rafmacalaba/data-use-mentions-extended huggingface dataset:
sectors are regulated jointly, bribery increases and social welfare declines. By contrast, if formal and informal sectors are regulated separately (what Echazu and Bose term as “horizontal centralization”), rent extraction cannot be coordinated and maximized, thus increasing social welfare. Both models, however, do not include differential effects of bribery exposure _within_ the informal sector.

Empirical evidence on the informal sector’s exposure to bribery is also lacking. The number of studies using micro-level data to study corruption remains limited, and the share incorporating the informal sector is smaller still. While not using data on the informal sector directly, Safavian _et al_ . (2001) study microenterprises and corruption in the Russian Federation and find that larger firms are more likely to report constraints due to corruption. Svensson (2003) controls for firms in Uganda paying taxes (a proxy for formality), finding that those that do are more likely to encounter a bribe request. Rand and Tarp (2012) use data for Vietnam and include a sub-set of un-registered businesses in their analysis of bribery incidence, which is found to be lower among informal businesses: they speculate that this lower probability is due to the less visibility. A reading of the literature yields few studies using micro-data for informality and bribery in Sub-Saharan Africa, specifically. Lavallée and Roubaud (2018) are an exception. They use a sample of informal-sector businesses found through household surveys<sup>6</sup> in six West African countries. In line with results from analysis of the formal sector, they find that a business’s ability to pay (proxied by sales per worker) is associated with a higher likelihood of facing a bribe request.

# III. Informality, Corruption, and Harassment in Zimbabwe _<u>Informality and Employment in Zimbabwe</u>_

Zimbabwe’s statistical office (ZimStat) reports some figures on the informal sector in its 2014 labour force survey (LFCLS). According to the LFCLS, about 14% of employment is provided by the
Nigeria, at 23% and 5% respectively, but still considerably larger than the divergence observed between the objective measurements.

Self-reported measures result not only in higher average deviations, but in dramatic systematic error as the size of small plots is overestimated by anywhere from 30% (Nigeria) up to a factor of six (Tanzania), with the over-estimation declining almost monotonically as plot size increases and eventually results in under-estimation in the larger plot size classes in Nigeria and Ethiopia. These results comparing objective and subjective area measures are in line with findings of previous literature (Carletto et al. 2015; de Groote and Traore, 2005). The scatter plots on the right side of Figure 3 convey the same message in graphic form.

> 9 Comparing self-reported to GPS yields exactly the same results, even when analyzed using data from the nationally representative LSMS surveys in Malawi (2010/11) and Tanzania (2010/11), therefore we limit the comparison to self-reported and CR.

22
committee of the Poverty Reduction Strategy to relate the narrower, targeted program to broader initiatives to reduce poverty. This work responds to the Government request made at the CG meeting, which asked the PWG to work on a _comprehensive_ poverty reduction strategy. Work on this has already begun. In a recent meeting of the PWG, members have looked at important sectoral, structural and macro issues which influence the creation of opportunities, the promotion of equity and the reduction of vulnerability for the poor.

In July, a three-day retreat was arranged for members of the PWG, other Government agencies, donors and representatives of civil society to brainstorm on the main elements of poverty reduction strategy for Vietnam. There was energetic discussion and debate on important policy measures needed to take forward a comprehensive poverty reduction strategy. Sensitive issues such as corruption, ethnic inequality, domestic violence, the status of urban migrants were all discussed openly. Findings from the PPA studies have informed this debate and many feel that they have played an important role in allowing participants to feel more comfortable discussing these issues. The committee drafting the Poverty Reduction Strategy is using the proceedings from the workshop as background material.

# **Wider Impacts**

# **_Strengthening relationships_**

A very positive aspect of the work in 1999 was the opportunity it offered for agencies 21
>30 (exports small relative to output) and _z_<sup>M</sup> <30 then it is categorized as a pure import good. In the data, in sectors where one ratio is greater than the cutoff of 30, the other is considerably less than 30, so that the cutoff clearly separates the exported commodities from import-competing ones.<sup>31</sup>

> 29 Hoekman, Ng and Olarreaga (2004).

30 The range of included production subsidy measures can influence imports as well as exports -- production subsidies can curtail imports (by increasing domestic production) or promote exports (by lowering production costs). For goods with one-way trade (goods that are purely exported or imported), it is clear what the subsidies achieve. For goods with two-way trade, we solve the problem by replicating them in the sample -- once for imports, and once for exports. Protection data are used only with the imports sub-sample, while subsidy data are used only with the exports sub-sample.

> 31 M X This applies to commodities with zero imports (so that _z_ is infinite), and zero exports ( _z_ =∞).

14
Policy Research Working Paper 10599

# **Abstract**

Over nine years of violence and conflict have profoundly altered the Republic of Yemen’s economy. The war has shattered the country’s already fragile socioeconomic equilibria, affecting nearly every facet of life. Since the onset of the conflict, economic diagnostics have focused on descriptions of the deteriorating macro-fiscal and poverty conditions, lack of food security, and loss of capital accumulation. However, relatively little attention has gone toward the development of a forward-looking vision for the country, rooted in Yemen’s current economic structure. This paper helps to fill this gap by presenting and analyzing a set of scenarios for Yemen’s economy up to 2030. The analysis is based on a new version of the Sustainable Development Goal Simulation model, a dynamic computable general equilibrium (CGE) model, which is applied to a new social accounting matrix (SAM) for Yemen. The new social accounting matrix has the virtue of consolidating sparce and often inconsistent Yemeni data from multiple sources (the World Bank, the International Monetary Fund, and the United Nations system) into a coherent framework that reflects the basic structure of the economy, both at the macro and sectoral levels. The simulation analysis is built around three broad scenarios spanning 2022 through 2030. The results suggest that if the conflict subsides, governance is strengthened, and the donor community provides crucial aid, considerable progress, including reduced poverty rates and improved living conditions, can be achieved by 2030. Given Yemen’s low levels of infrastructure and human development, the potential payoffs from investments in these areas are great.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group. 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 gmele@worldbank.
investment outcomes are created or might be reduced. _What is the effect of investment organizations’ evaluation practices on gender disparities in funding innovation?_ <mark>Examining this question requires access to investment organizations’ evaluation templates, investors’ evaluation practices, and investment outcomes by gender. This research is d</mark> ifficult to conduct at <mark>scale because most investment organizations prefer not to share their evaluation processes and outcomes publicly (e.g., Da Rin et al. 2013). To examine this question, we employ unique data from Village Capital (</mark> Vilcap <mark>), a global investment organization that</mark> selects qualified early-stage startups for consideration by its own investors, and introducing startups to a broader and more diverse set of potential follow-on investors.

Typically, investment organizations evaluate startups using organization-level criteria over three months (Tyebjee and Bruno 1984, Fried and Hisrich 1994, Cohen et al. 2019b, Gompers et al. 2020). Investors receive information – i.e., a pitch deck or overview, meet founders, and then make a decision on whether to continue to diligence (or inquire about) the startup. This process repeats as investors progress through deeper stages of diligence before making a decision to invest in a startup. Working with Vilcap, we <mark>designed a two-part intervention to reduce gender disparities in their investment outcomes by changing their evaluation templates that structured inquiry, specifically how Vilcap prompted individual investors to inquire about risk in stage one, and startup progress in stage two. We examined the effects on investors’ evaluation, analyzing over 3</mark> 1,000 scores investors allotted to startups during the period of study.

In the first stage of the field experiment, we used a cross-sectional design to assess the first stage of investment outcomes – an investor’s decision to conduct further diligence on a startup, or to exclude it from further consideration. We randomized a diverse set of 278 investors into a treatment
million. However, the risk of sampling is also large – if the selection of households or individuals is not properly done, statistics constructed from the sample do not represent the characteristics of the population of interest. This is the reason why sampling is so critical when NSOs are designing a survey.

This paper describes the sampling design for the 2016/17 Bangladesh Household Income and Expenditure Survey (HIES). The HIES, a large household survey collected by the Bangladesh Bureau of Statistics (BBS), is the main government instrument for monitoring welfare and living conditions in Bangladesh. The HIES is a well-established sample household survey in Bangladesh and has been conducted about every five years since 1991-92, with the latest three rounds collected in 2000, 2005, and 2010.<sup>1</sup> BBS decided to redesign the sample of the HIES 2016/17 to produce more frequent and more disaggregated poverty estimates than the previous rounds. Specifically, the samples of the latest three rounds of the HIES survey are designed to provide reliable annual poverty estimates at the division level for both urban and rural areas.<sup>2</sup> But in the HIES 2016/17, BBS decided to produce reliable annual district poverty statistics and quarterly national poverty statistics in addition to annual poverty estimates at the division level for urban and rural areas. As a result, BBS needed to change the design of sampling for the HIES 2016/17 significantly. For example, the sample size for the HIES 2016/17 needed to increase almost fourfold compared to the HIES 2010. This substantial increase in the sample size also forced a revision of the sampling frame to accommodate the larger number of household field interviews.

The main challenge when designing the HIES 2016/17 was how to achieve multiple goals with one sampling design. As mentioned above, the objective of the HIES 2016/17 is three-fold: (i) producing reliable annual poverty and welfare statistics at the district level; (ii) producing reliable quarterly poverty and welfare
2|
|Housing and Infrastructure|917,359|26|
|Housing|484,891|14|
|I|||
|Special Events|175,891|5|
|<br>Health and Sports|<br>100,442|3|
|Education and Culture|39,606|1<br>|
|||25|
|Total Expenditures on Social|3,540,529|100|
|Development and Social Benefits|||

' Subsidies in kind to employee **s** uch as subsidized food, vouchers for transport, etc. _Source:_ Ministry of Statistics, Republic of Belarus 25 percent of all "social" expenditures by enterprises. These "social benefits" included subsidies for such things as commuting and food. The transportation subsidies were and are important in tight housing markets. The importance of food subsidies is seen in the fact that food expenditures in 1995 accounted for 62 percent of consumer budgets in Belarus-ten times as much as the next largest category of consumer expenditure, which was clothing.

10. Enterprise provision of many of these social benefits in former Soviet Union countries has been observed to be superior in aggregate to that available to other members of the public.<sup>7</sup> To an outside observer, differences in provision of nonmonetary benefits are most evident by size of enterprise, however. The dental benefits provided by a large enterprise such as Belarus Railways greatly exceed both in quality and availability those extended to the ordinary member of the public through state clinics.

11. Aggregate statistics support this observation that enterprise spending on fringe benefits is systematically correlated with size of firm. As can be seen in Table 2, the quartile comprised of the largest

> 7 "Enterprise kindergartens were considered to be superior in quality to municipal facilities, and these differentials appear to have persisted: 1994 data for Kazakhstan show per child expenditures in enterprise kindergartens to be twice that in those run by the Ministry of Education." Klugman
the program. In 2011, as per the population census data in the sample areas, there was almost no difference in the treatment and control villages(see Table A 5)<sup>17</sup> . However, in 2016-17 we estimate that there is a 5.5 percent increase in overall WPR in treatment areas and an even higher increase of 7.7 percent among women of productive age, when compared to control areas. Interestingly, the increase in the paid livelihoods amongst adult women almost the same at 7.3 percent in treatment areas when compared to control areas.

This is a significant result from a policy point of view for two reasons. First, the increase in WPR is for the entire village and not just for program beneficiaries. Second, results seem to suggest that, in treatment areas, the program might have been able to arrest the decline in female labor force participation. To illustrate, in 2011, for every 1,000 women in a village, 490 were working before the program. After the intervention, in 2017, the number of working women in the control areas went down to 404 keeping with the overall trend but in the treatment villages, the decline was much less to 459. So, in that village, due to the intervention, 55 more women have joined or been retained in the labor force – amounting to a 13.6% increase in WPR.

**Table 5: Changes in WPR for full village - 2011 to 2016-17**<sup>**18**</sup>

|**Work Participation Rate**|**Control**<br>**Villages**|**Treatment**<br>**Villages**|**Difference between control**<br>**and treatment villages**|
|---|---|---|---|

> 15 High-value crops are all crops except cereals and millets
-------------------|--------------------------||||
|TOTAL|7.428.142|<br>1.918,677|<br>1.630.755|1.584,096|<br>19,082|
|MEXn O||(th|ousand<br>pesos)|||
|2021 Wheat flour<br>manufacturing|5.944.296|1.532,561|1.215.666|965,606|7.522|
|2022 Corn flour<br>manufacturing|2,299.211|641.220|529.702|488,202|2.884|
|<br>2029 Manufacturing<br>of other<br>flour<br>and<br>mill||||||
|products<br>based<br>on grain<br>and leguminous||||||
|<br> <br> <br>plants|879,618|418.003|235.899|226.339|2.096|
||--------------------------------|--------------------------||||
|TOTAL|9.123,125|<br>2,591.784|<br>1.981.267|1.680.147|<br>12.502|

Sources: United States (1977) and (1975) from US Census of Manufactures. Industry Series, table la.
represents the zone between the potential of full job automation and augmentation with generative AI. For example, business and administration professionals could see GenAI software streamline complex data analysis, associate professionals might use it to manage logistics more efficiently, while customer service clerks might rely on GenAI for support with query resolution. As the technology evolves, the balance between augmentation and automation of tasks might shift, potentially redefining some of the jobs in customer query roles significantly faster and exposing them to a higher risk of full automation than other occupations in this category. It should also be noted that a large share of customer query clerks is already found in the category of high automation potential, alongside other groups of clerical occupations. The fact that, in most cases, such jobs already use a computer at work further shortens the distance to the potential full automation of these occupations, making potential shifts between the “big unknown” and full automation more fluid.

# 4.5. Differential exposure across earnings levels

As the final step of the analysis, we look deeper in the relationship between labor earnings and the degree of occupational exposure to GenAI, focusing exclusively on the occupations that report already using a computer. To do that, we use the latest available micro data for each country and

> 37 This clearly assumes that such technologies would be adopted in a way that supports workers’ tasks, rather than imposed to increase the level of algorithmic control and limit worker agency, which can have negative effects on working conditions (Adams-Prassl et al., 2023; ILO, 2023c).

28
(CEPII). The CEPII database contains information on bilateral distances between the major cities for each pair of countries, whether two countries share a border, as well as information on land area and whether a country is landlocked.<sup>9</sup> Population data is taken from World Bank’s World Development Indicators for the period 1970-1999. Exporter and importer population is the only variable in our gravity speci...cation that changes over time.

The data on ...nancial development comes from the database originally compiled by Beck, DemirgucKunt, and Levine (2000). We use a version that has been checked for accuracy by Loayza and Ranciere (2005). Following the standard in the literature, our preferred indicator of ...nancial development is the ratio of credit by banks and other ...nancial institutions to the private sector as a share of GDP (“private credit”). The controls in our estimation include overall trade openness (imports plus exports as a share of GDP) and PPP-adjusted GDP per capita income, both of which come from the Penn World Tables (Heston, Summers and Aten, 2002). Finally, we use information on countries’legal origin as de...ned by La Porta et al. (1998), extended to include the socialist legal system.

The ...nal sample includes 96 countries, and is an unbalanced panel of 5-year averages from 1970-74 to 1995-99. Appendix Table A1 presents the data on the external ...nancing need of exports, EFNXct, for our sample of countries for the most recent 5-year period, 1995-99. Aside from EFNXct, the table contains information on the top two export sectors, the share of the top two sectors in the overall manufacturing exports, overall trade openness, private credit, as well as the sample means of these variables. It is clear that while there is some correlation between per capita income and the ...nancial content of trade, income is far
Assuming that time series data for factor market variables at the sector level is not available,<sup>2</sup> this section describes how sectoral representation of MS models can be improved. In MFMod, each country has a minimum of three sectors: agriculture ( _Pt_<sup>_AGR_</sup> _Yt_<sup>_AGR_</sup> ), industry ( _Pt_<sup>_IND_</sup> _Yt_<sup>_IND_</sup> ), and services ( _Pt_<sup>_SRV_</sup> _Yt_<sup>_SRV_</sup> ), where _P_<sup>_i_</sup> is the value added deflator and _Y_<sup>_i_</sup> is value added at constant prices for sector _i_ . Input-output (IO) tables are available for many (but not all) countries for certain years, enabling us to establish a _top-down_ correspondence between intermediate demand and changes in sectoral value added.

As described in Burns et al. (2021), the integration of an IO table improves the intersectoral responses to shocks. However, this option remains unsatisfactory given that most of the shocks occur from the demand side and are then mapped to sectors, precluding us from modeling sectoral shocks that then translate back to the demand side of the economy. For example, a shock to water tariffs should affect agriculture demand for water as well as manufacturing and in turn affect aggregate demand through the price channel. These relative price shifts are not accounted for in previous methodologies. One way to include relative price shifts, is to model the sector in the form of translog cost functions (Berndt and Wood, 1975), which will be the starting point of the specification of sectoral accounts under the constraint that we do not have data on wages and rental rates.

In Burns et al. (2021), aggregate real value-added (GDP at basic prices), _Y_<sup>_FCST_</sup> , is already determined. Given
47

- <u>Figure 11: HEFPI financial protection data processing steps</u>

<!-- Start of picture text --> Identify HH surveys<br>Access HH surveys<br>Compute harmonized variables<br>Compute CATA Get PPP and CPI data<br>Merge PPP and CPI data into microdata<br>Compute IMPOV<br>Collapse micro‐data to country‐year‐indicator level Get PovcalNet, WBI and GHED data<br>Merge PovcalNet, WBI and WHO‐NHA data into meso data<br>Conduct data quality checks<br>Drop rejected datapoints<br>HEFPI FP meso data set<br><!-- End of picture text -->

# <u>Comparisons across subpopulations</u>

For the reasons indicated above, and in line with findings reported in the World Bank-WHO 2017 Global Monitoring Report on UHC (WHO and World Bank 2017), we avoid reporting inequalities in catastrophic health expenditures across consumption quintiles, and report inequalities only across income quintiles. Inevitably, therefore, data on inequalities in catastrophic expenditures are available only for high-income and some upper middle-income countries.

Inequalities in the incidence of catastrophic health expenditures are computed using the same code underlying the Stata module FPRO (Eozenou and Wagstaff 2018).

# <u>Data-quality checks</u>

As explained above, on the health equity side of the HEFPI data set, there are published data we can check our results against for some country-year-survey-indicator combinations. This is not the case with the financial protection side of the data set. For sure, there are multi-country studies of catastrophic and impoverishing health expenditures, but these studies vary in the methods they use, and none uses exactly the same methods and data sets we do. The two papers by