rafmacalaba/datause-encoder-data huggingface dataset:
The model on internet use relying on more specific information on Latin America (Table 7) provides interesting additional insights. First, GDP and PC access are the dominating variables when compared to the determinants of internet hosts. This explains the rejection of the world-wide model for Latin America and suggests that the determinant of internet use (as opposed to the diff-usion of internet hosts) is somehow dominated by different forces in Latin America. A 10% rise in GDP per capita increases the number of internet users by about 13% (1.371*10%) while a 10% rise in lines per capita tends to increase the number of internet users by about 7% (.735*10%).

_Table 7._ **_Dependent variable: Ln internet users_** _pc_ **_in Latin America_**

||(first|column|iscoeffi|cient,sec|ondisp-v|alue)|||||
|---|---|---|---|---|---|---|---|---|---|---|
||||1|1<br>2|3|r|<br>4|r|<br>**5**||
||||entire|sample||||r|estricted|sample|
|Lagged dependent variable|=|=|-1.120|0.570|||.|||_-|
|LnGDPpc|1.371|0.008|1.622|0.053|0.358|0.687|0.478|0.324|0.314|0.573|
|Ln Costs|0.33|0.360|-0.686|0.377|-0.282|0.477|0.091|0.775|0.202|0.610|
|LnLinespc|0.735|0.061|1.432|0.291|0.033|0.950|-0.912|0.033|-0.728|0.094|
|LnPC'spc|0.32|0.571|1.785|0.489|-0.477|0.327|-1.527|0.047|-1.44|0.093|
|InGini||=|||||-10.49q|0.014|-10.204|0.041|
|Regulation||=|=<br>_|_|_____-0.16|8||||0.582|
|Regulation*privatization||=|||||||0.288|0.435|
|Obs.|**63**||63||36||36||36||
|R2|.979||.941||.984||.992||0.992||
|Allspecificationscont|rol for add|itiveyea|randcount|ry effects|||||||

Second, internet costs become somewhat more significant when compared to the determinants usage in the world - although not enough to be able to consider it to be a clear determinant factor. Again, the results are sensitive to the introduction of specific controls. As above in Table 5, we restrict our sample to those countries with information on inequality and institutional variables. Overall, our sample size falls from 63 observations to 36 observations. A comparison of columns 3 and 1 should make us wary of the possibility of extending the results for the restricted sample to the whole of Latin America. The coefficients change significantly suggesting that the restricted sample might be non-randomly selected in terms of users per capita. Again, however, inequality enters significantly with a negative sign suggesting that a 10% rise in the Gini halves internet diffusion. As before, it is difficult to find any independent effect for privatization and regulation. Lines per capita now enter with a negative sign but we cannot reject the hypothesis that its coefficient is statistically undistinguishable from zero at 5% significance level.

16
The rest of the paper is organized as follows. Section 2 presents a number of stylized facts on UK‐EU trade agreements and trade relations. The empirical analysis of the impact of trade agreements on UK trade with the EU and other partners is in section 3. The future scenario analysis is presented in Section 4. Concluding remarks follow.

# 2. Trade agreements and UK‐EU trade

This section presents some key stylized facts on UK‐EU trade relations. The first subsection looks at the trade policy regime, while the second one focuses on trade flows.

## a. Trade agreements

Part of the current public debate on trade policy presents trade agreements merely as institutions aimed at lowering tariffs among member countries and thus sees the trade effects of undoing trade agreements as the result of changes in preferential market access in goods. Modern trade policy institutions are, however, more complex than this. How have trade agreements evolved? How does the EU compare to other agreements?

A new database by the World Bank (Hofmann et al., 2016) reviews 279 PTAs, offering new insights on the changing nature of trade agreements. The approach followed by Hofmann et al. (2016) for the identification of provisions and their legal enforceability follows closely the seminal paper by Horn et al. (2010) and the World Trade Report 2011 (WTO, 2011). Horn et al. (2010) identify 52 policy areas included in PTAs signed by the EU and US. They consider a policy area to be legally enforceable if the language is sufficiently precise, implying clear legal obligations, and if the area is not excluded from dispute settlement procedures under the PTA. Using this approach, WTO (2011) constructed a data set mapping the same set of provisions for 100 PTAs in force in 2011 signed by mostly developed countries that contribute to more than 90 percent of world trade. In an effort to include a larger number of developing countries, Hofmann et al. (2016) updated the WTO data set and coded all the remaining agreements notified to the WTO and in force until 2015.

The number of trade agreements and their content changed dramatically since the early 1990s (Figure 1). The number of PTAs in force increased slowly in the 1960s and 1970s and then remained constant until the beginning of the 1990s when a large number of agreements entered into force. The number of PTAs has increased more than fivefold from 51 agreements in 1990 to 279 in 2015. Along with the number, the content of trade agreements has changed. The number of provisions included in PTAs raised over time suggesting an increase in the “depth” of agreements. The majority of PTAs signed after 2003 include at least 10 legally enforceable provisions. That is, modern PTAs are “deep” in the sense that they cover substantially more policy areas than traditional (or “shallow”) PTAs that focused mostly on tariff liberalization.

4
Henriksen, & Foshee, 1998; Smith, 2011). Some dimensions of parenting are not applicable at all ages (e.g. inductive reasoning is not applicable in infancy), and specific parenting behaviors may be appropriate at some ages but not others (e.g. leaving the child alone in their room may be an appropriate discipline strategy for a preschooler, but not for an infant). As a result, both the broader parenting constructs and the specific items used to assess them needed to be mapped against the ages of intended use. We therefore standardized our measures to the age of the child when estimating econometric models.

We present descriptive statistics on these measures in Panel A of Table A6. These figures indicate that there is variation on these practices within our sample, and also that the caregivers we study are over-represented in the bottom of the distribution of these scores. When we examine differences by literacy, we see that women who can read are substantially more likely to show warmth and consistency in their parenting, and less likely to show hostility.

# **2.3.2 Activities**

Caregivers were also asked to complete an inventory asking whether or not the child took part in a series of typical play and stimulation activities in the past week. This is akin to the inventory of activities employed by time-use surveys in Attanasio et al. (2020) and Andrabi et al. (2012). The descriptive statistics are presented in Panel B of Table A6. Across the board we see high level of engagement in stimulating activities, but also substantial engagement in household chores. We also looked at differences between children of literate and of illiterate mothers, and found that, as expected, the former are more likely to be engaged in reading and music-playing (13 p.p.), but also in every other activity (albeit with smaller differences across groups).

# **2.3.3 Investments in human capital**

In addition we use information from the section on the child’s health history to identify whether the child has received a full set of immunizations by the date of the survey. We also collect information on the types of food consumed in the past week and the number of days in the past week which 14
robustness analysis capital income is also taken into account assuming its distribution over activities is equal to the labor income distribution.

More formally, data on exports is used for a particular country-year to derive a vector **_l_** of dimensions ( _i_ x 1) with typical element representing export income by workers in industry _i_ as (1) )<sup>−1</sup> , with **e** a vector of exports (ll= vv� (II−AA _i_ x 1),<sup>DD</sup> ee the domestic input coefficients matrix ( _i_ x _i_ ) and **I** an identity matrix ( _i_ x _i_ ) such that )<sup>−1</sup> is the Leontief inverse matrix, and the diagonal matrix of AA<sup>DD</sup> labor income to gross output ratios ( _i_ x _i_ ). The values of export activities are derived by multiplying (II−AA<sup>DD</sup> vv� export income by industry with the occupation income distribution for each industry from the activity data set. Appendix A1 provides a more detailed technical exposition. Data for all variables vary across countries as well as over time allowing for meaningful cross-country and temporal comparisons.

# **3. Export activities along the development path**

How do export baskets evolve in terms of activities as countries grow richer? Figure 1 shows the development of export activity over GDP per capita. It is estimated using a non-parametric LOWESS smoother on data for 59 countries over a time span of 20 years.<sup>7</sup> For parsimony, panel A aggregates the 455 activities into 5 broad groups: engineering, managerial, production, support, and other activities across all industries. Panel A suggests that at lower levels of economic development, production activities account for a major part of export income. The production share steadily declines when countries grow richer: from more than 50 percent of total exports at GDP per capita levels below 5,000 US$ to about 30 percent at levels above 40,000 US$. Engineering, managerial, and support activities account for the majority of labor income from exporting at higher levels of income. Panel B further details production activities in four broad industry groups. It shows that income from production activities within agriculture, mining, and “light” manufacturing industries (including food and textiles industries) shifts towards production

> 7 For the purpose of Figure 1, data for eight countries has been added to the dataset, in particular data for some low-income countries, see Appendix G. However, the data for these eight countries have a lower level of detail and hence are not used in the rest of the paper.

9
<!-- Start of picture text --> Figure 1. Country correlations between cyclical components<br>of real government spending and real GDP, 1980-2020<br>AE EMDE<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2 AE Average = -0.06<br>EMDE Average = 0.42***<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>AzerbaijanChina<br>GreeceNicaraguaEcuadorUruguayGuyanaLiberiaArgentina<br>Antigua and BarbudaBrazilNamibiaEquatorial GuineaLebanonNigerPakistanIndiaBoliviaHungaryJordanKenyaBelarusZimbabweFijiRomaniaSerbiaAngolaQatarCyprusRwandaOmanCongo, Rep.Timor-LesteAfghanistanTrinidad and Tobago<br>SudanTongaSenegalMaliLatviaTuvaluBurkina Faso<br>BahrainCongo, Dem. Rep.BelizeJamaicaPolandBhutanLithuaniaKyrgyz RepublicRussian FederationPapua New GuineaLao P.D.R.VanuatuSyriaCabo VerdeGeorgiaUgandaGrenadaCameroonIndonesiaHondurasMoroccoSurinameTogoSeychellesEstoniaMaltaSri LankaSt. Kitts and NevisDjiboutiSt. Vincent and the GrenadinesIran, Rep. of MadagascarPhilippinesAlgeriaUzbekistanMyanmarBarbadosSouth AfricaParaguayTajikistanLesothoMicronesiaGhanaBurundiSaudi ArabiaBeninTurkmenistanMontenegroPanamaMozambiqueMarshall IslandsBosnia and HerzegovinaMoldovaChadComorosThe GambiaCambodiaColombiaTanzaniaLibyaCentral African RepublicCroatiaTurkeyGabonIcelandEthiopiaKiribatiMongolia<br>NigeriaPortugalIsraelPeruSouth SudanBulgariaEritreaSolomon IslandsCzech RepublicIraqAlbania<br>Haiti<br>SamoaMexicoSt. LuciaSierra LeoneKazakhstanGuinea-BissauMauritaniaCosta RicaBotswanaVietnamEswatiniSlovak RepublicTaiwan, ChinaSan MarinoArmeniaMaldives<br>SloveniaDominican Republic<br>Chile ZambiaItalyNauruSpainMauritiusTunisiaGuinea<br>PalauKuwait DominicaThailandLuxembourg New Zealand<br>SwedenMalaysiaFranceNorwayBelgiumJapanMalawiGermany NetherlandsEl Salvador The Bahamas<br>KoreaIrelandAustriaAustralia Guatemala Brunei Darussalam United Arab Emirates<br>Finland<br>North Macedonia<br>Egypt<br>Singapore United Kingdom<br>Denmark United States<br>Switzerland<br>Canada<br>São Tomé and Príncipe<br>Somalia<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real Hong Kong SAR, China<br><!-- End of picture text --> Notes: Black (dark) bars denote AE. Red (light) bars denotes EMDE. Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on WEO (IMF) data.
51 helpful to know whether it is correct -- or the degree to which it is correct -- so as to better understand whether Bank policy should be concerned with transacting patterns. The empirical challenge here is daunting, but, as usual, something might be better than nothing.

Topics / actionable items.

(i) Structured, short-term longitudinal case studies might provide a viable methodology. In a particular country, identify a few "prototype" firms in each of several taxonomic categories, where the taxonomic categories reflect the character of transacting partners in input and output markets (and perhaps other considerations) . Obtain baseline data, and then follow the firms for a few years. Attempt to determine whether there are patterns of stasis and dynamism at the micro level that support the idea that transacting partners are important.

(ii) Conceivably, there are some statistical data sets that are of sufficient quality to warrant exploration from the same viewpoint.

# **Concluding Coment**

It is obviously not possible to study the problem as a whole; the quest for good research topics is a quest for feasible approaches to illuminating some fraction of the subject. It is worth reiterating, however, that the Bank's most
**Table 8. Real Monthly Labor Earnings in Urban Areas (Constant 1994 pesos)**

|||**1988**<br>|||**1994 **<br>|||**1999**||
|---|---|---|---|---|---|---|---|---|---|
|**Te of Occuation**||**Media**|||<sup>**Medi**</sup>|||||
|**yp p**|**Mean**|**n**|**S.D.**|**Mean **|<br>**an**|**S.D.**|**Mean**|**Median**|**S.D.**|
|Primary Teacher in Public School|862|826|239|1,660|1,590|598|1,286|1,229|917|
|Primary Teacher in Private School|836|875|343|1,614|1,391|910|928|819|607|
|Lower-Secondary Teacher in Pub. School|1,042|972|410|1,872|1,688|898|1,491|1,366|638|
|Lower-Secondary Teacher in Priv. School|836|804|495|1,770|1,341|1,520|1,170|956|817|
|Upper-Secondary Teacher in Pub. School|1,345|1,458|720|1,755|1,590|1,003|1,548|1,366|961|
|Upper-Secondary Teacher in Priv. School|1,024|875|742|1,661|1,490|914|1,395|1,093|1,123|
|University Teacher in Public School|1,840|1,702|922|2,357|2,186|1,436|2,690|1,912|3,548|
|University Teacher in Private School|904|972|666|2,431|1,987|1,871|1,991|1,366|1,523|
|**Professionals with Upper-Secondary in**<br>**Education not teaching**|1,026|851|568|1,959|1,570|1,528|1,567|1,101|1,627|
|**Professionals with an University degree in**<br>**Education not teaching**|2,249|1,653|1,207|2,867|1,987|2,553|1,981|1,639|2,194|
|**The mixed-skilled group**|**1,199**|**826**|**2,501 **|**1,573**|**994**|**5,006**|**1,069**|**734**|**1,356**|
|The agricultural group|878|486|1,549|1,139|641|2,682|911|440|3,616|
|The low-skilledgroup|835|656|2,018|817|641|1,979|589|440|641|

Source: Own calculations based on ENEU survey Tables 9 and 10 present the teachers’ hourly labor earnings (mean monthly labor earnings divided by the total number of hours-worked per month, non adjusted and adjusted respectively). In both tables, teachers’ hourly earnings are higher in primary public schools than they are in primary private schools. At the lower secondary school level the difference is small. Interestingly, teachers hourly labor earnings and adjusted hourly labor earnings (taking into account two months vacation) are substantially above other worker’s hourly labor earnings but slightly below other _Professionals with B.A in Education not teaching_ . In 1999, median hourly earnings for primary public school teachers was above median earnings of all comparable groups.

15
found no statistically significant gap in the means of household consumption across surveys and national accounts, while household income means from surveys were 33 percent lower than GDP in national accounts. Deaton found that means for household consumption were 22 percent lower in surveys than in national accounts and means of income from surveys were 43 percent lower than means from national accounts.

We also find that the size of the gaps varies systematically along income levels of countries, with the discrepancy being largest for middle-income countries. In contrast to previous assessments of gaps, we find relatively small differences between income and consumption estimates across all countries. Overall, the gaps across NAS and HHS for income and consumption measures are similar, which contradicts Ravallion’s (2003) suggestion that the gap is mainly due to underreporting of incomes in surveys. In contrast to Deaton (2005) and Karshenas (2003), we show that the gap is narrowing as countries get richer for both income and consumption measures, possibly reflecting better integration of NAS and HHS data in highincome countries due to improved efforts to align survey and national accounts in recent years. We also find that growth rates from national accounts are higher than from surveys, particularly in middle-income countries, in line with the economic gradient in the gaps in levels.

We illustrate potential implications of these observed gaps for the measurement of global poverty and inequality. The implications depend on assumptions about the origin of the gaps and the corresponding adjustments made to the measures. We discuss implications of assuming that the gap is fully due to errors in survey data and that these errors are distribution neutral (proportionally uniform across the income distribution). This is similar to scaling up survey means to national accounts means, in line with the methods applied by Bhalla (2002), Sala-iMartin (2006) and Pinkovskiy and Sala-i-Martin (2014; 2016). Such adjustments generally show that both global poverty and inequality are lower and falling faster than measures based on surveys only.

We also examine a more nuanced explanation that assumes the gap in means is due to measurement (and definitional) differences across surveys and national accounts, but also with surveys disproportionately failing to fully capture the consumption and incomes of the richest households. We consider an adjustment similar to the approach proposed by Lakner and 4
# III. Trends in government spending over time, by region, by income group

In Table 4 we display the evolution over time in spending as a share of GDP for the three sectors and across different income groups. In our discussion, we will refrain from comparing the size of spending in the different sectors, since the definition of spending (including capital or not) changes across the three sectors.

**Table 4** : Government spending (as a percent of GDP) by sector and income group

||**20**|**00-20**|**03**|**2**|**004-20**|**07**|**20**|**08-201**|**1**|**20**|**12-201**|**6**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|Min|Max|Mean|Min|Max|Mean|Min|Max|Mean|Min|Max|
||||**Gover**<br>|**nment s**<br>|**pendin**<br>|**g on heal**<br>|**th**<br>||||||
|Overall|3.1|0.1|16.7|3.2|0.2|12.7|3.3|0.2|12.3|3.5|0.5|13.5|
|High income|4.5|1.1|7.8|4.7|1.0|8.0|5.2|1.3|8.8|5.5|1.9|11.4|
|Upper middle income|3.4|0.4|16.7|3.4|0.4|12.7|3.6|0.4|12.3|3.8|0.5|13.5|
|Lower middle income|2.2|0.2|10.5|2.2|0.2|10.5|2.3|0.2|8.9|2.4|0.5|7.6|
|Low income|1.4|0.1|3.2|1.5|0.2|3.0|1.3|0.4|2.6|1.3|0.5|3.1|
|||<br>|**Govern**<br>|**ment spe**<br>|**nding**<br>|**on educa**<br>|**tion**<br>||||||
|Overall|4.5|1.2|11.6|4.5|1.2|11.5|4.6|0.8|13.0|4.6|1.4|12.5|
|High income|5.0|2.2|8.2|4.9|2.5|7.9|4.9|1.6|8.2|5.1|1.6|7.6|
|Upper middle income|4.5|1.2|10.5|4.3|2.0|9.8|4.6|1.8|9.6|4.5|2.3|10.3|
|Lower middle income|4.5|1.2|11.6|4.7|1.7|11.5|4.8|0.8|13.0|4.8|1.8|12.5|
|Low income|3.2|1.4|4.9|3.5|1.2|5.5|3.5|0.8|6.3|3.9|1.4|7.4|
|||**Gov**|**ernmen**|**t spend**|**ing on**|**social pr**|**otection**||||||
|Overall|||||||1.5|0.2|7.1|1.6|0.0|10.1|
|High income|||||||0.8|0.8|0.8|2.0|0.5|3.5|
|Upper middle income|||||||1.6|0.5|3.9|1.7|0.2|3.5|
|Lower middle income|||||||1.6|0.2|7.1|1.3|0.0|7.0|
|Low income|||||||1.3|0.2|2.6|1.5|0.0|10.1|

The average share of domestic current spending in health has seen a moderate increase over time. Specifically, it increased from 3.1% of GDP in the period 2000-2003 to 3.5% of GDP in the period between 2012 and 2016. Not surprisingly, regions with higher levels of income spend more. The time trend is steeper in high-income countries, while it is flat in lower-middle income (LMICs) and low-income countries, with the latter displaying a dismayingly low level of current spending in health (1.5% of GDP). Unlike other income groups, LICs have experienced a sharp decline in domestic government health expenditure as a percent of total government expenditure (Figure A.1). The acceleration in high-income countries (HICs) and the consequent increase in the gap between them and other income groups took place after 2008 (Figure 1). The reason for this increase is potentially linked to the economic consequences of the financial crisis, since total expenditure in absolute terms for HICs did not experience a dip similar to that seen in GDP (in other words, a decrease in the denominator that is larger than the decrease in the numerator would mechanically generate an uptick, such as that seen in Figure 1). For instance, EUROSTAT reports that for the 28 EU countries, total government expenditure notably increased in 2009 (to 50.0%, from 46.2% in 9