rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix C. Validation of Final Database**

We validate the representatives of the final database by calculating the ratio of the sum of employment and gross output in the database to their corresponding aggregates, in the same manner as Gopinath et al. (2017). Aggregates for employment and gross output are obtained from Eurostat’s Structural Business Statistics Database (SBS). Tables C.1 and C.2 show the coverage of our sample by country, separately for non-manufacturing and manufacturing sectors.

Table C.1: Coverage of Final Database Relative to Eurostat (SBS) - Non-Manufacturing

|**Country**|||**E**|**mployme**|**nt**||||||**Turnove**|**r**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|
|Austria|19%|21%|28%|32%|26%|31%|26%|16%|18%|34%|42%|36%|39%|36%|
|Belgium|44%|41%|43%|45%|44%|44%|43%|55%|56%|55%
# 3   Fieldwork—Establishing a quantitative baseline

## Target institutions and documents for collection

This chapter identifies and defines documents that are to be collected in support of setting the baseline. Spending data are among the most complex infrastructure data to be collected. The standardized collection of spending data requires the potentially elaborate recoding of national budgets, which are country specific and vary in complexity. It also demands the careful reclassification of financial information of nonfinancial public institutions‘ as to guarantee the comparability and consistency of spending categories across operators, sectors, and countries.

Norms and levels of transparency vary by country. In some countries making budget documents and financial accounts available to the general public is the exception rather than the norm. This is why the first step (and challenge) in setting the fiscal baseline is to have access and collect budget and financial account documents. To decide which institutions are the best sources of such documents, see the updated institutional map presented in template 1.

Fiscal data documents are generally collected from central ministries (finance and economic planning) as well as from key line infrastructure ministries, autonomous agencies, parastatals, and NGOs. The involvement of key official and technical personnel is therefore critical to the document collection process.

There are essentially five types of documents to collect:

- _Approved budgets_ . These are final budget books that have been authorized by parliament in the form of budget laws. They often consist of both original estimates and supplementary budgets.

- _Actual budgets_ . These are reports of funds that have actually been spent. When budget books are available, the actual execution of budget estimates for the previous year is reported in the same documents.

- _Annual reports/financial accounts_ . These are documents published or made available internally by SOEs or special funds as they report their annual performance at the end of each fiscal year.

- _Audited budgets and annual reports_ . These are the final actual budgets and annual reports, prepared after auditing. They may not be made available across all countries or to all entities within a country
three to six months after the completion of the training event.

> 13 The definition of “gainful” employment was increased in 2012 to 4,600 NRs (  60 USD). Throughout this report, we use the prevailing exchange rate during 2010 and 2011 of 75 NRS to 1 USD.

> 14 The T&E providers running these training events are not given hard quotas for how many AGEI trainees to reach, but any female trainees aged 16 to 24 that they do train are designated as “AGEI” trainees for accounting and monitoring purposes. Employment Fund records indicate that 4410 young women were reached over the three year initiative, as shown in Table 1, exceeding the target of 4375.

> 15 They reached only a bit more than half of their annual target of 1500.

5
# **1. Introduction**

Three consecutive National Poverty Assessments (NPAs) conducted in Mozambique between 1998 and 2010 (MPD-DNEAP, 2010; MPF/IFPRI/PU, 2004; MPF/UEM/IFPRI, 1998) have provided a wealth of information on national poverty patterns and their changes over time. The most recently published NPA, based on the new Mozambique Household Budget Survey (IOF,<sup>2</sup> 2008/09) shows that while welfare levels have generally increased, poverty remains widespread. The total percentage of Mozambicans living below the poverty line (based on per capita consumption measured at the household level) was estimated at 54.7 percent, a slight and statistically insignificant increase from 54.1 percent observed in 2002/03, with a marginal rise in rural poverty (from 55.3 to 56.9 percent) offsetting a small decline in urban poverty (from 51.5 to 49.6 percent).

However, the poverty profile that emerged from the latest NPA (MPD-DNEAP,<sup>3</sup> 2010), which is computed using a consumption-based money metric as its measure of welfare, does not seem to be consistent with other poverty indicators, from child anthropometrics (see Azzarri et al., 2011) to asset ownership figures to measures of access to basic services and regional productivity estimates. These and other factors raise concerns about the robustness of the poverty statistics presented in the latest NPA, and the discrepancies in the data become even more striking at the provincial level.<sup>4</sup> In the most recent NPA, the rural Niassa and Cabo Delgado regions, historically considered poor according to a variety of indicators, recorded the lowest poverty rates in the country. By contrast, the country’s Southern provinces and urban areas registered very high poverty rates despite their traditionally superior nonmonetary poverty indicators (e.g. child malnutrition) and higher regional productivity.

Three types of error may have contributed to
15.6%|

Source: Author’s calculations for 16 ECA countries

> 13 This taxonomy offers a harmonized approach to differentiate by the type of sector where SOEs operate independently of the country or political and economic system. For more details see (Dall'Olio, et al. 2022). We used the main sector of operation of the firm using the NACE 4-digit classification and combine it with the sector taxonomy that follows the same sector disaggregation and classification.

9
Finally we consider the broader impacts of the RSE on the sending communities. The most direct impact is through the monetary contribution that migrants make to their communities, either through remitting to a community group while abroad or contributing some of their repatriated earnings to this group upon return. We asked return migrants how much they had contributed in this way to the community. The mean response aggregated over the two years was 157 pa’anga in Tonga and 11733 vatu in Vanuatu – or approximately US$80-130 per migrant. Our expenditure module also collected expenditure on community obligations, but only for a recall period of one month, thereby likely missing one-off contributions made by migrants upon return. The difference-in-differences regression then gives a positive, but insignificant impact on this item.

To further gauge the impacts at the community level, we conducted surveys of community leaders. This was done at baseline and at the time of the second round survey in Vanuatu, and at baseline and at the times of the third and fourth round surveys in Tonga. These data are thus less useful for Vanuatu, since they only measure immediate effects while most workers were still away. The Tongan surveys reveal the mean (median) community saying it received 633 (500) pa’anga from RSE workers, which is consistent with the community surveys giving a median of 5 workers per village participating coupled with the amounts reported by workers. The main use of this fund in 83% of cases in Tonga was funding the village water supply in the first year. In the second year, villages were also using this for street lighting, a school scholarship fund, community halls, and in one case, adding internet to a community hall.

Village leaders were directly asked the main benefit and main disadvantages of the RSE for their community. In Vanuatu this was only asked in wave 2, 6 months into the RSE. The main benefits reported at this stage were job opportunities for people in the village, money to support the village church, and improvements in housing.
program impacts. Behrman, Parker, Todd, (2009, 2011) find that extended time participating in the program leads to significant improvements in grades completed, about 1 full grade for children who participate in the program for 6 years beginning at ages 9 to 12, compared to nonparticipating children. Studies based on structural estimation also find program effects of a similar magnitude (Todd and Wolpin, 2006; Attanasio, Meghir and Santiago, 2012). Parker and Vogl (2018), using nationally representative Census data from 2010, find that long-term impacts on total schooling attainment for children who grow up in a household with PROSPERA benefits are 1.4 grades both for men and women, compared with those in households not offered the program during childhood.

Overall, these diverse studies provide consistent evidence that the program significantly increases schooling levels. The evidence from the medium- and longer-term evaluations currently suggests program impacts of increasing final levels of schooling attainment by between 15% and 20%. However, these previous studies are all based on self-reported information on school enrollment and attendance obtained from a household survey, raising concerns about whether there could be some reporting biases that are systematically related to program participation. For instance, beneficiary households, knowing that benefits are conditional on their children attending school might over-report their children’s enrollment and attendance. Our study avoids 7
.

In this paper, we examine trade participation and innovation activities and how they are intertwined in MENA. Using cross-sectional and panel data on more than 6,000 private firms in six MENA economies (the Arab Republic of Egypt, Jordan, Lebanon, Morocco, Tunisia and the West Bank and Gaza), we explore i) structural determinants of trade participation and innovation activities before the COVID-19 outbreak; and ii) the responses of traders and innovators and their performance during the COVID-19 crisis. While the level of trade participation of firms in the MENA region is similar to other lower-middle income (LMI) and upper-middle income (UMI) economies, we show that innovation rates are particularly low. This lack of innovation, which has been deteriorating in recent years, is also reflected in the low level of investment in intangible assets. This paper does not solve the difficult puzzle of why innovation is low in MENA. Instead, it attempts to identify the characteristics of firms that participate in trade and invest in innovation and how they benefit from these activities.

On the one hand, firms that trade in international markets tend to innovate more. Moreover, we find that traders tend to grow faster when they also invest in innovation, confirming the self-selection hypothesis of more productive, larger and innovative firms into trade activities. On the other hand, trade participation impacts positively the innovation process, even after controlling for the potential sample selection bias, confirming the learning by exporting effect. In addition, the use of a foreignlicensed technology company appears to have a key role for innovation, even after controlling for the effect of trade participation.

We also find significant differences in the average size and productivity levels between traders and non-traders. In particular, compared to non-traders, we find evidence of large productivity and firm 2
# **7 Conclusion**

In this paper we presented a fully stochastic framework to model and predict food crisis dynamics. We build on the recent advancements in food security analysis and measurement, most notably the IPC framework. A key novelty in our approach is to leverage food insecurity information of an entire population distribution, rather than calculating a single food insecurity metric. Consequently, the model provides a holistic picture of food crisis risk to decision-makers. It also lends itself to construct stochastic catalogues and exceedance probability curves that can serve as hazard modules for insurance and risk management products.

In order to model entire food insecurity distributions with conventional statistical methods, we introduce a convenient transformation, namely the compositional transformation. With it we can fit our model to the data, identify drivers of food insecurity, simulate alternative scenarios, and make forecasts into the near future. Due to the brevity of recorded food insecurity’s historic data, it is difficult to estimate a separate model for each country. We therefore employ a panel approach where we pool the data of 15 countries and estimate a joint multi-country model.

Food insecurity research has shown that agronomic, weather, conflict and economic variables are major drivers for food crises. However, which exact variables or transformations thereof should be included to predict either high or low states of food insecurity is not immediately clear. We therefore construct a total set of 1,670 candidate variables for each of the above categories. Using a statistical variable selection method, we identify the 30 most important drivers. We find that food insecurity states are indeed past-dependent and that states of low food insecurity are more likely to transition to high states than vice versa. Furthermore, conflict variables in the recent past are more predictive for high food insecurity levels while agricultural and weather-related variables are more important for lower levels. Food prices are predictive for both cases.

Finally, our model was designed to complement expert opinion. It leaves ample room to incorporate subjective judgements and tailor the model to specific applications. This is essential
Policy Research Working Paper 10028

# **Abstract**

Women’s labor force participation in the Kurdistan Region of Iraq is very low, at 14 percent. This paper investigates a number of social and psychological barriers to participation, using recent methods in the measurement of social norms and cultural beliefs and primary data collected from all three governorates. Furthermore, since greater growth in employment generation is expected in the private sector, the paper explores women and men’s perceptions toward working in the private sector in detail. The findings show that while 70 percent of women and men support women’s participation in the private sector. Several challenges remain in both information about the sector, as well as perceived risks and discrimination. More broadly, the findings show that traditional gender role expectations may still impede women’s labor force participation. Perceptions of common societal practices and beliefs of other members from the same household are all correlated with women’s work. The paper explores additional mental barriers using a smaller sample of younger and more educated female job seekers, who are registered with a jobs agency, and finds that both perseverance in the job search process and trust and engagement with formal institutions are additional behavioral barriers.

This paper is a product of the Mind, Behavior, and Development (eMBeD) Unit,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 zafif@@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
30</sup> Compare this with an average of 0.41 for developed countries noted before. This evidence on Latin America and Caribbean is interesting as the countries in this region also experienced some of the highest income inequality during this period (De Ferranti (2004)). Thus, high cross-sectional inequality was coupled with low intergenerational mobility, a doubly undesirable distributional outcome. Among the countries in

> 29In the income data used by Bingley and Cappellari (2019), the null hypothesis of stationary distributions is not rejected. Stationary distributions are also assumed by Solon (1999).

> 30The only other study that reports estimates of sibling correlation in schooling for Latin American countries is Dahan and Gaviria (2001). However, as discussed earlier, their estimates are not comparable to our estimates or other estimates in the literature.

17
on the World Bank’s June 2022, _Global Economic Prospects_ report. Commodity production for aluminum, copper, zinc, lead, nickel, oil, tin, and zinc is drawn from the World Bureau of Metal Statistics. Quarterly averages of monthly data are available for 2000—2022Q1.

# **3.4 Oxford Economics Model (OEM)**

The OEM is a macroeconometric model that is routinely used for growth forecasting in many international institutions and central banks (Guenette and Yamazaki 2021). It includes price series for world food, world beverages, world agricultural raw materials, aluminum, copper, iron, lead, nickel, tin, zinc, coal, oil, and natural gas.<sup>15</sup> These are extracted from the latest OEM forecasts.

The OEM is a macroeconometric model with 46 countries, 6 regional blocs and the Eurozone (Oxford Economics 2019). Most components are specified as error correction models. In the short run, shocks to demand generate economic cycles that can be influenced by fiscal and monetary policy. Over the long-run, output is determined by supply side factors: investment, demographics, labor participation and productivity. The resulting dynamics of short-run fluctuations and long-run trend are integrated in terms of cointegration. We use biannual (Q1 and Q3) forecast data from – 2015Q1 2022Q1 due to data availability.<sup>16</sup>

## **3.5 Machine learning approach**

The EMD-based support vector regression and GARCH model of Zhang, Zhang and Zhang (2015) is selected as a machine learning approach, in line with its generally successful forecast

> 14 We adopt the Gibbs-sampler, an estimation approach that allows for structural identifications such as elasticity and sign restrictions and prior beliefs about future economic events, to estimate a restricted VAR. Although the identification procedure is not required for forecasting, we impose sign-restrictions to ensure compatibility between prediction and scenario analyses.

> 15 Natural gas is a composite of Henry Hub, Japan, and European natural
# Bridging the Gap in Trade Reporting: Insights from the Discrepancy Index

Sonja Mitikj Siddhesh Vishwanath Kaushik JEL classification: F14, C55, C82 Keywords: International Trade data, Discrepancy Index, Data Quality, Mirror Statistics.

Acknowledgment: We are grateful to Ana Margarida Fernandes, Bob Rijkers, Hiau Loo Ki, Markie Muryawan, and Michael Ferrantino for their insightful comments and feedback that significantly improved this work. We also extend our appreciation to Umar Serajuddin and Daria Taglioni for their leadership and support throughout the project.
.** 1997. “Multilateral Tariff Cooperation during the Formation of Free Trade Areas.” _International Economic Review_ , 38(2): 291–319.

- **Bagwell, Kyle, and Robert W. Staiger.** 1999. “An Economic Theory of the GATT.” _American Economic Review_ , 89(1): 215–48.

- **Bagwell, Kyle, and Robert W. Staiger.** 2011. “What Do Trade Negotiators Negotiate About? Empirical Evidence from the World Trade Organizaion.” _American Economic Review_ , 101(4): 1238–1273.

- **Baier, Scott L., and Jeffrey H. Bergstrand.** 2007. “Do Free Trade Agreements Actually Increase Members’ International Trade?” _Journal of International Economics_ , 71(1): 72– 95.

- **Baldwin, Richard.** 2012. “WTO 2.0: Global Governance of Supply-Chain Trade.” Centre for Economic Policy Research Policy Insight No. 64.

33
working-age family members Employment+earnings Nonlabor income Consumption-to-income ratio<br>Source: Figure 1.1 (page 3) in Inchauste and J. Saavedra‐Chanduvi, (2014); using Data from SEDLAC,<br>various years; FAO n.d.; and national household surveys<br>Contribution to change in poverty, percent<br>Bangladesh, 2000–10 Ghana, 1998–2005 Nepal, 1996–2003 Cambodia, 2007–10 Mongolia, 2007–11 Philippines, 2006–09 Sri Lanka, 2002–09 Vietnam, 2004–10 Argentina, 2000–10 Brazil, 2001–09 Chile, 2000–09 Colombia, 2002–10 Costa Rica, 2000–08 Ecuador, 2003–10 Honduras, 1999–2009 Panama, 2001–09 Paraguay, 1999–2010 Peru, 2004–10 Thailand, 2000–09 Moldova, 2001–10 Romania, 2001–09<br><!-- End of picture text --> At the same time, the state of agricultural modernization varies greatly across countries. Accordingly, in order to leverage the agricultural sector for labor‐intensive growth and poverty reduction, it is important to tailor policies to align them with a country’s context. The 2008 WDR provides a useful typology:

> 14 The 2008 World Development Report _Agriculture for Development_ calculates poverty based on an international poverty line of $1.08 in 1993 purchasing power parity dollars.

31
question, we restrict the sample to Middle East and North Africa (MENA) regions, for which we have establishment-level data in the Arab Republic of Egypt, Jordan, Lebanon, Morocco, Tunisia, West Bank and Gaza. The survey is representative of the non-agricultural private sector of each country and provides information on all size classes, including small firms with less than 20 employees. This feature is of high importance for our analysis, as smaller companies are also more likely to suffer the competition from the informal sector.

The analysis takes advantage of the longitudinal dimension of the WBES to deal with simultaneity bias or unobserved factors in the empirical strategy. From the total sample of 13,000 company-year observations we focus on about 2,000 firms, for which we are able to match at least two consecutive waves of the survey.<sup>3</sup> We discuss possible selection issues in Section 2.

Our main measure of interest, _Constrained by informal_ , is a binary variable identifying firms that perceive the competition from the informal sector as a major constraint. The original WBES survey explicitly asks to what degree practices of competitors in the informal sector are an obstacle to the current operations of the firm. We classify a company to be constrained by the informal sector if it declares such practices to be a major or very severe obstacle (the top two categories).<sup>4</sup> Our measure has the great advantage of capturing an idiosyncratic component that goes beyond the mere diffusion of informal practices within the operating sector of a country. Instead, it represents a specific proxy for how much the company perceives informal competition as a jepardizing factor for its own business. As such, it is likely to be accounted for when firms make their decisions and form their own expectations about the future.<sup>5</sup> In this regard, the WBES dataset represents the only available survey providing forward-looking expectations on firms’ future sales for MENA regions. This is a critical piece of information