rafmacalaba/data-use-mentions-extended huggingface dataset:
>17</sup> Tables 18 and 19 report the estimation results using the travel time and ad valorem measures, respectively. The results suggest that reducing travel time by one day can increase total FDI inflows to BRI countries by 7 percent, FDI flows within BRI countries by 6 percent, FDI flows from OECD countries to BRI countries by 8 percent, and FDI flows from non-BRI countries to BRI countries by 8 percent.

When using the ad valorem time barrier measure, the results suggest that a 10-percent decrease in the ad valorem time barrier is associated with a 11-percent increase in total FDI inflows to BRI countries, a 10-percent increase in intra-BRI FDI flows, a 13.2-percent increase in FDI flows from OECD to BRI countries, and a 13.3-percent increase in FDI flows from non-BRI to BRI countries.

# 4.2 Determinants of Chinese ODI

In this sub-section, we focus on China’s outward investment; given the available information in the CGIT data set, we can differentiate China’s investment across types and sectors. This enables us to explore potential interactions between China’s construction contracts and its ODI and how the former might serve as a catalyst for the latter.

For China’s investment, the following specification is considered:

# ChinaFDI�� �α� � α� MktSize�� � α� SkillDiff��

# � α� Transport� �α�Infrastructure� �α�PTAdepth� �α� Construction����

> 17 We thank the authors for kindly providing the shipment time and trade cost estimates.

29
persistent lobbying by the business community, as well as firm surveys that showed that high severance pay obligations were a serious concern, the easing of severance pay requirements became a major part of investment-

> 2 See, for example, World Bank (2011) finds that 40 percent of wage workers report earnings below the minimum wage in Indonesia in 2007. This is broadly consistent with reported estimates that noncompliance rates are as high as 33 percent in Costa Rica (Gindling and Terrell, 2007) and 45 percent in South Africa (Bhorat et al, 2010).
The NUTS1 spatial effects interacted with a vector of other observable household characteristics have shown to be a good proxy for welfare dynamics and increase the accuracy of the imputation model, as shown by a number of external validation exercises conducted (See Annex). Poverty trends using imputed income can be compared at the NUTS1 level between the LFS and SILC. Figure 30 illustrates an external validation exercise for 2007 where observed income poverty using the SILC data set is compared with imputed income poverty using the LFS at the NUTS1 level. As the results show, the imputed regional income poverty rates from the LFS are matched closely to the SILC. It is impossible to externally validate the LFS imputed income poverty at the NUTS2 level since there is no other data set where households can be regionally identified at the NUTS2 and also have a full income profile.

In terms of imputed income poverty, Turkey has been able to reduce its poverty level at the $5/day 2005PPP (moderate) line but has not been able to expand this performance to the $2.5/day 2005PPP (extreme) poverty line between 2006 and 2013 (Figure 13). During this period, moderate poverty decreased slightly from 23.80 to 22.22 percent while extreme poverty rose from 5.46 to 7.62 percent. A more detailed look at the annual change in Turkey’s poverty reduction shows that the country’s financial crisis experience significantly affected the number of poor. With the impact of the crisis, moderate poverty and extreme poverty both increased between 2006 and 2008. In the immediate aftermath of the crisis, Turkey was able to recover swiftly as poverty rates fell between 2009 and 2011. However after 2011, extreme poverty started to increase once again despite the continuing improvement in moderate poverty. This decoupling between the trends suggests that the poorest of the poor was not able to escape extreme poverty while those among the poor that were closer to the $5 line moved upward after 2011.

The overall findings are consistent with the poverty trends
# **_Regulatory heterogeneity_**

As discussed, regulatory heterogeneity forms a significant part of the total costs in services trade. Regulatory heterogeneity covers the variability in the administration regarding the rules and requirements that apply to the delivery of the services and the regulatory processes to adhere to between countries. To date, the most sophisticated empirical counterpart for measuring regulatory heterogeneity is the OECD’s **Regulatory Heterogeneity index** in services based on the OECD STRI (see Nordås, 2016). Given that the in this index, the entire OECD index is used, it includes both the up-front or explicit barrier to trade related to market access and national treatment as well as to the extent that STRI covers for it the issue of regulatory transparency and barriers competition in services (see above).

The OECD’s STRI is specific to each country pair and indicates whether the countries have a similar regulatory frameworks in services or not. For each country pair and each sector, the indices reflect the (weighted) share of measures for which the two countries have different regulations. Note that in this respect, all categories of the OECD STRI are taken into account and not just specifically the areas of Regulatory transparency and / or Barriers to competition only. However, the OECD does have information on the level of regulatory heterogeneity for each of the five STRI components separately. Finally, the OECD developed two indicators of regulatory heterogeneity. One is computed on the basis of the qualitative answers as found in the database, the other one the basis of the actual index scoring.

# **3.2 Determinants of TFiS: Empirical Models and Results**

In this section, we develop an empirical approach to help identify the relative importance of different factors as potential drivers of TFiS, by focusing on the determinants of cross-border services trade as indicators of the sources of trade costs that arise in a cross-border setting. The empirical counterparts of the core dimensions of TFiS developed in the previous section serve as a basis for the analysis, but some
**Figure 1: Output of the informal economy across regions**

<!-- Start of picture text --> 40<br>35.5<br>34.3<br>35<br>30 27.1<br>25.4<br>25<br>21.6 21.6<br>20<br>15<br>11.2<br>10<br>5<br>0<br>Sub Saharan Latin South Asia Eastern East Asia Middle East North<br>Africa America Europe and and Pacific and North America<br>and Central Asia Africa<br>Caribbean<br>2015-2020)<br>Output originating int he informal<br>economy (% of official GDP, average over<br><!-- End of picture text --> <!-- Start of picture text --> Latin<br>America<br>and<br>Caribbean<br><!-- End of picture text --> Source: Authors’ own calculations based on World Bank’s Informal Economy Database as compiled by Elgin et al. (2021).

37
provisions.<sup>19</sup> We conclude the econometric analysis with two additional sets of experiments that further capitalize on the richness of the DCRTA data set to shed light on the links between DTA provisions and FDI.<sup>20</sup> First, we complement the analysis of Laget et al. (2021), who study the impact of several alternative PTA disciplines/provisions on FDI, by investigating the eects of all provision types from DCRTA on aggregate FDI. To this end, we rely on the dummy-variable specication from column (3) of Table 2 by sequentially replacing the

> 19 ˆ Specically, to obtain these bounds, we used the expression (exp(ˆ _βINVij,t_ + _βINV_  DEP T Hij,t × Nmin,max_ ) _−_ 1) _×_ 100 , where _β_<sup>ˆ</sup> _INVij,t_ and _β_<sup>ˆ</sup> _INV_  DEP T Hij,t_ are the corresponding estimates from column (4) of Table 2), and _Nmin,max_ denotes the minimum (7) and the maximum (42) number of investment provisions in our sample.

> 20 We are very grateful to Nadia Rocha and Vanessa Alviarez for suggesting to investigate the eects of additional provisions and alternative measures of the depth of DTAs.

27
Second, unions act as a transmission mechanism between the CPV and working people and are supposed to represent these people in the highest levels of government decision making. Third, unions are tasked with protecting workers’ rights and representing workers’ interests vis-à-vis firm management and providing welfare services to workers. The explicit roles for employers’ associations are similarly varied.

**Figure 6 – Union membership (in percent of wage employment)**

<!-- Start of picture text --> 90.00%<br>80.00%<br>70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br>China Vietnam Vietnam New Australia Japan Singapore Philippines Indonesia Korea Malaysia<br>(public (private Zealand<br>sector) sector)<br><!-- End of picture text -->

**Sources: World Bank staff calculations based on 2013 labor force survey, VGCL (2014) and World Bank (2012a).**

**Notes: Data for Indonesia and the Philippines are for 2005, those for Vietnam for 2013, all others for 2010. Public sector includes government, public services and state-owned enterprises, private sector includes household enterprises, domestic private firms and foreign-owned private firms.**

Figure 7 uses data from the VGCL to show the time series of informal wildcat strikes in Vietnam. In the 1990s, Vietnam generally saw less than 100 wildcat strikes per year. The time series of wildcat strikes has since broadly tracked Vietnam’s inflation rate. At the same time, the number of strikes has increased significantly, reaching a peak of almost 900 in 2011. Figure 7 also shows that almost no strikes occur in SOEs. In the SOE sector, wages and benefits are relatively high, and relations between workers’
-21-

rate tax of 20% or the application of a comprehensive income tax would have a negligible effect on average tax rates. The exception is the highest income decile which would experience an increase of the average tax burden to 37.5% (compared to 35.2% in case of no taxation of capital income). The problem, however, is that the highest income decile undertakes the most ,,tax-planning" activities and that the benefit of using such a tax on high income earners would be nullified by tax avoidance behavior. Evidence from TARKI and from other sources clearly point to a regressive effect of legal and illegal methods of tax planning.<sup>2 8</sup>

**Table 8: Hungary: Simulating the Distributional Impact of Various Capital Income Tax Schemes (using 1994 tax return data)**

||Income|(in HUF)|Income|Tax Payments (in|HUF)*****|A|verage Tax Rate*****|*****|
|---|---|---|---|---|---|---|---|---|
|Decile|Total<br>income|of which:<br>capital income|0% on capital<br> <br>income|20% on capital<br>income|CompIT***|0% on capital<br>income|20% on capital<br>income|CompIT**|
|1|49,202|182|0|36|0|0.00|0.07|0.00|
|2|113,698|91|721|740|740|0.63|0.65|0.65|
|3|159,564|364|10,300|10,373|10,391|6.46
knowledge on these cost components, and overall are 7.9 percentage points more likely to correctly identify the costs of a remittance transaction. The effect size is about half (4.6 percentage points) for the family-only training, and insignificant for the migrant-only training. Despite these differences in magnitude, we cannot reject equality of impacts across the various treatments.

# **3.3 Impact on Real Outcomes: Remittances**

There are two main channels through which financial literacy training might be expected to impact on real outcomes for remaining household members: it may affect how much money they receive in remittances, and it may affect how households use the money that they do receive.

We start first with remittance behavior. Higher financial literacy may change the extent to which remittance transfers take place through the formal financial system. However, in the case of our Indonesian sample, almost all remittances occur through formal channels, and thus column 1 of Table 5 shows no impact on greater formality given this starting point. This is consistent with separate survey evidence from the Bank of Indonesia which has found most remittance transactions tend to occur through formal channels.

Financial literacy training may also change the frequency and amount remitted. Migrants may send more remittances if financial literacy training enables them to manage their money abroad better and have more savings, or if they feel that financial literacy training has improved their receiving household’s financial management. A better understanding of the costs of remitting may reduce the frequency of remitting if migrants bundle transactions into fewer, larger, transfers in order to reduce transaction fees, or may increase the frequency of remitting if financially informed migrants are able to seek out cheaper methods for sending small transactions (Gibson et al, 2006; Aycinena et al, 2011).

Table 5 shows that financial literacy training had no significant impacts on the likelihood or frequency of remitting, or on the amount of remittances received. Moreover, the point estimates on the amount received are negative, suggesting that, if anything, households which took
- Net farm income would be enhanced in all developing country regions other than South

Asia (where job growth would be greater in non-farm activities).

To realize that potential gain, it is in agriculture that by far the largest cuts in bound tariffs and subsidies are required. The political sensitivity of farm support programs, coupled with the complexities of the measures introduced in the Uruguay Round Agreement on Agriculture and of the modalities set out in the Doha Framework Agreement of July 2004, make that a daunting task. However, with global gains of the order of $290 billion per year at stake from removing trade barriers, even if no reforms were forthcoming in services, and even if the counterfactual is the status quo rather than protectionist backsliding, the political will needs to be found to bring about such reform. The WTO’s Doha Development Agenda is an obvious vehicle for moving down this path (Anderson and Martin 2005a, b). Multilateral cuts in tariff bindings are especially helpful because they can lock in previous unilateral trade liberalizations that otherwise would remain unbound and hence vulnerable to reversals to higher protection; and they can be used as an opportunity to multilateralize previously agreed preferential trade agreements and thereby reduce the risk of trade diversion from those bilateral or regional arrangements. It remains to be seen whether the political will can be mustered to bring that Doha round to a successful conclusion.

The results concerning the extent of bias in trade policies against or in favor of agriculture are very much dependent on the levels of distortion in the GTAP database of course. Those for high-income countries are reasonably reliable, thanks in large part to the protection estimates provided by the OECD (2005 and earlier). Currently available estimates of (particularly agricultural) trade distortions and subsidies in developing countries are less reliable. Nor are many estimates provided in the GTAP database of export taxes or tax equivalents of 18
December 2014 and because of the winter there were very few consultations conducted at the facilities. The teams reached less than 30 percent of the target number of observations. Therefore, in the follow-up survey, the survey methodology was changed so that also home visits are observed, and the teams stayed for a longer duration in each health center. The target number of observations for each type of consultation was raised to 15.

# Household Survey

Within each selected catchment area, two villages were randomly selected to serve as sampling units for the household survey. The survey teams conducted full listings of all households in the selected villages to identify households eligible for inclusion in the survey. In all catchment areas, households with a female member with a pregnancy in the preceding two years were selected. In a third of the catchment areas - those randomly selected to not implement neither the collaborative quality improvement nor the citizen scorecard intervention - households with a member 40 years old and above were also selected.

The same villages visited in the baseline survey were also selected for the follow-up survey, but a new set of households was identified by the listing exercise.

# Survey tools and data

The baseline survey tools and data sets can be accessed with the following links: Household survey: http://microdata.worldbank.org/index.php/catalog/2798 Facility-based survey: http://microdata.worldbank.org/index.php/catalog/2799 39
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

the wake of the crisis. The conclusion states the case that firms in the developing world will, in one or all of the ways described, soon come to play a more central role in driving the innovative trajectory of the industry by leveraging the full complement resources that have become available in GVCs.

# **The Electronics Industry’s Role in Global Value Chain Formation**

Each year, the electronics industry generates a mushrooming array of products and services increasingly used in nearly every human endeavor.<sup>2</sup> Now deeply entwined in our social fabric, electronics products and systems now support critical aspects of communication, education, finance, recreation, and government. Thousands of companies from dozens of countries contribute to the industry on a daily basis. Even a single product can contain work carried out by dozens of firms in multiple countries. Because there is less need for co-location of engineers than in other technology-intensive sectors, such as with the co-location of design with manufacturing, it is relatively easy for electronics firms to engage in the twin strategies of outsourcing and off-shoring. Global sourcing is common. Factories can be relocated with relative ease and produce a wide variety of end products. As a result, GVCs in the electronics industry are more geographically extensive and dynamic than in any other goods-producing sector.

Evidence of the importance of the electronics industry in GVC formation can be found in statistics on intermediate goods trade. Trade in intermediate goods is indicative of GVCs because fragmented production processes require that parts, components, and partially manufactured subassemblies cross borders—sometimes more than once—before finished goods are shipped to final markets (Feenstra 1998; Dean, Fung, and Wang 2007; Brülhart 2008). Table 1 shows the relative importance of various goods-producing industries in GVCs: intermediate electronics and automotive goods dominate total trade in the top-50 manufactured intermediate products (a combined 64.7 percent in 2006). Next important is a group of undifferentiated
Two girls<br>_(=1 if frst two children were girls)_|7480|0.247|(0.431)|0|1|
|Same sex<br>_(=1 if frst two children have the same sex)_|7480|0.502|(0.500)|0|1|
|Mixed sex<br>_(=1 if frst two children have diferent sex)_|7480|0.497|(0.500)|0|1|

_Notes:_ The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and the 2012 LSMS 34
prospective international investors. Most of the papers cited above have, in fact, used these subjective indicators as measures of good governance.

This section makes use of the information provided by two independent country-risk evaluators, Jnternational Country Risk Guide (ICRG) and Business Environment Risk Index (BERI), which present ratings for various aspects of governance. ICRG provides ratings on five variables, including the rule of law, measuring the extent to which there are mechanisms for peaceful dispute resolution; expropriation risk, assessing the risk of nationalization of enterprise equity; and repudiation ofcontracts by government, measuring the state’s commitment to fulfill its contractual obligations and proxying for its ability to enforce contracts between private-sector agents. It also analyzes corruption in government, evaluating the extent to which bribes to public officials are needed to expedite bureaucratic requirements, circumvent regulations, force a change in policy to benefit a given interest group, or obtain public contracts without a lawful bidding process; and quality of the bureaucracy, evaluating both procedural clarity and the technical competence of government officials. Although in principle these ratings measure different aspects of governance, in practice they are highly correlated. Because of this, an overall index is presented here. This index is based on the simple average of the ratings for the five variables. A similar procedure is used with the BERI ratings, which consist of three underlying variables: enforceability ofcontracts, nationalization risk, and bureaucratic delays. | The ICRG and BERI indices presented below provide information related to two aspects of good governance. The first is the quality of public institutions (evaluated by the variables corruption in government and quality of the bureaucracy/bureaucratic delays), which describes the ability of the state to implement its policies through both an appropriate structure of government and honest and technically competent bureaucrats. The second is the quality of public services (evaluated by the _ variables rule of law, enforceability of contracts/repudiation of contracts by government, and expropriation/nationalization risk), which determine an environment where contractual obligations are respected both by
characteristics are entered simultaneously. SSP had broad appeal, with few baseline characteristics predicting take-up and dropout. Strikingly, while heavy overdrafters were just as likely as light overdrafters to sign up for SSP, they were 12 percentage points more likely to drop out (column 2). This is consistent with the hypothesis that some heavy overdrafters are partially-naïve and underestimate their future desire to undo commitments.

# **3.2 Direct Results from Administrative Data**

We now analyze how SSP impacted study participants’ use of NVRB accounts. Here, we focus on use of overdrafts, overdraft fees paid, net deposits (total deposits minus total debits, a measure of savings flow), and the average daily account balance (a measure of savings stock).<sup>11</sup> Our administrative data span the commitment period, the post-commitment period covered by follow-up surveys, and a post-commitment, post-survey period. We therefore estimate separate treatment effects for these three periods to understand how effects evolve over time.<sup>12</sup> We use the following regression specification: Where _yit_ is the outcome of interest for account holder _i_ in month _t_ , _treati_ is a dummy variable equal to one if account holder _i_ is in the treatment group, _duringt_ identifies the commitment period, _after_ 1 _t_ identifies the post-commitment, follow-up survey period, _after_ 2 _t_ identifies the post-commitment, post-survey period, _γs_ are strata fixed effects, _δt_ are month fixed effects, and _yi_ 0 is the average outcome of interest, measured in the year before the survey.

The first two columns of Table 2 show that during the commitment period SSP had no impact on overdrafts. After SSP savings were released, the share of accounts in overdraft declined by 6 percentage points (18 percent), while overdraft charges declined by GHS 3.77 (21 percent) per month – both significant at the 10 percent level. In the longer run, however, participants slid back into debt cycles: we find no treatment effect on overdrafts