rafmacalaba/datause-encoder-data huggingface dataset:
4

# **2. Mauritania’s Main Labor Market Indicators**

_The Mauritanian labor market appears to be less ‘dynamic’ than that of its neighbors._ As shown in Table 1, 49.3 percent of the Mauritanian working-age population participates in the labor market, a lower figure than in nearby countries. The employment-to-population ratio is 42.1 percent, lower than in Senegal (62 percent), Burkina Faso (81.5 percent) and Mali (70.2 percent). Besides, 14.7 percent of the labor force is unemployed in Mauritania, which is above unemployment rates displayed by neighboring countries (5.8 percent for Senegal, 8.8 percent for Mali, 11.2 percent for Morocco). Female participation, which stands at 28 percent, is also well below that displayed by neighboring countries.

**Table 1. Main Labor Market Indicators – Comparison with Neighboring Countries (in %)**

||_Mauritania_<sup>b</sup>|_Mali_<sup>c</sup>|_Morocco_<sup>c</sup>|_Burkina Faso_<sup>c</sup>|_Senegal_<sup>d</sup>|
|---|---|---|---|---|---|
|Labor force|493|||||
|participation<sup>a</sup>|.|79.3|55.4|85.0|70.7|
|Employment-to-<br>population ratio|42.1|70.2|46.9|81.5|62|
|Unemployment rate a|14.7|8.8|11.2|...|5.8|
|Female participation in<br>the labor force|28.0|47.5|28.7|46.5|42.5|

Figures are displayed in percentage; a Standard ILO definition. Sources: b: EPCV 2004 ; c: World Banks 2005, 2006 ; d: “Analyse des Conditions du Marché du Travail dans les Pays Sous-Développés – Cas du Sénégal”, World Bank 2007.

## **_2.1 Non-Participation_**<sup>_7_</sup>

_The overall non-participation rate for Mauritania stands at 50.7 percent and is similar for the poor and the non poor_ (Table 2 and Table 3). Female non-participation drives this statistic, as 72 percent of working-age women do not participate in the labor market. Striking discrepancies between female and male non-participation arise within the prime-age group (25-54). This same group reports family or household obligations, which fall upon women, as the chief reason for being out of the labor force (Table 4). This finding is dramatically at odds with the corresponding averages found in Sub-Saharan Africa (SSA), where around 70 percent of women are considered active participants in the labor market; but it is comparable with those found in the Middle-East and North Africa region (MENA), where around only 40 percent of women participate actively in the labor market (Kapsos, 2007).

7 The definitions of the labor market indicators used in this paper are provided in Annex 1.
capacities. In our sample, 67 percent of plants have a capacity lower than 30 MW.<sup>2</sup> Next, we augment this dataset with market-level measures of the degree of hydrological drought faced by hydropower plants at monthly frequency during the 2000 to 2020 period. The process of computing these measures of drought involves several steps. In the first step, we follow existing literature and use runoff anomalies to measure hydrological drought (Herrera-Estrada et al., 2018; IEA, 2021; Qiu et al., 2023). Runoff is the depth of water accumulated over time in the soil and is a valuable indicator of drought or flood conditions. Runoff anomalies indicate a period where water availability is above or below normal. Specifically, we define runoff anomalies as the difference between the monthly runoff and its corresponding average value over the reference period (2000 to 2019). To measure runoff anomalies, we use data from the International Energy Agency Weather for Energy Tracker database<sup>3</sup> (IEA and CMCC, 2022). This database provides information derived from the ERA 5 reanalysis on monthly total runoff anomalies (surface and subsurface) measured in millimeters per hour (mm/h) at a spatial resolution of 0.25<sup>_◦_</sup> x0.25<sup>_◦_</sup> .

In the second step, we determine the watershed of each hydropower plant in the sample. The watershed is the area over which water would accumulate for use by the hydropower plant. To compute these areas, we use the hydro basin polygons (areas where water collects and may flow) from the HydroSHEDS database<sup>4</sup> (Lehner and Grill, 2013). This dataset is produced from digital elevation maps and hydrological models. Next, following standard engineering practices for each hydropower plant, we delineate the watershed by tracing all of the upstream basins (Pfastetter level 12) that flow in the direction of the power plant. The resulting dataset provides a watershed for each hydropower plant.

In the third step, we overlay the information on runoff anomalies with the watershed delineations and compute the average monthly runoff anomalies for each watershed. Figure A1 in the appendix plots the evolution of runoff anomalies for the region in panel A and for sub-regions defined by the IEA in panel B. The figures reveal considerable sub-regional heterogeneity even during periods of significant overall drought, such as 2015 to 2020.

> 2Power plants with a capacity exceeding 30 MW are usually categorized as large plants by the US Department of Energy.

> 3https://www.iea.org/data-and-statistics/data-tools/weather-for-energy-tracker

> 4https://www.hydrosheds.org/products/hydrobasins

6
# **Figure 3. Tobacco Consumption, by Per Capita Consumption Decile, Bangladesh, India, and West Bengal**

<!-- Start of picture text --> a. Share of households consuming b. Share of tobacco consumption in total<br>tobacco household consumption<br>80.0% 4.0%<br>70.0%<br>3.0%<br>60.0%<br>2.0%<br>50.0%<br>40.0% 1.0%<br>30.0% 0.0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>India West Bengal Bangladesh India West Bengal Bangladesh<br><!-- End of picture text --> _Source:_ Calculations based on data of the Bangladesh HIES 2016/17 and the India National Sample Survey and Consumption Expenditure Survey, 2011–12.

_Note:_ Tobacco includes cigarettes, bidis, tobacco leaf, gul, betel leaf, betel nut, zorda, lime, khoer, rolled betel leaf, and other smoked or smokeless products. Consumption deciles are defined based on household consumption per capita. The consumption aggregate includes similar broad food and nonfood components; however, they may not be fully comparable due to the differences in survey design, data collection, and methodologies in computing the aggregate.

By type of product, one finds additional differences in consumption patterns across deciles (figure 4). The share of households consuming cigarettes is at 26–30 percent for almost the entire distribution, except the poorest decile, among which the share of households consuming cigarettes is significantly lower (19 percent). Meanwhile, bidi consumption clearly declines as household resources increase. While a quarter of the poorest households consume bidis, this is only true of 6 percent of the richest households. For smokeless products, consumption is similar, except among the top deciles. This is consistent with other studies describing the socioeconomic gradient in tobacco use; the gradient is not seen in cigarette consumption, but is noticeable in the consumption of bidis and smokeless products (Barkat et al. 2012). On average, the consumption of smokeless products accounts for the largest share, 1.4 percent of the total household budget. Cigarettes follow at an average expenditure share of 1.3 percent. The cigarette budget share is even across the distribution.

The median quantity of cigarettes consumed was 152 a month per adult man age 15 years or older; the corresponding number of bidis was 348 per month per adult man (figure 5).<sup>9</sup> Assuming daily smoking, this is equivalent to an average of 5 cigarettes and 12 bidis per adult man per day. These household figures are consistent with data on individuals from the Global Adult Tobacco Survey among adult men (Barkat et al. 2012). The poorest decile consumed about 20 fewer cigarettes per adult man relative to the richest decile (132 compared with 152 cigarettes per adult man per month). However, among deciles 4–10, the median quantities were similar. One may see the opposite pattern in the case of bidis, that is, the richest consume slightly fewer bidis.

> 9 The data do not allow the identification of the household members who are smoking; so, these results should be interpreted with care.

7
# **IV. Misinformation**<sup>24</sup>

1. What percent of the Colombian population do Venezuelans represent today? (Please choose a number between 0% and 100%)

2. In Colombia, an average citizen has around 6 years of education (which is equivalent to having finished primary school). How many years of average education do you think Venezuelan migrants in Colombia have?

3. Please choose the statements that best describe your opinions (you may choose more than one option):

- a. Venezuelan immigrants have access to the Colombian’s subsidized health system.

- b. Venezuelan children can attend public schools in Colombia.

- c. The children of Venezuelans who are born in Colombia, are Colombians (they have the right to get the Colombian nationality).

4. Imagine two people, Carlos and Diego, currently living in Colombia with their families. Carlos is born in Colombia, while Diego legally moved to Colombia five years ago from Venezuela. They are both 35, have three children, and earn the same low income from their jobs. In your opinion, does Diego pay more, the same, or less in income taxes than Carlos?

- a. A lot more

- b. More

- c. Same

- d. Less

- e. A lot less

5. In your opinion does Diego, who is a Venezuelan immigrant, receive more, the same, or less government transfers (such as e.g., public assistance, Familias en Acci ́on, formation programs like SENA, unemployment benefits during unemployment spells, educational scholarships or housing subsidies) than Carlos?

- a. A lot more

- b. More

- c. Same

- d. Less

- e. A lot less

# **V. Attitudes Towards Venezuelan Migrants**<sup>25</sup>

1. List experiment: How many of the following issues annoy you? [Please tell us how many not which of them]

- a. Homeless people sleeping on the street

- b. People talking loud close to me

- c. People who cut in line

- d. People from other countries coming to live in my country (randomly assigned to all participants)

2. Which relates the most with a Venezuelan immigrant living in Colombia that is poor?

- a. Lack of effort on his or her own part

- b. Circumstances beyond his or her control

> 24Questions 3 and 4 are adpated from Alesina, Miano and Stantcheva (2022)

25Question 2 is adapted from (Alesina, Miano and Stantcheva, 2022) 66
in Mongolia. The MVTP follows key aspects of the so-called demand-driven training approach which is designed to align prospective employees’ skills with the real needs of local employers (Betcherman et al., 2007). The rationale for demand-driven training programs is twofold. First, private institutions can offer relevant, up-to-date training that decentralizes the traditional supply of vocational training by public institutions. Established training providers therefore usually bid to offer training in trades with high labor demand. Second, by combining traditional classroom education with on-the-job training in the form of internships, MVTP can provide training in trades that the market demands.<sup>4</sup> The Employment Promotion Service Center (hereafter, EPSC) and the Metropolitan Employment Department (hereafter, MED), both within the Mongolian Ministry of Labor, oversaw the implementation of this training program. The MVTP is financed by the State Employment Promotion Fund and, according to administrative data from the Ministry of Labor, the total number of participants was 8,000 by 2011 and total program expenditure was approximately 3.5 billion MNT (USD $2.1 million). The EPSC selects training institutions through a competitive bidding process. The selection criteria for training institutions include evidence of ability to provide adequate training, legal registration, curriculum quality, teaching quality, adequacy of training sites, and, importantly, the ability to place trainees in internship positions with registered private employers.

Although the MVTP began in 2003, its effectiveness has never been assessed. This paper focuses on the 2013 call that purposely used an RCT to identify and measure MVTP impacts. In that year, the EPSC selected forty-seven training institutions in Ulaanbaatar that offered 141 courses in six family vocational skills, including construction, hairdressing, cooking, and heavy-machinery operation. The length of training varied from twenty to forty-

> 4 The MVTP is different from “technical vocational training education,” which is considered a form of formal secondary education and last for years. This type of technical education has led to gains in employment and wages (Field et al, 2019).

9
Our effect is roughly the same size as that found in Dechezleprˆetre et al. (2019). This is somewhat surprising given the dramatically higher levels of ambient pollution in India when compared to Europe. On average, in their sample, the population faces annual average exposure of 10 _μg/m_<sup>3</sup> compared to average exposures of 50 _μg/m_<sup>3</sup> in our sample. But our effects are roughly double those found in Avila Uribe (2023), more consistent with the hypothesis that higher levels of exposure may lead to more severe impacts.

The effects of these differences in ambient concentrations are likely exacerbated by differences in the sources of economic activity between India and Europe and the United States, and the implied differences in vulnerability to air pollution. Data from the Reserve Bank of India suggests that as much of 50 percent of India’s GDP comes from sectors that are exposed to heat - a rough approximation for the share of GDP generated by outdoor work. This is compared to less than 25 percent of European GDP generated by the same sectors based on European Central Bank figures.

Indian workers work in an environment with substantially higher levels of ambient pollution than European workers and a substantially larger share of Indian GDP is generated by – – workers who are likely to be frequently working in conditions outside that expose them to ambient pollution. Both of these facts suggest that the impact of increases in pollution on GDP should be larger in India than Europe.

On the other hand, recent work examining the mortality impacts of very high levels of air pollution using exposure to wildfire smoke in the United States suggests a strongly concave dose-response function for air pollution exposure (Miller et al., 2017). While small changes in pollution have large mortality effects at low levels of pollution, similarly small changes at higher levels of pollution have much smaller effects. This is supported by work examining the consequences of exposure to pollution on outdoor workers around the world that finds a similarly concave dose-response function (Burnett et al., 2018). It remains an open question how this dose response function for mortality translates for other outcomes but our results are consistent with the pattern being similar for labor productivity effects, for example.

Our point estimates are small relative to the overall variation in economic growth from year-to-year and across districts in our sample. A 1 _μg/m_<sup>3</sup> year-to-year increase in PM2 _._ 5 pollution, roughly 25 percent larger than the median change in our sample, reduces year-toyear GDP growth by 0.16 of a standard deviation. This effect is economically meaningful but small relative to secular changes in GDP growth.

We can further benchmark our results against estimates of the direct impacts of air pollution in India on the mortality and morbidity of Indian citizens. Pandey et al. (2021) employ the methodology from the Global Burden of Disease to estimate how exposure to air pollution impacts mortality and morbidity in India. They find that the monetized reductions in 15
|**Scenario**|**World Total Hig**|**h Income**|**US**<br>**Table 3: I**|**EU**<br>**mpact on Em**|**Low and**<br>**Middle**<br>**Income**<br>**issions Reduc**|**China**<br>**tions**|**Brazil**|**India**|**SSA**|
|---|---|---|---|---|---|---|---|---|---|
|||**% Chang**|**e in Emissions**|**Relative to**|**Business as Us**|**ual(BAU) in**|**2020**|||
|NBTA17|-9.3|-28.4|-33.5|-30.0|1.0|0.6|1.3|0.8|3.0|
|BTAFU|-10.9|-28.5|-33.5|-30.0|-1.5|-1.7|0.6|-1.6|0.7|
|BTADU|-9.8|-28.4|-33.5|-30.0|0.3|0.0|0.8|0.3|2.3|
|BTADE|-10.0|-28.5|-33.5|-30.0|0.0|-0.3|0.8|-0.2|2.2|
|BTFAR|-10.6|-28.5|-33.5|-30.0|-1.0|-1.3|0.6|-0.5|1.5|
|BTADR|-9.8|-28.4|-33.5|-30.0|0.3|0.0|0.6|0.4|2.3|
||||**% Chang**|**e in Emissio**|**ns Relative to**|**2005**||||
|NBTA17|56.9|-11.4|-17.0|-17.0|122.5|183.5|37.6|156.5|82.5|
|BTAFU|54.1|-11.6|-17.0|-17.0|117.2|177.0|36.6|150.3|78.4|
|BTADU|56.2|-11.5|-17.0|-17.0|121.1|182.0|36.8|155.0|81.4|
|BTADE|-17.0|-11.5|-17.0|-17.0|120.4|181.2|36.8|153.7|81.1|
|BTFAR|54.7|-11.5|-17.0|-17.0|118.2|178.3|36.5|153.2|79.9|
|BTADR|56.2|-11.5|-17.0|-17.0|121.1|181.9|36.6|155.3|81.3|

_Notes:_ **_NBTA17:_** _Industrial countries alone reduce emissions by 17% and take no trade policy action;_ **_BTAFU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in imports;_ **_BTADU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in domestic production;_ **_BTADE:_** _Industrial countries alone reduce emissions by 17% with import tariffs on all merchandise imports and rebates on all merchandise exports based on carbon content in domestic production;_ **_BTAFR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in imports;_ **_BTADR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in domestic production_ 26
Policy Research Working Paper 10223

# **Abstract**

Household businesses make up the majority of firms in developing economies. This paper uses a novel tax census database that covers the universe of tax-registered household businesses to analyze the entry and exit of owner-operated firms in Vietnam during January 2018 to August 2020. It documents new stylized facts about the survival dynamics of informal businesses. First, the entry and exit rates were about 5–6 percent a year for tax-registered household businesses during the pre-pandemic period. Second, an additional 25 percent of household businesses suspended their activity in a year on average, with the annual suspension duration exceeding 2.5 months. The suspension rate spiked to 40 percent during the onset of the COVID-19 pandemic in 2020. Third, the findings show that the pandemic-related effects were more pronounced for businesses dependent on face-to-face interactions with customers and suppliers. However, these effects were short lived, and activity and earnings rebounded by August 2020. The findings may reflect the relatively short COVID-19 distress in Vietnam during the first phase of the pandemic, but they illuminate both the vulnerabilities and resilience of the household business sector.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dle6@worldbank.org and eislamaj@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
We assume that when the restrictiveness index in country _i_ changes, the other variables in equations 5.1 and 5.2 (restrictiveness index in country j for other than commercial presence, the distance between trading partners and the intensity of trading among partners) do not change. Further, we follow Kimura et al., (2004a) and estimate the shipping margins for countries that are not in the sample of Kang. Following Kimura et al., we assume that for countries outside the Kang sample, the differences in shipping margins between country i and the average for the group, differ only due to differences in the restrictiveness indices. Then, in equations 6 below, we may ignore the values for _R oj_<sup>,</sup> _D jj_<sup>, and</sup> _S jj_<sup>in calculating the change in the shipping margin</sup> in country i due to a change in the restrictiveness index in country i.

**Table A.1. Indexes of restrictiveness of international trade in maritime services for other economies**

|Industrialized Economies||DevelopingEconomies||
|---|---|---|---|
|Singapore|0.207|Argentina|0.331|
|United Kingdom|0.239|Colombia|0.469|
|Denmark|0.284|Mexico|0.478|
|Finland|0.315|Chile|0.503|
|Canada|0.32|Malaysia|0.52|
|New Zealand|0.352|Brazil|0.521|
|Germany|0.39|Indonesia|0.558|
|Hong Kong SAR, China|0.403|Korea, Rep.|0.582|
|Japan|0.408|Thailand|0.601|
|Sweden|0.415|India|0.605|
|Australia|0.416|Philippines|0.644|
|United States|0.6|||

Source: Jong-Soon Kang (2000) Table 11.2 Original source: McGuire et al (2000) 58