rafmacalaba/datause-encoder-data huggingface dataset:
There are no panel data that directly allow an exploration of the effects of becoming a widow. However, it is feasible to compare the situation of widows and non-widows across religious groups in Nigeria. We do so in this paper using the Nigeria 2008 and 2013 DHS.

The data indicate a striking disparity in the nutritional status of Muslim and Christian women in Nigeria. Christian women as a group have a significantly higher Body Mass Index (BMI)—a standard measure of nutritional status given by weight (in kg) divided by the square of height (in meters)—than Muslim women, with the differential rising with age. This can be seen in Figure 1, which plots BMI and the 95% confidence intervals, by religion and age using nonparametric regressions.<sup>3</sup> Low BMI may reflect heightened stress and undernourishment.<sup>4</sup> As striking an observation, but one not documented before to our knowledge, is the difference in the association of nutritional status with widowhood. In Figure 2, the sample underlying Figure 1 is disaggregated, separately by religion, into women who have never been widowed and those who have. It is arresting that among Christians, the ever-widowed show significantly lower BMI than non-widowed women from their late 20s. Equally notable is the dissimilar pattern displayed for Muslims. Women have a similar BMI trajectory until the everwidowed overtake non-widowed women in their mid-30s, although these last differences are not statistically significant. Strikingly, the religious gap in BMI is largest for non-widows, and considerably smaller for widowed women. This is true especially at older ages where more widows are found. In fact, the BMI gap is not statistically significant at ages over 40.

These marked disparities beg explanation. What factors underpin the differences in nutritional status?

Muslims account for roughly half the population of Nigeria but they tend to live in different areas to Christians. The North of the country, where Muslims predominate, has historically been disadvantaged. Poverty levels are much higher there and access to basic social and infrastructure services is considerably worse (World Bank 1996). However, there are overlaps—places with both groups present. Also, while Muslim women tend to receive less schooling, there are overlaps in the distribution of schooling. Given the overlaps in

> 3 In this paper, we use kernel weighted local polynomial regressions.

> 4 An individual is typically considered underweight if BMI is less than 18.5; and overweight if it is 25 and above. Based on the pooled DHSs for 2008 and 2013, rural Muslim Nigerian women have an average BMI of 21.6 compared to 23.0 for Christian women.  In urban Nigeria, the averages across the two groups are 23.2 and 24.3 respectively.

3
|Large scale<br>(0/1)|1924|0.470|0.499|0.000|1.000|
|---|---|---|---|---|---|
|ONI|1924|-0.155|0.811|-1.400|1.800|
|Recruit effect (-<br>1/0/1)|1924|-0.005|0.355|-1.000|1.000|
|ONI X recruit<br>effect|1924|-0.010|0.331|-1.800|1.800|

Table 2. Relationship Between La Paz and La Nueva Viga (LNV) Prices

||Common<br>|name of spe<br>|cies (in Spa<br>|nish)<br>||||||
|---|---|---|---|---|---|---|---|---|---|
|Variable|Almeja|Calamar|Camaron|Cazon|Jurel|Lisa|Mojarra|Ostion|Pulpo|
|LNV|0.313**|0.606***|-0.146|0.102***|-|0.145***|0.124|-|0.123***|
|log price|||||0.231***|||0.207***||
||(0.133)|(0.026)|(0.133)|(0.020)|(0.012)|(0.027)|(0.085)|(0.025)|(0.040)|
|Constant|-0.182|0.484***|5.769***|2.777***|3.691***|1.971***|1.851***|5.939***|3.637***|
||(0.449)|(0.069)|(0.655)|(0.073)|(0.035)|(0.076)|(0.292)|(0.113)|(0.172)|
|Obs|42|80|35|1528|1903|110|152|113|103|
|R2|0.121|0.877|0.035|0.016|0.170|0.207|0.014|0.373|0.085|

Note: All specifications use OLS with the log of the price in La Paz as the dependent variable.

4
conflict).<sup>9</sup> Thus, in this dataset, the dyads may exit the risk set if they experience armed conflict following a settlement, and then reenter on the condition that a new settlement is reached. Note that whenever a settlement is reached by any of the rebel groups in the conflict, the settlement becomes analytically relevant for all dyads in the conflict, regardless of whether these are signatories or not. Some of the dyads may engage in post-settlement violence more than once. For example, in the conflict between the Israelis and the Palestinians, several peace agreements have been signed, and the non-signatory Hamas have engaged in violence with the Israeli government following more than one of these agreements. Hence, I employ a duration model that can handle such repeated events.<sup>10</sup>

# **_Dependent Variables_**

The post-settlement period is often more complex than peace simply prevailing or breaking down, therefore it is important that our dependent variables reflect what a durable peace may entail. Given that the signatories as well as the non-signatories may use violence after an agreement has been signed, it is important to take into account both these dimensions of peace duration.<sup>11</sup> Since the unit of analysis is different for the two sets of data, the dependent variables used for the settlement and dyadic level are not the same.

In the analysis at the settlement level, two dependent variables are employed in order to capture different dimensions of peace duration, that is, the signatories’ commitment to peace, but also the overall peace duration in the conflict. The variable _Signatory Peace Duration_ measures the number of years of peace from the signing of the agreement until the government and at least one of the other signatories engage in

> 9 Some dyads drop out of the data earlier either because the incompatibility is resolved or due to the fact that they cease to exist. For instance, if a rebel group completely dissolves or takes over the government the dyad drops out of the dataset. Based on information in the Uppsala Conflict Database, each of the parties in the dataset was evaluated according to these criteria in order to determine whether the dyad should continue to remain under observation or not (UCDP, 2006).

> 10 More specifically, I use a conditional gap time model, see Box-Steffensmeier & Jones (2004: 159–161).

> 11 It can be the government as well as any of the rebel groups that is the first to return to armed conflict after an agreement. While this is an interesting aspect to consider, the data used here does not make it possible to determine which actor that first resorts to violence.

10
# **3.7 India (IND)**

India’s antidumping process has a single track determination of injury and dumping, which falls under the jurisdiction of the Directorate General of Antidumping and Allied Duties in the Ministry of Commerce. The Designated Authority initiates and carries out both the dumping and the injury investigations, and makes a ruling based on its findings. Information on these investigations are made public through The Gazette of India: Extraordinary (an issue of the Ministry of Commerce) and is available online at <u>http://commerce.nic.in/ad_cases.htm. More information regarding the Indian antidumping process can</u> be found in Kumaran (2005) and Banik (1998).

In addition to the standard antidumping information described in section 2, additional data collected for India is described below:

**Table 3.7.1 : Additional Indian AD Data Included in AD-IND-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-IN**|**D-Master :**||
|**U.**|**NOTES**|Comments|
|**In AD-IN**<br>**C.**|**D-Domestic-Firms :**<br>**PET**|A dummy variable taking a value of 1 if the firm/entity in COLUMN B<br>filed or supported the AD petition and 0 if the firm/entity forms part of<br>the domestic industry and is mentioned in the investigation but did not<br>file or act in support of the petition.|

## **In AD-IND-Foreign-Firms :**

**D. F_AD_MARGIN** The firm-specific final antidumping margin imposed **E. F_AD_DUTY** The  firm-specific final antidumping duty imposed **F. P_AD_MARGIN** The firm-specific preliminary antidumping margin imposed **G. P_AD_DUTY** The firm-specific preliminary antidumping duty imposed

25
Policy ReseaRch WoRking PaPeR 4623

# **Abstract**

Drawing on a compilation of data from household surveys representing 130 countries, many over a period of 25 years, this paper reviews the evidence on levels and recent trends in global poverty and income inequality. It documents the negative correlations between both poverty and inequality indices, on the one hand, and mean income per capita on the other. It points to the dominant role of Asia in accounting for the bulk of the world’s poverty reduction since 1981. The evolution of global inequality in the last decades is also described, with special emphasis on the different trends of inequality within and between countries. The statistical relationships between growth, inequality and poverty are discussed, as is the correlation between inequality and the growth elasticity of poverty reduction. Some of the recent literature on the drivers of distributional change in developing countries is also reviewed This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to is part of a larger effort in the department to document the levels and changes in global poverty and inequality. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fferreira@worldbank.org and mravallion@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
data comes from Penn World Tables version 10.1. _TFP_ is taken directly from the variable _rtfpna_ in the database, while the investment rate is given by _cshi_ . The lines in gure 3.1 correspond to simple averages among countries within each group. Average size dynamics for Singapore, Japan, and Korea 3.2 are constructed based on the data in Buera and Shin (2013). The average rm size for Chile and Romania was constructed from the supplementary material accompanying Bartelsman et al. (2009). For Hungary, the data comes from Varela (2017).

In terms of computing the average size dynamics for China, we have two data sources that we use for dierent purposes: the Census Yearbooks for 1995, 2004, and 2008; and the Annual Survey of Industrial Production conducted by the National Bureau of Statistics for the years 1998 through 2007. Since part our calibration of the Chinese economy in 1998 relies on matching the average size ratios with the US, we need to make sure that the dataset covers most rms in the economy in order to avoid biasing the calibration of the underlying distortion. Thus, for calibration purposes, we appeal to data from the Census Yearbooks as reported in Brandt et al. (2014). They report the total number of rms and the employment level from the Census Yearbooks of 1995, 2004, and 2008, allowing us to compute the average size in these years. The average size for 1995, our calibration target in the model, amounts to 166 workers. We plot this number along with the other two available data points in gure 3.2<sup>20</sup> of motivating facts.

We appeal to the alternative dataset, the NBSsurveys, to provide a longer and more continuous point of comparison for the model with respect to predictions about the evolution of average rm size during the reforms. The Annual Survey of Industrial Production conducted by the National Bureau of Statistics covers all non-state rms with 5 million yuan in revenue or more. Even though we nd this data useful for illustrating the evolution of the average rm size for a longer period of time (see gure 5.3), appealing to it for calibration purposes would have delivered a much higher value of the at component of the prot tax distortion. That is because in the surveys, the average size of an industrial rm in China in 1998 was 341 workers, twice as large as the magnitude emerging from the Census. Matching this target would have required a stronger disincentive to entrepreneurship in the model.

> 20The gure also shows a data point for 1993. We thank Gueorgui Kambourov for calculating this number for us. The source is the same as in Brandt et.al (2014), which did not report number of rms and employment data for the year 1993 in their work.

41
- DONATO, K., G. MILLER, M. MOHANAN, Y. TRUSKINOVSKY, AND M. VERA-HERNÁNDEZ (2017): “Personality Traits and Performance Contracts: Evidence From a Field Experiment Among Maternity Care Providers in India,” _American Economic Review_ , 107, 506–510.

- DONOVAN, K., W. J. LU, AND T. SCHOELLMAN (2023): “Labor Market Dynamics and Development,” _The Quarterly Journal of Economics_ , 138, 2287–2325.

- DUDLEY, N. M., K. A. ORVIS, J. E. LEBIECKI, AND J. M. CORTINA (2006): “A Meta-Analytic Investigation of Conscientiousness in the Prediction of Job Performance: Examining the Intercorrelations and the Incremental Validity of Narrow Traits,” _Journal of Applied Psychology_ , 91, 40.

- EDIN, P.-A., P. FREDRIKSSON, M. NYBOM, AND B. ÖCKERT (2022): “The Rising Return to Noncognitive Skill,” _American Economic Journal: Applied Economics_ , 14, 78–100.

- FLETCHER, J. M. (2013): “The Effects of Personality Traits on Adult Labor Market Outcomes: Evidence from Siblings,” _Journal of Economic Behavior and Organization_ , 89, 122–135.

- GLEWWE, P., Y. SONG, AND X. ZOU (2022): “Labor Market Outcomes, Cognitive Skills, and Noncognitive Skills in Rural China,” _Journal of Economic Behavior & Organization_ , 193, 294–311.

- GROH, M., N. KRISHNAN, D. J. MCKENZIE, AND T. VISHWANATH (2016): “The Impact of Soft Skills Training on Female Youth Employment: Evidence from a Randomized Experiment in Jordan,” _IZA Journal of Labor and Development_ .

- HECKMAN, J. J., J. STIXRUD, AND S. URZUA (2006): “The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior,” _Journal of Labor Economics_ , 24, 411–482.

- LAAJAJ, R., K. MACOURS, D. A. PINZON HERNANDEZ, O. ARIAS, S. D. GOSLING, J. POTTER, M. RUBIO-CODINA, AND R. VAKIS (2019): “Challenges to Capture the Big Five Personality Traits in Non-WEIRD Populations,” _Science Advances_ , 5, eaaw5226.

- LEJUEZ, C. W., D. R. HOPKO, AND S. D. HOPKO (2001): “A Brief Behavioral Activation Treatment for Depression: Treatment Manual,” _Behavior Modification_ , 25, 255–286.

- LINDQVIST, E., AND R. VESTMAN (2011): “The Labor Market Returns to Cognitive and Noncognitive Ability: Evidence from the Swedish Enlistment,” _American Economic Journal: Applied Economics_ , 3, 101–128.

- LIST, J., A. SHAIKH, AND Y. XU (2019): “Multiple Hypothesis Testing in Experimental Economics,” _Experimental Economics_ , 22, 773–793.

19
to privacy concerns. To overcome this challenge, I geo-reference the city/towns in which firms in the dataset are located and match them with other spatial datasets.

# **3.2.2 Ethiopian Large and Medium Manufacturing and Electricity Industries Survey**

The large and medium manufacturing and electricity industries survey (LMMIS) is an annual census of all large and medium manufacturing firms with at least ten employees and rely on electricity for production in Ethiopia. The data is collected by the Central Statistical Authority (CSA), and all firms that meet the criteria are mandated by law to comply with the requirements of the CSA and participate in the survey (Essers et al., 2021). As a result, the LMMIS captures the universe of all formal large and medium scale manufacturing entities in the country (Essers et al., 2021). The main limitation of the data, however, is that it mainly captures formal firms thus excluding firms in the informal sector.

The data provides information on inputs, outputs, investments and capital expenditure, capacity utilization, duration of operation, as well as the main issues confronting firms. Unlike the WBES, the LMMIS data does not have explicit questions on the intensity of outages faced by firms. Instead, the survey asks firms to list the major issues (including electricity outages) confronting them and in some instances whether these issues affected their ability to operate fully during the calender year. I leverage these responses and measure firms’ exposure to unreliable electricity supply based on whether firms cite outages as a major issue confronting their operation. Specifically, I construct a dummy variable equal to 1 if a firm indicates electricity outages as a major issue either: (i) currently facing the firm; (ii) responsible for not operating at full capacity; or (iii) responsible for not operating all year round; and 0 if otherwise. Data from 2011 to 2017 are used in this paper.

Further, using this census data, I compute for each district (Woreda<sup>14</sup> ), the number of firms operating in a given year, and firm density (number of firms per 1000 people<sup>15</sup> ) for

> 14Third administrative region in Ethiopia

> 15Subnational population data on Ethiopia were obtained from https://data.humdata.org/dataset/ ethiopia-population-data- ~~-~~ admin-level-0-3

13
**Table 5: Guarantees Contribution to the Hidden Deficit (CZK bn)**

||**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|
|---|---|---|---|---|---|---|
|Very high (90%)|0|0|0|9.7|46.5|27.9|
|High (30%)|0|0|0|4.9|6.1|0|
|Medium (15%)|0.7|0|2.0|0.4|0.9|1.2|
|Low (5%)|0.2|0|0.1|0|0|4.4|
|Sub Total|0.9|0|2.1|15.0|53.5|33.4|
|Budget paid out(-)|-0.8|-0.4|-0.8|-0.1|-2.0|-6.7'|
|Total|0.1|-0.4|1.3|14.9|_51.5_|26.7|
|_(as % of GDP)_|_0.0_|_-0. 0_|_0.1_|_1.0_|_3.1_|_1.5_|

It **turns** out that the "true" fiscal deficit in the Czech Republic (table 6), though significantly higher that the deficit calculated through conventional methods, is comparable with deficits of other Central European countries. Therefore the Czech Republic's fiscal performance, contrary to the widely accepted view, is not noteworthy for its fiscal restraint. Moreover, if left to grow as in the past, the off-budget risk to future fiscal stability can increase significantly. Presently, there is no institutional mechanism in the country mandated to keep a check on government off-budget obligations and the ensuing fiscal risk. And demands on new guarantees and further programs to be fmanced through various off-budget agencies are growing.

**Table 6: Czech Republic "True" Fiscal Deficit, 1993-1998 (% of GDP)**

||**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|
|---|---|---|---|---|---|---|
|Reported fiscal deficit|_-0.5_|-1.3|<br>0.3|_0.5_|<br>1.1|<br>2.1|
|"Hidden" fiscal deficit in the special institutions<br>(KOB, CI, CF and NPF)|3.2|1.9|1.0|_0.5_|<br>1.0|<br>2.0|
|"Hidden" fiscal deficit in guarantees<br>net hidden subsidy (risk-adjusted)|0|0|0.1|1.0|3.1|1.5|
|**"True" fiscal deficit**|**2.7**|**0.6**|<br>**1.4**|**2.0**|**5.2**|<br>**5.6**|
|(including the special institutions and guarantee net hidden subsidy)|||||||

The first troubling fact implied by the above discussion is the sharp increase in the amount and risk of guarantees issued by the state. The bulk of the increase has emerged from the government's support to banks and to the Czech Railways. In 1997 and 1998 the government issued CZK 22bn (1.4% of GDP) guarantee to Czech National Bank on some of its very risky 14
Depending on the input indicators (particularly if they are generally measures of financial viability), the FEI may as a whole be used to identify the likeliest candidates for PPP, though this is not the case for this study. The FEI in this IPF study includes NRW levels that represent the current levels of economic inefficiency of existing supply schemes and not of the newly‐ proposed projects themselves. Therefore, areas with existing high levels of NRW are prioritized as they represent a greater economic _need_ in terms of intervention. When considering PPPs, however, projects with the highest likelihood of greater economic efficiency and lower water losses are likely the best candidates for private participation.

Further, it is to be noted that this is a preliminary analysis and must be supported with exhaustive PPP project appraisal. PPPs have great potential to harness the financial resources and expertise of the private sector to expand the space for infrastructure development, but they require careful consideration with respect to commercial viability and sustainability, as well as the legal, regulatory, and organizational underpinnings required to support long‐term contracts.

32
# **3.1 Integrating Banking and Securities Supervision**

As shown in Table 6, there seems to be a high degree of integration between banking and securities supervision in the countries covered in the survey. The average level of integration, measured through an index formulated on the basis of the responses of countries to question 4.6 of the questionnaire (please see Annex 1), indicates a degree of integration of 73 per cent, in a scale ranging from 0 per cent (no integration at all) to 100 per cent (full integration). The areas of higher integration include accounting rules and off-site monitoring and analysis, both with a degree of integration of 79 per cent. The areas with less integration between banking and securities supervision include minimum capital adequacy requirements and requirements for licensing, with a degree of 69 per cent and 63 per cent of integration, respectively.

**Table 6. Harmonization between Banking and Securities Supervision**

|**asofDec.2001**||
|---|---|
|**Variables**|**Index**|
|1.Accountingrules|79%|
|2.Off-sitemonitoringandanalysis|79%|
|3.Consolidatedsupervision|76%|
|4.Componentsofcapital|73%|
|5.On-sitesupervision|72%|
|6.Minimumcapitaladequacyrequirements|69%|
|7.Licensing requirements|63%|
|**Average**|**73%**|

Countries included in the sample: Denmark, Estonia, Hungary, Iceland, Korea, Latvia, Luxembourg, Malta, Mexico, Norway, Singapore, Sweden, and the United Kingdom.

In most countries covered in the survey, the accounting principles used for the valuation of securities tend to be similar for both banks and securities firms. There are, however, some differences in the accounting treatment used in both types of intermediaries with regard to the adjustments required when computing the net adjusted capital of securities companies. In Singapore, for example, securities firms have to calculate adjusted net capital after deducting investments in subsidiaries and associated companies. Banks calculate their capital adequacy ratio at the group level by consolidating the value of such investments net of goodwill and treating them as risk-weighted assets.

# **Off-Site, On-Site, and Consolidated Supervision**

Most countries reported a high level of consistency in their practices for off-site supervision of banks and securities firms.1<sup>8</sup> Reportedly, most integrated agencies utilize similar procedures for collecting data from banks and securities companies. In fact, in some countries, such as the UK, a single financial reporting framework has been recently introduced to collect information from 18 The survey covers only issues related to the supervision of securities firms. The scope of securities supervision is broader. Besides securities firms, securities supervisors monitor securities markets, exchanges, collective investment schemes and disclosure by issuers.

19