rafmacalaba/datause-encoder-data huggingface dataset:
# **I Introduction**

The first big move in online sales came with the founding of Amazon.com in 1994. At the time, the company sold only one product, books, but founder Jeff Bezos had bigger plans to sell “Anything with a capital A.” By 2016, the company had succeeded in that plan and sold $136 billion in goods.<sup>1</sup> Meanwhile, in 1999, not long after Bezos launched Amazon, Jack Ma established Alibaba.com, a business-to-business marketplace based in China. In 2018, Alibaba set a milestone for online sales with its Singles’ Day sale, which netted $30.8 billion in transactions, doubling the combined sales for the two biggest sales days in the United States, Black Friday and Cyber Monday.<sup>2</sup> E-commerce is a new technology and business mode that allows buyers to make online transactions and receive local package delivery or pickup from sellers. It has reshaped consumption patterns in the years since it was introduced. The welfare implications of e- commerce have been discussed in the literature, mostly focusing on developed countries (Brown and Goolsbee 2000, Hortaçsu et al. 2009, Gorodnichenko and Talavera 2017). On the one hand, e-commerce offers consumers lower search cost and more product variety than traditional retail stores, improving their welfare. Consumers may also gain from accessing merchants online who do not have local brick-and-mortar stores (Dolfen et al. 2019). On the other hand, the intense competition of e-commerce can result in efficiency and welfare gains for the entire society through effects on prices. However, the intense competition may also squeeze out brick-andmortar stores and smaller retailers, which in turn depresses offline shopping. For example, facing online competition, many physical bookstores have closed (Goldmanis et al. 2010). However, there is a net market expansion effect for consumer electronics, although e-commerce diverts sales from offline, and consumers benefit more than firms from the introduction of e-commerce (Duch-Brown et al. 2017). The net impact of e-commerce depends upon the relative importance of the above factors.

In developing countries, a large proportion of people live in remote areas with limited access to offline retail stores. In these countries, e-commerce may have greater potential to reach a wider range of consumers who are otherwise constrained by limited access to markets than in developed countries. Building a multiregional general equilibrium model and using city-level data, Fan et al. (2016) show that e-commerce development in China disproportionately improves consumer welfare in remote cities. Drawing data from eight counties in three provinces where

> 1 <u>http://www.latimes.com/business/la-fi-amazon-history-20170618-htmlstory.html#</u>

> 2 https://www.businessinsider.com/black-friday-cyber-monday-vs-singles-day-sales-2018-12.

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**Table 6: Effects using Additional Controls**

||NECESSITY|NOWAGE|
|---|---|---|
|Probit|7.8%***|10.5%***|
|(S.E.)|(0.9%)|(3.6%)|
|LATE (2SLS)|51.6%***|52.7%***|
|(S.E.)|(12.1%)|(13.5%)|
|ATE (Biprobit)|14.9%***|19.4%***|
|(S.E.)|(2.0%)|(2.8%)|

*** p<0.01, ** p<0.05, * p<0.1 Note: Controls include (log) sales, (log) employees, (log) age of owner, dummy for female=1, type of premises FE, education-level FE, stratum FE, sector FE, and harassment ‘donut’ rate. Results here add dummies for if married (Y=1), if there are children (6 y.o. or under) in the HH (Y=1), if the largest owner is the primary income owner in HH, dummy if the business is isolated residential (more than 500m from commercial or industrial area), and if the business uses the electrical grid (Y=1). HH: household, FE: fixed effect.

Results from the estimations with additional co-variates bolster previous findings. ATE estimates for both NECESSITY and NOWAGE are positive and significant, with magnitudes of 15 and 19%, respectively. Significant effects from the second stage for those in isolated residential areas (less likely to face a bribe request) and breadwinners as well as those with children in the household (both positive, significant) suggest some justification for their inclusion. LATE estimates are significant for both measures are positive and significant.

As noted above, the functional form of 2SLS may not be appropriate for binary outcomes with binary endogenous regressors. Interpreting 2SLS effects as a local treatment effect requires that the instrument (starting up in an area of activity post-2009) have a monotonic effect on NECESSITY (NOWAGE). That is, that there are no cases where switching from a 0 to 1 value for the instrument results in a _decreased_ likelihood of NECESSITY(NOWAGE)=1. With a binary outcome measure, this monotonicity assumption is not readily testable, and so the LATE magnitudes should be taken with some caution.

However, two generally suggestive conclusions can be drawn. First, the LATE and ATE effects are consistently positive and significant, establishing a relative robustness of their direction and salience. Secondly, recalling that the LATE is estimated for so-called ‘compliers’—those operating out of necessity or households de-linked from formalized employment—the 2SLS effects can be regarded as the effect of operating under inelastic opportunities to formalize. That is, the effect on bribe exposure among those most segmented from formalized opportunities. Such effects will be more relevant if compliers are notably different than other informal sector businesses. By contrast, the biprobit ATEs provide the difference in overall bribe rates if all operators started out of necessity (or (29)
where the actual numbers of total and professional staff in 2000 were 12 and 5 respectively but where the predicted numbers were 7-19 and 3-5 (with the range bounds depending on whether the equation used for prediction was the whole 60 country sample or the 26 developing country sub-sample.)<sup>32</sup> The problem of inadequate staff and professional staff numbers that emerges from the estimates in DPS (2000) does not seem to be as acute as previous work may have suggested but is still far from trivial – at least in Africa and the Caribbean. Whether or not it affects regulatory outcomes (eg investment and output growth) is a separate issue to which we will return in our companion paper.

However, case studies have shown that many DTE regulatory agencies, even some of the most lavishly staffed, have difficulties in handling key issues in regulatory economic practice (eg establishing a proper regulatory asset base, setting interconnection/access prices, etc). The apparent resource deficits reported from the DPS results are less relevant if there is a resource deficit and it is primarily on the _quality_ of the key regulatory staff rather than a _quantity_ issue relating to the numbers of total or professional staff. Unfortunately, as yet, no data seems to have been collected systematically on the _quality_ of regulatory staff in different countries. It is another _regulatory practice_ topic. In addition, it is a difficult topic on which to collect data, particularly across the range of DTEs. It might perhaps be collected within a systematically structured cross-country case study approach.

> 32 See DPS (2002), pp 33-42.

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These measures could not be further from the policy response to the deadliest viral outbreak of modern times, the 1918-1920 Spanish influenza.<sup>1</sup> That pandemic, which claimed at least 50 million worldwide, occurred against the backdrop of World War I. This fact alone precludes drawing meaningful comparisons regarding the effects of the COVID-19 pandemic _per se_ on the global economy. In fact, in 1918, the year in which influenza deaths peaked in the United States, real GDP (driven by wartime activity) rose 9 percent. By contrast, as shown in Figure 1, the October 2020 _World Economic Report_ (WEO) shows that 183 countries of 197 covered (95 percent) and including the United States are estimated to have had a contraction in real per capita GDP in 2020. In many cases, these were record-shattering declines.

Figure 1. Share of countries posting a decline in real per capita GDP: 1981-2020 (annual, in percent) <!-- Start of picture text --> 100<br>COVID-19<br>90<br>80<br>70<br>GFC<br>60 Onset of 1980s<br>50 debt crisis<br>40<br>30<br>20<br>10<br>0<br>1981 1985 1989 1993 1997 2001 2005 2009 2013 2017<br>Percent of countries<br><!-- End of picture text --> Sources: International Monetary Fund, World Economic Outlook, October 2020, and author’s calculations.

This synchronicity is uncommon. True, financial and sovereign debt crises engulfed the developing world in the early 1980s, while the global economy suffered a deep recession. By 1983, however, the United States and other advanced economies were enjoying a robust recovery coupled with lower inflation rates even as many emerging markets and developing economies

> 1 See Barry (2009) for an excellent account of this episode and the government response.

3
Perry (2000) sample includes observations on the crises in Asia, Russia, and Brazil, and thus comprises episodes with very fragile economies.

This paper complements the previous research on rating agencies by also examining these possible cross-country and security-market spillover-effects of rating changes. It also contributes to the literature on contagion and international transmission of shocks by examining the effect of domestic vulnerability, as measured by the ratings of international agencies, on the extent of international spillovers. Our results can be summarized as follows.

**First** , rating changes significantly affect bond and stock markets, with yield spreads increasing on average 3 percent and stock returns declining about 1 percent following a downgrade.

**Second** , rating changes also contribute to contagion or spillover effects, with rating changes among emerging markets triggering changes in both yield spreads and stock returns in foreign countries.  Still, the effect is smaller than that of rating changes of the domestic economy.

**Third** , similar to the findings in the literature on contagion, the “contagion” effects of rating changes are of a regional nature.<sup>4</sup>

**Fourth** , fragile economies, as measured by the international ratings, are more severely affected by changes in U.S. interest rates.  In fact, interest rates hikes in financial centers fuel increases in sovereign risk 50 percent larger in vulnerable circumstances, relative to the changes when countries have more healthy economies.

> 4 See, for example, Kaminsky and Reinhart (2000a).

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Table 6: Summary statistics by propensity score group

||Fu|ll-time s|chools||C|omparis|on school|s||
|---|---|---|---|---|---|---|---|---|---|
|||||Full|Sample|Clo|sest 5|C|losest|
|Variable description|Obs|Mean|St.Dev.|Obs|Mean|Obs|Mean|Obs|Mean|
|Score in language, out of 24|710|12.48|4.62|4641|13.26|2260|12.81|809|12.87|
|Score in math, out of 24|710|10.25|3.68|4641|10.95*|2260|10.59|809|10.55|
|Student is female|710|0.51|0.50|4641|0.51|2260|0.51|809|0.52|
|Student repeated >=1 grade|706|0.41|0.49|4618|0.35***|2246|0.38|804|0.36|
|Student repeated >=2 grades|706|0.13|0.34|4618|0.11|2246|0.11|804|0.08**|
|Student’s years of preschool|673|1.59|0.91|4495|1.57|2183|1.57|786|1.59|
|Household size|710|5.64|2.34|4641|5.37**|2260|5.59|809|5.53|
|Household’s nr of rooms|710|3.50|1.37|4641|3.47|2260|3.44|809|3.44|
|Mother has no education|710|0.27|0.45|4641|0.20***|2260|0.23*|809|0.20**|
|Mother completed primary|710|0.73|0.45|4641|0.80***|2260|0.77*|809|0.80**|
|Mother attended secondary|710|0.42|0.49|4641|0.52***|2260|0.48*|809|0.51**|
|Mother ...nished secondary|710|0.14|0.35|4641|0.17|2260|0.15|809|0.16|
|Mother completed tertiary|710|0.08|0.27|4641|0.09|2260|0.07|809|0.07|
|Father has no education|657|0.30|0.46|4244|0.22***|2072|0.26|749|0.27|
|Father completed primary|657|0.70|0.46|4244|0.78***|2072|0.74|749|0.73|
|Father attended secondary|657|0.37|0.48|4244|0.49**|2072|0.45**|749|0.44|
|Father ...nished secondary|657|0.13|0.34|4244|0.18|2072|0.15|749|0.14|
|Father completed tertiary|657|0.09|0.28|4244|0.09|2072|0.07|749|0.06|
|Teacher experience <=1 yr|35|0.09|0.28|183|0.03|93|0.04|32|0.03|
|Teacher experience = 2-3 yr|35|0.06|0.24|183|0.05|93|0.08|32|0.09|
|Teacher experience= 4-5 yr|35|0.20|0.41|183|0.04**|93|0.04*|32|0.06|
|Teacher experience = 6-9 yr|35|0.09|0.28|183|0.16|93|0.17|32|0.13|
|Teacher experience >= 10 yr|35|0.57|0.50|183|0.72|93|0.67|32|0.69|
|Average Class Size|27|21.57|6.50|110|27.19***|57|25.99**|20|26.31**|
|School equipement|27|0.18|0.61|110|0.37|57|0.38|20|0.39|
|School auxiliary Buildings|27|-0.49|0.25|110|-0.40|57|-0.43|20|-0.50|
|School socio-econ. quintile|27|4.48|0.70|110|4.17*|57|4.39|20|4.50|

Notes: This table reports summary statistics on key observables at the time of the baseline, for full-time schools and for three di¤erent sets of comparison schools: the full sample, the set of 5 closest schools in terms of propensity scores, and the set of closest schools. The table excludes schools that are missing any of the key variables used in the second stage analysis. School Equipment and School Auxiliary Buildings are estimated using Principal Component Factors. A t-test was performed for the equality of means between each comparison group and the group of full time schools. The t-test results are reported using star coding with * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

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**Figure 8:** Selected UMICs overperformers

<!-- Start of picture text --> a) China b) Malaysia<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Other non-overperformers (i) LMICs Other non-overperformers (i) LMICs<br>Other non-overperformers (ii) UMICs Other non-overperformers (ii) UMICs<br>Overperformer (CHN) Overperformer (MYS)<br>c) Kazakhstan<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Other non-overperformers (i) LMICs<br>Other non-overperformers (ii) UMICs<br>Overperformer (KAZ)<br>Digital Infr. Digital Infr.<br>Digital Infr.<br>Human K. & Labor Mkt Human K. & Labor Mkt<br>Human K. & Labor Mkt<br>Reg. & Ethics Reg. & Ethics<br>Reg. & Ethics<br>Innov. & Integr. Innov. & Integr.<br>Innov. & Integr.<br><!-- End of picture text --> <u>Source: The AIPI score and pillars for 2023 are obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI]. Notes: Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020).</u> Weighted average AIPI scores per group in parenthesis. Dotted yellow shows the weighted average pillar scores for non-overperforming LMICs and dotted red shows them for non-overperforming UMICs. The “spider” package is taken from Naqvi (2024a).

Spiderweb charts illustrate that China and Malaysia excel in all AIPI pillars relative to the average of non-overperformers within their income group. Notably, China demonstrates exceptional proficiency in digital infrastructure (Figure 8a), whereas Malaysia shows significant strengths in regulation and ethics, as well as human capital and labor market policies (Figure 8b).

As already underlined, China's AI readiness strategy heavily emphasizes the development of robust digital infrastructure as a cornerstone for its technological ambitions (de Seta, 2023). Central to this effort is the “New Generation Artificial Intelligence Development Plan” or AIDP (China State Council, 2017), which outlines a roadmap to position China as a global AI leader by 2030, underscoring the 17
We use our empirical strategy to test to what extent income shocks explain the surge in the Boko Haram conflict since 2010. In order to do that we construct the weights of the price indices on the basis of the 2008/09 Nigerian household survey, which is closer in time to the period of analysis relative to the 2003/04 survey. As state-level domestic prices are available to us only until 2010, we cannot compute _CI_ and _PI_ based on domestic prices. As an alternative we construct the price indices using directly the international prices as in equation (4) but using the 2008/09 data to construct the weights.

These measures are likely to be exogenous as international prices are arguably exogenous to local level conditions and as the weights are based on production and consumption baskets before the start of the period of analysis. These measures are close to those used by Dube and Vargas (2013) and Bazzi and Blattman (2014).

**Table 8: Summary statistics for the Boko Haram conflict measures, 2010-13**

||Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|_All states_||||||
|Boko Haram (BH) event|148|5.5|23.2|0|194|
|Non BH event|148|13.7|15.3|0|74|
|BH fatality event|148|4.1|19.1|0|171|
|Non BH fatality event|148|5.4|7.9|0|51|
|Fatalities in BH events|148|35.5|201.1|0|2301|
|Fatalities in non BH events|148|32.0|78.4|0|621|
|_Northern states_||||||
|Boko Haram (BH) event|80|10.2|30.9|0|194|
|Non BH event|80|13.5|16.4|0|74|
|BH fatality event|80|7.6|25.5|0|171|
|Non BH fatality event|80|6.6|9.8|0|51|
|Fatalities in BH events|80|65.6|270.6|0|2301|
|Fatalities in non BH events|80|47.1|102.3|0|621|

_Source: Authors’ elaboration on ACLED data_ In addition from the ACLED database, we compute the state-wise yearly number of conflict events, number of conflict events with fatalities and number of fatalities related to Boko Haram.<sup>30</sup> The summary statistics of these variables show the severity of this conflict in the context of the overall country in the period 2010-13, especially in the Northern regions (Table 8). On average 30% of all conflict episodes in the period were related to Boko Haram and over half of the conflict-related fatalities. The Boko Haram conflict varies in intensity more than the other conflict as shown by the comparatively larger standard deviation and ranges of the Boko Haram vs. non Boko Haram conflict measures. Indeed the figures are starker for the Northern states, where the Boko Haram conflict is concentrated.

> 30 A conflict event involves Boko Haram is one of the two actors is defined as Boko Haram in the ACLED data.

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that may not be controlled for. Osnago, Rocha and Ruta (2016) adopt an instrumental variable (IV) approach to address this type of endogeneity using as an instrument for the depth of the PTA between country i and country j the (weighted) average depth of all the agreements signed by i and j with any other country excluding the agreement(s) they have in common. Results of the IV analysis confirm the relevance of deep agreements in boosting GVC trade.

An alternative approach that allows to determine the importance of deep PTAs for GVCs is to look at the effect of depth on different sectors. The effect of deep PTAs should be stronger in sectors that are more integrated in GVCs. In order to test this, Osnago, Rocha and Ruta (2016) exploit the decomposition of gross exports into value added components available for 13 manufacturing sectors in WIOD for the period 1995-2011. They augment a sector-level gravity regression with an interaction term between depth and an index of vertical specialization. The coefficient of the interaction term is consistently positive and significant across specifications and using different variables of depth and provisions. The results suggest that deep PTAs have a larger impact on GVC intensive sectors.

# **5. Undoing Trade Agreements: Brexit and GVCs**<sup>**10**</sup>

The previous section looked at the impact of PTAs in boosting GVCs trade. In this section, I ask the opposite question -i.e. whether the undoing of a PTA would negatively affect global value chains. As an application, I focus on recent work that attempts to assess the trade impact of Brexit, the UK’s exit from the European Union. Specifically, Mulabdic, Osnago and Ruta (2017) study the effect that EU membership had on trade of the UK, most notably with its European partners, and then use this information to assess the future of UK-EU trade under different scenarios.

The EU has been a precursor of deep integration. The World Bank data on the content of trade agreements indicate that the EU is the deepest PTA among the 279 currently in force. The relationship between the UK and the rest of the EU members before Brexit will actually happen is regulated by the European Community (EC) Treaty and the following enlargement agreements which cover 44 policy areas ranging from standards to movements of capital, to labor mobility. The EU is also the region that has the largest share of intra-regional trade. The UK economy is part of this intense network of trade relations. First, the EU is the most important trade partner of the UK accounting for 52 percent of UK’s exports of goods and services. Second, the UK is closely integrated in regional value chains. For instance, the share of intermediates value added in total domestic value added in UK exports (the majority of which goes to the EU) is close to 70 percent.

Mulabdic, Osnago and Ruta (2017) investigate the extent to which the depth of the EU and its trade agreements with third countries contributed to boost UK trade using a gravity equation

> 10 This section is based on Mulabdic, Osnago and Ruta (2017).

11
It is also important to keep in mind that the study compared outcomes between sectors in which cooperatives were paid for reporting health indicators and outcomes in sectors in which cooperatives were paid for performance on indicators (conditional on completed reports). The regular reporting by CHWs already orients CHWs towards the prioritized indicators. It could be that as volunteers they have pro-social preferences and they are more motivated by improving the community health indicators than by financial rewards. It could also be that given their health knowledge and available resources and time there is little scope for additional improvements in performance on these indicators.

Consistent with other data sources, such as the Rwanda DHS, the data collected for the CPBF impact evaluation show overall increase in utilization of maternal and child health services. Ongoing policies and newly introduced interventions were targeting the same outcomes. For example, in the duration of the study, the ministry of health introduced and scaled up a program of community-based provision of family planning methods directly by community health workers (Chin-Quee et al. 2016).<sup>11</sup> Given the other interventions, it might have been hard to achieve additional marginal improvements in utilization through the performance payments to cooperatives.

> 11 The program was scaled up at the district level and therefore did not pose a threat to the internal validity of the experiment in which treatment was randomized at the sub-district level.

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