rafmacalaba/data-use-mentions-extended huggingface dataset:
**A1.2 The Wall Street Journal Central European Economic Review (CEER)**

The Central and Eastern Europe Review (CEER) is part of the European branch of the Wall Street Journal (WSJ), an American publishing company. As of May 1999 the CEER can be reached at: http:/linteractive.wsj.com/public/current/summaries/ceer98-2.htm Since 1995, CEER carries out an annual survey of business analysts in Europe and the United States regarding the economic situation in Eastern Europe. Respondents are asked to rate 27 Eastem European countries on their attractiveness as a place to do business over the coming year, based on their perceptl ons of factors including corruption, rule of law and political stability. The factors considered in tShe survey are reported in Table A1.2. To the best of our knowledge, respondents in this surveBy are not provided with definitions of the specific concepts on which they are asked to rate countries.

CEER reports the average response in their December/January issue. We use data from the December/January 1999 issue for political stability and rule of law, and data from the December/January 1998 issue for corruption.

<!-- Start of picture text --> Table A1.2: The Wall Street Journal<br>Central European Economic Review<br>Concepts Measured in 1998 Survey<br>1 Economic Strength<br>2 Balance of Payments<br>3 Business Ethics<br>4 Integration into World Economy<br>5 Liquidity/Ease of Buying Stocks<br>6 Rule of Law<br>7 Price Stability<br>8 Productivity<br>9 Currency Stability/lnvestment Climate<br>10 Political Stability<br>Concepts Measured in 1997 Survey<br>11 Economic Growth<br>12 Price Stability<br>13 Integration into World Economy<br>14 Currency Stability/lnvestment Climate<br>15 Rule of Law<br>16 Ease of Portfolio Position
15|>30|
|**Charges**|||||
|Container handling (US$ per TEU)|135–275|**110–243**|100–320|80–150|
|General-cargo handling charge (US$ per tonne)|6–15|**11–15**|8–15|7–9|

_Source:_ AICD ports database. _Note:_ TEU = twenty-foot equivalent unit.

Individual port performance varies significantly across southern Africa. Some ports are almost as good as the best international ports; others require significant improvement. The ports of Durban and Walvis Bay are among the most efficient in terms of key performance parameters and exceed the performance of all other ports by a large margin (table 2.7). Container dwell time in Beira—a port that could serve northern Zimbabwe and even Malawi—records container dwell times of almost 3 weeks, as does Maputo—which could serve Zambia and Zimbabwe. Crane productivity in Luanda is half that of Durban. Even though prices in Durban are very high and exceed global averages, Durban remains competitive as a transshipment hub. Walvis Bay, which charges half as much as Durban and which is undergoing significant improvements, seeks to claim a growing share of the region’s traffic.

**Table 2.7 Port performance compared across southern Africa**

|**Performance and price**|Cape Town,<br>South Africa|Durban, South<br>Africa|East London,<br>South Africa|Port Elizabeth,<br>South Africa|Walvis Bay,<br>Namibia|Luanda, Angola|Beira,<br>Mozambique|Maputo,<br>Mozambique|
|---|---|---|---|---|---|---|---|---|
|Container dwell time—average (days)
<!-- Start of picture text --> 8 Refugee data for Uganda, 2000-2016 3 Refugee data for Uganda, 2000-2016<br>So So<br>s8<br>®8<br>s s<br>2s gs<br>83 83<br>2es® = 2 Sre<br>8 S<br>7+ 7<br>o<br>8 8<br>sS<br>NN<br>o o<br>2000 2005 2010 2015 2000 2005 2010 2015<br>Year Year<br><!-- End of picture text -->
.0 of the Penn World Tables (Feenstra et al., 2015).

**Recent work shows that the quality of tertiary educated workers also varies substantially across countries.** Martellini et al. (2022) use data on the earnings of migrants across many host and origin countries to show that those educated in universities in developing countries earn less than their peers from rich countries, everything else equal. Moreover, migration reinforces cross-country differences in the quality of the tertiary educated labor force, as migrants from poor to rich countries are positively selected in human capital. These results reinforce the conclusion (already discussed for primary and secondary schooling in Section 2.3.1) that closing educational gaps between countries requires more than increasing enrollment in the developing world.

10
# **4 Study Design**

This study uses a randomized control trial design to measure impacts of the _Choices_ and _EMB_ programs, and to explore the pathways of the observed changes. The evaluation strategy builds on household-level randomization. Households could be randomly assigned into one of the following three groups: (1) _Choices Only_ : Adolescents invited to participate in _Choices_ training; (2) _Choices+EMB_ : Adolescents invited to participate in _Choices_ training and adult household members (typically parents) invited to participate in _EMB_ sessions; and (3) Control group: Adolescents not invited to the _Choices_ training.

For those households that were assigned to the _Choices+EMB_ treatment arm, the biological parents of the adolescents were invited to attend an _EMB_ session if they were living in the household. If there were no biological parents present in the household, any male and/or female primary caregiver was invited to attend _EMB_ sessions.

The randomization was stratified by community cluster, gender of the adolescent, school enrollment, and gender of the household head. There could be more than one eligible adolescent per household.<sup>26</sup> A household level randomization ensured that adolescents within a household were not assigned to different treatments which minimizes possible spillover or contamination effects. Our study design is not set up to capture or examine spillover effects which can arise if treated adolescents or parents share information and material from the trainings within their networks. If knowledge spillovers occur between treated and control households then our results are likely biased downward which means that our analysis will underestimate the program’s effect. Table A5 in the Appendix shows that gender attitudes in the control group are fairly stable over time from baseline, midline to endline which suggests that spillover effects are not a major factor.<sup>27</sup>

# **5 Data**

## **5.1 Survey Data**

In order to establish a sampling frame, the implementing partner conducted a community mapping exercise and identified project sites
- Sales tax: The sales tax is currently Bhutan’s primary source of indirect taxes and is levied on goods entering the country and on a limited number of domestically supplied services such as entertainment and hotel services. The current sales tax has 9 ad-valorem and two specific tax slabs, with tax rates ranging from 0% to 100%, and is mostly applicable on imported goods. Only six types of goods and services are subject to a sales tax at point of sale while around 37% of total imports are subject to a 0% sales tax.

- Excise tax: The excise tax is levied on select domestically produced and imported items and ranges from 30% to 75% (Table A.2). The government also collects fees for liquor exports and excise permit fees for the import of beer.

- Green Tax: A green tax was introduced in 2012 to better protect the environment and reduce carbon dioxide. It is levied on motor cars and vehicles, with the rate varying between 5% and 30% depending on vehicle type. Starting in 2014, petrol and diesel are also imposed a green tax.

- Custom Duty: Imported goods generally pay custom duties ranging from 10% to 100%. Select items are exempt such as import of goods by diplomatic missions, international organizations and their personnel; import of goods by Bhutanese organizations and agencies; import of industrial plant, machinery, their spare parts and raw materials; electric or hybrid vehicles; re-exported durable and consumable household and personal effects or such goods.

While the household survey collects information on the consumption of many different items, not all taxable goods can be identified. In addition, custom duty on cars varies depending on whether the vehicle is new or used, but that information is not collected in the household survey. Table A.1 in the appendix shows Custom Duty, Sales Tax, and Green Tax Rates in 2017 for select items identified in the household survey.

# **_Social insurance contributions_**

There are broadly pension-related contributions
**TABLE 2. DISTRIBUTION OF FIRMS’** **<u>CHARACTERISTICS</u>**

|Size||Region|
|---|---|---|
|Micro (less than 16 employees)|90.2%|North<br>19.3%|
|Small (16-100 employees)|7.8%|Center<br>49.2%|
|Medium (100-250 employees)|1.2%|South<br>21.0%|
|Large (more than 250 employees)|0.7%|Mexico City<br>10.6%|
|Divisionofactivity||Firm's exports aremore than50% ofoutput|
|Food, beverages, and tobacco|35.6%|No<br>99.1%|
|Textiles, clothing and leather|14.3%|Yes<br>0.9%|
|Wood and wood products|12.9%|Thefirm investsin research& development|
|Paper, paper products, printing, and publishing|6.8%|No<br>92.8%|
|Chemicals, oil derivatives, and coal|3.2%|Yes<br>7.2%|
|Non-metallic mineral products|8.1%|Thefirm has quality controlcertifications|
|Basic metallic industries|0.1%|No<br>96.8%|
|Metallic products, machinery, and equipment.|17.3%|Yes<br>3.3%|
|Other manufacturingindustries|1.8%|Thefirmadoptednew technology since1999|
|Foreigncapital is over50%||No<br>61.6%|
|No|99.5%|Yes<br>38.4%|
|Yes|0.5%||

Source: Calculations based on ENESTYC 2001.

With respect to firm size, 90 percent are micro in
Mobarak 2014).<sup>6</sup> Nevertheless, our indicator for estimated effects after one year is statistically insignificant and negative, implying noisy, but on average lower average effects on yield after the first year of the subsidy program (table 3, column 4).

Lastly, we examine whether input subsidies may be associated with spillover effects. Among the studies included in our meta-analysis, study 1 (Carter, Laajaj, and Yang 2021) is the only one with estimated spillover effects. We include an indicator for estimated spillover effects and find that while the estimated relationship is positive, it is not statistically significant (table 3, column 5). This is likely due to the fact that only 6.5% of the estimates are spillover effects. Nonetheless, the possibility of spillover in this setting could improve the cost-benefit calculations of input subsidy programs since nonbeneficiaries may adopt the technology without receiving the program. We also include all indicators in one regression and find similar estimated coefficients.

_Table 3 The relationship between program characteristics and estimated effect on yield_

||(1)<br>RCT|(2)<br>100% subsidy|(3)<br>Other interventions|(4)<br>Long term|(5)<br>Spillover|
|---|---|---|---|---|---|
|Program characteristics|-0.020|0.075|-0.143**|-0.093|0.194*|
||(0.053)|(0.054)|(0.051)|(0.096)|(0.099)|
|Observations|32|32|32|32|32|
|Mean indep. var.|0.250|0.625|0.188|0.156|0.062|

Notes: Each column comes from a separate
0.02)|(0.21)|(0.18)|(3.88)|(0.02)|(0.02)|(0.13)|(0.03)|(0.03)|(0.03)|(0.03)|(0.08)|
|RI p-values|.63|.94|.43|.28|.27|.11|.07|.09|.16|.51|.65|.2|
|Control Mean|.2|3.89|4.69|51.92|.1|.93|7.52|.4|.77|.55|.59|2.22|
|Obs|983|971|985|985|945|944|828|952|956|952|952|969|

* ** *** _Notes_ : This table estimates specification (1) in the paper. p _<_ 0.1, p _<_ 0.05, p _<_ 0.01. Robust standard errors in parentheses. All regressions have strata fixed effects and control for variables chosen using lasso.
. The subsequent scaling back of that program in the face of the sector’s projected surplus of supply is the result of undue political influence on planning. Different perspectives are possible on balancing the pragmatic and the visionary, but it is clear that the ERC’s LCPDP process is undermined by the ambitious political targets and pressure to push those targets into technical plans.

The LCPDP was originally meant to be updated annually (and then biennially). An update of the 2011 plan did not come until 2016, and this was updated again in 2018 (before being recalled for more consultations with government. These updates have been fed into the regulator’s periodic cost-ofservice study. The review process for these plans has provided an opportunity to challenge technical assumptions. Nevertheless, linkages between planning and implementation could be further strengthened.

Kenya also scores well against other countries with respect to procurement (table 2). Procurement of generation projects can be done in several ways, as discussed in Part 2. Some projects identified in the LCPDP are earmarked for development by KenGen, which then floats tenders for engineering, procurement, and construction contracts. Alternatively, KPLC (the off-taker) issues tenders for IPP projects, especially for fossil-fuel-fired thermal technologies. In either case, the procurement is conducted in good time, taking into account projected online dates and project lead times.

Interventions to accelerate renewables began in 2008, with the development of a feed-in tariff policy. Under the policy, KenGen or IPPs develop projects according to the milestones stipulated and ultimately negotiate PPAs with KPLC. The MoEP can also offer private companies geothermal power concessions or generating licenses; timelines are stipulated in the concession agreement or license. Similar scenarios apply to coal and gas concessions.

More recently, Kenya’s county governments have been given a mandate to undertake planning and development with respect to the regulation of electricity and petroleum. Following the promulgation of the current constitution in 2010, MoEP formed an Energy Sector Committee on the

> 12 For the detailed planning and procurement index, see
0|10.4|13.5|20.2|33.9|39.3|36.2|
|2 children|3.05|3.28|3.25|3.24|17.2|12.2|12.7|16.0|21.9|36.3|33.7|35.8|
|3 and more children|3.03|3.17|3.28|3.20|23.1|17.5|12.5|15.1|23.1|32.8|33.5|32.9|
|singleparent|3.11|3.37|3.36|3.43|14.9|11.6|13.0|9.2|24.1|41.6|39.8|42.3|

Source: Authors’ estimates based on HSS data.

32
a partial account of balance sheets.<sup>10</sup>

**Ghana.** The data comes from the 2014 Integrated Business Establishment Survey (IBES) conducted by the Ghana Statistical Service. The IBES is an economic census of all business establishments (registered and unregistered) in the non-agriculture sectors of the economy. It has detailed information about registration with administrative agencies, tax payment, accounting practices, age, employment size, state ownership, and foreign equity. The data contain records of an establishment’s registration status with government agencies, and formality is defined based on registration status with Registrar General’s Department (RGD) and keeping formal accounts.<sup>11</sup>

**Rwanda.** The source of the data is the 2014 Establishment Census (EC) conducted by the National Institute of Statistics of Rwanda (NISR). The EC constitutes a virtually exhaustive business registry

> 8 Our dataset is more comprehensive by enumerating all establishments. For example, Hsieh and Olken (2014) have census establishments with 20 or more employees, but they have a sample of those with 20 or fewer workers in the case of Indonesian manufacturing. Similarly, for Indian manufacturing, their dataset comprises a complete enumeration of formal establishments with 100 or more workers and a sample of formal establishments with fewer than 100 workers and informal ones. The exception is the Mexican manufacturing data, which comprises all establishments with fixed locations.

> 9 National Institute of Statistics and Demography (INSD). Analysis report of the XVII Industrial and Commercial Census. November 2018.

> 10 National Institute of Statistics of Cameroon (INS). General Census of Enterprises In 2009. September 2010.

> 11 Ghana Statistical Service. Integrated Business Establishment Survey: Summary Report. September 2015.

7
Dao, Thu Hien, Frédéric Docquier, Chris Parsons and Giovanni Peri (2018) “Migration and development: Dissecting the anatomy of the mobility transition”, _Journal of Development Economics_ 132: 88-101.

de Janvry, Alain, Kyle Emerick, Marco Gonzalez-Navarro, and Elisabeth Sadoulet (2015) “Delinking Land Rights from Land Use: Certification and Migration in Mexico” _American Economic Review_ , 105 (10): 3125-49 Deryugina, Tatyana, Laura Kawano and Steven Levitt (2018): “The Economic Impact of Hurricane Katrina on its Victims: Evidence from Individual Tax Returns,” _American Economic Journal: Applied Economics_ , 10(2): 202–233.

Dustmann, Christian, Hyejin Ku and Tanya Surovtseva (2021) “Real Exchange Rates and the Earnings of Immigrants”, CREAM Discussion Paper no. 10/21 Dustmann, Christian and Ian Preston (2019) “Free Movement, Open Borders, and the Gains from Labor Mobility”, _Annual Review of Economics_ 11: 783-808.

The Economist (2021) “Automatic for the people”, August 14.

Fernandez, Raquel and Dani Rodrik (1991) “Resistance to Reform: Status Quo Bias in the Presence of Individual- Specific Uncertainty”, _American Economic Review_ 81(5): 1146-55.

Frederick, Shane, Nathan Novemsky, Jiang Wang, Ravi Dhar, and , Stephen Nowlis (2009) “Opportunity Cost Neglect”, _Journal of Consumer Research_ . 36, (4), 553-561 Gabaix, Xavier and David Laibson (2017) “Myopia and discounting”, NBER Working Paper no. 23254.

Gazeaud, Jules, Eric Mvukiyehe and Olivier Sterck (2021) “Cash transfers and migration: Theory and evidence from a randomized controlled trial”, _Review of Economics and Statistics,_ forthcoming.

Giambra, Samuele and David McKenzie (2021) “Self-employment and migration”, _World Development_ , 141: 105362.

Gibson, John and David McKenzie (2011) “The microeconomic determinants of emigration and return migration of the best and brightest:
expected forest clearing and biodiversity loss that would be difficult to anticipate without this kind of analysis.

In summary, our methodology combines high-resolution spatial databases and appropriate econometric estimation to produce measures of expected forest clearing and biodiversity loss from secondary road upgrading in Bolivia, Cameroon and Myanmar. These three cases are purely illustrative, since the same supporting data are available for all tropical forest countries.

Since road upgrading will inevitably accompany rural development programs, we believe that explicit identification of ecologically-vulnerable areas and road corridors can provide two valuable types of information. First, with limited budgets, it can help steer road upgrading programs toward corridors where expected biodiversity losses will be minimized. Second, it can inform the adoption of appropriate protection measures in vulnerable road corridors and neighboring areas. Since our methodology can be applied to any country case at relatively low cost, we believe that more widespread application could provide useful assistance for environmentally-sensitive infrastructure planning.

We recognize that a full application of our approach requires investments in data assembly, geographic information systems and econometric analysis that may not be feasible for all potentially-interested parties. However, we believe that important insights can be gained by combining basic elements of our approach in a geographic analysis that roughly quantifies the agricultural potential and biodiversity value of forested areas in corridors where new road investments may occur. Such an analysis would flag potential hotspot areas with both high biodiversity value and high agricultural conversion potential.

59
**English Knowledge Score:** This is measured the knowledge score of each student in the English test for each school.

**Total Knowledge Score:** This is measured by the knowledge score of each student averaged across both subjects for each school.

We also measure intermediate impacts variables, combined into Anderson indices: _resource utilization_ , including the ratio of PTRs between lower and upper primary, lower primary PTRs, and lower primary pupil-textbook ratios; _teacher management_ , including frequency of observation of teachers by head teachers and of use of rewards for teacher performance; _teacher effort_ , including teacher attendance and the share of teachers at a school who were found in the classroom on MLSS visit; assigned or marked homework during observed lessons; reported satisfaction with their job; or were above the median in terms of the amount of time they spent teaching on a typical day; _teaching practices_ , including whether teachers asked students questions, employed teaching aids, and employed local materials to help deliver lessons; _remedial classes_ , including the frequency of such classes and the share of grades in which they are offered; and _school cultures_ , including the existence of clear rules on bullying and the incidence of bullying by teachers and students. For more detail on the construction of these indexes and the underlying variables, see section A.0.2.

# 2.8 Data

We draw on data from the Malawi Longitudinal School Survey (MLSS). The MLSS is an independent, nationally representative survey that provides data on conditions, practices and outcomes in Malawi’s primary schools. The MLSS was designed to align with the MESIP project cycle to both provide information to the government on implementation and outcomes of the project and to support a number of impact evaluations of pilot interventions under the project, following rigorous experimental designs; as well as to inform general education policymaking. Visits are unannounced.<sup>12</sup> Data is collected through observations and interviews – for example, observations of school and classroom facilities; observation of lessons
In this paper, we use data from nationally-representative household surveys that were conducted in Cambodia, Ethiopia, Malawi and Tanzania over the period 2016-2020 with support from the World Bank Living Standards and Measurement Study-Plus (LSMS+) program and that conducted intra-household, private interviews with adult household members regarding asset ownership. The LSMS+ supported surveys included cross-country comparable questionnaire modules that were designed based on the international guidelines for collecting individual disaggregated survey data on asset ownership and rights (UNSD, 2019) and that asked adult women and men to self-report regarding their personal ownership, control and valuation of a comprehensive set of physical and financial assets. The self-reported information on asset ownership and values allow us to create individual-level wealth aggregates, whose analysis reveals a number of findings that have important implications for both survey design and understanding of wealth inequalities within populations.

There are several headline findings from our research. First, across countries and different asset classes, there are substantial gender differences in incidence of asset ownership and values of asset holdings, underscoring the importance of disaggregated data collection (i.e. elicited from individuals, and at the asset-level) for characterizing gender inequities in wealth in low- and middle-income countries. Second, inequality as measured by household per capita wealth far outpaces more standard measures based on household per capita consumption expenditure, and individual-level variation in wealth leads to even higher measures of inequality across countries, similar to recent findings on intra-household consumption poverty (Brown et al., 2018; Bose-Duker et al., 2021). Intra-household wealth inequality has a substantial role in explaining overall wealth inequality, and land, in particular, is an important driver of inequality, although there is variation by country as to whether residential or non-residential land plays a greater role. Third, self-reporting on asset valuation matters and leads to higher measures of inequality compared to 4