rafmacalaba/data-use-mentions-extended huggingface dataset:
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<!-- Start of picture text --> Employment Levels Since Peak in January 2008<br>Manufacturing and Construction<br>2 LO —PePe _<br>SX a<br>NO —<br>2008m1 2009m1 time;; 2010m1 2011m1<br>—— Manufacturing ——— Construction<br>——— Durable goods ———— Non Durable goods<br>Source: suthor's calculations based on Buresu of Labor Statistics<br><!-- End of picture text --> <!-- Start of picture text --> Employment Levels Since Peak in January 2008<br>Total Employment in Private Sector: Goods vs Services<br>;<br>a<br>"<br>2008m1 2009m1 time;; 2010m4 2011m1<br>— Total Private ————— Goods Producing<br>——— Services<br>Source: author's calculations based on Bureau of Labor Statistics<br><!-- End of picture text -->
organization provides that information and regions with an active groups making sovereignty claims are coded _sd_ = 1 and 0 otherwise. Most claims are non-violent. We also identify claims that are made violently ( _violent_sd_ ). These are usually low-to-intermediate level armed conflicts and, in a few cases, separatist wars.

We have coded 222 regions as having a movement demanding more sovereignty, 47 of which are violent. Conflict was coded on the basis of encyclopedic sources (Minahan 1996; Dagenhard 1988), news archives (Keesings, Lexis-Nexis) and country case studies, so we are not limited by the sample selection problems that arise in the Minorities at Risk (MAR) database or other comparable sources since we collected data for all countries in our sample and not just those with a significant level of minority discrimination. We have data for small groups such as the Friulians in Italy, or nonviolent groups in developed democracies, such as the Lombardians (again in Italy), which would typically be excluded from MAR and other datasets of conflict.

Two risks associated with including non-violent groups is that we can count even fringe groups that have little popular support; and that it is difficult to establish the start and end-dates of activity for nonviolent movements. One can quibble over which movements are active in a given year, or which cases represent legitimate movements. Our approach is to flag those cases that are potentially ambiguous and discuss them in detail in the supplement, explaining why some were dropped from the analysis and others included. We drop some groups that are mentioned in encyclopedic sources if closer investigation reveals them to be too small (e.g. Koryaks and Itel’men in Russia’s Kamchatskaya oblast); if their political organizations represent minor fringes of groups with extremely low levels of public support in their group (e.g. Newfoundlanders and Westerners in Canada; the Lusatian Sorbs in Germany’s Saxony and Brandenburg regions); or if the group is too diffuse, representing a loose association of a regionally-based identity group (e.
family,<sup>21</sup> their incentive to do so declines post-reform since the amount of money at the father’s disposal to bequeath is smaller.

Testing the bargaining channel amounts to finding evidence that, within a particular type of family setup, women exposed to the reform have better autonomy outcomes. Examining the partition channel involves finding evidence that the following key patterns hold: (a) _A preference for nuclear households:_ Women in nuclear families should have higher autonomy than those in joint ones.

(b) _Partition driven by an increase in wives’ inheritance:_ Couples in reform states married after the reform should be less likely to be residing in joint families.

(c) _Partition driven by a decrease in husbands’ inheritance:_ Men in the reform states should switch to nuclear households if their income streams are potentially constrained by the reform.

In the next section, I empirically examine whether both these channels are in operation. I do not make any claims about the relative importance of the two. I first provide evidence for the partition channel and then explore whether the bargaining channel is in effect as well.

# VI.  EMPIRICAL EVIDENCE FOR CHANNELS

The NFHS data allows me to determine the family status of each married woman (joint or nuclear). I observe a list of all the members of each household in the Household Schedule of the data, and whether each of them is a usual resident in the household, which lets me identify which women live in the same establishment with family elders (i.e., a joint family for my purposes). A _joint_ family is defined as a family where a parent-in-law of a woman is present in the household;<sup>22</sup> the dummy

> 21. Botticinni and Siow (2003) shows that parents would want to leave bequests to those children who reside with them.

> 22. For instance, the woman identifies herself as the daughter-in-law of the household head or the woman reports she is the
br>**Africa**|General Household Survey 2018|Jan 2018|Dec 2018|n.a.|n.a.|n.a.|
|**Sudan**|National Household Budget & Poverty Survey 2014/2015|Jul 2014|June 2015|$1,635|$1,263|$1,394|
|**Tanzania**|Household Budget Survey 2017-2018|Nov 2017|Jan 2019|$797|$437|$564|
|**Uganda**|Uganda National Household Survey 2016/17|Jul 2016|June 2017|$716|$396|$486|
|**Zambia**|Zambia - Living Conditions Monitoring Survey VII 2015|Apr 2015|May 2015|$943|$255|$549|

_Source_ : World Bank staff analysis of household survey data.

_Note_ : The South African survey did not ask enough questions about expenditures to enable calculation of the total household expenditure for each household and instead asked households which of the 10 expenditure categories they belonged to. n.a. = not available.
expenditures on (i) food; (ii) public goods, defined as education, health, clothing, and furnishings; (iii) housing and utilities; (iv) adult goods, defined as alcohol, tobacco, transport, and communication; and (v) other goods, inclusive of recreation, hotels and restaurants, and miscellaneous items. We rectify the absence of household fixed effects in Equation 2 by estimating SURs with correlated random effects instead, as explained below.

> 8 We use the real (and disaggregated) consumption aggregates that are disseminated with the IHPS public use data, which include a re-released unit-record data tied specifically to the panel sub-sample of the IHS3 2010/11 (with adjusted sampling weights) and the corresponding 2010 consumption aggregate, which was re-visited in 2013 following the improvements in the ancillary CPI and food conversion factor data and due to the need to adjust the approach to spatial and temporal deflation of nominal consumption in each round in view of the shorter-than-12month-fieldwork underlying the panel data collection. The 2010 consumption aggregate is expressed in 2013 prices to facilitate comparisons in real terms and accounts for 132 percent inflation in between the survey rounds. The household gross income in 2010 is expressed in 2013 terms also using the same inflationary adjustment. For more information on the methodology underlying the computation of the IHS3 and the IHPS consumption aggregates for the panel sub-sample, please visit https://goo.gl/WzuRZF.

15
average size of exporters in their entry year could be underestimated. We investigate this possibility in the case of Peru, for which we have access to monthly export transaction data and find that in any given year (i) entrants are very small (relative to incumbents) regardless of the month when they start exporting and (ii) the probability of entering export markets is quite similar across months. Appendix B provides further details.

A final element in the analysis of export entrepreneurship at the firm-level is the contribution of new exporters to short-run export growth. Figure 4 shows the average annual growth rate of total exports for each country in the period 2005-2007 in Panel A and 2008-2009 in Panel B. The average contribution of the three terms in the export growth decomposition -- for new, incumbent, and exiting exporters; see Eq. (1) -- is presented for each country with Panel C covering 2005-2007 and Panel D covering 2008-2009.<sup>22</sup> During the steady growth period, incumbent exporters played a dominant role in explaining export growth in all LAC countries, which was also the case for all comparator countries in the groups of natural resource countries, processing countries and broad manufacturing export base countries. This finding confirms the evidence found for individual rich countries (such as the U.S. by Bernard, Jensen, Redding, and Schott, 2009). New exporters contributed very little to export growth in Chile, Peru, and Costa Rica but played a non-negligible role in the Dominican Republic, and to a lesser extent in Brazil, Ecuador, Guatemala, Nicaragua as well as in Bangladesh and Cambodia, the comparators for processing countries. Exiting exporters reduced total export growth importantly in Costa Rica and Ecuador, as well as in the comparator for broad manufacturing export base countries, Egypt. All in all, the contribution of new exporters was more important in LAC countries experiencing moderate export growth (Costa Rica, Dominican Republic) than in LAC countries experiencing fast export growth (Chile, Peru)
water); (iii) hot water; and (iv) natural gas (although the number of institutions using gas is negligible). Most of the energy used in education facilities is for space and water heating.

There are very few energy audit research studies of preschool educational facilities, but even these sparse findings indicate that there is an existing potential for energy savings. For example, an energy audit and energy consumption analysis of 10 operating kindergartens connected to the district heating transmission networks in Saint-Petersburg showed that the actual heat losses of the kindergarten buildings exceeded the normative standard by 30 to 40 percent. Many buildings are over-heated due to the degraded condition of buildings, a lack of thermal insulation, poor energy management, and the absence of any controls or meters to regulate the temperature (Vatin and Nemova, 2012). According to Moscow City data, the technical potential for thermal energy savings in education facilities in Moscow constitutes 25 to 80 percent, especially in heating (35 to 70 percent) and electricity (15 to 25 percent) (Guzhov, 2012). According to the estimates of the International Finance Corporation and the World Bank, the overall technical potential for energy savings in educational institutions in Russia might be as much as 80 percent (IFC/World Bank, 2008).

Some Russian preschool buildings have had some energy-efficient retrofitting done, but the most popular measures have been the reinforcement and thermal insulation of doors and windows rather than an upgrade of the overall envelope of a kindergarten building (Russian Ministry of Education and Science, 2013). Any rehabilitation of heating systems has tended to involve the installation of metering equipment rather than of new heating 11
.e. the Police (4,500).

# Turkey

Data on Central, Local, Education and Health employment are taken from OECD PUMA: Public Management, OECD Country Profiles, 1992 and relate to 1991. Data on military employment include conscripts (415,200), but exclude personnel in paramilitary units, i.e., Gendarmerie/National Guard (180,000), under the authority of the Ministry of Interior in peace time and the Ministry of Defense in war, and the Coast Guard (2,200). GDP is taken from World Tables 1995 and relates to 1993. Data on wages in manufacturing (monthly basis) are from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993. Data on paid employment in non-agricultural activities is from the International Labor Office's Yearbook of Labor Statistics 1995 and refers to 1993.

# Former Soviet Union

Armenia Data on paid employment in non-agricultural activities are taken from the Statistical Handbook 1995: States of the Former USSR and are for 1994.

Central Government, Non central government, education and health employment are taken from Armenia: Public Administration draft discussion paper of 24 September 1995 and relates to 1995. Central Government employment includes 90,800 employees of the in law enforcement. Local Government employment data includes 49,281 for the Yerevan City council, and 90,111 Local Councils at employees. Armenian authorities have several discrepant
segregation between crop types versus lower access to inputs and returns from these inputs, within food and export crops.

The second main contribution of our paper is that while most existing studies rely on one cross-sectional estimate due to data limitations, our paper uses two cross-sections, allowing for an analysis of trends in productivity gaps over time as well as changes in its determinants. We use two nationally representative surveys from Côte d’Ivoire: the 2008 household survey Enquête Niveau de Vie des Ménages (ENV 2008), which is a traditional household living standards survey that was collected between June and September 2008, and the latest employment survey Enquête Nationale sur la Situation de l'Emploi et le Secteur 2
|Dimension|Brief Description|
|---|---|
||Availability of Goal 1-6 indicators, measured by an average score. The primary data<br>source is the UN SDG database. While this is a database with comprehensive coverage<br>that all countries have signed up to, many countries are not yet submitting all their|
|Dimension 3.1: social<br>statistics|<br>available national data. Scores for some countries thus may not capture their<br>performance in calculating the indicators. For OECD countries, we supplement the UN<br>SDG database with comparable data submitted to the OECD following the<br>methodology inMeasuring Distance to the SDG Targets 2019: An Assessment of<br>Where OECD Countries Stand.|
|Dimension 3.2:<br>economic statistics|Availability of Goal 7-12 indicators, measured by an average score. See 3.1.|
|Dimension 3.3:<br>environmental statistics|Availability of Goal 13 & 15 indicators, measured by an average score. Goal 14 - Life<br>on Water - is not included because land-locked countries do not report on these<br>indicators. See 3.1.|
|Dimension 3.4:<br>institutional statistics|Availability of Goal 16-17 indicators measured by an average score. See 3.1.|
||Availability of recent censuses and surveys covering broad areas. The following<br>censuses and surveys are considered: Population & Housing census, Agriculture|
|Dimension 4.1:<br>censuses and surveys|<br>census, Business/establishment census, Household Survey on income/ consumption/<br>expenditure/ budget/ Integrated Survey, Agriculture survey, Labor Force Survey,<br>Health/Demographic survey, Business/establishment survey. Source: NSO websites,<br>World Bank microdata library, ILO microdata library, IHSN microdata library|
||Availability
- 11 -

**Figure 9. Historical monthly precipitation data: Selected Districts**

<!-- Start of picture text --> 1,800 1,800<br>District: Banke District: Makwanpur<br>1,600 1,600<br>1,400 1,400<br>1,200 1,200<br>1,000 1,000<br>800 800<br>600 600<br>400 400<br>200 200<br>0 0<br>1,800<br>District: Siraha<br>1,600<br>1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>Source: Author’s calculation based on data from the Ministry of Environment, Nepal.<br>Figure 10. Actual precipitation and AR-predicted expectations during the original work schedule<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>0 1000 2000 3000 4000 5000<br>Rain eAR<br>Raina<br>Monthly precipitation (mm) Monthly precipitation (mm)<br>Jan-00 Jan-05 Jan-00 Jan-05<br>Monthly precipitation (mm)<br>Jan-00 Jan-05<br>Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09<br>Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09<br><!-- End of picture text --> Source: Author’s calculation based on data from the Ministry of Environment, Nepal.

Finally, in Equations (1) and (2), _X_ controls for contract-specific heterogeneity, such as length of
Policy Research Working Paper 9648

# **Abstract**

This paper proposes a new method for improving the design effect of household surveys based on a two-stage design in which the first stage clusters, or primary selection units, are stratified along administrative boundaries. Improvement of the design effect can result in more precise survey estimates (smaller standard errors and confidence intervals) or reduction of the necessary sample size, that is, a reduction in the budget needed for a survey. The proposed method is based on the availability of a previously conducted poverty mapping, that is, spatial descriptions of the distribution of poverty, which are finely disaggregated in small geographic units, such as cities, municipalities, districts, or other administrative partitions of a country that are linked to primary selection units. Such information is then used to select primary selection units with systematic sampling by introducing further implicit stratification in the survey design, to maximize the improvement of the design effect. The proposed methodology has been implemented for the new 2021 Household Budget Survey in Tunisia, conducted under a cooperation project funded by the World Bank. The underlying poverty mapping is based on the 2015 Household Budget Survey and the 2014 Population and Housing Census.

This paper is a product of the Poverty and Equity Global Practice. 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 vmolini@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
trends compare across data sources and definitions. We then focus on one survey, the Russia Longitudinal Monitoring Survey (RLMS), which has rich and detailed information on employment, and complement the analysis with other surveys when appropriate. For all data sources, we maintain a definition of informal employment that is consistent with the legalistic view of informal employment. This definition is in line with the majority of Russian literature and is deemed more appropriate for transition economies.

The top panel of Figure 1 presents informal employment rates across countries from the ILOWIEGO database for the most recent year for which there exists an estimate for that country. Informal employment rates range between 40-70 percent in Latin American countries but are significantly lower in the Europe and Central Asia region. Russia ranks towards the bottom among all these countries – however it should be noted that this list conspicuously does not include any high-income countries where the share of informally employed is expected to be considerably lower.

1/67 Informal Employment and Worker’s Wellbeing in Russia
As a result, imports of digital goods are expected to increase only by 0.3% for the whole region.

We then estimate the effects of a full import tariff liberalization on digital goods imports of AfCFTA members towards all countries. We find that such a scenario would lead to an 8% increase in the imports of digital goods. These findings suggest that AfCFTA nations should consider including tariff concessions on digital goods imports from non-member countries, potentially lowering costs significantly, especially in the poorest countries with the highest tariffs on digital goods.

In addition to the articles cited above, our paper is related and contributes to several strands of existing research. First, our work relates to a broader literature on product quality and trade, including Verhoogen (2008), Bastos and Silva (2010), Crozet, Head, and Mayer (2012), Manova and Zhang (2012), Brambilla, Lederman, and Porto (2012), Johnson (2012), Hallak and 4This is aligned to patterns of prices for digital machinery, equipment, ans software observed in the ICP data, which are higher in Africa than other regions, including high-income economies (Cruz, Cirera, Hjort, Lang, Konte, and Edet, 2024).

> 5See: `https://au-afcfta.org/`

3
Baird, Sarah, Craig McIntosh, and Berk Özler. 2011. 'Cash or condition? Evidence from a cash transfer experiment', _The Quarterly Journal of Economics_ , 126: 1709‐53.

Bandiera, Oriana, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul, and Munshi Sulaiman. 2019. 'Women’s Empowerment in Action: Evidence from a Randomized Control Trial in Africa', _American Economic Journal: Applied Economics_ , Forthcoming.

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- Banerjee, Abhijit, Rukmini Banerji, James  Berry, Esther Duflo, Harini Kannan, Shobhini Mukerji, Marc Shotland, and Michael  Walton. 2016. 'Mainstreaming an Effective Intervention: Evidence form Randomized Evaluations of "Teaching at the Right Level" in India', _National Bureau of Economic Research Working Paper No. 22746_ .

- Barrera‐Osorio, Felipe, David S Blakeslee, Matthew Hoover, Leigh L Linden, Dhushyanth Raju, and Stephen P Ryan. 2017. 'Delivering education to the underserved through a public‐private partnership program in Pakistan', _World Bank Policy Research Working Paper No.8177_ .

- Benhassine, Najy, Florencia Devoto, Esther Duflo, Pascaline Dupas, and Victor Pouliquen. 2015. 'Turning a shove into a nudge? A" labeled cash transfer" for education', _American Economic Journal: Economic Policy_ , 7: 86‐125.

- Berlinski, Samuel, and Matias Busso. 2017. 'Challenges in educational reform: An experiment on active learning in mathematics', _Economics Letters_ , 156: 172‐75.

- Berlinski, Samuel, Matias Busso, Taryn Dinkelman, and Claudia Martinez. 2016. 'Reducing parent‐school information gaps and improving education outcomes: Evidence from high frequency text messaging in Chile', _Unpublished Manuscript_ .

- Botea, Ioana, Shubha Chakravarty