rafmacalaba/datause-encoder-data huggingface dataset:
# **8. Works cited**

ACOSTA, M., VAN WESSEL, M., VAN BOMMEL, S., AMPAIRE, E. L., TWYMAN, J., JASSOGNE, L. & FEINDT, P. H. 2020. What does it Mean to Make a ‘Joint’ Decision? Unpacking Intra-household Decision Making in Agriculture: Implications for Policy and Practice. _The Journal of Development Studies,_ 56 **,** 1210-1229.

AGARWAL, B. 1997. ''Bargaining'' and gender relations: Within and beyond the household. _Feminist economics,_ 3 **,** 1-51.

ALKIRE, S. 2008. 455Chapter 24 Concepts and Measures of Agency. _In:_ BASU, K. & KANBUR, R. (eds.) _Arguments for a Better World: Essays in Honor of Amartya Sen: Volume I: Ethics, Welfare, and Measurement._ Oxford University Press.

ALKIRE, S., MEINZEN-DICK, R., PETERMAN, A., QUISUMBING, A., SEYMOUR, G. & VAZ, A. 2013. The women’s empowerment in agriculture index. _World development,_ 52 **,** 71-91.

ANNAN, J., DONALD, A., GOLDSTEIN, M., MARTINEZ, P. G. & KOOLWAL, G. 2021. Taking power: women’s empowerment and household well-being in Sub-Saharan Africa. _World Development,_ 140 **,** 105292.

ARUGAY, A., DONALD, A., JARVIS, F., JOHNSON, H. C. & VALENCIANO, A. 2024. When Does Decision-Making Reflect Agency?

ASABA, R. B., FAGAN, H., KABONESA, C. & MUGUMYA, F. 2013. Beyond distance and time: gender and the burden of water collection in rural Uganda. _wH2O: Journal of Gender and Water,_ 2 **,** 31-38.

BANDURA, A. 2001. Social cognitive theory: An agentic perspective. _Annual review of psychology,_ 52 **,** 1- 26.

DONALD, A., KOOLWAL, G., ANNAN, J., FALB, K. & GOLDSTEIN, M. 2020. Measuring Women’s Agency. _Feminist Economics,_ 26 **,** 200-226.

HOU, X. & MA, N. 2013. The effect of women’s decision-making power on maternal health services uptake: evidence from Pakistan. _Health policy and planning,_ 28 **,** 176-184.

JACHIMOWICZ, J. M., DUNCAN, S., WEBER, E. U. & JOHNSON, E. J. 2019. When and why defaults influence decisions: A meta-analysis of default effects. _Behavioural Public Policy,_ 3 **,** 159-186.

KABEER, N. 1999. Resources, agency, achievements: Reflections on the measurement of women's empowerment. _Development and change,_ 30 **,** 435-464.

KABEER, N. 2001. Reflections on the measurement of women’s empowerment.

KABEER, N. 2005. Gender equality and women's empowerment: A critical analysis of the third millennium development goal 1. _Gender & Development,_ 13 **,** 13-24.

KAHNEMAN, D., KNETSCH, J. L. & THALER, R. H. 1991. Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias. _Journal of Economic Perspectives,_ 5 **,** 193-206.

MAHMOOD, S. 2016. Feminist theory, embodiment, and the docile agent: Some reflections on the Egyptian Islamic revival. _Readings in the Theory of Religion._ Routledge.

1
Graph 2: Allocation of Credit to the Private Sector by Type of Bank Poland <!-- Start of picture text --> 40<br>30<br>20 -<br>D; ,,,* t-~~~~~~~.-.. ............... *<br>10<br>Dec,89 Jun,90 Dec,90 Jun,91 Sept,91 Dec,91<br>--- State-Owned Special --- State-Owned Commer w Private Cammerci&l<br><!-- End of picture text --> <!-- Start of picture text --> CSFR<br>30 -.<br>A<br>Jo<br>O-Jan-91 0 lMar-SI T- 3 13po 1Sat- On JuBank iep-91 I~~~M Nkv-sI<br>|.- State-Owned Banks -^-- Other Banksl<br><!-- End of picture text -->
_Annex A. Methodology_ _23_

# _Data on causes of death_

The cause of death data for countries and territories used in this analysis can be divided into two groups. Table A2 lists those countries, mostly developed, for which the cause of death data are reasonably reliable, although comparability may still be uncertain. Table A3 lists those countries for which the data are less reliable and presents two indicators of reliability: coverage, as computed from the annual estimated number of deaths, and quality, as measured by the proportion of deaths coded as due to illdefined conditions. Some countries that report data have been exduded because coverage and qualicy are sufficiently poor to render the data unusable.

Of the countries included, China, India, and the U.S.S.R. have data with special characteristics. The Chinese data are from a survey covering 57 million urban residents and 42 million rural residents spread over the eastern part of the country. The 580 thousand deaths recorded, though only 7 percent of estimated annual Chinese deaths, should be reasonably indicative of the mortality pattern for 70-80 percent of the population. ICD-9 codes were used, and all the major causes and the majority of specific causes can be identified. Sample deaths were weighted to reflect the total population.

The Indian data are from a 1986 cause of death survey of 1,200 rural primary health care centers spread throughout India, and covered 10,075 male and 8,187 female deaths (0.2 percent of estimated Indian deaths). Coding was idiosyncratic, but the major causes and some of the specific causes were still distinguishable. Without the base population, mortality rates could not be calculated from the survey. We arbitrarily assumed that age-specific urban mortality rates were fixed percentages of rural rates (at age 1-4, 37.5 percent; at ages 0, 5-14, and 15-44, 67 percent; at age 45-65, 75 percent; and at age 65 and above, 100 percent). With the rural population at 74.5 percent of the total and with a particular age structure of mortality for the country as a whole drawn from Bank projections (see below), we then generalized the survey results to cover the rural population and estimated urban deaths by cause from the regression equations dussed below.

For the U.S.S.R., the data reported to WHO permitted identification of deaths due to diarrhea, most circulatory system diseases (exduding cirrhosis of the liver) and measles. Regression estimates were used to fill in the other causes.

For most other countries, data were available on each cause of death. The major exceptions were the Latin America and Caribbean countnes, for which 1970 but not 1985 data were available on other causes and four specific causes (chronic obstructive pulmonary disease, polio, mental disorders, and micronutrient disorders). Again, regression estimates filled in for the missing data.

# _Projecting mortality_

As noted above, the projection of mortality involved separate projections of life expectancy and infant mortality and the selection of 'split' life tables.
|P152653|2016|2019|12/13/2018|7 Active|Pacific Islands|
|---|---|---|---|---|---|
|P152653|2016|2019|6/19/2019|8 Active|Pacific Islands|
|P152653|2016|2020|12/17/2019|9 Active|Pacific Islands|
|P152653|2016|2020|6/19/2020|10 Active|Pacific Islands|
|P152658|2015|2016|3/2/2016|1 Closed|Georgia|
|P152658|2015|2017|4/26/2017|2 Closed|Georgia|
|P152658|2015|2018|3/13/2018|3 Closed|Georgia|
|P152658|2015|2019|5/17/2019|4 Closed|Georgia|
|P152659|2016|2017|9/23/2016|1 Closed|Gambia,The|
|P152659|2016|2017|3/31/2017|2 Closed|Gambia,The|
|P152659|2016|2018|10/16/2017|3 Closed|Gambia,The|
|P152659|2016|2018|6/29/2018|4 Closed|Gambia,The|
|P152659|2016|2019|1/9/2019|5 Closed|Gambia,The|
|P152659|2016|2020|8/6/2019|6 Closed|Gambia,The|
|P152659|2016|2021|7/16/2020|7 Closed|Gambia,The|
|P152670|2016|2017|7/15/2016|1 Closed|Ghana|
|P152670|2016|2017|6/26/2017|2 Closed|Ghana|
|P152670|2016|2018|8/3/2017|3 Closed|Ghana|
|P152670|2016|2018|6/29/2018|4 Closed|Ghana|
|P152670|2016|2020|10/16/2019|5 Closed|Ghana|
|P152693|2016|2016|2/5/2016|1 Active|Vietnam|
|P152693|2016|2017|8/14/2016|2 Active|Vietnam|
|P152693|2016|2017|2/8/2017|3 Active|Vietnam|
|P152693|2016|2018|8/1/2017|4 Active|Vietnam|
|P152693|2016|2018|1/23/2018|5 Active|Vietnam|
|P152693|2016|2019|7/24/2018|6 Active|Vietnam|
|P152693|2016|2019|1/21/2019|7 Active|Vietnam|
|P152693|2016|2019|6/19/2019|8 Active|Vietnam|
|P152693|2016|2020|12/12/2019|9 Active|Vietnam|
|P152693|2016|2020|6/15/2020|10 Active|Vietnam|
|P152698|2017|2017|6/27/2017|1 Active|India|
|P152698|2017|2018|11/1/2017|2 Active|India|
|P152698|2017|2018|6/30/2018|3 Active|India|
|P152698|2017|2019|1/9/2019|4 Active|India|
|P152698|2017|2019|6/5/2019|5 Active|India|
|P152698|2017|2020|12/4/2019|6 Active|India|
|P152698|2017|2020|3/11/2020|7 Active|India|
|P152698|2017|2020|6/18/2020|8 Active|India|
|P152705|2015|2016|12/30/2015|6 Closed|Djibouti|
|P152705|2015|2016|6/8/2016|7 Closed|Djibouti|
|P152705|2015|2017|12/18/2016|8 Closed|Djibouti|
|P152705|2015|2018|7/11/2017|9 Closed|Djibouti|
|P152705|2015|2018|3/6/2018|10 Closed|Djibouti|
|P152705|2015|2019|9/13/2018|11 Closed|Djibouti|
|P152705|2015|2019|1/3/2019|12 Closed|Djibouti|
|P152705|2015|2019|4/2/2019|13 Closed|Djibouti|
|P152705|2015|2020|12/3/2019|14 Closed|Djibouti|
_ANDR EE_<sup>_ ́_</sup> _DECEMBER - 2021_ _36_ <!-- Start of picture text --> Mozambique 2007-01-01 / 2021-08-01 1. 8 Myanmar 2007-03-01 / 2021-08-01<br>2. 0<br>1. 7<br>1. 8<br>1. 6<br>1. 6 1. 5<br>1. 4 1. 4<br>1. 2 1. 3<br>1. 0 1. 2<br>0. 8 1. 1<br>1. 0<br>0. 6<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>1.3 5 Niger 2007-01-01 / 2021-08-01 2. 0 Nigeria 2011-09-01 / 2021-08-01<br>1.3 0 1. 9<br>1.2 5 1. 8<br>1.2 0 1. 7<br>1.1 5<br>1. 6<br>1.1 0<br>1.0 5 1. 5<br>1.0 0 1. 4<br>0.9 5 1. 3<br>0.9 0 1. 2<br>0.8 5<br>0.8 0 1. 1<br>0.7 5 1. 0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Sep Sep Sep Sep Sep Sep Sep Sep Sep Sep Aug<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Somalia 2007-01-01 / 2021-08-01 South Sudan 2007-01-01 / 2021-08-01<br>1. 7<br>1. 6 7 0<br>1. 5 6 0<br>1. 4<br>1. 3 5 0<br>1. 2 4 0<br>1. 1<br>1. 0 3 0<br>0. 9 2 0<br>0. 8<br>0. 7 1 0<br>0. 6 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2007 2008 2009 2010 2011 2012 2013 2014 2 015 2016 2017 2018 2019 2020 2021<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Sudan 2007-01-01 / 2021-08-01 Syrian Arab Republic 2011-03-01 / 2021-08-01<br>1 0<br>3 5<br> 9<br>3 0 8<br>2 5 7<br>2 0 6<br> 5<br>1 5 4<br>1 0 3<br> 5 2<br> 1<br>2007 2008 2009 2010 2011 2 012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> Figure A4. Estimated food prices and intra-month volatilities in Mozambique, Myanmar, Niger, Nigeria, Somalia, Sudan, South Sudan and the Syrian Arab Republic.

_Note:_ Price of food baskets in local currency, local-market average, January 2015 =1. _Source:_ Figures prepared by the author for this paper. A Live version of all graphs, including graphs at the subnational level, are maintained in the on line data repository.
offers made to individuals in the country of Paraguay, they find that political agents target vote buying to individuals with greater feelings of reciprocity. A large literature on get-out-the-vote campaigns in the US finds that personal or direct contact with households are the most persuasive in getting them to vote on election day, compared to other strategies that are more broadly targeted, such as distributing pamphlets (DellaVigna and Gentzkow, 2009).

This argument might logically extend to comparing the marginal vote-returns from spending on broad public goods versus privately targeted transfers—broadly provided public goods might not generate the same strength of reciprocal feelings in individual voters as do private, one-to-one transfers. Even when an incumbent government provides broad public goods, voters that are targeted with private benefits from a rival party might still vote against the incumbent. Political studies of vote-buying, largely concentrated in Latin America and in poorer constituencies in the US, have provided theoretical arguments as well as empirical evidence that low-income or poor voters are more likely to be targeted with vote-buying offers, and more likely to comply (Harding, 2008; Stokes, 2005; Brusco et al, 2004).

Others have argued for countries like India that since the poor live at or near subsistence levels they are more likely to reward with a vote even a small welfare transfer made directly to their household rather than broad performance in the delivery of public goods (Varshney, 2000; Acharya, 2004). Their private voting action may therefore be at odds with their publicly declared preferences for public goods, such as when they answer questions about priority problems for the village where they invariably mention village infrastructure (Banerjee et al, 2007). Bardhan et al (2008) included a secret ballot in their survey of rural households in West Bengal to examine the correlates of voting for the incumbent Left Front political parties. They find that an individual household’s vote for the incumbent party is significantly correlated only with private and recurring benefits received, in the form of personal help in times of need or benefits from welfare schemes, and not with the availability of local infrastructure.

**Decentralization under heterogeneity of preferences?** Within this political economy framework, would a central political incumbent choose to decentralize the

14
Table 4: On-farm labor supply response to coffee price shocks

||Household|Male|Female|Spouse|Child|
|---|---|---|---|---|---|
||Panel A|: On-farm|labor supp|ly on all|felds|
|Land share of cofee * LogPrice|2.119***|1.876***|-0.804|-0.532|-0.124|
||(0.556)|(0.647)|(0.794)|(0.812)|(0.785)|
|_N_|9870|9870|9870|9870|9870|
|_R_<sup>2</sup>|0.754|0.770|0.691|0.688|0.544|
||Panel B:|On-farm|labor suppl|y on cof|ee feld|
|Land share of cofee * LogPrice|2.508***|1.902***|0.967|1.335*|-0.066|
||(0.657)|(0.627)|(0.702)|(0.681)|(0.637)|
|_N_|9870|9870|9870|9870|9870|
|_R_<sup>2</sup>|0.892|0.869|0.803|0.778|0.557|
||Panel C:|The share|of labor al|located t|o cofee|
|Land share of cofee * LogPrice|0.183***|0.144*|0.084|0.112|0.083|
||(0.069)|(0.079)|(0.087)|(0.083)|(0.098)|
|_N_|9870|9870|9870|9870|9870|
|_R_<sup>2</sup>|0.818|0.803|0.679|0.657|0.529|

Notes: This table is based on three rounds (2011, 2013, and 2015) of ESS survey. Robust standard errors in parenthesis. All regressions include household and zone-year fixed effects, and log rainfall. All dependent variables are measured as inverse hyperbolic sine (IHS) transformation of hours per year to deal with zero values. The share of labor allocated to coffee is defined as the ratio of labor supply on coffee fields to one _plus_ labor supply on all fields, to account for zero values. Male labor supply includes labor supply by all male members above 13 years of age (including the husband if any). Female labor supply includes labor supply by all female household members above 13 years of age (including the spouse if any). Children are defined as household members who are _≤_ 13 years of age. The labor supply measure includes the sum of planting and harvesting labor applied on a field. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _LogPrice_ is the log of average coffee price over the four quarters before the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Table 5: Off-farm labor supply and income

||Worked|Number of|PSNP|Worked as|Number of|Laborer|
|---|---|---|---|---|---|---|
||in PSNP|PSNP days|income|laborer|laborer days|income|
|Land share of cofee|0.037|21.640**|372.586**|0.106|6.459|-263.042|
|* LogPrice|(0.078)|(8.994)|(184.920)|(0.153)|(16.145)|(413.961)|
|_N_|9750|9750|9757|9779|9779|9765|
|_R_<sup>2</sup>|0.668|0.608|0.597|0.572|0.518|0.458|

Notes: This table is based on three rounds (2011, 2013, and 2015) of ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. _Worked in PSNP_ and _Worked as laborer_ are dummy variables indicating whether any member of the household worked in PSNP or as daily laborer. _Land share of coffee_ is the fraction of household land allocated to coffee in 2011. _LogPrice_ is the log of average coffee price over the four quarters before the survey month. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 27
Policy Research Working Paper 7459

# **Abstract**

This paper relaxes the single-factor model of intergenerational educational mobility and analyzes heterogeneous effects of family background on children’s education in villages, with a focus on the role of nonfarm occupations. The analysis uses data from rural China that cover three generations, and are not subject to coresident sample selection. Evidence from a battery of econometric approaches shows that the mean effects of parents’ education miss substantial heterogeneity across farm-nonfarm occupations. Having nonfarm parents, in general, has positive effects, but children of low educated non-farmer parents (with higher income) do not enjoy any advantages over the children of more educated farmer parents. Estimates of cross-partial effects without imposing functional form show little evidence of complementarity between parental education and nonfarm occupation. The role of family background remains relatively stable across generations for girls, but for boys, family background has become more important after the market reform. The paper explores causality using three approaches: Rosenbaum sensitivity analysis, minimum biased inverse propensity weighted estimator, and heteroscedasticity-based identification. The analysis results suggest that the advantages of having more educated parents, especially with nonfarm occupations, are unlikely to be due solely to selection on genetic transmissions. However, the estimated positive effects of nonfarm over farmer parents among the low educated households may be driven entirely by moderate selection on genetic endowment.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. 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 ysun5@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
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Figure 23 presents the results obtained in relation to contributory resources and public transfers, and the contributory and social pension expenditures.

Figure 23. Resources, Expenditures and Financial Result of Pensions per Scheme. Years 2018 – 2080.

a) Resources and expenditures b) Financial result <!-- Start of picture text --> 14%<br>2%<br>12% 0%<br>10% -2%<br>8% -4%<br>6% -6%<br>4% -8%<br>2% -10%<br>0% -12%<br>Contributions Contributory Results Non-contributory Results<br>Public transfers (40%) Total Result<br>2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078 2018 2022 2026 2030 2034 2038 2042 2046 2050 2054 2058 2062 2066 2070 2074 2078<br><!-- End of picture text --> Source: author elaboration based on PROST The introduction of a universal non-contributory scheme generates, of course, an increase in the expenditure level which shifts from 1% of the GDP in 2019, to 2.6% by 2050 and to 4% by 2080 (Figure 23.a). This trend relates to population aging maintaining the contributory coverage relatively stable. However, the fiscal effort that has to be made, if the rule setting transfers to the IESS at 40% of social security expenditures remains in force, is not only increasing but also higher than the noncontributory requirements. As a consequence, fiscal (non-contributory) resources compromised by the IESS system are higher than the financing needs for universal social pension, and in the medium and long terms there will be a surplus (Figure 23.b).

In brief, it is possible to identify several alternatives for reforming the income protection system for older people, both parametric as well as structural, that could relieve the impact of population aging in the medium term and expand the coverage level. The public policy alternatives that were previously analyzed are not exclusionary and a mixture of parametric and structural reforms could be simultaneously taken into account.

33
Table 1: Panel Unit Root Test Results <!-- Start of picture text --> Variables Levin and Lin (1993) Test Statistics<br>._____ _ P=1 P=2<br>TFP -9.42 -6.72<br>NRDN -25.95 -7.49<br>NRDOT -37.66 -5.88<br><!-- End of picture text --> Note: Model under test is specified as:

**_pi_** _Ayi =_ **_aOj_** _+ al jt + 3,yi ,__ **_+_** _t9iLAYi,I-L + i,,_ . The _L=l_

critical values at 1%, 5% and 10% confidence levels are -2.94, -2.23 and -1.84 respectively.

16