rafmacalaba/datause-encoder-data huggingface dataset:
|Without SGRY control<br>With SGRY control<br>Attends School<br>Works<br>Attends School<br>Works<br>**Panel A: Full Sample**<br>NREGA<br>-1.097<br>1.808_∗∗∗_<br>-1.275_∗_<br>1.863_∗∗∗_<br>[0.718]<br>[0.495]<br>[0.748]<br>[0.516]<br>Observations<br>238,479<br>238,479<br>238,479<br>238,479<br>Mean DV<br>0.80<br>0.11<br>0.80<br>0.11<br>**Panel B: Ages 13-17**<br>NREGA<br>-3.541_∗∗∗_<br>4.195_∗∗∗_<br>-3.693_∗∗∗_<br>4.360_∗∗∗_<br>[1.065]<br>[1.003]<br>[1.119]<br>[1.054]<br>Observations<br>84,619<br>84,619<br>84,619<br>84,619<br>Mean DV<br>0.70<br>0.25<br>0.70<br>0.25<br>**Panel C: Ages 5-12**<br>NREGA<br>0.202<br>0.677_∗_<br>0.056<br>0.609<br>[0.787]<br>[0.366]<br>[0.805]<br>[0.384]<br>Observations<br>153,860<br>153,860<br>153,860<br>153,860<br>Mean DV<br>0.85<br>0.027<br>0.85<br>0.027<br>District FEs<br>Yes<br>Yes<br>Yes<br>Yes<br>Year FEs<br>Yes<br>Yes<br>Yes<br>Yes<br>Age FEs<br>Yes<br>Yes<br>Yes<br>Yes<br>_Source_: Author’s analysis based on NSS 2004 – 2010 (Rounds 61, 64, and 66) and program expenditure data for<br>SGRY from 2004 to 2007. Expenditure data is from the Ministry of Rural Development, Government of India.<br>_Notes_: All estimates are of the order 10_−_2. This table reports estimates of _β_, the effect of NREGA on children<br>who report their “primary activity” as school attendance or work. The coeffcients are from the OLS estimation of<br>equation5. The dependent variable in columns 1 and 3 is a dummy if primary activity of the individual is “attends<br>school”. The dependent variable in columns 2 and 4 is a dummy if primary activity of the individual is any kind of<br>work. Other controls include district fxed effects, year fxed effects, and a vector of year-child-age fxed effects.<br>The standard errors in square brackets are clustered at the district level. All regressions include district, year, age,<br>and sex fxed effects. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guaran-<br>tee Act.<br>_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p <_0_._01.|
|---|
Table 8 Impact of Aid on the Quintile Income Groups (the Late 1990s-2000s)

|Dependent Variable: PeriodAve|rage Growth|Rate ofPer|Capita Inco|me ofthe Qui|ntile (real ter|ms)||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Ec|onomic Aid|||||Social Aid||||Ag|gregateAid|||
||qd)|(2)|3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(1)|(12)|(13)|(14)|(5)|
|Quintile Income Groups|Ql|Q2|Q3|Q4|Q5|Ql|Q2|Q3|Q4|Q5|Ql|Q2|Q3|Q4|Q5|
|Period Average Growth Rate|1.2749***|1.0826***|1.0412***|1.0007***|0.9912***|1.2195***|1.0584***|1.0261***|0.9971***|— 1.0069***|1.1838***|1.0367***|1.0124***|0.9903***|1.0202***|
|of Per Capita Income#|(6.59)|(9.12)|(12.87)|(19.11)|(14.17)|(7.16)|(10.24)|(14.71)|(21.82)|(16.50)|(7.07)|(10.36)|(15.05)|(22.75)|(17.03)|
|Aid/GDP|0.0019|0.0015|0.0008|0.0002|-0.0011|0.0013***|0.0008***|0.0004**|0.0001|-0.0006***|0.0007***|0.0004***|—0.0003**|0.00008|-0.0003***|
||(0.89)|(1.12)|(0.86)|(0.35)|(1.24)|(3.00)|(3.33)|(2.52)|(0.88)|(3.41)|(2.73)|(3.13)|(2.44)|(1.04)|(3.34)|
|Crises Dummy|-0.0046|-0.0001|0.0009|0.0013|-0.0014|-0.0036|0.0006|0.0013|0.0014|-0.0019|-0.0040|0.0003|0.0012|0.0013|-0.0017|
||(0.77)|(0.04)|(0.36)|(0.76)|(0.61)|(0.61)|(0.15)|(0.52)|(0.83)|(0.84)|(0.69)|(0.08)|(0.46)|(0.81)|(0.74)|
|No. of Observations|138|138|138|138|138|138|138|138|138|138|138|138|138|138|138|
|R-squared|0.29|0.53|0.69|0.83|0.72|0.31|0.54|0.70|0.83|0.72|0.31|0.54|0.70|0.83|0.72|
|Notes:<br>i) Absolute value oft-stati|stics calculate|d with White-|corrected stan|dard errors is|in parentheses.|*, ** and ***|indicate signi|ficance at th|e 10%, 5% and|1% levels, respe|ctively.|||||
|ii) Panel two-stage least squares m<br>iii) # denotes variable is instrumen<br>iv) Quintiles go from the poorest,|ethods is used<br>ted with the i<br> Q1 to the riche|for these ana<br>nitial level an<br>st QS.|lyses.<br>d period avera|ge growth rat|e ofthe previou|s spell.||||||||||
|Source:Authors'compilation||||||||||||||||
30 differ on the measurement of capital inputs. Many studies ignore capital expenditure. Bahl et al. allocate 5 percent of new capital expenditure as capital services accruing in the current year and allocate repayments on existing debt as a proxy for the services from the existing capital stock. In contrast, Meerman estimates the flow of services from the education capital stock as the (estimated) capital stock multiplied by an interest rate of 15 percent (i.e., depreciation and opportunity costs).

Another area of methodological weakness involves life-cycle questions. For instance, James and Benjamin (1987) in a study of Japan show that families with younger household heads are more likely to have both young children and low incomes while families with older household heads have older children and higher incomes due simply to the age-earnings profiles. Selden and Wasylenko (1992) find a similar pattem in Peruvian household age composition and income. At any point in time then primary education expenditures will look more pro-poor and higher education expenditures more pro-rich than they would if education expenditures were viewed from a lifetime perspective based on the permanent income of the household and total education received by household members. Another life-cycle issue missed by all studies is that in some countries coUege graduates are entidtled to government positions they may not otherwise obtain. Thus, govemment spending on such individuals does not always end with graduation.

FinaUy, the studies all ignore changes in relative prices, incomes, and location. For instance, because rural-urban migration may be due to geographic differences in public services as well as wage differentials, and because education expenditures exhibit an urban bias, the urban benefits to education expenditures may be at least partially dissipated through increased urban crowding, lower wages, and higher land prices. In addition, benefit studies typicaUy do not account for educational service quality differences between urban and rural areas.

Despite these caveats, benefit incidence studies can yield important insights into proposed changes in education, such as reallocating resources from higher education to primary and secondary education. A second policy might be to increase user fees for all or some levels of education. Here the evidence to date suggests that this may have adverse consequences for the poor, particularly if fees are increased for primary education. A third policy might be to target
# **I. Introduction**

In this paper, we examine the Israeli and Indian track records to draw generalized conclusions about the viability of diaspora bonds as a development financing instrument. The rise of various diasporas and their economic status in their adopted countries are fast becoming a source of pride as well as financial resources for developing countries. If seeking remittances is a way of tapping into diaspora income flows on a regular basis,<sup>1</sup> issuance of hard-currency-denominated bonds to the diaspora is a way of tapping into the latter’s wealth accumulated abroad.

Diaspora bonds are not yet widely used as a development financing instrument. As discussed below, Israel since 1951 and India since 1991 have been on the forefront in raising hard-currency financing from their respective diaspora. Bonds issued by the Development Corporation for Israel (DCI), established in 1951 to raise foreign exchange resources from the Jewish Diaspora, have totaled well over $25 billion. Diaspora bonds issued by the government-owned State Bank of India (SBI) have raised over $11 billion to date. The Government of Sri Lanka has also sold Sri Lanka Development Bonds (SLDBs) since 2001 to several investor categories including non-resident Sri Lankans raising a total of $580 million to date.<sup>2</sup> South Africa is reported to have launched a project to issue Reconciliation and Development (R&D) bonds to both expatriate and domestic investors (Bradlow 2006). Although the Lebanese government has had no systematic program to tap its diaspora, anecdotal evidence indicates that the Lebanese diaspora has also contributed capital to the Lebanese government.<sup>3</sup> Diaspora bonds are different from foreign currency deposits (FCDs) that are used by many developing countries to attract foreign currency inflows.<sup>4</sup> Diaspora bonds are

> 1 Remittance flows to developing countries have increased steadily and sharply in recent years to reach over $200 billion in 2006 (Ratha 2007). The World Bank believes that unrecorded remittance flows to developing countries are one-half as large (World Bank 2005).

> 2 As per the Central Bank of Sri Lanka press release of September 13, 2006, the last issue of SLDBs for $105 million was sold through competitive bidding on September 12, 2006 at an average yield of LIBOR+148.5 basis points.

> 3 Indirect evidence may be that the Lebanon’s government bonds are priced higher than the level consistent with the country’s sovereign credit rating.

> 4 A Bloomberg search of FCD schemes identifies well over 30 developing countries. Moody’s and Standard and Poor’s have foreign currency short-term debt ratings for 60 and 68 developing countries respectively.

2
Haryana is one of India’s richest states as measured by per capita GDP, but it ranks among the worst in terms of female disadvantage. In 2001, the lowest sex ratios for those aged 0-6 years were observed in north India, particularly in Haryana (820 girls per 1000 boys) and Punjab (793 girls per 1000 boys). Studies also document a strong pattern of female disadvantage in child survival, health and schooling. Sudha and Rajan (1999) show that sex ratios at birth in Haryana and neighboring Punjab are abnormally low, suggesting a high incidence of sex-selective abortion. Using data from 1992-93, Filmer, King and Pritchett (1998) estimate that girls in Haryana are two times more likely than boys to die between the ages of one and four. A large gender gap is also present in school enrollment for 6-14 year olds among the poorest 40% of households (Filmer and Pritchett, 1998).

The _Apni Beti Apna Dhan_ (ABAD) program was introduced in October 1994 to improve parents’ perceived value of daughters by offering them economic incentives. Upon the birth of a daughter, mothers are entitled to a monetary award of Rs. 500 (approximately $11) within 15 days of each birth, to cover post-delivery needs. It also endows each girl with a longer-term monetary investment of Rs. 2,500,<sup>3</sup> (approximately $55) in government fixed-deposit securities, redeemable for a guaranteed sum of Rs 25,000 (approximately $550) on her 18th birthday provided she remains unmarried. This yields an implicit annual return of approximately 13%. A bonus of Rs. 5000 is awarded is if the girl has received at least a Standard 5 education, and a further Rs 1000 is awarded if she has studied up to Standard 8.

To be eligible for this scheme, a girl would need to be the first, second or third child in the family. The family would have to be below the poverty line (BPL). Non-poor families with a disadvantaged caste background - formally identified in India as belonging to a “Scheduled Caste” (SC) or “Other Backward Caste” (OBC) - would also be eligible. The wealthiest in this latter group would be excluded via a restriction on gazetted government employees or income tax payees (effectively between 1-4% of the national population over most of our study period (Banerjee and Piketty, 2006)).

In 1995 the Haryana government expanded the scheme by offering a higher maturity amount for girls willing to defer redeeming their securities: Rs. 30,000 for two years, or Rs. 35,000 for 4 years. In addition, they would also receive a credit subsidy for entrepreneurship loans.

The novel design, scale and potential social impact of this program all point towards the importance of understanding its effects. A beneficiary assessment conducted in three districts in 1998-99 found that most government officials were aware of eligibility criteria, and funds were disbursed in a timely manner to successful applicants (MODE, 2000), but no impact evaluation was conducted. A rigorous program evaluation, however, is complicated because the program’s implementation was not _ex-ante_ designed to

> 3 This represents about one-fifth of an eligible households’ annual income (MODE, 2000).

4
Yet, it is important to note that when we assess media articles as percentage of total media outlets in each economy, East Asia and the Pacific stands out as the region with the highest proportion of media articles covering _Doing Business_ . Inversely, Latin America and the Caribbean, OECD high income and Middle East and North African countries have the lowest percentage of media articles in total media outlets (see figure 3a). Furthermore, as numbers of media articles discussing _Doing Business_ can substantially fluctuate from year to year, this pattern varies with each _Doing Business_ publication cycle. For example, if we isolate the period of 2016/17 and perform the same analysis, a different pattern emerges (see figure 3b).

**Figure 3:** East Asia and the Pacific has the highest proportion of media articles on Doing Business in total media outlets

# _Figure 3a_

_Figure 3b_ <!-- Start of picture text --> Media articles as % of outlets Media articles as % of outlets, latest year only<br>62.31<br>125.86<br>116.60<br>39.40<br>70.34<br>26.90 24.88<br>35.49 32.02<br>11.74 11.40<br>8.84 13.49 8.38<br>South Asia<br>South Asia<br>East Asia & Pacific Europe & Central AsiaSub-Saharan Africa High income: OECD Sub-Saharan Africa East Asia & PacificEurope & Central AsiaMiddle East & North AfricaHigh income: OECDLatin America & Caribbean<br>Middle East & North Africa Latin America & Caribbean<br>60 150<br>40 100<br>% %<br>50<br>20<br>0<br>0<br><!-- End of picture text --> _Source:_ Factiva.

Across all income groups (see figure 4), lower-middle income economies had the highest _Doing Business_ media coverage in 2016/17, averaging at 24 articles per country. Among them, press in Pakistan, India, Philippines and Nigeria cited the report the most, which positively correlates with these countries’ active reform implementation efforts given regulators’ ambitious reform agendas. Among the low-income countries, media outlets in Zimbabwe, Nepal, Uganda and Rwanda publicize _Doing Business_ the most. Notably, Rwanda is the regional and this income group’s leader in terms of reforming its business regulatory environment, with more than 50 reforms implemented from 2004 to 2017. Most of these reforms impacted the areas of dealing with construction permits, getting credit and trading across borders. Media outlets in Nepal and Uganda were among the first ones within the lowincome group of countries to cite _Doing Business,_ in 2008 and 2009 respectively. By contrast, Zimbabwe’s press, despite the country’s active implementation of business regulatory reforms, commenced discussing the report’s rankings only five years ago.

Interestingly, data show that there is more _Doing Business_ media coverage in countries with less free press. One explanation could be that after successful reform implementation, nondemocratic governments encourage local media to emphasize and promulgate positive changes. Media in four of the top 10 countries, in terms of press coverage of the _Doing_ 14
11 percent in 1987 and only 0.7 percent in 1988.3 However, the argument is developed below that the primary source of this decline was not the external debt problem, but the adverse growth effects of high domestic inflation. As discussed below, those countries that did achieve favorable performance on domestic adjustment were able to obtain relatively high growth in the Baker Plan period (1986-88).

INTERNATIONAL ENVIRONMENT -- An assumption of the Baker Plan wIs that the international economy would not collapse and make it impossible for the debtor countries to increase their exports and "grow their way out" of the debt problem. The industrial countries more than fulfilled this prerequisite. Thus, average growth in industrial countries in 1986-88 stood at 3.3 percent, comfortably above the range of 3 percent (or, after LIBOR subsided to single digits, 2-1/2 percent or lower) that had been considered necessary for progress on debt.' International interest rates also remained in a range compatible with emergence from the debt problem. For 1986-88 as a whole, LIBOR averaged 7.3 percent, less than half the level of 1981-82. Although the rate rose by 200 basis points from the fourth quarter of 1987 to the fourth quarter of 1988 (when it stood 3 Economic Commission for Latin America, Balance Preliminar de la Economia Latinoamericana: 1988 (Santiago: ECLA, December 1988). Hereafter referred to as ECLA 1988.

4 IMF, World Economic Outlook. October 1988, p. 59; William R. Cline, "International Debt: Analysis, Experience and Prospects," Journal of Development Planning, No. 16, 1985, pp. 25-56.
Vietnam, 2017).<sup>5</sup> The Formosa incident wreaked havoc on the livelihoods of local coastal communities residing in the four affected provinces, which happen to rely heavily on saltwater fishery. In May 2016, for the first time in history, the Vietnamese government announced a double-ban on both fishing activity and the processing and selling of seafood caught within 20 nautical miles of central Vietnam provinces, worrying that contaminated seafood in the region might not meet safety standards (VOA, 2016b). The ban would eventually last six months and was lifted in September 2016. However, all near-shore (within 20 nautical miles) deepwater fishing activity continued to be restricted, and would only eventually be lifted in May 2018, after series of inspections from the Health Ministry concluding that seafood caught in the area had met safety standards, and that marine resources had recovered (Vnexpress, 2018).

Figure 1 shows the map of Vietnam (divided into provinces) with a focus on the study area. The shaded provinces in central Vietnam are those directly affected by _Formosa_ : Ha Tinh, Quang Binh, Quang Tri, and Hue (from north to south). The location of Formosa steel plant is geo-coded and shown as the green asterisk on the southern tip of Ha Tinh province. The thick dashed line (in red) indicates the near-shore fishing-restricted zone demarcated by the government that was in effect for six months, between April and September 2016.<sup>6</sup> The thin dash line (in blue) indicates the Maritime Exclusive Economic Zone (EEZ) of Vietnam.

# **3 Data and Empirical Methodology**

## **3.1 Data and summary statistics**

We analyze the impact of _Formosa_ on fishery workers in central Vietnam using two main data sources. We collect labor-market information of fishery workers from the Labor Force Surveys of Vietnam (“LFS”) in 2015 and 2016. The second set of data comes from the Visible Infrared Imaging Radiometer Suite (“VIIRS”), which is a remote-sensing data source storing satellite-imaged night-light luminosity and administered by the National Oceanic and Atmospheric Administration (“NOAA”). We specifically utilize VIIRS’ Boat Detection Module (“VBD”), which processes worldwide night-time light emitted from fishing boats to attract catch. Importantly, VBD also implements an automatic boat detection identification system

> 5 See the detailed time-line pertaining to the Formosa environmental pollution incident in the Appendix.

> 6 As mentioned earlier, this region also defines the _deepwater_ fishing-ban zone which was effective until May 2018.

5
# An Assessment of Water, Sanitation & Hygiene Access in Bangladesh’s Community Health Clinics

George Joseph, Bushra Binte Alam, Anne Shrestha , Khairul Islam, Santanu Lahiri, Sophie Ayling

## JEL: I15; I18, Q01

Key Words: SDG, Health Clinics, Health Centers, WASH access, Water Supply, Sanitation, Bangladesh Corresponding author: George Joseph (Water Global Practice, The World Bank, Washington DC, USA 20433, <u>gjoseph@worldbank.org); Anne Shrestha (Water Global Practice, The World Bank, Washington DC, USA 20433, ashrestha7@worldbank.org); Bushra Binto Alam( Health Nutrition and Population Global Practice, The World Bank, Dhaka,</u> Bangladesh; balam@worldbank.org; Khairul Islam, Water Aid Bangladesh, Dhaka , Bangladesh,khairulislam@wateraid.org; Santanu Lahiri, Water Global Practice, The World Bank, Dhaka, Bangladesh, <u>slahiri@worldbank.org); Sophie Ayling (sophie2@ayling.com, InterAmerican Development Bank, Washington DC, USA 20577</u> This paper is a product of an assignment conducted for the FAO and World Bank using data collected by Community Based Health Care (CBHC) with support from World Bank and WaterAid Bangladesh. This was made possible by the Swedish International Development Cooperation Agency and benefited from funding from the Government of Japan through the Japan Trust Fund for Scaling Up Nutrition. We would like to thank the valuable contributions of Prof. Dr. Md. Abul Hashem Khan, Dr. Ashish Kumar Saha, Er. Md. Ziaul Haque, Dr. Barendra Nath Mandal, Dr. Farzana Munmun, Ms. Soma Ghosh Moulik, Mr. Arif Ahamed, Ms. Rokeya Ahmed, Mr. Abu Ahmed Mansoor Kabir, Mr. Mohammed Abu Hamid, Mr. SM Rezaul Islam, Mr. Mohammed Abu Hamid, Mr. Bakhtiar Sohag, Mr. Sahil Deo, Ms. Sanjana Krishnan, Mr. Akramul Haque and Mr. Tareq Mahamud.

The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of FAO or The World Bank, its Board of Executive Directors, or the governments they represent. FAO or The World Bank does not guarantee the accuracy of the data included in this paper.
# What the Mean Measures of Mobility Miss: Learning About Intergenerational Mobility from Conditional Variance<sup>1</sup>

Md. Nazmul Ahsan, Saint Louis University M. Shahe Emran, IPD, Columbia University Hanchen Jiang,University of North Texas Forhad Shilpi, World Bank JEL Codes: I24, J62, O12

> 1Emails for correspondence: nazmul.ahsan@slu.edu (Md. Nazmul Ahsan); shahe.emran.econ@gmail.com (M. Shahe Emran); Hanchen.Jiang@unt.edu (Hanchen Jiang); fshilpi@worldbank.org (Forhad Shilpi). We would like to thank Matthew Lindquist and Charlie Rafkin for valuable comments on an earlier draft. The standard disclaimers apply.