rafmacalaba/data-use-mentions-extended huggingface dataset:
# C. Trade Remedies

As noted earlier (in Section IIi), anti-dumping and countervailing actions, in particular investigations, are being reviewed in this study as potential indicators of market access restrictions, irrespective of their consistency with WTO provisions. The evidence shows that the frequency of antidumping and countervailing actions (as well as other price related controls) in developed countries declined significantly over the period 1994-1997. Several developed countries (Japan , Switzerland, Norway) did not use such mneasures at all; while in others (EU, Canada) the frequency of their use fell to less than 1 per cent of the total tariff lines.

This evidence is corroborated by detailed analysis of antidumping actions initiated by developed countries based on the WTO antidumping data base. Table 16 shows that the annual average number of anti-dumping investigations initiated by developed countries fell from 160 cases in the period 1989-1993 to 95 in the period 1994-1997; although they increased in 1997, after declining for four straight years. Also, in recent months there has been a significant increase of announced anti-dumping actions by the US involving steel products. And there have been press reports that the EU will take similar actions. It is too early to say whether this is the beginning of a series of actions It is worth noting also that the share of developed countries in anti-dumping investigations fell to less than 50 per cent of the total antidumping investigations in the latter period - i.e. in the latter period, developing rather than developed countries initiated the majority of anti-dumping investigations. The use of antidumping as a trade remedy in a number of developing countries has given rise to requests from many others for technical assistance from international organizations to help establish similar capacity to bring antidumping actions.

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approach is that the product sophistication measures do not cover all Chilean innovators due to difficulties in establishing concordances between the 7-digit ISIC level and the HS classification of the trade data.

We examine the third dimension of market risk by considering in Table 8 alternative measures of innovation that attempt to capture the various challenges associated with getting new products to be successfully sold on the market. First, as is widely discussed in the innovation literature, the value of an innovation depends on the actions of competitors; its value will be much lower if competitors introduce novel products that are very close to the plants‟ innovations. Columns (1)-(3) show the results from estimating our main specifications distinguishing across product innovations in a 7-digit category with more than versus less than 10 competitors who introduce innovations concurrently. The estimates indicate that product innovation improves Chilean plant survival odds significantly _only_ in industries with a lower degree of competition.

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Thus, even if small-scale experiments are unbiased within sample, they may be biased and also imprecise for population-level inference. In other words, there is likely to be a trade-off between the potential omitted variable bias in our panel-data estimates on one hand, and the advantages of greater precision, “as is” implementation, and unbiased site selection on the other. We do not attempt to quantify this trade-off in this paper (since we have no objective basis of doing so). However, one way of reconciling this trade-off is to conduct a substantial nationwide expansion of school inspections by hiring more staff in the context of a large experimental evaluation. From a decision-theoretic perspective, our results are strong enough to support such a policy even if there is only a 1% chance that our estimates are causal. In Appendix B, we formally show that, barring extreme priors, a policy-maker interested in lowering effective student-teacher ratio will find it cost-effective to invest in or scale-up monitoring of teachers.

# **7 Conclusions**

The central and state governments in India have considerably increased spending on primary education over the past decade. We contribute towards understanding the impact of these substantial nationwide investments in primary education in India by constructing a unique nationally-representative panel data set on education quality in rural India. We find that there has been a substantial improvement in several measures of school quality including infrastructure, student-teacher ratios, and monitoring. However, teacher absence rates continue to be high, with 23.6 percent of teachers in public schools across rural India being absent during unannounced visits to schools.

Using village-level panel data, we find two robust correlations in the panel data that provide external validity in nationally-representative data to results established in smallerscale experiments. First, reductions in student-teacher ratios are strongly correlated with increased teacher absence, suggesting that increased spending on hiring additional teachers was accompanied by increased inefficiency, which may limit the extent to which additional spending may improve outcomes. Second, increases in the frequency of inspections are strongly correlated with lower teacher absence, suggesting
We start with a group of 51 “serial defaulters.” These are countries that have defaulted on their external debt at least twice since 1815 (or independence).<sup>41</sup> Appendix B5.4 shows that the results are similar when using a stricter definition of serial defaulters, that is, for countries with at least three or at least four defaults. The second group of 40 countries (“Others”) includes non-defaulters and one-time defaulters.<sup>42</sup> The default data are from Reinhart and Rogoff (2009), updated by Reinhart and Trebesch (2016) and here.

Table 5: Bond returns by country group and era

||Arithmetic<br>mean<br>(annual)|<br>Geometric<br>mean<br>(annual)|SD|Excess return<br>(mean, above<br>UK/US bonds)|
|---|---|---|---|---|
|Total Sample (1815-2016)|||||
|Serial defaulters<br>|7.1<br>|5.8<br>|16.8<br>|4.6<br>|
|Other countries with ext. bonds|5.6|5.1|10.4|3.4|
|UK/US government bonds|2.9|2.5|9.3||
|Early 19th Century (1815-1869)|||||
|Serial defaulters<br>|7.0<br>|5.5<br>|17.8<br>|2.9<br>|
|Other countries with ext. bonds|5.8|5.4|10.1|1.8|
|UK government bonds|5.1|4.6|10.2||
|Pre-WW1 (
# **_1. Introduction_**

The COVID-19 pandemic was a shock that provided impetus for increased usage of digital payment services, and perhaps spurred deeper financial inclusion. In fact, between 2017 and 2021, the share of adults reporting having used the internet to make or receive digital payments in the previous year rose, on average, by 7.7 percentage points. However, the standard deviation of these changes is also high, at 8.5 percentage points, indicating substantial variation across countries. Because it was unanticipated and because countries had differing responses in terms of increased use of digital payments, the episode provides an opportunity to identify the drivers of digital payments and digital financial inclusion. Using the 2021 round of the Global Findex survey and other new datasets describing bank regulation and supervision and legal and regulatory enablers and safeguards for digital commerce and (personal) data sharing, we measure the influence on digital payments of dimensions that have not been quantified well to date, while also measuring the influence of drivers such as human capabilities (educational attainment), infrastructure (electricity, internet usage), and the usage of mobile phones.

The literature on the effects of digital payments, and digital financial services (DFS) in general, is growing and surveys of it were available prior to the COVID-19 pandemic (Karlan et al., 2016; Abbasi and Weigand, 2017) and in the context of the pandemic (Agur et al., 2020). Key themes that emerge from the literature on impact evaluation, much of which is focused on field experiments (i.e., randomized controlled trials or ‘RCTs’) are that: (1) digital payments and transfers can strengthen informal risk-sharing networks, providing a source of resilience to unanticipated shocks that can contribute to higher household savings and consumption (Lee et al., 2021; Jack and Suri, 2011, 2014, 2016; Suri et al., 2012); (2) digital systems are proving a cost effective and secure method for
we use for our core analysis average management scores over the most senior divisional-bureaucrat reports where there are multiple directors.

The scores on each question are converted into normalized z-scores (so are continuous variables with mean zero and variance one by construction) and are increasing in the commonly understood notion of ‘better management’. For example, we assume greater monitoring corresponds to better management practice. We then take unweighted means of the underlying z-scores to aggregate the questions on monitoring shown in Table A3 and separately all other management practices.

We complement our measure of alignment with a second measure by asking civil servants directly about the extent of alignment they feel to the organization and their daily activities. In the civil servants survey, we asked officials, “To what extent do you believe that the organization’s mission is aligned to your own mission?” and “How consistent with your mission are the various tasks and activities assigned to you on a day-to-day basis?” To measure the extent to which the organization as a whole and the individual tasks assigned to the civil servant are consistent with her own mission, we create an alignment index that is the mean of the organization average response to the former of these questions and the proportion of employees in the organization that state “I am contributing to fulfilling that mission on an everyday basis” to the latter.

# **Basic Determinants of Bureaucrat Errors**

What are the descriptive determinants of these errors? Table A4 undertakes complementary ANOVA and OLS analyses where the dependent variable is the z-score of errors across variables outlined above. We focus on variables related to the hierarchical structure of the bureaucracy, and the marginal costs and benefits of information acquisition. Column 1 of Table A4 presents partial sum of squares for these variables with corresponding F-statistic p-values presented in parentheses. Column 2 provides coefficients for the corresponding variables within a formal regression framework for consistency with our later tables.

The first panel tests whether the hierarchical structure of government matters for the quality of officials’ information along three margins. We see
of 0.5. The Central Highlands have the highest R-squared of 0.74. Other regions have an R- squared above 0.6.<sup>3</sup>

# **_4.2 Spatial patterns of poverty_**

Table 1 presents the regional estimates of the poverty rate and per capita expenditure which are computed directly using per capita expenditure data of the 2010 VHLSS and those estimated from the poverty mapping method. The 2012 VHLSS is representative at the regional level, and the regional poverty rate directly estimated from expenditure data can be thus regarded as the benchmark against which to compare the poverty map estimates. Table 1 reveals that estimates of the poverty rate are quite similar across the two approaches.

Table 1: Per capita expenditure and poverty indexes

||Estimates from<br> <br>|the 2010<br>|VHLSS<br>||Predictions fr<br>|om SAE<br>|||
|---|---|---|---|---|---|---|---|---|
||Per<br>capita<br>expenditure<br>(thousand<br>VND)|P0|P1|P2|Per capita<br>expenditure<br>(thousand<br>VND)|P0|P1|P2|
|Northern Mountain|10927.1|44.87|0.1558|0.0701|10826.4|43.85|0.1483|0.0679|
||(250.2)|(1.54)|(0.0069)|(0.0042)|(340.9)|(1.76)|(0.0082)|(0.0046)|
|Red River Delta|21546.0|11.95|0.0265|0.0088|20515.2|10
# **1. Introduction**

Vietnam represents one of the great international success stories in achieving poverty reduction during recent decades. Concerns have been raised, however, that the pace of poverty reduction has slowed in recent years. In response, the Government of Vietnam has launched a variety of policies and initiatives to restore momentum in poverty reduction, and it is perceived that in order to buttress the impact of these policy measures, programs must become better targeted at the poor. One route towards better targeting is to exploit geographic variation in welfare outcomes. International evidence suggests that transfers will have a larger impact on poverty when they are targeted to finely delineated communities and localities (Baker and Grosh, 1994; Bigman and Fofack, 2000; Elbers _et al._ 2007).

Geographic targeting of the poor may be most effective in those settings where economic growth originates in areas with high factor productivity, which then results in the emergence of lagging regions. The 2009 World Development Report published by the World Bank, entitled “Spatial Disparities and Development Policy”, demonstrates that economic activity and growth is spatially concentrated in many developing countries due to agglomeration benefits deriving from networks, technological change and human capital externalities. The report argues that countries should embrace this development rather than insist on geographically balanced growth. However, the report further argues that policy makers should explore opportunities to ensure that the benefits from such spatially concentrated growth are distributed broadly across the population. One means to that end is to promote the movement of people; barriers to trade and factor mobility should be removed. Another is to implement spatially targeted re-distribution policies that permit countries to support these growth processes while pursuing an equitable distribution of wellbeing.

A major obstacle to detailed spatial targeting is scarcity of reliable information on welfare levels at the local level. Estimation of poverty in small geographical units such as districts and communes poses considerable data demands. Income or expenditure data are commonly used as indicators of economic wellbeing and such information are routinely collected in household sample surveys. While representative
increase in the volume and types of data available to inform their development plans, track performance, and improve government accountability to the public. New global entities, such as the Global Partnership for Sustainable Development Data and the SDSN Thematic Research Network on Data and Statistics, fostered collaborations between official and nongovernmental data producers. The expectations for official statisticians to abide by open data policies also increased.

As a result of these initiatives, measures of statistical capacity, openness, and funding modestly increased.<sup>1</sup> The expanding requirements on NSSs to tap new data sources, find ways to make data more accessible, and expand their measures of development outpaced capacity improvements, however, reinforcing the need for NSSs to continually engage with users—particularly state actors—to ensure that capacity improvements are made in the areas vital to fostering better development outcomes.

The UN High-Level Group for the Modernization of Official Statistics identifies trends, threats, and opportunities to modernize NSSs. For more than a decade, it has overseen the development of standards and models for describing statistical production processes, organizational structures, and metadata (UNECE 2020b). It has provided guidance on communication and relationship management with users, integrating data from varied sources, big data, and managing partnerships and human resources. Operations in many high-performing NSSs now conform to its standards and guidelines and develop a culture of experimentation and innovation.

# **_Measures of Statistical Capacity_**

Statistical capacity is difficult to measure because it is only partially revealed by achievements or other observable characteristics of the NSS. A system may have the capacity to produce goodquality data but not have yet done so, or it may no longer have the capacity to produce good data despite having done so in the past.

> 1 The Statistical Capacity Building Indicator (established by the World Bank in 2004) increased by about 2 percent between 2004 to 2017 (World Bank 2020b). The Open Data Inventory Index, which assesses the coverage and openness of official statistics, increased by 9.4
**Figure 1: Trade measures for medical products (weekly, January-July 2020)**

<!-- Start of picture text --> Export restrictions Import liberalization<br>Weekly evolution over 1 Jan-17 July 2020 by implementation date Weekly evolution over 1 Jan-17 July 2020 by implementation date<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>Number of restrictive measures for medical products 0 Number of liberalising measures for medical products 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28<br><!-- End of picture text -->

**_Source_** _:_ COVID-19 Trade Policy database, own calculations. **_Note:_** The data at the end of each week do not consider the measures that were removed (with a removal date in that week).

**Figure 2. Duration of trade measures for medical products by implementation date**

<!-- Start of picture text --> 2/1/2020 3/1/2020 4/1/2020 5/1/2020 6/1/2020 7/1/2020<br>Implementation date<br>Liberalising measures Restrictive measures<br>1000<br>800<br>600<br>400<br>Duration in days<br>200<br>0<br><!-- End of picture text --> _Source:_ COVID-19 Trade Policy database, own calculations _._ High and upper middle-income countries, according to the World Bank income classification, enacted more trade policy measures targeting the medical sector than other countries (Evenett et al. 2020
Customer type varies greatly with the sector in which informal businesses operate. The sectoral breakdown of typical customer types given in Panel B of Figure 9 offers a rather interesting pattern. Manufacturing businesses are most heavily reliant on personal connections, with more than half of all businesses in this sector reporting such connections with their typical customer. Unfamiliar contacts comprise the typical customer for less than 1 in 6 informal manufacturing businesses. In contrast, less than a third of retail businesses report personal connections with their typical customers, with 1 in 4 reporting the typical customers to be unfamiliar. This stark pattern is consistent with the potential explanation that institutional settings, particularly mechanisms of fulfilling agreements, shape informal businesses' customer base. The manufacturing sector is particularly prone to contract enforcement challenges such as the hold-up problem (e.g., Nunn, 2007). For example, for manufacturing to begin, customers may need to issue advance payments, and manufacturers need to be able to get paid in the event of disputes after the product is finished. In contrast, retail businesses, e.g., resellers in market stalls, or businesses in other services sectors, such as personal transportation or hairdressing, are less complicated in terms of ensuring that agreements are fulfilled since the provision of the service and payment can occur almost simultaneously. The increased risk that transacting parties may renege on agreements in the manufacturing sector likely contributes to considerably higher reliance on personal connections in this sector than in retail or other services.

Suppliers to the informal businesses are also very likely to be informal. Informal Sector Enterprise Surveys also enable a look at the upstream integration of the informal businesses within their local markets. In particular, businesses in Somalia and Zambia were asked whether their main suppliers are themselves, individuals, businesses, or others. Figures 10 displays data for five cities where this question was implemented. The most common suppliers to informal businesses are individuals, with a large variation across cities—from 43.5% in Lusaka (Zambia) to 88.5
indicator, is that it captures well some of the features of a country's social capital, in particular trust. The correlation between a generalized trust indicator in Paldam and Svendsen (2006) and rule of law, 0.51, is larger than for other indicators of governance.

As an alternative, we averaged the six governance indicators. Many previous studies have averaged or summed governance indicators, with the rationale being that averaging reduces measurement error if the indicators pertain to similar underlying concepts of governance and have independent errors (e.g. Wheeler and Mody, 1992; Mauro, 1995; Chong and Calderon, 2000). In Section 4 we report results for the rule of law index but the results were robust to the use of an aggregate indicator.

> 11 Adult mortality rates (AMR) are calculated as the weighted average of male and female adult mortality rates (series SP.DYN.AMRT.MA and SP.DYN.AMRT.FE, respectively) available from the WDI (2008). For Australia, Canada, Germany, Italy and the US, 2005 rates are missing so 2004 values are used instead. ASR=1-AMR.

> 12 In the original dataset, the rule of law index ranges between -2.69 and 2.12; we rescaled it between 1 and 100.

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homicide outcome is not significant, but remains negative and of similar magnitude (appendix A, table A4). The analysis also increased the sample size to include the top two quintiles of population rather than only the top quintile. The results were slightly smaller and slightly less significant, reflecting the greater number of zero observations in the outcome data (appendix A, table A5). Some of the control variables—those indicative of women’s empowerment— <mark>may be jointly determined or potential mediating variables, rather than true confounders. Thus, the analysis contrasts results with and without these sets of controls and finds consistent estimates across specifications (a</mark> ppendix A, <mark>table A6).</mark>

> 11 For the alternative log specification, 0.001 instead of 0.01 is added to each outcome count before the transformation. The alternative log specification had a larger and more significant coefficient on homicides, while the hyperbolic sine had a smaller and nonsignificant coefficient. Across all outcomes and specifications, the signs were negative.

> 12 Population data used to calculate the rates and generate the linear time trend is from the DATASUS population projections, which are computed based on inter-Census projections made by the health research institute RIPSA (Rede Interagencial de Informações para a Saúde - Interagency Network of Information for Health), in partnership with the Brazilian National Statistics Bureau (Instituto Brasileiro de Geografia e Estatística, IBGE).

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