rafmacalaba/data-use-mentions-extended huggingface dataset:
(1999) propose using the geographical and linguistic characteristics of an economy as instrumental variables for their social infrastructure. We focused on distance from the equator and the percentage of the population speaking a Western European language as their first language today.

Another problem with the IV strategy is that it limits the analysis to a cross-section as the instrumental variables are available only at a point in time. We chose the year 1995 since the data for years of schooling for 1995 are arguable measured with less error than for 2000 and 2005, where extrapolations were used. The first instrumental variable, logarithm of settler mortality rates, further limits the analysis to 59 countries that are ex-colonies. Thus, the results from the IV analysis should be interpreted with caution and considered only tentative.

# _4.2 Panel estimation results_

Table 5 shows the results from the estimation of equation (11). The columns differ in terms of the estimation technique and the variable measuring human capital per worker. For column (1) we replicate the results in World Bank (2006); that is, we just pool the observations over the years 1995, 2000, and 2005 and use OLS so that  _i_   _i_ and  _t_  0  _t_ . The indicator of human capital is simply years of schooling from Barro and Lee (2001). In column (2) we also use OLS on the pooled sample, but the indicator of human capital is given by equation (3). Both the coefficients on our indicator of institutional quality and years of schooling exhibit positive signs and are highly significant. An additional point in the rule of law index is associated with an increase in intangible wealth per capita of around $3,000. An additional year of schooling is associated with an increase in intangible wealth per capita of over $11,000 and a unit increase in human capital per worker with an increase in intangible wealth per capita of around $45,000.

The regressions in columns (1) and (2)
exporters<br>-5<br>50<br>-10<br>45<br>-15<br>-20 40<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br>Sources: Haver Analytics; Oxford Economics; World Economic Outlook, International Monetary Fund; World<br>Bank Development Indicators; PRS Group; World Bank.<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> A. Weighted averages.

C. Long-term averages are country-specific and refer to available data over 1990-2008.

E. F. Investment-weighted averages.

46
will draw on existing literature and also present data from our own research. Second, we will discuss the policy choices and possibilities for reform posed by this landscape.

# **Methodology**

This paper has several sources of data. The World Bank Gender Norms Survey<sup>3</sup> (WBGNS) is a nationally representative survey of 3,000 women from two age-groups – 15-25 year olds and 4560 year olds. In addition, it also surveyed 1,500 male heads of households and 500 community leaders like Union Parishad members, religious leaders, teachers and businessmen. Among other questions, the survey had a few on conflict in the community and many more on spousal and other forms of violence against women. The survey also asked if in the last year the respondent had heard of certain criminal incidents in the community or acts of violence against anyone in their community (village or urban neighborhood). To our knowledge this is the first nationally representative survey on conflict despite the fact that the number of questions is few. It is also the only survey that asks questions about spousal violence of both men and women.

A companion qualitative field study was also conducted in 2006 in tandem with the WBGNS. Thirty-two focus groups of on average 7 participants each were conducted at the ward level in the districts of Dinajpur, Satkhira, Sunamgonj and Mymensingh. The distribution of focus groups was as follows: four each with adolescent boys and girls (separately) in school, four each with adolescent boys and girls (separately) out of school, four each with mothers of adolescent girls from poor backgrounds, four each with mothers of adolescent girls from elite/rich backgrounds, four each with fathers of adolescent girls from poor backgrounds, four each with fathers of adolescent girls from elite/rich backgrounds. The questions were on changes in the community, changes in the lives of girls due to education, marriage practices, access to justice, participation in political processes, aspirations of young men and women and select gender
22

**Figure 7** : _Trade Facilitation Benchmarks for Bulgaria in comparative perspective against the EU-15 average level_

<!-- Start of picture text -->
1.0<br>0.9<br>0.8 Bulgaria<br>0.7<br>Romania<br>0.6<br>0.5<br>Estonia<br>0.4<br>0.3 Lithuania<br>0.2<br>0.1 CEEC-8 average<br>0.0<br>Ports Customs Regulation IT infrastructure<br><!-- End of picture text -->

_Source:_ Wilson, Mann, Otsuki database (2004) and Wilson, Luo and Broadman (2004).

The gap between performances in terms of regulatory environment measures in which Bulgaria’s score is higher than the average for CEEC-8 than on other three indicators of trade facilitation appears to stem from relatively recent vintage of regulatory changes. Better regulatory arrangements usually lead, albeit with some lead-time, to improvements in performance.

Telecommunications is one of these areas where new laws are yet to significantly alter the quality of IT infrastructure. Their availability and pricing of telephone services deteriorated in comparison to CEEC-8 countries in 1999-2002 on all major indicators except in density of telephone mainlines (Table 10). But the latter was exceptionally high in Bulgaria even before the collapse of central planning. It still exceeded the average for CEEC-8 in 2001 and was higher than in Hungary in 2002. Yet, despite a significant fall in the length of a telephone-waiting list between 1999 and 2002, the gap vis-à-vis CEEC-8 had expanded.

The gap in terms of pricing local telephone calls had also fallen, although the telephone average cost of a local call was still well below that in CEEC-8 and Romania in 2002. But such a low cost of local telephone calls—more than five-times lower than on average in CEEC-8 or Romania—is not the result of high efficiency but cross-subsidization.
pulling hair, pushing or shoving and twisting arms or legs (Elayyan 2007). Some Palestinian students living in refugee camps in Lebanon were reported to refuse attending school because of the unsafe environment, the violence and unkind treatments they suffered from their teachers (Abu Sharar 2009). While one in four Lebanese students taking part in a school-based health survey in 2005 reported being physically assaulted by a teacher or school staff, and almost 56 percent of students in the Republic of Yemen, aged 12-17 reported being violently assaulted by a teacher or school staff (WHO 2007; Ba-Saddik 2013). Corporal punishment is also reinforced at home, where parents use violent forms of discipline at a higher rate compared to other regions (El Kogali and others 2015).

Teachers in the MENA region mainly receive training on subject content and pedagogical practices, much of which is still enforcing traditional approaches to learning. Training is not provided on student aggression, behavioral, or social problems, and these have now become an even more serious problem with the unfolding refugee crisis, and the increased occurrence of students dealing with trauma or posttraumatic stress. Moreover, aggression prevention programs, and student psychological and social support services are not equitably provided across and within countries. Positive relationships between teachers and students are one means for managing classroom misconduct that has yet to infiltrate teaching practices and policies across the region.

How schools, school leaders and teachers foster positive relationships with students, even when faced with extreme situations of misconduct, is a key determining factor in the ability of a student to perform better (Hamre and Pianta 2001; O’Connor, Dearing and Collins 2011). Positive relationships are more likely to foster self-esteem and to enhance the quality of relationships with peers. As students feel more secure, they are more likely to learn more easily, adopt appropriate norms and behaviors, and feel more confident in raising their own expectations. Moreover, socially disadvantaged students tend to benefit more from positive relationships, perhaps because some may be more likely to experience stress and
to corporate income tax. The authors conclude that the creation of these MTOs -whose objective was to simultaneously expand enforcement reach and facilitate compliance (through customer service) through increased local staff -led to sustainable, long-term impacts on tax filing and payment behavior, and they estimate that MTOs more than doubled revenue collection in the nine-year study period. On the deployment side, a study in Peru (Kapon et al. 2022) finds that Prioritized Iterative Enforcement (PIE) -where small groups of high risk tax payers are targeted in batches -can improve property tax collection by as much as 10 percent. Other studies look at how the assignment of tax officers to jurisdictions can improve compliance (Bergeron et al. 2023).

The remainder of the paper is organized as follows; section 2 will discuss the Tanzanian context in greater detail; section 3 will discuss the study design; section 4 will summarize the experimental impacts from the intervention (identified through the survey and administrative data, with validation from a follow-up survey); and section 5 concludes with a discussion on policy implications.

6
<!-- Start of picture text --> London Barcelona<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br>1 3 5 7 9 11 13 15 17 19 21 23 25 1 3 5 7 9 11 13 15 17 19 21 23 25<br>DISTANCE FROM CBD (KM) DISTANCE FROM CBD (KM)<br>i) j)<br>Washington DC Seoul<br>250 300<br>200 250<br>200<br>150<br>150<br>100<br>100<br>50<br>50<br>0 0<br>1 3 5 7 9 11 13 15 17 19 21 23 25 1 3 5 7 9 11 13 15 17 19 21 23 25<br>DISTANCE FROM CBD DISTANCE FROM CBD (KM)<br>Source: Authors calculations using 2012 Landscan data. Landscan is a global dataset that models<br>population densities on roughly 1x1 km grid. The city center was identified using the location of<br>oldest building as a proxy, or a government building if necessary. This location was then visually<br>verified using google maps and night time lights.<br>PERSON/HECTARE PERSON/HECTARE<br>PERSON/HECTARE PERSON/HECTARE<br><!-- End of picture text --> Figure 2 above show the density profiles for Nairobi, Kigali, Addis Ababa, Dar es Salaam, Dakar and other major international cities. In the African cities of Nairobi, Kigali, Addis Ababa, Dar es Salaam, there appears to be a high concentration of population near the CBD and a rapid decline as one moves towards the periphery, suggesting a strong monocentric pattern in all cases. Dakar’s is a bit of an anomaly, with its unique physical geography strongly influencing its gradient. Located on
- 12 -

**Figure 4: Contractual savings portfolio distribution (%)**

<!-- Start of picture text --> 100%<br>90%<br>1 5 17 20~~~~z 21 21 25<br>80% 30 2<br>70%<br>60%5 22202220<br>_ ~~ ~ 15~~~~~~~~~~~~~~~~~~~17<br>40%<br>30%,<br>20%<br>1982 1984 1986 1988 1990 1992 1994 1996<br>0<br>a0 C 5 . 6 a-d d.po.4% *8Bi, -dO b-nd. IL... s nsh,- a *OIhe<br><!-- End of picture text -->

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

Non-life insurance companies hold a much larger proportion of liquid financial assets (more cash and deposits and a lower proportion of illiquid loans), as shown in Figure 5.

**Figure** 5: **Non-life insurance portfolio distribution (%)**

<!-- Start of picture text --> 100%<br>90%<br>80% is 17 is 23 21 25 25<br>70%<br>30%<br>20%<br>10%<br>1982 1984 1986 1988 1990 1992 1994 1996<br>'2 Casl, and dolosdSi *Bills. nd bonds Loans 03Shas.. *Otbar<br><!-- End of picture text -->

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

It is important to show the difference in portfolio allocation between countries with common law legal origin and countries with other legal origins. Figure 6 shows the
(and untested) assumption for causal DD DD YY DD DD ZZ inferences based on the DML regression equations (2), (2a) and (2b) is that 1 2] = 0 , and for .<sup>11</sup> The key difference between the ad-hoc approach and DML that allows for EE[εε|ZZ, DD , DD causal inferences is the assumption that ii 1 or 2 are conditionally exogenous (or as good as randomly EE[vv |ZZ] = 0 ii= 1,2 assigned conditional on the covariates ) is more defensible. The vector of the confounding variables DD DD is of much higher dimension than the vector which is composed of the few confounding variables ZZ ZZ chosen in ad-hoc manner. Thus, in the ad-hoc approach the estimates of 1 and 2 may subject to φφ omitted variable bias thus preventing one from making causal inferences. ββ ββ In contrast to the ad-hoc approach, the vector in the DML approach, as applied in this study, consists of 375 possible control variables for the analysis using the MICS from the Republic of Congo and 109 ZZ

> 9 The DML approach has a more relaxed sparsity requirement than double selection (Belloni et al. 2014b), that adds robustness since the sample is split and coefficients are obtained from one sample and used in another independent sample (see the Appendix for more details).

> 10 The sequence of steps involved in applying the DML method is presented in detail in the Appendix.

> 11 An alternative approach is to employ instrumental variables for the 1 and 2 treatment variables and apply a variant of the DML approach explored here. The difficulty common to most applications of the instrumental variable approach is to identify variables that are excluded from equation (2) but correlated with the treatment variables DD DD 1 and 2 and uncorrelated with the error term . DD DD εε

12
23 transfers to the urban poor in Egypt, and a 19 percent decline in the value of such transfers for the rural poor. In other words, eliminating the subsidy on sugar would have just about the sanie impact on the poor as that of eliminating the subsidy on oil.

The third possible reform, doubling the price of a loaf of subsidized baladi bread from LE 0.05 to ILE 0.10,36 would have the most iraportant economic and distributional effects on the poor in Egypt. On the one hand, simulations based upon the food characteristic 'demand system suggest that government costs on total food subsidies would decline by 47 percent. On the other hand, because consumption of baladi bread is so high for the poor in urban areas, a doubling of the price of subsidized baladi bread would lead to a 61 percent decline in the value of income transfers to the urban poor. As a result of this large decline, there would be a significant negative effect on urban incomes: simulationL **s** uggest that total nonfood expenditures in urban areas would fall by approximately 7 percent. In rural areas, the food consumption and income effects of such an increase in subsidized baladi bread prices would be smaller, because rural people consume less subsidized bread than their urban counterparts.

# 8. Conclusion

In the past, the ability of the Egyptian food subsidy system to target and improve the welfare and nutritional status of the poor in Egypt has been much-debated. This debate has taken place because the Egyptian food subsidy system is still a general, untargeted program that is essentially open to all Egyptians, poor and non-poor.

This paper has used new household survey data to show that the Egyptian food subsidy system is targeted in the sense of being self-targeted to the poor because it
TABLE 1: POLICE AND CRIME AGAINST FIRMS (LOSSES DUE TO CRIME/SALES)

||% of Losses<br>Due to Crime<br>Over Sales|% of Losses<br>Due to Crime<br>Over Sales|% of Losses<br>Due to Crime<br>Over Sales|% of Losses<br>Due to Crime<br>Over Sales|
|---|---|---|---|---|
||1|2|3|4|
|Police per 100,000 persons|-0.0011**|-0.0020**|0.0052*|0.0004|
||[0.0005]|[0.0009]|[0.0027]|[0.0006]|
|Police x GDP per Capita Growth||0.0003**<br>[0.0001]|||
|Police x GINI|||-0.0002**<br>[0.0001]||
|Police x Large City or Capital||||-0.0022***<br>[0.0005]|
|GDP per Capita Growth|-0.0808***|-0.1679***|-0.0706***|-0.0821***|
||[0.0208]|[0.0471]|[0.0230]|[0.0199]|
|Firm with Female Owner|0.1181*|0.1078*|0.1143*|0.1069*|
||[0.0632]|[0.0630]|[0.0616]|[0.0622]|
|Real GNI per capita (in 100s)|-0.0058***|-0.0061***|-
carbon<br>monoxide (CO) emissions<br>by 5–15 percent|
|Goel & Gupta<br>(2017)|Delhi|2004-2006|Pollution and weather data<br>(hourly, daily and weekly)|Regression<br>discontinuity|The expansion of Delhi<br>Metro result in a 34<br>percent localized reduction<br>in CO in the short run.|
|Gendron-<br>Carrier et al.<br>(2018)|43 cities<br>across the<br>world|2000-2014|Latitude, longitude and date<br>of opening of 171 subway<br>systems consist of 8,889<br>stations, Aerosol Optical<br>Depth (AOD), recorded by<br>the Moderate Resolution<br>Imaging Spectroradiometer<br>(MODIS)|Event study|Particulate concentrations<br>dropped by 4 percent on<br>average following the<br>opening of a new subway<br>system.|

44
_Note: Energy includes electricity, natural gas, town gas, liquified energy sources, liquid fuels, coal, other solid fuels, and heat energy_ _Figure 7: Energy spending share (over expenditure) and energy poverty incidence rates, by definition (2021)_ _Figure 8: Energy spending share (over income) and energy poverty incidence rates and, by definition (2021)_ <!-- Start of picture text --> 70 62.3 60.0 51.9<br>60 50.0<br>50<br>40.0<br>40<br>30.0<br>30<br>20 13.5 9.9 9.5 7.9 20.0 12.7 12.7 9.5 7.9<br>10 10.0<br>0 0.0<br>2M M/2 M/2 (equ.) 2M M/2 M/2 (equ.)<br>(expenditure-based) 10 percent measure (income-based) 10 percent measure<br>Energy spending share Energy spending share<br><!-- End of picture text --> _Notes: The energy spending share is the average expenditure on energy divided by the average household income (on the right) and by the average household expenditure (on the left). We consider the following measures of energy poverty: (1) A household is considered energy poor as soon as the share of energy expenditure (compared to equivalised disposable income) is above twice the national median of energy spending shares (2M); (2) a household is energy poor as soon as absolute energy expenditure is below half the median of absolute energy expenditure (M/2); (3) a household is energy poor as soon as equivalized energy expenditure is below half the median of absolute energy expenditure (M/2 (equ.)); (4) A household is considered
with these children during the most critical ages of their life and are charged with promoting their development. Thus, it is of vast importance to understand what these teachers know about childhood development and how to promote it. If there are misunderstandings about any of the domains of development among the teachers, there are large consequences for children. Without teachers who understand the importance of these early stages, children’s future productivity, mental health, and success is at risk (Human Capital Index (2018)).

# **3 Data**

During the months of March-May, 2020, we collected data on 225 head teachers and 206 teachers in ECED centers in Eastern Nepal. In our district in Eastern Nepal, there are 367 ECED centers. A large proportion of teachers have not received government ECD training, and this is what our sample focuses on. We selected 200 schools where teachers had not received government ECD training and then conducted a phone based survey that covered demographics, knowledge of early childhood development, and leadership skills of teachers and head teachers. 4 We also collected data on mental health using questions from the GHQ-12, a 12-item questionnaire that measures anxiety, depression, and general mental health (Goldberg and Hillier (1979)).

Head teachers of 200 randomly selected schools in an Eastern district in Nepal were called, and, once surveyed, asked for information about teachers in their schools. Most ECED centers have more than one teacher; we interviewed one teacher per school that met our selection criteria. There were three selection criteria that were used. Teachers in our sample cannot be assistant teachers, must not have government ECD training, and must be government employed teachers. Assistant teachers do not have as large of a role in ensuring student success; thus, we eliminated them from our sample. Teachers in our sample have lots of interaction with children in the most important time frame in their lives: ages 3-5. It

> 4Due to COVID-19 and school closures, we had to collect these data over the
issues as they apply to Belarus and suggest appropriate policy actions.

4. The Russian Government was the first to ask the World Bank for assistance with respect to enterprise social asset divestiture in early 1994. Since then, the Bank has undertaken several loans in support of enterprise divestiture of functions such as housing, kindergartens, medical care, enterprise privatization and related issues in social protection programs in Russia, Kazakhstan, and the Kyrgyz Republic. Staff Appraisal Reports for these loans are a rich source of infonnation on this problem. As well, an extensive published literature is being built up on the issue. Aggregate surveys of the problem have been undertaken for specific countries by authors within and outside the Bank,<sup>2</sup> and are complemented by in

> **1** This document was produced as part of the author's responsibility in the 1996 Belarus Country Economic Memorandum Mission headed by John Hansen. I wish to thank Elena Klochan, Nikolai Lisai and Konstantin Senyut of the World Bank's Resident Mission in Minsk, and John Hansen and Yuliya Merkulova for assistance in preparing this report. I have also benefited from specific comments from Jorge Martinez-Vasquez and Richard Bird, and from extensive discussions at the Bank with Lev Freinkman and Philip O'Keefe, both of whom are engaged in operations work associated with this issue.

- 2 See Adrienne Cheasty, "Social Divestiture in Ukraine-A Budgetary Perspective," IMF Fiscal Affairs Department, OcL 1995; Simon Commander and Richard Jackman, "Providing Social Benefits in Russia: Redefming the Roles of Firms and Government." Pblicy Research Working Paper 1184, Economic Development Institute, The World Bank, 1993; Simon Commander and Une Lee with Andrei Tolstopiatenko, "Social Benefits and the Russian Industrial Firm," Joint Conference of the World Bank and the Ministry of Economy of the Russian Federation, _Russia: Economic Policy and Enterprise Restructuring,_ June 1995; Lev M. Freinkman and Irina Starodubrovskaya, "Restructuring of Enterprise Social Assets in Russia," Policy Research Working