rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Balance for Soft Skills

||(1)<br>Leadership|(2)<br>Teamwork|(3)<br>Neither|(4)<br>Difer|(5)<br>ences|
|---|---|---|---|---|---|
|||||(1)-(3)|(2)-(3)|
|Greater Kuala Lumpur|0.63|0.65|0.62|-0.01|-0.03|
||(0.48)|(0.48)|(0.49)|(0.02)|(0.02)|
|Small Firms|0.45|0.45|0.43|-0.02|-0.02|
||(0.50)|(0.50)|(0.50)|(0.02)|(0.02)|
|Salary (USD)|751.22|763.36|751.99|0.77|-11.37|
||(269.54)|(326.23)|(274.43)|(15.33)|(17.06)|
|Days to Process|16.22|17.19|16.79|0.57|-0.40|
||(9.51)|(9.32)|(9.37)|(0.46)|(0.45)|
|Pre-scan Questionnaire|0.54|0.55|0.56|0.02|0.01|
||(0.50)|(0.50)|(0.50)|(0.02)|(0.02)|
|Number of questions|1.88|1.86|1.85|-0.03|-0.01|
||(2.25)|(2.22)|(2.20)|(0.10)|(0.10)|
|Business degree|0.49|0.49|0.50|0.01|0.00|
||(0.50)|(0.50)|(0.50)|(0.02)|(0.02)|
|Engineering degree|0.26|0.26|0.25|-0.01|-0.01|
||(0.44)|(0.44)|(0.44)|(0.02)|(0.02)|
|Number of job applicants|140.58|152.95|145.64|5.06|-7.32|
||(222.81)|(286.54)|(223.20)|(10.06)|(11.55)|
|Applicants with more education|6.68|7.33|6.87|0.18|-0.47|
||(11.87)|(15.64)|(11.16)|(0.52)|(0.61)|
|Applicants with higher exp. salary|67.52|69.72|67.58|0.06|-2.13|
||(127.89)|(137.63)|(117.52)|(5.54)|(5.75)|
|Number of foreign job applicants|6.91|6.11|5.16|-1.75**|-0.95|
||(18.61)|(13.64)|(10.55)|(0.81)|(0.64)|
|Observations|993|1004|998|||
|_Note_: This table reports descriptive statist|ics for the samp|le by soft skill|and balance|tests. Colu|mns 1|
|to 3 report the mean and standard deviati|on for each soft|skills across dif|erent set of j|ob and com|pany|
|characteristics. Columns 4 and 5 present th|e diference betw|een column 1 a|nd 3, and 2 a|nd 3 respect|ively.|

18
careful scrutiny would show otherwise). Believing that a fact is true is not identical to believing that a target is worthwhile, but in the case of development goals the two are probably related. People who cannot understand a target are unlikely to support it with enthusiasm. Perhaps one of the most infamous findings on salience in the development literature is the result that people in donor countries significantly overestimate the amount of money their governments commit to overseas development assistance, and when asked what the right amount should be, suggest a number that is much higher than current assistance levels.<sup>4</sup> But this finding is not only, and perhaps not primarily, a reflection of cynical views regarding government or stinginess toward outsiders; rather, similar problems arise in a variety of questions involving policy issues, budgets, and large numbers (Kuklinski, Quirk, Jerit, Schwieder, & Rich, 2003). Larger numbers are hard to grasp. Discussing millions and billions of dollars is not an everyday matter. The same is true of complex measures, such as ratios and technical terms.

This line of consideration suggests that development targets designed to nudge should avoid using large numbers, proportions, and technical terms wherever possible. If there is to be a target for official development assistance, for instance, the “standard” target of 0.7% of GNI, which has the disadvantage of including both a percent and technical term, should be replaced. A more salient number would be dollars of development assistance per person, from each donor country, which intuitively informs people how much of their tax money is going to development assistance, and allows them to compare that contribution to how much they spend on their own purchases. Consider, for instance, how much easier it would be to imagine the impact of a decline in ODA if it were expressed, not as a reduction from 0.5% to 0.27% of GNI, but as a contribution of, say, USD$218 person falling to $132 per person (roughly the decline in Austria from 2007 to 2011). Of course, ODA per person does not measure the same quantity as share of GNI; it would shift benchmarks of donor generosity to the benefit of richer countries. Still, the percentage of GNI target, while more “fair” from a certain point of view, does not

> 4 The finding has been replicated several times. See, for instance, the World Public Opinion survey of 2010: <u>http://www.worldpublicopinion.org/pipa/articles/brunitedstatescanadara/670.php?nid=&id=&pnt=670&lb=. See</u> also this 2012 survey of American public opinion from the Kaiser Family Foundation: <u>http://www.kff.org/kaiserpolls/upload/8304.pdf.</u>

10
- First, four blocks were randomly selected from a total of 22 blocks in the district of Jaunpur, namely, Maharajganj, Shahganj, Sikrara, and Ramnagar.

- Second, 280 villages were randomly selected (out of 313 villages in these four blocks).

In each of the 280 villages, 10 randomly selected households were surveyed about the status of education services, perceptions of children’s learning achievement, and the role for public action to improve outcomes, as were all government primary schools headmasters, and all VEC members. Data on school resources and functioning were also collected through direct observation of the interviewing teams. The final sample consists of 2,800 household interviews, 316 school interviews and observations, and 1,029 VEC member interviews from the 280 villages.<sup>5</sup> Data on actual learning achievement of children were collected through a testing tool developed by Pratham. All children between the ages of 7 and 14 were tested from 30 randomly selected households in each village (including the 10 households from which the other information mentioned above was collected). The final sample consists of 17,608 children from these 280 villages.

# 3. **Findings of the survey**

The survey has provided new data on actual outcomes of education service delivery—the extent to which children are learning, in terms of the basic competencies of reading, writing, and simple arithmetic. In this section, we first describe what the basic learning outcomes are, and then contrast this with the stated perceptions of learning by parents, teachers, and VEC members. Although there is no clear evidence of a knowledge gap about the state of actual learning in the village as a whole, illiterate children in large part are not identified by their parents as such. Furthermore, parents show through responses to a range of questions that they have not paid much attention to the role of public action in improving outcomes. Quite strikingly, they are completely unaware of even the existence of a VEC, let alone being informed about its roles and responsibilities.

> 5 In obtaining averages for the survey as a whole, all responses and results are weighted by their relevant populations.

7
|Lower MIC<br>HIC<br>|**Region of study countr**|**y (omitted= North**|-2.084<br>(4.803)<br>8.227<br>(13.690)<br>**America)**<br>||
|---|---|---|---|---|
|Africa|||8.513|4.946***|
||||(7.575)|(1.557)|
|EAP|||10.44||
||||(11.050)||
|Western Europe|||1.073|-5.475**|
||||(7.991)|(2.060)|
|LAC|||5.299||
||||(7.612)||
|SAR|||11.75||
||||(15.470)||
||**Oth**|**er controls**|||
|Journal article|||-1.917|-1.855**|
||||(2.956)|(0.690)|
|Precision (1/sd)|-0.0037|0.0053|0.000834||
||(0.008)|(0.007)|(0.010)||
|Observations|102|88|88|88|
|AdjustedR2|0.578|0.771|0.854|0.835|

Note: Robust standard errors are in parentheses and clustered at study level. EAP = East Asia and Pacific; HIC = high-income countries; LAC = Latin America and Caribbean; LIC = lower-income countries; MIC = middle-income countries; MRA = meta-regression analysis; OLS = ordinary least squares; SAR = South Asia Region; sd = standard deviation. *** p<0.01, ** p<0.05, * p<0.1 42
(lesson two), males are expected to name at least one local service for victims of rape and violence. The key messages of this lesson are (1) “Boys and girls can listen to each other and respect each other, even though it can be difficult”; (2) “When communicating with someone of the opposite sex, remember to: find a safe place to talk, show respect to the person you are communicating with, make strong eye contact, and stay positive”; and (3) “In life, we should all stand up for females to protect them from abuse” (Grassroot Soccer, 2013). Similarly in lesson three, _Risky Partners_ , the key message is about having sex with individuals your own age and not pressuring younger females to have sex. In lesson ten, _Red Card_ , males are given scenarios worthy of a red card, such as bus drivers requiring sex from female passengers, older partners pressuring younger females to have sex, negative peer pressure to have sex, and gender-based violence. Therefore, this intervention focuses on the importance of preventing IPV, both as an instrument to reduce the risk of contracting and spreading HIV and as a goal in itself. See Appendix Table C1 for goals and key messages for all sessions.

**Goal Setting.** Goal setting is about self-regulation strategies and can be helpful when addressing emotional or behavioral difficulties. Oettingwen and Gollwitzer (2010) argue that framing goals in terms of positive outcomes vs preventing negative outcomes is more effective. It is a valuable tool often used in cognitive-behavioral therapy (CBT).

For the activity, facilitators asked female participants if they would be willing to set a goal to remain healthy and stay STI/HIV free for the following year. If they agreed, facilitators went through the S.M.A.R.T. process (Doran, 1981) with the females, where they were asked to identify and commit to 1-3 specific strategies to follow during the next 12 months in order to achieve the goal. S.M.A.R.T. stands for Specific, Measurable, Achievable, Relevant, and Timely.

The initial goal setting activity took place in August 2017. This activity took about 90 minutes and was done one-on-one with a trained enumerator. Of the 865 females randomly selected from the baseline survey to receive invitations to participate in the goal setting activity 789 participated (91%).<sup>7</sup> Of the 789 participants, 113 females (14.3%) 7Of the 174 females who did not participate, only 2 refused. The rest were either unavailable at the 10
Precarious areas in Kinshasa are patched in different parts of the city. Unlike other major African cities (for example, Kibera in Nairobi) where precarious areas (or informal settlements) are geographically concentrated, Kinshasa has multiple pockets of precarious areas spread across its city boundary. In many parts of the city, precarious areas are located right next to non-precarious areas. As discussed more in detail throughout this report, poorer households tend to reside in precarious areas that suffer from a perennial shortage of basic services, and expanding service access to these precarious areas remains a key challenge. Both the level and quality of key basic public services in Kinshasa, such as water supply and sanitation, can easily deteriorate due to the city’s inability to cope with the rapid increase of urban population adding pressure on its critical infrastructure and public service systems. Therefore, there is an immediate and increasing need for investing in the delivery of public services and core infrastructure to improve the living standards of Kinshasa residents.

# **2.3 Socio-economic characteristics by residential locations**

Urban centers and non-precarious neighborhoods are home to wealthier households. Figure 8 shows that the distribution of households across the five different quintiles of per adult-equivalent consumption expenditures varies significantly along these spatial dimensions.<sup>9</sup> More specifically, richer households live closer to the city core while poorer ones are largely excluded from the city core or its surrounding areas (Panel A). Similarly, non-precarious areas tend to host a higher proportion of richer households compared to precarious areas (Panel B). As discussed further in the following sections, these rich neighborhoods come with higher housing prices/rents and are largely inaccessible to poorer households who have no choice but to live in more affordable and yet precarious areas that are disconnected from core urban areas and suffer from poorer living conditions.

The wealthier status of those residents in the preferable neighborhoods allows them to purchase better access to key public services, which residents in the non-preferable neighborhoods may not be able to afford. While households living in the city core or non-precarious neighborhoods enjoy better access to services, their spending on these services is accordingly much larger than those households living in the peri-urban or precarious neighborhoods. For instance, the amount that residents in non-precarious neighborhoods spend on bills for electricity and stocking water (for own consumption) are 40 percent and 54 percent higher, respectively, than residents in precarious neighborhoods.

> 9 The consumption expenditures are based on the 2018 Kinshasa Survey with adjusted sampling weights. See Batana et al. (2021) for details.

11
14. "It is common for **finrs** in my line of business to have to pay some irregular "additional payments" to get things done." This is true

**(1)** always (2) mostly [] **(3) frequently** 0 (4) sometimes _ElD01 (5)_ seldom El (6) never **El10**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>I X<br>30<br>20<br>i<br>Wodd LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text -->

**Percentage of firms who ticked (1), (2), or (3) for question 14**

The frequency of corruption varies clearly across regions: it is the lowest in the developing countries (15 percent), followed by Asia and MNA (about 35 percent), then CEE, Latin America and Africa (between 40 to 50 percent) and finally the CIS where more than 60 percent of entrepreneurs, considered this to be a major phenomenon.

# _Question 15: Predictability of amount of bribe_

This question distinguishes between "greasing" corruption and "blocking" corruption. "Greasing" corruption is predictable and acts like a transaction cost, whereas, "blocking" corruption is highly unpredictable causing large uncertainties. The following chart shows regional averages for those firms which answered positively to question 14. In other words only firms which made "additional payments" at least sometimes (meaning they ticked frequently, mostly, or always in question 14) are represented in this chart.

Caution should be used when interpreting these results because the sample of the firms was largely truncated to avoid misleading answers. For example in many cases (especially in developed countries), respondents who marked "never" (paid "additional payments") for question 14 also answered "never" (knew in advance about how much this "additional payment" is) for question 15. Question 15 then translates to an extremely high average score for unpredictability of corruption. Thus, truncation is necessary, even if the sample is heavily reduced in size.

31
Nevertheless, migrants are more likely to be making such investments in most countries than non-migrants are, and so our net effect in the last column, after taking out the mean for nonmigrants is typically positive, although we cannot reject that it is zero.<sup>12</sup> In addition, in answer to a separate question, none of the developing country migrants in our sample report holding shares in home country firms, showing that they are not making large investments in existing formally established companies.

In contrast, high-skilled emigrants are much more likely to be consumers of traded products from their home countries, often through what Orozco et al. (2005) term nostalgic trade. 87 percent of Ghanaian non-student migrants in our sample report having purchased Ghanaian food, drink or goods in their destination country, or having ordered goods directly from a Ghanaian retailer for their personal consumption. However, the mean (unconditional) value of goods ordered directly is only $183 and that of Ghanaian products purchased by migrants abroad is $443. Such nostalgic trade is also common among the New Zealand sample, with 87 percent of emigrants engaging in it,13 but less common amongst migrants from the small island nations: 47 percent of Micronesians engaged in such trade, with an unconditional mean value of such transactions of $337, and only 13 percent of Tongans did, with an unconditional mean value of $36. Given the small numbers of high-skilled migrants in any particular emigrant destination and the small size of these transactions, these high-skilled emigrants are therefore unlikely to spur trade by serving as a significant export market in and of themselves.

# **4.3 Non-financial Flows of Knowledge**

In addition to providing financial support to households and firms in their home countries, highskilled emigrants are often argued to benefit their home countries through knowledge transfer (e.g. Saxeenian, 2002, Newland, 2004, Kugler and Rapoport, 2005, Kerr, 2008). Although we are unable to place a monetary value on this knowledge transfer, our surveys at least allow us to provide empirical evidence on how common different types of knowledge flows are among the best and brightest, and to ask whether in fact emigrants engage in more of these types of knowledge flows than they would be doing had they not migrated.

> 12 We formally test for equality to zero by regressing the unconditional amount invested on a dummy for being a current migrant, restricting analysis to the current migrant and non-migrant samples of non-students. 13 The value of such trade was not asked for the New Zealand sample.

18
- 6 -

**TABLE** 1: LSMS Surveys

|I.|Data Now Available|Country|Years of Data Collection|
|---|---|---|---|
|||Cote d'Ivoire|1985, 1986, 1S87, 1988|
|||Peru|1985-86|
|||Ghana|1987-88, 1988-89|
|||Mauritania|1987-88|
|||Bolivia|1988, 1989|
|||Jamaica|1988, 1989|
|II.|Soon to be Implemented|Country|Expected Date in Field|
|||Laos|Fall, 1990|
|||Morocco|Spring,<br>1990|
|||Pakistan|Fall,<br>1990|
|III.|Discussions<br>Underway:|Latin<br>America/Carribean|Africa/Middle<br>East<br>Asia|
|||Brazil|Algeria<br>Nepal|
|||Colombia|Burkina<br>Faso|
|||Guatemala|Egypt|
|||Trinidad<br>and Tobago|Jordan|
|||Venezuela|Kenya|
# _Uuivhwiau I. OAc Arab CawtiWo_

|_County_|_Unky_<br>_& Locaion_|_Year_|_Lanpage_|_Acad_<br>_Staff_|_Sudes_|
|---|---|---|---|---|---|
|Jordan|Yamouk Ulnivernty,<br>rbid|1976|A, E|8485<br>479 F.T.<br>93 P.T.|13,359|
||Jordanian University<br>for Science & Tech.,|1986||||
|Kuwak|Kuwait Uni<br>rky<br>Kuwak|1966|A, E,<br>F|c. 600|c. 17,000|
|Lebanon|Lebanese University,<br>Beirut|1953|A, E,<br>F|c. 530|c. 39,000|
||Amerian Univerity of<br>Beirut, Beirut|1866|E|8-5<br>367 F.T.|84-5<br>_4,50_|
||StL Joseph University,<br>Beirut|1881|F,E|84E85<br>230 F.T.<br>681 P.T.|845<br>5f84|
||Beirut Arab University,<br>Beirut|1960|A, E|84-85<br>67 F.T.<br>194 P.T.|84-85<br>3,872+<br>13,54 ext|
||U. of the Hdy Ghost,<br>Jwdiehi|19',1|A,F|84F8<br>67 F.T.<br>298 P.T.|845<br>Z813|

MOB-Mlnly of Educatin; MOHESR-Minitry of Hher Education and Scientific Researcb b Ag-Agriculture; Ar-Architecture A-Art; Cs-Computer Sc.; D-Dentist, Ed-Education; E-Engineering Ft-Food Techn4 La-Languag; L-Law, M-Medicine; P=Plarmacy, R-Religlous Studies; S-Sciences; T-Tedunolog Vm-Veteriniry Med.

_42_
# **Table 2. Summary Statistics of Key Variables, Aggregated at the District level**

||Mean|s.d.|minimum|median|maximum|
|---|---|---|---|---|---|
|FDI|0.120|0.170|0.000|0.061|1.000|
|ln(L)|5.585|0.754|3.017|5.566|7.932|
|ln (firm age)|2.276|0.366|1.298|2.251|3.929|
|ln(GDP PC)|8.929|0.625|7.517|8.921|10.510|
|ln(city distance to port +1)|4.846|2.215|0.000|5.583|8.313|
|City phone density|3.975|0.647|2.407|3.965|5.455|
|ln(city avg wage)|9.554|0.302|8.792|9.532|10. 371|
|district average share of college graduate in L|0.174|0.086|0.029|0.159|0.671|
|city share of good air days|0.815|0.155|0.265|0.836|1.000|
|dummy: open city|0.115|0.319|0.000|0.000|1.000|
|ln(dist. avg of days passing customs)|2.501|0.456|0.928|2.510|4.071|
|dist avg of private firm access to loans|0.603|0.213|0.000|0.615|1.000|
|dist avg of private firm ETC burdens|0.013|0.008|0.001|0.010|0.069|
|dist avg of foreign advantage in ETC|0.427|1.081|-0.927|0.169|8.577|
|dist avg of private firm contract enforcement|0.645|0.199|0.000|0.687|1.000|
|dist avg of foreign advantage in contract enforcement|1.073|0.436|0.000|1.005|5.662|
|dist avg of private firm tax burdens|0.048|0.019|0.006|0.046|0.169|
|dist avg of foreign advantage in tax burden|0.169|0.742|-0.850|0.040|8.155|
|average PS tenure|2.942|0.977|1.000|3.000|5.667|
|ln(average PS tenure)|1.017|0.369|0.000|1.099|1.735|
|PSyoung|0.262|0.440|0.000|0.000|1.000|
|PSold|0.236|0.425|0.000|0.000|1.000|
|ln(average PS tenure)*PSyoung|0.242|0.458|0.000|0.000|1.609|
|ln(average PS tenure)*PSmiddle|0.513|0.563|0.000|0.000|1.735|
|ln(average PS tenure)*PSOld|0.262|0.504|0.000|0.000|1.609|
|Full foreign ownership|0.069|0.143|0.000|0.000|1.000|

Note. The statistics are at the district-level. The number of observations range from 909 to 916.

27
- Shorrocks, Anthony. 1999, “Decomposition Procedures for Distributional Analysis: A Unified Framework based on the Shapley Value”, mimeo, University of Essex, Department of Economics.

- Skoufias, Emmanuel, and Susan Parker, 2006, “Job Loss and Family Adjustment in Work and Schooling During the Mexican Peso Crisis”, Journal of Population Economics. 19: p. 163-181

- Smith, James P, Duncan Thomas, Elizabeth Frankenberg, Kathleen Beegle, and Graciela Teruel. 2002 “Wages, Employment, and Economic Shocks: Evidence from Indonesia,” _Journal of Population Economics,_ vol. 15, no. 1.

- Strauss, John, Kathleen Beegle, Agus Dwiyanto, Yulia Herawati, Daan Pattinasarany, Elan Satriawan, Bondan Sikoki, Sukamdi, Firman Witoelar. 2004. Indonesian Living Standards: Before and After the Financial Crisis  Rand Corporation, and Institute of Southeast Asian Studies.

- Verick, Sher. 2009, “Who is Hit Hardest during a Financial Crisis? The Vulnerability of Young Men and Women to Unemployment in an Economic Downturn,” IZA DP No. 4359.

Walby, Silvia, 2009. Gender and the Financial Crisis, mimeo, Lancaster University.

- World Bank, 2009, _Averting a Human Crisis during the Global Downturn: Policy Options from the World Bank’s Human Development Network_ , World Bank. Washington DC.

- World Bank, 2010a, _Household Welfare in the Face of Economic Slowdown: Result from Five Urban City Centers in Turkey_ , World Bank. Washington DC.

- World Bank, 2010b _Opening the Door to Good Quality Jobs: The Role of Human Capital_

- _Investment and Social Protection Policies_ , mimeo, World Bank, Washington DC.

- World Bank, 2010c. Coping Well or Barely Coping: Household and Government Responses to the Great Recession in Europe and Central Asia. World Bank, Washington DC.

25