rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 10: Results table for Kazakhstan, by occupation**

||**All**|**Store Clerk**|**Farm**<br>**Worker**|**Rec'ist**|**Software**<br>**Engineer**|**Manager**|**Medical**<br>**Doctor**|**Taxi Driver**|**Schoolteacher**|
|---|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|**_Random assignment sex_**||||||||||
|Male|0.046***|0.100***|0.136***|0.069*|0.075**|0.027|0.038|-0.061*|-0.019|
||(0.013)|(0.036)|(0.038)|(0.038)|(0.032)|(0.031)|(0.037)|(0.031)|(0.032)|
|**_Random assignment age_**||||||||||
|Age 35|0.057***|0.027|0.044|0.035|0.003|0.099**|0.140***|0.031|0.106**|
||(0.018)|(0.053)|(0.050)|(0.055)|(0.049)|(0.043)|(0.053)|(0.048)|(0.045)|
|Age 45|0.085***|0.019|-0.018|-0.016|0.004|0.192***|0.197***|0.122***|0.163***|
||(0.018)|(0.056)|(0.049)|(0.057)|(0.046)|(0.047)|(0.048)|(0.045)|(0.046)|
|Age 55|0.074***|0.042|0.054|0.019|0.015|0.093**|0.189***|0.074|0.112**|
||(0.018)|(0.054)|(0.052)|(0.051)|(0.046)|(0.045)|(0.051)|(0.046)|(0.048)|
|**_Random assignment pay_**||||||||||
|Pay KZT 300000|-0.485***|-0.556***|-0.480***|-0.489***|-0.447***|-0.465***|-0.334***|-0.475***|-0.656***|
||(0.021)|(0.060)|(0.056)|(0.074)|(0.058)|(0.062)|(0.056)|(0.053)|(0.053)|
|Pay KZT 400000|-0.796***|-0.717***|-0.809***|-0.731***|-0.809***|-0.719***|-0.813***|-0.795***|-0.985***|
||(0.022)|(0.057)|(0.052)|(0.077)|(0.057)|(0.053)|(0.055)|(0.053)|(0.047)|
|Pay KZT 500000|-1.062***|-0.953***|-0.959***|-1.048***|-1.031***|-0.963***|-1.103***|-1.104***|-1.317***|
||(0.023)|(0.063)|(0.057)|(0.076)|(0.053)|(0.050)|(0.053)|(0.052)|(0.047)|
|Pay KZT 600000|-1.204***|-1.070***|-1.070***|-1.167***|-1.144***|-1.148***|-1.277***|-1.251***|-1.518***|
||(0.025)|(0.064)|(0.057)|(0.078)|(0.056)|(0.053)|(0.057)|(0.054)|(0.052)|
|Constant|3.672***|3.394***|3.571***|3.459***|3.725***|3.602***|3.897***|3.758***|3.976***|
||(0.029)|(0.062)|(0.057)|(0.083)|(0.060)|(0.053)|(0.055)|(0.056)|(0.055)|
|Sample|All|All|All|All|All|All|All|All|All|
|Observations|22,416|2,802|2,802|2,802|2,802|2,802|2,802|2,802|2,802|
|R2|0.264|0.197|0.228|0.246|0.277|0.277|0.325|0.315|0.412|
|Adjusted R2|0.264|0.194|0.226|0.243|0.275|0.275|0.322|0.313|0.410|

notes: Standard error in parenthesis; .01 - ***; .05 - **; .1 - *; Ordinary least squares; Dependent variable: 1 = Severely overpaid, 2 = Somewhat overpaid, 3 = Fair, 4 = Somewhat underpaid, 5 = Severely underpaid; Controls for survey round not shown.

17
# **Figures**

## **Figure 1.   Demand for Representative CRTS good g in Region r**

<!-- Start of picture text --> Armington Aggregate Demand<br>for CRTS good g in RM r<br>CES<br>σ = sigmadm a/<br>Russia goods<br>Rest of World Imports<br>CES<br>σ = 1.5 * sigmadm<br>Own Regional Market Other Russian Imports<br>CES<br>σ = 2 * sigmadm<br>Imports from Imports from<br>Imports from<br>Regional Market 1 Regional Market q<br>Regional Market m<br><!-- End of picture text --> <u>a/ sigmadm = 3 in CRTS sectors, except in OTH (other goods producing sectors). For OTH</u> we rely on estimates from Ivanova (2005).

75
<!-- Start of picture text --> 45000<br>40000<br>35000<br>30000<br>25000<br>m 1977-2009<br>20000<br>@ 1993-2009<br>15000 m 3 Std. Dev. Outside<br>10000<br>5000<br>fe)<br>Russia OECD Resource China India Korea<br>Rich<br>Countries<br><!-- End of picture text -->
<!-- Start of picture text --> CAMERO N<br>UGANDTM”A<br>GABON CONGO ,<br>WANDA<br>BURUND<br>Legend<br>* = Kinshasa FAA NY’ Z .~<br>Travel Time to Kinshasa - Year 1900<br>Days : ;<br>a<:<br>ia: -< .<br>_<br>Mi s-12 .<br>HM 12-16<br>MM 20-24 | ZA rnB MALAW<br>Copyright: ©2043 Esri, DeLorme, NAVTEQ<br><!-- End of picture text -->
# **II. Statutory pension schemes**

The German pension system is characterized by strong fragmentation with respect to institutions, coverage, contributions and benefit levels. It consists of three main components: (i) the statutory public pension system for workers and employees, including the institutionally separate compulsory pension systems for farmers and artists. Public sector employees are also compulsorily insured but civil servants are not covered by the public pension system; their pensions are paid directly from the budget.The independent pension systems run by the professional associations of doctors, lawyers, and other groups of self-employed are usually also listed in this category since membership is compulsory.

(ii) the supplementary occupational pension schemes for private and public sector employees, and (iii) voluntary private retirement provisions, mostly in form of private life insurance contracts.

The statutory pension schemes provide about 70 percent of all pension benefits while pensions for the civil service account for approximately 10 percent of pension spending. Occupational pension schemes of the private sector provide 5 percent of all pension benefits, supplementary schemes for public sector employees 3 percent, and private retirement provision through life insurance accounts for about 10 percent.'

# Social insurance pension scheme

The public pension system is the most important pillar of old age security in Germany. In 1995, total expenditures amounted to DM 360 billion or about 10 percent of GDP. All dependent employees, i.e. approximately 85 percent of the economically active population, are compulsorily insured; depending on the type of occupation, they are affiliated either with the Workers' Insurance (ArV), the (white-collar) Employees' Insurance (AnV) or the Miners' Insurance (KnR).

The Workers' Insurance consists of 18 insurance institutions at the state level and separate institutions for the railway workers (now privatized) and sailors; the Employees' Insurance and the Miners' Insurance are administered at the federal level. Some occupational groups are insured in separate public pension institutions. The state heavily subsidizes most of the special schemes, particularly in sectors with a declining number of active workers such as agriculture.

Figures for 1993; Deutsche Bank Research 1995 4
13 It is important to emphasize that the descriptive statistic used - share of imports affected by NTBs (which is described in more detail in **Annex** III) - is not a measure of the restrictiveness of NTBe. Rather, it gives a measure of the incidence of NTBs, analogous to some extent to describing sectors as being affected by non-zero tariffs.<sup>9</sup> Table 10: EC IMPORTS FROM HUNGARY AFFECTED BY NTBS APPLIED IN 1988 BY BROAD IMPORT CATEGORIES

||IMPOR<br>1984<br>$'000|TS<br>PRICE<br>%|NTB T<br>THREATS<br>%|YPES<br>QRS<br>%|ALL<br>%|
|---|---|---|---|---|---|
|FOOD PRODUCTS|1123110|64.6|1.9|45.3|64.6|
|AGRIC. RAW MATERIALS|237789|0.1|2.3|5.7|7.6|
|ORES & METALS|450972|14.1|24.9|30.4|32.5|
|FUELS|397098|0.0|17.4|9.0|26.5|
|MNFRS (INC CHEMICALS)|1651383|17.0|3.8|19.4|24.4|
|TOTAL|3860352|27.7|7.0|26.3|35.2|

# Source: See Table 9.

In Table 11 information is given for each SITC division (excluding SITC 9) on the EC's NTB treatment of Hungary and other broad groups of countries (the same as were shown for tariffs in Table 8). The table shows a significantly higher incidence in the application of NTBs against Hungary's trade than against other groups of countries -- even other "socialist" countries -- and against the world in general.

In respect of individual SITC divisions, there are only a few in which the share of trade from Hungary affected by some kind of NTB is lower than that for other groups of countries. The final column also gives a more detailed breakdown of the sectors affected by NTBs than was given in Table 10, although only the figure for "All types of NTBs" is given. The breakdown by different types of broad categories of NTBs is given in Annex I, Table A4.

The textiles and clothing sectors are significant in having very high shares of imports from Hungary, other "socialist" countries, and developing countries affected by NTBs. In contrast, the share of imports from OECD countries affected by NTBs is negligible, reflecting their exclusion from the MFA or the EC textile r6gime. Other important sectors where this type of differential is marked include: petroleum and petroleum products (SITC 33), animals oils and fats (SITC 41), leather goods (SITC 61), paper and paperboard manufactures (SITC 64), iron and steel (SITC 67), sanitary and plumbing equipment (SITC 81) and footwear (SITC 85). However, there are some sectors where the incidence of NTBs against OECD countries is higher, e.g., transport equipment, reflecting the application of VERs by some EC countries against Japan.

9 Some recent econometric work suggests that, within certain bounds, this definition can be of some help in trying to explain trade flows.
on the banking crisis variables change only trivially compared to the full sample in Table 1. In equation 2, we retain all observations but add two variables: number of years and number of years squared from the onset of an ongoing crisis, i.e. they equal 0 for all years subsequent to the crisis end year. Again, results on the original set of banking crisis variables are changed only trivially, and coefficients for the added set are insignificant, and far smaller in absolute value. Time from crisis onset appears to matter equally for aid disbursements, whether or not the crisis has "officially" ended (as coded by CKLN). This finding is consistent with the finding, discussed below, that the fiscal effects of crisis linger long after the bank bailouts have ended.

Equation 3 of Table 3 includes the non-systemic crises coded by CKLN, in addition to the LV systemic crises, more than doubling the number of crises in the sample. This change does nothing to reduce the estimated depth of the crisis-induced fall in aid, and in fact deepens it somewhat by reducing the coefficient on the quadratic term (and hence weakening the eventual rebound in aid). Including non-systemic crises expands the number of donors with multiple crises over our sample period, thus introducing more noise into our crisis variables, which count up from the onset of the first crisis. We therefore in equation 4 drop the four countries (Turkey, Iceland, UK, and US) with multiple crises. Point estimates on the crisis variables change very little, but significance weakens with this one-sixth reduction in the sample size.

To summarize, banking crises reduce predicted aid by perhaps 20 to 25 percent below trend, with the largest effect coming only about 10 years after crisis onset. Moreover, it is likely that the relationship is a causal one, flowing from financial-sector troubles to reduced aid. It is implausible that future reductions in aid outflows trigger banking crises, of course. And while it is theoretically possible that an omitted third variable (such as unbridled greed, or poor executive governance) could cause both banking crises and aid reductions several years after the crisis, we find this unlikely. In the case of the current downturn, for example, the Bush Administration had increased aid substantially even as the conditions for a financial crisis were gathering.

_Effects of donor-country income levels_ As expected, the level of per-capita income (in logs) of a donor country is a strong, robust, and very significant predictor of the amount of ODA that the country gives each year. In the Table 1 regressions predicting disbursements _,_ we find that the coefficient on log GDP per capita is 13
Policy Research Working Paper 8573

# **Abstract**

Women’s ownership, use, and control over property matter for their well-being and agency and can influence outcomes for the second generation—women’s daughters and sons. Additionally, gender gaps in property ownership induce allocative inefficiencies and foregone economic output, thus having economywide implications. This paper uses data for 28 countries in Sub-Saharan Africa to shine a spotlight on gaps between men and women in land and housing (property) ownership and analyze patterns across and within countries. The results indicate that men are about three times as likely as women to claim sole ownership over property. Gender gaps are smaller if joint ownership is taken into consideration, but still materially disadvantage women. Men are significantly more likely to own property than women even after controlling for a host of other factors. This paper is an important step toward a better understanding of gender gaps in property ownership in Africa and outlines an agenda for future data collection and analytic efforts.

This paper is a product of the Gender Global Theme and a background paper to the World Bank report Accelerating Poverty Reduction in Africa. 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/research. The authors may be contacted at igaddis@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
# **C Contributions of Breastfeeding and Vaccination**

Our estimates suggest that ultrasound access reduced under-5 mortality by 1.996 p.p. (column (3) in Table 3) and increased the likelihood of being breastfed for at least 24 months by 27.5 p.p. (result available upon request). Since there was no significant effect on breastfeeding during the first year of birth, we assume that the 27.5 p.p. increase took place between 12 and 24 months from birth. According to the World Health Organization (2000), breastfeeding between the ages one and two decreases mortality by 50 percent relative to no breastfeeding. Applying this factor to the share of children who are being breastfed and the mortality rate during 12-24 months,<sup>62</sup> the implied mortality rate for breastfed children is 1.35 percent<sup>63</sup> and is 2.7 percent (1.35 * 2 = 2.7) for non-breastfed children in the 12-24 months range. This implies that not being breastfed during the 12-24 months age range increases the risk of mortality by 1.35 p.p. (2.7 - 1.35 = 1.35). If breastfeeding disparities (during 12-24 months) were the only cause of under-5 mortality differences by gender, the EFM decline due to improvements in the breastfeeding gender gap would be 0.16 p.p. (0.275 * 1.35 = 0.371). Thus breastfeeding explains about 19 percent (0.371/1.996 = 0.186) of the estimated EFM decline.

Moreover, we find that ultrasound availability increased the probability of a child receiving at least one vaccination by 0.076 to 0.118 (triple-interaction coefficients in column (6) of Table 4. The average number of vaccinations (conditional on receiving at least one vaccination) for girls preceded by a firstborn girl during the early diffusion period is 6.64 in NFHS data. Thus the estimated effects for at least one vaccination translate into an average increase in the number of vaccinations of 0.505 (6.64 * 0.076 = 0.505) to 0.784 (6.64 * 0.118 = 0.784). Oster (2009) suggests that each vaccination reduces mortality during ages 1 to 4 by 0.26 p.p.. Thus the implied effect on EFM through vaccination is 0.131 p.p. (0.26 * 0.505 = 0.131) to 0.204 p.p. (0.26 * 0.784 = 0.204), which translates into 7 percent (0.131/1.996 = 0.066) to 10 percent (0.204/1.996 = 0.102) of the decline in EFM.

Note that the mortality measure is 12-36 months in Jayachandran and Kuziemko (2011) and is 1-4 years in Oster (2009). However, any exogenous change in these mortality measures would generate an almost one-to-one change in the mortality measure we use, i.e., death before age five.

> 62We assume that the share of children who are being breastfed and the mortality rate during 12-24 months are the same as those for 12-36 months used in Jayachandran and Kuziemko (2011).

> 63 Solving 0 _._ 481 _x_ + 2(1 _−_ 0 _._ 481) _x_ = 2 _._ 05 yields _x_ = 1 _._ 35, where 0.481 is the fraction of children aged 12 to 36 months that are being breastfed in the sample analyzed by Jayachandran and Kuziemko (2011).

72
OZIER - REPLICATION REDUX – p. 40 of 41

# Appendix A: Recalculating the headline number

## **Appendix Table A1: Estimated and calculated effects on school participation**

|||**Orig**<br>|**inal**<br>|**Rev**<br>|**ised**<br>|
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
||Treatment (direct effect)|0.0547**|0.0536**|0.0553***|0.0578***|
|**ient**<br>**es**||(0.0232)|(0.0233)|(0.0136)|(0.0139)|
|**ffic**<br>**at**|Treatment pupils ('000) 0‐3km|0.04797**|0.04567**|0.03801*|0.04461**|
|**Coe**<br>**stim**||(0.0192)|(0.0182)|(0.0209)|(0.0207)|
|**A.**<br>**e**|Treatment pupils ('000) 3‐6km|‐0.01268||‐0.02429||
|||(0.0153)||(0.0149)||
|**eans**|Treatment pupils 0‐3km|608.3046|608.3046|605.6553|605.6553|
|**B. M**|Treatment pupils 3‐6km|726.8933||1631.4675||
|**lity**<br>**s**|Average externalities 0‐3km|0.0292**|0.0278**|0.0230*|0.0270**|
|**rna**<br>**age**||(0.0117)|(0.0111)|(0.0127)|(0.0125)|
|**Exte**<br>**aver**|Average externalities 3‐6km|‐0.0092||‐0.0396||
|**C.**<br>||(0.0111)||(0.0243)||
|**lity**<br>|Total externalities above|0.0200|0.0278**|‐0.0166|0.0270**|
|**rna**<br>**als**||(0.0135)|(0.0111)|(0.0300)|(0.0125)|
|**Exte**<br>**tot**|Overall deworming effect|0.0747***|0.0814***|0.0387|0.0848***|
|**D.**||(0.0273)|(0.0258)|(0.0321)|(0.0172)|

Table notes: calculations above are author's original calculations based on data and replication files provided by Miguel and Kremer (2014), following and expanding upon some parts of Miguel and Kremer (2014) Table B2. Standard errors in parentheses; * denotes significance at the 10 percent level, ** = 5 percent level; *** = 1 percent level.

Panel A of Table A1 provides excerpted coefficient estimates from a regression of school attendance on a set of right‐hand‐side variables describing who was dewormed where. Columns 2 and 4 provide the original and revised estimates for a simple specification involving the effect of deworming a child, herself, and the externality effect of deworming each additional thousand children within 3 km.
Interaction of Infant Mortality and Fertility and The Effectiveness of Health and Family Planning Programs <!-- Start of picture text --> by<br>Howard Barnum<br><!-- End of picture text -->

# Table of Contents

<!-- Start of picture text --> I. introduction ........................................... 3<br>I. Indonesian Setting ........................................... 6<br>111. Analy" Framework ........................................... 8<br>nuant Mortaty .............................................. 9<br>Total Fertlity Rate .......................................... 10<br>Interaction of Mortality and Fertility .............................. 11<br>Data . 13<br>TV. Empirical Results.15<br>Structural Equation Estimates ......................... 17<br>Reduced Form Estimates ................................... 21<br>V. Policy Implications of Mortality-Fertility Interaction ........... 22<br>Cost Effectiveness of Preventing Births ........................ 24<br>Cost Effectiveness of Preventing Infant Deaths ........... 25<br>Sensitivity of Results .......................................... 28<br>Conclusion .......................................... 31<br>Bibliography........ 33<br>Ajpezndbc ....... 36<br><!-- End of picture text -->
The bulk of these exports were destined for the EU—primarily Italy and France— although this has changed over time. The EU share of Syrian exports declined from 68.3 percent to 40.2 percent between 2000 and 2006, with the MNA region taking up most of the slack (increasing over the same period from 7.8 percent to 23.1 percent) (Figure 2). Part of this can be explained by (anticipated and actual) Syrian entry into GAFTA in 2005. The EU-Syria Association Agreement (EUSAA) may once again shift export patterns between the EU and the MNA region, although this remains uncertain.<sup>6</sup> <!-- Start of picture text --> Share of total 80%<br>exports<br>70%<br>European Union<br>60%<br>50%<br>40%<br>30% Middle East and<br>North Africa<br>20%<br>Eastern Europe<br>10%<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Source: Authors' calculations, from UN COMTRADE (2010)<br><!-- End of picture text --> Figure 2: Changes in export destination patterns, Syria, 2000–2008. Export destination breakdown for the EU (MNA) region includes, in decreasing order of size, the main trading partners of Italy, France, the United Kingdom, and Spain (Saudi Arabia, Jordan, Lebanon, Iraq, and Egypt). Exports to other regions are negligible.

Traditionally, Syrian exports to EU countries have been in (unprocessed) petroleum and derivative oils, and this has likewise been affected by the relative decline in the share of oil exports in total exports by Syria over time. Between 2000 and 2007, nonoil exports (as a share of GDP) grew by almost 200 percent, which accounted for a significant share of the 18 percent growth rate of total exports (as a share of GDP). In contrast, exports from Syria to the other countries of the region have mainly been in food products; the growth of such exports has in fact been the main driver of the increase in MNA-related trade in nonoil products. Table 3, which lists the key products destined for Syria’s main export partners, captures this pattern vividly.

We consider the extent of diversification across products and destination markets more formally by employing several standard (and some nonstandard) measures of export

> 6Negotiations on the EUSAA were completed in 2004, but political circumstances precluded its ratification by the European Parliament. Following an improvement in the political climate, the document was (re)initialed in Dec 2008, and is currently awaiting passage.

8