rafmacalaba/datause-encoder-data huggingface dataset:
for women to spending time with men with whom they are sexually active, and this is more so for younger girls in villages struck by high levels of disruption. This has implications related to fertility and risky behaviors, that we come to below after examining the impacts of ELA on the social ties of young girls and women.

# **5.4.2 Social Networks**

As Figure 8 and Table 8 show, the reallocation of leisure time caused by ELA is not just from time spent with men, but also from time spent with friends, alone or engaged in volunteering/church activities: this is the case for both age cohorts and in low and high disruption villages.

The reduction in time spent with friends can have one of two interpretations. Girls might be substituting this with time spent at ELA clubs, or there might be an overall weakening of social ties between girls that persists post-crisis. Given the different implications of each, we probe further by exploiting a network survey module fielded at baseline and endline. This asked about the number (and identity) of ties to other girls in the village along four dimensions: friendship, for business (income generation), who intimate topics are discussed with, and for credit/finance.

Given network ties are censored at zero, we estimate (1) using a Tobit model. The results in Table 9 reveal the following. The estimates show that for older girls, high Ebola-related disruption weaken networks along two dimensions: business and credit ties. Such weakening of social network structures due to the aggregate shock has both micro and macro implications in the long run [Fogli and Veldkamp 2016].<sup>35</sup> However, ELA clubs curb the loss in social ties for both younger and older cohorts along multiple dimensions in high disruption villages. For younger girls, these offsetting impacts are _._ 337 friendship ties (16% of the baseline mean), _._ 459 for business ties (63% of the baseline mean), and _._ 579 for credit ties (66% of the baseline mean). We see similarly protective effects of ELA clubs on the social ties of older girls along three dimensions. The reported p-values on the significance of � _β_ 3 confirm the impacts of ELA clubs on social networks do not significantly differ between low and high disruption villages with two exceptions: for the older age group, there is a significantly greater protective effect in fostering ties for discussing intimate topics or for credit in high disruption villages relative to low disruption villages ( _p_ = _._ 046 _, ._ 003 respectively).

The fact the ELA intervention might help protect social ties is a strong marker of the longer run benefits of the intervention and the ability of young women to recover from the aggregate shock. Our findings on the impact of the intensity of an aggregate shock on networks are novel,

> 35For each cohort, around 75% of ties on each dimension are to others in the same age bracket. This further limits informational or behavioral spillovers occurring between age cohorts.

29
Figure 7.

# Table 5: Comparison of Hazard and Logit Models for Third Birth for Women with 8 or More Years of Education

||Ur|ban|Ru|ral|
|---|---|---|---|---|
||Logit|Hazard|Logit|Hazard|
|||1985–|1994||
|||1 boy an|d 1 girl||
|Starting population<sup>a</sup>|3506|3506|1909|1909|
|Third birth<sup>b</sup>|1397|1653|1057|1279|
|Number of abortions|34|47|62|79|
|Sex ratio (boys per 100 girls)|110_._4|111_._3|118_._4|119_._2|
|||2 g|irls||
|Starting population<sup>c</sup>|1681|1681|942|942|
|Has third birth<sup>b</sup>|1005|1149|719|820|
|Number of abortions|227|275|89|102|
|Sex ratio (boys per 100 girls)|168_._7|173_._8|135_._7|135_._6|
|||1995–|2006||
|||1 boy an|d 1 girl||
|Starting population<sup>a</sup><br>|2367|2367|1763|1763|
|Third birth<sup>d</sup>|502|740|543|887|
|Number of abortions|10|18|43|79|
|Sex ratio (boys per 100 girls)|109_._2|110_._3|123_._5|126_._2|
|||2 g|irls||
|Starting population<sup>c</sup>|1015|1015|842|842|
|Third birth<sup>d</sup>|396|566|430|607|
|Number of abortions|86|121|87|125|
|Sex ratio (boys per 100 girls)|165_._8|164_._5|160_._6|161_._6|

**Note.** The logit models are estimated using only uncensored observations. The hazard models are estimated using all observations and covers the period from beginning of spell to 7.25 years (29 quarters) after the birth of the second child for the period 1985–1994 and from beginning of spell to 5.75 years (23 quarters) after the birth of the second child for the period 1995–2006. a Number of women in period who had one boy and one girl as their first two children. b For logit model the number of women who are observed to have a third birth. For hazard model the predicted number of births that will occur between beginning of the spell and 7.25 years (29 quarters) after the birth of the second child.

> c Number of women in period who had two girls as their first two children.

> d For logit model the number of women who are observed to have a third birth. For hazard model the predicted number of births that will occur between beginning of the spell and 5.75 years (23 quarters) after the birth of the second child.

Table 5 presents the predicted outcomes for urban and rural women for the third spell split by the sex composition of their first two children and when the spell began.<sup>40</sup> For urban women who 40 There is no evidence of sex-selective abortions and very few births for women with two boys and those predictions 44
The results in Table 4 suggest that disease environment effects (the current monsoon rainfall shock) are more salient for boys in the first two years of life. This apparent disadvantage of boys, however, could just as well reflect the interaction of biological and environmental factors than any differences in behavioral responses to child illness on the part of parents. At any rate, based on the coefficients on last completed monsoon rainfall deviations, we do not find much evidence of “discrimination” against girls; the point estimates across columns 2 and 3 are statistically indistinguishable. Indeed, once we net out the larger disease environment effects for boys – using our arguments from Section 4 – the pure _income effects_ are extremely close for boys and girls.

# _Robustness_

As support for our argument that excess rainfall in the current or ongoing monsoon is associated with the disease environment, we examine the determinants of diarrhea episodes in our sample children. DHS surveys record the incidence of diarrhea in the two weeks preceding the day of the interview; 20% of children in our sample reported a bout of diarrhea. Results in Table 5 indicate a significant positive relationship between excess rainfall and contemporaneous diarrhea among children. Insofar as diarrheal morbidity inhibits nutrient uptake and thereby decreases body weight in the short-run, we take this evidence as corroboration that the current monsoon rainfall shock operates through a disease environment channel.

Turning next to the rainfall shock in the last completed monsoon, we perform two robustness checks. First, if our interpretation is correct, then we should see that the implied income effect is much smaller, if not nonexistent, for children residing in urban areas (see also Maccini and Yang, 2009). After all, urban households’ purchasing power is likely to be much less sensitive to the vagaries of the harvest than that of rural households. Indeed, the results reported in Table 6 suggest as much. Using the coefficients in the last column, we can bound the income effect of monsoon rainfall shocks for urban children at no greater than -0.014, which is actually negative but statistically indistinct from zero. This finding bolsters our argument that much of the positive rainfall effect on the body weight of rural children is mediated through fluctuations in agricultural income. Second, we perform a “placebo” test of whether a _future_ completed monsoon rainfall shock – that is, excess 13
Table 3 – continued: Summary Statistics

|Conflict Casualties Tercile|Low I|ntensity|Mediu|m Intensity|High I|ntensity|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Male|Female|Male|Female|Male|Female|
|Panel D – DHS Sample used to est|imate Eq. (1) in|the analysis of|marriage by2|1years old|||
|=1 if married by 21 years old||0.863||0.870||0.884|
|District casualties per 1000||0.272||0.649||1.519|
|||(0.101)||(0.160)||(0.883)|
|Maoist abductions per 1000||1.348||12.927||5.067|
|||(2.676)||(29.516)||(5.585)|
|Age at interview||28.112||28.356||28.526|
|||(5.084)||(5.269)||(5.114)|
|Observations||1259||964||955|

Definition of district controlled by Maoist based either on matches between People’s Army and government classifications as of 2003 (Definition 1) or based on government classification (Definition 2).

39
Despite the great volume of remittance flows, there has been little empirical work investigating remittance-related issues in Vietnam, presumably due to the limited availability of data. The data constraint is particularly acute for the analysis of remittance behavior among migrants since detailed data on migrants themselves are often absent in conventional Vietnamese household surveys (e.g., the VLSS or the VHLSS).

Fortunately for our purposes, data from the 2004 Vietnam Migration Survey have recently become available. This survey collected detailed information on migrants within Vietnam. The main research aim of this paper is to use these data to examine the key factors that influence the remittance behavior of internal migrants in Vietnam. Given the absence of data on recipients, our focus will be on the remitters. We specifically examine, among other things, how the circumstances of migrants at the destination, their link to relatives left behind, and the time spent at the destination influence their remittance behavior. According to the 2004 Migration Survey, more than one-half of migrants sent money/goods home to their relatives during the 12 months prior to interview. Among those who remit, the total value amounted to, on average, about 17% of migrants’ earnings, reflecting the potential importance of remittances to the origin households.

The remainder of the paper is structured as follows. The next section provides a review of the existing theoretical literature on remittance motives and frames some of the research questions of primary interest to this study. A description of the migration survey data used for the empirical analysis is provided in Section 3. Section 4 outlines and justifies the variables used in our empirical model and Section 5 discusses some econometric issues 5
Policy Research Working Paper 8150

# **Abstract**

The paper revisits the issue of poverty-specific purchasing power parities (PPPs), using the most recent (2011) International Comparison Program (ICP) results. The World Bank’s global poverty count uses a common international poverty line—currently $1.90 at 2011 international prices—based on the ICP PPPs for consumption. The use of these PPPs is often criticized for two reasons. First, the ICP PPPs are based on patterns of aggregate household consumption, not the consumption of the poor.

Second, the basket of goods and services used for collecting prices for the ICP is not poverty specific. On the first issue, using data from 28 African countries, the paper concludes that the poverty-specific PPPs estimated with household expenditure survey weights are very similar to the ICP PPPs. On the second issue, poverty-specific PPPs were estimated after removing items deemed to be irrelevant for the poor. The overall effect of removing these items from consumption PPPs is shown to be negligible.

This paper is a product of the he Development Data Group, Development Economics. 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 ydikhanov@worldbank.org, nhamadeh@worldbank.org, wvigiloliver@worldbank.org, tdegefu@worldbank. org, or isong@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
LMDI-I has all the desirable properties of perfect decomposition and consistency in aggregation. Perfect decomposition ensures that the decomposition results obtained do not contain a residual term<sup>7</sup> .

Adapting the Log-Mean Divisia Index-I decomposition, originally developed for sector level analysis, to firm level data, the general formula of our decomposition methodology becomes the following<sup>8</sup> : where s, f, E and Q stand for sector, firm, energy consumption and production, respectively.

Then, change in the energy consumption of the manufacturing industry between the base year (<sup>0</sup> ) and the end year (<sup>T</sup> ) can be decomposed into components as follows: In the equation above:

- Dman is the total change in the energy consumption in the manufacturing industry,

- Dact is the activity effect, the change in the aggregate associated with a change in the overall level of the activity in the manufacturing industry,

- Dstrs is the sector structural effect, the change in the aggregate associated with a change in the relative shares of sectors in overall manufacturing,

- Dstrf is the firm structural effect, the change in the aggregate associated with changes in the relative shares of firms within sectors,

- Dintf is the intensity effect, the change in the aggregate associated with changes in the firm energy intensities.

> 7 See Ang (2004, 2015) for a detailed comparison of different decomposition methods.

> 8 For the mathematical derivation of the formula please see Ang and Liu (2001).

11
harassed and belittled by Chinese employers. Domestic workers work like “slaves”, all day and all night with few breaks and little time to themselves.

Women’s actual experiences varied in how they compared to their perceptions prior to moving. Some found their working and living arrangements to not be very different from what they expected – they expected it to be hard work and it is. “I heard nothing good about it to tell the truth, they said it is difficult. Back then I could not tell what they were talking about but now I see... the hardships of staying here”, textile case study. “Paying for everyday needs is stressful because what we earn is little,” textile worker. Some were happy that it was not as bad as they thought it was going to be, although this group consists mostly of domestic workers who have very positive relationships with their employers: “I got good treatment to be honest but I still heard sad stories from other domestic workers about the nasty treatment they endured”. A third group, the largest, found living and working away to be tougher than expected or were disappointed that their lives did not improve immediately: “I initially thought my life would change, as well as the lives of my children”, textile case study.

Importantly, migrants’ needs and ability to support their children and family household outweigh any negative light in which they perceive migration and female migrants in general. While it is not ideal that they live away from their children – and most had a difficult time leaving those behind – female migrants do what they can to make their living and working arrangements tolerable. They tend to accept the conditions because the alternative is always worse: “I think people endure the pain because they know what is at stake when they lose the jobs they have. The problems which they have left back home are much worse than the bad treatment,” domestic worker.

32
31

# _Table 3_ : Determinants of Tax Morale in 10 Eastern European Countries

|Order probit|Coefficient|z-statistic|Marginal effect|Coefficient|z-statistic|Marginal effect|Coefficient|z-statistic|Marginal effect|
|---|---|---|---|---|---|---|---|---|---|
|||(5)|||(6)|||(7)||
|_Independent variables_<br>Demographic factors||||||||||
|AGE|0.009***|12.64|0.004|0.010***|13.66|0.004|0.010***|12.26|0.004|
|FEMALE|0.188***|8.05|0.074|0.185***|8.09|0.073|0.176***|7.35|0.070|
|EDUCATION|0.006**|2.20|0.002|0.006***|2.48|0.002|0.006**|2.17|0.002|
|Political concern/interest||||||||||
|DISCUSS POLITICS|-0.087***|-4.23|-0.034|-0.090***|-4.50|-0.036|-0.085***|-4.03|-0.034|
|FOLLOW POLITICS|0.083***|8.15|0.033|0.082***|8.17|0.032|0.085***|8.03|0.034|
|Maitial status||||||||||
|MARRIED|0.081***|3.42|0.032|0.074***|3.19|0.029|0.074***|3.05|0.029|
|Risk preference||||||||||
|RISK AVERSE|0.036|1.39|0.014|0.048*|1.90|0.019|0.040|1.49|0.016|
|Religiosity<br>CHURCH ATTENDANCE|0.050***|7.17|0.02|0.048***|7.08|0.019|0.048***|6.69|0.019|
|Employment status<br>||||||||||
|EMPLOYED/<br>SELF-EMPLOYED|-0.079***|-3.18|-0.031|-0.068***|-2.79|-0.027|-0.075***|-2.92|-0.030|
|Trust and Perceived Government Quality||||||||||
|TRUST IN JUSTICE SYSTEM<br>|||||||0.028*|1.76|0.011|
|TRUST IN THE<br>EUROPEAN UNION|0.029*|1.95|0.012||||0.015|0.88|0.006|
|GOVERNANCE QUALITY||||0.017***|2.99|0.007|0.015**|2.31|0.006|
|Country||||||||||
|HUNGARY|Ref. group|||Ref. group|||Ref. group|||
|BULGARIA|-0.357***|-6.72|-0.141|-0.345***|-6.62|-0.137|-0.333***|-6.06|-0.132|
|CZECH REPUBLIC|-0.717***|-14.47|-0.275|-0.747***|-15.01|-0.286|-0.738***|-14.47|-0.282|
|LATVIA|-1.126***|-21.82|-0.403|-1.152***|-22.34|-0.411|-1.168***|-21.86|-0.414|
|LITHUANIA|-1.235***|-23.55|-0.429|-1.221***|-23.92|-0.428|-1.248***|-23.01|-0.430|
|POLAND|-0.953***|-17.76|-0.352|-0.994***|-18.59|-0.365|-0.962***|-17.49|-0.354|
|ROMANIA|-0.996***|-17.69|-0.365|-1.034***|-18.22|-0.376|-1.014***|-17.36|-0.369|
|SLOVAK REPUBLIC|-0.922***|-16.55|-0.343|-0.920***|-16.37|-0.343|-0.951***|-16.11|-0.351|
|SLOVENIA|-0.451***|-8.84|-0.178|-0.473***|-9.30|-0.186|-0.468***|-8.99|-0.184|
|Number of observations|12,299|||12,808|||11,605|||
|Pro>chi2|0.000|||0.000|||0.000|||
|Pseudo R2|0.056|||0.057|||0.058|||

Notes: Coefficients in bold, z-statistics in parentheses, and marginal effects in italics. The symbols *, **, *** represent statistical significance at the 10%, 5% and 1% levels, respectively. As can been seen, HUNGARY is the reference group for the countries.We report the marginal effects of the highest tax morale score (3).
|Unit area value (UAV)|9|•<br>Gujarat<br>•<br>Himachal Pradesh<br>•<br>Jammu and Kashmir<sup>29</sup><br>•<br>Kerala<br>•<br>Delhi<br>•<br>Odisha<br>•<br>Mizoram<br>•<br>Tripura<br>•<br>Sikkim|
|---|---|---|
|Annual rental value (ARV)|12|•<br>Andhra Pradesh<br>•<br>Assam<sup>30</sup><br>•<br>Bihar<br>•<br>Chhattisgarh<br>•<br>Goa<br>•<br>Haryana<br>•<br>Jharkhand<br>•<br>Madhya Pradesh<br>•<br>Tamil Nadu<br>•<br>Uttar Pradesh<br>•<br>Uttarakhand<br>•<br>Meghalaya|
|Flat rate(FR)|1|•<br>Punjab|
|Multiple options for valuation<br>methodology|4|•<br>Maharashtra: CV or ARV<br>•<br>Rajasthan: UAV or by any other method<br>•<br>Telangana: CV or ARV or any such method as<br>prescribed<br>•<br>West Bengal: ARV or CV where ARV cannot be<br>estimated|
|State Acts where valuation<br>methodology is unclear or not<br>mentioned|2|•<br>Manipur (unclear)<br>•<br>Arunachal Pradesh (not mentioned)|

_Source:_ MoHUA 2020.

# Appendix 3.  Other types of financial benefits for female property

# owners

In addition to property-related tax discounts, female property owners may receive other financial benefits, including the following.

- 1) Lower interest rates on home loans: Several banks provide lower interest rates on home loans for properties registered in a female’s name. For instance, the State Bank of India provides a concession of five basis points or 0.05 percent on home loans given to female property owners.<sup>31</sup>

> 29. Jammu and Kashmir’s status changed from that of a state to separate union territories in October 2019. 30. As per the Guwahati Municipal Corporation Act, 1969, as amended up till 2012.

> 31. _Economic Times_ (June 2021), “4 personal finance benefits that women get,”

> https://economictimes.indiatimes.com/wealth/tax/4-personal-finance-benefits-that-women-get/stamp-duty-concessionon-registered-property/slideshow/77738108.cms.

38
several alternative measures of liquidity (turnover, and the already-mentioned Amihud, Amivest, LOT, and Roll measures) for 31 emerging markets over 1987–2000. While he does not perform any econometric test, he shows a significant within-country correlation between all liquidity proxies (with the exception of turnover) and a sharp increase during the crisis period 1997:Q3 to 1998:Q3 (again, with the exception of turnover, which appears unaffected). This type of casual evidence provides the most natural motivation for a deeper econometric study of the kind presented here.<sup>9</sup> The rest of the paper is organized as follows. Section 2 discusses the data. Section 3 presents the methodology and findings. Section 4 concludes.

# **2. Data**

We focus on a variety of emerging markets and crisis episodes over the period April 1994-June 2004. We use stock data rather than bond data, which is usually harder to collect and, when available, covers shorter time spans. The data come from Bloomberg and the Emerging Market Database (EMDB).

High-frequency (daily) financial data for emerging economies is often plagued by missing or, worse, misreported data and their accuracy cannot be taken for granted. We spent considerable time in the selection process to include only the most liquid stocks for each country, in order to minimize the effect of mismeasurement on our results. Indeed,

> 9 In a recent paper, Chen and Poon (2007) find that return volatility Granger-causes a widening of the Amihud ratio, and argue that it is not illiquidity that leads to sharp price declines, but vice versa. For more analysis on emerging market liquidity, see for example Levine and Schmukler (2006).

5