rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5: Impact of winning the lottery and of migration on household income**

||(1)<br>Control<br>group|(2)<br>ITT|(3)<br>ITT<br>(Controls)|(4)<br>ITT(IPW)|(5)<br>IV|
|---|---|---|---|---|---|
|ihs(Farm income)<br>FDR q-value|8.371***<br>(0.162)|0.111<br>(0.211)|0.159<br>(0.218)<br>[0.130]|0.168<br>(0.214)|0.275<br>(0.371)|
|ihs(Non-farm business income)|5.131***|-1.099***|-0.887***|-0.927***|-1.530***|
||(0.182)|(0.230)|(0.243)|(0.238)|(0.406)|
|FDR q-value|||[0.001]|||
|ihs(Rental and other income)|5.233***|-0.129|-0.0908|-0.103|-0.157|
|FDR q-value|(0.151)|(0.199)|(0.206)<br>[0.171]|(0.202)|(0.349)|
|ihs(Remittance income)|2.379***|4.675***|4.362***|4.389***|7.520***|
|FDR q-value|(0.186)|(0.273)|(0.293)<br>[0.001]|(0.285)|(0.444)|
|ihs(Labor income, home)|5.191***|-1.555***|-1.557***|-1.560***|-2.685***|
|FDR q-value|(0.174)|(0.238)|(0.250)<br>[0.001]|(0.248)|(0.419)|
|ihs(Labor income, away)|4.891***|3.759***|3.679***|3.700***|7.011***|
|FDR q-value|(0.202)|(0.305)|(0.310)<br>[0.001]|(0.305)|(0.493)|
|ihs(Labor inc., away --p10)|5.194***|4.247***|4.053***|4.082***|7.192***|
|FDR q-value|(0.198)|(0.279)|(0.284)<br>[0.001]|(0.277)|(0.421)|
|ihs(Total income, all sources)<br>FDR q-value|12.51***<br>(0.0789)|0.538***<br>(0.0931)|0.568***<br>(0.0912)<br>[0.001]|0.580***<br>(0.0870)|1.083***<br>(0.166)|
|ihs(Total income -- p10)|12.54***|0.602***|0.612***|0.603***|1.085***|
|FDR q-value|(0.0786)|(0.0991)|(0.0997)<br>[0.001]|(0.0958)|(0.168)|
|Index: HH income|0|0.193***|0.194***|0.189***|0.344***|
||(0.0332)|(0.0444)|(0.0467)|(0.0461)|(0.0805)|

_Note:_ This table shows the impact of winning the lottery (T1) and of migration on income aggregated at the household level estimated using Equation (2). The first column shows the control group mean. Column (2) shows the ITT estimates with only Upazila indicators, Column (3) adds other controls (applicant height, age, religion, and parental education). Column (4) presents the estimate of the reduced form with inverse-probability-weights (IPW) estimated using the controls. Column (5) presents the 2-SLS estimates of the impact of migration. The rows indicate the outcome variables. _ihs_ refers to the inverse-hyperbolic sine transformation of the variables. The bottom most outcome is a covariance-weighted index of all other outcomes with positive values representing increased income and labor supply. Standard errors, reported in parentheses, are clustered at the union level. Column (3) also presents the (False Discovery Rate) FDR adjusted q-values that adjust for multiple-hypotheses testing. *:p>0.1; **: p>0.05; ***: p>0.01 41
In 1994, about 20 percent of all employers were visited for supervision; about 5 percent were found to have contributed less than the mandated contributions.<sup>2</sup> Evasion does not appear to be an important problem in the German pension system. There is a tight network and cross-check system between the databases of the Tax Authorities, the Labor Office, and health, unemployment and pension insurance. Even minor employment contracts which are not subject to social insurance contributions are registered to verify total earnings per person and insurance exemption. Social insurance coverage is also in the interest of employees, particularly in respect of health insurance because family members are covered at no extra charge. Since social insurance coverage is comprehensive, workers are automatically also enrolled in pension and unemployment insurance.

The percentage of foreign workers insured in the public pension system is 9.3 percent with Turkish and former Yugoslav workers accounting for about half of the total. The share of foreigners in the total population was 9.5 in 1992; this indicates that evasion of social security contributions is low among this group of workers, at least with respect to registered legal foreign residents. With the opening of Eastern Europe, however, the number of workers in Germany without social security coverage has increased, particularly in the construction industry. Increasingly, foreign firms and contract workers are hired at wages below the collectively negotiated minimum and without social benefits.

The federal government provides a subsidy to the public pension system which is meant to cover benefits not directly related to the concept of old age insurance, such as credits for non-contributory periods due to education, military service or child-rearing, the cost of rehabilitation benefits, and health insurance of pensioners. Until 1992, the level of the government subsidy was determined ad hoc. Since the 1992 pension reform, the government subsidy has been tied to the growth of contribution rates and to the increase of gross covered wages in order to achieve an automatic balancing of income and expenditure.

Higher contribution rates will lead to an increase of the government subsidy; at the same time, higher contribution rates result in lower net wage increases (which in turn result in lower pension increases). With this mechanism, it is intended to distribute the burden of an aging population among workers, pensioners, and the government.

> 2 Collection supervision will be changed in the near future.The pension institutions will be responsible for the supervision of correct contribution collection through the sickness funds; in 1995, supervision was extended to cover 30 percent of all employers.

6
39 _Note: This figure shows return periods of 2 (top), 5 (bottom left), and 10 (bottom right) years using the benchmark model (without recently built infrastructure)._

# **_Modeling Exposure With Socioeconomic and GIS Data_**

Social and economic exposure depends on the geographic distribution of the poor and non-poor and of economic assets throughout the study area. However, detailed models of exposure do not exist for Ho Chi Minh City and even data about present conditions is tenuous. We have developed simple models of exposure given the kinds of data and projections that are available.

We use district-level estimates of population and poverty rates to calculate the exposure of the poor and non-poor in each district. GIS data from the Steering Center for Flood Control describes the distribution of semi-permanent and permanent buildings at the sub-district level. Assuming that the poor live in semi-permanent buildings and nonpoor live in permanent buildings, we can then map the exposure of the poor and nonpoor in each subcatchment.

For economic exposure, we use as a proxy indicator the contribution of each district to the city’s overall GDP, which can be approximated given available economic data. We again use GIS building data provided by the Steering Center for Flood Control to map subcatchment-level exposure. We assume that the fraction a district’s economic value that resides in a particular subcatchment is proportional to the fraction of the district’s permanent (i.e. wealthier) buildings that are in the subcatchment.

## **Poor and Non-Poor Exposure**

We use several variables to calculate poor and non-poor exposure. We first calculate PopulationDi, the population in the study area in each district Di.

PopulationDi = PopulationHCMC x PercentPopulationDi x PercentAreaDi where PopulationHCMC is the total population of Ho Chi Minh City; PercentPopulationDi is the percent of the total Ho Chi Minh City population in district Di; PercentAreaDi is the percent of district Di that is in the study area; Knowing the poverty rate PovertyDi in each district, we can calculate PoorDi and NonPoorDi, the number of poor and non-poor, respectively, in the study area in each district:

## PoorDi = PopulationDi x PovertyDi

NonPoorDi = PopulationDi − PoorDi While this gives us district-level exposure, our model calculates hazard by subcatchment. We use GIS data provided by the Steering Center for Flood Control to determine the distribution of the population in each subcatchment. The GIS data provides geographic information on the buildings in the study area, as well as the each
wage gap, i.e. for women and men with the same level of education, the gender wage gap is larger than the average gender wage gap.

_Figure 2: Gender wage gap widens when we control for education_ <!-- Start of picture text --> Gender gap in earnings of wage and salaried workers<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00<br>Raw gender gap 0.81 0.19<br>Controlling for education 0.69 0.31<br>Female earnings per $1 of male earnings Gender gap<br>Countries included: Cambodia, Indonesia, Philippines, Timor Leste<br><!-- End of picture text --> _Source: World Bank staff calculations using EAPPOV harmonized database_ If differences in education levels do not play a role in explaining the gender wage gap in East Asian countries today, what factors are responsible for observed differences in wages between men and women? One factor that has received increasing attention in the literature is occupational sorting. Peterson and Morgan (1995) find that wage differences between men and women are very small within an establishment for the same occupation. In their study, sorting into establishments and, in particular, occupations explains a large portion of the aggregate gender wage gap in the US. Similarly, Blau and Kahn (2016) show that human capital variables explained little of the observed gender gap in the US in 2010, in contrast with occupation and industry which account for over half of the gap. Fafchamps, Soderborn and Benhassine (2008) find that the sorting into occupations and firms explains the gender wage gap in a set of nine countries in Sub‐Saharan Africa and two countries in North Africa.

We build on this work by examining the role of occupational sorting in gender wage gaps in Vietnam. We also explore some potential channels for emergence of occupational sorting there. Vietnam offers a great setting for these analyses ‐ gender gaps in educational enrollment have been more or less closed and the rates of female labor force participation are relatively high (see Appendix I). From an analytical 3
explained by more generous transfers to households with children in Russia. At the same time, our results are consistent with those for Germany and Switzerland in terms of the smaller effect of additional children compared to additional adults.

Figure 2 compares our preferred BUC estimated scales with some other common scales, including the simple per-capita adjustment, the square-root adjustment, the OECD scales, and the poverty line scale , each normalized to a single adult.<sup>17</sup> For each additional adult (or child), while the per capita and OECD scales display a constant marginal cost, our estimated scales, as well as the square-root scale, have a decreasing marginal cost. Compared to our estimated scales, all the other scales overestimate the weights for either an additional adult or an additional child. Interestingly, our estimated scales also provide lower elasticities than the equivalence scale embedded in the official poverty line for Russia, particularly for large-size households.

# **5.2. Adjusted Poverty Lines**

A natural question arises. What are the implications of these decreasing marginal costs for both adults and children for poverty measurement? We present in Table 3 our proposed populationweighted poverty lines for different family types, based on the estimated parameters of equivalence scales, and compare them with the official poverty thresholds employed by Rosstat. Our absolute poverty lines are derived from Rosstat’s official poverty thresholds for different age groups. Our relative poverty lines are computed as two-thirds of the median income per adult equivalent for each household type, using the parameters that account for differences in economies of scale and composition in the household. To make Table 3 easier to read, we leave out the standard errors (see Appendix A, Table A.5 for the full results).

> 17 We offer a comparison of our results with those in studies for Germany and Switzerland that use similar estimation methods in Appendix A, Table A.4.

14
Policy Research Working Paper 9817

# **Abstract**

The COVID-19 pandemic has struck businesses across the globe with unprecedented impacts. The world economy has been hit hard and firms have experienced a myriad of challenges, but these challenges have been heterogeneous across firms. This paper examines one important dimension of this heterogeneity: the differential effect of the pandemic on women-led and men-led businesses. The paper exploits a unique sample of close to 40,000 mainly formal businesses from 49 countries covering the months between April and September 2020. The findings show that women-led micro-businesses, women-led businesses in the hospitality industry, and women-led businesses in countries more severely affected by the COVID-19 shock were disproportionately hit compared with businesses led by men. At the same time, women-led micro-firms were markedly more likely to report increasing the use of digital platforms, but less likely to invest in software, equipment, or digital solutions. Finally, the findings also show that women-led businesses were less likely to have received some form of public support although they have been hit harder in some domains. In a crisis of the magnitude of the COVID-19 pandemic, evidence tracing the impact of the shock in a timely fashion is desperately needed to help inform the design of policy interventions. This real-time glimpse into women-led businesses fills this need for robust and policy-relevant evidence, and due to the large country coverage of the data, it is possible to identify patterns that extend beyond any one country, region, or sector, but at the cost of some granularity for testing more complex economic theories.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Gender Global Theme. 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 at jtorrescoronado@worldbank.org, f.maduko@exeter.ac.uk, igaddis@worldbank.org, liacovone@worldbank.org, and kbeegle@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
# **Table 3.1: Airport charges at selected airports (1998)**

||Ton.|Ton.|Ton.|Ton.|
|---|---|---|---|---|
||Rio de JanerNo|No|No|<br>Yes|
|>|~~~~<br>~<br>hrs.|<br>~~~~3<br>hrs|2hrs|3__s|
|_<br>1|(|igg|_||01i~~~~~~~1|
|i<br>||~~~~~~Passe|nger<br>Company|*|Company|
|||<br>~~No||<br>No|No|No|
||<br>~~~~~~~~|~No|rYes|Yes|Yes|
|Nciise|No|No|Yes|No|
|111|<br>°|**N**|N|<br>N|
|g.g:|<br>~|~~~ o||o<br>o|

Source: Doganis (1992) adapted and actualized.

1MTOW: Maximum take off weight.

2MAW: MaxiTnum aircraft weight.

*It is not an airport charge. It is collected at ticketing point as Government levy.

The selection of the initial price structure will be the basis for the application of the regulatory mechanism. It should be an adequate guideline for future investment and also ensure an efficient allocation of resources. Economic theory states that if the price is established according to the service marginal cost, an efficient allocation of resources amnong users would be obtained. The paid fare would reflect the true service value, with those not willing to pay not being served. However, those airports that generally operate below available capacity present a very small marginal cost, not being able to produce enough revenues for the covering of total costs. In the airport industry, a great deal of costs are sunk, or there are historical costs that do not conformn to the service marginal cost. Therefore, the strict application of a charging policy that follows the marginal cost criterion, would inevitably lead to financial losses for those airports operating below available capacity.

26
|**Firm size**|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|6-10 employees|0.870***|0.739***|0.695***|0.773***|0.726***|0.744***|0.684***|0.724***|0.739***|0.010|
|11-50 employees|1.739***|1.556***|1.378***|1.619***|1.438***|1.582***|1.351***|1.434***|1.462***|0.026|
|Observations|1,112|1,112|1,112|1,112|1,112|1,112|1,112|1,112|1,112|1,113|
|R<sup>2</sup>|0.412|0.434|0.449|0.428|0.446|0.427|0.443|0.446|0.444|0.194|
|**Identifying variables**|||||||||||
|Distance to nearest tax office||||||-0.117***|-0.076***|||-0.076***|
|Distance to municipal office||||||-0.186***|-0.133***|||-0.133***|
|Firm had an inspection during the past year||||||0.349***|0.288***|0.331***|0.339***|0.288***|
|Average (parish-level) distance from tax office<sup>†</sup>||||||||0.106|||
|Average (parish-level) distance from municipal office<sup>†</sup>||||||||-0.178*|||
|First stage F statistic||||||81.480|38.210|18.730|18.290|36.460|
|<br>First stage partial R<sup>2</sup>||||||0.199|0.093|0.050|0.051|0.093|
|Over-identifyingrestrictions: Prob >χ2<br>*** p<0.01, ** p<0.05, * p<0.1.||||||0.613|0.470|0.260|0.127|0.207|

<mark>Note: Dependent variable is log of monthly profits. Distances are in logarithms. Confidence intervals calculated with robust standard errors. City and sector dummy variables are included in all specifications, but are not shown.</mark> <mark>† Location (parish-level) variables are calculated as excluded means.</mark>

> ‡ <mark>Dependent variable: log of monthly revenue per worker.</mark>

15
7 attention we should pay to its preservation. As a measure of taxonomic uniqueness, we use dummy variables to discriminate among three possibilities. First, a "Full Species' is our term for a genuine species in the generally accepted biological sense.'<sup>0</sup> Next, a "Monotypic Genus" is a full species that constitutes the sole representative of its genus." Finally, we use the term "Subspecies" to mean any taxonomic unit below the level of a full species. Of these three types, "Monotypic Genus" is the most taxonomically distinct, while "Subspecies" is the least.

Finally, a fourth factor to be considered does not relat directly to species value, but rather to the probability of preventing extinction.

4) Any preservation decision should pay some attention to the actual level of endangerment of the species in question. Other things equal, preservation dollars should go to reccver the more endangered species.'<sup>2</sup> Our data for endangerment comes from the Nature Conservancy (NC), which trcks an exhaustive subset of all vertebrate 'full species" in the U.S. and provides "global endangerment" ranks on a scale of I (most endangered) to _5_ (least endangered). Overall, the NC ranldng system is by far the most comprehensive and objective measure of species endangermen **t** hat we could find. Bach of the interval rankdngs 'The 'genemrly accepted" biological-species definition is typically ascribed to Ermst Mayr: "Species ar groups of actually or potentially interbreeding natural populations reproductively isolated from other such groups.

"The Genus is the taxonomic level just above species.

121n a fonnal model of biodiversity preservation, such as Weitzman (1993), a more appropriate statement is something like the following: other things equal, we should spend more money on species with higher marinal decreases in extinction probability per dollar spent. In practice, there probably is a high correlation between a species' 'absolute and 'marginal' levels of endangerment, so the two concepts may actually turn out to be similar. Throughout this paper we finesse the possible distinction between marginal and absolute lvev, of endangerment.
43

# <u>Annex 1: Definitions</u>

There is a legal obligation under the 1951 UN Convention on Refugees (UNCOR), to which nearly 150 UN members are party, not to return the refugee to his or her country of origin if that places them in harm’s way (non‐refoulement). At the same time, there is often confusion in the broad public about the distinction between an asylum seeker (who can be legally returned if his or her asylum application fails) and a refugee. There is also confusion between economic migrants – who are admitted based on very different criteria than a refugee, and who may arrive legally or illegally. For defining policy, it is important at the outset to clarify the distinction between economic and forced migrants and, within forced migrants between asylum seekers and refugees and, among returnees, between forced and voluntary returnees.

# **Annex Table 1 Term Definition**

|**Term**|**Definition**|
|---|---|
|**Economic migration**<sup>**44**</sup><br>|Economic migration is defined as a choice to<br>move to improve the_standard of living_by<br>gaining a better‐paid job.<br>|
|**Forced migration **<sup>**45**</sup>**and the forcibly**<br>**displaced**|Forced Migration is “a general term that<br>refers to the movements of refugees and<br>internally displaced people (those displaced<br>by conflicts within their country of origin) as<br>well as people displaced by natural or<br>environmental disasters, chemical or nuclear<br>disasters, famine, or development projects.”.<br>People that are part of a forced migration are<br>forcibly displaced.|
|**Refugee Status**|Refugees include individuals recognized<br>under the 1951 Convention relating to the<br>Status of Refugees, its 1967 Protocol, the<br>1969 Organization of African Unity (OAU)<br>Convention Governing the Specific Aspects of<br>Refugee Problems in Africa, those recognized<br>in accordance with the UNHCR Statute,<br>individuals granted complementary forms of<br>protection, and those enjoying temporary<br>protection. The refugee population also<br>includes persons in refugee‐like situations.<br>(A refugee, per the Convention, is unable or<br>unwilling to return to their country of origin<br>owing to a well‐founded fear of being<br>persecuted for reasons of race,religion,|

> 44 Source: <u>BBC</u>

> 45 Source: Columbia University, <u>Forced Migration Learning Module</u>