rafmacalaba/datause-encoder-data huggingface dataset:
regulation (in particular employment protection laws), tax payments, and social security contributions. It, therefore, excludes criminal activities and home-based production. We use Schneider’s estimates because, first, they are the most comprehensive estimates obtained using a unified method, and, second, they are used by a number of other studies. However, as with the measurement of any other latent variable, these estimates of the size of the informal sector should be considered with caution. They are likely to pick up a large amount of measurement error; and in the particular case of the DYMIMIC procedure, the estimates depend largely on the theoretical relation between the variable of interest and the indicators, which may be subject to debate. Although highly important and interesting, a detailed discussion of the estimation of the informal sector is beyond the scope of this paper.

As described in the previous section, our explanatory variables of interest in the growth and informality regressions are indices that quantify a country’s regulatory burden. We consider, in turn, the overall regulation index and its three main components-- the product-market, labor, and fiscal regulation indices. In extensions to the basic specification, we interact the regulation index with a governance proxy, which as already noted is constructed from information on experts’ perceptions on public accountability, absence of corruption, and rule of law, as reported by the International Country Risk Guide.

The set of control variables for the growth regressions consists of the initial level of per capita real GDP (to account for convergence effects), the initial rate of secondary enrollment (as proxy for human capital investment), the initial ratio of private domestic credit to GDP (to account for financial depth), and a Sub-Saharan dummy variable (to control for the particular conditions of civil conflict, mismanagement, and disease affecting this region).<sup>9</sup> For the regressions of the size of the informal sector, the control set is quite parsimonious consisting only of initial real GDP per capita. Despite its parsimony, this variable summarizes most elements of economic development and is crucially important as a control given its strong relationship with both informality and regulation (see, as illustration, the corresponding bivariate correlations in Table 2).

> 9 The “Africa dummy” has a long tradition in empirical growth studies; see for example Easterly and Levine (1997).

6
changing investor base for their debt. In this respect, improving market monitoring, the timely availability and quality of ownership statistics, and good investor communication and outreach to establish solid links with key participants (wherever they may be), will be ever more important.

**Table 2: Summary of inclusion criteria for some widely tracked EM / global local currency bond indices**

||**Rating/**<br>**income**<br>**level**|**Market**<br>**size/**<br>**liquidity**|**Issue**<br>**size/**<br>**liquidity**|**Coupon**|**Redemption**|**Embedded**<br>**options?**|**Maturity**|**Other**|
|---|---|---|---|---|---|---|---|---|
|**GLOBAL IN**|**DICES**||||||||
|**Citi World**<br>**Govt.**<br>**Bond**<br>**Index**|Min. A-/A3<br>by S&P or<br>Moody’s|Total of<br>all<br>eligible<br>issues<br>≥$50bn|Market<br>specific<br>(e.g.<br>MXN:<br>≥MXN<br>10bn)|Fixed or<br>zero only|Bullet or<br>sinking fund|Yes|≥1 year|Sovereign<br>must actively<br>encourage<br>fgn. investor<br>participation|
|**Barclays**<br>**global**<br>**aggregate**<br>**bond**<br>**index**<br>**EM-DEDIC**<br>|IG using<br>middle<br>ranking of<br>the big 3<br>ratings<br>agencies<br>**ATED INDICES**<br>|Market-spe<br>minimum i<br>E.g. $300m<br>equivalent<br>issues in U<br>pricing “reg<br> <br>|cific<br>ssues sizes.<br>/€300m<br>for EM<br>SD and EUR<br>ions”<br>|Fixed or<br>changed<br>according<br>to<br>schedule<br>(e.g. step-<br>up)<br>|Bullet or soft<br>bullet<br>|Yes<br>|≥1 year<br>|FX must be<br>freely<br>tradable and<br>hedgeable<br>|
|**JPM**<br>**GBI-EM**<br>**family**|Low or<br>middle<br>income<br>(World Bank<br>classification)|No minimu<br>universal li<br>standard, b<br>must be re<br>liquid (pric<br>tradable, r<br>spreads) a<br>most liquid<br>included|m size or<br>quidity<br>ut market<br>asonably<br>es available,<br>easonable<br>nd only<br>bonds|Fixed only|Bullet only|No|≥13<br>months|Cannot be<br>explicit<br>capital<br>controls|
|**Barclays**<br>**EM local**<br>**currency**<br>**govt.**<br>**universal**<br>**index**|A1 or lower<br>(middle<br>rating of big<br>3), or ≤Upper<br>Middle<br>income<br>classification<br>by World<br>Bank|Total<br>debt with<br>≥1y<br>maturity<br>must be<br>≥$5bn|Market-<br>specific<br>minimum<br>issue sizes|Fixed or<br>changed<br>according<br>to pre-<br>determined<br>schedule<br>(e.g. step-<br>up)|Bullet only|Yes|≥1 year|Market<br>investability<br>considered;<br>must be<br>reliable<br>pricing<br>source|

**Source: Barclays, Citigroup, JP Morgan bond index factsheets**<sup>**vi**</sup>

**13**
# **1. Introduction**

Poor management of steep, erosion-prone, and often fragile tropical soils increases the exposure and vulnerability of poor people to climate change; by contributing to loss of vegetation and soil fertility, it may also heighten the severity of climate-induced shocks. Measures such as terracing, bunding, micro-dam construction, tree planting, and establishing agro-forestry systems have thus gained popularity and attracted major resources. In Africa alone, since 2006 the World Bank has invested more than US$10 billion in sustainable land management (SLM) to reduce soil loss and increase soil moisture and vegetative cover. The goal is to help farmers adapt to the short-term effects of climate change and mitigate the impacts of climate-induced shocks over the medium term (World Bank 2017).

Yet while numerous studies have explored the biophysical variables that may be affected by SLM or soil and water conservation (SWC) activities and the associated microeconomic costs and benefits, there is as yet scant evidence on how much impact such investments have on watersheds or landscapes—the units at which these interventions are usually conducted. That makes it difficult to assess whether or how effectively these programs achieve their short- and longer-term objectives and to identify corrective actions in case they fail to do so.

One important issue is that SLM activities differ from the household-based interventions long analyzed by economists in three major ways, namely: (i) rather than improving livelihoods directly, a main goal is to better use vegetative cover to enhance the soil’s capacity to capture and store water, reducing run-off and arresting soil erosion; (ii) the relevant unit of treatment is a watershed or a landscape with micro-watersheds, and their geographical scale is considerably larger, encompassing a number of communities and hundreds or even thousands of households depending on the basin under consideration and the water discharge point; and (iii) outcomes for any period are strongly affected by climatic variables and can be properly measured only if those variables are taken into account.

Recent advances in computing platforms and access to data have greatly improved the ability to evaluate these programs. Free access to archived medium-resolution imagery and processing power on cloud computing platforms such as Google Earth Engine (GEE) allows anyone to conduct analysis that a decade ago would have been costly and time-consuming even for experts. To demonstrate how such data can provide evidence that can serve as a basis for informed decision-making, we use Ethiopia’s Tana Beles Integrated Water Resource Development Project (TBIWRD). Accessing and processing a large amount of readily available remotely sensed satellite images via GEE and combining them with information on the timing and location of interventions makes it possible to rigorously evaluate the impact of SWC activities on soil water content and photosynthetic activity at the pixel level.

2
of 8 categories: none, some primary school, completed primary school, some middle school, completed middle school, some secondary school, completed secondary school, and some college. We use age as a proxy for experience. Industry consists of the following categories: agriculture/fishing, mining/energy, manufacturing, construction, retail, trade, finance, public services, and other. Sample means for these variables by gender and country can be found in the Online Appendix in Tables C.1-C.5.<sup>3</sup> Table 1 provides summary statistics for these variables, pooling all cross-sectional data sets. The data are presented for the three main occupational categories available: agricultural employees, wage employees, and the self-employed. Tables A.1 - A.5 in the Online Appendix provide the same descriptive statistics for each country.

Table 1: Summary Statistics by Employment Type

||mean|sd|min|max|count|
|---|---|---|---|---|---|
|Agriculture||||||
|Primary Income in USD|27.92|57.66|0.00|664.14|1711.00|
|Education (Categorical, 1-8)|3.09|1.40|1.00|8.00|29321.00|
|Age in Years|35.96|13.25|15.00|65.00|37012.00|
|Male Dummy|0.48|0.50|0.00|1.00|37012.00|
|Marriage Dummy|0.67|0.47|0.00|1.00|37012.00|
|Paid Employment||||||
|Primary Income in USD|92.91|117.71|0.00|1138.52|13696.00|
|Education (Categorical, 1-8)|4.23|2.21|1.00|8.00|13346.00|
|Age in Years|33.73|11.49|15.00|65.00|14242.00|
|Male Dummy|0.69|0.46|0.00|1.00|14242.00|
|Marriage Dummy|0.51|0.50|0.00|1.00|14242.00|
|Self-Employed||||||
|Primary Income in USD|79.95|111.59|-29.20|1138.52|10999.00|
|Education (Categorical, 1-8)|3.80|1.71|1.00|8.00|12384.00|
|Age in Years|35.33|10.93|15.00|65.00|13383.00|
|Male Dummy|0.47|0.50|0.00|1.00|13383.00|
|Marriage Dummy|0.64|0.48|0.00|1.00|13383.00|
|Total||||||
|Primary Income in USD|83.30|113.31|-29.20|1138.52|26406.00|
|Education (Categorical, 1-8)|3.52|1.77|1.00|8.00|55051.00|
|Age in Years|35.34|12.46|15.00|65.00|64637.00|
|Male Dummy|0.52|0.50|0.00|1.00|64637.00|
|Marriage Dummy|0.63|0.48|0.00|1.00|64637.00|
|Observations|64637|||||

> 3The Online Appendix can be found at http://faculty.washington.edu/rmheath/research.html.

4
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE company’s capital (figure 4). In 2000 it was agreed that both power and water tariffs would be adjusted once fuel prices exceeded a threshold value of $0.42 per liter. These adjustments failed to take place. Instead, the state compensated for rising fuel prices with a direct subsidy that, as of 2005, amounted to $4.4 million annually. In April 2006 the authorities eliminated this oil subsidy and implemented a series of tariff hikes that failed to fully compensate for further increases in the price of oil. Thereafter, the regulatory agency introduced an automatic adjustment mechanism for electricity and water tariffs linked to the price of petroleum, although none of these mechanisms is being applied as stated by law. While the measures taken in the mid-2000s served to stem the hidden costs associated with underpricing in the sector, by 2006 they were growing again. Hidden costs associated with ELECTRA’s power service remained in the range of 1.0 to 1.5 percent of GDP for much of this period, making them material at the macroeconomic level (figure 5).

**Table 5. Benchmarking power indicators, 2006**

||Unit|Low-income countries|**Cape Verde**|Middle-income<br>countries|
|---|---|---|---|---|
|Installed power generation capacity|MW/million people|24.41|**153.9**|796.2|
|Power consumption|kWh/capita|99.5|**89.0**|4,473|
|Power outages|Day/year|40.6|**150.4**|5.6|
|Firms’ reliance on own generator|% consumption|17.7|**4.6**|0.5|
|Firms’ value lost due to power<br>outages|% sales|6.1|**8.9**|0.8|
|Access to electricity|% population|15.4|**66.0**|59.9|
|Urban access to electricity|% population|71|**—**|83.7|
|Rural access to electricity|% population|12|**—**|33.4|
|Growth access to electricity|% population/year|1.4|**—**|1.8|
|Revenue collection|% billings|88.2|**86.9**|99.9|
|Distribution losses|% production|22.1|**21.5**|15.7|
|Cost recovery|% total cost|90.0|**71.3**|125.7|
|Total hidden costs as % of revenue|%|67.5|**72.3**|3.5|
|||Countries using<br>predominantly thermal<br>generation|Cape Verde|Other developing<br>regions|
|Power tariff (residential at 75 kWh)|U.S. cents|16.0|**25.1**|5.0–10.0|

_Source:_ Eberhard and others 2008. Derived from the AICD electricity database (http://www.infrastructureafrica.org/aicd/tools/data) **.** _Note:_ GWh = gigawatt-hour; kWh = kilowatt-hour; MW = megawatts.

12
Policy Research Working Paper 9962

# **Abstract**

On-chain crypto-assets transaction volumes have grown rapidly, particularly during the COVID-19 pandemic. Crypto-assets activity appears to be a global phenomenon, although it still remains modest relative to gross domestic product for most countries. Panel regressions across more than 130 countries show that the variation in countries’ monthly crypto volumes is mostly driven by globally relevant factors such as real U.S. longer-term inflation expectations, U.S. real Treasury yields, and gold and crypto-asset prices, rather than recent country-level macroeconomic developments. Cross-sectional regressions offer tentative evidence that crypto activity is higher in countries with higher information and communications technology adoption and higher reliance on remittances. Taken together, the findings shed new light on the drivers behind crypto activity and offer support to the notions that crypto-assets are perceived as a risk asset, a potential macro hedge, and a potential tool to support cross-border transactions. However, the results come with caveats: a significant portion of the sample period includes extraordinarily loose global financial conditions; the crypto volume data have a short history, rely on important limiting assumptions, and do not represent all crypto activity; and crypto-assets represent a fast-evolving, increasingly diverse asset class and industry.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Information and Technology Solution Vice Presidency. 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 efeijen@worldbank.org, ykawashima@worldbankgroup.org, and rmittal1@worldbankgroup.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
<!-- Start of picture text --> 6<br>4<br>2<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Farm Enterprise, Ag Enterprise, Ind<br>Enterprise, Ser Wages, Ag Wages, Ind<br>Wages, Ser Wages, Unkn<br>6<br>4<br>2<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Farm Wages, Ag<br>Wages, Ind Wages, Ser<br>Wages, Unkn<br>Productivity ratio, output per person per year<br>Productivity ratio, output per hour<br><!-- End of picture text --> Figure 8: Productivity gaps by activity for rural households (ratio between mean values for each activity). Figure 8a (top) depicts the ratio between mean farm labor productivity per person per year and the mean labor productivity of other activities (i.e. NFEs and wage labor in different sectors). Figure 8b (bottom) depicts per-hour productivity gaps for the same activities.

28
growth model. The results, however, remain questionable given the proxies used to measure capital and labor destructions and the timing of the growth response.

Jaramillo (2007) presented a comprehensive analysis of the link between natural disasters and economic growth both in the short-run and long-run using a panel dataset of 113 countries over the period 1960-1996. However, disasters that develop through extended periods, such as droughts and famines, as well as insect infestations and epidemics are excluded from the analysis. The type of disasters examined by Jaramillo include earthquakes, floods, wild fires, wind storms, waves and surges, extreme temperatures, volcano episodes, and slides. Taking country and year fixed effects into account and controlling for trade openness and foreign aid, the author examined the short-run effects of disasters on economic growth, followed by an analysis of the longrun effects. For the short-run, Jaramillo assessed the impacts on GDP growth in the disaster year and the following year, whereas for the long-run, he tested for the cumulative disaster effects over the period 1960-1996 on the GDP per capital level in 1996. The regression results indicated that short- and long-term effects of disasters are determined by countries’ income level, population, and the type of disaster. On the whole, it was found that the effects of disasters on GDP growth rate varied from 0.9% decrease to 0.6% increase depending on the disaster type.

Focusing specifically on the long-term macroeconomic impacts of natural disasters, the first comprehensive empirical research was done by Skidmore and Toya (2002). In their cross-country analysis, the authors use average per capita GDP growth over the period 1960-1990 and the total number of significant disaster events observed in respective countries during the same period. The disasters studied cover climatic and geologic disasters. The results revealed that climatic disasters have positive effects on the long-run economic growth as they induce higher capital accumulation and total factor productivity than before. It is argued that total factor productivity is the predominant factor in promoting growth after disasters. By contrast, geologic disasters were observed 9
Table A6 Shapley Decomposition Urban- Rural Differences in Poverty in 2001

|||Difference|Scale|Inequality|
|---|---|---|---|---|
||Headcount|-22.30|-22.25|-0.05|
||Poverty Gap|-8.51|-8.20|-0.31|
|Urban|Squared Poverty Gap|-3.96|-3.80|-0.16|
||Watts|-11.90|-11.39|-0.50|
||Headcount|11.90|19.87|-7.97|
||Poverty Gap|4.54|9.40|-4.86|
|Rural|Squared Poverty Gap|2.11|5.03|-2.91|
||Watts|6.35|13.94|-7.59|

Source: Authors’ Calculations Table A7 Shapley Decomposition Urban- Rural Differences in Poverty in 2007

|||Difference|Scale|Inequality|
|---|---|---|---|---|
||Headcount|-27.73|-22.05|-5.68|
||Poverty Gap|-9.50|-7.69|-1.81|
|Urban|Squared Poverty Gap|-4.06|-3.44|-0.62|
||Watts|-12.60|-10.42|-2.18|
||Headcount|15.14|21.41|-6.27|
||Poverty Gap|5.19|10.47|-5.28|
|Rural|Squared Poverty Gap|2.22|5.64|-3.42|
||Watts|6.88|15.34|-8.46|

Source: Authors’ Calculations 39
21 _Women seem to fare better in urban areas than in rural areas_ (Table 2) _._ Female non-participation rates for instance are higher in rural areas than they are in urban centers (respectively 73.5 percent and 69.8 percent). The opposite is observed for employment-to-population: the female urban employment rate stands at 23.9 percent whereas the rural one stands at 20.3 percent. Unemployment is also lower for women living in urban areas (20.8 percent compared to 23.3 percent for rural areas). Hence, a better understanding of the gender gaps observed in the Mauritanian labor market requires distinguishing rural and urban areas.

# <u>3.2.1 Gender gaps in rural labor markets</u>

_Labor market indicators are somewhat worse for females in rural areas than they are for the overall population._ Indeed, labor force participation for women in rural areas stands at 26.5 percent compared to 28 percent for all working-age women (Table 2). The rural employment-to-population ratio is also slightly lower (20.3 percent against 21.8 percent). The reverse is true for males though: both their rural labor force participation and employment rates are approximately three percentage points higher than those exhibited by all working-age men.

_Prime-age women in rural areas mainly opt out of the labor market for family reasons._ As shown in Table 11, rural females across all age groups report family or household obligations as the chief motive for their non-participation in the labor force. Table 12 also provides evidence that women aged 25 to 49 decrease their participation in the labor market when living in a household with children (31.6 percent for those with no children against 27.7 percent for those with children). It should be noted however that the participation rate for females without children remains strikingly lower than that of males, which stands at 92.1 percent for those aged 25 to 49. Hence, gender gaps in participation among the 25-to-49 working-age groups cannot solely be attributed to childcare motives.

**Table 11. Out of the Labor Force – Motives for Women in Rural Areas (in %)**

|||_Age group_<br>||
|---|---|---|---|
||_15-24_|_25-49_|_50-64_|
|_No work_|16.4|8.1|1.7|
|_Slack season_|0.5|0.3|0|
|_Student_|21.8|0.6|0|
|_Family/household obligations_|52.8|85.7|60.5|
|_Too young/old_|6.3|3.4|35.7|
|_Disabled_|1.7|1.2|2.1|
|_Other_|0.5|0.7|0|
|_Total_|100|100|100|

Source: EPCV 2004. Figures are displayed in percentage.
# **Firm Entry, Exit and Suspension: Evidence from Household Businesses in Vietnam**<sup>∗</sup>

Ergys Islamaj<sup>†</sup> Duong Trung Le<sup>‡</sup> Thanh Minh Pham<sup>§</sup>

**_Keywords_** : informal sector, household business, entry, exit, suspension, COVID-19, Vietnam **_JEL Classifications_** : O12, O17, D22

> ∗We thank Aaditya Mattoo, Francesca de Nicola, Ivan Korolev, Solomon Polachek, David Slichter, Jonathan Timmis, and the Labor Group Meeting participants at Binghamton University for helpful comments and suggestions. The findings, interpretations, and conclusions are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the governments they represent.

> †The World Bank. East Asia and Pacific Chief Economist Office. Email: eislamaj@worldbank.org.

> ‡Corresponding author. The World Bank. East Asia and Pacific Chief Economist Office. Email: dle6@worldbank.org.

> §Mekong Development Research Institute. Email: thanhpham@mdri.org.vn.