rafmacalaba/datause-encoder-data huggingface dataset:
- 24 -

# (Table 6 continued)

|Variables|Public|School TyMe<br>Private 0|Private F|
|---|---|---|---|
|Fewer than 5|-0.38<br>(0.29)|0.02<br>(0.65)|0.87<br>(0.79)|
|Fewer than 3|0.01<br>(0.19)|0.63<br>(0.42)|-0.56<br>(0.43)|
|No response|-2.47**<br>(0.77)|-0.84<br>(1.55)|-2.07<br>(2.29)|
|Student/teacher<br>ratio|-0.01|0.01|0.05|
||(0.01)|(0.03)|(0.04)|
|Teacher's experience|0.001|0.03|-0.06|
|in years|(0.02)|(0.05)|(0.06)|
|Teacher's education|0.004|0.02|1.13***|
|in years|(0.06)|(0.26)|(0.34)|
|Class periods|0.03**|0.002|0.10**|
|taught elsewhere|(0.01)|(0.03)|(0.04)|
|Length of mathematics|-0.01|0.03|-0.03|
|period|(0.02)|(0.10)|(0.06)|
|Minutes on routine<br>administration|-0.002<br>(0.01)|0.02<br>(0.02)|0.02<br>(0.02)|
|Minutes on|-0.002|-0.05|-0.004|
|establishing<br>order|(0.01)|(0.03)|(0.02)|
|Lambda|-1.22|-2.91|-2.25|
||(0.71)|(1.91)|(2.14)|
|N (students)|1619|402|453|
|Adjusted R<sup>2</sup>|.20|.19|.60|

Note: Tne numbers are regression coefficients; the standard errors are in parentheses.

***p < .001, ** p < .01, * p < .05.
Policy Research Working Paper 8580

# **Abstract**

Despite the obvious positive health impacts of tobacco taxation, an argument raised against it is that poor households bear the burden of the increased prices because of their higher share of spending on tobacco. This note includes estimates of the distributional impacts of price rises on cigarettes under various scenarios using the Household Income and Expenditure Survey 2016/17. One contribution of this analysis is to quantify the impacts by allowing price elasticities to vary across consumption deciles. This shows that an increase in the price of cigarettes in Bangladesh has small consumption impacts and does not significantly change the poverty rate or consumption inequality. These findings stem from relatively even cigarette consumption patterns between less and more well-off households. These results hold even considering some small substitution through the use of bidis, which are largely consumed by the poor. The short-term consumption impacts are also negligible compared with the estimated gains because of savings in medical costs and the greater number of productive years of life.

This paper is a product of the Poverty and Equity Global Practice. 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 afuchs@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
|Water Catchment Project Indicators ...................................................................................................... 25|
|---|
|IPF Results: Water Catchment ................................................................................................................. 29|
|Water Catchment IPF Matrix .................................................................................................................... 33|
|Comparing IPF to Selection of Water Catchment Projects ............................................................... 34|
|Applying IPF to Road Transport ................................................................................................................36|
|Transport Policy Goals and Road Project Criteria ................................................................................36|
|Road Transport Project Sample ...............................................................................................................36|
|Transport Project Indicators ..................................................................................................................... 37|
|IPF Results: Road Transport ..................................................................................................................... 38|
|Road Transport IPF Matrix ........................................................................................................................ 39|
|Comparing IPF to Funding of Transport Projects ................................................................................ 39|
|Chapter 5. Conclusion ..................................................................................................................................... 40|
|References .......................................................................................................................................................... 42|
|Annex 1. Chilean Law Relevant to Project Appraisal and SNI ............................................................. 44|
|Annex 2. Water Catchment Raw Project Data ...................................................................................... 45|
|Annex 3. Water Catchment Project SEI Calculations, by Region ...................................................... 47|
|Annex 4. Water Catchment Project FEI Calculations, by Region...................................................... 50|
|Annex 5. Road Transport Raw Project Data ........................................................................................... 53|
|Annex 6. Road Transport Project SEI Calculations, by Region (with standard poverty rate) .... 54|
|Annex 7. Road Transport Project SEI Calculations, by Region (with multidimensional index|
|poverty rate) ...................................................................................................................................................56|
|Annex 8. Road Transport Project FEI Calculations, by Region .......................................................... 58|

3
# **Appendix Table 2: Kitagawa-Oaxaca-Blinder Decomposition of Labor Force Participation (Paid+Unpaid** **<u>Work) by Age Group</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Overall|Age 15-29<br>Endow.|Coeff.|Overall|Age 30-64<br>Endow.|Coeff.|
|Male|0.752***|||0.938***|||
||(0.008)|||(0.005)|||
|Female|0.667***|||0.828***|||
||(0.008)|||(0.007)|||
|Raw Difference|0.085***|||0.110***|||
||(0.011)|||(0.008)|||
|Difference due to|-0.012***|||0.021***|||
|Endowments|(0.005)|||(0.004)|||
|Difference due to|0.097***|||0.089***|||
|Coefficients|(0.011)|||(0.010)|||
|Individual Lives in an||-0.000***|0.016**||-0.002***|0.021**|
|Urban Area||(0.000)|(0.008)||(0.000)|(0.009)|
|Individual Age||-0.015|-0.422||0.006***|-0.060|
|||(0.012)|(0.537)||(0.001)|(0.429)|
|Individual Age Squared||-0.004|0.289||-0.008***|-0.032|
|||(0.012)|(0.277)||(0.001)|(0.207)|
|Number of Adults in HH||0.002|0.007||0.000|0.015|
|||(0.001)|(0.015)||(0.000)|(0.011)|
|Number of Children Aged 0-||-0.003|0.015*||-0.000***|0.004|
|<br>5 in HH||(0.002)|(0.009)||(0.000)|(0.008)|
|Individual owns a Financial||0.015***|0.007||0.014***|0.001|
|Account||(0.003)|(0.006)||(0.003)|(0.010)|
|Individual is Married||<br>-0.007|<br>0.005||<br>0.002**|<br>0.083***|
|||(0.005)|(0.011)||(0.001)|(0.031)|
|Individual is Divorced/Widowed/||0.001|-0.000||-0.003**|0.006*|
|Separated||(0.001)|(0.000)||(0.002)|(0.003)|
|Individual Completed Secondary||-0.008***|-0.010***||0.002**|-0.004|
|Schooling||(0.001)|(0.003)||(0.001)|(0.003)|
|Individual owns a Vehicle||0.002*|0.011||0.012***|0.004|
|(Bicycle, Motorcycle, Car)||(0.001)|(0.008)||(0.003)|(0.016)|
|Wealth PCA - Based on||0.004***|-0.000||-0.002***|0.000|
|Home Condition||(0.001)|(0.000)||(0.001)|(0.000)|
|Wealth PCA - Based on||<br>0.001***|<br>-0.001||<br>0.000|<br>0.000|
|Appliances Ownership||(0.000)|(0.001)||(0.000)|(0.001)|
|<br>Individual has a Disability||<br>0.000|<br>-0.000||<br>0.000**|<br>0.000|
|||(0.000)|(0.000)||(0.000)|(0.000)|
|Constant|||<br>0.179|||<br>0.050|
||||<br>(0.259)|||<br>(0.220)|
|Observations|10,711|10,711|10,711|11,941|11,941|11,941|
|Robust standard errors in parenthese|s. *** p<0.01,|** p<0.05, * p<0.1.|||||

44
# Exchange Rate Policy, the Real Exchange Rate, and Inflation: Lessons from Latin America

by Miguel A. Kiguel*

||Table of Contents||
|---|---|---|
|I.|Introduction|1|
|II.|Analytical Framework|2|
||i.<br>Long Run Determinants of the Real Exchange Rate<br>ii. Short Run Determinants of the Real Exchenge Rate<br>iii. The Scope for Exchange Rate Policy|2<br>4<br>5|
|III.|Exchange Rate Policy in Latin America|6|
||Exchange Rate Regimes|6|
|IV.|Exchange Rate Policies and the Real Exchange Rate|9|
||When Do Devaluations Work?<br>The Real Exchange Rate Under the Crawling Peg|9<br>11|
|V.|The Exchange Rate as a Nominal Anchor in Disinflation|13|
|VI.|Undervalued Real Exchange Rates and Inflation|16|
|VII.|Main Findings|19|
|Refe|rences|21|
|App|endix|22|

*F This paper was prepared for the conference on "La dinamica de los mercados internacionales y politicas comerciales para el desarrollo," El Escorial, Spain, July 1991. I am grateful to Jose Antonio Ocampo, Manuel Agosin, and conference participants at El Escorial for helpful commnents, and to Heidi Zia for research assistance.
premium gained by greater levels of education of girls who postponed childbirth.<sup>13</sup> Since wages are not reported separately for adolescent mothers and non-mothers, we use the average adult female expected wage as an earnings proxy for a girl who postponed childbirth and the average female youth expected wage as an earnings proxy for adolescent mothers. Then the difference between average female wage and the average teenage mother wage is a proxy for the ―cost‖ of early childbearing to lifetime economic productivity:<sup>14</sup> where _I_ is forgone annual earnings; _wF_ is mean annual adult female wage; _EmpF_ is adult female employment rate; _wYf_ is mean annual youth female wage and _EmpYf_ is youth female employment rate.

The above costs correspond to the average costs of early pregnancy for one girl in one year. To measure the total cohort cost per year, we multiply the average cost with the number of adolescent births in the observation year ( _Yf_ ). Assuming these girls will stop working when they are age 60 and these young mothers are age 15—the International Labor Organization’s standard age for the beginning of a work life—when they enter the labor market, we multiply the annual cost of the cohort by the years that the cohort will be working, which is 45 (retirement age minus age of first working). Finally, as above, we state the total country costs as a share of current year GDP.

Several sources of data are used. The wage data are those that were derived for the estimates of early school leaving, reported in section a, above. The World Bank’s HNPStats was used for incidence of adolescent childbirth, and the World Bank’s WDI database was used for GDP values.

While the observed wage gap between teen mothers and other girls persists throughout life, this may be more a factor of differences in innate ability rather than a

> 13 Ideally, we would have replicated the age earnings profiles discussed above in the ―education‖ section and applied the methodology to this section.  Unfortunately, our data only allow us to measure earnings by grade level.  Since adolescent mothers and non-mothers often differ in education level by only a few years, ―grade level‖ is not sufficiently disaggregated to identify wage differences across groups.  Our thanks to one of our referees for the recommendation to explore this methodological approach.

> 14 We could control for the labor market dynamic for females, however this varies substantially across countries and would make cross-country comparisons difficult to interpret.  Instead, we choose to use a constant change factor and provide comparative statics across countries.

12
Policy Research Working Paper 8146

# **Abstract**

Assets generate and help diversify income, provide collateral to access credit, alleviate liquidity constraints in the face of shocks, and are key inputs into empowerment. Despite the importance of individual-level data on asset ownership and control, and that most assets are owned by individuals, solely or jointly, it is typical for the micro data on asset ownership to be collected at the household level, often from only one respondent per household. Even when the data are collected at the individual level, with identification of reported or documented owners of a given asset within the household, the information is still often solicited from a single respondent. Further, the identification of owners is seldom paired with the identification of individuals who hold various rights to assets, limiting understanding of the interrelationships among ownership and rights, and whether these relationships vary across individuals. Through a review of the existing approaches to data collection and the relevant literature on survey methodology, this paper presents an overview of the current best practices for collecting individual-level data on the ownership and control of assets in household and farm surveys. The paper provides recommendations in three areas: (1) respondent selection; (2) definition and measurement of assess to and ownership and control of assets; and (3) measurement of the quantity, value, and quality of assets. Open methodological questions that can be answered through analysis of existing data or the collection and analysis of new data are identified for future research.

This paper is a product of the Gender Innovation Lab, Office of the Chief Economist, Africa Region and the the Living Standards Measurement Study, Development Data Group. 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 cheryl.doss@qeh.ox.ac.uk.

_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
Second, most studies focus on certain aspects of childcare, particularly women’s employment, since women are typically assumed to be the main caregivers for children. Our study looks at a wide range of outcome variables, including employment outcomes (i.e., selfemployed, employed, farm and non-farm, skilled employment, and wage work), householdlevel outcomes (i.e., income, poverty, household size, migration, and co-residence with grandparents), both in the short term and the medium term. <mark>In particular, w</mark> e show that childcare can significantly shift women’s occupations from self-employment to paid employment in Vietnam. By examining a larger number of outcomes, particularly in a developing country context, we aim to provide a more comprehensive analysis of the effects of childcare on maternal employment. Furthermore <mark>, our study is one of very few revealing a positive, but weaker, effect of childcare on men</mark> ’s labor market outcomes.

Finally, we offer the first study that rigorously examines the impacts of childcare for Vietnam. Vietnam is an interesting case to study for several reasons. The country has averaged a solid annual growth rate of around 6% during the past two decades. Yet almost half (44%) the working population is still self-employed in the agriculture sector, and more than two-thirds (68%) of those who work are self-employed.<sup>3</sup> Furthermore, gender inequality still remains a challenge in Vietnam. Women are less likely to participate in the formal work sector and whereas the proportion of men working in a wage job is 42%, the corresponding figure for women is only 30%. This lower female participation rate in the labor market is likely the result of women having to stay at home to take care of their young children. Indeed, although Vietnam has accomplished almost universal primary school enrollment (Dang and Glewwe, 2018), more than half (53%) of children age 1-5 do not attend childcare. Our findings for the strong effects of childcare on women’s labor market outcomes suggest that improving access to childcare, especially for small children, can lead to more effective labor policies and also to women’s empowerment.

This paper consists of six sections. We describe the data sets in the next section, before presenting in Section 3 descriptive analysis of childcare and maternal employment in

> 3 Unless otherwise noted, our estimates are based on the Vietnam Household Living Standards Survey (VHLSS) in 2016.

4
In this paper, I examine the asset gains experienced by households with a family member who migrates internationally and domestically. I focus on the state of Kerala, in southwest India. Kerala has for several decades seen its residents relocating for work both overseas and within India, primarily to neighboring states. For Keralites working abroad, the most common destination is the countries of the Arabian Gulf, where they hold temporary contract-based jobs.<sup>6</sup> Using panel data on migrant and non-migrant families from the 1998 and 2003 waves of the Kerala Migration Survey (KMS), I examine the assets accumulated—a measure of medium to long-term economic welfare—by Keralite households over a five-year period. The KMS is the only panel data set of Indian households currently available that can be examined for changes over time in the outcomes of households with members who migrated overseas or within India.

A key challenge in identifying the welfare consequences of migration is the non-random selection of migrant households. This can result in a biased estimation of the relationship between migration decisions and household-level outcomes. No easy solutions to this challenge exist. My strategy in this paper for mitigating concerns about endogeneity bias is to use panel data in conjunction with an instrumental variables approach. The identification strategy exploits two key facts during the period observed: First, Muslim households were far more likely to send a family member overseas and much less likely to send a member to work within India. Second, as migration for employment from Kerala was predominantly undertaken by males during this period, the likelihood of migration increases with the number of prime-age males in a household.

The results of the analysis show that the assets accumulated by households over the five-year interval in terms of housing investments and purchases of durable goods were positively associated with the migration of a family member for work. The asset gains are also statistically similar between households whose members migrated overseas and those whose members moved within India for

> 6 The 1998 and 2003 KMS data reported that 95 and 90 percent, respectively, of household members in a foreign labor force were residing in the Arabian Gulf.  The Arabian Gulf countries consist of the Kingdom of Saudi Arabia, United Arab Emirates, Qatar, Bahrain, Oman, and Kuwait.

3
**<u>Table 8: Impact of reservation on attendance and nature of participation in panchayat meetings</u>**

||**Attending meeting**|**Participated discussing**|**Relevant issues discussed**|
|---|---|---|---|
|**_Model 1_**||||
|Reserved now|-0.0540***|0.0216|-0.0339*|
||(-7.904)|(1.245)|(-1.934)|
|Reserved now|0.0890***|0.157***|-0.0141|
|* female|(11.98)|(5.172)|(-0.510)|
|Reserved lag1|-0.0430***|0.00547|0.0592***|
||(-5.912)|(0.333)|(4.542)|
|Reserved lag1|0.0463***|-0.152***|0.0879***|
|* female|(5.464)|(-4.687)|(3.412)|
|Observations|92,886|16,453|16,453|
|R-squared|0.344|0.202|0.093|
|Tests|41.39***<br>0.29|26.53***<br>19.14***|1.93<br>27.85***|
|**_Model 2_**||||
|Reserved now|-0.0514***|0.0180|-0.0365*|
||(-6.480)|(0.868)|(-1.699)|
|Reserved now|0.111***|0.212***|-0.0131|
|* female|(12.74)|(6.084)|(-0.427)|
|Reserved lag1|-0.0472***|-0.0298|0.0542***|
||(-5.820)|(-1.543)|(3.431)|
|Reserved lag1|0.0734***|-0.110***|0.107***|
|* female|(7.633)|(-2.892)|(3.762)|
|Reserved lag2|-0.0348***|-0.0784***|-0.0617**|
||(-4.163)|(-3.145)|(-2.374)|
|Reserved lag2|0.126***|0.102***|0.102***|
|* female|(14.77)|(2.717)|(3.337)|
|Observations|70,677|11,920|11,920|
|R-squared|0.389|0.175|0.112|
|Tests|82.56***|32.02***|1.60|
||13.77***|12.37***|25.91***|
||184.99***|0.30|0.94|

Note: In each column, the dependent variable is a zero-one indicator of meting attendance in column 1 and, conditional on having attended the meeting, whether issues relevant to the respondent were discussed and whether s/he participated discussing. Regressions are clustered at household level and information on education, age, gender, marriage status, household size as well as dummies for cast, religion, land ownership, and overall wealth are included in all regressions but not reported.

27
# **How Much Does Reducing Inequality Matter for Global Poverty?**

Christoph Lakner Daniel Gerszon Mahler Mario Negre Espen Beer Prydz<sup>*</sup>

**JEL codes:** I32, D31, O15

**Keywords:** Poverty, inequality, inclusive growth, simulation, machine learning

* All authors are with the World Bank. Negre is also affiliated with the German Development Institute. Contact information: clakner@worldbank.org, dmahler@worldbank.org, mnegre@worldbank.org, eprydz@worldbank.org. The authors wish to thank Shaohua Chen, Francisco Ferreira, La-Bhus Fah Jirasavetakul, Dean Joliffe, Aart Kraay, Peter Lanjouw, Christian Meyer, Prem Sangraula and Renos Vakis, as well as two anonymous referees for helpful comments and suggestions. The findings and interpretations in this paper do not necessarily reflect the views of the World Bank, its affiliated institutions, or its Executive Directors. Part of this work was funded by the UK Department for International Development through its Strategic Research Program (TF018888). This working paper is a substantially revised and updated version of Lakner et al. (2014). The earlier version focused on changes around the bottom 40% using a simple step-function growth incidence curve (GIC), whereas this paper considers more general distributional changes in the Gini index and more plausible functional forms of the GIC. Furthermore, this paper offers a more complete assessment of the potential tradeoffs between reducing inequality and increasing growth through the use of iso-poverty curves. Finally, this paper proposes a novel way to estimate the passthrough rate from GDP growth to growth in household survey income or consumption.
<!-- Start of picture text --> 09<br>08<br>07<br>0.6<br>0.5<br>04<br>03<br>0.2<br>0.1<br>0<br>ooanaaonoanqaaqaqgqruagugreqeaqgqneaqaqaagurdaqaqnnaqaqdagendqnrdqennaqdrindadqeagdqanga#gcoo oooqo gogoseo gooseeseesoescoco ceo ooeeseseresesoeeseeseseesoesceocrclucasesesee e e eeoeseseeee see esg&<br>Sggqsgsesadcagaetdaggegsgaggasagadgag<br>SHRAKARSASKLSTHRALSRS<br>Se ergcesee rey egGrFeceseecarlLsrGargarrceTT SITSs<br>a<br><!-- End of picture text -->