rafmacalaba/data-use-mentions-extended huggingface dataset:
export profit line has shifted leftward, bringing more firms into the export market. That is, the productivity cut-off for joining the export market falls from 68.72 to 38.09.

In our simulation, we fix the total number of workers at 400,000 in 281 firms. Originally, 16,552 work in the heterogeneous sector _b_ and the rest work in the reserve sector _a_ . The model is full-employment, general equilibrium prior to the trade agreement. In the heterogeneous sector, there are 46 firms selling to the international market that employ a total of 9,300 workers, with an average firm size of 202 workers. The remaining 235 firms in this sector produce for the domestic market, employ 2,752 workers total, with an average size of 11.7 workers.

We model an increase in the international (export) price of 5 percent (Table 4). The results show that employment in the export sector increases by 2,451 workers. This increase is broken down into three groups of firms: (i) “always exporters” increase employment by 642 workers (7 percent); (ii) “never exporters” reduce employment by 9 workers (with a drop in average firm size of about 0.1 worker per firm); and (iii) firms that switch from domestic to exporting increase employment by 1,819, an increase in average firm size of 30.8 workers. The change in employment in these three groups matches the overall increase in employment in the export sector. In the next section, we turn to firm-level data from Egypt to estimate actual employment changes and compare them to the general results of the model simulation.

# **4. Data and Descriptive Analysis**

## **_4.1 Sources_**

Our empirical analysis relies primarily on the World Bank’s Enterprise Surveys (ESs) for Egypt, a small panel of firms followed over time. The ESs are conducted across all geographic regions and cover small,
# **b. Sanitation**

In the sanitation sector, there is no clear case of concentration neither at the global level nor at the regional level. Although Ondeo and Veolia clearly dominate the market and are followed closely by the large German group RWE, there are other private operators (OPP). These OPP include many local players in many of the countries and serve about a third of the population of the world.

North America, is, once more, a clear border line under both types of market definition, suggesting that its sanitation market may be subject to some degree of market power by the few operators who dominate the market. The indicator is however formally inclusive. Eastern and Western Europe are also border cases when relying on the urban population indicator to define the market.

The developing regions of the world are much less concentrated than sometimes argued. This is largely due to the fact that coverage rates are fairly low. In many countries of the developing world, sanitation is a much less formal activity than in the developed part of the world.

**<u>Table 7: Regional HHI - Sanitation</u>**

||<br>**GLOBAL**|<br>**Africa**|<br>**Asia &**<br>**Oceania**|**Latin**<br>**America**|**North**<br>**America**|**Eastern**<br>**Europe**|**Western**<br>**Europe**|
|---|---|---|---|---|---|---|---|
|**Market indicator**<br>||**1. Urban p**<br>**2. Country**<br>|**
to 2015, was consistent with the lack of non-bank long-term financial instruments, including the limited development of contractual savings institutions, such as insurance companies, pension funds, and mutual funds. About half of the countries in the region have stock exchanges and few of them are liquid. Another indication of the short-term nature of African banking is the dearth of mortgage finance. While mortgage depth to GDP in the median African country was below 1%, it was above 2% outside Africa (Badev et al. 2013). These aggregate numbers match with anecdotal evidence that mortgage systems in many smaller African countries comprise just a few hundred mortgages, concentrated among wealthy individuals.

The ownership structure in the financial sector, particularly in banking, has also played a role in Africa’s financial performance and outcomes. As discussed in Beck et al. (2019), the ownership structure of Africa’s banking systems has undergone notable changes over the past decades, with foreign-owned banks playing an increasingly important role while government-owned banks have become less prominent. In the median African country, the share of assets held by foreignowned banks rose from 35% in 1995 to over 65% in 2019 (the average has increased from 43% to 61%), while there were no government-owned banks in 1995 or 2019 in the median African country (the average share of government-owned banks has declined from 17% in 1995 to 5% in 2019). For comparison, the asset share of foreign-owned banks in the median non-African developing country was 23% in 2019.<sup>10</sup> Although foreign-owned banks have been present in Africa since the colonial era, their

> 10 These data are based on Panizza (2023), which defines a bank as foreign- (or government-) owned if more than 50% of its ownership shares are held by foreign (government) entities.

14
Risk Index on the available values of legality and using the predicted value of 14.6 for China.

Alternatively, omitting the 98 Chinese firms from all of the subsequent analysis leaves the results and conclusions unchanged.

# **IV.   Analysis**

In this section we describe the tests of our hypotheses and the results from these tests. Our main hypothesis is that foreign debt allows firms which reside in countries with poor legal systems to overcome borrowing constraints in their local markets and thus to achieve their preferred level of debt financing. Our starting point is a descriptive examination similar to LaPorta et al. (1997) of external equity and debt capital that firms use and the relation to legal environment. We aggregate to the country level and compare external capital ratios with a country’s legal environment.<sup>6</sup>

## **A. Main Results**

Our sample of 1689 firms from 9 East Asian countries spans the range of legal systems well: three countries have an English legal origin (Hong Kong, Malaysia and Singapore), two have a German legal origin (South Korea and Taiwan), three have a French legal origin (Indonesia, Philippines and Thailand) and one has a socialist legal origin (China). Table 1 presents some descriptive statistics. Among the three primary legal origins the distribution of firms is fairly even ranging from 397 firms of French legal origin to 749 of English legal origin.

The next column shows the legality index for each country. Unsurprisingly, the highest average legality measures are found among countries with an English legal origin (average of 18.2), then among countries with a German legal origin (average of 15.8), and finally among countries with a French legal origin (average of 11.0). China has a legality index that is roughly the same as the average of the German legal origin countries.

> 6 Country level data based on median cash-flow yields qualitatively similar results to those presented in Table 1.
, December 2020_

|**By employment category**|**Due to**|**To avoid**|**Other**|
|---|---|---|---|
||**business or**<br>**job losses**<br>**from COVID**|**exposure to**<br>**the virus**|**reasons**<br>**(Including**<br>**non-COVID-**<br>**related)**|
|Public sector employees|3.9|0.6|12.4|
|All private sector employees<br>All private sector employees,|24.1|1.8|17.0|
|core sectors|29.0|-|-|
|Self-employed|27.2|3.1|29.8|
|Employers|33.9|0.0|4.0|

Source: the COVID-19 Georgia High-Frequency Phone Survey (GHFPS).

Notes: “Individuals who lost jobs because of job or business losses due to COVID-19”: Those who had a job in March 2020 but don’t have a job in December 2020 and answered that they stopped working because they lost jobs or no business because of COVID 19. Other reasons include contract end, validate the method we propose for quantifying the level of risk of job loss for PFPS jobs. An additional test of our methodology and its limits would be to see whether it can predict the realized distribution of net job losses across different categories of workers that would have most likely held the jobs that were lost.

# 5.4 Projected Job Losses by Sector

In this sub-section we assess how well projections of job loss align across various methods and sources of data by sector of employment. Because neither survey (WBES and LFS) on which
need support to supply these types of inputs (Amendolagine et al. 2019).

_The level of autonomy with which affiliates operate_ . Affiliates of MNCs that operate centralized sourcing policies are usually characterized by low levels of local sourcing, as evidenced by case study findings on the automotive industry in the Czech Republic (Pavlinek and Zizalova 2016). Affiliates that are allowed to operate with more freedom and greater flexibility tend to use more local suppliers and are able to benefit from opportunities that arise when local supplier bases improve (UNCTAD 2001). Survey findings for several Eastern European countries indicate that MNC affiliates with a relatively high level of autonomy over technological business functions (Giroud, Jindra, and Marek 2012) or supply, logistics, and product development (Jindra, Giroud, and Scott-Kennel 2009) are also characterized by higher levels of local sourcing. Similarly, a high level of autonomy fosters the provision

> 7 Foreign participation is important, as shown in a study by Jordaan (2013), who reports results from a survey among MNC affiliates and domestic producer firms in northeastern Mexico, showing that firms with foreign participation are significantly more involved in providing a variety of types of support to their suppliers than domestic firms .
World Bank’s LSMSs. Carletto et al. (2007) describe how the income data across the surveys were harmonized. It appears that the microdata are available for both rural and urban areas, in which case this is a potentially valuable source of income data to study the top tail of the income distribution. Kerr et al. (2019) undertook harmonization of 26 years of household surveys on earnings income in South Africa and made the harmonized data publicly available. Changes over time in the surveys, the sampling, and the questionnaires meant that obtaining comparable earnings distributions over time even for one country required a substantial amount of work.

We have described the method of Korinek et al. (2006) to adjust survey weights to correct for unit non-ignorable non-response and we discussed several papers that implemented these corrections. Ravallion (2022) notes that implementing this method requires regional non-response rates but that these are seldom available. He argues for the inclusion of PSU-level response rates and notes that this would be simple to include for survey data producers. Hlasny (2020) documents that some of the surveys available through the Luxembourg Income Study (LIS), including Brazil and South Africa, include non-responding households.

## _Administrative data_

The UNU WIDER project “Regional Growth and Development in Southern Africa” resulted in access to South African tax microdata through the South African National Treasury from 2015. This model of partnership between international organizations and national governments, including tax offices, has since been replicated by WIDER in Tanzania, Zambia, Uganda, and Rwanda.<sup>8</sup> Such data can be used to improve the measurement of the top tail of the income distribution in these countries, especially since publicly available household survey data on incomes exists for all these countries. Having a larger number of countries with tax and survey data and undertaking comparisons would allow for more general conclusions about the extent of the differences between survey and tax data for Africa, rather than just two as in Chancel et al. (2023).
worker. In the following, we describe the identification of deceased workers and incumbent workers in the administrative data. We also provide evidence that, when comparing peer groups with the same pre-death racial composition of employees, peer group characteristics do not predict whether a white or non-white worker dies. In addition, we verify that the death of a non-white worker, compared to the death of a white worker, leads to a lasting drop in the nonwhite share of the peer group. For these reasons, we can treat the race of the deceased worker as a conditionally exogenous and significant shock to the peer group’s racial composition and can estimate its effect on incumbent workers’ retention at the firm.

# **3.1 Sample Construction**

**Unexpected worker deaths.** We identify worker deaths based on the employer notifications in the RAIS data. When employers report contracts that were terminated because the employee died, they distinguish between deaths due to a work accident, deaths while commuting to work, and all other deaths. We exclude work accident and commuting deaths in order to avoid concerns that safety conditions at and on the way to work may affect both the likelihood of worker deaths and the retention decisions of workers. Moreover, to rule out spurious death notifications, we drop deaths of individuals for whom we observe an employment contract at least 30 days the date of death (less than 3% of all deaths).

In order to identify separations that are likely unexpected for coworkers, we focus on deaths of workers younger than 65 who have a full-time (with at least 30 hours) and permanent job. Moreover, we only include individuals with a minimum tenure of three months at the time of death, as a large share of contracts are terminated in the first three months when firms can dismiss workers without cost. We also exploit employer reports on workers’ sick leave periods and drop deaths of individuals who had any sick leave in the two years before the death (37% of all deaths)
make the two touch, because they are pointed in the wrong direction. To deal with cases like these, “bridges” were created as follows. All dangles were paired with the closest other dangle if it was within 100 meters using the Spatial Join tool, and connecting lines were created between these pairs of dangles. These bridges were added to the rest of the roads.

The AICD roads database was gathered with explicit reference to inter-city roads. Unfortunately, this means that in many cases, information on roads within cities was not collected, greatly reducing the connectivity of the dataset in many countries. “Spiders” were created to model missing city roads. For every dangle falling within a city, a (paved) road was created between the city centroid and the dangle. The implicit assumption is that radial road travel within cities is comparatively easy. The resulting spiders were added to the roads, and all spiderlegs and any roads that intersected them were “planarized”. Before planarizing, the topology of the network was such that a spiderleg could cross a road without being connected to it. Planarizing ended this.

Manual edits were necessary for several reasons. Recall that Extend did not close holes if a dangle was not pointed at another road, and that bridges were only created between two nearby dangles, not a dangle and a non-dangle. So extra segments of less than 10 meters each were created in 6 other places to fix dangles affecting routes to 16 cities. Recall also that spiders were created between a centroid and any dangles within a city. So once a spider is created, the spiderleg is the network location of the centroid, so if it connects to a dead end, other nearby roads cannot be reached even if they are very close. In these cases, deleting one or more spiderlegs fixed the problem. Five spiderlegs affecting 4 cities were deleted.

# **A.4 Route calculation**

To prepare for building the network, the roads were intersected with all land borders, so that the
Policy Research Working Paper 7824

# **Abstract**

This paper introduces a variety of concepts and methods to examine living standards improvements in Nepal in a dynamic perspective. Using data from three rounds of Nepal Living Standards Surveys conducted in the past two decades, together with data from a nationally representative survey that was implemented in 2014 specifically to collect information on social and economic mobility, the paper presents novel statistics on the extent of inter- and intra-generational mobility in Nepal. The findings suggest that there has been appreciable upward mobility in education; that is, Nepalis today are increasingly more likely to be better educated than their parents. However, inter-generational mobility of occupations has been much more muted, with 47 percent of Nepal today remaining in the same occupation as their parents. Upward mobility is higher for younger cohorts and for individuals who move from their rural areas of birth to an urban area. There are also significant differences in mobility by social groups, with Dalits and Terai caste groups having lower upward mobility odds. Examining mobility within generations using synthetic panel techniques, the paper finds that: (a) for every two people who escape poverty, one slides back, suggesting significant churning around the poverty line; (b) a large fraction of those who have escaped poverty remain vulnerable to falling back, with an overall vulnerable population of 45 percent; and (c) the share of the middle class—defined as those with sufficiently low likelihood of falling back into poverty— has increased steadily over the past two decades, reaching 22 percent in 2010–11. However, triangulating subjective well-being data from Gallup, it appears that a majority of even those who constitute the middle class are fundamentally insecure about their economic futures. The prevalence of a large vulnerable population and a nascent, growing but struggling middle class represents a key challenge to consolidating recent gains in moving people out of poverty.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to
(2004). We calculate as distance weighted average GDP of all other countries in our sample. The measure of distance originates from the CEPII data base and represents the geographical distance between the capital cities. We refer to these control variables as _Tropics, Pop100km,_ and _Remoteness,_ respectively _._ Finally, given the possible noise introduced by initial conditions in terms of natural resource endowments, we include dummy variables for countries with subsoil assets (World Bank 2006). We refer to this dummy variable as _Resources_ . It should be noted however that the effects of natural endowments on long term economic growth is unclear. Sachs and Warner (1997) find support to the hypothesis that countries rich in natural resources tend to grow slower. The authors include the ratio of natural resource (fuels and non-fuel primary products) exports to GDP in the base year and find it to be negatively correlated with economic growth. Similarly, Barro (1997) who includes a dummy variable for oil-rich countries in the growth regression finds it to be negative and statistically significant. However, several more recent studies found that the presence of natural resources does not necessarily present an impediment to higher growth; it depends on other policies pursued by the countries (e.g., Lederman and Maloney, 2007).

The data on GDP per capita and the amount of net oversees development assistance per capita (ODI) originate from the World Bank World Development Indicators data base.

# **3.2 Stylized facts**

As noted, Freedom House’s Civil Rights and Political Liberties indices are closely related (Table 1). When looking at the most recent data available i.e. 2007 we note that all observations on civil rights and political liberties are located along the 45 degree line with the highest single cluster of countries representing developed countries with high levels of civil liberties and political rights (Figure 1). The indices has been reversed as compared to the original Freedom House indices, so that the higher the level of index
# **4.4.1 Overview of the Survey**

Listening to South Caucasus (L2SC) is an expansion of a collaborative effort that has been conducted in multiple countries in the Europe and Central Asian region. This initiative aims to comprehensively monitor the views and well-being of a representative group of people as the government introduces social and economic reforms that affect every business and citizen. By reflecting on the experience of this group over the years, the study provides an up-to-date understanding of how policies reflect on people’s daily lives. The study comprises a nationally representative baseline survey and a high-frequency panel survey of a subset of the baseline participant households. The information collected through the L2SC initiative informs reform efforts directly by raising the profile of citizens’ views and enabling in-depth economic analysis.

While the L2SC survey covers Armenia and Georgia, this paper focuses on the baseline survey in Armenia— Listening to Armenia (L2Arm)—where the new national sampling frame based on pre-EAs has been proposed.<sup>17</sup>

# **4.4.2 Sampling Design**

The sampling design optimizes the spatial allocation of the household sample to provide valid representativeness at the national level for both urban and rural areas. A two-stage stratified cluster sampling design is employed to select participating households, ensuring a balanced sample distribution across regions<sup>18</sup> and accounting for differences between urban and rural areas, survey budgets, and discrepancies in population estimates. The L2Arm survey’s implementation highlighted the robustness of the sampling frame, as it successfully captured the population distribution across diverse geographic and demographic strata. The use of probability proportional to size (PPS) sampling ensured that the selection process was equitable and aligned with population estimates, further validating the practicality of the proposed approach.

In the first stage, a certain number of primary sampling units (PSUs) will be selected in each urban and rural stratum (urban and rural areas within each administrative region). In the second stage, the
-0.0245**|0.0460|
||(0.0125)|(1.12)|(1.75)|(1.50)|(1.43)|(-0.61)|(3.04)|(-0.18)|(2.33)|(0.87)|(-3.18)|(1.50)|
|Household Size|-0.0033|-0.00583|-0.00167|0.0131|-0.00566|-0.0199|-0.000743|-0.0431**|-0.00398|0.00253|<br>0.00724|0.0131|
||(0.0087)|(-0.20)|(-0.27)|(0.59)|(-0.78)|(-0.79)|(-0.13)|(-2.92)|(-0.53)|(0.12)|(1.00)|(0.59)|
|World Bank Law Index|0.0534**|0.0262|0.0189**|0.00523|0.0701***|0.0228|0.00470|0.0362|-0.00560|-0.0162|0.00658***|<br>0.00523|
||(0.0174)|(0.46)|(6.40)|(0.65)|(6.12)|(0.17)|(0.61)|(1.17)|(-1.85)|(-1.27)|(17.51)|(0.65)|
|Observations|10124|
) SAR (RHS)<br>EMDEs<br>excl. CHN<br><!-- End of picture text -->

## D. Female labor force participation

Sources: Penn World Tables, World Bank.

Note: AEs = advanced economies; CHN = China; EMDEs = emerging market and developing economies; Exp. = commodity exporters; Non-exp. = Commodity importers; EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MNA = Middle East and North Africa; SAR = South Asia; and SSA = Sub-Saharan Africa.

A. Baseline investment growth assumes investment forecasts of Consensus Economics, and investment surge assumes best ten-year improvement record for each country.

B. Baseline secondary school completion rate assumes the trend improvements in education and education improvements assumes best ten-year improvement record for each country.

C. Baseline life expectancy assumes the trend improvements in education and health improvements assumes best ten-year improvement record for each country.

D. Baseline female labor force participation (LFPR) assumes the predicted value of female LFPR based on the trend improvements in determinants of the LFPRs and improvements in labor market assumes best ten-year improvement record in female LFPR for each country.