rafmacalaba/datause-encoder-data huggingface dataset:
variance of student age may lead to increased behavioral problems, I replace the dependent variable in regression equation (1) with a set of variables measuring whether students experienced behavioral problems inflicted by others and whether they find school enjoyable. I also estimate the effects of classroom age heterogeneity on these measures of behaviors and attitudes separately for students who are younger than the median age of other fourth graders in their countries.

# _2.4 Differential Effects of Classroom Age Heterogeneity on Achievement_

Classroom age heterogeneity may have differential effects on students depending on their age and gender. For example, young students may require more teacher attention and if teachers tailor instruction to the median or average students, a diverse classroom may have a stronger negative effect on their achievement than on older students’ achievement. Similarly, the effect of age heterogeneity on achievement may differ depending on gender. For instance, boys are perhaps more likely to be distracted than are girls in heterogeneous classroom. It is also possible that girls are more vulnerable to age heterogeneity. To examine whether there exist differential effects, I estimate equation (1) separately by student age group and gender.

# **3. Data**

The data used are sourced from the Trends in International Mathematics and Science Study (TIMSS) in 2003 and 2007. TIMSS provides student-level data on mathematics and science 10
# Table 3: Disaggregating the form of new women-owned establishments in unorganized sector

||DV is|<br>logcount of ne|<br>w women-owned|<br> <br>establishments i|<br>n unorganized sec|<br>tor bystate-i|<br>ndustry:|
|---|---|---|---|---|---|---|---|
||Total<br>|Household<br>based<br>|Non-household<br>based<br>|<br>With three or<br>fewer<br>employees<br>|With four or<br>more<br>employees<br>|Without<br>external<br>financing<br>|With external<br>financing<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||||A|. Base estimatio|n|||
|(0,1) state has political reservations|0.391+|0.336+|0.063|0.336+|0.173|0.376+|0.144|
||(0.214)|(0.192)|(0.145)|(0.198)|(0.126)|(0.203)|(0.130)|
|Observations|3606|3606|3606|3606|3606|3606|3606|
|Adjusted R-squared|0.733|0.743|0.528|0.737|0.355|0.732|0.457|
|<br>Controls|||State x industry|and Industry x y|ear fixed effects|||
|||B. Panel|A including male|analogue of acti|vity in state-indus|try-year||
|(0,1) state has political reservations|0.333+|0.264|0.042|0.286|0.158|0.336+|0.132|
||(0.194)|(0.170)|(0.139)|(0.187)|(0.120)|(0.186)|(0.121)|
|DV analogue for male-owned|0.118+++|0.113+++|0.071+++|0.099+++|0.047+|0.092+++|0.049+|
|establishments in state-industry-year|(0.024)|(0.023)|(0.019)|(0.025)|(0.024)|(0.031)|(0.027)|
|Observations|3606|3606|3606|3606|3606|3606|3606|
|Adjusted R-squared|0.737|0.748|0.531|0.740|0.358|0.734|0.459|
|Controls|||State x industry|and Industry x y|ear fixed effects|||

Notes: See Table 2.
# **Figure 4. Ambient Air Pollution caused Mortalities and Morbidities by type of Diseases in**

# **2015 (Unit: Thousand and %)**

# **(a) Mortality**

<!-- Start of picture text --> Lung cancer ,<br>283.3, 7%<br>Lower<br>respiratory<br>infection, 675,<br>Ischaemic heart<br>16%<br>disease , 1521.1,<br>36%<br>Chronic Cerebrovascular<br>obstructive disease , 898.1,<br>pulmonary 21%<br>disease, 863.6,<br>20%<br><!-- End of picture text -->

# **(b) Morbidity**

<!-- Start of picture text --> Lung cancer ,<br>6,209, 6%<br>Ischaemic heart<br>Lower respiratory disease , 32,406,<br>infection, 28,360, 31%<br>28%<br>Cerebrovascular<br>disease , 19,243,<br>Chronic<br>19%<br>obstructive<br>pulmonary<br>disease, 16,848,<br>16%<br><!-- End of picture text --> Source: Cohen et al. (2017) 17
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<!-- Start of picture text --> /<br>/<br>O”<br>f - _s gO, MAURITANIA<br>- . puisTa<br>fw tae > -_<br>. =<br>. . eos . t poca<br>xii " a ke i= {g \vo<br>.s L! . = j<br>we ; : Ldye. ae a<br>"Fk i<br>omFa = “ae. - wow‘ally maui<br>W | _ Tamb: da = \<br>tre. : THE GAMBIA aed . Mx<br>I Ge hy :<br>SEBette L - at; “eS oe‘aati =?<br>BE a GUINEA-BISSAU ca Qo<br>SOR? = 4 GUINEA a<br>fe }<br>. po<br><!-- End of picture text -->

<!-- Start of picture text --> Saea Potietbal<br>TeWOW Preciouscee bop Metals + Diarronds<br>& oil rela<br>Crepland= 00% ExtentBut High Sunandiny<br>@ ion-son<br>@ -sm<br><!-- End of picture text -->
<!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>0.0 | —<br>Labelling Non-automatic Technical barriers Testing Traceability<br>requirements import-licensing to trade requirements requirements not<br>(B31) procedures other regulations on (B82) elsewhere<br>than transport and specified (B859)<br>authorizations storage (B42)<br>covered unde (E1)<br>TM Philippines || Average of Comparator Countries<br><!-- End of picture text -->
61 Table III.8

# Source of Contraceptives by Husband's Employment Status, Husband's Occupation and Area of Residence

|Country,<br>Employment<br>Status,<br>Husband's<br>Occupation|Governmen<br>(Percent)|t Commercial<br>(Percent)|a_NGO_<br>(Percent|Otherb<br>) (Percent)|
|---|---|---|---|---|
|EGYPT|||||
|All women|||||
|Employed:<br> <br>|||||
|Professional<br>Technical|||||
|or Managerial|20.0|78.4|1.3|.3|
|Clerical,<br>Sales and Service|<br>26.6|70.1|2.3|1.0|
|Agriculture<br>and Unskilled|31.6|66.1|.9|1.4|
|<br>Unemployed|30.4|68.4|.6|0.6|
|Urban<br>|||||
|Employed|||||
|Professional<br>Technical|||||
|or Managerial|17.6|80.6|1.4|0.3|
|<br>Clerical,<br>Sales and Service|<br>22.2|74.1|2.7|1.0|
|Agriculture<br>and Unskilled|29.6|67.7|1.4|1.3|
|Unemployed|29.5|68.6|1.0|1.0|
|Rural|||||
|Employed:|||||
|Professional<br>Technical<br>|||||
|or Managerial<br>|27.9|70.9|1.2|0.0|
|Clerical,<br>Sales and Service|<br>36.7|61.1|1.3|0.9|
|Agriculture<br>and Unskilled|34.1|64.1|0.2|1.6|
|Unemployed|32.1|67.9|0.0|0.0|
|MOROCCO|||||
|All Vomen|||||
|Employed:|||||
|Professional,<br>Technical|||||
|or Managerial|43.9|55.1||0.9|
|Clerical,<br>Sales and Service|<br>53.5|44.7||1.8|
|Agriculture<br>and Unskilled<br>|66.8<br>|30.6<br>||2.6<br>|
|Unemployed|67.2|32.8||0.0|
|Urban|||||
|Employed:|||||
|Professional<br>Technical|||||
|or Managerial<br> <br>|42.3<br>|56.6||1.1|
|Clerical,<br>Sales and Service|<br>51.5|47.2||.6|
|<br>Agriculture<br>and Unskilled<br>|<br>53.1<br>|<br>43.5<br>||3.5<br>|
|Unemployed|54.1|45.9||0.0|
|Rural|||||
|Emloed:|||||
|py<br>Professional<br>Technical<br>|||||
|or Managerial|53.8|46.2||0.0|
|<br>Clerical,<br>Sales and Service<br> <br>|<br> <br>59.4<br>|<br>37.0<br>||3.6<br>|
|Agriculture<br>and Unskilled|73.0|24.8||2.2|
|Unemployed|76.3|23.7||0.0|

# Source: CPS; DHS

a. Includes private physicians, hospitals, pharmacies, and any other private, non-NGO. b. Unspecified source, may encompass NW"s when private, nonporfits are not a category, and may include commercial vhere it s not a separate category.
and September-December. The PoI data has information on one’s employment status, time usage, and demographic characteristics like gender, education level, and marital status. The PoI data shows how much time a person spends on household activities, at work, and traveling. Reported time on travel is spent by a person traveling from one place to another for various purposes, including work-related activities. The CP survey does not ask specific questions about time spent commuting to work, searching for a job, or on leisure.

We use six waves of PoI from May-August 2020 to January-April 2022 (or from the 20th to the 25th wave). We also match the households in the PoI data to those in the CP data. Appendix Table D2 lists the study periods for the two sectional data sets. We restrict our sample to women (or households having women) aged between 15 and 65 at their in the data. Table D3 lists the variables used in the first appearance Appendix analysis and their definitions. Appendix Table D4 presents the summary statistics of our study sample. Panel A displays the household characteristics in December 2020. Differences between households in treated vs. control HRs regarding rural residence, number of people, and per-capita income and expenditures are relatively small. In panel B, we compare women in treated HRs to those in control HRs in May-August 2020. The distributions of age, marital status, and education are comparable for the two groups of women. Women in treated areas are less likely to participate in the labor market, but conditional on participation; they are more likely to be employed. They also tend to more time on household activities and work but less time on travel than women spend in control areas.

# **E.2 Delhi Primary Survey**

As explained above in footnote 28, we cannot include Delhi in our baseline analysis. To complement our inquiry, however, we use primary data collected in February 2020 by the Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India (Mahendru, 2022). This survey is specifically designed to evaluate changes in bus usage among women in Delhi following the implementation of the _Pink Slip_ scheme. The survey collected responses from 2,025 women, of which 1,294 had been using the bus before the scheme’s introduction; we refer to this group as “continuous users.” Additionally, 231 respondents who began using buses after the scheme’s introduction are termed “new users.” The remaining 500 respondents who did not use buses before or after the scheme are labeled as “non-users.” The was selected at attractions and across sample randomly major generation points 6
Policy Research Working Paper 8386

# **Abstract**

This paper discusses the results of a survey of multinational corporations with affiliates in developing countries. The paper explores corporate perspectives and decision making across the stages of the investment cycle: attraction, entry and establishment, operations and expansion, linkages with the local economy, and, in some cases, divestment and exit. Through interviews with 754 executives, the survey finds that political stability and a business-friendly regulatory environment are the top two factors influencing multinational corporations’ investment decisions in developing countries. Investors seek predictable, transparent, and efficient conduct of public agencies. The survey results also show that investors are heterogeneous, and their perceptions vary with motivation and size. Multinational corporations that are involved in efficiency-seeking investment are more selective than investors motivated by other considerations, and that relatively smaller multinational corporations are more sensitive to host country characteristics and investment climate factors than large firms.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 pkusek@ifc.org and asilva5@ifc.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
# **<mark>Figure 3. The proportion of enrolled school-age children and month of birth</mark>**

<!-- Start of picture text --> Panel A. All children Panel B. Sample of children age 1 and 2<br>Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May June<br>Month of birth Month of birth<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Panel C. Sample of children age 2 and 3 Panel D. Sample of children age 3 and 4<br>Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun<br>Month of birth Month of birth<br>Sample average within bin Polynomial fit of order 2 Sample average within bin Polynomial fit of order 2<br>Panel E. Sample of children age 4 and 5<br>Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun<br>Month of birth<br>Sample average within bin Polynomial fit of order 2<br>.2<br>.55<br>.5 .15<br>.45 .1<br>Child care attendance Child care attendance<br>.4 .05<br>.35 0<br>.4 .6<br>.3 .5<br>.2 .4<br>Child care attendance Child care attendance<br>.1 .3<br>.75<br>.7<br>.65<br>.6<br>Child care attendance<br>.55<br>.5<br><!-- End of picture text --> <mark>Note: The vertical axis gives the proportion of children attending child care, and the horizontal axis presents the birth months of children with contiguous birth months. Children born in December are 1 month older than those born in January.</mark> <mark>Sources: Authors’ estimation from VHLSSs 2010, 2012, 2014 and 2016.</mark> 29
11 as to incorporate clients' views into the initial project design. A book by Larry Salmon, Listen to the People (IBRD/Oxford University Press), summarizing the Bank's experience in this area, was published in early 1987.

These techniques were applied during preparation of Ethiopia Family Health (1986, Case 3). This study, which took less than six months and cost less than US$15,000, excluding the cost of Bank staff assistance, yielded information which appears critical for project planning purposes, and which could not easily have been obtained from a standard KAP survev. This appears to have been the first effort since some of the early nutrition projects (e.g., Brazil Nutrition 1976 and Indonesia Nutrition Project 1977, Case 6) in which the Bank has funded systematic collection of anthropological information about PHN program clients.

Information about service providers. The pattern of information availability is the same with regard to service providers. Of the appraisal reports, 42% contain a basic description of provider practices, or refer to the existence of information about these. Only a small minority of reports discuss the determinants of provider behavior. Once again, the information gap is cause for concern. It is unclear how appropriate training and incentive systems can be designed for service providers without an understanding of the inappropriate perceptions and practices whici need to be corrected.

Two recent projects attempted a systematic study of provider practices prior to appraisal, with very different approaches. Focus group techniques were used during preparation of the Nigeria Imo Health Project (1987, Case 4). They yielded useful qualitative information about both service provider and client attitudes and practices, took less than two months to implement, and involved about five (already trained) borrower staff for a month each, plus the time of one consultant. The project preparation facility for the Ethiopia Family Health Project (1986, Case 5) funded a functional task analysis of service providers, developing somewhat further a methodology last applied in a Bank PHN project in the first Pakistan Population Project (1983) but not subsequently used. This time-and-motion study yielded detailed information about the divergence between providers' job descriptions and actual practices for a cost of less than US$40,000 and took about five months.

# Learning During Imalementation

Information was collected during this review about three types of learning which have been built into the implementation phase of Bank PHN projects. First, learning about the effectiveness of IEC; second, learning about the effectiveness of service delivery; third, learning about performance from routine service statistics collected through the program's management information system. In addition, a number of projects have funded surveys to collect basic demographic, health, and nutrition data during implementation, to fill gaps in knowledge and to provide baseline information against which to evaluate project progress later. The number and type of these surveys were not quantified in this review.
**GLOSSARY OF EPIDEMIOLOGICAL TERMS USED**

_Asymptomatic transmission_ : For influenza, the transmission from person‐to‐person where the infecting individual does not manifest visible/detectable symptoms of influenza illness.

_Basic Reproductive Number_ , _R0_ : The mean number of secondary cases caused by a typical single infectious case in a completely susceptible population (i.e., with no prior immunity), in the absence of public health interventions.

_Case Fatality Rate*_ : The proportion of persons with a particular condition (cases) who die from that condition. The denominator is the number of incident cases; the numerator is the number of cause‐ specific deaths among those cases.

_Chemoprophylaxis_ : Using antivirals or other drugs to prevent infection with the disease (can be pre‐ or post‐exposure to the disease‐causing agent, e.g., the influenza virus).

_Epidemic_ : A generalized epidemic; or, a sequence of outbreaks in a large geographically defined area such as a city, a province, or a country.

_Gross Attack Rate_ : The proportion, usually of a country population, that has a clinical case of the infectious disease during a defined period of time, such as the duration of the epidemic.

_Herd Immunity*_ : The resistance of a group to invasion and spread of an infectious agent, based on the resistance to infection of a high proportion of individual members of the group. The resistance is a product of the number susceptible and the probability that those who are susceptible will come into contact with an infected person.

_Incubation period (influenza)_ : The longest period between the introduction of the virus into a host and the occurrence of the first clinical signs of the disease.

_Outbreak_ : A localized epidemic; or, a sequence of related infectious disease cases in a geographically defined area such as a neighborhood, town, or city. Sometimes considered synonymous with epidemic.

_Pandemic_ : An epidemic that is geographically widespread; occurring throughout a region or even throughout the world.

_Physical prophylaxis_ : Using masks or other physical devices to prevention infection with the disease.

_Prophylaxis_ : Prevention of nfection with a disease, pre‐ or post‐exposure to disease‐causing agent. i _Quarantine_ : The physical separation of healthy people who have been exposed to an infectious disease – for a period of time – from those who have not been exposed.

_Social distancing_ : A disease prevention strategy in which public health authorities limit social (face‐ to‐face) interaction to reduce exposure to and transmission of a disease. These limitations could include, but are not limited to, school and work closures, cancellation of public gatherings and closure or limited mass transportation.

_Virulence*_ : The proportion of persons with clinical disease, who after becoming infected, become severely ill or die.

* Source: CDC Epidemiological Glossary.  www.cdc.gov/reproductivehealth/EpiGlossary/glossary.htm

3
November 2018.<sup>12</sup> The water tariff, which we reproduce in Table 3, has been used since 2011 to the present date. Neither tariff structure makes a distinction between a private connection for the exclusive use of household members and a connection shared by more than one household.<sup>13</sup>

**<u>Table 2:  Tariff Structure for Electricity Provision Services in Addis Ababa (2016)</u>**

|<br>**C**|<br>**onsumption Ch**|<br>**arge**|<br>**Fixed Monthly Ch**<br>**Service***|<br>**arge for**<br>|<br>**Minimum C**|**harge**|
|---|---|---|---|---|---|---|
|**Tariff**<br>**Block**|**Monthly**<br>**Consumption**<br>**(KWh)**|**Tariff**<br>**(USD/KWh)**|**Monthly**<br>**Consumption**<br>**(KWh)**|**Tariff**<br>**(USD)**|**Monthly**<br>**Consumption**<br>**(KWh)**|**Tariff**<br>**(USD)**|
|**Block 1**|0-50|0.0132|0-25|0.068|0 - 20|2.03|
|**Block 2**|51-100|0.0172|26-50|0.165|20 - 220<br>220 -|0.98|
|**Block 3**|101-200|0.0241|51-105|0.330|100000000|0.49|
|**Block 4**|201-300|0.0266|106-300|0.495|||
|**Block 5**|301-400|0.0274|>300|0.660|||
|**Block 6**|401-500|0.0284|||||
|**Block 7**|>500|0.0336|||||

<mark>* Fixed charge that changes dependent upon the range of household monthly consumption.</mark>

**<u>Table 3: Tariff Structure for Water Provision Services in Addis Ababa (2016)</u>**

|**Tariff Block**|**Monthly**<br>**Consumption (m3)**|**Tariff**<br>**(USD/m3)**|**Solid Waste**<br>**Management Charge**<br>**(As percent of water**<br>**Bill)**|
|---|---|---|---|
|**Block 1**|0 - 7|0.08|20%|
|**Block 2**|8 - 20|0.18|20%|
|**Block 3**|21 - 40|0.23|20%|
|**Block 4**|41 - 100|0.29|20%|
|**Block 5**|101 - 300|0.36|20%|
|**Block 6**|301 - 500|0.45|20%|
|**Block 7**|> 500|0.56|20%|

> 12 The electricity tariff in Ethiopia has undergone major changes over the years. In 1959, there was a flat tariff rate for electricity with an average annual increase of about 5% (although the tariff increased in some years and not in others). Tariff increases were not linked to inflationary or other economic trends, however. During that period, tariffs differed depending on the type of customer and the type of system (self-contained vs. interconnected).

> 13 In other words, the same IBT is used to calculate the monthly bill for a household with a private electricity (or water connection) and for a group of households that share a connection.

6