rafmacalaba/data-use-mentions-extended huggingface dataset:
issues: (i) the survey data miss rich and poor people, thus more people are actually in the middle because the sample does not include the full population. On the other hand, there are misperceptions or misinterpretations of the questions: (ii) people simply do not know, and in absence of any additional information they bunch in the middle; (iii) people answer the question referring to their permanent income, rather than their current situation, and the dispersion of permanent income is lower than that of the current income. Lastly, it could be possible that individuals’ responses are based on their objective position not within the overall population within a smaller reference group - this reference group being specific for individuals who share some kind of characteristic, like their education level, their gender or their geographical location. This is (iv) the reference group explanation.

**Missing data.** First, the survey might miss persons that would rank themselves in the lowest and highest steps of the ladder. Household surveys like LiTS suffer from a known

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2008 Q2 2010 Q1<br>Q4-1972 Q4-1974 Q4-1976 Q4-1978 Q4-1980 Q4-1982 Q4-1984 Q4-1986 Q4-1988 Q4-1990 Q4-1992 Q4-1994 Q4-1996 Q4-1998 Q4-2000 Q4-2002 Q4-2004 Q4-2006 Q4-2008 Q4-2010<br>Q1-1998 Q4-1998 Q3-1999 Q2-2000 Q1-2001 Q4-2001 Q3-2002 Q2-2003 Q1-2004 Q4-2004 Q3-2005 Q2-2006 Q1-2007 Q4-2007 Q3-2008 Q2-2009 Q1-2010 Q4-2010 Q3-2011<br><!-- End of picture text --> Source: Author’s calculations based on data from OECD Statistics. Data from this common source are not available for period prior to early 1980s for a number of countries.

What did the international monetary system have to do with the 2008-2009 crisis? Economists have varied views. Most recently, for example, Lin and Treichel (2011) argue that the cause of the crisis was financial deregulation combined with lax monetary policy, and the latter was facilitated by the US dollar’s role as the dominant international reserve currency. In other words, the current structure of the international monetary system may be directly linked to the propagation of the worst financial crisis since the Great Depression. Others, for example, Caballero (2006) and Eichengreen (2011a) have also noted the roles played by the IMS and the dollar in the crisis; however, they approach the issue from a different analytic framework with different causality chains.

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The paper proceeds as follows: Section 2 describes the economic and satellite data used in the study, Section 3 compares the predictive power of nightlights and Landsat in the crosssection while Section 4 does the same in the time series. Section 5 concludes and discusses the results.

# **2 Data and Descriptive Statistics**

This paper uses different satellite imagery products to predict commune-level economic data from Vietnam. This section introduces the sources of the satellite measures and the economic data.

## **2.1 Economic Data**

This study uses economic data at the production commune/ward level in Vietnam for the years between 2004 and 2012. In Vietnam, around 9,000 communes and wards make up the third-level administrative subdivision after districts and provinces, and cover the whole country. In cities, these are called wards and in more rural areas they are called communes. For simplicity these are referred to as communes in the following. These units have a large variation in size and cover areas between 0.05 and 1,567 square kilometers, with a median area of 14.7 square kilometers. Communes therefore represent very different entities. While in cities, communes tend to be small and represent neighborhoods, there are several large communes which are sparsely populated and often sit along the western border with Laos. Even though the exact decision process of outlining commune borders is unknown, the formation of these units is highly endogenous. Unfortunately, the data does not come with population data from which a mechanism (such as a target population size within a commune) could be inferred. Figure 1 provides a graphical introduction into the 5
085|
|Rural household|-0.229***|0.069|
|**Woreda level characteristics**|||
|Average distance to road|-0.001|0.001|
|% of households in agriculture|0.304***|0.082|
|% of household heads employed|-0.101|0.093|
|% of household heads with at least secondary school education|0.429*|0.227|
|Average dietary diversity<br>|-0.009|0.022|
|σeıȷ<br>2<br>�|0.122||
|R sq for Level 1|0.402||
|Obs. Level 1 (household)<br>|30,141||
|σu0ȷ<br>2<br>�|0.042||
|R sq for Level 2|0.329||
|Obs. Level 2(community)|543||

Note: Interaction terms between woreda and a number of the household characteristics are also included in the regression but not reported.

*, ** and *** refer to statistical significance at 10%, 5% and 1% levels, respectively. Source: Author calculations from 2016 HCES/WMS.

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|**|Retail Sales – Bill collection rate, Are the following metrics published in a primary official document (by the utility, regulator or ministry and/or<br>government)?|
||6.3.4|re_6_3_4|Is the utilityoperatingan incidence/outage recordingsystem(or SCADA/EMS with such functionality)?|
||6.3.5|**|Is the utilitymeasuringthe SAIDI and SAIFI or anyother measurements for service reliability?|
||6.3.5.1|**|Are the measurements reported to the regulatorybody?|
||6.3.5.2|**|Are the measurements available topublic?|
|Carbon pricing and<br>ii CP|7.1|re_7_1|Is there a carbon pricing mechanism (eg carbon tax, emissions trading scheme) implemented in the country, covering part or all of the country’s<br>greenhousegas emissions?)|
|montorng ()|7.2|re_7_2|Is there a monitoring, reportingand verification system forgreenhousegas emissions inplace?|

_Source: ESMAP RISE dataset and authors’ elaboration based on dataset described in this paper. Notes: **Do not contain the year, cannot be used in a panel format._ 50
>D. EMDEs: Global supply shocks<br>Ratio Interquartile range Median<br>0.8<br>0.4<br>0.0<br>-0.4<br>-0.8<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>F. EMDEs: Oil price shocks<br>Ratio<br>1.0 Interquartile range Median<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>Trade Exch. Inflation Energy Current<br>openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> Note: Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. EMDEs = emerging market and developing economies; IT = inflation targeting. B.D.F. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications. Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

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**2 Literature**

This section briefly reviews the most recent studies assessing the role of tax-benefit policies in protecting household income during the COVID-19 pandemic. Additionally, we briefly discuss the literature focusing on automatic stabilizers beyond the context of the pandemic.

## **2.1 COVID-19-related studies**

Around the world, recent empirical applications have used EUROMOD for timely monitoring of the distributional impact of the COVID-19 pandemic and the cushioning effect of tax-benefit policies before micro data on the situation became available. Examples of international studies are Avellaneda et al. (2021) for Andean countries; Barnes et al. (2021) for South Africa; Jara et al. (2021) for Ecuador; Lastunen et al. (2021) for five countries in Africa and Wright et al. (2021) for Indonesia.

They all apply Atkinson’s (2009) “stress-test” methodology where micro data is shocked – in these cases based on external information on the adverse effects of the COVID-19 pandemic – to assess the resilience of the welfare state and to identify vulnerabilities of the system (Popova and Navicke, 2019).

While the analysis in these emerging and low-income economies is often based on the role of emergency measures, the European context allows for a more nuanced analysis of the role of monetary compensation schemes as well as automatic stabilizers.

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of normality, ELL’s performance is still worse than that of the Census EB update. In terms of bias, some areas under the traditional ELL show quite considerable bias, yet on average across areas the bias can be near zero. This suggests that, even if the bias across all areas of ELL can be small, it is not an accurate estimator in each area.<sup>28</sup> In fact, the empirical MSEs of the traditional ELL estimators for many areas are still much greater than those of the proposed Census EB ones in this experiment.

Even after the better performance of the Census EB procedure compared with the traditional ELL method under normality departure in the form of higher tails, model checking and diagnostics are extremely important, as in any model-based procedure. In fact, good performance of EB and Census EB

> 24Another slight modification made in separate simulations is that the location effect is simulated as _ηciid∼ N_<sup>�</sup> 0 _,_ 0 _._ 07<sup>2�</sup> .

> 25Results available upon request. Although it must be noted that under this scenario under many populations _σ_ ˆ _η_ <0, and thus many of the populations need to be re-simulated.

> 26For other FGT indicators see figures: 24, 25, 26, and 27.

> 27For other FGT indicators see figures: 28, 29, 30, and 31.

> 28Note that the purpose in SAE is not obtaining estimators performing well on average across areas since, in that case, the overall sample mean at the population level would be sufficient.

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4 countries)|
|---|---|---|---|
|Average Pair|1.04%|0.979|0.938|
|US; Japan|27.8%; 17.6%|0.645|-|
|NN|3.40%|0.934|0.81|
|SS|0.25%|0.995|0.985|
|NS|3.40%; 0.25%|0.964|0.893|
|Ratio NN:SS|13.34|0.939|0.822|

Table 1: Shares and multipliers. Shares of world GDP based on 2000 GDP values in dataset. Multipliers use formula from equations 5 or 6 with share values as arguments.

Why is MR more important when bigger countries are involved? MR is a GDP-weighted average of bilateral trade costs, so big “third countries” have a large weighting in the other countries’ baskets. Changes in trade costs with these big countries therefore have a big impact on multilateral resistance. For example, if the third country is Uruguay, which is small, then trade cost changes have a relatively 9 We can generalize for the change in trade between countries a and b when k countries sign an agreement: . is the applicable coefficient. The bigger is k, the more important is MR. The extreme case is where k=n ie all countries sign a deal except with themselves, which is the multilateral reduction case applied to distance in Behar & Nelson (2009).

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Chile and find that increasing the amount of time that students spend in school diminishes the likelihood of adolescent pregnancy by reducing risky sexual behavior.

The FSD format has been incorporated gradually since 1997. According to official statistics from the Ministry of Education, 80 percent of primary schools, or some 6,834 institutions, had joined the system by 2011 (figure 1).<sup>18</sup> The increase of the school day involved adding 232 hours per year for third to sixth graders, 144 hours annually for seventh to eighth graders, 261 for ninth and tenth graders, and 174 for students in their last two years of high school. In addition, as a consequence of the increase in lecture hours, schools had to allocate additional time for lunch. This may imply a positive income effect for those families. Therefore, the effective time of children at schools increased from 8 a.m. through 1 p.m.; to 8 a.m. through 4 p.m.. However, the program’s implementation was gradual. Restrictions linked to operational considerations and infrastructure meant that the incorporation of schools into the program was not instantaneous or homogenous.

Operational costs are financed through a 30% increase in government subsidies<sup>19</sup> while infrastructure-related costs are financed through the “capital contribution” subsidy, which it assigned to schools by the government through competition.<sup>20</sup> Schools that did not have a full school day prior to 1998 can apply for support to acquire the infrastructure necessary to

> 18. The Ministry of Education projected that FSD would be universal in municipal schools by 2007 and in private subsidized schools by 2010. However, the process of incorporating municipal schools into the system did not end in 2007, which led to the extension of the period through 2010. The law requires that publicly funded schools created after 1997 begin operations with a full school day.

> 19. This contribution is received once the Ministry of Education
Melitz and Ottaviano (2008) predict a negative relationship.

With regard to the empirical literature, Manova and Zhang (2012) is the main reference for this paper. The authors analyze a custom database on Chinese firms and establish stylized facts on the variation in export prices and imported-input prices across firms, products and trade-partner countries. They also compare their findings with the literature and the expected results of the various models of trade and heterogeneous firms. They conclude that there is not a single trade model that can explain all the facts observed in the data and that future work on international trade models should incorporate quality differentiation both across firms and trade partners within firms.

An attempt similar to Manova and Zhang (2012) is made by Görg, Halpern and Muraközy (2010), who analyze the relationship between gravity variables and fob export unit values using a Hungarian firm6
Source: World Bank Staff estimates using Escolano (2018, Eq. 19) and forecasts of public and publicly guaranteed debt at the end of 2018 from the February 2019 World Bank database for the Regular Economic Report for the Western Balkans.

**Of course, these estimates depend crucially on the assumed growth rate** . Nominal growth could well be higher than 5 percent a year, which would reduce the surpluses required to achieve the debt levels shown in Table 5. For example, with annual inflation of 2 percent, the assumed real growth rate underlying Serbia’s deficit rule implies nominal growth of roughly 6 percent a year. On the other hand, in the five years ending in 2018, none of the countries is expected to have achieved nominal growth as high as 5 percent.<sup>12</sup> Estimated by the model, rates range from a low of 2.4 percent in Bosnia and Herzegovina to a high of 4.8 percent in Albania. This period was, of course, affected by the global financial crisis, and future growth may be faster.

**To take account of random shocks and feedback from fiscal policy to the state of the economy, we can turn to macroeconomic modeling** . This modeling, developed by Jooste, Burns, and Song (2019), is based on the World Bank’s macro-econometric models for each of the six countries of the Western Balkans. Added to these models are fiscal-reaction functions that specify how the government responds to fiscal outcomes in the presence of fiscal rules. The models project the fiscal deficit and public debt, as well as other economic variables such as output and inflation. The models can be used to estimate how the economy would respond to changes in fiscal rules or to changes in the strength of the government’s commitment to complying with the rules. The models allow for random shocks, and the generation of some of the results relies on Monte Carlo simulation.

**The additional element of the modeling
# Introduction

Don Tapscott coined the term _digital economy_ in his 1995 best-seller _The Digital Economy: Promise and Peril in the Age of Networked Intelligence_ .<sup>1</sup> In the more than two decades since then, a dynamic global business landscape has emerged, filled with new types of firms and new ways of doing business by leveraging digital technologies. For example, of the top 10 most valuable firms by market capitalization in 2018, Apple, Alphabet, Amazon, Facebook, Tencent, and Alibaba share a common feature: each has established a digital platform to create value through facilitating exchanges between two or more interdependent groups. Meanwhile, more businesses are now providing goods or services online. Indeed, e-commerce has revolutionized the ways in which 1.79 billion people trade around the globe.<sup>2</sup> To respond to the new market players and business models, governments need to develop clear, coherent rules to facilitate digital economic activities. This is especially important for developing countries that have not yet fully reaped the benefits of the digital evolution for economic growth and job creation. However, the research on appropriate regulatory and policy approaches is limited.<sup>3</sup> Data sets that can serve as a reference point to assess a country’s regulatory environment for a digital economy are simply not available.

To support World Bank client countries in their efforts to better harness the benefits of digital technologies and address the potential risks associated with digital economic activities, several World Bank teams, including the Global Indicators Group of the Development Economics Vice Presidency, the Digital Development Global Practice, and the Macroeconomics, Trade, and Investment Global Practice, launched the Digital Business Indicators initiative in 2017. The initiative seeks to develop a benchmarking tool to measure the regulatory environment for the digital economy. The objective is to encourage competition among countries as they move toward more efficient, more transparent, and easier-to-implement regulations for the thriving digital economy. In 2018 the Digital Business Indicators project collected information on
# **3 MAMS for Moldova**

In this section we briefly present the analytical tool used in this analysis, which was developed at the World Bank: MAMS (Maquette for MDG Simulations), a dynamic CGE model for medium- and longrun strategy analysis. The strength of Computable General Equilibrium (CGE) models lies in that they combine theoretically and empirically solid accounts of the behavior of economic agents and institutions, real world data (from national accounts, household surveys and other statistical sources), flexibility in terms of data requirements and analytical focus, as well as the ability to test the impact of shocks (policy changes and/or other exogenous changes that are beyond the control of domestic policy makers) that are relevant to the future of the modeled economy. CGE models have a comparative advantage relative to partial equilibrium models when the shocks of interest have repercussions throughout the economy, especially if these repercussions also have a significant feedback on the parts of the economy that most directly were impacted by the shock.<sup>**5**</sup> This study uses a “core” version of MAMS, a dynamic CGE model developed at the World Bank, designed to analyze strategies for medium- and long-run growth and poverty reduction in developing countries.<sup>**6**</sup> Given the analytical focus of our study and in order to reduce data needs, it does not cover MDG indicators other than poverty.

The development of MAMS is and has been driven by a strongly felt need for an economy-wide approach to medium- and long-run development strategy analysis that is flexible enough to be linked to country-specific databases that may differ considerably in their characteristics (including disaggregation).

The starting point for MAMS is the static, standard CGE model developed at the International Food Policy Research Institute (IFPRI) (Lofgren et al., 2002). The core version of MAMS is significantly extended in that it (a) is (recursively) dynamic; (b) tracks
<!-- Start of picture text --> Inefficient Efficient<br>Miscoordination<br>coordination coordination<br>LL HH LH<br>1 5 1<br>2 4 2<br>3 3 3<br>4 2 4<br>5 1 5<br>−0.9 0 0.8 −0.9 0 0.8 −0.9 0 0.8<br>Cohort I Cohort II<br>LL HH LH<br>6 5 6<br>7 4 7<br>8 3 8<br>9 2 9<br>10 1 10<br>−0.9 0 0.8 −0.9 0 0.8 −0.9 0 0.8<br>Cohort II Cohort I<br>Period Period<br>Period Period<br><!-- End of picture text --> Figure 3: Period-by-period pair outcomes for the Stag Hunt, by pair type.

a plurality of both pair types were in miscoordination or in the inefficient equilibrium at period 5. This pattern is strikingly different from the finding of Van Huyck, Battalio and Beil (1990) that the frequency of efficient coordination in a Stag Hunt increased over the first five periods and then stayed at that high level for the remaining five periods of a ten-period game.

For periods 6 to 10, _LL_ pairs consistently maintained an efficient convention, with between 67 and 80 percent of pairs playing ( _Stag_ , _Stag_ ). _HH_ pairs initially started without a single pair at the inefficient equilibrium, though a majority started in miscoordination. However, the fraction of pairs playing ( _Hare_ , _Hare_ ) rose from zero in the initial period of the pairing to 33 percent in the last two periods, whereas there was no time trend to the proportion playing ( _Stag_ , _Stag_ ). Evidently, the _HH_ pairs learned to avoid disequilibrium by largely settling on an inefficient convention.<
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. But future research on the role of skills in GVCs integration could explore other ways of collecting more longitudinal skilled/unskilled labor data for developing countries in general and explore how SSA would perform in comparison with other developing regions, especially those that are performing better.

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