rafmacalaba/data-use-mentions-extended huggingface dataset:
br>|hare in w<br>|orld (in<br>|%)<br>||||||||
|Turkey|0.41|0.77|0.44|0.79|0.43|0.83|0.69|1.03|68|33|59|24|
|EU-8|1.32|1.64|1.58|2.14|1.86|2.34|2.80|3.15|111|92|50|35|
|Bulgaria|0.06|0.12|0.09|0.09|0.07|0.10|0.10|0.15|65|27|38|55|
|Romania|0.13|0.17|0.14|0.18|0.16|0.20|0.26|0.34|96|100|59|74|

_Notes_ : EU-8 countries are the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovak Republic and Slovenia.

_Source_ : IMF DOT Statistics.

Turkey’s export performance has been in line with that of EU-8 and Romania and well above Bulgaria’s performance. The latter’s share in world exports in 2003 was slightly below in 1993, whereas that of Turkey was 52 percent higher. The difference between Turkey and EU-8 and Romania is relatively small considering that dynamics of geographic reorientation of trade away from the former Soviet Union drove their trade in 1992-96 following the collapse of central planning. While between 1990 and 1993, exports from Turkey were growing at roughly the same pace as those from the EU-8, the situation subsequently changed. The ratio of EU-8 share in world exports to that of Turkey increased from 3.2 in 1990 and 1993 to 4.3 in 2000 and then fell to 4 in 2004 indicating Turkey’s stronger export
factors of severe cases with COVID-19: a meta-analysis’, _Epidemiology and Infection_ , 148, p. e175. doi:10.1017/S095026882000179X.

Rabie, T. and Curtis, V. (2006) ‘Handwashing and risk of respiratory infections: a quantitative systematic review’, _Tropical Medicine and International Health_ , 11(3), pp. 258–267. doi:10.1111/j.1365-3156.2006.01568.x.

Saunders-Hastings, P. _et al._ (2017) ‘Effectiveness of personal protective measures in reducing pandemic influenza transmission: A systematic review and meta-analysis’, _Epidemics_ , 20, pp. 1– 20. doi:10.1016/j.epidem.2017.04.003.

Smith, K.R. _et al._ (2000) ‘Indoor air pollution in developing countries and acute lower respiratory infections in children’, _Thorax_ , 55(6), p. 518. doi:10.1136/thorax.55.6.518.

Sturrock, H.J.W. _et al._ (2013) ‘The Use of Bivariate Spatial Modeling of Questionnaire and Parasitology Data to Predict the Distribution of Schistosoma haematobium in Coastal Kenya’, _PLoS Neglected Tropical Diseases_ . Edited by X.-N. Zhou, 7(1), p. e2016. doi:10.1371/journal.pntd.0002016.

Takahashi, S. _et al._ (2017) ‘The geography of measles vaccination in the African Great Lakes region’, _Nature Communications_ , 8(1), p. 15585. doi:10.1038/ncomms15585.

Talaat, M. _et al._ (2011) ‘Effects of Hand Hygiene Campaigns on Incidence of Laboratory-
mixed record in this regard. Their analysis is based on two successive rounds of the Russian Longitudinal Monitoring Survey (carried out with about 3,800 households in October 1996 and November 1998 respectively). In the crisis, mean household expenditure fell by about 20 percent and poverty headcount (national definition, expenditure) rose from 22 percent to 33 percent. On the other hand, inequality narrowed: the Gini coefficient for expenditure actually fell from 0.44 to 0.42; from 0.48 to 0.42 for income. Interestingly, there were on average expenditure gains by the poor between 1996 and 1998, and 42 percent of the 1996 poor had escaped from poverty two years later. The apparent paradox (gains by the poor, but increasing poverty headcount) is resolved by realizing that the overall increase in headcount reflected the very large number – over a quarter – of the previously non-poor that fell into poverty. Increases in poverty rates are lower if measured in terms of income rather than expenditure, and it seems that the expenditure responses reflected excessively precautionary behavior.

# _African crises of the 1990s_

Sizable short-run improvements in inequality were recorded in three African countries, Kenya, Uganda and Zambia, around the time that their banking crises broke in 1993-5 (Figure 2). Yet it may be unwise to jump to conclusions about the causality of such a correlation. For none of these three crises were associated with any large output dip (cf. the country entries in Caprio et al., 2005). The Uganda case relates to the decision to face up to the deep insolvency of a large state-owned bank at the center of the system. The Kenya and Zambia cases related to mismanagement and perhaps fraud at sizable banks unrelated to macroeconomic conditions. It is likely that the distributional changes that occurred in these countries during the mid-1990s were more closely related to terms of trade changes that were rather independent of the banking conditions.

These important cases highlight the possibility that banking crises can be accompanied by falling inequality. The next
More importantly, reported travel times from surveys are both unreliable and idiosyncratic. In the case of the HRVS data set we believe the variable accuracy of recorded GPS locations disproportionately influenced the rate of convergence where average travel times were low. GPS error within the HRVS data set was high and inconsistent due to the use of multiple tablet models when collecting survey data. As reported above, we discarded 117 GPS points (2% of the total) that fell outside the reported village (ward) boundary, deeming them erroneous. However, we could not assess the quality of points that fell within ward boundaries, many of which may have fallen far from the actual household location.

We believe this led to dramatic relative differences where average travel times were low. For example, 12 minutes modeled and 2 minutes reported yields a 600% difference in results, whereas 130 minutes modeled and 140 minutes reported elsewhere yields only a 7% difference. When assessed comparatively the former will return a poor coefficient of correlation even though in absolute terms the difference is insignificant.

This explains the substantially poorer R value for Province 2, where mean and maximum travel times are much shorter than in other provinces. Subdividing by geography (see Table 14 and Figure 28, Annex III) reinforces this analysis; correlations for households in the Terai are low, yet Terai data clusters close to the regression line in absolute terms. Explaining the low coefficient of correlation in mountain areas is more difficult. It may be that erroneous points there are more likely to return very high travel times because the majority of mountainous areas are very remote. Maps of model results clearly show islands of good access to services clustering near the very few roads and paths in mountainous areas, amidst figurative seas of high travel times (see Figure 27, Annex III). A misplaced GPS point in this environment could easily return an extreme value.

Paradoxically, in Figure 7 the estimated Loess regression lines and a 45 ° line are close in _absolute_ terms for short distances (roughly 30‐
Lerner index|-0.018<br>[0.294]|0.045<br>[0.261]|||
|Concentration 3|||0.180<br>[0.198]||
|Herfindahl index||||0.338*<br>[0.183]|
|LernerShare government banks|-3.068**<br>[1.281]|-4.131***<br>[1.123]|||
|Concentration 3Share government banks|||-1.491*<br>[0.860]||
|H IndexShare of government banks||||-2.452**<br>[0.940]|
|Log firm size|0.086***|0.088***|0.088***|0.088***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Manufacturing|0.015|0.011|0.019**|0.019**|
||[0.009]|[0.009]|[0.010]|[0.009]|
|Exporter|0.036***|0.034***|0.036***|0.036***|
||[0.008]|[0.010]|[0.008]|[0.008]|
|Foreign-owned|-0.076***|-0.088***|-0.080***|-0.078***|
||[0.011]|[0.012]|[0.011]|[0.
This version: 15 Feb 2024 60

# **<mark>APPENDIX NOTE B:</mark> Revisiting the dissonance between incomes and prices**

To better understand the dissonance between incomes and the cost of living in our qualitative analysis, this section provides additional/complementary evidence on trends in income and prices using various official data sources. The objective is to understand whether citizen frustration captured in qualitative data is consistent with trends in relative increases in wages vis-à-vis prices.

Compared to other ASEAN countries, annual real wage growth in Malaysia has been low (World Bank, 2023). To probe further, we reproduced the GIC using employment income. Figure B1 confirms that, during 1984-2016, wage growth has been pro-poor, which is also consistent with the trends in overall income gains from growth (see Figure 5). This is also true for each of our three study states, although the employment income GIC has been relatively flat for the state of Selangor (Figure B2). However, this may not have increased wages sufficiently in the long run. Figure B3 reports trends in median employment income during 2004-2016 across our three study sample states. Slow wage growth is evident even for the state of Selangor. Moreover, there is significant regional disparity. Sabah not only has the lowest median income, it also saw smaller increases in wage income, averaging RM 56 per year compared to RM 79 and RM 88 in Terengganu and Selengor, respectively.

60
no OECD input-output tables, we assign to each of those countries the input-output table from the country that is most similar in terms of three dimensions: level of development captured by GDP per capita, size captured by population and economic structure captured by share of manufacturing value added, all from World Development Indicators. We assign to countries with no input-output table the table of the country with the lowest aggregate difference across the three dimensions, which we aggregate using inverse-variance weighting.

11
past CEQ for Colombia, direct taxes and direct transfers slightly reduce inequality, while indirect taxes and subsidies appear to be neutral, and education and health spending has the most significant effect in terms of reducing inequality. In the current exercise, the effect of direct transfers and direct taxes seems to be higher, although not significant. The same goes for spending on education and health.

<!-- Start of picture text --> FIGURE IV-4 : GINI COEFFICIENTS FOR COLOMBIA<br>0.6<br>0.574<br>0.567 0.567<br>0.559<br>0.55 0.531 ENPH (Pensions<br>0.551 0.517 0.515 as deferred<br>income)<br>0.5 ENPH (Pensions<br>considered<br>0.454<br>direct transfers)<br>0.45<br>ECV 2010*<br>0.4<br>Market Income Disposable Income Consumable Income Final Income<br>(*) Lustig, Melendez, and Rodriguez-Castelán (2014)<br>Source: World Bank Group staff estimates based on ENPH<br><!-- End of picture text --> The redistributive effect of the tax system and social spending in Colombia is above the regional average (Figure IV-5). Argentina, Brazil, and Costa Rica are the only countries for which information is available that have a tax system and social spending that help reduce inequality to a greater extent than the Colombian system.

<!-- Start of picture text --> FIGURE IV - 5 : REDUCTION IN INEQUALITY WHEN MOVING FROM MARKET INCOME TO FINAL INCOME<br>0<br>-0.05<br>-0.1<br>-0.15<br>-0.2<br>-0.25<br>Contributory pensions as deferred income Contributory pensions as direct transfers<br
<u>https://www.wilmerhale.com/en/insights/blogs/wilmerhale-privacy-andcybersecurity-law/20190124-the-european-commission-adopts-adequacy-decision-on-japan.</u>

> 229 https://ec.europa.eu/info/law/law-topic/data-protection/international-dimension-dataprotection/adequacy-decisions_en.

> 230 See GDPR, arts 44-49. Indeed, our survey respondents indicated that they rely principally on standard contractual clauses for cross-border data transfer from the EU. Privacy Survey, at 23.

> 231 _See_ Chander, _supra_ note 52.

> 232 Aaditya Mattoo & Joshua P. Meltzer, _International Data Flows and Privacy: The Conflict and Its Resolution_ , 21 23 J. INT’L EC. L. 769, 771 (2018) (“the EU–US Privacy Shield offers a way of resolving the conflict between regulatory heterogeneity and international data flows”).

> 233 Privacy Shield Framework, _Privacy Shield Overview_ , https://www.privacyshield.gov/program-overview.

41
using the Political Rights indicator and Economic Freedom. The share of tertiary educated workers over the total residence labor force is not signi...cant anymore, probably due to the two variables being highly correlated (0.7145). Finally, the coe¢ cient on the GDP per capita is in general positive but not always signi...cant.<sup>20</sup> The estimations con...rm that democracy is very persistent. Moreover, considering the ...rst 3 columns in each table, the coe¢ cient on past democracy ranges between the estimated coe¢ cient in pooled OLS, which is usually biased upwards, and the estimated coe¢ cient for the ...xed e¤ect, which usually displays a downward bias.

The AR(2) test which tests the null hypothesis that the error term is not second order serially correlated, and the Hansen J test of overindentifying restrictions, indicate that the moment conditions are satis...ed and the instruments are valid.

In general, the results appear quite robust across speci...cations and indices.<sup>21</sup> To evaluate whether the skill composition of migration, and not just its size, a¤ects institutional quality at home, we introduce in column 6 the share of tertiary educated amongs migrants. The coe¢ cient of the share of tertiary educated migrants is negative but not statistically signi...cant for 3 indicators out of 4 and is only positive and signi...cant at the 10 percent when considering the EFW indicator. In spite or, rather, because of this inconclusive result, we will further investigate this issue in the next section using numerical simulations.

Finally, one may be concerned, as Acemoglu et al. (2005) were about their own study, that the presence of socialist countries in our sample may largely a¤ect the estimation results. Indeed, most socialist countries had high levels of education in the
Since the weekly phone arm was primarily intended as a proof of concept, the focus of this paper is the comparison of the weekly visit group and the recall group – in other words, we do _not_ include any analysis of the data from the weekly phone group.

While the overall design of this study is very similar to Arthi et al (2018), there is one significant difference: This study fielded a baseline survey to _all_ households, including recall households, which allowed us to collect a baseline listing of plots and household workers. This listing of plots and household workers plays a crucial role, because all data on farm labor were collected at the person-plot level. However, respondents could add plots and household workers that were not listed at baseline during subsequent visits – that is during visits 1 to 24 (weekly visits, endline) in the weekly visit group, and during the endline in the recall group. This design variation follows from the results in Arthi et al (2018), who document that underreporting of plots and household workers in the recall group counteracts overestimation of farm labor per person per plot. Having access to a baseline survey for all households allows us to explore in greater detail bias in listings of plots and household workers, but it may also explain some of the difference in results between this study and Arthi et al (2018). We will revert to this issue in section 4.5, where we compare the results obtained for Ghana and Tanzania.

Our field work design randomized households within villages. We are confident that intra-cluster contamination among households is minimal given that villages tend to be large and the sampled households are rather dispersed. The within-village randomization is aimed at balancing micro-agroecological characteristics that may affect the allocation of household labor and are difficult to be captured in the available survey and satellite data.

Although the initial sample was 240 households per experiment arm, we had some attrition at different stages. In the weekly visit group, 20 households dropped out of which 9 were replaced with a
Policy Research Working Paper 10223

# **Abstract**

Household businesses make up the majority of firms in developing economies. This paper uses a novel tax census database that covers the universe of tax-registered household businesses to analyze the entry and exit of owner-operated firms in Vietnam during January 2018 to August 2020. It documents new stylized facts about the survival dynamics of informal businesses. First, the entry and exit rates were about 5–6 percent a year for tax-registered household businesses during the pre-pandemic period. Second, an additional 25 percent of household businesses suspended their activity in a year on average, with the annual suspension duration exceeding 2.5 months. The suspension rate spiked to 40 percent during the onset of the COVID-19 pandemic in 2020. Third, the findings show that the pandemic-related effects were more pronounced for businesses dependent on face-to-face interactions with customers and suppliers. However, these effects were short lived, and activity and earnings rebounded by August 2020. The findings may reflect the relatively short COVID-19 distress in Vietnam during the first phase of the pandemic, but they illuminate both the vulnerabilities and resilience of the household business sector.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. 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 dle6@worldbank.org and eislamaj@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do
Our results show that skilled labor is the most important determinant of backward and forward GVC participation in SSA. However, unskilled labor, which is the most available labor form in SSA economies, has adverse effects on both backward and forward GVC integration. This suggests that SSA countries need to improve their skilled labor content in exports to better engage in manufacturing GVCs. Public policies that upgrade unskilled to skilled labor should be encouraged in SSA countries. Investing in professional and skills developments that are required to support direct and indirect GVC related activities is needed for SSA countries to maximize the gains from GVC participation. In addition, our findings show that net migration as a share of population has a strong positive effect on backward and forward GVC participation. To this point, migration is an important determinant of integration in manufacturing GVCs for SSA countries. This is because skilled foreign workers contribute in increasing the participation of SSA countries in manufacturing GVCs especially in countries where there are skilled-labor shortages. Therefore, regional agreements that encourage and support intra-regional skilled mobility can be a great vehicle to upgrade countries’ integration in backward and forward global and regional value chains.

Moreover, our results suggest that countries with greater human capital endowment (high HCI) tend to engage more in manufacturing GVCs than those with low initial endowments. Since SSA countries lag behind the rest of world in initial human capital endowment, our results show that the impact of HCI on GVC participation is minimal in SSA compared to the rest of the world. This signals the need for policies to be directed towards improving the human capital index of SSA countries. Investing in children today is a great opportunity to boost long-term skilled endowment which has proven to register the highest impact on both backward and forward GVC participation in SSA. Finally, it is important to note that our analyses focus on manufacturing GVCs integration, but the methodology employed can also be extended to the primary and service sectors. Another limitation of this study is the small size of the SSA sample due to limited available skills related data.
a household’s income-generating potential and ability to cope with future shocks.

Third, disruptions to schooling can widen learning gaps between children from different socioeconomic strata, reducing intergenerational mobility. In most low-income countries covered by HFPS, children in fewer than 66 percent of households participated in any continued learning when the schools were closed. Learning losses can have irreversible long-term costs for individuals and society alike. Evidence from past disasters suggests that disrupted schooling and the trauma of shocks can adversely affect learning among children that are observable years later.<sup>26</sup> These effects increase educational inequality, as suggested by early data from high-income countries (Belgium and the Netherlands). As children learn from home, social inequalities become more salient. To compound this effect, as vulnerable households in developing countries suffer income losses, some may also be less likely to send children back to school after a long period of closure. The closure of schools could thus widen already existing gaps in education between children born into different socioeconomic strata.

Fourth, long-term effects on inequality can also occur due to the interruption of critical health services caused by the pandemic, including maternal and child health care, which can leave lasting impacts on the health of future generations from vulnerable families. Elston et al (2017) find that in areas affected by the 2014–15 Ebola outbreak in West Africa, maternal and delivery care, malaria admissions for children under the age of 5, and vaccination coverage fell significantly. Such disruptions to health care, as well as to nutrition, could undo decades of gains in health outcomes in many low- and middle-income countries.

> 24 Jacobson, LaLonde, and Sullivan (1993) and Stevens (1997). They argue that firm closings can be thought of as exogenous employment shocks after conditioning on pre-displacement earnings.

> 25 The adult earnings of men whose fathers were displaced from their jobs due to firm closures are estimated to be 9 percent lower than earnings of similar individuals whose fathers did not experience an employment shock,
# **Appendix Table 5. Nutrition/health metrics for women of reproductive age**

|**Nutrition/health outcome**<br>**variables**|Data source|Data availability|Notes|
|---|---|---|---|
|**Anaemia**pregnant women (15-<br>49 years) and non-pregnant<br>women (15-49 years)|UNICEF Global Database<br>(based on WHO GHO)|All countries,<br>Latest year: 2019||
|**Underweight**in WRA (20-49<br>years)|UNICEF Global Database<br>(based on NCD-RisC)|All countries, Latest<br>year: 2016|Percentage of women 20-49 years of age with a<br>BMI <18.5 kg/m2<br>Data also available for men from NCD-RisC.|
|**Overweight and obesity**in<br>WRA|UNICEF Global Database<br>(based on NCD-RisC)|All countries, Latest<br>year: 2016|Percentage of women 20–49 years of age with a<br>BMI ≥ 25 kg/m<sup>2</sup><br>Data also available for men from NCD-RisC.|
|**Low-birth weight**prevalence<br>and prevalence of newborns<br>without birthweight data|UNICEF Global Database|N=148 (54 no LBW<br>estimate)<br>Latest year:**2015**<br>(will be updated later<br>this year)||
|**Stillbirth**rate and number of<br>stillbirths|UNICEF Global Database|Latest year: 2021||
|**Preterm birth**|Modeled estimates by<br>Chawanpaiboon et al. 2019<sup>5</sup>|Latest year: 2014