rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2:** Employment outcomes of IDPs (age > 14).

||Mean|S.D.|Min.|Max.|N|
|---|---|---|---|---|---|
|**Employment status**||||||
|Employed|0.55|0.50|0|1|6721|
|Paid labor for someone else|0.37|0.48|0|1|6659|
|Unpaid labor in family non-farm business|0.30|0.46|0|1|6662|
|Non-agricultural self-employment|0.24|0.43|0|1|6648|
|Agricultural self-employment (own or family)|0.08|0.27|0|1|6646|
|Unpaid training or workshop|0.05|0.21|0|1|6624|
|**Among workers**||||||
|Number of activities|1.87|1.03|1|5|3665|
|Hours worked|34.76|25.40|0|160|3803|
|Months worked|7.91|3.70|1|12|3437|
|Sector of main activity is agriculture|0.35|0.48|0|1|3675|

Note: Employed equals 1 if an individual engaged in at least one of the following five forms of work in the past 7 days: paid labor for someone else, unpaid labor in an off-farm family business, non-agricultural self-employment, agricultural self-employment; and participation in a training or workshop.

21
Pension Fund along with pension, hence people have difficulty in recalling it. The latter are difficult to detect because the discounts are included in the payment due on utility bills. Regarding birth grants and childcare allowances up to 1.5 years, the number of recipients seems to be under-simulated by 50–60 percent, which may be due to underrepresentation in the sample of families with young children. Families with 3 children are few in general, and they seem to be underrepresented as well. For the means-tested benefits, a good match is achieved by calibrating the take-up rates with administrative data. The receipt of maternity capital matches the external number quite well.

**Table A-5. Programs included but not simulated in RUSMOD, number of recipients/payers (in thousands), 2017**

||RUSMOD|External|Ratio|
|---|---|---|---|
|**Benefits**||||
|Public pensions|53,826|45,182|1.19|
|Unemployment benefit|1,413|848|1.67|
|Scholarships|2,329|||
|Monthly cash payment|11,279|26,154|0.43|
|Categorical benefits for housing/utilities|17,536|36,688|0.48|
|**Taxes and Social Insurance contributions**||||
|Property and vehicle taxes|5,914|||
|Social allowance onpersonal income tax|868|||

Source: Rosstat and RUSMOD for 2017 policy year and 2016 RLMS-HSE data

**Table A-6. Programs simulated in RUSMOD, number of recipients/payers (in thousands), 2017**

||RUSMOD|RLMS-<br>HSE|Ratio|External|Ratio|
|---|---|---|---|---|---|
|**Benefits**||||||
|**_Non means-tested_**||||||
|Birth grant|877|||1,795|0.49|
|Maternity leave allowance|520|||||
|Lumpsum allowance for pregnant women|520|||552|0.94|
|Child allowance up to 1.5 years*|1,493|1,129|1.32|4,067|0.37|
|Compensation of childcare costs|2,854|||||
|Maternity capital|776|||700|1.11|
|**_Means-tested_**||||||
|Child allowance up to 16(18) years**|4,619|4,368|1.06|4,950|0.93|
|Allowance for the third child up to 3 years|41|||421|0.10|
|State social assistance|1,762|1,888|0.93|1,732|1.02|
|Other social assistance***|1,866|||||
|Housing subsidy|2,872|4,552|0.63|3,353|0.86|
|**Taxes and Social Insurance contributions**||||||
|Social insurance contributions|45,174|||||
|Personal income tax|46,729|||||
|Value added tax|59,708|||||
|Excise duties|40,952|||||

Notes:

* The original RLMS-HSE variable includes all child benefits received up to age of 1.5 years.

** The original RLMS-HSE variable includes all child benefits received by children aged 1.5+ years.

*** Only monthly cash payment is simulated; the original RLMS-HSE variable includes categorical benefits for transportation, medicines and recreation.

Source: Rosstat and RUSMOD for 2017 policy year and 2016 RLMS-HSE data Results for tax and benefit amounts are presented in the tables below. Expenditures on pensions are overestimated by 25 percent compared with external data (Table A-7). Expenditures on unemployment benefits are under-predicted by 27 percent, even though the number of recipients is 41
# **Table 8. Sources of provision of health services before reforms: % structure by labor sector in 1981**

|**Where did theperson receive attention**|**S.E.**|**I**|**F**|
|---|---|---|---|
|Public network|47.80|58.93|46.65|
|Private network|51.01|40.33|52.26|
|Both|1.19|0.74|1.09|
|**Whopaid the attention:**|**S.E.**|**I**|**F**|
|Particular|**55.93**|**39.86**|**30.50**|
|Social Security|**33.39**|**46.30**|**35.37**|
|Pre paid|3.83|2.30|6.63|
|Employer|3.16|5.84|**24.15**|
|Other|3.02|4.88|2<br>.44|
|More than one|0.67|0.82|0.91|

Note: These results are based on the sample of working household heads and the members of their household (the employment category of the household head is assigned to the rest of the members of the household) Source: PNAD 1981, non urban dwellers excluded.

**Table 9. Shares of Formal and Informal sectors with respect the Working ages population (in percentages)**

||Survey|Urban|Rural|Metropolitan|Non Metropolitan|
|---|---|---|---|---|---|
|F|PNAD 1981<br>PME 1983|57.37|18.51|65.57<br>63.25|38.92|
|I and S.E.|PNAD<br>PME|42.63|81.49|34.43<br>36.75|61.08|
|Workforce under 15 y.o|. (PNAD) in MM|**55.12**|**19.56**|**24.81**|**49.87**|
|F|PNAD 1990<br>PME|54.57|26.68|61.04<br>61.34|42.16|
|I and S.E.|PNAD<br>PME|45.43|73.32|38.96<br>38.66|57.84|
|Workforce under 15 y.o|. (PNAD)|**70.20**|**22.52**|**30.07**|**62.66**|
|F|PNAD 2001<br>PME|46.78|20.00|51.52<br>52.96|38.95|
|I and S.E.|PNAD<br>PME|53.22|80.00|48.48<br>47.04|61.05|
|Workforce under 15y.o|.(PNAD)|**102.80**|**18.21**|**39.87**|**81.14**|

Source: PNAD and PME. PME’s figures correspond to September of the corresponding year.

46
**Table C.27: Role model analysis: Effect on assignable goods expenditures and remittances**

||(1)<br>Women||(2)<br>Girls<br>(A|(3)<br>Men<br>ll outcomes in|Taka)|(4)<br>Boys|(5)<br>Remittances|
|---|---|---|---|---|---|---|---|
|exposed|_−_283_._05|**|10_._95|_−_228_._85|***|_−_26_._02|_−_382_._25<br>*|
||(123_._69)||(62_._34)|(68_._64)||(70_._26)|(214_._88)|
|N|488||255|488||262|483|
|Non-exposed mean|1306_._69||2_._41|939_._44||2_._35|1823_._53|
|Non-exposed SD|1402_._55||2_._50|856_._85||2_._41|2485_._34|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The nonexposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the production line level in parentheses. Controls chosen using pdslasso from demographic controls and bargaining measures, their squares, and factory, enumerator and month dummies.

**Table C.28: Role model analysis: Effects on other expenditures**

# **Panel A: Outcomes in % of household income**

||(1)<br>Food|(2)<br>Furnishing<br>durables|(3)<br>Entertainment<br>durables|(4)<br>Kitchen<br>durables|(5)<br>Housing|(6)<br>Savings|(7)<br>Recreation|
|---|---|---|---|---|---|---|---|
|exposed|_−_0_._78|0_._47*|0_._28|_−_0_._01|_−_0_._01|_−_0_._63|_−_0_._21 ***|
||(2_._65)|(0_._27)|(0_._21)|(0_._03)|(1_._31)|(0_._91)|(0_._07)|
|N|488|488|488|488|488|486|488|
|Non-exposed mean|36_._77|0_._26|0_._22|0_._08|17_._06|4_._59|0_._63|
|Non-exposed SD|26_._60|1_._35<br>**Pan**|1_._62<br>**el B: Outcomes**|0_._33<br>**in Taka**|15_._23|9_._74|0_._82|
|exposed|_−_176_._80|92_._68|59_._99|1_._61|173_._35|_−_71_._68|_−_24_._55 **|
||(479_._27)|(61_._50)|(46_._35)|(5_._28)|(263_._43)|(191_._04)|(9_._90)|
|N|488|488|488|488|488|486|488|
|Non-exposed mean|7071_._61|65_._81|49_._72|14_._87|3264_._08|961_._86|115_._13|
|Non-exposed SD|4419_._98|394_._59|343_._51|52_._67|2473_._79|2004_._59|120_._92|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The non-exposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the production line level in parentheses. Controls chosen using pdslasso from demographic controls and bargaining measures, their squares, and factory, enumerator and month dummies.

91
|(iii)|Low Poverty Inci-<br>dence & Low Ele-<br>vation|Gbegbeyise<br>Korle Lagoon<br>Area|8<br>2|240|256|
|---|---|---|---|---|---|
|||Pig Farm|2|||
|(iv)|Low Poverty Inci-<br>dence & High Ele-<br>vation|Abeka<br>Accra New<br>Town<br>Mamobi|3<br>4<br>5|240|249|
|Total|||48|960|1006|

# **Appendix 2 – SWIFT methodology for estimating household consumption expenditure**

SWIFT (Survey of Well-being via Instant, Frequent Tracking) is a rapid poverty assessment tool. Developed in-house at the Poverty and Equity Global Practice of the World Bank. It can produce accurate poverty data through household expenditure and poverty data in a very timely, cost-effective and userfriendly manner. It has also been used to improve availability and frequency of official poverty statistics.

Compared with a typical household consumption data collection, SWIFT is much faster and more costeffective for producing consumption or income data and poverty statistics. This is because instead of collecting primary household consumption or income data, SWIFT collects only 10 to 30 questions on poverty-correlated variables, then projects household income or expenditure from them using a custom-built model, and estimates poverty and inequality statistics from the projected income or expenditure data. The poverty correlates typically include variables such as household size, household head’s educational attainment, household head’s employment status, ownership of consumer durables and housing conditions. To collect responses to the select questions from a household, we usually need only 7 to 10 minutes. This is much faster than a typical household consumption or income data collection, which takes at least one hour. Furthermore, the SWIFT approach is very quick to estimate poverty and inequality statistics from data collected – in 1 minute or less. This is in contrast with traditional methods that often require over one year to process consumption data collected by an official household survey and estimate poverty and inequality statistics.

# _Basics and Assumptions_

SWIFT collects only 10 to 30 questions on poverty correlates, projects household income or expenditure from them using a model, and estimates poverty and inequality statistics from the projected income or expenditure data. The poverty correlates usually include household size, household head’s educational attainment, household head’s employment status, ownership of consumer durables, housing conditions, etc. To do this accurately, model development is critical.

32
at 20 percent for India; while these indexes are not perfectly comparable, they can provide qualitatively similar results.

Higher education levels are clearly associated with a higher chance of upward mobility and a lower chance of downward mobility for all three countries. Consistent with our earlier finding of increasing (overall) mobility for the US, Vietnam, and India in this order, Figure 2 also shows a qualitatively corresponding order of increasing mobility by education levels despite the different levels of education achievement for each country. For example, the percentage of the upward movers in the US (i.e., the percentage of the population in the poor or vulnerable categories in the first period that move up one or two income categories in the second period) is roughly 25 percent for those with a high school education or lower, but jumps to almost 40 percent for those with a college education or higher. The corresponding figure for India is highest at 53 percent and 73 percent respectively for those with a secondary education (i.e., completed grades 9-12) and college education.

Figure 2 thus confirms the (perhaps standard) finding that education is an important driver behind welfare mobility in developing and richer countries alike.<sup>28</sup> It is rather straightforward to apply a similar analysis to study the relationship between mobility and other key variables of interest.

# **4. Conclusion**

We propose in this paper two approaches towards setting a vulnerability line that are constructed based on the existing poverty lines and the risk of falling into poverty, which can be flexibly adapted to welfare objectives in both low-income and high-income country settings. These vulnerability lines could replace the current _ad hoc_ practice to determine the vulnerability

> 28 Further examples of profiling of welfare transitions using synthetic panel data are provided in Dang, Lanjouw, Luoto and McKenzie (2014) or Dang and Lanjouw (2013).

29
‐ <u>Table 3. Share of TBT measures imposed on Egyptian exports (by product HS2 level)</u>

|Labels|Share|
|---|---|
|Inorganic chemicals; organic or inorganic compounds of precious metals, of rare‐earth|24.9%|
|Organic chemicals|20.5%|
|Miscellaneous chemical products|12.2%|
|Tanning or dyeing extracts; tannins and their derivatives; dyes, pigments and other coloring|7.3%|
|Essential oils and resinoids; perfumery, cosmetic or toilet preparations|3.4%|
|Soap, organic surface‐active agents, washing preparations, lubricating preparations|3.4%|
|Albuminoidal substances; modified starches; glues; enzymes|3.4%|
|Photographic or cinematographic goods|3.4%|
|Pharmaceutical products|2.9%|
|Explosives; pyrotechnic products; matches; pyrophoric alloys; certain combustible prep.|2.9%|
|Fertilizers|2.4%|
|Animal or vegetable fats and oils and their cleavage products; prepared edible fats; animal ...|1.3%|
|Fish and crustaceans and other aquatic invertebrates|1.2%|
|Meat and edible meat offal|1.1%|
|Others|9.2%|
|Total|100%|

_Source: Own construction using WTO’s TBT STCs database._ iii.Sanitary and Phyto‐sanitary (SPS) Measures The distinction between SPS and technical measures is not always clear, in particular because both apply to agricultural and industrial products. Yet, SPS measures refer to requirements imposed to protect life or health of animal, plant or human from pertinent health risks (such as additives, contaminants, toxins or disease‐causing organisms in foods, beverages or feedstuffs), whereas technical barriers – as described above – refer to the products regulations and standards.

The positive impact of lower tariffs in Egypt has been offset by the number of SPS measures imposed on Egypt ( 18). More specifically, SPS measures are among the largest obstacles facing exporters of agricultural products. SPS measures imposed on Egypt’s exports mainly originate from EU Member countries, which are also the most important trade partner for Egypt (Figure 19). The EU mainly imposes SPS measures on leguminous vegetables, beans and seeds, with food safety and protection of humans, animals and plants from pests and diseases being the reasons for imposing such measures. At the HS2 level, edible vegetables are subject to the highest number of SPS measures. SPS measures applying to this category represent more than triple those on meat and meat offal, and live animals which arrive at the the second and third places respectively (Figure 20). In recent years, Egyptian exports to the GCC countries also started to face SPS measures. For instance, between 2012 and 2019, six notifications of SPS measures and Specific Trade Concerns (STCs) were raised by UAE and Saudi Arabia against agricluture products, with egyptian exports of agriculture and animal products being suspended or banned entry to these markets (WTO, 2019). If repeated, these concerns may result in Egypt losing its comparative advantages in favor of other competitors. In the framework of the EU‐Egyptian Association Agreement, Egypt is currently working on the development of an Egyptian policy on food safety, and on the establishment of a unified Food Safety Authority.

19
||**_Table_**_6:_<br>**(I)**<br>**Deathsper**<br>**1000livebirths**<br>**1990**|_Under-5mortalityrat_<br>**(2)**<br>**Deathsper**<br>**1000livebirths**<br>**2000**<br>._____________________|_es_<br>**(3)**<br>**Averageannual**<br>**rateofchange**<br>**1990-2000**(%)<br>_____________________|**(4)**<br>**Averageannual rateof**<br>**changenecessaryto**<br>**achievegoal(%)**<br>**(2000-2015)**|
|---|---|---|---|---|
|Alldevelopingcountries|88|84|-0.5|-7.0|
|EastAsiaandPacific|55|45|-2.0|-6.0|
|Europe and CentralAsia|34|25|-3.1|-5.3|
|LatinAmericanandCaribbean|49|37|-2.8|-5.5|
|MiddleEastandNorthAfrica|72|54|-2.9|-5.4|
|SouthAsia|121|96|-2.3|-5.8|
|Sub-SaharanAfrica|155|162|0.4|-7.6|
|Source:WHO/UNICEF|||||

In the 1990s, all regions except Sub-Saharan Africa reduced under-5 mortality rates, as seen in Table 6. However, no region is on track at this time to meet the 2015 goal. Annual reductions in child mortality of between 5.3% and 7.6% will be required to meet this goal. Fortunately, much is known about the causes of infant and child mortality, including the importance of clean water and the benefits of oral rehydration therapy for diarrhea, the role of vaccinations, especially against measles, the value of insecticide treated nets and prophylactics for malaria prevention & treatment, the importance of pre-natal and post-natal interventions in reducing mother to child HIV transmission and education of mothers in basic infant and young child care.

Unfortunately, progress in under-5 mortality has been slowest among the poorest countries where the problem is most acute. Between 1990 and 2000, 31 low and middle-income economies reduced their under-five mortality rates fast enough to achieve the goal of a two-thirds reduction by 2015. Of these, 9 were upper middle income, 16 lower middle income, but only 6 were low income. Of the 15 that suffered increasing mortality rates, 10 were low income. Still the regional and income-group averages disguise much variation. Two of the countries making the fastest progress over the decade were Tajikistan and Azerbaijan, both low income countries, while Botswana, an upper middle income country that has been badly affected by the spread of HIV/AIDS, saw under-five mortality rates rise from 62 to 100 per 1,000 live births in just 10 years.

Maternal mortality is much more difficult to measure accurately. Deaths as a result of pregnancy or child birth are relatively rare and may not be captured in general purpose surveys or those with small sample sizes. Furthermore, maternal deaths may be underreported in countries that lack good administrative statistics or where many births take place outside of the formal health system. What makes maternal mortality such a compelling problem is that it strikes exclusively young women undergoing what should be a normal process and because the difference in outcomes is so different between those who live in rich countries - where the average maternal mortality ratio is around 21 deaths per 100,000 live births - and those who live in poor countries where the ratio may be as high as 1,000 deaths per 1 00,000 live births. (AbouZhar 2000) 9
Africa, but many other countries have followed since (Lesotho, Nigeria, Niger, and Rwanda), and an increasing number of countries are considering its implementation.

This paper assesses the impacts of the PSI-PMI program on students’ learning outcomes. To assess the program’s impact, we focus mainly on the performance of 12<sup>th</sup> grade students on the compulsory high school leaving examination, the West African Secondary School Certificate Examination (WASSCE). We used matching procedures to construct two groups of non-program students to serve as the control group. In the first control group, <mark>given that the selection of pilot schools was not random, we used administrative data and propensity score matching to select a comparable group of control schools. We then obtained student-level data through a survey,</mark> significantly oversampling control group student <mark>s to further match at the student level. The second control group exploits the fact that within the pilot schools, not all students were exposed to the program due, among other things, to capacity constraints. As a result, we were able to build a within-school control group, which addresses the limitation of the matching at the school level given the small sample. A</mark> t the time of data collection, the sampled students were assessed on an exam based on the Gambian curriculum but designed by educators and the researchers. In addition to our survey data, we have access to a significant amount of administrative data, including the students’ performance on their nationwide grade 9 examination, the Gambia Basic Education Certificate Examination (GABECE).

<mark>The results of our analyses show</mark> that the PSI-PMI program in The Gambia significantly improved student performance in mathematics and English.<sup>3</sup> The program improved students’ average mathematics score by 0.54 standard deviation or 11.9 percentage points, from 38.8 to 50.7, on the researcher designed exam and by 0.59 standard deviation or 9.2 percentage points on the WASSCE exam. The program also improved students' English score by 3.87 percentage points. Although these results are impressive, they were driven by high-performing students, as reflected in their scores on the GABECE, the 9<sup>th</sup> grade national exit exam. The analyses further show that the interactive nature of the program contributed to greater student participation in the classroom and increased student interest in learning science and mathematics. Teachers also

> 3 These two subjects are compulsory for all students.

3
WPS3792

**Measuring the Impact of the Investment Climate on Total Factor Productivity:**

**The Cases of China and Brazil**

# **Uma Subramanian, William P. Anderson and Kihoon Lee**

**Abstract** :  This study measures the impact of investment climate factors on the total factor productivity (TFP) of firms in China and Brazil. The analysis is conducted in two steps: first, an econometric production function is estimated to produce a measure of TFP at the firm level; in the second step, variation in TFP across firms is statistically related to indicators of the investment climate as well as firm characteristics. The result yields a number of insights on the factors that underlie productivity.  In both countries and in a variety of industry groups, indicators of poor investment climate, especially **_customs clearance delays_** and **_utility services interruptions,_** have significant negative effects on total factor productivity.  Reducing customs clearance time by one day in China could increase TFP by 2-6%.  Indicators such as **_email usage_** have positive effects on TFP.  In the case of China, state-owned firms and firms located in the interior are shown to be much less productive than privately owned firms and firms located in the East.  In Brazil, the results present an interesting contrast between the apparel industry and the electronics industry.  In the apparel industry, older firms in competitive markets are more productive while in the case of electronics, newer firms with higher market shares are more productive.

# World Bank Policy Research Working Paper 3792, December 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ William P. Anderson is at Boston University, and Uma Subramanian and Kihoon Lee are at FIAS, World Bank-IFC. The authors graciously acknowledge support provided by the World Bank for this research. Comments from Jose Guilherme Reis and Bert Hofman of the World Bank are much appreciated by the authors.

Corresponding author: Uma Subramanian, Usubramanian@worldbank.org.