rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 60<br>50<br>.<br>City 40<br>economy<br>worsening<br>(%)<br>30<br>20<br>10<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>25<br>Standard 20<br>of living<br>worsening<br>(%)<br>15<br>10<br>5<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>60<br>Approval<br>of country's 4,<br>leadership<br>(%)<br>20<br>)<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> <!-- Start of picture text --> 45<br>40<br>Confidence<br>_ nN<br>institutions 35<br>(0-100)<br>30<br>25<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text -->
Thus, a new poverty line was estimated based on the poverty estimation methodology outlined in Section II.

As mentioned earlier, the new poverty line was set at RWF 159,375 per adult equivalent per year in January 2014 prices, and poor households in 2013/14 were identified using the following inequality: ����/�� where e����/�� denotes nominal consumption expenditure per adult equivalent in EICV4, and π����� is the price index (COLI) used to deflate nominal consumption expenditure within EICV4 to January 2014 prices. This yielded a poverty rate of 39.1 percent in January 2014 (see NISR 2015).

III.2. Comparability of poverty rates over time Welfare comparisons based on absolute poverty lines are only valid if the poverty lines represent the ‘same purchasing power’ over time. This can be accomplished by adjusting a ‘given’ poverty line and welfare aggregate for changes in prices/purchasing power, and then comparing them against each other to arrive at poverty headcount rates.

# III.2.1 Theoretical framework

Theoretically then, poverty rates estimated from household surveys conducted at two different points in time are comparable when both the welfare aggregate and the poverty threshold from these time periods are appropriately adjusted for price variations between the two survey rounds. The following is an illustration of how the expenditure data and poverty lines need to be updated for robust poverty comparisons over time.

 Estimation of poverty rates in round t�: Suppose we start with a poverty line (z����) that is estimated using a household survey conducted in year t�, and is adjusted to prices in month m� of the same year (t�). Similarly, the consumption aggregate from year t� is expressed in terms of month m� of year t� prices. Then the poor in t� are simply identified by: Where e����is household i’s nominal consumption expenditure per adult equivalent in month m of year t�, π�������� is the inflation rate derived from a region-specific COLI valued in terms of month m� of year t� prices, and z���� is the poverty line, also valued in terms of month m� of year t� prices.<sup>7</sup>

> 7 The theoretical example mentions the per adult equivalent consumption expenditure and region-specific cost-ofliving index normalized to a certain month to reflect the Rwanda case more closely.

9
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and Tobago and about $135 million in sugar) the share of Caribbean exports by air rises to approximately 36 per cent. As such, the Caribbean countries reliance on air transport is far higher than that for LAFTA (about 10 per cent of total exports by air), CACM (10 to 14 per cent air freight), or other developing countries in Africa.

While the comparative costs of air and ocean transport will later be analyzed in detail, Table 3 summarizes information on Caribbean countries overall freight costs as reflected in U.S. trade data (see equation (1)) for 1985 through 1987. In addition, the table also provides similar information for selected developing countries in South America and other geographic regions so comparisons of freight rate levels can be made. However, a key point to note is that the nominal freight rates reported in Table 3 should vary with factors such as distance to the United States, the volume of goods shipped (if transport economies of scale exist), or the composition of each countries' exports. 11/ Another potentially important factor affecting the data in Table 3 relates to the relative use and cost of air as opposed to ocean transport for each country's exports. As such, these data must be interpreted with caution and are mainly useful for an initital assessment of the relative importance of transport costs on exports at the national level. Given these qualifications, the statistics in Table 3 do not indicate that the Caribbean countries' exports suffer major adverse effects from higher than average transport costs. Overall, the Caribbean countries had a 1987 11/ Yeats (1981, pp. 150-165) provides an assessment of how factors like port facilities, technology employed (i.e. containerized versus non-container transport systems) and economies of scale affect nominal transport costs. Scale economies, and the use of larger vessels are apparently quite important for some primary commodities like petroleum, metal ores and wood.
sanctions before.’ The Head of the KAN also realized that both parties were equally guilty. ‘But the lineage elder wanted to give a lesson. Now many young people do not respect their _mamak_ .’ He also acknowledged that there was social envy that the woman had managed to start a successful business and build a house in the _nagari_ , whereas the lineage head was still living in a bamboo hut with no secure income.

The woman had not been formally adopted into a local clan and thus had no clan head to speak on her behalf. She was in _adat_ terms still an outsider. If she had had a local lineage head to represent her, the case might have been solved between the lineage heads instead of going straight to the KAN. Having learned from the experience, she was adopted into the clan of _Datuk_ Basa Nan Tinggi, who will represent her in future _adat_ cases. (World Bank 2008, p. 27-28) To conclude, local-level legal institutions, like their formal counterparts, do not generally protect the interests of marginalized individuals and reduce economic inequalities. This may not, however, be related primarily to the content of local customs, which are in fact adaptable, but to the relative disparities in prestige, power, and material resources of the disputants, which can be used to influence legal decisions and evade sanctions. If true, this suggests that reforming locallevel legal institutions may not be as daunting as changing local cultures, and that supportive institutions, such as NGOs, locally embedded paralegals, and other organizations, might be able to help local-level legal institutions promote economic equalities.

_Legal institutions and human capabilities_ Sen (1999) argues that development does not take place merely when income or utility increase, although those are common correlates of development. Development is best conceived, instead, as the expansion of freedom to perform the essentially human activities whose emergence constitutes, as Aristotle would have put it, human flourishing. Sen calls these the capacity to perform these activities “capabilities.” Although Sen has not presented a definitive list, Nussbaum (2000) proposes one: living a life of a normal length and in good health, living 20
The remainder of this paper is organized as follows: Section 2 presents the data, while Section 3 describes the methodology employed for analysis. Results are interpreted and discussed in Section 4. Section 5 draws key conclusions and policy implications.

# **2. Data**

We retrieved necessary data for the United States, Brazil, India, the United Kingdom, 26 of the European Union (EU) member states,<sup>4</sup> and Norway and Switzerland<sup>5</sup> from January 2018 to September 2021 – a period that includes approximately two years before the beginning of the pandemic and 21 months within the pandemic. Data sources are summarized in Table 1.

# **Table 1: Summary of data sources**

|Category|Data|Source|
|---|---|---|
|Electricity generation|Daily electricity generation by energy type;<br>Monthly installed capacity by energy type|US: EIA<br>Brazil: ONS<br>India: Ministry of Power<br>Europe: ENTSO-E|
|COVID Severity|Daily new cases per million population;<br>Daily new death per million population;<br>Stringency index|CSSE at Johns Hopkins University<br>(JHU);<br>Oxford<br>COVID-19<br>Government<br>Response Tracker|
|Temperature|Daily population-weighted CDD;<br>Daily population-weighted HDD|NOAA<br>Global<br>Historical<br>Climatology<br>Network<br>Daily<br>(GHCN-D);<br>Eurostat<br>and<br>OECD<br>regional<br>database.|
|Fuel prices|Monthly fuel prices by major energy hub (Coal,<br>Australia; Coal, South Africa; LNG, Asia; Natural<br>Gas, EU; Natural Gas, US; Henry Hub Gas; Dubai<br>Crude; Brent Crude; WTI Crude)|<br> <br> <br>IMF Primary Commodity Prices|

As shown in Appendix Figure A. 1, fossil fuels account for around 61% and renewables (wind, solar, hydro, and others) for around 17% of electricity generation in the United States. As

> 4 All except for Malta due to data availability issues.

> 5 Norway and Switzerland are not part of the European Union but belong to the European Free Trade Association (along with Iceland and Liechtenstein). Iceland and Liechtenstein are not included in our sample due to lack of data.

5
across households, then purchases and sales of liquid stocks by one group of investors will be uncorrelated with that of another group. To test for this independence, I form 5,000 pairs of non-overlapping investor groups containing 500, 1,000 and 5,000 investors. For each _IlliqBSItG_ , I then remove the effects of common dependence due to the market factor and common variation in all household trades by running the following regression: <!-- Start of picture text --> G G MKT G BSI G<br>0 MKT t BSI t t<br><!-- End of picture text --> In the equation above, _MKTt_ is the month _t_ market return in excess of the risk free rate, and _BSIt_ is the buy-sell imbalance for _all_ households in a given month _t,_ defined as: <!-- Start of picture text --> i N i N<br>V i V i<br>t , Buy t , Sell<br>i N i N<br><!-- End of picture text --> _Vti_ , _Buy_ and _Vti_ , _Sell_ are the total value of purchases and sales, respectively, of investor _i_ in month _t_ . I aggregate over all _N_ investors. The reason for this regression is to remove the common component in the households‟ net demand for liquid securities due to market movements and changes in overall household demand unrelated to liquidity. I then compute correlations of the residuals, _tGG_ , for different pairs of investor groups.

<!-- Start of picture text --> tGG<br><!-- End of picture text --> The results are reported in Table 10. The correlation values range from 18% to 32% depending on the number of investors used in the simulation. All correlations are statistically different from zero. These results suggest the existence of a systematic component in the demand for liquid securities across households. The results support hypothesis _H3a_ , that there is systematic variation in households‟ trades of illiquid securities.

# **6.2 Aggregate Market Liquidity and Household Demand for Liquid Securities**

(2009) uses this methodology to investigate dynamic style preferences of individual investors. I obtain similar results examining shifts in portfolio positions as in Kumar (2009).

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# Where on Earth is Everybody? The Evolution of Global Bilateral Migration 1960–2000

_Çağlar Özden, Christopher R. Parsons, Maurice Schiff, and Terrie L. Walmsley_ Keywords: emigration, immigration, labor migration, international migration, gender JEL Codes: F22, O15, J11, J16 Çağlar Özden (corresponding author; cozden@worldbank.org) is a senior economist in the Development Research Group of the World Bank. Christopher Parsons (lexcrp1@nottingham.ac.uk P) is a consultant at the World Bank and a doctoral candidate at the University of Nottingham. Maurice Schiff (mschiff@worldbank.org ) is a lead economist in the Office of the Chief Economist for Latin America at the World Bank. Terrie Walmsley [twalmsle@purdue.edu] is an associate professor and executive director of the Global Trade Analysis Project, Purdue University, and an associate professor at the University of Melbourne. First and foremost the authors thank the United Nations Population Division for spearheading the creation of the Global Migration Database. In particular, they thank Bela Hovy and Hania Zlotnik for their close support and shared vision that ensured the completion of this project. They are grateful to Richard Black, Ronald Skeldon, and especially L. Alan Winters for having the foresight to initiate this project and for their unwavering support. They also extend thanks to the librarians at the British Library, the Library of Congress, and the London School of Economics and to Lorraine Wright at the United States Census Bureau for assistance beyond the call of duty. The authors thank Steven Vertovec and Norbert Winnige of the Max Planck Institute for the Study of Religious and Ethnic Diversity for helping surmount the data issues concerning Germany and the former Soviet Union. They thank Michel Beine, Frédéric Docquier, and the journal editor, as well as three anonymous referees, for advice and comments. They gratefully acknowledge financial support from the World Bank Knowledge for Change Program and Ivar Cederholm’s help with administration of the funding. The findings, interpretations, and conclusions expressed in this article are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
_'Mexico: The 1997 Pension Reform_ _29_ <!-- Start of picture text --> Govt Ccxtiib. as %ofG DP<br>0.35 _c<br>0.30 R1 1<br>1g7 2Do4 2011 2018 2025 20m 2039 206 21Y53 zeo) 2tE7<br>*Social QJoa * flpoiiknal (nibifnl f] lot<br><!-- End of picture text --> nine years of the reform. Later, it will decline as a result of (JU **l**<sup>2</sup> growth, which will predominate over that of growth in number of workers. The following graph depicts how total cost practically coincides with the cost of the social quota since the level of prorated contributions cost arc minimal .

**Transition workers.** The transition group comprises three categories of workers:

- **1 .** All workers that at the effective date of the reform (September 1, 1997) were active andl therefore acquired rights during the effectiveness of the previous law.

1. Workers that contributed to the IMSS at some time but were not active at the effective date of the reform and may later begin to contribute again.

2. Workers that were receiving an IMSS pension prior to September 1, 1997.

As discussed earlier, upon retirement workers in categories 1 and 2 may choose between the boenefits under the old PAYG system and the pension benefits accumulated under the individual accounts. In the model, workers compare the net present value of each flow of payments (PAYG pension vs. benefits deriving from accumulated balances) and choose the highest<sup>6</sup> .

**Current pensioners.** Pensions in course of payment will be deemed to mean those being paid by the IMSS up to December 31, 1996. Beginning when the IMSS's reserves are exhausted, which will happen during the second year, the government will assume responsibility for financing them. The margin of reliability of these estimates is all the greater in that:

- *This is a closed group, i.e. its population does not grow over time;

- *The number of pensioners and the amount of their pensions at the end of 1995 is known and it is necessary to estimate only the number of persons that will take this pension up to September 1, 1997. By 1995 its cost was close to 0.4% of GDP,

- These workers are not affected by the interest rate since they lack individual accounts and their pensions are funded entirely out of fiscal resources.

6 Modeling workers with pre-reform acquired rights who currently are not contributing but that can return to the system is an area that lacks a proper actuarial model in Mexico. The IMSS has no model or estimated probabilities in this area
Figure 4. Albania: Actual and Fitted Changes in the CPI, 1993:01-1997:09 <!-- Start of picture text --> 0.15<br>Ii ~0.10<br>Fitted 0.05<br>Actual Ft<br>o.021 \ t t~~~~~~~~~ MS U~.0<br>0.02 -' 02<br>0 .0 1 - - ---------- R s d a - ------ -- ,i~ -.... i .......... . . .I .. 0 0<br>-0.00<br>-0.02<br>Residual-----Actual ---- Fitted<br><!-- End of picture text --> 29
# **1 Introduction**

The diffusion of global production networks has called for new statistical tools providing a representation of complex production linkages between and within economies. New types of data sources, the Inter-Country Input-Output (ICIO) tables, and new analytical frameworks have been developed to measure supply and demand contributions of countries and sectors in global value chains (GVCs).<sup>1</sup> In this paper we describe `icio` , a new Stata command that computes countries’ and sectors’ participation in GVCs as well as relevant measures of trade in valueadded, following the conceptual framework proposed by Borin and Mancini (2019), – – which in turn extends, refines and reconciles the other main contributions in this strand of the literature.<sup>2</sup> The command is flexible in many aspects. It allows to choose from different accounting methodologies, called perspectives. Each of these perspectives is best suited to address specific empirical questions, such as tracking production-demand linkages, assessing countries’ participation to the global production sharing, quantifying value-added embedded in countries’ and sectors’ exports, evaluating the potential exposure to macroeconomic and trade policy shocks. It exploits the most famous ICIO tables - the World Input-Output Database (Timmer et al. 2015), the OECD TiVA database (OECD, 2018), and the Eora Global Supply Chain Database (Lenzen et al. 2013). Moreover, any user-provided ICIO table can be straightforwardly loaded and used to compute value-added trade and GVC participation measures.

More specifically, `icio` encompasses the most relevant measures of value-added in exports and imports at the aggregate, bilateral and sectoral levels. For a given trade flow, it disentangles the source country/sector and the destination country/sector of value-added content. Moreover, for export flows at any level of disaggregation, `icio` computes the component related to GVC trade, i.e., the one entailing more than a single border crossing. This measure - and its backward and forward GVC participation sub-components, is featured in the World Bank World Development Report 2020 (WDR, 2020).<sup>3</sup> In addition, the `icio` command can be used to retrieve, from the ICIO tables,

> 1See, among others, Johnson and Noguera (2012), Wang et al. (2013), Koopman et al. (2014), Borin and Mancini (2015), Los et al. (2016), Nagengast and Stehrer (2016), Johnson (2018), Miroudot and Ye (2018), Los and Timmer (2018).

> 2The essential features and the algebraic derivation of the conceptual framework are reported in Appendices A to F.

> 3 GVC measures are based on Borin and Mancini (2015, 2019), which consistently refine the vertical specialization index proposed by Hummels et al. (2001).

2
bution of treatment effects in gray. For comparison, we show quantile treatment effects at each decile in black. We calculate these quantile treatment effects using the empirical deciles of the partial residuals of a regression of average test scores in 2011 on grade level. While the distribution of treatment effects is calculated using the full panel, our panel functional form implies that treatment effects are the same in every year and should therefore be comparable to the quantile treatment effects calculated using only 2011 data. Pointwise confidence intervals for the QTTs are calculated using one thousand iterations of the pointwise bootstrap described in Subsection 6.2.1. In this case, deciles of the distribution of treatment impacts look quite similar to the quantile treatment effects at each decile. The deciles of the distribution of treatment impacts are increasing by construction, but the QTTs trend upwards at almost exactly the same timates suggests the WSDP did not cause much mobility across the potential outcome distributions. In other words, schools that ranked high in the control group distribution also ranked high in treatment group distribution. While none of the decile QTTs are significantly The analyses in this section so far amend Blimpo, Evans and Lahire’s (2016) finding of a zero than offsetting gains and losses. This demonstrates that DIA can provide further guidance for illustration of what else one can learn from DIA and such panel data and how failures of the assumptions can affect the results. To improve the illustration, we replicate our distributional cally, we draw a simulated panel of 8 observations for each of 120 “schools” from the following data generating process: With the following distrib tio al m os: ~N(O,1),( )=n([ | ~~_~~

|

<mark>~</mark> term to ex ore the i of violating the deconvoluti sumptions on the estimate particular, presents estimates o t e average and variance of treatment effe d Makarov Bounds on the proportion of sch_ Is who benefit from the program when is O so that the deconvolution assumptio are satisfied.

estimated is due to changing
We explore this heterogeneity using the YL data by separating the sample in two groups: mothers with less than primary education and mothers with at least primary education completed. Our estimations are presented in Table VI. Unlike the existing literature, there are no apparent differences in the effect of maternal mental health for mothers who have completed or not primary education.

However, when we combine the heterogeneity by household wealth and maternal education, we find out that our results are still in line with the existing literature for two reasons. First, the papers finding that lower maternal education exacerbates the negative effect of maternal mental health on early vocabulary argue that this low maternal education can be associated to sub-optimal childcare practices or to restricted access to quality material inputs and opportunities. Access to quality inputs that help to improve children’s vocabulary is restricted to less wealthy households as well. Second, there is extensive evidence of a strong correlation between mother’s education and socioeconomic status. In this sense, the expected differences in terms of lack of resources that allow to overcome the negative effects of maternal mental health on child language are captured not by maternal education but by household wealth in this particular context.

In addition, we explore whether the effect of exposure to maternal mental health issues during a child’s young age varies depending on whether the mother has a partner, given that this factor may modulate the impact of depression. The literature suggests that the presence of other members in the household that provide support to the mother can buffer the effect of depression on children. Our results are presented in Table VII. Our estimates suggest that having a partner actually worsens the negative effects of maternal mental health issues on child vocabulary (Columns 1-3).

To understand these unexpected results, we further explore the characteristics of the partner. First, we separate the sample of women living with a drinking (columns 4-6) and heavilydrinking partner (columns 7-9). Our estimations indicate that Z-scores of children with mothers whose mental health was 1 sd worse at _t-1_ and lived with a drinking partner are, on average, 0.52 to 0.56 standard deviation lower than Z-scores of children of mothers who were also living with a drinking partner. These coefficients are statistically significant at the 5 percent level. Moreover, the estimated coefficients for the effects of mentally ill mothers living with heavily-drinking partners indicate that this group is driving the effects described before.

16