rafmacalaba/data-use-mentions-extended huggingface dataset:
the 37<sup>th</sup> position among 190 parliaments in the world in terms of the number of women in the parliament (OSCE, 2020). But despite these remarkable improvements, women have remained underrepresented in leadership positions, especially in local government. For instance, in 2020 women still made up only 3 percent of the country’s 162 district governors (Petrov, 2020).

Broad indexes of gender equality suggest that Uzbekistan has achieved rather mixed overall performance. The 2019 Human Development Index value for women in Uzbekistan was 0.70 compared to 0.74 for men, resulting in a Gender Development Index (GDI) value of 0.94. This places Uzbekistan in the middle of neighboring countries’ performance, with GDI values for the Kyrgyz Republic somewhat higher 0.96, and somewhat lower in Tajikistan at 0.82. The 2019 round of the global Gender Inequality Index (GII) in 2019 ranked Uzbekistan in 62<sup>nd</sup> place out of 162 countries (UNDP, 2020). Benchmarking the index components reveals that women in Uzbekistan were not reaching their full human development potential mainly due to gender inequalities across three dimensions: i) reproductive health, ii) empowerment (measured by educational attainment and political participation), and iii) labor market participation. At 70.6, Uzbekistan scored modestly according to Women Business and the Law index for 2021, though its ranking has improved in recent years (World Bank, 2021).

> 1 The list of jobs prohibited to women from December 1999 to May 1, 2019 can be found here. This list was approved by the Ministry of Employment and Labor Relations (on December 24, 1999) and the Ministry of Health (on December 22, 1999) in consultation with the Council of the Federation of Trade Unions of Uzbekistan.

> 2 Available at https://lex.uz/ru/docs/4230938

> 3Available at https://regulation.gov.uz/
(Jayne _et al._ 2014a).<sup>1</sup> The extent to which such policies improved productivity, local economic activity, and equity remains unclear, largely because there are no systematic data on large farms.

We piggy-backed on the 2020 census in Zambia’s Eastern Province to obtain a list of all large farms that can serve as a sample frame, basic evidence on their activities and rights, and a map of cultivated fields. Overlaying this map with survey and sketch plans from the registry that were specifically digitized for this purpose,<sup>2</sup> allows distinguishing farms’ _de facto_ occupation from their _de jure_ rights. An in-depth survey of a representative sample of 3,000 large farms was then conducted to obtain more detailed data for analysis. The census provides an estimate (at most 26%) of the province’s agricultural area owned by large farms.<sup>3</sup> Discrepancies between official records and ground reality are large, consistent with the notion of weak

> 1 Zambia’s National Statistical Agency (Zamstats) defines large and medium farms as those larger than 5 ha and we refer to them as ‘large farms’ throughout the paper.

> 2 Digitization of cadastral data was supported by the Zambia Integrated Landscape Management Project (ZIFLP).

> 3 If applied nationally, even 26% of area owned by large farms would mean that 10 million ha was converted from customary tenure since 1995.

2
–|
|Nigeria|–||Pakistan|0.092|
|Morocco|0.000||Bangladesh|0.000|
|Tunisia|0.010||Malaysia|–|
|Iran|–||Indonesia|0.672|

_Source_ : Authors’ analysis of the Gwartney, Lawson, and Hall (2017) Economic Freedom of the World (EFW) index. Additional data come from the Polity IV dataset, Freedom House, the Database of Political Institutions (DPI), and the JuriGlobe database. _Note_ : Table presents estimated country weights to construct Synthetic Jordan based on the EFW index as the outcome. OIC denotes the full Organisation of Islamic Cooperation donor pool; OIC minus PAK denotes the OIC donor without Pakistan; and OIC SAL denotes the donor pool limited to OIC countries that received Structural Adjustment Loans (SALs) from either the World Bank or International Monetary Fund (IMF).

sizable gaps in Real and Synthetic Jordan’s EFW score in 2000 and 2005 correspond to a difference in EFW score of more than one standard deviation (see table S1.2 in the supplementary appendix S1). This result suggests that the improvements in economic institutions resulting from the refugee shock led to long-term improvements in Jordanian economic institutions.

Table 1 columns (3) and (5) are the gaps between the Real Jordan and Synthetic Jordans constructed from the OIC (minus Pakistan) and OIC-SAL pools of countries. These fits are poorer as the pretreatment RMSPE for OIC (minus Pakistan) and OIC-SAL were 0.104 and 0.119, respectively. However, the increase in Real Jordan’s EFW relative to the Synthetic Jordans in these two additional donor pools are similarly large to the preferred Full OIC specification and statistically significant in the same years of 2000, 2005, and 2010.

The RMSPEs in table 2 rows (2), (3), and (4) show the degree to which Synthetic Jordan fits Real Jordan - relative to the placebo synthetic control in pretreatment
frequent. Figure S1.6 in appendix S1 confirms that the differences are more important at higher levels of consumption.<sup>12</sup> As may be expected, we find more inequality in PSF than in ESPS data. Using per capita household expenditures, the Gini index is 47.1%, quite a bit higher than what we find in ESPS (38.9%) (using the series restricted so as to be comparable to PSF) and in the WDI (40.3% for 2011).<sup>13</sup> With such level of inequality, Senegal would be placed about 37 ranks higher in the ranking of countries by inequality level (computed from standard consumption data), from the 62nd position to the 25th one, between Venezuela and Chile.<sup>1415</sup> As we shall see in the next section, factoring in intra-household inequality will push Senegal even upward on the ladder of inequalities.

How does PSF modify the assessment of poverty? In order to answer this question we first need to define poverty. Two poverty lines are selected, following the basic needs approach. The lowest, nutrition, line corresponds to the cost of the food basket that provides at least 2400 kcal per day. The second line is a basic needs poverty line, used as the national poverty threshold in Senegal. It is obtained by augmenting the food poverty threshold with the amount of resources that is necessary to cover individual basic needs other than nutrition. This amount is established through the observation of the average non-food consumption of households for which food consumption per adult equivalent belongs to an interval of plus or minus 5% around the food poverty threshold (see Appendix S1).

Values of the two poverty lines for Dakar, other towns and rural areas separately are reported in table 2. The nutrition poverty line is very close to the $1.25 (PPP 2005) international line (that would be equal to 366 CFA francs at the time of
<!-- Start of picture text --> Dhaka - 2007 Dhaka - 2013<br>160<br>160<br>140<br>140<br>120<br>120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Storm induced outages Storm-induced outages<br>Annual average (all causes) Annual average (all causes)<br>Number of outages per month<br>Number of outages per month<br>January February March April May June July August September October November December January February March April May June July August September October November December<br><!-- End of picture text --> _Source: authors based on BREB data and ES data_ Overall these results provide two key insights. Firstly, the absolute frequency of outages is significantly larger than in developed countries studied earlier.<sup>18</sup> In particular, this is true for the number of outages caused by storms, providing evidence for relatively low resilience of Bangladeshi electricity supply. Secondly, the data indicates that the _share_ of storm-induced power outages in Bangladesh is significantly _lower_ than in the US or Europe – while increasing over time. This suggests that even for increasingly wellmanaged power systems natural shocks continue to pose a threat which grid operators struggle to deal with.

Spatial distribution of storm-induced power outages The number of storm-induced power outages varies significantly across divisions. Figure 3.8 shows the disaggregation of power outage reports by the seven Bangladeshi divisions<sup>19</sup> . The majority of storms that induced outage during the period 2000-2017 were recorded in Dhaka, Chittagong and Rajshahi, while none are attributable to Barisal. The reason for missing data for Barisal is unclear. While we had to exclude several PBS Zones in Barisal due to reporting and date issues, it is unlikely that Barisal did indeed not experience any storm-related outages in the included
in a regional war complex).

2. If the coding of civil war is expanded to include “internationalized internal armed conflict” (type 4, denoted by I when there is no overlap with type 3 denoted by O) and the intensity continues to be “war,” some countries that were not coded as having a civil war now are Congo-Brazzaville, Guinea, Guinea-Bissau, and Senegal. In addition, the conflicts of some countries are denoted by F; these countries intervene in violent conflicts in other countries but do not experience associated violence within their own boundaries, which is hardly anyone’s idea of a civil war. Mauritania, Namibia, and Tanzania would not even appear in table 1 except for these types of conflicts. Such a conflict seems to be the only reason that Namibia appears in table C1 of Miguel et al. (2004), and such coding is sometimes material for their econometric inferences (Jensen and Gleditsch 2009). Tanzania seems to appear as a result of its invasion of Uganda after Amin attempted to annex part of Tanzania, so these dates for both countries seem closer to an interstate war than either type of internalized conflict. Ugandan exiles in Tanzania were also involved but probably could not have undertaken hostile action against Amin alone. Libya appears almost only for its interventions, except that in 1987, it was burned in its intervention in Chad, leading to the recording of an I.<sup>1</sup> 3. Even those countries that would be coded by battle death–based rules as having had civil wars under the “war” intensity (1,000 battle-related deaths) in the PRIO/Uppsala data often show relatively few years of warfare (row 1 of table 1). Similarly, there are often oddly isolated years in what would seem, from narrative accounts, to have been sustained periods of conflict over the monopoly of force. Liberia is such an example, which experienced violent contestations of the monopoly of force following the Doe coup in 1980
These micro-data sets are complemented by administrative data on transfers and taxes from the Ministry of Treasury and Finance (MoTF), as well as the Turkish tax code and laws to understand the design features of each specific fiscal intervention.

# _Empirical Approach_

There are largely three steps involved in the empirical approach of a CEQ. First, assess the coverage of the study. Second, measure taxes and transfers in the survey. Third, construct poverty and inequality measures for distributional analysis. While these are all important steps on its own, the second step is, by far, the most complex and labor-intensive component of the exercise.

_Step 1_ : The first step in the approach is to examine the HBS questionnaire, assess what information is being collected about the interaction of the household with the economy and its different markets, evaluate how questions are asked, and inventory which fiscal interventions can be plausibly studied. This determines the coverage of the study.

As a result, it was assessed that the following taxes and transfers can be analyzed using the HBS questionnaire, and subsequently captured in the study:

- _Direct taxes:_ Payroll Income Tax, Personal Income Tax, (including Agricultural Income Tax, Rent Income Tax from Workplace, Interest Income Tax, Private Pension Income Tax(Individual Retirement System Income Tax)), Motor Vehicle Tax, and Residence Property Tax.

- _Indirect taxes_ : VAT and Special Consumption Taxes.

- _Direct transfers:_ Total Direct Transfers, Old Age Transfer, Family Transfers, Disability Benefit, Home Care Benefit, Health Premium Fee Waiver, Disability Contributory Benefit, Scholarship Transfers, Unemployment Transfers, Near Cash Transfers, Sickness Transfer, Agricultural Support Transfer, and AGI.

- _In-kind transfers_ : health and education services.

_Step 2:_ The second step in the approach is to measure taxes and transfers for households in the HBS. Using laws, codes, program rules, plausible assumptions, and household survey responses we calculate how much each household is paying in taxes, and how much is receiving in transfers.
17 Ultimately the goal for universal access to clean energy is abandonment of polluting energy. This study therefore also examined if, in the countries selected for abandonment, there were other households who had not used LPG and used only electricity. The selection criterion—only those areas where one-fifth of the population cited LPG as their primary cooking fuel—meant exclusion from the sample of Botswana, Eswatini, Namibia, South Africa, and Zambia, where few cooked with LPG and most households reliant on clean energy used electricity. As a result, there were few households that had used electricity to meet all their household needs (Figure 10).

**Figure 10  Share of population abandoning polluting fuels**

<!-- Start of picture text --> Electricity only<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : * = urban areas only.

Another question is the extent to which adoption of LPG as the primary cooking fuel is correlated with abandonment of polluting fuels (Figure 11). Virtually all households citing LPG as their primary cooking fuel kept using polluting fuels in urban Burkina Faso. In Cameroon, Ghana, urban Kenya, and Lesotho, more than three-fifths of the population continued to use polluting fuels. By contrast, virtually all households in Mauritius had abandoned polluting energy for all activities, closely followed by Gabon. In Côte d’Ivoire, Mauritania, and urban Nigeria, once a household adopted LPG as the primary cooking fuel, more than three-fifths abandoned all other fuels. When the results were broken down by expenditure quintile, there were no marked differences between lower and higher quintiles. Such findings about expenditure quintiles, however, should be interpreted with caution because the sample size of those citing LPG as the primary cooking fuel is small in lower quintiles in many countries. For example, the percentage of the population living in households in the bottom quintile citing LPG as the primary cooking fuel exceeded 10 percent only in Gabon,
(2011 PPP), percent of population (2011 PPP), percent of population<br>Government expenditure on<br>education (PPP$ millions) in logs<br>Public health expenditure per capita (PPP current international $) in logs<br>ages 15 and above<br>Physicians, per 1,000 people<br>Illiteracy rate, percent of people<br><!-- End of picture text --> _Source_ : Authors’ calculation based on data from World Bank.

_Note_ : Data are for latest observation available including 142 countries from the World Bank’s POVCALNET database. Poverty headcount ratio uses the poverty measure of $3.20 per day in 2011 purchasing power parity (PPP) terms.

30
**Table 3.6.1 : Additional EU Data Included in AD-EUN-v1.0.xls**

|**Column**|<br>**Variable Name**|**Description**|
|---|---|---|
|**In AD-E**|**UN-Master :**||
|**U.**|**INIT_GP**|The government publication containing the initiation notice|
|**V.**|**P_AD_DUTY**|The preliminary antidumping duty imposed. If there were several duties<br>imposed, this column contains the maximum dumping duty|
|**W.**|**F_AD_DUTY**|The final antidumping duty imposed. If there were several duties<br>imposed this column contains the maximum dumping duty|
|**X.**|**P_AD_MARGIN**|,<br>The preliminary dumping margin assessed. If there were several<br>dumping margins assessed, this column contains the maximum dumping<br>margin found|
|**Y.**|**F_AD_MARGIN**|The final dumping margin assessed. If there were several dumping<br>margins assessed, this column contains the maximum dumping margin<br>found|
|**Z.**|**P_GP**|The government publication containing the preliminary outcome of the<br>investigation|
|**AA.**|**F_GP**|The government publication containing the final outcome of the<br>investigation|
|**AB.**|**NOTES**|Comments|
|**In AD-E**|**UN-Products :**||
|**D.**|**CN_ P_ex**|This column indicates whether the HS code (referred to as the CN code<br>in the European Union
high school or completion of a four-year college degree. We expect the likelihood of account ownership to increase with the individual’s level of education.

_Married_ indicates whether a respondent is married, and _Divorced/Separated_ indicates whether a respondent is divorced or separated. The variable _Household Size (log)_ is the logarithm of household size, including the number of children. We speculate that adults who live in larger households (including a spouse) are more likely to use someone else’s account, and less likely to own their own.

For employment status, each respondent falls into one of four categories, represented by four variables. The variable _Wage Employee_ captures those respondents who, either full time or part time, are employed by an employer. _Self-Employed_ captures respondents who work for themselves (and do not report also earning a part-time wage). _Unemployed_ equals 1 if the person does not have a job and is looking for one. _Out of Workforce_ is a dummy that takes the value 1 when the individual does not have a job and is not looking for one _._ In general, we expect employed individuals to be more likely to have a bank account, since employers may require accounts to pay salaries.

Aside from controlling for individual-level variables, our estimations also consider a large set of country-level characteristics and policies that might influence the different dimensions of the use of bank accounts (captured by x1i , x2i, x3i, and x4i in equations (1)-(4) above). The variable _GDP per capita (log)_ is the logarithm of gross domestic product (GDP) per capita in constant 2000 U.S. dollars in 2009 and comes from the World Development Indicators of the World Bank (2012). The remaining explanatory variables relate to account costs, documentation requirements, proximity to bank outlets, the regulatory environment, banking 19
and economic growth), SDG 9 (infrastructure), SDG 10 (inequality), SDG 16 (peace, justice, institutions), and SDG 17 (partnerships). The WBG is also involved in the monitoring of another 22 indicators, under an even wider range of goals.

- _Atlas of Sustainable Development Goals._ Since 2017, the WBG has produced the Atlas of Sustainable Development Goals, which provides a visual guide to the SDGs. The WBG’s _World Development Indicators_ includes many of the data indicators that are tracked under the SDGs.<sup>14</sup>

- _Human Capital Index._ The WBG’s Human Capital Index assesses how much human capital a child born today can expect to acquire by the age of 18, given the risks related to health and education prevailing in the country where the child was born (Figure 2B). The index considers indicators closely linked to the SDG targets—specifically, under-5 mortality

> 12 An increasing number of countries conduct voluntary national reviews, from 22 in 2016, the year the process was introduced, to an expected 51 in 2019.

> 13 As “custodians,” international agencies work with national statistical offices to develop methodologies for indicators to measure progress on the SDGs. The agencies also work with countries to compile data for SDG indicators, which they submit to the UN Statistics Global SDG database.

> 14 SDG-related indicators can be explored in the _SDG dashboard_ (http://datatopics.worldbank.org/sdgs/), a web-based interface that allows further exploration of the data (e.g., showing how a particular economy is performing across the 17 goals).

10
# **_Social Standards_**

While product and labor standards have become codified, as governmental regulations in many markets and across most GVCs, suppliers had to adapt to the increasing importance of voluntary standards. In many instances, voluntary standards have become de facto mandatory obligations and a commercial imperative (Swann 2010). Their emergence reflects social concerns “unleashed by globalization” over inadequate government regulation of the social and environmental impacts of international trade (Mayer and Gereffi 2010). The governance of GVCs is increasingly determined by what the buyer requires, and not merely what a supplier can offer.

Higher _social standards_ could increase the demand for goods and services from reputation-conscious foreign investors or international buyers, but also increase labor costs (for example, due to higher contributions to unemployment, pension, or health insurance), while not necessarily improving the quality of those inputs. This could have a positive or negative effect on a country’s attractiveness. Higher labor costs, in turn, can trigger local firms to save costs, for example, via increased offshoring, or to raise their productivity levels. The extent of generated spillovers via GVC integration is therefore not clear.

However, a growing number of studies now show that profits, productivity, and firm survival are all positively associated with improvements in working conditions (World Bank Group 2015). Several papers have taken various approaches to try to identify the relationship between firm performance and working conditions. A discrete-time survival analysis model of 595 Cambodian garment factories by Jetha and Linsen (2015) found that increased compliance with social protection standards is associated with reduced odds of factory closure. A survey of management and laborers in the Lao People’s Democratic Republic’s garment sector finds that failure to increase worker well-being has led to dissatisfaction, high labor turnover, low productivity, and impaired firms’ opportunities to strengthen participation in garment GVCs (World Bank 2012).

# **_Environmental Standards_**

Finally, higher _environmental standards_ may lead to growing demand for inputs from foreign investors or