rafmacalaba/data-use-mentions-extended huggingface dataset:
no differences between the three largest cities. Overall, about 10 million people across the country are currently in need for adequate housing. Brazil’s HH survey is one of the few surveys that provides information on ethnic origin. The difference between indigenous groups, Afro-Americans, and others is small but significant. Indigenous peoples are less likely to live in adequate housing (93 percent), compared to AfroAmerican peoples (95 percent) and Whites (95 percent).

# **5. Estimating the Stock and Flow Housing Deficit**

The main issue in developing an empirical estimate of the housing deficit is conceptualizing what a stock or flow housing deficit precisely encompasses. The computation of the _AHI_ presents the

> 17 Research has shown that adequate housing is strongly associated with better educational outcomes, particularly in children as it is associated with lower numbers of disruptive moves, evictions, or crime (e.g., WHO 2018). Yet, the direction of the association can also be reverse: education of HH heads can also have an impact on housing adequacy as more educated HHs might place higher importance to adequate housing conditions, or as more educated HHs might have more financial means or information to access housing finance. 18 This may also reflect that HHs employed in the agriculture sector are more likely to live in rural areas.
Figure 4: Size Distribution of POEs and SOEs <!-- Start of picture text --> (a) By Employment (b) By Operating Revenue<br>SOEs vs POEs SOEs vs POEs<br>2011-19 2020 2011-19 2020<br>Note: Tests of differences in means and medians between SOEs and POEs, p-valules are, respectively, 0.000 and 0.000 Note: Tests of differences in means and medians between SOEs and POEs, p-valules are, respectively, 0.000 and 0.000<br>POE 10-24.9% 25-49.9% 50% or more POE 10-24.9% 25-49.9% 50% or more POE 10-24.9% 25-49.9% 50% or more POE 10-24.9% 25-49.9% 50% or more<br>10 25<br>8<br>20<br>6<br>15<br>4<br>Log employment<br>Log operating revenue10<br>2<br>0 5<br><!-- End of picture text --> Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. SOEs are defined as firms where national or subnational governments have ownership stake of at least 10%, while POEs are firms where national or subnational governments own 0-9.9%.

# 5. Main Findings

The first part of this section presents the main results regarding whether ownership stakes (i.e., majority and minority) in SOEs and control levels (i.e., directly vs. indirectly owned and centrally vs. locally owned) of SOEs matter for firm performance. It also presents results of whether performance differences between POEs and SOEs depend on the type of sectors they operate, and whether ownership also matters for subsidy allocation. The second part presents the results of whether SOEs could act as buffers in attenuating some of the effects of the COVID-19 crisis. The final part presents how SOEs’
51 Figure IV: Dynamic Ban Effects on Firms’ Agricultural Exports (High vs Low Fertilizer Intensity) <!-- Start of picture text --> −4 −3 −2 −1 0 1 2 3 4<br>Quarter relative to period when treatment first changes (t=0)<br>.4<br>.3<br>.2<br>.1<br>0<br>−.1<br>−.2<br>−.3<br>Effect on log(exports) −.4<br>−.5<br>−.6<br>−.7<br><!-- End of picture text --> _Notes_ : The chart shows De Chaisemartin and d’Haultfoeuille (2022) estimates of the effects of fertilizer import bans on firms’ agricultural export value, relative to the last quarter before the ban takes effect, using a firm-year level export panel. The sample covers the Q1/2019-Q3/2022 period (but only coefficients in Q2/2020-Q2/2022 are reported) and includes all firms who exported at least one of 23 crops for which we have fertilizer intensity data in 2017-2019. Each firm’s fertilizer exposure is computed as a weighted average of crops’ fertilizer intensities with weights given by each crop’s share in the firm’s 2017-2019 exports. The treatment group consists of firms whose fertilizer exposure is above the sample’s 75th percentile. Firms below the 75th percentile serve as the control group. The treatment period is defined as the set of all quarters when any fertilizer import bans were in place. The y-axis shows the effects of fertilizer import bans on log value of agricultural exports, where negative values correspond to a decrease in exports. The p-value of the joint significance test for the placebo estimators is 0.22.
5 needs to be added domestic support to primary agriculture in non-OECD countries, which is another $7 billion in 2001 according to the GTAP database (Dimaranan and McDougall 2005, Ch.16b). These estimates are shown in row 1 of Table 1.

For market price support provided through trade measures, the GTAP database relies on applied tariff rates including preferential rates where applicable, plus export subsidy notifications by members to the WTO Secretariat (Dimaranan and McDougall 2005, Chs.16d and 16e). By contrast, the OECD relies on domestic-to-border price comparisons to capture the combined effect of all trade measures, both tariffs and such non-tariff barriers as quarantine restrictions. In Table 1 the cost to producers of having to pay higher prices for intermediate inputs that are subject to tariffs or export subsidies has been netted out of the gross benefits to downstream producers of market price support measures in the GTAP database.<sup>5</sup> For primary agriculture in OECD countries, the two market price support estimates differ substantially – $46 billion in the GTAP database compared with $139 billion in the OECD’s PSE estimate for 2001 (see first two columns of row 3 of Table 1). This is mainly because the GTAP method does not capture the protective effect of nontariff barriers (NTBs) such as Sanitary and Phyto-Sanitary (SPS) measures or other technical barriers to imports that may provide additional economic protection, in contrast to the OECD measure which is does. Such NTBs are still a major trade restriction despite GATT and WTO attempts to convert all non-tariff barriers to tariffs.<sup>6</sup> When direct subsidies are added to market price support the total support is the equivalent of a 22 percent ad valorem subsidy equivalent, whereas the comparable rate for the OECD Secretariat’s estimate is 44 percent (which is equal to its Producer Support Estimate (PSE) of 31 percent of the support-inclusive value of production). That difference between the GTAP and OECD’s rates is very consistent with a
We have constructed a data set for _30_ Chinese provinces, and for the years 1994-97.9 We have estimated data for the non-state sector by deducting state sector figures from economy-wide figures, assuming the economy is made up of the state and non-state sector. As constructed, the non-state sector includes collective owned units (urban and rural), joint owned, sharing holding and foreign funded firms. The series we focus on are the total industrial value-added, fixed capital asset, and the total number of industrial employees in state and non-state sectors. Industrial value-added and total fixed capital assets of industrial enterprises are those reported for industrial enterprises with independent accounting systems. Limiting our analysis to those "independent accounting units" of the industrial sector introduces some bias towards large firns, but it still captures a lot of the action in state industry, as it accounts 95 percent of SOEs gross industrial output. Both fixed capital asset and industrial value-added are deflated by a price index of investment in fixed asset and ex-factory price indices of industrial outputs based on 1996 price.

Our data set illustrates well several of the features described above. The difficulties encountered by containment in the mid-nineties are clearly apparent. While the share of total labor and capital received by the state sector has remained roughly the same (60+percent of each), its share in value added has been falling (from 54 to 46 percent between 1994 and 1997). This fall in efficiency supports the notion that labor and capital are not as productively employed in the state 9 Due to different methods of data compilation used before 1994 in the China Statistical Yearbook, we cannot obtain consistent private sector data on industrial value-added, fixed capital asset and industrial labor. Our analysis is therefore limited to the past four year's data. While aggregate data is known to be of poor quality, this is perceived by many analysts to be especially the case for the 1995 series, which we treat below with more caution.

8
and usage of mobile internet.

Research findings have provided evidence on the development impacts of mobile internet access and usage. Katz and Callorda (2018) estimate that a 10 percent increase in mobile broadband penetration is associated with a 1.8 percent increase in gross domestic product (GDP) in middle-income countries, and a 2 percent increase in GDP in low-income countries. Mobile broadband coverage is also shown to have large and positive impacts on household consumption levels (Bahia et al. 2020). Mobile internet access and usage are also found to have a positive impact on people’s happiness and well-being (GSMA and Gallup 2018) and women’s empowerment (Bailur and Masiero 2017). It is argued that access to and usage of information and communication technology (ICT) reduce poverty, by fostering access to and exchange of information, and improving the transparency and accessibility of public services—all benefits that would also apply to mobile internet (Cecchini and Scott 2003; Roller and Waverman 2001; Waverman, Meschi, and Fuss 2005).

Despite the potential of mobile internet to help achieve development goals, there is a significant digital divide among and within countries. According to the Organisation for Economic Co-operation and Development, the digital divide refers to the “gap between individuals, households, businesses and geographic areas at different socio-economic levels with regard to both their opportunities to access ICTs and to their use of the Internet for a wide variety of activities”. According to the Global System for Mobile Communications (GSMA 2019a), those who are not connected through mobile internet are disproportionately rural, women, or illiterate. For instance, in developing countries, women still remain 10 percent less likely than men to own a mobile phone, and 23 percent less likely than men to use mobile internet services. Similarly, rural populations are 40 percent less likely to use mobile internet than urban populations (GSMA 2019b).

> 2 TeleGeography. 3 ITU statistics. 4 World Development Indicators. 5 World Development Indicators.

> 6 World Development
.437)<br>(0.266)||
|Treated*No-Contract|0.061**|0.069**|
||(0.026)|(0.032)|
|No. of Observations|4,587<br>4,587<br>2,925|2,925|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: All regressions are using the Model 3 specification (see note to Table 2). For Heckman model, the 95% bootsraping percentile confidence intervals are in the square brackets, which are calculated by pair cluster bootstrap with 1000 replications. The sample size of Panel A is 4,587, and the sample size of Panels B and C is 2,881. For reweigting results, robust standard errors clustered at workplace of household head are in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

34
(INEGI) to understand the income, spending, socio-demographic, and employment situation of the country. It is a nationally representative, cross-sectional household survey. The 2014 MCS-ENIGH survey covers 58,125 households, of which approximately 75% are urban and 25% are rural.<sup>10</sup> The survey samples 896

> 10 Rural areas are defined as localities with fewer than 2,500 inhabitants.

11
to induce more negative employment effects among females during the pandemic recession, studies have also demonstrated the possibility of the ‘added-worker effect’, where non-employed spouses (more often to be female, particularly in

> 7 It is equivalent to USD 149 billion at PPP exchange rate of 1 USD = 4,675.22 in 2020.

> 8 Using O*NET data, Albanesi and Kim (2021) showed that women are overrepresented in high-contact and inflexible occupations; 73% of workers in these sectors were female.

8
4,423||

Notes: _(1)_ = Estimations controlling by the youth’s age and gender variables; provider’s age, gender and schooling. _(2)_ = additional to the variables of (1), entities of the sample of ENCEL, _marginalization_ (poverty) index in 1995 of the villages in the sample, and identification of the control or treatment village of the experimental evaluation of 1997-2000. _(3)_ = additional variables of (2), speaking an indigenous language and migrant. OLS= Ordinary least squares. Clustered Standard errors in parentheses. Level of significance *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1. Source: estimations performed based on data from the Evaluation of Rural Households Survey (ENCEL) 2017, complemented with data from the panel of ENCEL 1997-2000.

For both the health and education indicators, the rank-rank models provide larger estimations of mobility potential than the estimations that result from directly using the level of the indicator (58% and 71%, respectively). As indicated in the methods section, the estimation of mobility with the rank-rank models are more robust because they reduce biases stemming from extreme values.

25
The practice of assigning different courses of action to different groups and comparing outcomes dates back thousands of years. In the Old Testament, King Nebuchadnezzar orders a group of his subjects to eat rich meat and drink wine while another group is made to adhere to vegetarianism in order to evaluate the merits of the two diets (1 Daniel 11–16, New International Version). Versions of this approach have since been used in countless other efforts to evaluate competing hypotheses, from eighteenth century studies of scurvy treatment to the A/B testing now common in technology firms.

One particular version of this approach is the randomized controlled trial (RCT). An RCT is usually a large-scale study, prospectively designed to test a small set of hypotheses by randomly assigning treatment(s) to participants. Medical scientists have implemented hundreds of thousands of RCTs since the mid-1940s to test hypotheses about treatment options and inform care decisions<sup>1</sup> . In the 1980s, several studies showed that RCTs in medical research yielded less biased treatment effect estimates than observational studies. The method has been adopted in several scientific fields, as well as by the US Food and Drug Administration (FDA) and other government agencies, as the “gold standard” of empirical evidence (Vader 1998).

Not all RCTs are created equal, however. Meta-analysis of thousands of medical RCTs has revealed several pitfalls that skew effect estimates and lead to erroneous conclusions (Jüni, Altman, and Egger 2001). Broader conclusions based on flawed studies have led to the use of drugs or procedures that bring no measurable benefit and, in some cases, even cause harm (Goldacre 2014). In the past two decades, medical

> 1. There were 183,991 trial protocols registered in the US Government’s Clinical Trials database (www.clinicaltrials.gov) as of 11 February 2015. It is perhaps impossible to know how many trials have ever been conducted, as public registration of trial protocols was not common practice until the late 1990s
|33.1|31.1|30.4|29.2|-6.1|-2.3|-3.7|
|Center gulf region|35.0|34.5|33.6|32.5|-1.3|-2.6|-3.3|
|North central region|33.8|33.6|31.6|29.9|-0.4|-6.1|-5.3|
|North pacific region|42.9|42.7|41.6|40.1|-0.4|-2.5|-3.7|
|Central pacific region|35.2|34.8|33.3|32.4|-1.2|-4.4|-2.6|
|South pacific region|28.9|28.2|26.4|26.5|-2.5|-6.3|0.3|
|Peninsula region|35.4|34.3|33.2|31.4|-3.1|-3.2|-5.5|
|Border|40.4|40.4|39.0|35.6|-0.1|-3.4|-8.7|

**Source: Own calculations using data from ENOE.**

The distribution of wage changes by economic activity show some interesting patters. In 2009 average wages fell in all sectors but two: manufacturing and government services. Note that manufacturing was also the sector that endured the largest job losses, showing a clear pattern of adjustment to the crisis through quantities rather than prices. The case of government services is perhaps due to the fact that wages in the public sector are less sensitive to economic crisis, particularly if the crisis does not have budgetary implications
Policy Research Working Paper 9121

# **Abstract**

Intra-household inequalities have long been a source of concern for policy design, but there is very little evidence. The current practice of ignoring inequality within households could lead to an underestimation of both overall inequality and poverty levels, as well as to the misclassification of some individuals as regards to their poverty status. Using a novel survey for Senegal in which consumption data were collected at a disaggregated level, this paper quantifies these various effects. In total, two opposing effects, one on mean and one on inequality, compensate each other in terms of the overall poverty rate, but individual poverty statuses are affected. Intra-household consumption inequalities accounts for 14 percent of inequality in Senegal. The authors uncover the fact that household structure and organization are key correlates of intra-household inequality and individual risk of poverty.

This paper is a product of the Knowledge and Strategy Team, Development Economics. 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 sylvie.lambert@psemail.eu.

_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
cherry-coffee crops. Thus, and similar to the main rainy season, there are fewer opportunities for monitoring child labor activities in the harvesting season relative to the short rainy season.

We thus estimate the following multivariate equation, _yijt_ = α + β 1 _SRij_ + β 2 _Gij_ + β 3 ( _SRij_ × _Gij_ ) + β 4 ( _SRij_ × _rainit_ ) + β 5 ( _SRij_ × _harvit_ ) + β 6 ( _Gij_ × _rainit_ ) (2) + β 7 ( _Gij_ × _harvit_ ) + β 8 ( _SRij_ × _Gij_ × _rainit_ ) + β 9 ( _SRij_ × _Gij_ × _harvit_ ) + _X ijt_ ' γ + _St_ + _c j_ + ε _ijt_ where _rainit_ is and indicator for the main rainy season and _harvit_ is an indicator for the harvesting season. The coefficients of interest are β 8 and β 9 . We formally test the null that β 8 = β 9 = 0

# **4. Results**

# **4.1 Differences in child labor statistics by respondent type and by season**

Table 2 presents differences in means for child labor indicators by treatment assignment and disaggregated by gender for the pooled data and each season separately. The upper panel shows means at the extensive margin while the lower ones at the intensive margin. In each case, we test for differences in means between self- and proxy statistics based on a 30-day recall period. Descriptive statistics for the 7-day recall period depict similar patterns and thus are not discussed but are available in the Appendix Table A.1.2.

By looking at the left panel for the pooled data, we observe that participation in farm work obtained from the child (60.4%) is statistically significantly different from the one obtained from the proxy-respondent (56.1%). This discrepancy is driven by the girls’ subsample that reports 7.2 percentage points (or 15%) higher rate of participation than the mean participation obtained from the proxy. On the contrary, the difference in reports is marginal
Policy Research Working Paper 10672

# **Abstract**

Various challenges are thought to render female-headed households (FHHs) vulnerable to poverty in the Arab region. Yet, previous studies have had mixed results and the absence of household panel survey data hinders analysis of poverty dynamics. This paper addresses these challenges by proposing a novel typology of FHHs and analyzes synthetic panels constructed from 20 rounds of repeated cross-sectional surveys spanning the past two decades from the Arab Republic of Egypt, Iraq, Jordan, Mauritania, the West Bank and Gaza, and Tunisia. The paper finds that the definition of FHHs matters for measuring poverty levels and dynamics. Most types of FHHs are less poor than non– FHHs on average, but FHHs with a major share of female adults are generally poorer. FHHs are more likely to escape poverty than households on average, but FHHs without children are the most likely to do so. While more children are generally associated with more poverty for FHHs, there is heterogeneity across countries in addition to heterogeneity across measures of FHHs. The findings provide useful inputs for social protection and employment programs aiming at reducing gender inequalities and poverty in the Arab region.

This paper is a product of the Development Data Group, Development Economics. 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 hdang@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
, and the Adult Literacy and Life Skills Survey (ALL), carried out between 2003 and 2008. Based on these surveys, UNESCO began the Literacy Assessment and Monitoring Programme in 2003, which aimed to measure the literacy and numeracy skills of youth and adults in developing countries (OECD 2016a). PIAAC and STEP modules are based on the Skills, Technology, and Management Practices survey, which was developed for the U.S. based on the Current Population Survey and the National Adult Literacy Survey.

> 7 STEP has two surveys: a housheold survey and a firm survey. The household survey includes a direct reading assessment and an indirect (self-reported) assessment of other competencies and job-relevant and behavioral skills (Pierre et al. 2014). The firm module asks about the skills gap that employers perceive at an aggregated occupational level (management, professionals, and technicians and associate professionals).

4