rafmacalaba/data-use-mentions-extended huggingface dataset:
economy. From a forward-looking stand, the diagnostic identifies some potential entry points for policy change. The large-scale Minimum Subsistence Allowance program ( _AGI_ in Turkish) presents an opportunity if its design were more targeted to the poor and vulnerable, while keeping a neutral fiscal position. Overall, the paper offers a platform to simulate the distributional implications of a variety of policy changes and inform the policy debate among stakeholders. Finally, the analysis provides a building block for future policy research to dig deeper into the reasons that could explain Turkey’s performance relative to peers.

> 3 EUROMOD is another initiative that aims to comprehensively analyze taxes and transfers. However, since it is based on household income, it does not consider household spending and therefore does not estimate the impact of indirect taxes like VAT.

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30 for 61 percent of the income gap. Estimates in the literature range from one third or less (Hall and Jones, 1999; Bils and Klenow, 2000; Caselli, 2005) up to four fifths (Jones, 2014), but recent work by Hendricks and Schoellman (2018) finds that human capital accounts for 62 percent of the income gap. Lagakos et al. (2018a) find a value of 74% when estimating returns to education and experience among immigrants in the US. Thus, our results are broadly consistent with the recent literature.

What is the impact of education and experience _separately_ ? Assume that demographic and labor market factors are the same in the developing economy as in the developed economy, but that the returns to experience are the same, so that the only difference is in the quantity and returns to schooling. In this case, GDP is 75 _._ 6%, leaving 33 _._ 9% of the output gap unaccounted for. Instead suppose that demographic and labor market factors are again the same, but that the quantity and returns to education are the same, so that the only difference is in the returns to experience. Now GDP is 75 _._ 2%, leaving 34 _._ 4% of the output gap unaccounted for. Thus, separately, schooling and experience each account for about a third of the income gap.

Figure 4 displays _pcGDPt_ in the developing economy after various reforms that might impact the schooling distribution and/or the returns to schooling and experience. First, in the NW panel, we increase the quantity of schooling, the returns to education and the returns to experience in the developing economy to the levels of the developed economy. Relative _pcGDPt_ rises steadily for about 50 years before slowing and settling around 87 percent. Output per worker converges to 92 percent of that in the developed economy (not shown) – closer, because it takes account of the fact that participation is lower in the developing economy, but still some
e., at time _t_ =-1. Table 2 presents summary statistics for the data used in the empirical analysis, and the Appendix provides more information on the underlying sources of the data.

16 Indeed, Bown and Crowley (2013a) consider the role of WTO disciplines for high income economies. While the estimated IRRs from that paper are in line with theoretical expectations, they are not precisely estimated. One explanation for the imprecision is the lack of inter-temporal and cross-sectional variation in WTO disciplines across the five high income economies during this sample period.

> 17 To clarify, we might also expect the _level_ of a country’s WTO disciplines to impact TTB determination. I.e., policy-imposing countries that have bound less than 100 percent of their tariffs (see column 1 of Table 1) might be less likely to use TTBs than others because there is no WTO discipline over products with unbound tariffs. However, because there is no inter-temporal variation in the share of a country’s MFN tariffs that are bound during the WTO period, any level differences are captured by the importing country indicator variables.

<mark>13</mark>
# Appendix D: Structural Vector Autoregression

To understand the relative importance of external factors in driving each country’s business cycle or output fluctuations around trend, we estimate a VAR per country with a set of external and internal factors and calculate the impact of each factor in determining short term movements of real output growth. This effort translates into computing the variance decomposition of each VAR system. We run a VAR for 54 countries with quarterly data typically ranging from the early 1990s to 2017.

The variance decomposition proceeds as follows. Using an orthogonalized VAR system, we can compute an accounting of the forecast error variance; that is, what percent of the k-step ahead forecast error variance is due to which variable. Formally, we start with the moving average representation of our orthogonalized VAR system:<sup>27</sup> , E( ′)=I, )=I, =I, where is a vector including our variable of interest, real domestic output growth, a set of external xxtt = CC(LL)ηηtt ηηttηηtt , E( ′)=I, )=I, =I, where factors represented by the log change in CTOT, US real output growth, and change in real US policy tt rates and a set of internal factors represented by the change in real public expenditure, change in real xx exchange rate, change in domestic real interest and change in real trade balance. C(L) = C0 + C1L + C2L2 + ... where C is the matrix of orthogonalized coefficients and L the lag operator.

In our specification, we use one lag in all VAR systems and we report the variance of the 8th step ahead forecast errors. The choice of lag length was based on several information criterion tests. The choice of the 8th step ahead was made to ensure that the role of different factors had already begin to stabilize (some factors may take more than one period to affect growth).

> 27 See Cochrane (2005) for a complete textbook
1.3 per thousand.<sup>23</sup> Only 3 percent of inexperienced potential migrants and 11 percent of those who have migrated before expect the mortality rate to be lower than what it actually is. The overestimation at the mean is 21 times the actual figure for the inexperienced ones and 13 times for those who have migrated previously. The extent of overestimation is smaller at the median, but still 8 and 4 times the actual rate for both inexperienced and experienced (those who have

> 21Note the change in definition of experienced migrants for this part. For this part, experienced also includes those who are back on vacation and have an existing employment contract abroad.

> 22In the data collected by The World Bank (2011), returnees who earned more are more likely to express a desire to migrate again in the near future. Those who earned above the median during their foreign-migration experience are 18 percent more likely to express a desire to migrate again.

> 23To put this number in perspective, the mortality rate of average Nepali men with the same age distribution as the sample is 4.7 per 1000 for a two-year period. The mortality rate of average US men with the same age distribution as the sample is 2.85 per 1000 for a two-year period. Note that this information on relative risks was not provided to the potential migrants.

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products may themselves become inaccurate with the passage of time as their source data in the census becomes outdated.<sup>19</sup> 18 Similarly, an earlier unpublished version of Facebook’s population data which we worked with, only yielded a correlation coefficient of 0.5 with the 2012 Sri Lankan census at the village level because Facebook initially used the 2001 Sri Lankan census for calibration. Their estimates were later updated by using the most recent census, which now gives an _R_<sup>2</sup> of 0.841 and a correlation coefficient of 0.917.

> 19 We also assess the consistency of four publicly available built-up area measures, namely, GUF, GUF+, GHSL, and Facebook, against each other, and the validate the built-up area estimates for the 55 sub-districts by Engstrom et al. (2017) against Facebook’s. All estimates of built-up area at the village level are reasonably consistent with each other. At the national level, the _R_<sup>2</sup> between estimates from Facebook and each of the first three sources are 0.76 or greater (supplementary Table S4). In the 55 sub-districts, the _R_<sup>2</sup> between Facebook estimates and those from Engstrom et al. (2017) is also high at 0.794 (supplementary Table S5). These patterns also hold if correlation coefficients are employed for the analysis (see supplementary tables S6 and S7).

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# **5 Conclusion**

This paper has sought to focus attention and find insights regarding a most remarkable PISA 2012 result - the superlative performance of Vietnam, a country with the lowest per capita income amongst all PISA participants. Vietnam, with a mean PISA math score of 511 is not one of the very top performers. However, when compared with other lower middle income countries that took part in PISA 2012, Vietnam is a clear outlier. The following three concluding points can be made as a result of the analysis presented in this paper.

**1. Half the gap can be explained:** Even though the PISA dataset is rich and covers many aspects related to the achievement of student scores with international standardization of measures, with all the available variables, we could explain at best about 50% of the performance gap of Vietnam. The PISA 2015 application will be especially interesting to study as it will provide another important data point and enable a trend analysis to be conducted.

**2. Cultural factors are likely very important:** A combination of three sets of factors appear to be the most potent explanation for Vietnam’s performance: First, Vietnamese students work harder - we see they have less instances of skipped classes and being late for school, spend about the same time or more learning in school and substantial extra time studying after school. While at school, Vietnamese students are more disciplined and focused on their studies. Second, Vietnamese teachers appear to benefit from a closer supervision of their work by the school principal and others, and there may be a stronger harmony between the hard working students and their teachers. Third, parents may have an important role to play, by taking an active part in combining high expectations of their children, following up with their children’s teachers and contributing at school.

**3. Resources do appear to matter:** When we compare PISA performances across the range from lower income non-OECD countries to the high income OECD countries,
2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; SAR = South Asia. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-21. A.-D. Period averages of annual GDP-weighted averages. Potential growth estimates based on production function approach.

A. Sample includes 53 EMDEs, of which 3 economies are from SAR region. “Other factors” include trend improvements in human capital, and stable investment growth relative to output growth.

- B. Sample includes 53 EMDEs, of which 3 economies are from SAR region.

- D. LFPR = labor force participation rate.

- E. Sample includes eight SAR economies.

- F. Based on data from 1980-2021. “Cost per event” in current 2021 US dollars. Occurrences of natural disasters.
market prices from La Nueva Viga, a large national fish market in Mexico City connecting sources to distributors. The La Nueva Viga data contain information on marine fish, crustaceans, freshwater fish, and mollusks/others. B.C.S. is listed as a source only for the fourth category. This, coupled with the fact that other sources of La Nueva Viga catch have only a partial overlap of species with La Paz, limits the number of species that can be matched to the logbook data. In cases where a market price is not available for a particular date in the logbook, the average price for the corresponding week or month is again used. All cooperative and market prices are converted into 2010 Mexican pesos using a Consumer Price Index obtained from the OECD.

We aim to understand how cooperative pricing responds to natural variation that alters population growth rates. The Oceanic Niño Index (ONI) is a three-month running mean of an 5. Available at http://www.economia-sniim.gob.mx/i_default.asp.

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the domestic market by inferior efficiency. In addition, by raising competitive pressures in the domestic market, international trade lowers firms’ markups over marginal cost and encourages organizational change and production upgrades to boost within-firm productivity (Melitz and Ottaviano 2008).

# II.2 Empirical literature

The relationship between international trade and long-term output growth has been investigated by a large empirical literature, using cross-country and firm-level data. In addition to aggregate effects, studies have identified specific channels through which trade integration boosts productivity, capital accumulation, and employment growth—the fundamental drivers of long-term economic growth.

Trade and output growth. Most cross-country studies have found a positive link between international trade and output growth.<sup>2</sup> However, the direction of causality and the role of third factors remain matters of debate. Some studies find clear growth-enhancing effects of trade liberalization (Dollar 1992; Bhagwati and Srinivasan
on international financial transactions are often imposed to give the government greater control over the flow of credit within the economy, as well as greater control over the exchange rate. These restrictions can take various forms, from transactions taxes to outright restrictions on inflows and/or outflows. We measure the extent of capital account restrictions by the widely used Chinn-Ito index. This is an index measuring a country’s degree of capital account openness initially introduced by Chinn and Ito (2006) and updated frequently.<sup>19</sup> The index is based on the tabulation of restrictions on cross-border financial transactions reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions. It is therefore a measure of _de jure_ restrictions. We take the latest available value of the series, which is available up to 2017. Given that higher values of the Chinn-Ito index imply greater capital account openness, we expect a positive association with our measure of interest rate repression.

- **Entry barriers** : To maintain control over credit allocation, governments may restrict the entry into the financial system of new domestic banks or of other potential competitors, for example foreign banks or non-bank financial institutions. Entry barriers may take several forms, including outright restrictions on foreign banks; bank activity restrictions; or excessively restrictive licensing requirements. We proxy entry barriers by an index of legal requirements needed to obtain a banking license. This indicator, first developed by Barth et al. (2001), can be calculated using data systematically collected by the World Bank as a

> 16 While an important financial repression measure used in many jurisdictions, we do not include lending quotas that, to the best of our knowledge, are not systematically tracked and cannot be meaningfully proxied by a quantitative variable.

> 17 We did not calculate correlation coefficients for deposit IRCs due to the low number of observations. There are only 9 countries with deposit rate ceilings, and while there are 25 countries with deposit rate floors, 8 of them are ECCU members and
from those designed to improve structural factors. Moreover, measuring the lasting success in the fight against poverty using social indicators with large cyclical fluctuations could be misleading since the analysis would be highly sensitive to the time span under consideration. In other words, given the significant influence that business cycles have on the cyclical component of some social indicators like the poverty rate, a policymaker would draw very different conclusions if the response of poverty were evaluated during a boom or a complete (boom-bust) business cycle. In fact, the importance of the cyclical component in social indicators is magnified for the case of emerging markets subject to large external shocks, such as changes in the terms of trade, global liquidity, and world economic activity.<sup>9</sup> All these shocks are cyclical in nature and thus will tend to amplify emerging markets’ business cycles and, in turn, the cyclical components of social indicators.

To formalize the intuition discussed above, we need to compute the relative importance of the cyclical and trend components in each social indicator. We do so with a simple variance decomposition analysis. First, we decompose each time series into a cyclical (C) and trend (T) components: . (1) CC TT tt tt tt We proxy the trend component with a simple linear trend.SS = SS + SS<sup>10</sup> Then, taking the variance of (1), we can

> <sup>10</sup> Then, taking the variance of (1), we can compute the portion of the variance corresponding to each component:<sup>11</sup>

> 9 See, for example, Vegh _et al_ . (2018).

10 Our definition of trend-driven change calls for truly long-term factors driving the trend component in our set of social indicators. As social indicators data are not available for time spans longer than three decades, the identification of this type of low frequency signals is more difficult. Thus, applying any
in the robustness checks section.

> 5 A major technical issue with any (long-running) panel survey is attrition over time; analyzing panels of varying lengths can help provide better comparison and more robust results. We also experiment with other ways to divide the time periods and discuss results in the robustness check section.

> 6 As an example, the China Health and Nutrition Survey (CHNS) collects panel data and was implemented as early as 1989, but does not offer nationally representative data. A more recent panel survey, the China Family Panel Study (CFPS) provides more coverage but was started only in 2010. Alternatively, statistical techniques have recently been developed that allow the construction of synthetic panels from repeated cross sections (Dang _et al._ , 2011; Dang and

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, including among the most vulnerable populations such as IDPs and refugees. Child marriage should be acknowledged as a potential consequence of conflict and, in contexts where this is a concern, post-conflict reconstruction plans should aim to address this form of GBV.

# **_6.4 Strengths and limitations_**

This study is among the first quantitative multi-country studies using population-based data to examine the impact of conflict (both contemporaneous and lagged effects) on girl child marriage. This paper draws upon two robust data sources – DHS and UCDP – to examine this relationship, and to strengthen the literature on how conflict may impact child marriage across the globe.

While this study has a number of strengths, it also has some notable limitations. Firstly, DHS data did not allow us to add a number of covariates to the models which might have been informative. Few questions in the DHS asked about the respondent’s experiences before getting married, making it difficult to include variables about individual risk factors prior to marriage. Age at marriage is also a recalled question and is subject to measurement error (Neal & Hosegood, 2015). Individuals may also have moved (and particularly done so in relation to conflict) but the DHS does not include a full residential history to account for this.

Although we do control for time and geographic fixed effects, identifying the relationship between conflict and child marriage based on local variation in the timing of conflict in conflictaffected areas, our results are primarily associations, as we lack a strong causal identification strategy. Local time-varying omitted variables may thus bias estimates, driving both conflict and early marriage, but detailed time-varying data on local conditions are not available to overcome this challenge.

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manage the exponential increase in data traffic and provide ultra-fast and ultra-reliable internet connections. Such an effort also requires working at the local, regional, and international level to reduce interference.

# _Payment_

Digital payment infrastructure is recognized as the bedrock to ensure smooth transactions among all market players in the digital economy. According to Findex 2017 data, globally 52 percent of adults have made or received a digital payment in 2016/2017. Payment using a debit or credit card through a mobile money account or the internet are included in this finding. Developing countries’ use of digital payments is growing rapidly. In low-income countries, the share of the adult population using digital payments increased from 15 percent in 2014 to 26 percent in 2017. (figure 6).

# **Figure 6. Digital payments have been rising across all countries in recent years**

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shows that childhood stunting in Ethiopia is still as high as 37%, which makes it among the highest in the world (EPHI and ICF 2021).

This study investigates the effects of public transfers, services, and taxes on children’s wellbeing in the context of a Sub-Saharan African country and simulates the impact of potential policy choices aimed at alleviating child poverty. Specifically, the study answers the following questions:

1. How do the burdens of taxation and the benefits from government transfers and spending differ between children living in rural and urban settings, boys and girls, as well as between poorer and richer children?

2. What do government transfers, spending, and taxes contribute to the reduction of child monetary and multidimensional poverty and inequality?

3. What impact do potential changes to the social transfer system have on poverty and inequality among children?

The study applies the Commitment to Equity for Children (CEQ4C) methodology, which is an extension of the Commitment to Equity approach (Cuesta, Jellema, and Ferrone 2021). The methodology compares welfare indicators before (pre-fiscal) and after (post-fiscal) taxes and/or transfers, and ultimately evaluates the distributional effects of fiscal policy (Inchauste et al. 2017; Lustig 2018). This study specifically examines how children in Ethiopia are affected by fiscal actions following a recent cohort of studies that extended the CEQ4C method to children (Cuesta, Jellema, and Ferrone 2021; Save the Children 2021; Bornukova et al. 2020; Save the Children 2022). As a result, in this study, individual children, rather than households (as is often the case in fiscal incidence analyses), are the unit of analysis. The study primarily employs data from the Ethiopia Socioeconomics Survey (ESS) 2018/19, an LSMS-ISA survey which also collected data on certain taxes including business taxes, land use fees, and agricultural income taxes. We also integrate these survey data with administrative data obtained from various ministries and their subsidiary agencies.

The study finds that the fiscal
Table 14: Monitoring and Sanctioning Processes

|Civil SocietyLea<br>|Mean Control<br>der’s Status - Incr<br>|Effect of VH<br>Training<br>eased Direct Pre<br>|Effect of VH<br>+ CL Training<br>ssure<br>|CL Effect<br>||
|---|---|---|---|---|---|
|VH usually meets CL at CL’s home|0.076|-0.075**|-0.060|0.015|191|
|(0/1) (CL survey)|(0.267)|(0.038)|(0.038)|(0.043)||
|CL’s perceptions of whether respected|0.685|-0.048|0.001|0.046|193|
|by all in village (0/1) (CL survey)|(0.467)|(0.083)|(0.085)|(0.097)||
|Fire Alarm<br>|- Increased Press<br>|ure from Above<br>||||
|VH’s perceived independence from govt|2.448|0.037|0.081|0.044|244|
|(1-4) (VH survey)|(0.996)|(0.149)|(0.159)|(0.182)||
|No. times per year exchange information|0.287|0.613**|-0.243|-0.857**|191|
|btwn CL and district administrator (CL survey)|(0.911)|(0.283)|(0.301)|(0.341)