rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.31|
|Corr. Coeff. (Contribution fee)|-0.36|-0.52|0.04|-0.28|-0.27|0.74|0.47|-0.02|
|Corr. Coeff. (AM fee)|-0.43|-0.09|-0.43|-0.25|-0.52|-0.59|0.15|-0.31|
|**Independent**|||||||||
|Average Rate of Return|16.9|17.4|6.1|7.5|8.3|4.5|16.9|11.1|
|Average Rate of Return (weighted)|17.2|18.0|4.8|7.0|7.4|4.7|16.4|10.8|
|Standard Deviation|1.9|1.0|4.2|1.0|1.7|0.5|0.8|1.6|
|Coefficient of Variation|0.11|0.06|0.86|0.15|0.23|0.10|0.05|0.22|
|Corr. Coeff. (Contribution fee)|-0.33|-0.39|-0.75|-0.71|-0.90|0.92|-0.97|-0.45|
|Corr. Coeff.(AM fee)<br>Source: HFSA raw data|0.77|0.64|0.74|-0.87|-0.72|1.00|-0.97|0.09|

34. The MPFs sponsored by financial institutions charge on average higher asset management
that limited use of instructional time is a contributor to poor student learning and high levels of student disruption can lead to teacher burnout and decreased effectiveness (Bruns and others 2015; Ptero-Lopez 2009). The majority of classroom observations have been conducted in regions outside MENA; however, a recent example from the West Bank and Gaza may illustrate the importance of teachers staying on task (box 1).

# Box 1: Classroom Observations in the West Bank and Gaza

The Stallings’ classroom observation tool established a good practice benchmark of 85 percent of total class time to be used for instruction. In schools across the West Bank and Gaza, territories where students undoubtedly are faced with personal, economic and political stresses, student performance is on average high compared to other MENA countries. The Stallings observations in classrooms find that teachers are spending 90 percent of their time on instruction, 9 percent on classroom-management and only 1 percent time-off-task (Yarrow and others 2014). While this is not indicative of what could be occurring in classrooms with high levels of disciplinary issues, it is suggestive that even in contexts of conflict and fragility, effective teaching practices can ensure high quality of learning. It is also indicative, that students with a higher ability for self-regulation are better able to learn.

**Table 4. Marginal Impact of School Safety on Mathematics Performance, TIMSS data 2015**

||MENA cou<br>|ntries<br>|Other cou<br>|ntries<br>|
|---|---|---|---|---|
||Estimated|sd error|Estimated|sd error|
|Socioeconomic background (individual)|13.1|0.3|21.8|0.4|
|Socioeconomic background (school)|56.0|2.7|55.7|2.1|
|School safety|6.5|0.8|8.1|1.0|
|N
# Appendix A4: Economic Reform News

To compile our database of economic reform articles, we use carefully selected keywords in the 28 languages available in the Dow Jones database. In particular, we search for all articles which contain the words ‘economy’ or ‘economic’ _and_ ‘reform’ or ‘reforms’ in the text of the article. Table A4.1 presents our translations of these terms in the 28 languages.

**Table A4.1 Keywords for economic reforms articles**

|Language|Economy|Economic|Reform|Reforms|
|---|---|---|---|---|
|Arabic|اقتصاد||الصلحات<br>||
||اقتصادیة||اصلحات<br>||
||اقتصادي||إصلحات<br>||
||||لصلحات<br>||
||||الصلح<br>||
||||إصلح<br>||
||||لصلح<br>||
||||اصلح||
|Malay|ekonomi|ekonomi|pembaharuan|pembaharuan|
|Indonesian|ekonomi|ekonomis|pembaruan|reformasi|
|Bulgarian|икономика|икономически|реформа|реформи|
|Catalan|economia|econòmic|reforma|reformes|
|Chinese - Simplified|经济|经济|改革|改革|
|Chinese - Traditional|經濟|經濟|改革|改革|
|Czech|hospodářství|hospodářský|reforma|reformy|
|Danish|økonomi|økonomisk|reform|reformer|
|Dutch|economie|economisch|hervorming|hervormingen|
|||economische|||
|English|economy|economic|reform|reforms|
|Finnish|talous|taloudellinen|uudistaa|uudistuksia|
|French|économie|économique|réforme|réformes|
|||économiques|||
|German|wirtschaft|wirtschaftlich|reform|reformen|
|||wirtschaftsreformen|reform|reformen|
|Hungarian|gazdaság|gazdasági|
prove to be of paramount importance in explaining Africa’s disadvantage relative to similar income countries. Party monopoly also plays an important role. The longer a single political party remains in power, the lower are firm productivity and sales growth rates. In contrast, many elements thought to be important for explaining African performance are found to matter less: geography, crime, domestic and international competition do not matter as much.

This paper also adds to the literature on the effects of the (broadly-defined) business environment. We obtain several novel findings. First, modern telecommunications development is of critical importance. Second, even relative to formal finance, trade credit plays an especially important role in developing countries. Third, political monopoly is negatively associated with productivity growth rates and firm expansion. Finally, different policies have distinct implications for structural change. Telecom development, for instance, is structure-neutral: it helps manufacturing and services about equally and facilitates both low- and high-tech manufacturing. In contrast, development of banking helps services more than manufacturing, and corruptions hurts services more than manufacturing. Some tariff protection helps high tech manufacturing but not low-tech manufacturing. Party monopoly hurts manufacturing much more than services.

In each of the survey countries of the World Bank Enterprise Survey (WBES) included in this paper, firms of all sizes and ownership are covered for both manufacturing and services. The survey questions are broad, including detailed quantitative measures which allow us to infer firm performance such as labor productivity levels and growth rates, TFP, sales growth, investment rates and export intensity. Moreover, the survey asks detailed questions, both subjective and objective, on the political, institutional, and business environment that a firm faces, such as infrastructure issues, regulatory burdens, corruption, crime, and access to finance.

4
Hoy, C., and F. Mager. 2021. “Why Are Relatively Poor People Not More Supportive of Redistribution? Evidence from a Randomized Survey Experiment across Ten Countries.” American Economic Journal: Economic Policy 13 (4): 299–328.

Inchauste, G., and D. Victor, eds. 2017. The Political Economy of Energy Subsidy Reform. Directions in Development. Washington, DC: World Bank.

IEA (International Energy Agency). 2023. “Fossil Fuel Subsidies Database.” https://www.iea.org/data-and-statistics/data-product/fossil-fuel-subsidies-database.

IMF (International Monetary Fund). 2021. “Global Debt Reaches a Record $226 Trillion.” https://www.imf.org/en/Blogs/Articles/2021/12/15/blog-global-debt-reaches-a-record226-trillion IMF (International Monetary Fund). 2023. “Fossil Fuel Subsidies Database.” https://www.imf.org/-/media/Files/Topics/energysubsidies/ EXTERNALfuelsubsidiestemplate2023new.ashx.

IPCC (Intergovernmental Panel on Climate Change). 2023. “AR6 Synthesis Report: Climate Change 2023.” https://www.ipcc.ch/report/ar6/syr/.

ITU (International Telecommunications Union). 2023. “Statistics.” https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx.

Karadja, M., J. Mollerstrom, and D. Seim. 2017. “Richer (and Holier) than Thou? The Effect of Relative Income Improvements on Demand for Redistribution.” Review of Economics and Statistics 99 (2): 201–12.

52
.<sup>15</sup> First, we calculate the percentage change in monthly NDVI. A month is labeled as a growing month if the percent increase is greater than 20 percent. Second, the start of the harvest season is defined as the first month in which the NDVI falls by at least 5 percent. Third, the lean season is defined as the three months preceding the start of the harvest season.

For a graphical illustration of the use of monthly NDVI averages to estimate harvest cycles, please refer to Figures A-1a to A-1e in the appendix. There are subtle differences in harvest cycle patterns even within a country. For example, in Mozambique (Figure A-1b), the South-Western province of Manica has a slightly earlier harvest cycle than the NorthEastern province of Nampula. In Manica, the average November NDVI is greater than

> 14These are districts in Malawi, regions in Tanzania, and provinces in Mozambique, Zambia and Zimbabwe.

> 15Our purpose is to standardize these definitions so that they can be used to build an automated (i.e. rulesbased) definition of a drought shock. This is a prototype; in practice we could use NDVI data at more disaggregate temporal frequencies, allow for differences in harvest cycles at more refined spatial scales, and incorporate other data (e.g. soil moisture) into this algorithm.

8
2002 to 2006 and household assets in 2004-2006. The main variables used in our estimation are age, schooling level, schooling level of the spouse, an indicator for the birth of a child in the current year, ages of all children, number of years the respondent worked in the formal sector, number of years the respondent worked in the informal sector, labor sector choice, labor sector choice of the spouse, annual earnings and private household wealth.<sup>36</sup> We merge the household survey data with the administrative data on pension savings accumulations.

To arrive at our estimation sample, we impose the following sample restrictions: (i) Our model incorporate the rules of the AFP pension system. We excluded from the estimation sample workers who reported only making contributions to a pension system other than AFP. We do, however, incorporate those workers who worked before 1980 and accumulated some pension rights under the previous pension system and then switched to the AFP system. In the model, the value of these rights is captured through the value of their Recognition Bond (“bono de reconocimiento”), which we add to the funds accumulated in the AFP account upon retirement.<sup>37</sup> (ii) Incorporating marriage decisions is not feasible given the model’s complexity. To limit the impact of this abstraction, we set the initial age in our model to 35, an age at which most people’s marital status has been determined. We use in estimation individuals who are 35 or older in 2004, the initial year of our sample. We excluded respondents who reported getting married after the age of 35.

(iii) We excluded households with missing information on key variables and with inconsistencies across survey rounds with respect to age, education and civil status (2,502 respondents).

The final sample contains 6109 households, some consisting of a single person and some of a couple, for a total of 5482 women and 4843 men.

Tables 1 and 2
data sets and indicators of trade facilitation is provided in the following sections.

## **2.1 General Economic Indicators 2.1.1 Investment Climate Indicators**

## _<u>World Bank’s Rapid Response Unit</u>_

Rapid Response is a knowledge service specializing in policy advice on business environment reform and privatization policy in developing countries. Areas of expertise include comprehensive assessments of the business environment in developing countries, through both country-specific reports as well as comparative data used for benchmarking purposes. Their products include:

## _<u>Doing Business database</u>_

The Doing Business database provides objective measures of business regulations and their enforcement. The Doing Business indicators are comparable across 155 economies. They indicate the regulatory costs of business and can be used to analyze specific regulations that enhance or constrain investment, productivity and growth.

The topics covered include: 5 ____________________________________________________________________________________________________________
**_<u>________________________________________________________</u>_**

people and country analysts, including surveys of residents, both local and expatriate. TI focuses on corruption in the public sector and defines corruption as the abuse of public office for private gain.

_<u>The EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS)</u>_ BEEPS was developed jointly by the World Bank and the European Bank for Reconstruction and Development, is a survey of managers and owners of more than 20,000 firms across the countries of Central and Eastern Europe, the former Soviet Union, and Turkey. The survey has been carried out in three rounds: <u>1999, 2002</u> and 2005.

The BEEPS is designed to examine the quality of the business environment as determined by a wide range of interactions between firms and the state, including in the following areas:

- Problems Doing Business

- Unofficial Payments and Corruption

- Crime

- Regulations and Red Tape

- Customs and Taxes

- Labor Issues

- Firm Financing

- Legal and Judicial Issues

- Infrastructure

<u>2005 Questionnaire</u>

# **2.2 Trade Indicators**

_<u>OECD-WTO: Doha Development Agenda Trade Capacity Building Database (TCBDB)</u>_ The TCBDB has been established by the WTO jointly with the OECD to provide information on trade-related technical assistance and capacity building projects. It covers national as well as regional projects. It is an on-going activity and the 2004 Joint WTO/OECD Report on TradeRelated Technical Assistance and Capacity Building was circulated in December. At present, the period of coverage is 2001, 2002, 2003, partial 2004 and subsequent years. Data is reported from bilateral donors and multilateral/regional Agencies.

A search of the database can be performed either by trade category or beneficiary country or donor country or agency. The statistical analysis option allows quantitative analysis of trade capacity building data by these categories.

# _<u>COMEXT</u>
**We find that a weighted predictive mean matching model is the best-performing model.** The weighted PMM approximates the underlying model well and allows us to weight regressions by survey weights. This result is in line with the previous literature, showing that PMM works well in the context of imputations (Kleinke, 2018). Given that we plan to use survey weights in future analyses performed on the generated dataset, we consider the weighted PMM most appropriate. We show that results across the multiple imputations are consistent. In addition, the distribution of the average imputed energy spending share closely resembles the observed distribution of energy spending shares when restricting the sample by households' gender composition and income categories. Results differ slightly by matching variables but are sufficiently similar. The same applies to results by non-matching variables. They also hold when using an alternative number of neighbors for the PMM.

**We use the generated dataset to demonstrate that the energy-poor and monetary-poor overlap.** Based on the synthetic EU-SILC, we show that all monetary poor, defined via the relative at-risk of poverty measure, are also energy poor. At the same time, not all the energy poor are at risk of poverty. Moreover, we show that energy expenditure shares are higher for lower-income quintiles. An important limitation of our analysis is that these insights highly depend on our chosen measure of energy poverty. Our main results rely on an income-based measure of energy poverty and a 10 percent threshold to identify the energy poor<sup>5</sup> . The overlap between energy and monetary poverty measures differs when using different thresholds or measuring energy poverty from a different angle.

**Our paper makes an important contribution to recent efforts exploring the potential of statistical matching techniques to improve the data environment in the EU.** Over the years, there has been an increasing effort in the EU to explore the potential of statistical matching to combine information from different datasets (Leulescu and Agafitei (2013); Serafino and Tonkin (2017); Moretti
th</sup> percentile in the world. Consider now rural Indonesia in the bottom of the figure. Here the span is from the 4<sup>th</sup> percentile in the world to the 56<sup>th</sup> percentile. The 29 This is an estimate based on the Theil decomposition between inequality due to the differences in incomes between six country groups, namely (Africa), (Asia), (Japan, Korea and Taiwan), (Latin America), (Eastern Europe), and (Western Europe and its offshoots) and inequality within country groups. Since there are no data on income distributions for most countries in the world prior to 1950, Bourguignon and Morrison use estimates for a few select countries to “impart” the same distributions to other countries in the group. Their between-component accounts for some 30 percent of global inequality. Obviously, if they had data on all countries’ distributions, the between component would have been larger. However it is unlikely to have exceeded one-half of global inequality.

30 Both calculated from Maddison (2004) data.

> 31 Note that in a world of large between-county income differences, and very small within-national inequalities, there would be no overlap at all, and 100% of global inequality would have been “caused” by between-national differences.

17
reform, (2/1)`|`Daily expenditure per`<br>`capita after reform and`<br>`proposed Government`<br>`scheme(3/1)`|
|---|---|---|---|
|poorest|`100%`|`93%`|`108%`|
|2|`100%`|`93%`|`104%`|
|3|`100%`|`93%`|`102%`|
|4|`100%`|`93%`|`101%`|
|5|`100%`|`93%`|`100%`|
|6|`100%`|`93%`|`99%`|
|7|`100%`|`93%`|`98%`|
|8|`100%`|`93%`|`96%`|
|9|`100%`|`94%`|`94%`|
|richest|`100%`|`95%`|`95%`|
|`Total`|`100%`|`94%`|`97%`|

Source: Forecasted numbers from HIES 2018/19 are used.

Table 11. The total (direct and indirect) impact on poverty and inequality after increase in gasoline prices after providing cash transfers under proposed Government scheme

|Pre-Ref|Post-Ref|Post-Ref after transfers,<br>Government scheme|
|---|---|---|
|Poverty (%)<br>11.2|14.2|9.7|
|Inequality (%)<br>39.7|40.3|38.1|
|Source: Authors’ calculation using forecasted HEIS 2018/2019.|||
|
for regulations, we obtain more objective indicators that are less influenced by stages of economic development or recent events.<sup>3</sup> These indicators, in turn, can then be used to analyze specific regulations that enhance or constrain investment, productivity and growth.

Out of seven indicators in the database, we have chosen those that are clearly applicable to test the linkages between the inflow of foreign investment and economic growth in the host economy. That leaves us with five sub-components, namely, starting a business, labor market regulations, contract regulations, creditor rights and insolvency regulations. The starting-abusiness indicator is the (arithmetic) mean of the average number of procedures to start a business, the number of days and the costs required to complete that process,<sup>4</sup> while the labor market regulation index is an average of three indexes: flexibility of hiring, conditions of employment and flexibility of firing. The contract regulation index is an average of three indexes covering the number of judicial procedures to enforce a contract, the duration and the cost, whereas the creditor rights index measures four powers of secured creditors in bankruptcy. Finally, for the insolvency index we use the goals of insolvency index from the Doing Business dataset that reflect the difficulties in closing down a business.

To get an overall index of regulations we compute a weighted average of the five individual components of regulations, taking factor loadings in principal components analysis as weights.<sup>5</sup> Higher values for all five sub-components (and, thus, for the aggregated regulation index) reflect more regulations, that is, regulations with a lower quality for business operations. While the Doing Business indicators are available for a total of 145 countries, we had to restrict our sample to 89 countries. Included in the following analysis are all countries for which data on regulations, the dependent and all independent variables are available.<sup>6</sup> Base year for the regulation index is January 2003.
income group**

<!-- Start of picture text --> Sector: Mobile telecom<br>L LM UM H<br>STRI 2008-11 STRI 2016<br>50<br>40<br>30<br>20<br>STRI 2008-11, 2016<br>10<br>0<br><!-- End of picture text -->

> 19 In Colombia the number of licenses was fixed in the 1993 bidding process. In 2016, the quantitative restrictions were removed. For India, the maximum number of firms or suppliers in the sector was four in 2008. This restriction has been lifted and no quantitative limit is applied anymore.

23
to the local communities (services, RD contracts, etc.)

- extension services operating in agriculture or manufacturing

- techno-parks which have to be evaluated from the viewpoint of enterprise numbers, employment, growth rate, etc.

For all the above, what matters is to evaluate against what is realistic to expect in function of the level of development. This is why it is important to constitute sets of data which allow relevant benchmarking and comparisons.

It is also important to bear in mind that for innovation programs, tangible results in terms of turnover, export, or jobs created do not appear overnight. Generally five to ten years are necessary for tangible economic results. Innovation programs have therefore to be assessed through intermediary variables such as increase in research and innovation expenses, intensification of linkages between university and industry in various forms, and other variables attesting of a change of behavior favorable to innovation.

> 31 As designed by the Euro pean Union (Innovation trend charts) and the OECD.

> 32 Most of this expertise lies in international consulting firms and remains in the form of ”proprietary technology”.

32
29 and under, where women are more affected, and youngest and oldest employees experience higher wage increases and job losses. The minimum wage increase significantly impacts the accommodation and food services sector, affecting almost half of employees. In contrast, fewer than 1% of employees in the electricity, gas, steam, and air conditioning supply sectors are affected. Interestingly, sectors with moderate employment impact tend to experience the most substantial wage hikes. Across all scenarios, wage growth remains close to zero.

**Similarly, increasing the statutory minimum wage to a living wage estimate is expected to result in a slight short-term increase in average wages. Still, it could lead to significant job losses, particularly among the youngest workers.** The impact of the minimum wage increase on income inequality in this scenario depends on the approach chosen, with a more substantial impact than in Scenario 1, resulting in a reduction in the Gini index and an increase in the bottom 50% income share. However, the policy simulations under both scenarios show that the minimum wage increase does not result in significant wage hikes among the formal employee population, with average wage increases of 0.40% in the short term and 0.04% in the long run.

# **Several policy recommendations arise from this analysis:**

**First, the legal minimum wage should be set based on an assessment of its adequacy, among many other factors. It is also critical to analyze the dynamics of the leading macroeconomic indicators to mitigate the potential negative impact on employment.** A living wage assessment is a key tool to determine how the existing minimum wage covers the basic consumption basket, encompassing food and non-food essentials **.** Moreover, evolving, these adjustments should at least be anchored to inflation rates to uphold the purchasing power of individuals. Our results show that increasing the minimum wage to the level of the consumption basket can generate significant income gains for employees who keep their jobs. However, a significant formal employment