rafmacalaba/datause-encoder-data huggingface dataset:
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1558 In Search of Price Rigidities<br>(Recent Sector Evidence from<br>Argentina|Jacques Morisset|December 1995|N. Cuellar<br>37892|
|WPS1559 Have Transport Costs Contributed<br>to the Relative Decline of Sub-<br>Saharan African Exports? Some<br>Preliminary Empirical Evidence|Azita Amjadi<br>Alexander J. Yeats|December 1995|S. Lipscomb<br>33718|
|WPS1 560 Trade and Fluctuations|Aart Kraay<br>Jaume Ventura|December 1995|R. Martin<br>39065|
|WPS1561 Income Inequality and Aggregate<br>Saving: The Cross-Country Evidence|Klaus Schmidt-Hebbel<br>Luis Serven|January 1996|E. Khine<br>37471|
|WPS1562 Catching Up with Eastern Europe?<br>The European Union's Mediterranean<br>Free Trade Initiative|Bernard Hoekman<br>Simeon Djankov|January 1996|F. Hatab<br>35835|
|WPS1563 Equity and Growth in Developing<br>Countries: Old and New Pespectives<br>on the Policy Issues|Michael Bruno<br>Martin Ravallion<br>Lyn Squire|January 1996|P. Sader<br>33902|
|WPS1564 From Plan to Market: Patterns of<br>Transition|Martha de Melo<br>Cevdet Denizer<br>Alan Gelb|January 1996|C. Rollison<br>84768|
|WPS1565 Housing Finance in Transition<br>Economies: The Early Years in<br>Eastern Europe and the Former<br>Soviet Union|Bertrand M. Renaud|January 1996|R. Garner<br>37670|
|WPS1566 Liquidity, Banks, and Markets:<br>Effects of Financial Development<br>on Banks and the Maturity of<br>Financial Claims|Douglas W. Diamond|January 1996|D. Evans<br>38526|
|WPS1567 Population Growth, Factor<br>Accumulation, and Productivity|Lant Pritchett|January 1996|S. Fallon<br>38009|
|WPS1568 Determinants of Diarrheal<br>Disease in Jakarta|Anna Alberini<br>Gunnar S. Eskeland<br>Alan Krupnick<br>Gordon McGranahan|January 1996|C. Bernardo<br>37699|
|WPS1569 Improving Water Resource<br>Management in Bangladesh|Rashid Faruqee<br>Yusuf A. Choudhry|January 1996|C. Anbiah<br>81275|
# **East Asia – Rise of China with Functional Upgrading: Winners**

In East Asia, China has not only increased its share of overall exports, but it has also significantly diversified its export partners. In 1996, Japan and Hong Kong represented nearly 60% of China’s apparel exports of $25 billion, with the United States and the EU-15 accounting for another 22.6%. By 2008, China’s apparel exports nearly quintupled to $120 billion, and the EU-15 and the United States were the top two export partners, but they accounted for only 39.3% of China’s apparel exports, while Japan and Hong Kong held 21.1% (see Table 6). Thus, China’s top four export markets in 2008 had about the same share of China’s total exports as did Japan and Hong Kong alone in 1996. In this respect, China is lessening its dependence on its traditional export partners while adding important new markets, such as Russia and countries from the former Soviet bloc. This pattern can help China to withstand the demand slump in advanced industrial markets.

It is also important to recognize the size of China’s apparel production for its domestic market. In 2007, the estimated value of sales to the Chinese apparel market totaled $93 billion for the year, indicating that 56% of the overall apparel production activities in China were for local consumers (Clothesource, 2008).

**Table 6: China’s Top 10 Apparel Export Markets: 1996, 2002, & 2008**

[Values in $US Millions; %: Partner’s Share of China’s Annual Apparel Exports to World]

||**1996**|||**2002**|||**2008**|||
|---|---|---|---|---|---|---|---|---|---|
||**Partner**|**Value**|**%**|**Partner**|**Value**|**%**|**Partner**|**Value**|**%**|
|1|Japan|8,170|32.6%|Japan|11,197|27.1%|EU-15|28,760|23.9%|
|2|HongKong|6,600|26.4%|HongKong|7,084|17.2%|USA|18,566|15.4%|
|3|USA|3,187|12.7%|USA|5,325|12.9%|Japan|17,686|14.7%|
|4|EU-15|2,467|9.9%|EU-15|4,672|11.3%|HongKong|7,757|6.4%|
|5|Rep.Korea|649|2.6%|Rep.Korea|2,250|5.4%|Russia|5,640|4.7%|
|6|Russia|635|2.5%|Russia|1,300|3.1%|Kyrgyzstan|5,091|4.2%|
|7|Australia|453|1.8%|Australia|1,027|2.5%|Rep.Korea|3,340|2.8%|
|8|Poland|275|1.1%|Canada|731|1.8%|Kazakhstan|3,022|2.5%|
|9|Canada|267|1.1%|Mexico|618|1.5%|Canada|2,956|2.5%|
|10|Saudi Arabia|192|0.8%|Singapore|617|1.5%|Australia|2,473|2.1%|
||World|25,034||World|41,302||World|120,405||
|**Val**|**ue of Year’s Top**|**10 and %**|**Share of**|**China’s Annu**|**al Appare**|**l Exports**||||
|||22896|91.5%||34821|84.3%||95290|79.1%|
|**Wo**|**rld Apparel Exp**<br>|,<br>**orts & Ch**|**ina’s Sha**<br>|**re**<br>|,|||,||
||166,077||15.1%|203,664||20.3%|361,888||33.3%|

Source: UN Comtrade: SITC code 84 rev. 3: Exports from China World Textile Export Values from WTO Statistics Database

# **South Asia: Steady Winners**

In the long-term, the South Asian countries have all increased market share to both the EU-15 and the United States. Post-MFA and during the crisis, Bangladesh has performed well in both markets, but India, Sri Lanka, and Pakistan have performed differently to the two markets. The U.S. market share and export value of India, Sri Lanka, and Pakistan has been decreasing, 10
**36**

# **References**

Adams, Richard H.J. (2004): “Economic Growth, Inequality, and Poverty: Estimating the Growth Elasticity of Poverty”; _World Development_ 32(12), pp.1989-2004.

Anand, Sudhir, and S.M.R. Kanbur, (1985): “Poverty under the Kuznets Process”; _Economic Journal_ 95(0) (Supplement), pp. 42-50.

- Arellano, Manuel, and Olympia Bover (1995): “Another Look at the Instrumental Variable Estimation of Error-Components Models”; _Journal of Econometrics_ 68, p.29-51.

Bernard, A., and C. Jones (1996): Productivity across Industries and Countries: Time Series Theory and Evidence, _Review of Economics and Statistics_ 78(1), pp. 135-146.

Bond, Stephen. (2002): “Dynamic Panel Data Models: A Guide to Micro Data Methods and Practice” _Centre for Microdata methods and Practice Working Paper 09/02,_ The Institute for Fiscal Studies, Department of Economics, Université Catholique de Louvain.

Bourguignon, Francois, and Christian Morrisson (1998): “Inequality and Development: The Role Dualism.” _Journal of Development Economics,_ 57: 233-257.

Bravo-Ortega, Claudio and Daniel Lederman (2005): “Agriculture and National Welfare around the World: Causality and International Heterogeneity since 1960”; _World Bank Policy Research Working Paper_ 3499, February 2005.

Byerlee, Derek, Xinshen, Diao, and Chris Jackson (2005): Agriculture, Rural Development, and Pro-Poor Growth, Country Experiences in the Post-Reform Era, _Agriculture and Rural Development Discussion Paper 21_ , World Bank: Washington D.C.

Chen, Shaohua, and Martin Ravallion (2004): “How Have the World’s Poorest Fared Since the Early 1980s?” _World Bank Research Observer_ 19(2), pp. 141-170.

Christiaensen, Luc, Lionel Demery, and Stefano Paternostro (2005) “Reforms, Remoteness, and Risk in Africa: Understanding Inequality and Poverty during the 1990s”, in R. Kanbur, and
7 have narrowed over time (cf. Bredenkamp et al. 2014). One can document changes and differences in ‘financial protection’ in health; one can see, for example, whether the incidence of ‘catastrophic’ and ‘impoverishing’ health expenditures varies across countries (cf. Wagstaff and Eozenou 2014) or has changed over time in a specific country, before or after a reform, or relative to trends in neighboring countries. One can analyze (equality in) service coverage and financial protection simultaneously under the UHC umbrella (cf. Wagstaff et al. 2015; Wagstaff et al. 2016). More generally, the database is likely to be useful for analyzing any health system regarding how well it does in terms of delivering health services and improving health outcomes, but not compromising families’ ability to pay for goods and services other than health care.

This paper provides a brief history of the HEFPI database, gives an overview of the contents of the 2018 version of the database, and then gets into the detail of the construction of its two sides – the health equity side, and the financial protection side. It also provides illustrative uses of the data set.

# **A brief history of the HEFPI database**

The 2018 HEFPI database is, in effect, the fourth in a series of similar World Bank databases, all of which draw exclusively on data from household surveys – see Figure 1. The first two (Gwatkin et al. 2000; Gwatkin et al. 2007) showed gaps within and between countries on various indicators of service coverage and health outcomes in the MDG areas of maternal and child health (MCH), and communicable disease. The 2000 data set covered just 42 countries and drew data from just 42 surveys in the Demographic and Health Survey (DHS) family. More DHS surveys were added in 2007. The 2012 database (Bredenkamp et al. 2012b, c, d, e, a) also included data from UNICEF’s Multiple Indicator Cluster Survey (MICS) and the World Health Organization’s (WHO’s) World Health Survey (WHS). Data on service coverage and health outcomes in all three databases were presented for the population and for ‘wealth’ quintiles, the latter being formed by applying principal components analysis (PCA) to a variety of indicators capturing the ownership of assets (e.g. car and
Policy Research Working Paper Series

||Title|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS1719|Shifting Responsibility for Social<br>Services As Enterprises Privatize in<br>Belarus|David Sewell|February 1997|0. Lawrence<br>32216|
|WPS1720|The Distributionof Foreign Direct<br>Investment inChina|Harry G. Broadman<br>Xiaolun Sun|February 1997|J.Grigsby<br>82423|
|WPS1721|EU Accession of C0entral and<br>Eastern Europe: Bridging the<br>Income Gap|Luca Barbone<br>Juan Zalduendo|February 1997|L. Barbone<br>32556|
|WPS1722|Uncertainty, Instability, and<br>Irreversible Investment:Theory,<br>Evidence, and Lessons for Africa|Luis Serv6n|February 1997|E. Khine<br>37471|
|WPS1723|The Productivity Effects of<br>of Decentralized Reforms: An<br>Analysis of the Chinese<br>Industrial Reforms|Lixin Colin Xu|February 1997|P. Sintim-Aboagye<br>38526|
|WPS1724|Debt Maturity and Firm Performance<br>A Panel Study of Indian Companies|Fabio Schiantarelli<br>Vivek Srivastava|February 1997|B. Moore<br>38526|
|WPS1725|Access to Long-Term Debt and<br>Effects on Firms' Performance:<br>Lessons form Ecuador|Fidel Jararnillo<br>Fabio Schiantareili|February 1997|B. Moore<br>38526|
|WPS1 725|Roads, Population Pressures,<br>and<br>Deforestation in Thailand.<br>1976-89|Maureen Cropper<br>Charles Griffiths<br>Muthukumara Mani|February 1997|A. Maranon<br>39074|
|WPS1727|The Economics of the Informal<br>Sector: A Simple Model and Some<br>Empirical Evidence from Latin<br>America|Norman V. Loayza|February 1997|R. Martin<br>39026|
|WPS1728|Regional Labor Markets during<br>Deregulation in Indonesia: Havethe<br>Outer Islands Been Left Behind?|Chris Manning|February 1997|J. Israel<br>85117|
|WPS1729|Does Mercosur's T-rade Performance<br>Raise Concerns about the Effects<br>of Regional Trade Arrangements?|Alexander Yeats|February 1997|.J. N.gaine<br>37947|
|WPS1730|Have Trade Policy Reforms Led<br>to Greater Openness in Developing<br>Countries?|Shuby Andriamananjara<br>John Nash|February 1997|J. Ngaine<br>37959|
|WPS1731|Pension Reform, Growth, and the<br>Labor Market in Ukraine|Michelle Riboud<br>Hoaquan Chu|February 1997|R. Hablero<br>33971|
funding were provided to some village funds in 2003 and 2004, and well-run funds were allowed to borrow up to a million baht more from the BAAC (or GSB or KTB).

_VF Management and Operation_ In this and subsequent sections we rely heavily on the results of a purpose-built questionnaire that was administered by the National Statistics Office in May 2010 to 3,091 village funds. Every village or urban area that was covered by the 2009 Socio-Economic Survey was included in the sample frame, and an attempt was made to gather information on the Village Fund in each of these localities by interviewing in person one of the Village Fund officers. In rural areas the villages and Village Funds apply to the same area, so the results of the survey can be matched village-to-village with those of the 2009 SES. However, in urban areas the areas covered by the Village Funds did not necessarily coincide with the enumeration areas used in the SES, and so the correspondence is imperfect: for each urban district covered by the SES, a Village Fund was interviewed, but the geographic coverage of the latter overlapped, but was not identical to, the urban enumeration area used by the SES.

The 2010 VF Survey shows that of the village fund committees that were in operation in 2009, 54% were established in 2002, and a further 36% in 2003 or 2004. By the end of 2005, 91% of villages had an operating VF committee, and almost all of the rest have set up their committees subsequently.

Villagers are not automatically “members” of the VF, but anyone aged 20 or older may join, typically by purchasing a share or making an initial deposit. The median number of VF members is 113 per village, of which slightly under half were men; this compares with a median number of households per village of 210, and population of 850 (see Table 3).

The village funds are run by local committees, with on average of eleven members, evenly split between men and women. There is some variation in committee size, as Table 4 shows; most committees have an odd number of members, with the most popular sizes being 9, 11, and 15. The original government guidelines recommended committees with 15 members, but most villages favor smaller committees, perhaps because of the difficulty of finding enough volunteers, or perhaps because larger committees are more difficult to run.

It is standard policy for VF committee members to serve for two-year terms – this is the case in 81% of the villages surveyed – but in practice many members serve more or less indefinitely: on average, 6.7 of the original members of the VF committee (out of an average of 11) were still serving as of 2010. There is 7
Table A.20: Comparison of technology sophistication between original and replacement sample

|VARIABLES|Brazil|Brazil|Vietnam|Vietnam|Senegal|Senegal|
|---|---|---|---|---|---|---|
|Original sample|-0.014|-0.037|0.030|0.043|0.021|0.028|
||(0.048)|(0.047)|(0.050)|(0.048)|(0.018)|(0.018)|
|Observations|638|637|1,484|1,484|1,753|1,666|
|R-squared|0.299|0.335|0.262|0.320|0.377|0.437|
|Controls:|||||||
|Sector|Y|Y|Y|Y|Y|Y|
|Size group|Y|Y|Y|Y|Y|Y|
|Region|Y|Y|Y|Y|Y|Y|
|Age||Y||Y||Y|
|Exporter||Y||Y||Y|
|Foreign owned||Y||Y||Y|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Data are from the Brazil, Vietnam, and Senegal FAT surveys. For each country, technology sophistication ( _MOSTj_ ) is regressed on a dummy for the original sampling list with controls for the stratification (sectors, size groups, and regions) and/or firm characteristics (age groups, exporter, and foreign owned). Robust standard errors in parenthesis.

abnormal interviews or outliers by running regressions of firm-level sophistication on enumerator dummies and firm controls as discussed in the text. Table A.21 shows that enumerator dummies are not significant for Brazil, Ghana, India, and Korea. For Bangladesh, Senegal, Vietnam, and Kenya, no more than 20% of enumerator dummies are statistically significant with respect to their distribution towards the type of firms they interview across various strata. Table A.22 compares the average technology sophistication ( _MOSTj_ ) for GBF, excluding the firms with abnormal enumerators and in the entire sample. We find no economic or statistical difference between mean sophistication in these countries.

**Minimizing respondent bias** . A critical factor to minimize respondent bias is to identify the right respondent. The protocol for the implementation of the survey required that the survey should be ideally answered by the top manager. About 47% of the survey was answered by the owner or CEOs, while the other responses included factory managers, other managers, administrative staff, and accountants. Almost 80% of the interviews were conducted through one visit in person interview with the main respondent. In circumstances in which the main respondent did not have all the information about a general topic of the questionnaire, especially in modules B and C, they were requested to consult with other colleagues.

To assess the relevance of response bias, we conduct a parallel pilot in Kenya where 108
our sample is 28,270 kyats (USD17). Furthermore, working children spend more time at work than adults, working an average of 6.3 days per week compared to 5.9 for adults.

Most children carry out one or more wage or domestic activities with almost all children doing wage work being also involved in household chores. In addition, the vast majority of children working for pay or profit are child laborers according to the definition of child labor used for this study (see the Data and Methodology section). Table 1 presents the total number of children employed in each type of work and doing any work at all. Overall, 40 percent of children in Myanmar are working. However, it is important to note that one child may do more than one type of work.

**Table 1 Basic statistics on child labor/work in Myanmar, 2015**

||Boys|Girls|Total|
|---|---|---|---|
|**Proportion of children engaged in different types of work**||||
|Wage work:|6.2|5.6|6.0|
|Share of wage workers in agriculture|54.0|59.2|56.5|
|Share of wage workers in industry|26.1|16.1|21.4|
|Share of wage workers in services|19.8|24.4|22.1|
|Household chores|30.9|39.4|35.2|
|Household production:|10.9|11.8|11.4|
|Farming activities|7.5|6.8|7.1|
|Unpaid family work|4.1|3.5|3.8|
|_Any type of work_|_36.6_|_42.2_|_39.7_|
|**Place where child workers mainly work (wage labor)**||||
|Inside the home|1.6|5.5|3.4|
|Workspace next to/in front of the home|6.0|5.0|5.5|
|Private house of employer/client|20.7|21.4|21.1|
|Fixed location with permanent structure|6.3|8.3|7.2|
|Fixed location with temporary structure/kiosk/stall|1.2|1.0|1.1|
|Fixed location without any structure|2.3|1.6|1.9|
|No fixed location (street vendors)|3.8|3.4|3.6|
|Farm, agricultural plot, sea, river, construction site|57.6|53.5|55.7|
|Other|0.5|0.4|0.4|
|Total|100.0|100.0|100.0|
|**Position held by child workers (wage labor)**||||
|Employee|54.2|57.4|55.7|
|Paid apprentice/intern|1.3|0.6|1.0|
|Employer (with regular employees)|0.1|0.0|0.1|
|Own-account worker (without regular employees)|4.8|4.8|4.8|
|Co-operator of household/family business|15.6|13.3|14.5|
|Unpaid help in household/family business|24.1|23.9|24.0|
|Total|100.0|100.0|100.0|

10
<!-- Start of picture text --> East Asia & Pacific Europe & Central Asia Latin America & Caribbean<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Year Year Year<br>Low Aid 2nd Quart. Low Aid 2nd Quart. Low Aid 2nd Quart.<br>3rd Quart. High Aid 3rd Quart. High Aid 3rd Quart. High Aid<br>Middle East & North Africa South Asia Sub-Saharan Africa<br>1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010<br>Year Year Year<br>Low Aid 2nd Quart. Low Aid 2nd Quart. Low Aid 2nd Quart.<br>3rd Quart. High Aid 3rd Quart. High Aid 3rd Quart. High Aid<br>1 .8 .4<br>.8 .6 .3<br>.6<br>.4 .2<br>.4<br>.2 .2 .1<br>0 0 0<br>Proportion of Countries in Conflict Proportion of Countries in Conflict Proportion of Countries in Conflict<br>.8 .8 .4<br>.6 .6 .3<br>.4 .4 .2<br>.2 .2 .1<br>0 0 0<br>Proportion of Countries in Conflict Proportion of Countries in Conflict Proportion of Countries in Conflict<br><!-- End of picture text --> FIGURE A5: NONLINEAR TRENDS IN THE PROPORTION OF COUNTRIES EXPERIENCING CONFLICT BY REGULARITY OF AID RECEIPT AND REGION Notes: The y-axis is the proportion of all countries experiencing a conflict in each year. The trend line shows the lowess plot of an indicator for whether a country is experiencing a conflict in a given year by year. “Low Aid” is the quartile of countries receiving aid in the fewest proportion of years in the data. “High Aid” is the quartile of countries receiving aid in the highest proportion of years in the data. Data are from the NQ replication file.

48
employer ownership type, and then consider the newer schemes targeting urban residents who lack access to social insurance through their employers.

**_Employer-Based Social Insurance Programs_** . Social insurance for employed workers in urban China, which includes the ostensibly mandatory urban enterprise pension, unemployment and medical insurance, combine social pooling with individual accounts. Table 3 provides the summary information on the basic contribution rates for employers and employees for different social insurance programs in which workers and urban enterprises are supposed to participate.

Civil servants and public institution ( _shiye danwei_ ) employees enjoy a traditional defined benefit pension, although some provinces and the central government have recently implemented local pilots experimenting with reform of these pensions for public institution employees. Urban enterprise employees are required to participate in urban social insurance programs, with employers and employees contributing 29 to 31 and 11 percent of wage income, respectively, to the complete set of programs (Table 3). In spite of these mandates, however, evidence from household survey data presented below suggests that they are difficult to enforce and that coverage rates remain low.

The urban employee pension insurance scheme requires a total contribution of 28 percent of payroll, with employers providing 20 percent to a social pooling account and employees contributing 8 percent of their wage income to individual accounts.<sup>16</sup> The pension benefits received upon retirement include a basic pension from the social pooling account equivalent to 20 percent of the local average wage of urban employees, and an individual benefit equal to the individual account accumulation divided by 139. The vesting period is 15 years for the basic pension, and if contributing for less than 15 years, beneficiaries receive a lump-sum payment from their individual account accumulation upon retirement.

The urban employee medical insurance scheme requires a total contribution of 8 percent of salary. Employers contribute 6 percent, of which 70 percent of the contribution goes to a pooling fund account, and 30 percent is allocated to the employee’s individual account. Employees then contribute 2 percent of their wage to individual accounts. The insurance scheme covers both outpatient and impatient health services. Participants in the scheme typically have a deductible equal to 10 percent of the average annual wage, with reimbursements capped at four times the local average annual wage. The pooling funds finance 80 percent of the reimbursement, and employees co-finance 20 percent from their individual accounts. Those health services expenditures below the minimum required payment are paid from

> 16There is considerable scope for local variation in these rates. In Guangdong, for example, some prefectures have contribution rates as low as 13 percent. The rates listed here may be viewed as maximums, although they are adhered to by many prefectures. Some jurisdictions lowered the rates in the wake of the 2008 Global Financial Crisis.

8