rafmacalaba/datause-encoder-data huggingface dataset:
sectors, SMEs play a significant role in tenns of number of firms, employment, manufacturing value-added, and exports.

Researchers obtained lists of SMEs from each sector's respective trade association. For the factory automation sector, which lacks its own trade association, researchers compiled a list of SMNEs from the Directory of Automation Equipment Manufacturers (1992). From these lists, firms were selected on the basis of a multistage stratified sampling technique in which the finn population was sampled in each of the following size groups (measured by number of workers): less than 50, 50-99, 100-199 and 200-plus. Our study samxled 122 firms in the four chosen sectors: 42 producing woven textiles, 20 producing auto part.. 20 producing electronic parts, and 40 involved in the production of factory hatomatiorn products. Table 1.3 shows the distribution of sampled SMEs. Data was collected from the chosen SMEs through interviews. To develop the inLerview protocol, the researchers visited the key organizations managing technical and marketing support systems in Korea, and interviewed knowledgeable individuals there.

11.2 **Summary** of **the** SME Sample The SMEs in our sample differ significantly across sectors in terms of ownership, manpower, technological level, degree of subcontracting, and export orientation.

Ownership. Most of the SMEs (68 percent overall) are owned by individuia entrepreneurs. About a quarter of the sample SMEs are collectively-owned (corporations and partnerships). Two firms in each sector (except for woven textiJes) are joint venures with foreign firms, while two of the SMEs in the factory automation sector are subsidiaries of large conglomerates.

The overall level of educational achievement among Korean manager/entrepreneurs is very high. Almost 75 percent of entrepreneurs in our sample have a college education and more than 10 percent have graduate education (see Table 11.4). About half of them have earned a science or engineering degree, often direcdy related to their line of business. Beyond this, however, the age and educational background of entrepreneurs at the sample SMEs reflect the charactenstics of their sectors, with the youngest and best-educated found in the technologically dynamic factory automation sector.

3
Table 4: Total Public Spending on Energy Subsidies (selected energy sources, latest available year, 2013)

|**_Indicator_**|LPG|Gasoline|Diesel<br>(50<br>ppm)|Diesel<br>0.2%|Electricity|
|---|---|---|---|---|---|
|Subsidy rate (%)|68|15|16|62|27/50|
|Total consumption at sale price (MD)|225|884|230|103|2169|
|Price increase estimated from elimination of<br>subsidies (%)|214|23|22|165|30|
|**Expenditures**||||||
|Amount (MD)|483|199|50|170|1671|
|As a % of GDP|0.7|0.3|0.07|0.2|2.4|
|Amount estimated end-2013 (MD)|749|321|75|214|2569|
|As a % ofGDP|1|0.4|0.1|0.3|3.4|

_Source:_ WB staff calculations using data from Ministère des Finances and Ministère de l’Industrie. _Notes:_ All numbers from April 2013 unless otherwise noted. April 2013 GDP in 2012 prices estimated at 70,400 million TND (MD).

# **4. The Socioeconomic Profile of Energy Subsidies**

# **4.1. Residential Consumption and Expenditures on Energy**

Previous sections have discussed the complex structure of energy prices (in terms of progressivity) and the estimated subsidy rates underlying current price structures. To determine the extent to which such price and subsidy structures lead to pro-poor welfare outcomes, the consumption of different socioeconomic groups, their expenditures, and their benefits from subsidies all need to be factored in. First, consumption and spending on energy will be disaggregated by socioeconomic group. This socioeconomic analysis covers both Tunisian individuals and households grouped by their consumption levels in quintiles. Quintile 1 refers to the poorest, while quintile 5 refers to the richest individuals and households.

Panels A and B in Table 5 show that _total_ consumption of energy across quintiles varies by energy sources. Richer quintiles consume more energy, with significantly large differences for gasoline and diesel among these quintiles and the rest. Consumption of the top two quintiles represents 80% and 90% of the consumption of diesel and gasoline, respectively. The bottom 40% consumes 2% and 8% of the total consumption of these two sources, respectively. For the other energy sources, the distribution of consumption across quintiles is not so skewed: the share of the top two quintiles (bottom 40%) consumption of LPG and electricity represents 45%–52% (28%–34%).

12
Over 2001-17, Morocco had one of the highest investment rates globally, varying between 25 and 38 percent of GDP. Most of this represented public sector investment in infrastructure. In the latest (2017) survey, the country ranked 42nd in quality of infrastructure, having risen more than 20 spots in a decade. Despite this achievement, infrastructure investment needs remain large owing to growth in demand for infrastructure services arising from population growth and urbanization (World Bank 2020d). Over the next decade the country will need average infrastructure investment of 6.2 percent of GDP annually, mainly in the energy and transport sector (Oxford Economics and Global Infrastructure Hub 2017).

Lebanon faces signi fi cant infrastructure de fi ciencies, including a dysfunctional electricity sector, water shortages, and inadequate waste and wastewater management (Harake and Kostopoulos 2018; Le Borgne and Jacobs 2016). The port explosion in Beirut in 2020 and the country’s ongoing economic crisis have highlighted the need for infrastructure investment. The explosion is estimated to have caused damage equivalent to 15-19 percent of 2020 GDP (World Bank, European Union, and United Nations 2020). Large numbers of Syrian refugees in Lebanon (and Jordan) have added to strains on the provision of public goods.

Countries involved in armed con fl ict risk the large-scale destruction of physical capital. In Syria, the war that began in 2011 has devastated the economy: in 2019, income per head was no higher than in the early 1990s (World Bank 2022j). The cost of rebuilding infrastructure damaged or destroyed by the con fl ict has been estimated to be in the range of $100-200 billion in 2015 prices, the lower bound being about ten times the country’s 2015 GDP (Gobat and Kostial 2016). Iraq also faces large infrastructure investment needs, increased by con fl ict. It has been estimated that some $200 billion in 2018 prices would be needed to restore “hard” infrastructure to pre-ISIS levels, almost equal to Iraq’s 2018 GDP (Gunter 2018). In Yemen, recovery and reconstruction costs are estimated at $2025 billion, equivalent to 1.1-1.3 times its 2020 GDP (World Bank 2020a).

GCC countries also have infrastructure needs, predominantly in electricity generation, although the pandemic has highlighted the need to invest also in digital infrastructure. Saudi Arabia’s infrastructure investment needs over the next decade are estimated at 2.8 percent of GDP, mainly in energy and road transport. With higher income levels, these countries’ plans for public spending on infrastructure in the medium term generally track their needs.

# 8.4 Regional policy priorities

Policy priorities di ff er across the region. In most of MNA, policy priorities include addressing low-quality education, reducing youth unemployment, improving governance, and decreasing the state’s economic footprint. In agriculture-dependent economies and those with large populations along the coast, adaptation to climate change is a priority. In economies that have faced con fl ict, a priority is to restore essential services and
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aid nor the initial level of indebtedness of the country significantly affects the growth impact of natural disasters. On the other hand, he finds that the level of economic development does influence the impact of natural disasters. It is this dimension of the heterogeneity across countries that we emphasize in this paper.

Before proceeding with the literature review, we now provide the outline of the paper. Section II presents the description of the data, including details on the sample regarding countries, periods, and frequency of observations; and on the variables used in the analysis concerning definitions, sources, and summary statistics, with special attention to the measures of moderate and severe natural disasters. Section III introduces the econometric methodology, including an exposition of the VARX method, and two important specification tests dealing with exogeneity assumptions and lag structures. Section IV presents the basic results, discussing and contrasting the effects of droughts, floods, earthquakes, and storms, focusing mostly on the sample of developing countries. Section V offers some concluding remarks.

# **Literature review**

Economic research in this field is still in an early phase of development. In general, the results on the macroeconomic impact of natural disasters seem to be ambiguous. A close examination in recent studies further demonstrates that these effects may depend on economic, social, and institutional conditions, as well as on the type of natural disaster and sector of the economy.

Rasmussen (2004) assessed the impacts of natural disaster incidences using a cross-country sample for the period 1970 through 2002. The data were obtained from the EM-DAT database of the Centre for Research on the Epidemiology of Disasters (CRED), which is the major source of data on natural disasters used in most studies. According to CRED, a natural disaster is defined as a situation or event which overwhelms local capacity, necessitating a request for external assistance. The database consists of disaster events which fulfill at least one of the following criteria: ten or more people reported 5
Contributions to pension funds and premiums for direct insurance enjoy limited tax advantages through the application of a flat 20 percent tax instead of the higher corporate tax rate. The preferential tax, however, will only be applied up to a limit. Employers' contributions represent taxable income for the employee. Pension benefits of pension funds and direct insurance are taxed only on the portion that represents interest earned; lump-sum distributions from direct insurance are tax-free. Benefits from book reserve schemes and support funds, however, represent fully taxable income for the beneficiary.

Unfortunately, there is no reliable comprehensive information on the benefits provided by the different occupational pension plans. In a survey for the state of Bavaria, almost all companies with pension schemes offered defined benefit plans, some with fixed benefits and some earnings-related, while defined contribution plans were offered by only 2 percent of the companies. Among the companies with more than 1,000 employees about 12 percent had defined contribution plans.

German companies continue to prefer book reserves due to the tax advantages of this option and, more generally, the easy access to financing without having to provide collateral and savings in transaction costs compared to external financing. A further reason why book reserves are popular is the fact that there are no limits imposed on the level of benefit promises and the formation of corresponding book reserves. The combination of internal financing and pension commitments is seen as the major reason why supplementary pension provision has expanded in Germany despite the voluntary nature of second pillar.The experience with the book reserve schemes has so far been favorable and the PSV has proven to be an effective mechanism of protecting members' interests. For employers using book reserves, however, the risk of being able to finance the pension promises remains if reserve planning and investment is not conducted adequately .

# Public supplementary pensions

All public sector employees are insured in the supplementary pension institution of the federal and the state governments; in addition there are 22 municipal and 5 church institutions providing supplementary pensions. Participation is quasi-mandatory as it is based on employment negotiations which have been declared valid for all public sector employees; civil servants, however, are excluded. At the end of 1991, approximately 4 million employees were covered.

The public supplementary pension institutions are partially funded and are supervised by the Ministry of Finance. Employees switching from the public to the private sector are eligible for the accrued benefits upon retirement but benefits are not adjusted for inflation during the waiting period.

15
et al. (2022) show that, in similar analyses, using the obfuscated coordinates has no meaningful impact on results.

In the remainder of this section, we describe the sources of weather data and household data. We then discuss descriptive statistics on the combined weather-household datasets.

# **3.1 Earth Observation Weather Data**

In this study, the most important data are our EO weather data. To align with the use of these data by economists, while also representing a wide range of the types of available data in this domain, we use several public weather data sources that represent different modeling types, input sources, and spatial resolutions. To ensure consistency and to ensure the creation of common metrics across the analysis, we imposed two inclusion criteria. The source had to have (1) high temporal resolution (daily), and (2) a minimum 30-year length of record at least up to 2021.<sup>2</sup> Table 1 describes each data source, including the length of record, spatial and temporal resolution, and the type of data recorded.

We classify EO weather data products by the method through which they generate precipitation and temperature estimates. The first type of product we use merges gauge data with data from meteorological satellites, which provide indirect information at full coverage. EO products of this type include the African Rainfall Climatology version 2 (ARC2), the Climate Hazards group InfraRed Precipitation with Station Data (CHIRPS), and Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT) (Novella and Thiaw, 2013; Funk et al., 2015; Tarnavsky et al., 2014).

ARC2 is a merged gauge data and EO product that provides daily rainfall outputs for the African continent. The dataset, produced by the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) provides improvements over ARC1 and a longer length of record compared to the rainfall estimate (RFE), the operational dataset of USAID’s Famine Early Warning Systems Network (FEWSNET) program. Inputs are Global Telecommunications System (GTS) rain gauge data over Africa, geostationary Meteosat infrared (IR) imagery, and polarorbiting microwave Special Sensor Microwave Image (SSM/I) and Advanced Microwave Sounding Unit (AMSU-B).

Validation efforts by Novella and Thiaw (2013) found that low reporting rates for some GTS stations degrades model performance in those regions. Other findings are a general tendency to underestimate rainfall, which is enhanced in areas of high relief or complex topography.<sup>3</sup> Like ARC2, the CHIRPS rainfall dataset builds on established techniques for merging gauge and

> 2These criteria resulted in the exclusion of some EO products that have been frequently used by economists, including the monthly _Terrestrial Air Temperature and Precipitation_ from the Center for Climatic Research at the University of Delaware.

> 3Data and technical documentation are available for download from `https://www.cpc.ncep.noaa.gov/products/ international/data.shtml` .

7
iv

|Table 19. Self-Employed – Monthly Earnings by Gender and Education (in thousands of MRO)<br>Table 20. Wage Earners – Monthly Earnings, by Living Area, Gender, and Type of Employer|30<br>(in|
|---|---|
|thousands of MRO)|31|
|Table 21. Wage Earnings – by Gender and Education (in thousands of MRO)|31|
|Table 22. Wage Earners - Econometric Estimates of Wage Determinants<br>-- 0 --|34|
|Annex Table A Education Level by Gender and Age Group (in percentages)<br>....................................|. 38|
|Annex Table B. Types of Employment and Employer by Poverty Status (in percentages)<br>................|. 38|
|Annex Table C. Sector of Employment – Males in Rural Areas (in percentages)<br>..............................|. 38|
|Annex Table D. Sector of Employment – Females in Rural Areas (in percentages)<br>...........................|. 39|
|Annex Table E. Sector of Employment – Males in Urban Areas (in percentages)<br>..............................|. 39|
|Annex Table F. Sector of Employment – Females in Urban Areas (in percentages)<br>..........................|. 39|
|Annex Table G. Main Labor Market Indicators – Youth and Poverty (in percentages)<br>......................|. 40|
|Annex Table H. Self-Employed – Monthly Earnings and Poverty (in thousands of MRO)<br>................|. 40|
|Annex Table I. Incidence of Poverty by Region and Relative Contributions (2000-2004)<br>.................|. 40|
# **I. Introduction**

An extensive literature consistently finds that conditional cash transfer (CCT) programs increase children’s school participation (Baird et al., 2014; Fiszbein and Schady, 2009; Saavedra and Garcia, 2012) usually while decreasing their participation in work (de Hoop and Rosati, 2014; Edmonds 2008; Edmonds and Schady, 2014; Fiszbein and Schady, 2009). In this paper, we present a counterexample from the experimental pilot of a conditional cash transfer program in the Philippines that shows that cash transfers can, under certain conditions, increase both school enrollment _and_ participation in paid work.

The program, _Pantawid Pamilya Pilipino Program_ or simply _Pantawid_ , supports poor households (those with income less than US $2.15 per capita per day) by providing two transfers, one conditioned on child health-related behavior and the other on schooling.<sup>1</sup> The randomized evaluation (World Bank, 2013) of _Pantawid_ demonstrated that the program broadly achieved its primary objectives by increasing school participation of eligible children (those aged 6 to 14 from preidentified poor households) by almost 5 percentage points and improving the health and nutrition of eligible 0-14 year olds (World Bank, 2013; Kandpal et al., 2016).<sup>2</sup> Using data from the _Pantawid_ impact evaluation, we find that the increase in school participation was accompanied by a concomitant increase in children’s participation in paid work outside the home. This increase is on the order of 5 percentage points, as compared to a control mean of 12 percentage points in the rate of child work-for-pay. In particular, the program appears to have encouraged children who would otherwise be neither in school nor in work to attend school _and_ to start working.

1 The program has been in place since 2008 and now covers over 4.5 million poor households. 2 Children younger than age 5 in treated areas had higher height-for-age z scores, were less likely to be severely stunted, and more likely to eat protein-rich foods and use health services. Older children (aged 6 to 14) were more likely to be offered de-worming medication.

2
30

**Table 19. Self-Employed – Monthly Earnings by Gender and Education (in thousands of MRO)**

||_All_<br> <br>|_Male_<br> <br>|_Female_<br> <br>|
|---|---|---|---|
||_Mean_<br>_Median_|_Mean_<br>_Median_|_Mean_<br>_Median_|
|_All_|29.2<br>20<br>_(4,403)_<sup>*</sup>|35.1<br>21<br>_(3,141)_<sup>*</sup>|14.6<br>10<br>_(1,262)_<sup>*</sup>|
|_None_|22.3<br>17<br>_(2,330)_|27.5<br>20<br>_(1,474)_|13.3<br>10<br>_(856)_|
|_Primary_|31.1<br>20<br>_(526)_|36.4<br>20<br>_(391)_|14.6<br>10<br>_(135)_|
|_Secondary_|53.2<br>30<br>_(314)_|56.3<br>35<br>_(263)_|35.1<br>20<br>_(51)_|
|_Higher_|114.1<br>50<br>_(45)_|116.4<br>50<br>_(43)_|4.9<br>7<br>_(2)_|

Source: EPCV 2004. Numbers in parenthesis correspond to unweighed observations.* The number of observations is greater than the sum of observations over all education levels, due to missing data on education.

_The earnings of the self-employed feature substantial gender gaps._ Indeed, as evidenced by Table 19, male earnings are approximately 2.5 times higher than females’. Most self-employed women are engaged in agriculture and trade. Their earnings in the former sector are nearly half what they are in the latter: 11,300 MRO on average for agriculture compared to 19,600 MRO for trade (medians are respectively 8,000 MRO and 15,000 MRO). These figures stand at 22,600 MRO and 44,000 MRO for males, i.e. double that of females (medians at 20,000 MRO 30,000 MRO).<sup>17</sup> The gender gap is also highest among the non poor: non poor men earn approximately 60 percent more than non poor women whereas for the poor the differential comes to 50 percent (Table H) _Besides, at the same level of education, women’s earnings in self-employment are still lower than men’s (Table 21)._ Females with no education earn approximately half of what males with no education earn. Women with primary schooling fare even worse when compared to men, as their average earnings only amount to 40 percent of men’s with primary education.

# <u>3.4.2 Wage earners</u>

_Wage earning is the second form of employment in Mauritania_ (Section 2.3) _. Mean wages in the public sector are lower than those observed in the private sector_ (Table 20). Average wages amount to 34,400 MRO in the public sector compared to 39,200 MRO in the private sector (with medians respectively at 28,000 MRO and 30,000 MRO). On average men earn more in the private than in the public sector, but in both sectors, median wages stand at 30,000 MRO. This is confirmed by the results obtained in the wage regression (see Table 22), as the coefficient for the private sector variable is significant and positive for males. Female wage workers are predominantly employed in the public sector and by individual households and are almost absent from the private sector despite the

> 17 Results not shown here. Comparisons between earnings by gender for other sectors are rendered impossible due to a too small number of observations.
**Figure 14: Output growth volatility and volatility of inflation in non-OECD East Asia**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Volatility of Inflation Output Volatility<br>197019721974197619781980198219841986198819901992199419961998200020022004<br>Volatility<br><!-- End of picture text --> Sources: author’s own calculations using WDI data. Volatility of inflation is measured as weighted average of the standard deviation of CPI growth using a five year window.

**Figure 15: Volatility of inflation in East Asia (by income level)**

<!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>OECD High Income Middle Income Low Income<br>No-OECD<br>1970-80 1980-90 1990-2000 2000-06<br><!-- End of picture text --> OECD: Korea, Rep., New Zealand, Australia, Japan. High Income non-OECD: French Polynesia, New Caledonia, Singapore, Hong Kong. Middle Income: Philippines, China, Thailand, Malaysia, Fiji, Indonesia, Kiribati.

# **Lower Frequency of External and Exogenous Shocks**

While countries have some leverage in reducing the frequency and magnitude of internal shocks, they are also subject to external shocks upon which they have little control – whose frequency nonetheless reduced in the last decade. These external shocks, such as terms of trade shocks, fluctuations in international interest rates, and aid volatility, also contribute considerably to the volatility of output growth, particularly in developing countries (see, for 18