time-Varying Mun. Controls<br>**Notes.** Dependent variable is the log<br>100,000 inhabitants in 2007 (general <br>Federal Government and include all d|No<br>of total number of <br> homicide rate of 8<br>eaths by "presumed|No<br>teachers for colum<br>.3 and drug related<br>delinquent rivalry"|No<br>ns (1) to (5) and th<br> homicide rate of <br>. Standard errors|No<br>e log of the total nu<br> 2.6). Drug-related <br>clustered at county|Yes<br>mber of schools for<br> homicides are colle<br>level in parenthese|No<br> columns (6) to (10).<br>cted by the National<br>s. *** p<0.01, ** p<0|No<br>Homicide rates ar<br>Council of Public S<br>.05, * p<0.1|No<br>e normalized accor<br>ecurity (Consejo N|No<br>ding to the nationa<br>acional de Seguri|Yes<br>l homicide rate per<br>dad Pública) of the|

Table 10 looks at the impact of homicide rates on learning, as measured by the Enlace test. We do not find a statistically significant impact on learning. Two things are important to note, however. First is that the results are much less precisely estimated. Second is that the reported migration changes the selection of students who take the test, so that even tighter estimated results would not be easy to interpret without knowing the test scores of those who migrate.

31
<!-- Start of picture text --> Figure 7.2: Access to Infrastructure Services by location in Haiti, 2001 (percent)<br>100.0<br>90.0 Metropolitan<br>80.0 Urban<br>70.0 Rural<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Electricity Water Trash Paved road Dirt road Landline Mobile<br>collection phone<br>Source: Own calculations based on HLCS 2001.<br>Percent<br><!-- End of picture text --> There are significant differences in access to energy. Households in the metropolitan area have far more access than those elsewhere. Energy and electrification projects help improve living conditions. The supply facilitates social integration, helps increase production value, and promotes diversification. As regards energy sources for cooking, rural residents mainly use charcoal while urban dwellers use propane, which entail both health and safety risks. Different localities have disparate levels of access to the electrical network. In the regions, public electricity connection reaches from 2.3 percent for the population in the Northwest to 58.2 percent in the West region (Table 7.5).

Large differences exist among locality with regard to access to the electrical network and the type of energy used, a commonly used energy source for cooking for rural residents is firewood or charcoal. Only 9.8 percent of rural households have access to electricity as compared to 91.4 percent of households in the metropolitan area (Table 7.4).

Table 7.5: Household Access to Basic Infrastructure by Region in Haiti, 2001 (percent)

||Arti-<br>bonite|Center|Grand-<br>Anse|North|North<br>-east|North-<br>west|West|South|South-<br>east|
|---|---|---|---|---|---|---|---|---|---|
|Electricity|15.1|13.3|11.1|15.9|9.3|2.3|58.2|9.1|8.7|
|Water|14.8|10.9|5.8|9.2|5.9|9.0|16.5|25.6|3.0|
|Trash collection|6.3|7.0|1.8|8.8|9.0|2.3|14.5|0.0|1.4|
|Paved road|14.9|3.4|6.1|9.8|3.2|0.3|12.9|4.4|2.0|
|Dirt road|41.7|40.4|25.8|47.0|62.9|25.8|34.7|43.0|30.1|
|Landline|1.4|3.2|0.4|4.4|0.8|0.0|7.7|0.3|0.4|
|Mobile phone|0.1|0.0|0.0|1.3|0.5|0.0|5.9|0.2|0.0|

_Note:_ Water: Supplied from private or public water company. Trash: Collected by private or public company. Paved road: Paved and partly <u>paved road. Dirt road: Dirt and gravel road.</u> _Source:_ Own calculations based on HLCS 2001.

41
30

# Scenario 10. Starting **the** funded **tier** with the genertion younger than 30 In 1997, increasing retirement ages to 65 and maintaining a 33% tax **rat**

||1995|1996|1997|1998|2000|2005|2010|2020|<br>2030|
|---|---|---|---|---|---|---|---|---|---|
|As % of formal GDP||||||||||
|Payroll contributions (all)|8.43|9.14|9.42|9.96|10.62|11.97|12.40|<br>13.12|<br>13.42|
|Total PAYG expenditure|8.49|9.64|9.99|10.23|10.32|8.30|8.28|8.99|<br>8.98|
|Balance|-0.06|-0.50|-0.57|-0.27|0.30|3.67|4.12|4.12|<br>4.44|
|Funded Tier (trillions of Kbv., curr. price)||||||||||
|Total assets (stock)|||198|514|1629|8867|28232|175051|765451|
|Gov't borrowing from Funded tier|||256|315|477|414|1364|8326|<br>28228|
|(as % of formal GDP)|||2.5%|2.6%|2.8%|1.4%|2.8%|6.4%|<br>8.3%|
|Stock of govt debt|||256|614|1731|4857|12603|88118|438412|
|(as % of formal GDP)|||3%|5%|10%|16%|26%|67%|<br>128%|
|Extra resources needed for PAYG|||-57|-32|0|0|0|0|<br>0|
|(as % of formal GDP)|||-1%|-0.3%|0.0%|0%|0%|0%|<br>0%|
|Average replacement ratio|29%|29%|30%|31%|31%|31%|31%|31%|<br>31%|
|System dependency ratio|111%|118%|114%|107%|99%|71%|68%|71%|<br>69%|
|Share of formal sector employment|59%|59%|62%|65%|69%|71%|71%|72%|<br>72%|

Note: It is assumed that in 1997 all workers younger than 30 will contribute to funded tier, in 1998, workers younger than 31, etc. For these workers 32% tax goes into their personal account, 1 % is used for social pensions. Real interest rate on funded tier balance is 3% annually.

# Scenario 11. Starting the funded tier with the generation younger than 30 in 1997, lowering social security tax to 23% for all workers in 1997, increasing retirement ages to 65, and maintaining constant replacement ratio under PAYG

||1995|1996|1997|1998|2000|2005|2010|2020|2030|
|---|---|---|---|---|---|---|---|---|---|
|As % of formal GDP||||||||||
|Payroll contributions (all)|8.43|9.14|6.61|7.04|7.46|8.40|8.70|9.21|9.42|
|Total PAYG expenditure|8.49|9.64|9.99|10.23|10.33|8.30|8.29|9.01|8.99|
|Balance|-0.06|-0.50|-3.37|-3.19|-2.87|0.10|0.41|0.20|0.44|
|Funded Tier (trillions of Kbv., curr. price)||||||||||
|Total assets (stock)|||137|358|1132|6148|19569|121306|530399|
|Gov't borrowing from Funded tier|||137|197|365|1026|2153|<br>9248|28578|
|(as % of formal GDP)|||1.4%|1.6%|2.1%|3.4%|4.4%|7.1%|<br>8.4%|
|Stock of goVt debt|||137|358|1132|6118|18743|115560|508777|
|(as % of formal GDP)|||1%|3%|7%|20%|38%|<br>88%|149%|
|Extra resources needed for PAYG|||-341|-389|-490|0|0|0|0|
|(as % of formal GDP)|||-3.4%|-3.2%|-2.9%|0.0%|0.0%|0.0%|<br>0.0%|
|Average replacement ratio|29%|29%|30%|31%|31%|31%|31%|<br>31%|31%|
|System dependency ratio|111%|118%|114%|107%|99%|71%|68%|<br>71%|69%|
|Share of formal sector employment|59%|59%|62%|65%|69%|71%|71%|<br>72%|72%|

Note: For workers enrolled under the new plan, 22% tax goes into their personal account, 1 % is used for social pensions. Real interest rate on funded tier balance is 3% annually.
recently announced poverty estimates that come from asking households about their consumption during two visits at two distinct times of the year. These weekly estimates are then averaged and multiplied by 52 to get annual consumption. Similarly, in the case of Jordan, households are visited at least four times during the year and are asked about their consumption levels and patterns.<sup>6</sup> The estimated prevalence of poverty currently treated as the official Government of Jordan poverty rate is based on taking the average value of consumption from these four visits, annualizing the average, and then identifying a household as poor if their annualized average falls below the poverty line.

We argue that this approach is not comparable with the more commonplace approach based on the single-visit estimate of poverty. This lack of comparability matters in terms of forming public policy that addresses the volatility of consumption and it also matters when international organizations such as the United Nations and the World Bank produce global counts of the poor.<sup>7</sup> The remainder of this paper develops this argument and then proposes an alternative conceptual view of poverty that is more sensitive to the within year variation of wellbeing observed throughout much of the world. Section 2 provides a description of the data from Jordan that are used in our analysis, along with a discussion of the country context. Section 3 examines multiple ways of using poverty data from repeat visits and contrasts each with how the data are currently being used in Jordan (which we refer to as the status quo). A key finding in this section is that the estimated poverty rate based on an approach that is consistent with data from single visits is

> 6 Households are asked about food consumption on a quarterly basis and nonfood consumption on a monthly basis.

> 7 See World Bank (2014) for an estimated global count of the poor, and Jolliffe et al. (2014) for methodological details on how these estimates are produced and how sensitive they are to methodological differences across countries.

4
- 13 -

The extent to which the service quality of infrastructure has improved varies from country to country. Figures 13 to 15 show the difference between the 2002 and 2005 BEEPS results for each country. Obviously, more advanced countries tend to have experienced relatively moderate improvements simply because they already achieved the high level of quality and did not have much room for further improvement. Armenia, Georgia, Kyrgyz, Moldova and Uzbekistan are generally among the best performing countries in all sectors. Azerbaijan achieved the most spectacular change in electricity. Interestingly, however, the sector performances are not necessarily consistent with each other within a country. This must result from the difference in the development strategy and priority across countries.

Figure 13. Quality Improvements in Electricity in ECA <!-- Start of picture text --> (160)<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>Source: BEEPS.<br>(74)<br>20<br>10<br>0<br>-10<br>-20<br>-30<br>-40<br>-50<br>Source: BEEPS.<br>Changes in electricity outages (days)<br>Azerbaijan Uzbekistan Kyrgyz Moldova Georgia Ukraine Kazakhstan Romania Macedonia Armenia Slovakia Russia Estonia Czech Poland Croatia Slovenia Hungary Lithuania Bulgaria Belarus Turkey Albania<br>Changes in water supply suspensions (days)<br>Moldova Uzbekistan Armenia Ukraine Georgia Macedonia Romania Azerbaijan Russia Kyrgyz Estonia Czech Croatia Hungary Belarus Slovakia Poland Bulgaria Turkey Lithuania Slovenia Kazakhstan Albania<br><!-- End of picture text --> Figure 14. Quality Improvements in Water Supply Services in ECA
Table S.14: Information on lighting sources at follow-up

# **Panel A: No. of light sources** **<u>(852</u> households)**

||Trea|tment A|Trea|tment B|C|ontrol|
|---|---|---|---|---|---|---|
||Mean|Std.|Mean|Std.|Mean|Std.|
|Flashlight|0.04|0.20|0.10|0.31|0.12|0.33|
|Kerosene lanterns|0.02|0.15|0.07|0.30|0.27|0.48|
|Kerosene lamps|0.31|0.61|1.14|0.61|1.66|0.83|

# **Panel B: Per-day hours using light sources based on family** **<u>questionnaires,</u> not a time diary** **<u>(852</u> households)**

||Trea|tment A|Trea|tment B|C|ontrol|
|---|---|---|---|---|---|---|
||Mean|Std.|Mean|Std.|Mean|Std.|
|Flashlight (rainy)|0.09|0.52|0.25|0.77|0.31|0.90|
|Flashlight (of-rainy)|0.13|0.68|0.41|1.24|0.46|1.28|
|Kerosene lanterns (rainy)|0.06|0.42|0.21|0.91|0.76|1.39|
|Kerosene lanterns (of-rainy)|0.07|0.46|0.25|1.05|0.97|1.73|
|Kerosene lamps (rainy)|0.58|1.22|2.75|1.97|4.26|2.03|
|Kerosene lamps (of-rainy)|0.77|1.69|3.57|2.48|5.19|2.41|
|S2 (rainy)|2.12|0.85|-|-|-|-|
|S2 (of-rainy)|2.76|0.84|-|-|-|-|
|S10 (rainy)|2.30|0.81|-|-|-|-|
|S10 (of-rainy)|2.92|0.74|-|-|-|-|
|S250 (rainy)|2.38|0.87|2.86|0.85|-|-|
|S250 (of-rainy)|3.09|0.82|3.17|0.69|-|-|

# **Panel C: Expenditures** **<u>(in</u> Bangladesh Taka, BDT)**

||Treatm|ent A||Treatm|ent B||Con|trol||
|---|---|---|---|---|---|---|---|---|---|
||Mean|Std.||Mean|Std.||Mean|Std.||
|Food (12 mth)|61558.06|20218.79||61218.47|18070.34||63414.54|19610.40||
|School fees (6 mth)|3196.00|1901.03||3196.26|2035.94||3105.54|1859.43||
|Medical fees (12 mth)|1891.12|1589.65||2045.54|2040.55||2265.76|4747.81||
|Kerosene (12 mth)|134.65|358.85||787.61|587.00||1542.79|912.75||
|Other non-food (12 mth)<br>**Panel D: Major activities **|10537.33<br>**by light sou**|3420.76<br>**rces**||10757.64|2847.53||10755.38|3285.35||
|||Treatment A|||Treatment B|||Control||
||Flashlight|Kerosene|Kerosene|Flashlight|Kerosene|Kerosene|Flashlight|Kerosene|Kerosene|
|||lanterns|lamps||lanterns|lamps||lanterns|lamps|
|Reading/studying|0.10|0.50|0.54|-|0.84|0.48|0.14|0.97|0.89|
|Social interaction|-|0.16|0.06|0.19|0.07|0.17|0.32|0.08|0.17|
|Cooking/eating at night|-|0.16|0.68|-|0.61|0.84|0.06|0.43|0.94|
|Walking outside at night|0.90|0.66|0.40|0.85|0.38|0.58|0.84|0.33|0.71|
|Tending livestock|-|0.16|0.06|0.14|0.15|0.19|0.06|0.07|0.23|
|Income generating activities|-|-|-|-|-|0.00|0.02|0.00|0.02|
|Nightlight for security|0.80|0.33|0.37|0.71|0.15|0.26|0.58|0.17|0.34|
|No. of households|10|6|61|21|13|168|50|110|401|
||S2|S10|S250|S2|S10|S250|S2|S10|S250|
|Reading/studying|0.28|0.07|0.73|-|-|1.00|-|-|-|
|Social interaction|0.65|0.25|0.18|-|-|0.11|-|-|-|
|Cooking/eating at night|0.73|0.23|0.50|-|-|0.88|-|-|-|
|Walking outside at night|0.23|0.66|0.06|-|-|-|-|-|-|
|Tending livestock|0.06|0.49|0.16|-|-|-|-|-|-|
|Income generating activities|0.00|0.20|0.19|-|-|-|-|-|-|
|Nightlight for security|-|0.02|0.10|-|-|-|-|-|-|
|Recharge cell phone|-|-|0.05|-|-|-|-|-|-|
|No. of households|238|239|239|-|-|188|-|-|-|

59
**Figure 15. Euro area deposit growth**

<!-- Start of picture text --> Percent, y-o-y Corporates Households<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: European Central Bank, World Bank. Notes: Year-over-year growth rates of outstanding deposits at the end of the period (stocks). Outstanding deposits at all euro area Monetary Financial Institutions. The vertical line indicates the start of NIRP (June 2014) in the euro area. Last observation is May 2016.

In the euro area, deposits continue to grow at a broadly similar pace before and after NIRP, showing no sign of disintermediation risk as retail deposit rates stabilize around zero (Figure 15). More generally, there is no evidence as yet of a significant increase in cash hoarding across NIRP countries (Malinovskaya and Wessel 2016).

# **6. What are the implications of NIRP for emerging market and developing countries?**

As the United States embarks on a gradual normalization of policy interest rates, NIRP along with other unconventional expansionary policies by major central banks could help contain the ensuing increase in global interest rates and support the appetite for riskier, higher-yielding assets, including those of EMDEs, supporting capital flows to these economies. While negative interest rates in Europe have already led to a notable pickup in euro-denominated bond issuances by EMDEs, increasing yield differentials between Europe, Japan and the United States could also maintain upward pressure on the U.S. dollar. The broad-based U.S. dollar appreciation since 2014 has been mirrored in considerable strain on some EMDE currencies, contributing to a higher cost of debt servicing and broader balance sheet pressures. This has been reinforced by deteriorating growth prospects and rising credit risks, particularly among commodity-exporting EMDEs.

This section first analyzes how NIRP could have an impact on financing conditions for EMDEs. It then considers the results of an event study on the responses of EMDE financial variables to NIRP announcements. It concludes with a brief discussion of policy responses to possible risks associated with NIRP.

35
Development Mechanism has adopted a default unsustainable percentage for Ethiopia of 88% (UNFCCC, 2012) little is probably known about the details of the Ethiopian context. Perhaps this is the most important arena where local context matters, but it may be that such granularity is not necessary if the first two conditions are met. After all, are only areas with known and continued unsustainable fuelwood harvests to be rewarded with better technologies? Such an approach appears counterproductive and potentially could create incentives for unsustainable harvest.

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