rafmacalaba/data-use-mentions-extended huggingface dataset:
When interpreting these results, two features of the performed DEA should be kept in mind. First, for any given country there are three possible factors behind an increase in DEA scores: an increase in the country’s revenue relative to its GDP, a decline in the DEA input index (corresponding to less favorable economic conditions), and changes in the frontier due to developments in other countries. As can be seen in Table 2, changes in the DEA input index were generally very small over this period. The improvements in efficiency can thus be attributed either to increases in revenues or to changes in the frontier.<sup>19</sup> The fact that there are several cases in Table 2 in which DEA scores increased despite declining revenues (as a percentage of GDP) suggests that changes in the frontier play a crucial role for the changes in DEA scores.

Moreover, when looking at changes in countries’ revenue mobilization performance across time, it is important to note that the frontier analysis is designed to track countries’ relative performance within the sample (i.e., in comparison to other countries during the same time period) rather than countries’ absolute performance over time. This means that the DEA scores (as well as the values of the input index) cannot be directly compared across time. Instead, similar scores in two periods indicate that the _relative_ performance of a country within the sample remained about the same. This approach has the advantage that it controls for general time effects, i.e., events that affect all countries simultaneously in a given period. For example, suppose country A achieves a score of 0.4 in the first period and 0.5 in the second period. It might be the case that revenues in fact declined from the first to the second period. This might be the case if a global shock (such as the Financial Crisis) negatively affected revenue mobilization in all countries, but country A managed to cope with the shock relatively better
18. CARE-Perú. 2008. “Cuarta Medición de Indicadores de los Objetivos 2-4 del Componente VIH”. http://www.bvcooperacion.pe/biblioteca/handle/123456789/6135 19. Bautista, C.T., Sánchez J.L., Montano S.M., Laguna-Torres V.A., Lama J.R., Kusunaki L., y Carr J.K. 2004. “Seroprevalence of and risk factors for HIV-1 Infection among South American men who have sex with men”. Sex Transm. Infect. 2004 80:498-504 20. Ministerio de Salud y Deportes, ONUSIDA, OPS/OMS, USAID, CDC, IBIS-HIVOS. 2008. ” Estudio de Sexualidad Masculina y VIH en Bolivia (SEMVBO) ”. La Paz, Bolivia 2010 21. Ministerio de Salud, Estrategia Sanitaria Nacional de ITS/VIH/SIDA, Coordinadora Nacional en Salud, ASAP, Perú. 2009. “Base de Datos y Referencias para una Estrategia Basada en Evidencia”.

22. Programa Nacional de Prevención y Control del VIH/SIDA-ITS del Ministerio de Salud Pública, ASAP, Ecuador. 2009. “Base de Datos y Referencias para una Estrategia Basada en Evidencia”.

23. Ministerio de Salud y Deportes Programa Nacional ITS/VIH/SIDA, ASAP, Bolivia. 2009. “Base de Datos y Referencias para una Estrategia Basada en Evidencia”.

24. Maart S, Jelsma J. 2010. “The sexual behaviour of physically disabled adolescents”. Disabil Rehabil. 2010;32(6):438-43.

25. Heidari S, Kippax S. 2009. “Special theme on HIV and disability - time for closer bonds”. J Int AIDS Soc. 2009 Nov 9;12(1): 26. Rohleder P, Braathen SH, Swartz L, Eide AH. 2009. “HIV/AIDS and disability in Southern Africa: a review of relevant literature”. Disabil Rehabil. 2009;31(1):51-9. PubMed PMID:
likely be part of the reason why linkages of local manufacturers in Ghana are not as strong as those of their counterparts in other resource rich economies such as the Democratic Republic of Congo, South Africa, and Zambia. These additional factors include countries’ physical, cultural, political proximity to larger economies, as well as their trade policies and membership in regional trading blocs. An estimated gravity model of sources of the import content of manufactured exports and destinations of the indirect component of those exports highlights and quantifies the role of these factors.

Furthermore, using firm-level data from World Bank Enterprise Surveys, the paper shows that the domestic actors in these linkages are typically relatively large establishments of 100 or more employees that have been in operation in for five years or longer. These manufacturers are also more likely to have foreign equity holders or foreign technology licenses.

The next section of the paper describes the conceptual framework and analytic methods underlying these findings. This is followed by an account of the data sources in section 3 and how and why countries are classified in producing those findings. Sections 4, 5, and 6 provide details of the findings. Section 7 concludes.

# **2.  Conceptual Framework and Data**

## 2.1. Conceptual framework

The term “value chain” refers to the sequence of stages of productive or value-creating activities or tasks leading to the delivery of a product as a final good or service (that is, a good or service in end use) and starting from its conception and design via intermediate phases of a production plan and its execution. The concept relates to the analytic technique of value chain analysis, which is a method of identifying cost-saving or product differentiation opportunities across the various stages of production or delivery of a good or service. A value chain is said to be a global value chain (GVC) if it involves processes and tasks carried out in the framework of contractual relations between firms across international borders that are not necessarily in the same region.

In a GVC, firms
552|0.156|0.168|0.178|0.135|
|Birth Cohort FE||Yes|Yes|Yes|Yes|Yes|
|Country X Year FE||Yes|Yes|Yes|Yes|Yes|
||_Panel B: _|_Ignore chil_|_d sample_||||
|Post-marriage||-0.186***|-0.165***|-0.214***|-0.262***|-0.108***|
|||(0.01)|(0.02)|(0.01)|(0.05)|(0.02)|
|Post-marriage _×_ Higher education||0.075***|0.083**|0.071***|0.065|0.124***|
|||(0.02)|(0.04)|(0.02)|(0.09)|(0.05)|
|Post-marriage _×_ Husband higher e|ducation|-0.025|-0.008|0.004|0.134|-0.068|
|||(0.02)|(0.04)|(0.03)|(0.11)|(0.05)|
|No. of obs.||9583|2163|4413|570|2434|
|R-squared||0.608|0.152|0.215|0.2|0.106|
|Birth Cohort FE||Yes|Yes|Yes|Yes|Yes|
|Country X Year FE||Yes|Yes|Yes|Yes|Yes|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. SAR - South Asia Region,
the evolution of preferences.

Respondents were given the exact same choice question in 2013 and 2016 and the treatment group in the RCT received improved firewood stoves, whereas the control group continued to use traditional stoves. This setting allows us to use a difference-in-difference framework to analyze the results from the choice experiment, the first instance of a difference-in-difference choice experiment analysis that we are aware of. Further, we exploit the heterogeneity in use in the treatment group to analyze the evolution of preferences in response to the effectiveness of the stoves.

We conduct a national survey in Ethiopia starting with 504 households in 36 communities in 2013, and we are able to conduct the study with 486 of the same households in 2016, giving an extremely low attrition rate of just 4%.

14
alone as a proxy for general skills severely underestimates the returns to human capital and obscures our understanding of how the labor market rewards various skills.

In response to the conceptual and methodological limitations of the returns-to-schooling studies noted above, a substantial literature has examined how cognitive skills, and to a lesser extent noncognitive skills, are rewarded in the labor market (Cawley, et al., 1996; Hanushek and Zhang, 2006; Hanushek et al., 2013). Although available metrics are fairly heterogeneous and seldom comparable across data sets, _cognitive skills_ tend to be measured through student achievement tests and reading and numeracy assessments. _Noncognitive skills_ are typically estimated through self-reported measures of behaviors and personality traits (such as the Big Five personality inventory dimensions).

Overall, findings indicate that, like years or level of schooling, cognitive skills tend to have a statistically significant effect on wages—the magnitude of which varies by the specific

> 5 “Mincer’s empirical innovation has perhaps been too successful as it has also led researchers to ignore many important and continuing measurement issues. Implicitly the Mincer formulation assumes that schooling is the sole systematic source of skills differences” (Hanushek et al., 2013, p. 4).

4
Has this come with higher average household living standards? Macro-level economic growth, as measured in the national accounts (NAS), does not automatically translate into similar growth in average household living standards, as measured by the mean for the distribution of household consumption or income on which poverty measures are based. There are invariably gaps between NAS aggregates and the grossed-up consumption or income aggregates from the household surveys used to measure poverty. There are a number of reasons for these gaps. In practice, the way that the NAS are constructed means that there is nothing exactly corresponding to household consumption as measured in surveys, so full agreement should not be expected. Measurement errors in both sources also play a role, as do differences in accounting periods and sampling problems. A likely source of discrepancies between the two data sources is underreporting of incomes or consumptions in sample surveys or selective compliance in the randomized assignments that are used in implementing the surveys. The rich will undoubtedly have a stronger incentive than the poor to underreport their incomes or consumption and will be less likely to be available for interviews. Indeed, in one assessment (using data for the United States), selective compliance—in which the rich are less likely to participate in surveys than the poor—resulted in a sizeable underestimation of inequality using the unadjusted sample survey data but made little difference to poverty measures (Korinek et al. 2006).

Compiling evidence on how average living standards have been evolving over time in the developing world requires a large number of household surveys, which come irregularly over time. Thus the calculations are slightly more complicated than when using annual NAS data. Drawing on some 900 household surveys for 125 countries, Chen and Ravallion (2012) provide estimates of poverty measures for “reference years” spanning 1981–2008 at three yearly intervals, which have been updated to include 2010 for this paper. Given the irregular and unsynchronized spacing of surveys, Chen and Ravallion (2010, 2012) use an interpolation method that employs growth rates in private consumption from
systematically different perceptions of and attitudes to objective shocks (Bertrand and Mullainathan, 2001). In the section that follows, we therefore investigate the relationship between subjective crisis impact and coping strategies used by households.

# **3. Household coping strategies and subjective impact**

The subjective impact of the crisis goes beyond lower aggregate consumption and may depend on the coping strategies adopted. In fact, 13 percent of those who reported that their household was affected "a fair amount" and 9 percent of those reporting "a great deal" also answered that they did not reduce their consumption. One-quarter of these households experienced job loss and one-half had lower wages, but they were able to maintain consumption using private and public safety nets as well as increasing their earnings. The LiTS II allows an examination of formal and informal coping strategies, beyond adjustments in consumption and labor supply: the survey collected information on a wide range of coping mechanisms (as declared by respondents), although the list is not exhaustive. For example, respondents were not asked if they reduced education expenditures<sup>6</sup> .

# **3.1. Coping strategies: Descriptive statistics**

# **_Typology and summary statistics_**

Following Lokshin and Yemtsov (2004), we combine coping strategies into “active”, “safety nets” and “passive” strategies. We further distinguish between public and private safety nets, and between cutting expenditures on necessities (staple foods and health) and other expenditure, thus analyzing how households differed in terms of their use of 5 broad coping categories:  _Active strategies_ . These include getting a second job or increasing working hours to compensate for reduced wages, enrolling in further education because of a lack of job opportunities, selling assets or moving to a new location.

 _Safety nets_ . Households can draw upon public safety nets (state benefits from government) and private safety nets (borrowing money from relatives, friends or a bank).

> 6 However, World Bank
between the treatment and control group, where the treatment group shows earnings that are approximately 10 percent lower than the earnings of the control group. Even though the difference is marginally statistically significant (p-value of 0.076), all our estimations include this variable as covariate. Moreover, when we conduct a test of joint significance of differences of all pre-treatment characteristics, we do not reject the null hypothesis that all characteristics between treatment and control group are the same (F-test=0.68 with a p-value of 0.835).

Given that Table 3 uses only data from our surveys, the set of variables we show there is richer than the one described in Table 2, particularly for variables regarding education and skills. A few additional things are worth mentioning about our sample characteristics that were not described in section 3.2. We observe that about 30 percent of our sample has specialized on a STEM degree<sup>20</sup> during their education. The proportion of the sample that holds a degree in Computer Science is significantly lower: only 5.5 percent. About a third of our sample has taken a coding training course before the bootcamp. Importantly, all these variables that measure skills as well as the Big 5 social skill index are perfectly balanced between treatment and control groups in our sample.

As mentioned in section 3.1, we repeat the balance tests for the sample that responds the follow-up online survey. Results are shown in columns 4-6 in Table 2. Three features are worth mentioning. First, characteristics of the overall sample remain the same between the whole sample and the sample of non-attriters. Second, as shown in Table 3, the sample of non-attriters continues to show balance between treatment and control groups.<sup>21</sup> Last, the F-test of overall significance keeps a similar magnitude and statistical significance. Given this, our main estimates do not adjust for this attrition. Moreover, in Table 10 we show that our main estimates hold even after
market incomes above the EU median of more than 11.4 percentage points. Less affected is defined as experiencing changes below the EU median but above 0. The non-affected groups include individuals with no changes. Finally, results are also presented for population subgroups among those severely affected.

# **4.1 Total welfare change**

Although on average all EU countries experienced losses in gross market incomes, the simulated losses based on transitions from LFS data have been comparably small (below 5 percent) for the majority of countries (Blue bars in Figure 1). The countries with the highest losses (10 percent or more) are Malta, Ireland, the Slovak Republic, Italy and Greece, with Malta experiencing the highest losses (15 percent). A second group of countries with losses above 5 but below 10 percent are France, Cyprus, Belgium, Austria, Slovenia, Spain and Lithuania.

Thus, the starting point for tax-benefit systems to cushion the adverse effects of the COVID-19 pandemic varies across countries. In most countries, mean changes in disposable incomes are significantly smaller than changes in mean market incomes. Mean disposable incomes decreased by less than 2 percent in 18 out of 27 countries and even slightly increased in Austria, Croatia, Czechia and Luxembourg. Thus, on average, most tax-benefit systems provided sufficient support during the first year of the pandemic. Although this is also partly true for Malta and Ireland, the countries with the highest changes in market incomes, mean changes in disposable income are still comparably high with 6 and 4 percent.

_Figure 1: Mean change in market vs. disposable incomes (in percent)_ <!-- Start of picture text --> MT IE SK IT EL FR CY BE AT SI ES LT PT DE HR EE CZ RO HU PL LV BG SE LU FI DK NL<br>Market (Scenario A vs. D) Disposable (Scenario C vs. D) Disposable (Scenario A vs. D)<br>2<br>0
# **Appendix B: SHIW empirical evidence on the value of use rights**

In sections 3 and 4, we have examined the theoretical reasons why use rights associated with usufruct should be quantified and included in the net wealth of the households benefiting from them (and subtracted from that of the bare owners). We have also recalled that in Italy, as in many other countries, the use rights of houses are valued on the market, although this market is sometimes of a limited extent, and we have shown some possible estimation strategies using survey data.

In this Appendix, we will attempt to provide some empirical evidence of the appropriateness of this line of reasoning based on the data collected in the SHIW. We have considered a model in which the subjective judgment on the household's economic well-being is explained by the economic resources in terms of household income and wealth, controlling for a set of socio-demographic conditions (year, number of components, geographical area, municipality size, gender, age, education, and civil status of the adult with the higher income).

In the first experiment, wealth is measured without considering the value of use rights. To assess whether usufruct affects the perceived economic well-being, two dummy variables were introduced in the model, indicating whether the household is a usufructuary or a bare owner. According to the thesis advocated in this study, the coefficient of these dummies should be significantly different from zero, with a positive sign in the first case, and a negative in the second.

The analysis is conducted using an ordinal probit model, as the dependent variable has an ordinal nature. To avoid the potentially adverse effects of outliers often present in income and wealth distributions, both variables were subjected to the inverse hyperbolic sine (IHS) transformation, which is similar to the logarithmic transformation but allows for the treatment of negative values as well (Friedline et al., 2015).<sup>11</sup> The results show that, conditionally to all the socio-demographic conditions
pensions, government transfer programs, rent, interest, remittances, and other monetary sources. The third measure, which we will call full income, adds the value of home-produced consumption, or auto-consumption, to monetary income. The final measure is total expenditure, which includes both monetary expenditure and the value of autoconsumption. The correlation between household labor income and total household monetary income averages 0.91 across the six surveys for urban households with heads in the age range 25-49. We should therefore expect similar results for these two measures. The correlation with full income is not much less, at 0.87, while the correlation with total expenditure is 0.60.

Figure 9 plots the fitted cubic income mappings for these four measures of household welfare. The curves are plotted at the average of the cohort specific intercepts, with a measurement error correction made as in equation (16) under the assumption that the reliability ratio is 0.5. First, we note that shape of the estimated curve for labor income is very similar to that from the ENEU, seen in Figure 7. Secondly, total monetary income of the household has very similar income dynamics as labor income, showing that the omission of secondary job and non-labor earnings does not appear to have a sizeable impact on the estimated dynamics. However, thirdly, we note that the shape of full household income, which includes the value of home-produced goods, and the shape of the expenditure relationship do exhibit more curvature than labor earnings. Nevertheless, most importantly we see that all four measures of household welfare have mappings which cross the 45 degree line from above, hence showing that our finding of no evidence for poverty traps extends to these other measures.

# 6 Conclusion

This paper has shown how pseudo-panel methods can be used to investigate non-linear income dynamics in the presence of measurement error and individual heterogeneity. Panel data estimates suggested that income was very mobile, conditional on individual fixed effects, and that no individuals have income 22
|..|3.9|
|Total AD/CV/SG|0.5|0.5|0.7|0.7|3.8|1.8|1.1|0.1|0|0|0|0|3.3|4.0|

Notes: X = licensing for health, sanitary, safety, environmental and/or defense.

- = less than 1%.

** = antidumping measures in effect in 2003 for Canada, 91; EU, 156; and US, 279.

- *** = Data are taken from Bown, C. (2012), Emerging Economies and the Emergence of South-South Protectioniem, PRD #6162, World Bank. (Table 1 and Appendix Table A.1)

.. = data not available Sources: WTO Trade Profiles database, WTO TPRs, and OECD, 1997 (table 5.1); and Bown (2012).

The lower panel of Table 10 shows the changes that have been in the use of trade remedies by developed countries over the period 2007-2011. The trade weighted share has increased in this period for the US primarily because of the use of additional countervailing measures, but has decreased both for the EU and Japan.<sup>26</sup> The overall conclusion from this analysis of the frequency of use of trade remedies by developed countries is that, while still substantial, it is certainly not more extensive than in the nineties. Combined with the earlier discussion of the declining trends in the use of other core non50