rafmacalaba/datause-encoder-data huggingface dataset:
opening up of the economy to foreign investment, with the result that China has become the largest recipient of direct investment flows in recent years. Thus, compared to other labor-abundant countries in Asia, China has been more open to foreign trade and investment.

The opening up measures would not have had such substantial impact if they had not been accompanied by improvements in investment climate. This is probably one of the least understood features of China’s recent development. There are literally dozens of Chinese coastal cities that have developed quite good investment climates. In these cities the private sector accounts for 90% or more of manufacturing assets and production. A genuine Chinese private sector has emerged that is highly profitable: in 2005 average pretax rate of return for domestic private firms was around 20%, similar to that for foreigninvested firms (Dollar and Wei, 2006). World Bank investment climate surveys have documented the differences in the objective conditions of production in Chinese cities, compared to ones elsewhere in developing Asia. For example, firms lose a lot of output as a result of unreliable power supply: 3.3% of output in Indonesia, 4.9% in Pakistan, 5.9% in Philippines, 7.9% in India. The figure for coastal Chinese cities was 1.0% (Table 4). Similarly, most manufacturing firms are importing some parts and material: customs clearance time for imports is low in Chinese cities (3.2 days) compared to those in Indonesia (4.8), India (6.6), Philippines (7.2), Bangladesh (10.6), or Pakistan (17.1).<sup>3</sup> On a whole range of practical matters that affect production, Chinese coastal cities outperform the best locations in Bangladesh, India, Indonesia, Pakistan, and the Philippines. The only large Asian countries that have similarly good investment climate 9
27 incentives for Improving the effectiveness and efficiency of public expenditures on this development. To accelerate the supply response to these incentives by public and private training institutions, the study suggests the importance of taking positive steps to eliminate binding regulations on private VET and to subject public VET to market competition. These steps promise to expand the base of private resources available for skills development and result in a more cost-effective use of public resources. The findings of the study, of course, based on a comparative study of six developing countries, are subject to further validation. This would include expanding the number of countries studied and improving the measures of VET structure and performance used. Broadening the regional mix of countries would be especially importan **t** o reduce the potential regional bias of the study. Future investigations of this topic should also explore its dynamic dimensions. That is, research should be directed to how changes in trade regimes affect the liberalization of labor markets, and subsequently, how these changes influence shifts in the structure and performance of VET. Attention should be given to the identification of conditions under which VET systems respond rapidly to changing trade regimes and market signals.
the term “deep restructuring” – introducing new products and process, finding new markets for their goods, supported by significant investment.

The BEEPS surveys start in 1999, after the end of the transformational recession and the period of large-scale defensive restructuring by firms. Most of the region has experienced an economic recovery during the period covered by the surveys. Actions by firms that would have earlier been described as part of the defensive restructuring process intrinsic to the transition process are better thought of as the lower tail of the distribution of firm-level growth. This section therefore focuses on deep restructuring activities.

The BEEPS surveys are rich in measures of restructuring and innovation activity. However, many of these measures are hard to use because of calibration problems. If 30% of the sample of firms in a country introduce a new product, is this high or low? By benchmarking against what is observed in market economies, we can answer this question. We are also interested in measuring the pace of restructuring activity in TEs over time. The scale of misallocation of capital at the start of transition was enormous, meaning that there were many profitable market niches, large and small, for firms to move into. As transition progressed and the scale of the initial misallocation moved further into the past, the size and number of these profit opportunities would have decreased. One hypothesis we will explore is whether the scale of restructuring activity in TEs has been declining and approaching the levels observed in DMEs. We will also use the BEEPS data to explore the determinants of restructuring activity. We are interested in particular in the relationship between certain features of the business environment – in particular, competition, finance and ownership – and restructuring activity.

The BEEPS measures of restructuring activity that we use are, for each firm, indicators of whether the firm:

1. developed successfully a major new product line/service in the 36 months prior to the survey;

36
Figure 1 <!-- Start of picture text --> Hungary: Total AQsets of Financial Institutions,<br>Nominal ("L)P, & CotnsumerPrices<br>(in bililon Forints, year end)<br>GDP<br>(2,070 bF)<br>GDP<br>(170b) TOWa<br>Asi.tC CPI<br>(1,621 bP)<br>GDP<br>(l.409 bF)Tol<br>Asmse<br>1.2b F) CPI<br>TotaLl TOtaW ,0<br>Assets Assets CPI<br>(1,004 ci Cpt) 7 72. 1,019 bF<br>-800<br>-400<br>1987 1988 1989 1990<br>U Large Banks 12 Small & Medium Banks * Savings Banks & SFls<br>Soumes: National Bank of Hur and Bank tafl Est'rates<br>Miscellaneous Indicators<br># of Institutions Total Asset Shares of Total Assets Average Total Assets GDP<br>1987 1990 Growth '87-90 1987 1990 1988 1990<br>Total 19 30 61% 100% 100% 7cX/o 70%<br>Large Banks 4 4 33% 58% 48% 40% 34%<br>Small & Medium Banks 10 14 378% 5% 14% 4% 8%<br>Savings Banks 1 3 64% 35% 36% 26% 26%<br>Special Financelnst. 4 9 116% 2% 2% 1% 1%<br>Consumer Price Growth '87-90 Nominal GDP Growth '87-90<br>75% 69%<br>Sourcs: Natonal Bank of Hungary and Bank StWl Estimates<br><!-- End of picture text --> 2a
Policy Research Working Paper 8865

# **Abstract**

Sectoral segregation is often used to explain a large part of a well-documented gender earnings gap in business profits. Women tend to sort into different sectors than men, and the sectors dominated by women tend to be less profitable. This paper investigates the horizontal dimension of sectoral segregation by studying global data on female and male enterprises operating in sectors that are typically dominated by the same and opposite sex. The analysis uses the novel _Future of Business dataset_ , which spans 97 countries and was administered to enterprise owners, managers, and employees who use Facebook. The analysis finds that some of the earnings gap can indeed be explained by sector choice: female-owned businesses in male-dominated sectors make significantly higher profits than those in traditionally female sectors. The evidence points to a hierarchy of earnings, with male-owned businesses in male-dominated sectors earning the most, women in male-dominated sectors and men in female-concentrated sectors in the middle tier, and women in female-concentrated sectors at the bottom. Correlational analysis suggests that women who own businesses in male-dominated sectors are younger, married, and more likely to have inherited the business than women in female-concentrated sectors. They have similar education to women in female-concentrated sectors and present higher self-efficacy but lower entrepreneurial identity and commitment to the sector. Male support networks appear to be key for female-owned firms, with co-ownership with husbands and male role models factoring into the decision to cross over.

This paper is a product of the Gender Innovation Lab, Africa Region and Gender Global Theme. 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://www.worldbank.org/prwp. The authors may be contacted at mgoldsten@worldbank.org, pgonzalezmartine@worldbank.org, and spapineni@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
Market expansion also directly affects the choice of technique in grain mills and bakeries, which it does not seem to do in the production of cinder blocks. This also comes out in Table 5 and Table 7, where market expansion is seen to reduce TFPR in bakeries per the first table by a higher proportion than in Table 7. Part of the effect seen in Table 5 must therefore be associated with the choice of the scale or factor intensity in this case also. The picture is similar in grain mills, where market expansion reduces TFPR in Table 5, but does not have any such effect in Table 7, suggesting that the effects we read in Table 5 are influences transmitted via choice of the scale or technique.

_H. Determination of TFPR in textiles, garments, footwear, plastic products, wood work, metal works, and the furniture industry_ How does the pattern of results reported for the four industries in Tables 3 to 7 hold up across the other seven industries? Do market size, transport costs, and entry regulation affect aggregate productivity across any of these industries as they seem to do in the production of cinder blocks? Or, is the effect of any one of the three factors contrary to what economic theory predicts?

We address this question by analyzing data on seven four‐digit ISIC industries and report our findings in Tables 8 through to 11. The tables relate to growth in TFPR only, to which our comparison across the 11 industries is limited. This is because we do not have unit product price data for the industries that the four tables cover.

Estimation and test results on the textiles industry (ISIC 1710), wearing apparel (ISIC 1810) and the footwear industry (ISIC 1920) are reported in Tables 8 and 10. Results for wood processing and wood products (ISIC 2000), manufacturing of plastic products (ISIC 2520), structural metal products (ISIC 2811) and furniture making (ISIC 3610) are reported in Tables 9 and 11.

The first column of Table 8, underscores that unweighted estimates of the revenue productivity equation of section 3.3 on data on firms pooled across the sector would conceal effects that would be observed in relatively homogenous industries. Thus, per this column, productivity would respond to changes in local market size as the theory would predict but not to shifts in transport costs or to entry license fees. This contrasts sharply with what we see in Tables 3 to 7 21
Regarding health impacts, individuals who live closer to the camps have a higher probability of contracting viral diseases than those who live farther from the camps. Estimates indicate that a decrease in distance to refugee camps by 30 km is associated with a 2.1 pp increase in the likelihood of experiencing diarrhea, a 3.7 pp increase in the likelihood of experiencing fever, and a 2.7 pp increase in the likelihood of experiencing a cough. Adindividuals closer to are more to seek health care from ditionally, refugee camps likely NGO providers. These results are in line with those documented by Dagnelie, Mayda and Maystadt (2023), who use DHS data to examine the effects of 30 refugee camps in Africa. The authors report negative effects on height-for-age and weight-for-age scores of children under five years of age and suggest that the effects stem from heightened rates of malaria transmission. Importantly, this mediating factor might also affect our context as Zhao et al. (2023) note that Myanmar bore the heaviest malaria burden in the Greater Mekong subregion between 2011 and 2017.

Greater proximity to refugee camps is also evident in heightened self-reported safety concerns. The estimates indicate that an increase in proximity to the camps by 30 km is associated with a higher likelihood of experiencing serious injuries by 2.6 pp, encountering combat situations by 2.1 pp, facing near-death experiences by one pp, and witnessing homicides of strangers (one pp). These correspond to large and meaningful effects that represent increments of at least 20 percent in the mean values of these outcomes. The results underscore unease about the effects of to on the proximity refugee camps safety and security of individuals in surrounding areas.

Finally, a decrease in distance to the camps is associated with more positive perceptions among hosts regarding the benefits their families receive from the presence of the Rohingya. Additionally, those living closer to the camps are less likely to believe that support for camp residents should come only from external sources. However, respondents nearer the camps are also more worried about safety due to the Rohingya’s presence.

Overall, the individual-level analysis suggests complex but extremely consistent effects of the Rohingya refugee camps on host communities. The positive effects of closer proximity to these camps are all associated with the presence of humanitarian institutions: individ5
**Table 2. Average interview length by method and country (in minutes)**

||||Difference<br>|
|---|---|---|---|
|Country|24-hour(TD)|7-day (SQ)|(TD - SQ)|
|Bangladesh|12.88|10.63|2.25***|
|Uganda|7.86|7.97|-0.11|

**Source:** Authors’ calculations based on WEAI 2.0 pilot data.

**Notes:** N=356 for Bangladesh; N=315 for Uganda. Paired _t_ -tests performed comparing the mean survey times between methods. TD = time diary. SQ = stylized question. ***, **, and * denote statistical significance levels at 1%, 5%, and 10%, respectively.

26
Policy Research Working Paper Series

|T,tle|Author|Date|Contact<br>**for**paper|
|---|---|---|---|
|WPS1760 Credibility of Rules and Economic<br>Growth: Evidence from a Worldwide<br>Survey of the Private Sector|Aymo Brunetti<br>Gregory Kisunko<br>Beatrice Weder|April 1997|M. Geller<br>31393|
|WPS1761 Bending the Rules: Discretionary<br>Pollution Control in China|Susmita Dasgupta<br>Mainul Huq<br>David Wheeler|May 1997|E. de Castro<br>89121|
|WPS1762 Strategies for Pricing Publicly<br>Provided Health Sertvices|Paul J. Gertler<br>Jeffrey S. Hammer|May 1997|C. Bernardo<br>31148|
|WPS1763 China and the Multilateral Investment<br>Guarantee Agency|Yushu Feng|May 1997|E. Beers<br>36175|
|WPS1764 A Reversal of Fortune for Korean<br>Women: Explaining 1983's Upward<br>Turn in Relative Earnings|Yaba vab der Neykeb<br>Rodgers|May 1997|D. Ballantyne<br>87198|
|WPS1765 Explaining Agricultural and Agrarian<br>Policies in Developing Countries|Hans P. Binswanger<br>Klaus Deininger|May 1997|D. Housden<br>36637|
|WPS1766 New Systems for Old Age Security:<br>Theory, Practice, and Empirical<br>Evidence|Estelle James|May 1997|S. Khan<br>33651|
|WPS1 767 Pension Reform: Is There a Tradeoff<br>between Efficiency and Equity?|Estelle James|May 1997|S. Khan<br>33651|
|WPS1768 Is There a Quantity-Quality Tradeoff<br>as Enrollments Increase? Evidence<br>from Tamil Nadu, India|P. Duraisamy<br>Estelle James<br>Julia Lane<br>Jee-Peng Tan|May 1997|S. Khan<br>33651|
|WPS1769 Information, Incentives, and<br>Commitment: An Empirical Anialysis<br>of Contracts between Government<br>and State Enterprises|Mary M. Shirley<br>L. Colin Xu|May 1997|P. Sintim-Aboagye<br>38526|
|WPS1770 Poverty and Social Transfers<br>in Hungary|Christiaan Grootaert|May 1997|G. Ochieng<br>31123|
|WPS1771 Government Employment and Pay:<br>A Global and Regional Perspective|Salvatore Schiavo-Campo<br>Giulio de Tommaso<br>Amitabha Mukherjee|May 1997|A. Panton<br>85433|
|WPS1772What Drives Deforestation in the<br>Brazilian Amazon? Evidence from<br>Satellite and Socioeconomic Data|Alexander S. P.Pfaff|May 1997|A. M. Maranon<br>39074|
WPS3932

**WELFARE MEASUREMENT BIAS IN HOUSEHOLD AND ON-SITE SURVEYING OF WATER-BASED RECREATION:**

**AN APPLICATION TO LAKE SEVAN, ARMENIA**

Craig Meisner and Hua Wang Development Research Group The World Bank and Benoît Laplante Independent Consultant, Montreal, Canada _Keywords_ – On and off-site sampling, recreation demand, zero-inflated models, truncated count data models, endogenous stratification, Armenia.

World Bank Policy Research Working Paper 3932, June 2006 _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._ Correspondence should be addressed to: Craig Meisner, MC2-205, World Bank, 1818 H Street, NW, Washington, DC 20433, <u>cmeisner@worldbank.org.</u>
Table (G10) Impacts of the Program on Savings and Loans

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|At least 30 JD in savings|-0.12*|(0.07)|[0.34]|0.32|(0.47)|1,102|
|Value of loans taken in past year, USD PPP|-144.22|(1,980.97)|[1.00]|4,128.63|(12,492.84)|1,089|
|Value of loans given in past year, USD PPP|-113.90|(635.12)|[1.00]|613.62|(4,147.31)|1,084|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: At least 30 JD in savings is an indicator equal to 1 if the household has at least 30 JD in savings, in a bank or otherwise. Value of loans taken in the past year includes formal and informal loans taken out by the household. Value of loans given includes all formal and informal loans made by the household to other individuals.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 10 in the pre-analysis plan.

Table (G11) Impacts of the Program on Physical and Mental Health

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Life Satisfaction (SD)|-0.32**|(0.15)|[0.06]|0.01|(0.98)|1,097|
|Subjective Health (SD)|-0.30*|(0.16)|[0.06]|0.02|(0.99)|1,102|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: Life satisfaction corresponds to a standardized measure from “All things considered, how satisfied are you with your life as a whole these days on a scale of 1 to 10?” Subjective health is a standardized measure from “Would you describe your general health as good, fair, poor, or very poor?”

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the pre-analysis plan.

48
32% do not know when their buildings were constructed. 83.2% families live in houses owned by them. This is a significant finding which suggests that for most families, their house is their main asset given that they do not have enough savings in financial form. As these houses are highly vulnerable to floods, the main asset of the families comes under threat due to flood hazard.

The types of houses differ from slums and shanties to multi-story buildings and skyscrapers. Slums are informal settlements created to accommodate the influx of labor force into the city. These are often small units constructed with durable material like bricks but inadequately maintained and lack basic facilities like water and sanitation. Most of them do not have land ownership where they are located.<sup>32</sup> Shanties are small single units constructed with non-durable material and have no regular electricity, water or sanitation.<sup>33</sup> They are found on vacant government or private land and face the threat of eviction any time. Majority of them in Colombo are built on land that is subject to frequent flooding.<sup>34</sup> Temporary huts are structures made from non-durable material and used for temporary shelters. These households shift out eventually to slums or shanties.

There are significant differences in the types of houses depending on the category of households as seen in Table 6. The BPL households either live in shanties, slums or single-story buildings. Among poor households, majority live in single-story buildings followed by slums and multi-story buildings. The same applies to non-poor households, but the percentage living in buildings is far higher. Since 60% of non-poor households live in single-story buildings, they are also directly impacted by flood waters.

_Table 6: Category-wise types of houses_

|**Type of house**|**BPL**|**Poor**|**Non-poor**|
|---|---|---|---|
|Slum|14.7|21.4|2.3|
|Shanty|44.1|12.1|7.0|
|TemporaryHut|5.9|7.5|-|
|Single storeybuilding|26.5|46.8|60.4|
|Multi storeybuilding|5.9|11.6|27.9|
|Skyscraper|-|0.6|2.3|

> 32   Sevanatha Urban Resources Centre (2003), Partnerships to improve access and quality of public transport – A case report: Colombo, Sri Lanka, Water, Engineering and Development Centre, Loughborough University.

> 33 _Ibid._

> 34 _Ibid._

19