rafmacalaba/data-use-mentions-extended huggingface dataset:
2013 2015 2016<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2014<br><!-- End of picture text --> Source: Consensus Economics; Dincer and Eichengreen 2014; International Monetary Fund; World Bank. Note: EMDEs = emerging market and developing economies.

A.B.E.F. Inflation expectations are five-year-ahead expectations of annual inflation.

A. Based on a sample of 24 advanced economies during 1990H1-2018H1.

- B. Based on a sample of 23 EMDEs during 1995H1-2018H1.

- C. Based on a sample of 24 advanced economies.

D.F. Based on a sample of 23 EMDEs. F. High (low) transparency countries are defined as those with central bank transparency above the 75th (below the 25th) percentile of EMDEs.

20
means in the Anchored Test X and Reference Test Y as follows: where 푙푖푛푘푖푛푔푌푝푙(푋) is the equation for converting observed scores on Anchored Test X to the scale of Reference Test Y. A limitation of this methodology is that it is sensitive to the scale of each test being similar. We address this potential limitation by using tests with a mean of 500 and standard deviation of 100.<sup>2</sup> This ensures that ratios capture differences in test difficulty rather than differences in scaling.

Below we specify each step in the process. The conversion rate is calculated as follows. Mean scores for tests X and Y, 휇(푋) and 휇(푌), are first calculated for a given round r, subject s and level l. We consider tests to be in the same round if they are five years apart and optimize to have the rounds as tight as possible. Most often this translates to within one to two years. In some cases, this extends to three to five years apart. In a few

> 2 In rare cases where tests do not have initial scores on this scale, we rescale the test using the microdata.

10
-9 -

availability of qualified faculty. But the poor provinces are deprived of more qualified faculty. Based on relative faculty ratio (RFR) the richest quintile of provinces--Beijing, Shanghai, Zhejiang, Liaoning clearly have an oversupply of professor and assistant professor, as the value of RFR far exceeds one (1.60 and 1.20); conversely, the poorest quintile of provinces--Guizhou, Xinjiang, Anhui, Henan, Guangxi and Sichuan --have an undersupply of such more qualified faculty (Table 12, Annex 1).

# **Outcomes and Efficiency**

This section focuses on outcomes that are internal to the educational system-specifically, cohort survival rates and transition rates, and outcomes that are external to the system, such as private and social rates of return to educational investments. Both sets of outcomes are critical, since together they help determine the global efficiency of the educational system. Yet, few efforts are made to assess the impact of public policy on these outcomes in China. The dearth of such assessments is due in part to the scarcity of appropriate data and in part to the fact that these outcomes were not important to policymakers under the command economy.

(a) Internal **efficiency.** Conventionally, an education system is internally efficient if it generates the maximum output at minimum cost. Outputs are measured by such indicators as transition rates and cohort survival rates or the proportion of students who start grade 1 and complete each subsequent cycle, students' cognitive and technical skills, scientific knowledge, improved and the like. Ideally, a combination of individual survey and time-series data are used for this analysis. However, in the context of the present study, readily compiled provincial-level time-series data are used to provide results that would be helpfuil for policy interventions for improving system's internal efficiency which also improves quality.

At the aggregate level, educational outcomes vary directly with income. The transition rates from primary to secondary and secondary to higher level of education are higher in
related economic disruptions? We look into adjustments on employment, digitization and the propensity to pivot product-mix. _Third_ , what is the role of policy in supporting South Asian firms during crisis and recovery? This paper argues that size and sector are imprecise targets for policies supporting firms, given the wide variation in the impact of the pandemic within these groups. Results underscore the possible mismatch in the perspective of the firms and policy makers, with firms’ preferred instruments being attributed predominantly to the most significant channel of transmission of the shock as opposed to their external attributes. Consequently, it provides a framework for customizing firm support by looking into the channels of transmission of the shock.<sup>2</sup> In addition to its impact on the region’s economies, the COVID-19 pandemic may also aggravate the existing differences in dynamics by firm size, exacerbate gender inequality and expose the vulnerability of export industries. For instance, self-employed and micro-enterprises represent over 80% of employment in South Asia, compared to about 50% in the East Asia and Europe and Central Asia regions (Mukhtarova, 2020). In the BPS data, only 9% of South Asian firms are women-led, compared to 35% and 36% in EAP and LAC, respectively.<sup>3</sup> Within sectors, COVID-19 paralyzed the export-oriented tourism in Nepal and caused a massive decline in Bangladesh’s ready-made-garment

> 1For information on BPS, see Apedo-Amah et al. (2020) and Appendix Figure A.1 for the timing of surveys in SAR. Although the survey was conducted in India along with other SAR countries, we do not divulge country specific figures for any of the outcomes.

> 2COVID-19 affects firms through five distinct channels: (i) reduction in demand (ii) disruption in production and supply chains (iii) unavailability of liquidity and financial services (iv) uncertainty of the extent and time horizon of the shock, (v) lock-down restrictions (Apedo-Amah et al., 2020).

> 3Moreover
;<br>|
|�<br>_Crime_: security costs, cost of crimes, and use and performance of police services;<br>�<br>_Capacity, innovation, and learning_: utilization, new products, planning horizon, sources of<br>technology, worker and management education, and experience; and<br>�<br>_Productivity information_: employment level, and balance sheet information (including income,<br>main costs and assets).<br>The sample size was relatively large with 408 firms and includes a representative sample of the<br>composition of the Serbian economy. The PICS survey was undertaken in the aftermath of the<br>assassination of the Prime Minister at a time when the country was experiencing a great deal of<br>uncertainty, so the survey results must be viewed in this light.|
|**Business Environment and Enterprise Performance Survey (EBRD and World Bank):**The<br>BEEPS utilizes a standard survey instrument applied to nearly all countries in Eastern Europe and<br>Central Asia, thus ensuring comparability. BEEPS II is a follow-up of an earlier BEEPS effort. In<br>Serbia the BEEPS II survey had a sample size of approximately 230 firms in 2002 (BEEPS I did not<br>cover Serbia). Generally, the sampling strategy in BEEPS II differs from that of the PICS, as the sample<br>design of the BEEPS is highly skewed toward smaller firms.|

# **Total Factor Productivity in the International Context**

Increases in the level of total factor productivity (TFP) of firms are an essential feature of economic growth: rich countries are countries with firms that are highly productive. This section demonstrates how TFP at the firm level is related to GDP per capita at the country level, and compares the level of TFP in Serbian firms with the levels in comparator countries. These comparisons use the large PICS-BEEPS dataset of about 27,000
data limitations, they only include capital cities in seven countries.

The limited attempts to measure multidimensional job quality in developing countries reflect the lack of comprehensive quality data in these countries. This paper contributes to the literature by developing a multidimensional JQM that is global in scope and applicable to developing countries across all regions. Similar to Brummund, Mann, and Rodríguez-Castelán (2018), it proposes four dimensions of job quality: income, stability, benefits, and working conditions. Given the data limitations, especially in the poorest countries, the proposed JQM measure allows a greater flexibility in the choice of indicators, which is discussed in the methodology section. It also focuses only on wage employment because information on the self-employed is lacking in many countries.

# **3. Job Quality Measure Methodology**

# **a. Dimensions of job quality**

Over the past decade, there has been a proliferation of job quality frameworks proposed by international panels of experts both on the macro and micro levels. Prominent efforts include decent work indicators (ILO 2013), the job quality index (Eurofound 2012), indicators of job quality (European Union Employment Committee), good jobs for development (World Bank 2012), quality of employment (UNECE 2015), and the OECD job quality framework (Cazes, Hijzen, and Saint-Martin 2016).<sup>9</sup> The main dimensions that overlap across these frameworks are (1) earnings, (2) benefits (social protection), (3) job security, and (4) working conditions (physical and mental well-being). Table 1 shows the types of indicators at both the micro and macro level that are common in the literature on the measurement of these dimensions.

> 8 In González et al. (2021) having a contract applies only to wage employees, while, among the self-employed, the indicator is measured as lack of higher education.

> 9 For the indicators of job quality, see “Annex 1: Definitions of Job
particulates (PM2.5) use the MODIS measure of aerosol optical depth (AOD) and two S5P measures: the absorbing aerosol index (AI) and tropospheric NO2 density. We obtain highlysignificant results for MODIS AOD and S5P NO2, controlling for the effects of three weather variables: temperature, humidity and wind speed. We perform an equivalent test for the CAMS-3 NO2 measure and find high significance for S5P’s tropospheric NO2 density.

Since traffic congestion is a major source of air pollution in the study area, we use Google Traffic to develop a spatial panel database of high-resolution traffic congestion measures for the Dhaka region. We test the relationship between traffic congestion and air pollution by matching hourly pollution data from CAMS-3 with hourly weather data and hourly Google Traffic congestion in the surrounding .01-degree cell (Figure 1). Controlling for the weather variables, we obtain highlysignificant results and predictive fits that exceed the accuracy of satellite-based measures in the majority of cases.

We conclude that all three global technologies can provide useful information for air pollution monitoring in the Dhaka metro region. Our results also suggest that they should be viewed as complements rather than substitutes. MODIS, S5P and Google Traffic all track CAMS-3 PM2.5 with high significance; S5P and Google Traffic perform similarly for CAMS-3 NO2. Google Traffic can track air pollution in metro Dhaka at higher spatial resolution, but for vehicular sources only. MODIS and S5P capture the effects of emissions from other sources. The two satellite platforms also measure pollution at all locations, while Google Traffic is often confined to the major metro areas of developing countries.

17
than 0.5% of Chinese overseas lending since 2000 and their average volume is just US$ 12.9 million, with a median of US$ 6.3 mn (Horn et al. 2021). The loans are canceled regularly as part of China’s development aid program, often in the context of high-level diplomatic visits and largely independent of the economic cycle of the debtor country (Gardner et al. 2020; Acker et al. 2020; Appendix I).

To place Chinese debt restructurings in the broader context of debt workouts, we compare our data with a chronology of credit events involving private or other official creditors. For private external creditors we use the updated database of sovereign debt restructurings with private external creditors (bondholders and banks) by Asonuma and Trebesch (2016), which covers 192 restructurings between 1975 and 2021. To add restructurings with other official creditors, we use data on the Paris Club deals from Cheng et al. (2019), which we update to bring the total tally of Paris Club restructurings to 460 cases from 1956 to 2021.<sup>6</sup> It is important to note that there is no systematic database that covers debt restructurings with official creditors outside of Paris Club umbrella. To date, restructurings of “South to South” loans remain largely undocumented, despite evidence that such lending is on the rise (Cerutti et al. 2020; Broner et al. 2020). More details are provided in Appendix I.

# **3. China’s overseas loans and “hidden defaults”:  Stylized facts**

**_Frequency and incidence._** Figure 1 reveals that restructurings of China’s overseas loans now account for a major fraction of sovereign credit events in developing countries. Since 2008, we record 71 restructuring operations involving Chinese loans, but only 21 on external bonds or loans involving private creditors. The high number of recorded Chinese loan restructurings poses an important knowledge gap, given that relatively little is known about
preference. Evaluation of the _Apni Beti Apna Dhan_ scheme that was implemented in Haryana in 1994 is a case in point (see Section A3, Appendix).

Holla et al.’s (2007) evaluation of the scheme draws on pooled birth history data from the first two rounds of NFHS, and a difference-in-difference approach that uses variation in the eligibility criteria, and the implementation of the program in Haryana versus neighboring states. The results from the evaluation suggest that the program had a positive effect on the SRB (probably due to the cash payment that is made quickly after the female child’s birth). However, the positive effect of the program for girl children erodes over time despite the staggered payments that are promised if the girl remains alive, unmarried, and fulfills educational and health requirements by age 18. Vaccination rates for girl children did not improve following the initiation of the program, and as a result, the program had a negligible effect on improving child sex ratios.

Sinha and Yoong (2009) identify more successes of the program, but they also find that these successes were limited. They use pooled birth-history data from the NFHS, but from all three rounds (with three rounds of data they can evaluate outcomes for girls in the short and medium term), and a difference-in-difference approach that exploits variation in the eligibility criteria. Their evaluation results suggest that while beneficiary households were more likely to give birth to girls, and ensure their survival, there is mixed evidence on whether these girls saw improvements in their quality of life. Beneficiary households made more health investments in their young daughters, however this did not necessarily translate into better health status for the girls in the medium term. Furthermore, school-age girls in beneficiary households were not more likely to attend school than their counterparts in non-beneficiary households; however, if they did start attending school, they were more likely to continue attending school. The program does not have a clear, positive effect on mothers’ preference for girl children either.

# _China_
8 Total household expenditures are the same as those used to calculate the poverty rate in the countries and, where temporal and spatial deflators are available, adjusted accordingly. Annual expenditures per capita in 2022 U.S. dollars ranged from US$318 in Ethiopia to US$4,000 in Mauritius. Per capita expenditures were converted from the local currency to U.S. dollars using the official exchange rate and adjusted to 2022 U.S. dollars. As explained in annex 1, some governments maintained substantially over-valued exchange rates, making per capita expenditures much higher than those based on market-based rates. Because electricity prices are unaffected by spatial deflators and LPG prices are also unaffected in many countries, when they were used in calculations—such as comparing spending on LPG and electricity by various households—their values were not deflated. Expenditure shares were similarly calculated using un-deflated values.

Although national household expenditure surveys have the advantages mentioned in the previous section, there are limitations:

- For energy items, households are typically asked how much they had spent on a given item in the previous month or in the last 30 days or in a typical month, all of which are intended to calculate the average monthly household expenditure on the item. However, it is clear from the responses that many report how much they had paid for the last purchase or in the last month. As an illustration of the pitfalls of this approach, if a household refills an LPG cylinder every six weeks, depending on the timing of the survey, the respondent may report zero spending on LPG. A clear example of data inconsistencies arising from this type of problem is the Mexican household survey, in which the relatively large sizes of LPG cylinders and tanks commonly used in Mexico appear to result in many LPG-using households reporting zero expenditure in the previous month. For example, in the 2008 survey, 80 percent of households reported that LPG was their primary cooking fuel but only 60 percent had reported non-zero expenditures because they were asked to tell the enumerator how
areas, which is also where most wage workers are located. Considering that 59% of the population of SubSaharan Africa lives in rural areas,<sup>22</sup> the concentration of teachers in urban areas demonstrates a potential mismatch, consistent with findings from previous research (Bashir et al. 2018; UNESCO, 2019).<sup>23</sup> In terms of school type, about 20% of teachers work in private schools in these countries.<sup>24</sup> This is consistent with the rise of private schools in Sub-Saharan Africa in recent years (World

> 20 Note that we include these neither in the group of teachers nor in the “other wage workers” group.

> 21 We present more detail on the age distribution of teachers in Appendix Table A6. Some countries, like Burkina Faso, have relatively low variation in teacher age; others, like DR Congo, have higher variation. The cross-country average of the 25<sup>th</sup> percentile in teacher age is 31 years old, and the average 75<sup>th</sup> percentile in teacher age is 45 years old.

> 22 Data from World Development Indicators (World Bank, 2021).

> 23 The surveys only collected the area an individual lived but not necessarily where the person worked, which would be the ideal way to distinguish whether teachers both lived and worked in urban areas or lived in urban areas but traveled to work in rural areas. The latter is not uncommon in many African countries, which means that our data are not ideal for analyzing urban-rural differences in teacher labor markets. Furthermore, many countries have centralized allocation of teachers rather than local hiring.

> 24 Teachers were considered working in private schools unless they reported government (at any level) or any public entity as their employer.

13
Policy Research Working Paper 7922

# **Abstract**

The automation and out-sourcing of routine, codifiable tasks are seen as driving polarization in labor markets in high-income countries. This paper first offers several explanations for why developing countries might show differing dynamics, at least for the present. Census data then confirms this, showing on average no evidence of polarization in developing countries. However, incipient polarization in a few countries as well as major drives to automate in some large, labor intensive producers suggests this may not remain the case. This raises concerns first about the impact on equity within those countries, but second the possibility that the traditional flying geese pattern”— whereby low skilled jobs are progressively off-shored to poorer and poorer countries—may be short circuited.

This paper is a product of the Equitable Growth, Finance and Institutions Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<mark>WPS3564</mark>

# **Trade Costs and Location of Foreign Firms in China**<sup>**♦**</sup>

Mary Amiti<sup>*</sup> International Monetary Fund and CEPR Beata Smarzynska Javorcik<sup>#</sup> World Bank and CEPR

**Abstract:** This study examines the determinants of entry by foreign firms, using information on 515 Chinese industries at the provincial level during 1998-2001. The analysis, rooted in the new economic geography, focuses on market and supplier access within and outside the province of entry, as well as production and trade costs. The results indicate that market and supplier access are the most important factors affecting foreign entry. Access to markets and suppliers in the province of entry matters more than access to the rest of China, which is consistent with market fragmentation due to underdeveloped transport infrastructure and informal trade barriers.

## JEL Classifications: F1 F23.

Keywords: foreign direct investment, trade costs, market access, supply access.

World Bank Policy Research Working Paper 3564, April 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ♦ We would like to thank Caroline Freund, Mary Hallward-Driemeier, Will Martin, Stephen Redding, John Romalis, Tony Venables, Shang-Jin Wei, and participants at the Workshop on National Market Integration (World Bank and Development Research Council) in Bejing, September 2003, the Workshop
# Zambia’s Infrastructure: A Continental Perspective

## **_Vivien Foster and Carolina Dominguez_**

### **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably, Kapil Kapoor (country manager), Boris Enrique Utria (Sector Leader), Gael Raballand (roads), Pierre Pozzo di Borgo and Lucien Andre Aegerter (railways), Diep Nguyen-Van Houtte (power), Midori Makino and Elisabeth Sherwood (water), and Gareth Locksley (ICT).