rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3 Evidence**

Proposition 1 summarizes the main theoretical results concerning the correlation between mean quality growth and variance of quality growth as well as the positive relationship between the mean and the variance of quality growth and financial development in a crosssection of countries. We now turn to the empirical evidence for these predictions.

## **3.1 Data**

We use data from disaggregated (HS10) bilateral exports to the United States for the years 1989 to 2001, sourced from the NBER Trade Data Set, a detailed description of which is provided by Feenstra, Romalis, and Schott (2002). The data sum across U.S. ports and modes of transportation. In total, there are 16,926 HS10 products and 178 countries. The number of products per country ranges from 1 to 9907, with a mean number across countries of 5385. We focus on manufacturing industries (SITC 5-8). In keeping with the spirit of the underlying theoretical analysis, we exclude “homogeneous goods” as defined by Rauch (1999a).

As is well known, the trade data in the NBER data set are quite noisy. In addition to data reporting and entry errors, there are product classification errors due underlying product heterogeneity. Further, as Besedes and Prusa (2006) note, there are changes in product categorization across time. For instance, a given HS10 product category may split into several HS10 codes over time and, alternately, multiple HS10 codes may be merged into one HS10 category, due to the evolution of new products as well as the attrition of old ones. To deal with the noise in the data, we trim the data along two dimensions. First, we exclude all observations for which the dataset reports a quantity of one unit or value less than $7500 1989 dollars. Second, as in Khandelwal (2010), we remove varieties with extreme unit values that fall below the 5<sup>th</sup> percentile or above the 95<sup>
enrollment increases attributed to _PROGRESA_ – as estimated by the _ex post_ evaluations of Skoufias and Parker (2001) and Schultz (2000) – are 2.4 and 7.5 percent for children of age 10-13 years and 14-17 years, respectively. The Attanasio et al model is thus able to generate predictions that match up quite well with _ex post_ evaluation results, even as its complexity and challenges of implementation are considerable.<sup>4</sup> Leite (2007) applies the BFL model to data from PNAD ( _Pesquisa Nacional por Amostra de Domicílios_ ) 1999 in Brazil to forecast an increase of 3.9 percent in the enrollment rate of poor

> 3 Our computations involve extrapolating from the enrollment difference presented in Figure 1 of Attanasio et al (2005), by assigning weights to each age group in Figure 1 and using the baseline survey. 4 These authors also propose the use of a structural model to analyze issues that remain unexplored by standard Difference-in-Difference (henceforth DID) estimators. For example, they estimate that the performance of _PROGRESA_ could have been improved by offering more resources to older age cohorts of children such as secondary school students and less to younger age cohorts or primary school students.

6
4 natural resources correlates with the construction of tall buildings whose economic rationale is questionable, for example very tall buildings that are more expensive to construct than other types of buildings. Since resource-rich countries do not have a larger construction sector, as measured by cement use or construction GDP, but use more cement in their tall construction sector than industrialized economies, the expansion of the sector might have occurred at the expense of non-tall construction.<sup>4</sup> Fourth, an important theme in the macro-urban literature is the fact that urban-biased policies cause urban primacy (Ades and Glaeser, 1995). In particular, governments use resource rents to implement policies that disproportionately favor the largest city (Bates, 1981; World Bank, 2020). However, using both country- and city-level data, we do not find that resource-rich countries have relatively higher urban primacy rates. Indeed, resourcerichness appears to have the same consequences for _all_ population size categories of cities.

we various that that more Finally, perform analyses suggest having consumption cities might have economic implications for a country. Using evidence on the number of years of schooling, education quality, and the returns to education in urban areas globally, we actually do _not_ find that consumption cities have significantly less human capital than production cities. Yet, for a given level of human capital, consumption cities have more informality and higher employment in urban non-tradables than production cities. This implies that the human capital in consumption cities is employed in less productive sectors and jobs, at least in the short to medium run. Indeed, controlling for selection and sorting, we find large wage gaps between urban tradables and urban nontradables. In addition, we find that returns to experience in urban areas are overall lower in countries with more consumption cities. Finally, we discuss various studies showing that agglomeration economies are possibly weaker in urban non-tradables.

In addition to the structural change literature, we contribute to the macroliterature on the determinants and characteristics of urbanization across development countries (Gollin, Lagakos and Waugh
POi-icY RFSEAR wH WXORKING PAPiR 2278

# Summary findings

Budina. Garrersen, and de _jfong_ use firm level data on Bulgaria to investigate the impact ol liquidlity constraints on firms' investment performance.

Internal funds are an important determinanit of investmnent in rnost industrial economies. The authors use a simple accelerator model of investment to test wshether liquidity constraints are relevant in Bulgaria's case. Their estimates are based on data for 1993-95, before Bulgaria's financial crisis of 1 996-97.

It turns out that Bulgarian firms are liquidityconstrained and that firms' size and financial structure help to distinguish between firms that are inore and less liquidity-constrained.

In the authors' view, liquidity constraints in transition economies should be interpreted in different ways than those in indusstrial economies. In Bulgaria, liquidity constraints and hence access to external funds should be seen in the context of soft budget constraints and the financial system's failure to enforce the efficient allocation of funds.

The relationship between liquidity constraints and firm characteristics may actually be thie opposite of what is nurmally the case in industrial countries. In Bulgaria, lack of liquidity constraints may be a sign of financial weaklness.

This paper - a prodtict of Macroeconomics and Growth. Development Research Group - is part of a larger effort in **the** group to study transition economies. Copies of the paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contactNina BEudina, room MC3-353, telephone 202-458-2045, fax 202-522-3518, emrail address obudina(oiworldbank.org. Policy Researclh Working Papers are also posted on the Web at www.worldbank.org/ research/ivorkingpapers. The other authors may be contacted at h.garretsen(Wjbw.kun.nl or e.dejong(@xbw.kun.nl. January 2000. (28 pages)

> _The Policy Research Workinmg Pater Series dissemninates the fin dings of uork in progress to encourage the exchange of ideas about tdeveloposent issnes. .An obective of the series is to get the find ogs or
As in the standard LTGM, the initial stock of capital-to-GDP ratio is calculated using the most recent observation from PWT 10 (although earlier versions of PWT are also available).<sup>33</sup> The initial capital stock is split across activities to equalize the initial marginal revenue product of capital across .<sup>34</sup> Information on the initial stock of reserves in industry is taken from the BPjj∈{0, ... NN} Energy Dataset for oil, natural gas and coal, and from the U.S. Geological Survey ii Database (USGS) for mining industries, such as copper, gold, and iron. As a stock variable, initial reserves are set to match the most recent observation (usually 2017).

The External-Balance Model also requires initial public and private external debt. Reliable data on the decomposition of external debt into private and public debt is often unavailable for developing commodity-exporting countries. Hence, in the baseline calibration, we assume that all initial external debt is public, and initial private external debt is zero. This is obviously an extreme assumption, and countryspecific data should be used where available. We calibrate public external debt equal to the most recent observation on total external debt taken from WB-WDI.

**Trajectory of exogenous variables** . The LTGM-NR requires the trajectories of a number of exogenous variables from 2021 until 2050. The key assumption of baseline simulations is that recent trends will continue in the long-term. In this case, we assume that historical averages (such as 2000-2019) will continue until 2050.

The first assumptions are the paths for actual and structural commodity prices. A structural price should reflect its perceived long-term value, so the user could use a long historical average or a proper estimate of the long-run price. For example, in
aged 5-12 engaged for at least one hour per week; (3) children aged 13-14 engaged for more than 24 hours per week or working nights (6 pm to 6 am); and (4) children aged 15-17 engaged in work for more than 44 hours a week. A stark statistic is that nearly a third of all children aged 15-17 are classed as child laborers while only a very small share (1.3 percent) are not<sup>4</sup> (Department of Labour 2016). Nevertheless, 35 percent of children are involved in household chores such as cleaning, cooking and childcare and 11 percent in unpaid household subsistence activities such as farming, collecting firewood and fetching water.

At the time of LFS 2015, about 78 percent of children were attending school and not working. Older children, however, were less likely to be enrolled in school and more likely to be working as nearly half of children aged 15 to 17 had quit school (Department of Labour 2016). Myanmar has improved its school enrollment in the last decade: the net primary enrollment rate increased from 88 percent in 2010 to 94 percent in 2017. But there is still a large dropout of students during the transition from primary to middle school with just 71 percent of school-age children enrolled in middle or high school in 2017 (CSO, UNDP and World Bank 2020). Enrollment rates drop at middle and high school, which is consistent with the ages at which children join the labor market.

For the purpose of this paper, we focus on children aged 5 to 17 doing any type of work that involves wage work, household chores, unpaid family work or household subsistence activities. Given the importance of child work in Myanmar and the concomitant persistent problems of non-enrollment and dropout after primary school, the objective of this study is to identify what factors explain child work and how child work affects schooling outcomes. Besides looking at children’s participation in market and household activities, the study seeks to understand how
Bai et al. (2007) and Jin et al. (2008) confirm that Chinese food producers increasingly use voluntary standards. Based on surveys of Chinese firms, the authors argue that improved product quality and safety and access to foreign markets are the main incentives to use voluntary standards and apply for certifications. In the next section, we briefly introduce the institutional background on standardization in China.

# **3. Food Safety Standardization in China**

A number of government agencies are involved in regulating food safety in China: the Ministry of Agriculture, the Administration of Quality Supervision, Inspection and Quarantine (AQSIQ), the Food and Drug Administration, the Ministry of Health, the Administration for Industry and Commerce, and the Standardization Administration of the People‟s Republic of China (SAC). The large number of agencies with regulatory responsibilities in this area has been subject to criticism as it sometimes leads to an uncoordinated and confusing system (Asian Development Bank 2007). Other problems often cited include low levels of enforcement of standards and regulations by food safety officials and the lack of inspectors supervising an ever increasing number of food producers (Broughton, Walker 2010).

SAC is the agency responsible for approving and publishing Chinese standards and adopting international standards, including food standards. According to Wang Ping, the Executive Governor of the Science and Technology Committee of the China National Institute of Standardization, the standard-setting process in SAC takes place in 450 national technical committees in which stakeholders from government agencies, industry and research organizations participate. SAC nominates the participants and the technical committees are jointly run by SAC and some ministries (Ping 2010). SAC also represents Chinese interests in international organizations. China is actively involved in the Codex Alimentarius Commission (CAC), the organization responsible for setting international standards for food products.<sup>4</sup> Setting food safety standards is a general priority of the Chinese government. In the Eleventh Five-year Plan of Standardization development, agriculture and food safety were named key areas for standardization
and limited ability to repay. It took creditors more than a decade to realize that serial rescheduling would not resolve the debt overhang.

# **4. Concluding remarks**

China’s overseas lending boom to developing countries is resulting in a growing number of defaults and debt restructurings. If history is any guide, multi-year debt workouts and serial restructurings lie ahead. Lack of transparency on the extent of indebtedness and on “hidden” debt restructurings makes it even more difficult to assess whether progress in restoring debt sustainability for the debtor countries is being accomplished. Filling in these knowledge gaps is a step in that direction.

# **References**

- Acker, Kevin, Deborah Brautigam, and Yufan Huang. 2020. Debt Relief with Chinese Characteristics. CARI Working Paper No. 2020/39.

- Acker, Kevin, Deborah Brautigam, and Yinxuan Wang. 2021. Global Debt Relief Dashboard: Tracking Chinese Debt Relief in the Covid-19 era. China Africa Research Initiative, Johns Hopkins University School of Advanced International Studies.

- Asonuma, Tamon, and Christoph Trebesch. 2016. Sovereign Debt Restructurings: Preemptive or Post-Default? _Journal of the European Economic Association,_ 14(1), 175-214.

- Asonuma, Tamon, Dirk Niepelt and Romain Ranciere. 2017. Sovereign Bond Prices, Haircuts and Maturity. IMF Working Paper No. 17/119.

- Bon, Gatien and Gong Cheng. 2020. China’s Debt Relief Actions Overseas and Macroeconomic Implications. EconomiX Working Papers No 2020-27.

- Bon, Gatien and Gong Cheng. 2021. China’s Overseas Sovereign Debt Relief Actions: What Insights Do Recent Cases Provide? _International Economics,_ 166, 23-41.

- Broner, Fernando, Tatiana Didier, Sergio L. Schmukler, Peter von Goetz. 2020. Bilateral International Investments: The Big Sur? World Bank Policy Research Working Paper No. 9501.

- Cerutti, Eugenio, Catherine Koch, and Swapan-Kumar Pradhan. 2020. Banking across borders: Are Chinese banks different? BIS Working Papers No
disasters affect industrial facilities and create local pollution). The framework proposed here is for socioeconomic resilience, not for a broader concept of resilience.

Issues related to conflicts and government stability are not explicitly recognized, even though they indirectly influence our results since fragile governments usually provide little social protection and have limited ability to respond to shocks. We also do not account for the possibility that a disaster (or the response to it) magnifies pre‐existing conflicts.

Average losses for poor and non‐poor people may not capture the full impact of the disaster: in each category, losses are heterogeneous and some households may lose everything, and experience long‐term effects or fall into _poverty traps_ . During the July 2005 floods in Mumbai, India, household surveys show that the median asset and income loss per capita was approximately Rs. 9,300, while the average loss was substantially higher at around Rs. 13,700 (Patankar and Patwardhan 2016). Losses across the population follow a lognormal distribution with a long tail: median losses are moderate, but some households lost almost all their income. For the people experiencing large losses, the welfare impact of the shock is not only related to the net present value of the flow of consumption losses, but also to possible long‐term effects, such as reduction in food intake, health effects and disability, and exclusion from job markets, which can lead households to fall into poverty traps (Barnett et al., 2008; Carter et al., 2007; Kraay and McKenzie, 2014; Maccini and Yang, 2009). The risk of poverty traps is particularly acute for children, as severe health impacts or interruptions in education can have lifelong impacts on earnings. We disregard this risk as it is difficult to monetize. (Barrett and Constas, 2014) propose a definition of _development resilience_ that focuses on the capacity of people to avoid such poverty traps.

Also, the ability of individual firms to cope with the shock and continue to produce in the
and wives’ optimal home production time. Adding an additional member’s labor decisions will increase the household labor market states to 64, plus home production optimal time allocation per agent. Including that dimension (extended families rather than spouses making choices) into the current specification is computationally costly and complex to program/code.

**Second, this paper uses search cost and taste/productivity/social norms to explain nonparticipation, but other sources of nonparticipation such as nonlabor income and health status should also be studied.** The nonparticipation rates for Roma are high compared to international standards. Among the usual explanations for high nonparticipation rates are the disincentive effects of nonlabor income (which also includes household subsidies, social assistance schemes, pensions, and transfers from other households) or health status. The RRS includes these dimensions but analyzing them was out of the scope of this paper. These are areas that can be further explored in future research.

**In this direction, even when we included gender norms over leisure, home production, and search cost, we are not explicitly modeling cultural norms such as the imposed restriction on females to only do housework and not enter the labor market.** Therefore, our results should be interpreted as an upper bound for household reactions to different policies. Further work could explore the introduction of such social barriers into the modeling.

**Another useful future extension could produce a General Equilibrium Search Model like the one developed by Hanming and Shepard (2019), but this will rely on future data availability.** Hamming and Shepard have labor supply and demand information and employer provided health insurance. In the present exercise we did not have firms’ information. Our estimated labor market parameters did not allow us to ascertain if the values found were coming from less labor market productivity or from employer discrimination against Roma males or females. Considering this limitation, in the policy experiments we one increased male and female labor parameters to simulate if there is a reduction in discrimination, or if Roma
(e.g., financial crisis, COVID-19 pandemic, etc.), some of which rely on individual-specific scales (e.g., FAO 2020; United Nations 2020; World Bank 2020; Adjognon et al. 2021; Amare et al. 2021; etc.). However, given that both well-being and individual specific scales could be moving at the same time, it might be difficult to precisely interpret these types of empirical strategies.

Second, although many difficulties in interpreting subjective welfare metrics have been highlighted before, it is an empirical issue to understand how difficult such measures are to interpret. Previous results have found substantial differences between subjective and traditional welfare metrics (e.g., Beegle et al. 2012; etc.). However, rather than simply making it more difficult to detect changes in consumption and employment, these results demonstrate that subjective welfare metrics can possibly produce estimates that move in the _opposite_ direction of traditional metrics that are needed to target critical social protection programs during crises.<sup>4</sup> Although subjective welfare metrics can still be informative to policy decisions, these results demonstrate that such metrics cannot always be substitutes for traditional metrics of deprivations, and it is important to utilize subjective welfare indicators alongside more traditional welfare metrics during especially tumultuous times.<sup>5</sup> The rest of the paper is structured as follows. Section 2 presents a conceptual frame-

> 4This finding is also related to Headey (2012), which found in a cross-country comparison that subjective assessments of food security improved between 2005 and 2007/2008 using two simple subjective questions about food security from the Gallup World Poll. This time period was a time of both rapid income growth and rapid increases in food prices. However, unlike the case described here, subjective assessments of food security and more traditional metrics are aligned in Headey (2012), where higher food prices are associated with worse subjective assessments of food security in the data and larger income growth is associated
Storm year Storm year,L1 Storm year,L2 Storm year,L3 Storm year,L4 Storm year, L5 Sum of lags<br>EF<br>=sy<br>e ¢ s ry<br>f uN vf # a Sues go x<br>% a ‘e fn ® .<br>= z<br>: y “a t “i F bi<br>Avg. in-migration, non-storm years aa ¥ Avg. in-migration, storm years ‘ee $ an 3<br>oO 50 a 100 150 200 250 0 50a100 150 200 250<br><!-- End of picture text -->
_Figure 8: Share of officials agreeing with statements on gender equality._ a. “Schools should try to promote gender equality.” b. “Girls face additional challenges in accessing and completing their education compared with boys in this country.” <!-- Start of picture text --> 100%<br>100%<br>50% 50%<br>0% 0%<br>IDN LAO MNG PHL VNM Non-EAP IDN LAO MNG PHL VNM Non-EAP<br>Strongly agrees Agrees Strongly agrees Agrees<br>Disagrees Strongly disagrees Disagrees Strongly disagrees<br>Does not know Does not know<br><!-- End of picture text --> Source: Calculations from CDG–World Bank 2022 microdata.

- 4.4 Officials favoring gender equity tend to promote access for children with disabilities.

Policy makers’ support for statements associated with gender equity is correlated with their support for statements of inclusion of children with disabilities.<sup>10</sup> The survey includes several questions measuring beliefs using a Likert scale.<sup>11</sup> Some are linked to beliefs about gender, views about children with disabilities or about EdTech and the effectiveness of more funding for education. We use factor analysis to identify potential latent variable measuring views about equity.<sup>12</sup> While there are large cross-country disparities in this index, respondents from EAP countries overall tend to have higher scores on the index than those from non-EAP countries (Figure 9). The index average is statistically higher in the EAP than in the non-EAP group.<sup>13</sup> Compared with non-EAP respondents, officials in EAP strongly support both gender equity and inclusion when surveyed. This index provides support for a range of beliefs and values on both topics that are meaningful for the World Bank and other development partners to be aware of when engaging with public officials in EAP countries. We find no statistically significant link between this index and