rafmacalaba/data-use-mentions-extended huggingface dataset:
country during November 2017 that had an adverse impact on food access and access to a number of basic services, most likely through a large increase in fuel prices (e.g., Tandon and Vishwanath 2020).

# **3. Data**

As discussed above, it is very difficult to collect data that can assess how food assistance responds to food emergencies given the difficult environments in which many of these events occur. This article utilizes the mobile Vulnerability and Assessment Mapping Survey (mVAM) conducted by the World Food Programme (WFP). The survey is the most geographically complete survey covering both the time before and the time after the 2017 IPC announcement. Furthermore, the survey is conducted every month, which further allows us to more completely analyze the persistence of changes in food assistance and food access.

Specifically, the monthly mVAM began in August 2015 and has been conducted in nearly every month since.<sup>7</sup> The 10-minute survey is conducted by mobile phone and reaches approximately 2400 households every month through random digit dialing. The survey is stratified by governorate, where potential respondents continue to be contacted until a sufficient number of 6One example is the Famine Action Mechanism announced by the World Bank immediately following the global reporting on food emergencies. The mechanism aimed to identify famines and food emergencies earlier and also automatically link funding to well-defined triggers. The countries cited in the development, and included in the piloting of the mechanism, were the four countries identified in the U.N. Security Council meeting and subsequent news reporting- Nigeria, Somalia, South Sudan, and Yemen (e.g., World Bank 2018b).

> 7In the 2016 IPC classification, the mVAM was actually the key component of determining food access across the country.

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Policy Research Working Paper 8646

# **Abstract**

The production of export goods has become increasingly unbundled, and countries positioning to become more integrated in the global economy are increasingly looking toward global value chains. This paper uses the Organisation for Economic Co-operation and Development/ World Trade Organization’s Trade in Value Added Database to assess Brazil’s current integration in global value chains. It uses a structural gravity model estimated with parts and components to analyze the scope for Brazil to increase global value chain–related trade. One avenue to raise participation in global value chains is through (deeper) preferential trade agreements, and to this end the paper characterizes the level of integration of Brazil’s current preferential trade agreements. Brazil has witnessed high growth in total domestic value added embodied in gross exports since 1995, yet it exhibits lower international engagement in global value chains, but tends to be stronger as a seller than a buyer. Most of the participation on the selling side comes from indirect linkages with domestic input sectors, and services sectors have been important for growing the indirect value added in global value chain–oriented exports. A deep integration agenda focusing not only on border measures, but also on beyond-the-border measures, would help Brazil to maximize the benefits from participation in global value chains. Other than its natural partners, Brazil should integrate with countries where global value chains are taking place. New agreements signed by Brazil and Mercosur with other regional members such as the Pacific Alliance should also take into consideration provisions such as investment, competition policy, and intellectual property rights, which are demonstrated to be very important for integration in global value chains.

This paper is a product of the Macroeconomics, Trade and Investment 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/research. The authors may be
present mixed evidence, with either a positive, a negative, or an insignificant effect of variety of inputs on the growth rate of total factor productivity.

However, not all the existing evidence supports the hypothesis of a negative impact of natural resources on economic development. Davis (1995) compares the longrun economic development indicators of minerals-based economies and non-mineralsbased developing economies.<sup>11</sup> He finds that the minerals-based economies as a group significantly outperform the non-minerals-based economies. More recently, Rigobon and Manzano (2001) have found that Sachs and Warner’s results are not robust to small changes in econometric procedure when panel data are used. They specifically analyze the impact on growth of natural resources exports as a share of GDP. They find that when the model is estimated on panel data using fixed effects, the negative impact of natural resources on growth vanishes, but that it remains in the cross-sectional estimations. Rigobon and Manzano argue that the high prices on commodities during the 1970s led developing countries to use them as collateral for debt. During the 1980s, commodity prices fell sharply, leaving developing countries with massive debts and a reduced flow of foreign resources with which to pay it back. Finally, Lederman and Maloney (2002) find that their preferred measure of natural resource abundance appears to be positively correlated with economic growth, and that export concentration reduces growth. They use as a measure of resource abundance the net exports of natural resources per worker.

# **4.2 Empirical Methodology and Results**

We estimate the main empirical implications of our model using panel data for the period 1970-90. The data used in the regressions are from the Penn World Tables, the Barro and Lee (1994) Educational Data Set, and the World Tables from the World Bank (1993-96). We describe the data and their sources in more detail in appendix B.

We regress the growth rate of GDP per capita on various explanatory variables, using random and fixed effects to test the robustness of our measures of
|43.9|38.4|
|Mutual Funds|3.9|5.4|
|Insurance Companies|0.8|0.8|
|Pension Funds|3.1|4.1|
|Retail Investors|20.2|3.1|
|Foreign Investors in euro bonds|0.0|14.3|
|Foreign Investors in other debt|20.6|23.7|
|Other Government Entities|7.4|10.2|
|Total|100.0|100.0|

Source: Estimated on the basis of Bank of Greece, Ministry of Finance, IMF and OECD data.

Before concluding this section, three other important aspects of public debt management should be briefly discussed. The first concerns the very high interest cost of a large public debt that has been issued at high interest rates. The interest cost was kept low during the era of financial repression when banks were forced to invest in treasury bills and interest rates were highly negative in real terms. But after 1985, interest rates that were highly positive in real terms were paid on government debt and this was reflected in a rising interest cost relative to GDP. The cost reached a peak in 1994 when it amounted to 14 percent of GDP, up from 4.8 percent in 1981. The interest cost declined significantly in the late 1990s but it still amounted to 8 percent of GDP in 2000 and 6 percent in 2003 (Table 3.11).

38
Swaffield, 2002). Bhorat et al. (2013) found that in South Africa employers also adjusted to the rise in costs by cutting back on workers’ hours.

# **d. Fourth Adjustment Channel: Leaving the Formal Sector, Partially or Entirely**

The fourth strategy is to get out of the formal sector, either partially or entirely, and operate on an informal basis with employees who are not protected by labor legislation. This strategy is quite common in developing countries, where informal employment is especially prevalent in sectors that are more human capital‐intensive and productivity is low. In Latin America, it has been found that labor rigidity, coupled with little oversight of the businesses could lead some of them to operate illegally to avoid having to pay the required social benefits and taxes (Loayza, Oviedo, and Servan, 2005). Businesses that operate partially on an informal basis can decide which employees to assign to formal positions. In theory, businesses would choose to pay social benefits to their most productive workers, whose marginal productivity compensates for the additional cost (Bosch, 2006). There is little empirical evidence on decisions made by businesses to hire personnel under formal or informal arrangements. Most of the predictions are based on theoretical models (for example, Bosch, 2006). However, country‐level studies based on panel data have shown that increased labor costs tend to increase the size of the informal sector (for example, Loayza et al., 2005; Hazans, 2011; Fialová and Schneider, 2011; and Lehmann and Muravyev, 2012).

A study by Hazans (2011) based on 30 European countries over the period 2004‐2009 found that rises of 1 percentage point in the minimum wage increased informal employment by about 0.3 percentage points. Similarly, Fialová and Schneider (2011), studying panel data for 2000‐2007, concluded that the work of labor institutions helped to promote the growth of the informal sector in Europe. The authors found that the strictest
countries collected at different points of time. For many countries, especially low-income and fragile or conflict-affected states, it has data only for a single year. The GTAP 10 database combines detailed data on bilateral trade, transport, and protection characterizing economic linkages among regions, together with individual country input-output (I-O) databases which account for inter-sectoral linkages within regions for the most recent year available (2014).

The I2D2 database is used to compute informal employment shares in total employment in each of nine sectors, comprising agriculture, mining, manufacturing, transport and communication, finance, construction, trade, utilities, and other services. According to the operational definition used by the International Labor Organization, informal workers include all (i) contributing family workers, (ii) informal employers, own-account workers, members of cooperatives if they own an informal economic unit, and (iii) employees whose employer has no contribution to social security and who are not entitled to and do 9
being. The questionnaire for the husbands covered the labor supply of household members, income from labor, time use, household expenses on items usually bought by male members (e.g., khat, cigarettes, transport, etc.), intra-household decision making, personality traits, and mental health and well-being.

Subsets of modules from the household survey were administered as part of the midline weekly surveys. These modules covered time use, expenditures, food diversity and security, school participation, program knowledge, and public works delegation. For women who delegated the public works opportunity to another household member, the interview collected self-reported information on the women and their delegees’ mutually agreed upon income-sharing rule.

_Weekly Surveys on Employment and Intra-household Transfers_ A key innovation in our data collection was to improve measurement of the working status of the target population in a setting with volatile and irregular rates of self-employment and casual labor. We based our survey instruments on the labor diaries developed by Dupas et al. (2020). The weekly surveys were administered to both the female beneficiary and her husband, if present (95 percent of beneficiaries were married), for three consecutive weeks both at midline and at endline. Enumerators visited each household once a week and asked whether the respondent had worked, the amount of time worked, and the type of work performed for each of the seven days prior to the interview. Work is defined as time on any income-generating activity. In one of the three weeks, the female beneficiary was also asked about the labor force participation and earnings of other adult household members (excluding husbands) in the seven days prior to the interview.<sup>10</sup> In addition, the weekly survey included a special section on intra-household transfers. Specifically, the section documented whether any transfers had occurred between the woman and the rest of household members as well as the amounts of the transfers in either direction. The module was intended to measure potential intra-household reallocation of income in response to the intervention, i
oldest of the mashup indices still in use, the HDI. Its weights were set 20 years ago, with equal weight to the (scaled) sub-indices for health, education and GDP.<sup>16</sup> Equality of the weights was, of course, an arbitrary judgment, and it might have been hoped that the weights would evolve in the light of the subsequent public debate. But that did not happen. The weights on the three components of the HDI (health, education and income) have not changed in 20 years, and it is hard to believe that the HDI got it right first go.<sup>17</sup> Setting initial weights and revising them in the light of subsequent debate would also point to the need to know the tradeoffs in the most relevant space for understanding what the weights really mean. Arguably, the fact that MIPs have assigned weights in the deprivation space rather than the attainment space does not make it easy for the debate to proceed on a wellinformed basis. I would conjecture that most people will find it easier to attach a monetary value (or market-good equivalent) to a non-market good than to assess what tradeoff is acceptable between the corresponding two poverty measures. If the welfare dimension for which prices are missing is (say) health status, it is surely easier for people to judge how much money they would pay for better health than what tradeoff they would accept between poverty in income space and poverty in health space. Indeed, given the opaqueness about the tradeoffs in the primary attainment dimensions built into MIPs such as the Alkire-Santos index, it can be argued that users (including policy makers) may end up tacitly accepting, and acting upon, tradeoffs that they would find objectionable when revealed. This can hardly be helpful in advancing open public debate about the weights. The weights need to be transparent. Here again it is far from clear that the deprivation space is superior to the attainment space for this purpose.

A seemingly natural hybrid can
.12069.

- Ricker-Gilbert, Jacob, and Thomas S Jayne. 2017. “Estimating the Enduring Effects of Fertiliser Subsidies on Commercial Fertiliser Demand and Maize Production: Panel Data Evidence from Malawi.” _Journal of Agricultural Economics_ 68 (1): 70–97.

- Ricker-Gilbert, Jacob, Thomas Jayne, and Gerald Shively. 2013. “Addressing the ‘Wicked Problem’ of Input Subsidy Programs in Africa.” _Applied Economic Perspectives and Policy_ 35 (2): 322–40.

- Tanko, M, S Ismaila, and S Abu Sadiq. 2019. “Planting for Food and Jobs (PFJ): A Panacea for Productivity and Welfare of Rice Farmers in Northern Ghana” 7 (1).

https://doi.org/10.1080/23322039.2019.1693121.

- Wossen, Tesfamicheal, Tahirou Abdoulaye, Arega Alene, Shiferaw Feleke, Jacob Ricker-Gilbert, Victor Manyong, and Bola Amoke Awotide. 2017a. “Productivity and Welfare Effects of Nigeria’s e- Voucher-Based Input Subsidy Program.” _World Development_ 97: 251–65.

- ———. 2017b. “Productivity and Welfare Effects of Nigeria’s e-Voucher-Based Input Subsidy Program.” _World Development_ 97: 251–65.

- Yi, FJ, WY Lu, and YH Zhou. 2016. “Cash Transfers and Multiplier Effect: Lessons from the Grain Subsidy Program in China” 8 (1): 99. https://doi.org/10.1108/CAER-07-2015-0078.

- Zaveri, Esha Dilip, Jason Daniel Russ, Sebastien Gael Desbureaux, Richard Damania, Aude-Sophie Rodella, and Giovanna Ribeiro Paiva De Souza. 2020. “The Nitrogen Legacy: The Long-Term Effects of Water Pollution on Human Capital.” _World Bank Policy Research Working Paper_ , no. 9143.

# **Appendix**

# **A1. Key word search**

Database 1: Web of Science 18
of households that report not sending all children to school in at least one survey. By four surveys and above, over half of respondents report not sending all children to school in at least one survey.

The data further illustrate the dire food security situation for the entire mobile phone-using population over the course of the survey. Over 30 percent of households had inadequate Food Consumption Scores (FCS), which is defined as an FCS of less than 42;16 food assistance was the primary source of food for nearly 25 percent of households; and approximately 48 percent of households reported their primary difficulty being underemployment and low wages.

However, there are important caveats to note about the data. First, the survey can only be representative of the mobile phone-using population following the beginning of the conflict. Although it is difficult to identify exactly how access to mobile phones has changed since the onset of the conflict, all evidence suggests that access has remained high. The share of the population that lived in a household that owned at least one mobile phone was high prior to the conflict across the entire country and among vulnerable groups,17 there is anecdotal evidence

> 15Additionally, the survey tracks the number of active mobile phone numbers to which the household has access, primary sources of income in the past month, and the primary difficulty faced by the household in the past month.

> 16Food Consumption Scores are the weighted sum of the number of times households consume from specific food groups. They capture both quantity and quality of food consumption.

> 17Prior to the escalation of the conflict, 85 percent of the total population lived in a household that owned at least one mobile phone and would be represented by the household-level survey conducted by the WFP.

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Many students facing uncertain futures try to stay connected to both educational systems. Findings from the World Bank survey indicate that 25% of children are engaging in online Ukrainian schooling while being enrolled and attending Italian schools. The lack of certainty means children are trying to prepare for further studies (e.g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children.

Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy.

**The mental distress** resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16% of children and 24% of refugee caregivers reported experiencing psychological distress

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, _d_ = 1 _, . . . , D_ . In step 3, the true values of the bootstrap indicators are obtained from the bootstrap census vectors, _δd_<sup>_∗_(</sup><sup>_b_)</sup> = _hd_ ( **y** _d_<sup>_∗_(</sup><sup>_b_)</sup> ). In step 4, the bootstrap sample vector **y** _ds_<sup>_∗_(</sup><sup>_b_)</sup> is not extracted from **y** _d_<sup>_∗_(</sup><sup>_b_)</sup> ; instead, it is generated again using the values of the same auxiliary variables, but this time taken from the survey, **X** _ds_ , _d_ = 1 _, . . . , D_ .

Corral Rodas, Molina and Nguyen (2021) extended the model-based simulation experiment of Molina and Rao (2010) to more realistic scenarios with a much better explanatory power of the model, including contextual variables, using much larger area population sizes and much smaller sampling fractions, generating errors from a Student’s _t_ 5 distribution instead of a normal distribution, and also decreasing the overall sample size and the area sample sizes. Additionally, Corral et al. (2021) performed a design-based validation study, using the Mexican Intracensal Survey
also distinguish between the poor and non-poor according to their housing facilities, a differentiator that may be important when considering exposure to flood. The GIS data provided by the Steering Center for Flood Control suggests a poverty rate of 2.4% under the assumption that the area of semi-permanent homes relative to the area of permanent homes indicates poverty levels. Another study cites the percent of housing the government classifies as “slum” or “temporary” as 25% (Coulthart _et al._ 2006). Clearly there is much variation in recent estimates, and little can be said with confidence about future estimates. We therefore use a wide range of values for poverty rate -- 2.4% as a lower bound and 25% as an upper bound. We assume that the relative poverty rates of districts in our study area remain the same as estimated by the 2003 Inter ‐ ministerial Poverty Mapping Task Force.

_________

> 29 The Vietnam Household Living Standards Survey of 2008 estimated the overall poverty rate in Vietnam at 13.4% and estimated a poverty rate of 0.3% in Ho Chi Minh City. The Ho Chi Minh City estimate has been questioned for having methodological shortcomings (UNDP, 2010). The UNDP’s Urban Poverty Survey was aimed at overcoming these shortcomings.
br>Colombia India - Ukraine<br>30% India – AP Rajasthan<br>50% Egypt, Arab<br>20% KenyaPakistanRajasthanIndia - Ukraine 40% TanzaniaSenegal TajikistanRep.<br>10% Senegal India –AP Vietnam 30% Morocco India - Odisha<br>Dominican Tajikistan Dominican Vietnam<br>0% Republic Tanzania Egypt, Arab Rep. 20% Republic<br>-50% Morocco 0% 50% 100% -50% 0% 50% 100%<br>Change in Full-Cost Recovery Change in Full-Cost Recovery<br>Utility Governance Index<br>Private Sector Participation<br>Private Sector Participation Utility Governance Index<br><!-- End of picture text --> _Notes:_ *The Philippines saw a decline in cost recovery, but post-reform cost recovery was still close to 100 percent on average. _Source:_ World Bank staff.

_62._ However, privatization as a stand-alone measure is not always a successful strategy for improving utility performance, as was the case in Odisha and the Dominican Republic, where all privatized utilities had their licenses revoked for nonperformance.<sup>13</sup> In Odisha, the Regulator revoked WESCO’s distribution

> 13 It also has to be noted that some level of operational or limited capital cost recovery may be necessary to attract private sector participation. In many developing countries the performance of the vertically integrated state-owned enterprise is so poor that it would be hard to attract bids for the entire utility, so unbundling or improving performance would be a prerequisite

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