rafmacalaba/data-use-mentions-extended huggingface dataset:
|All Regi<br>|ons<br>||||||
|---|---|---|---|---|---|---|---|
||Region<br>1|Region<br>2|Region<br>3|Region<br>4|Region<br>5|Region<br>6|Total|
|A) Region's share of<br>national industry<br>employment|0.050|0.070|0.150|0.250|0.290|0.190|1|
|B) Regions share of<br>national total<br>employment|0.100|0.150|0.200|0.150|0.180|0.220|1|
||A-B|*0.5|0.025|0.040|0.025|0.050|0.055|0.015|0.21|

25
the north. Second, mining plays a dominant role in the production network, especially in the south.

# **Intermediate Input Use and Input Downstreamness Index**

We compute the intermediate use share as the percentage of sectoral output used as intermediate inputs for other sectors, and its change between 2004 and 2013. Appendix figure A4 (panel a) compares the change in the intermediate share across sectors. In both the north and the south, the share of intermediate use in mining has increased by 11 and 13 percentage points, respectively. In the south, the change in the intermediate use in WRT services (by 32 percentage points) is more the four times the same in the north (by 7 percentage points).

We next compute downstreamness (Miller and Blair 2009; Antras et al. 2012; Antras and Chor 2013) at the sector level for 2004 and 2013 using the national and regional I-O tables. The downstreamness index measures the average distance from primary input 11
, faced an increased risk of falling into poverty which meant that they were no longer part of the middle class. The values reported in table 2 show that in 10 countries, the middle-class lower threshold increased by more than 10 percent between 2008 and 2018. The largest increase was in Bulgaria, one of the poorest countries in Europe, where the predicted income associated with the lower middle-class threshold soared 70 percent, increasing from $30 to $51 per day. Within Central Europe and the Baltic countries, the lower threshold increased from $42 to $52 per day in Latvia and from $44 to $50 per day in in Estonia. In the rest of Europe, substantial increases were seen in Austria, Denmark, Italy, the Netherlands, and Norway. Only in Cyprus did the threshold drop by more than 10 percent. That drop likely reflects the sharp decline in incomes after the global financial crisis, which disproportionately affected the bottom half of the

> 12 In Table A.5 we report the change of the vulnerability threshold between 2015-18 and 2017-20, that is, including the effect of the COVID-19 pandemic. In most countries for which data is available there was a decrease in the vulnerability threshold in that time period, most probably explained by the social protection mitigation measures implemented during 2020. There is not yet a 4-year panel in EU-SILC that completely excludes the years of the pandemic, so it is still early to know whether this change is permanent or not.

10
The fact that the program impact is not heterogeneous along the gender and the student socio-economic dimension alleviate equity concerns about the impact of the program. Nevertheless, the program could still be disproportionately benefiting high SES students if they are disproportionately represented in the group of high performers. We assess in our sample by looking at the composition of students who scored credit and above in GABECE. Column 1 in Table A6 shows that the share of the student who scores credit and above is similar in each of the first three quartiles of student SES (ranging 42-43%) and 53% in the top quartile. There is also a 5 percentage point gap in favor of boys (47% vs 42%). Both of these variations are not disproportionately large as anticipated. Furthermore, column 2 in the same table shows that of all the students who scored credit and above in GABECE, 25.4% are in the bottom quartile of SES and 61% are in the bottom half of SES, of which the gender composition is about balanced as shown in the bottom panel of Table A6. Surprisingly, there are more girls than boys high performers at baseline. Of all the high performers at baseline, (60.4% were girls and 39.6% were boys, and thus the program benefited more girls than boys. If the observation from our sample is true at the national level, then we can conclude that the program does not pose a significant equity issue in terms of student socio-economic background and does benefit girls more than boys. This may not be surprising because the poor kids who make it to high school are often among the high performers.

# 5. Qualitative Assessment

To arrive at a fuller picture of the program’s impact and draw relevant policy implications, we complement the rigorous estimation of the program’s impact on test scores with qualitative analysis. The qualitative analysis includes a qualitative survey, as well as focus group interviews with teachers, school administrators, and the Ministry of Education officials. The
responses and variance decompositions are evaluated at the forecast horizons of 2, 4, 6, and 8 quarters (Table 1). Quarterly data from 1991Q1-2015Q3 are used in the estimation.

- **Estimating weights for dispersion** _._ The weight assigned to each risk factor is here estimated as the share of the global growth forecast error variance explained by each of the risk factors (Table 1). The variance decomposition is derived from a recursive identification based on the assumption that oil prices are mostly driven by supply factors as in the spillover analysis of World Bank (2016a). The ordering of the variables used in the Cholesky decomposition is as follows: global term spread, S&P500, oil prices, and global growth. The derived dispersion of the forecast error distribution is scaled to match the mean square root of past forecast errors since 2010.

- **Estimating weights for skewness** _._ The weight assigned to each risk factor in the estimation of global growth skewness is estimated using the impulse responses of global growth at the forecast horizons of interest (2, 4, 6, and 18 quarters) from the same VAR (Table 2). The Cholesky ordering is the same as for the estimation of weights for dispersion.

As a result, each of these recovered statistics characterizing the shape of the global growth forecast distribution is time-varying, reflecting shifts in the distribution of the underlying risk factors.

# **4. Balance of risks to global growth**

The resulting fan chart shows confidence intervals at 50, 80 and 90 percent probability around the growth projections in the June 2016 _Global Economic Prospects_ (Figure 2). The fan chart uses information available up to May 2016. It illustrates that the uncertainty surrounding global growth forecasts has risen marginally above the long-term average and is increasingly tilted to the downside.

The period around the global financial crisis in late 2008– early 2009 illustrates the risks captured in the fan chart. After sharp corrections in most asset prices (financial,
Assessing the Value of Market Access from Belt and Road Projects<sup>1</sup> Tristan Reed Development Research Group, World Bank Alexandr Trubetskoy Department of Statistics, University of Chicago

# Updated February 2021

JEL Classification Codes: R42, O18 Keywords: market access; transportation infrastructure; cost benefit analysis; Belt and Road

> 1 GIS shapefiles of the database of Belt and Road Initiative projects developed for this study are available here: https://github.com/sashatrubetskoy/bri_market_access. We thank Sam Asher, François de Soyres, Caroline Freund, Indermit Gil, Bert Hofman, Gabriel Kriendler, Somik Lall, Martin Melecky, Megha Mukim and Michele Ruta and participants at the Harvard Cities and Development conference for helpful comments. Robert Mwanamanga assisted with the compilation of Appendix A. The views expressed in this paper are those of the authors and do not necessarily represent those of the World Bank Group or its Directors. Correspondence to: treed@worldbank.org.
to bunch below the standard business tax rate threshold. Yet, it is not easy to compare the effective tax rates between the presumptive tax and the standard business tax because in the presumptive tax system, business expenses, such as labor costs, materials, and depreciation of assets, cannot be deducted from the taxable income when it is assessed on turnover.<sup>16</sup> In the ESS data, profits could not reliably be estimated because there is fungibility between business expenses and household expenses, which makes the effective tax rates comparison between the two tax systems difficult. There are also compliance costs related to paying the standard business taxes because of the requirement for financial record-keeping and the possible need for hiring accountants to comply with taxes. The tax rate of 2.5% is also the percentage that the government could charge without losing tax revenue, which is discussed in more detail in Appendix 9.

Table 7 shows the estimated effective tax rates of this hypothetical system and Figure 3 disaggregates the results by turnover quartiles. There are three main findings from the analysis. First, the tax burden is lower for female than male businesses (row 1) because most female enterprises earn less than the 50,000 birr threshold. Second, the effective tax rates for all businesses (row 1) as well as for those conditional on positive tax payments (row 2 in Table 7) under the hypothetical presumptive tax are lower compared to the effective tax rates in the current system (rows 2 and 4 in Table 6, respectively). This is largely due to the exemption of low revenue firms, which reduces the overall effective tax rates. Third, the hypothetical presumptive tax is more progressive, as larger firms would face higher burdens than smaller firms (Figure 3) and larger firms would pay more taxes in the hypothetical system than under the existing system. The results are similar when a 2% tax on turnover is used, except that the effective tax rates are 2% for firms with positive tax liabilities
(11.33)|−0.89|22.14<br>(11.33)|19.28<br>(12.19)|2.85<br>***|
|Proportion of men|0.51<br>(0.50)|0.67<br>(0.49)|−0.16<br>***|0.58<br>(0.49)|0.66<br>(0.47)|-0.09<br>***|
|Proportion with<br>vocational training|0.36<br>(0.48)|0.05<br>(0.22)|0.31<br>***|0.34<br>(0.47)|0.11<br>(0.32)|0.23<br>***|
|Number of workers|564|824||1429|2664||

_Source:_ Author's calculations based on VLSS and VHLSS data.

# _<<B>>The “public sector dummy approach”_

We first consider the simplest method to compute the public-private wage gap, controlling for the differences in the workers' characteristics between the two sectors. This method implicitly assumes that the returns to the workers' characteristics are the 17
# 1. Introduction

State-owned enterprises (SOEs) play a large and increasing role in the global economy. According to the International Monetary Fund (IMF),<sup>1</sup> global assets of SOEs more than tripled between 2000 and 2018, from US$13 trillion to US$45 trillion, about half of global GDP. SOEs are also large employers in many countries. For instance, despite several decades of privatization, SOEs still account for half of all public employment in EBRD countries (EBDR, 2021). An analysis of the role of firms with state participation across countries requires firm-level data. Aggregate data on SOEs can provide a fair overview of the relative size of the state footprint in terms of GDP or total employment. However, such aggregate figures provide overall estimates that are not precise enough for meaningful cross-country comparisons, due to differences in state ownership definitions and aggregation considerations. Hence, researchers analyzing government public provision of goods and services through SOEs, and the effect of SOEs on productivity, growth, and competitiveness, require access to firm-level data using a consistent definition of SOEs to allow for better cross-country comparisons. Firm-level data are also essential to analyze SOEs’ economic implications, design SOE reform plans, and assess the adjustment costs and mitigation measures considering the interrelations of SOEs across multiple sectors in the economy. Firm-level data also allows for more targeted programs, for example regarding divestiture measures, management arrangements, corporate governance, and regulatory reforms. However, reform strategies are often constrained or delayed by the lack of readily available information.

This paper describes a novel methodology to develop a harmonized cross-country firm-level database of state-owned enterprises, the Businesses of the State (BOS) database, which maps the state footprint within the corporate sector and across economic activities.<sup>2</sup> The database is based on a uniform definition, a global data source, and a standardized protocol to include supplementary information from complementary sources beyond ORBIS (e.g., business registries, central depositories
19 prices, 2020/21 prices, 2010 prices</mark> respectively <mark>. For the Malawi</mark> IHPSs and IHSs, c <mark>onsumption data are deflated to 2013 prices and 2010/11 prices r</mark> espectively.

> 12 For Bangladesh and Nigeria, we employ the international poverty line for analysis since official poverty data for these countries are based on different data sources that are not available to us, such as the Bangladesh <mark>Household Income Expenditure Survey (HIES) and the Nigeria Living Standards Surveys (NLSSs). However, note that Nigeria’s national poverty line calculated using NLSS 2018/19 is close to the international poverty line of $1.90 per person per day in 2011 PPP (Lain and Vishwanath, 2022). Furthermore, data comparability issues also exist with various rounds of the Bangladesh HIESs (Fernandez</mark> _<mark>et al.</mark>_ <mark>, 2024).</mark>

14
one or all of these tracks may be undesirable or impossible, due to insurmountable barriers to participation, if participation entails risks for the displaced, or when the absence of democratic structures to elect IDP leaders means that IDP interests are compromised. In situations where it is not possible to include Migration Online; Leckie, S. d. (2003), _Returning Home: Housing and Property Restitution Rights of Refugees and Displaced Persons,_ Transnational Publishers, pp. 15-16.

42 The right to return is founded enshrined by the right to return is Article 12 of the International Covenant on Civil and Political Rights, which protects liberty of movement and the freedom to choose one’s own residence. See: W Kälin, Guiding Principles on Internal Displacement: Annotations (rev edn, American Society of International Law and Brookings–Bern Project on Internal Displacement 2008) at 126, 129–30.

43 Principle 28 of the Guiding Principles on Internal Displacement states that IDPs ‘have the right to choose freely between return, local integration or resettlement.’ See: Francis Deng, _Compilation and Analysis of Legal Norms, Part II: Legal Aspects Relating to the Protection Against Arbitrary Displacement_ , U.N. Doc. E/CN.4/1998/53/Add.2 (11 February 1998). 44 International Covenant on Civil and Political Rights (adopted 16 December 1966, entered into force 23 March 1976) 999 UNTS 171 at Article 12(3). See also: UN Human Rights Committee, ‘General Comment No 27: Article 12 (Freedom of Movement)’ (2 November 1999) UN Doc CCPR/C/21/Rev.1/Add9.

> 45 Purkey A. (2016) _Justice, reconciliation, and ending displacement: Legal empowerment and refugee engagement in transitional processes_ , Refugee Survey Quarterly, 35, 1-25.

> 46 Fagen, P.W. (2009). _Peace processes and IDP solutions_ . Refugee Survey Quarterly, 28(1), pp.31-58.
but are further from the more recent work of Raffler et al. (2019) who also examine community monitoring of health clinics in Uganda, but find weak effects. A possible reason for these contrasting results may lie in the baseline health conditions under which the interventions were implemented. The Björkman and Svensson (2009) intervention was implemented in 2004-2005, when under-5 mortality in Uganda stood at 117 per 1,000 live births. Analogously, our accountability interventions in Sierra Leone were implemented when under-5 mortality stood at 149.8 per 1,000 live births. In contrast, the more recent study in Uganda implemented community monitoring over 2014-2016, when infant mortality had fallen to 59 per 1,000 live births. The lack of consistent effects in the latter context suggest that it may be more difficult to improve health outcomes when baseline conditions themselves have already improved substantially (Raffler et al. 2019).<sup>7</sup> The interventions we analyze harness social accountability and competition between providers to improve interactions between providers and the communities they serve. Other related approaches to improving health service delivery have examined the effects of financial incentives (Miller et al. 2012; Olken et al. 2014; Singh and Mitra 2017); combined technological monitoring with financial incentives (Banerjee et al.

> 6 Relatedly, Bandiera et al. (2019) find that an empowerment program for young women in Sierra Leone increases their capacity to cope with disruptions caused by the Ebola crisis.

> 7 When implemented under challenging baseline conditions, the effects of community monitoring appear to persist. A follow-up study of the original Björkman and Svensson (2009) intervention suggests a lack of convergence in health outcomes up to four years later (Björkman Nyqvist et al. 2017).

5
# **IV. A COMPARITIVE ASSESSMENT OF TPP COMMITMENTS**

The main findings are summarized in Figure 1 below. In the following sub-sections, we will compare TPP commitments with Doha offers, the best commitments under PTAs and applied policies.

# **Figure 1: STRI for TPP Members - Doha Offers, ‘Best PTA’, TPP Commitments and Applied Policy of 2015**

<!-- Start of picture text --> Overall STRI: Doha, PTA, TPP and applied policy<br>BRN MYS CHL SGP MEX CAN VNM USA PER JPN AUS NZL<br>Doha PTA<br>TPP Policy '15<br>Note: Absense of a bar means there are no restrictions (STRI score is zero)<br>100<br>75<br>50<br>25<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Note 1: BRN =Brunei, MYS =Malaysia, CHL = Chile, SGP= Singapore, MEX= Mexico, CAN= Canada, VNM = Vietnam, USA =United States, PER = Peru, JPN= Japan, AUS = Australia; NLZ = New Zealand. Note 2: The country STRI is an aggregate score of indices for services sectors covered, the country STRI comprises STRIs of five major sectors, financial, telecom, retailing, transportation, and professional services. These five sectors contain different subsectors and the relevant modes. Source: The World Bank TPP database, 2016.

# _Doha offers and the TPP commitments_

The TPP commitments of the majority of countries are a significant improvement on their Doha offers. On average, TPP countries’ commitments improved upon the Doha round offers by about 40 percent - the TPP members’ average Doha offer score was 46 and the TPP commitments score was 28. The largest improvements upon the offers are from Chile, Brunei, Mexico, Peru and 8
|2.2E+11|0.42|0.69|0.05|0.15|2|3|3|
|382|Machinery, except electrical|6989|1.5E+05|0.43|1.1E+12|0.67|0.67|0.25|0.25|3|4|4|
|383|<br>Machinery, electric|6513|1.5E+05|0.46|1.1E+12|0.74|0.81|0.22|0.26|3|4|5|
|384|Transport equipment|5714|2.0E+05|0.33|1.0E+12|0.49|0.76|0.09|0.16|2|5|4|
|385|Professional & scientific equipment|5195|1.1E+05|0.28|2.8E+11|0.45|0.81|0.08|0.24|3|4|5|

Source: Author's analysis based on data described in text. Notes: See Table S1a. Column 3 provides route-level observation counts by industry after first differencing. Columns 4 and 5 provide labor productivity estimates from the UNIDO database. Columns 6-8 report statistics from the World Trade Flows database, with volumes expressed in US dollars. Industries 356 and 390 are excluded. Columns 9-10 report the share and growth rate of patenting in United States that is used for the technology transfer measures. The last columns report the quintile to which the industry is assigned for the Rybczynski effect controls of country time trends x industry quintiles.
_(Table C.4 continued.)_

|Involvement<br>sions (Index)|in|deci-|From the phone surveys: Index of woman’s involvement in seven<br>decisions in the household:<br>1. Decisions about spending money on food,<br>2. Decisions about how woman spends leisure time,<br>3. Decisions about who does the household chores,<br>4. Decisions about medical or health care,|
|---|---|---|---|
||||5. Decisions about woman visiting a friend in the neighborhood,<br>6. Decisions about woman taking a bus to run an errand,|
||||7. Decisions about sending money or gifts to the family,<br>For each decision, an ordinal variable was coded such that<br>1=Woman is involved,<br>0=Woman is not involved.<br>I then compute a standardized index from these dummy variables<br>followingAnderson(2008)and O’Brien(1984).|
|softonly|||Dummy variable indicating that woman in the selected group was<br>assigned to the soft-skills only training group. Only available for the<br>selectedgroup.|
|hardsoft|||Dummy variable indicating that woman in the selected group was<br>assigned to the soft- & hard-skills training group. Only available for<br>the selectedgroup.|

72
and 36 observations of extension activities. Round one of the primary qualitative data collection took place during August-September 2021, and round two took place during May-June 2022. Both rounds of qualitative data collection were embedded in an impact evaluation of a video-based NSA intervention that is part of the second phase of Ethiopia’s Agricultural Growth Project (AGP2). AGP2 aims to increase agricultural productivity and commercialization of smallholder farmers in Ethiopia through a variety of interventions. Among other things, AGP2’s goals include strengthening the implementation of nutrition sensitive interventions in the agriculture sector with a focus on improving the consumption of diversified diets.

Digital Green, an international non-governmental organization, has an ongoing partnership with Ethiopia’s Ministry of Agriculture and Natural Resources. Digital Green trains the ministry’s extension agents at the woreda (district) level to produce and screen localized videos that are shown through their existing extension system. Under AGP2, Digital Green worked with the ministry to expand into 20 selected woredas with a modified curriculum to include agriculture and nutrition information. Dissemination of videos was done jointly by the agricultural and health extension workers.

The impact evaluation seeks to assess the impact of leveraging video-enabled extension to improve nutritional outcomes by diversifying and improving dietary consumption through the take-up and adoption of new practices, and changes in household behaviors. For the impact evaluation, a total of 200 kebeles (clustered villages) in 20 selected AGP2 woredas were randomized into three experimental treatment arms: 4
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**D. Differential between EMDEs and AEs in per capita investment and GDP growth**

## **E. Labor productivity growth in EMDEs**

<!-- Start of picture text --> Percent Percent<br>Investment growth<br>12 6<br>Labor productivity growth (RHS)<br>10 5<br>8 4<br>6 3<br>4 2<br>2 1<br>0 0<br>2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

## **F. Labor productivity growth in EMDEs with high and low investment growth, 2010‐15**

<!-- Start of picture text --> Percent<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>Low investment High investment<br>growth growth<br><!-- End of picture text --> Sources: Haver Analytics, International Labor Organization, International Monetary Fund, Penn World Table, World Bank. A. Number of years needed to catch‐up with 2015 real per capita GDP level in the United States, assuming average growth rates over each period denoted for each group. B. Unweighted averages. TFP calculated as residual from the growth‐accounting framework in Didier et al. (2015). Dashed lines indicate long‐term average for 1990‐2008 for each respective group. C. Correlation of change in investment growth from 2010‐15 with change in TFP growth over the same period. Red dotted line denotes the linear regression line. Includes 40 EMDEs. D. Weighted averages. Difference between EMDEs and