rafmacalaba/datause-encoder-data huggingface dataset:
which beneficiaries of the same program package would have more opportunities to interact among each other.

Finally, and as part of the program design, a subset of beneficiary women were selected during the registration assemblies to serve as _promotoras_ or leaders of small groups of beneficiary women (approximately 10 per group) in order to further enhance information flows and motivation and to enhance compliance with the various program requirements and conditionalities. Specifically, the _promotoras_ were expected to frequently meet with the beneficiaries in their groups to talk about the objectives and the conditionalities of the program. While these women self-selected to lead these groups, they were randomly allocated to one of the three program packages (see below). The program hence created a lot of new leadership positions for women in these communities, whereas before the program, leadership positions for women in these communities were limited mostly to positions as teachers and health coordinators.

# _2.3. Program randomization_

The program was targeted to 6 municipalities in the Northwest of Nicaragua. These were municipalities that met both criteria of having been affected by a drought the previous year and by the high prevalence of extreme rural poverty based on the national poverty map.<sup>12</sup> From the list of all communities in the 6 municipalities, 56 intervention and 50 control communities were randomly selected through a lottery to which the mayors of the 6 municipalities were invited to attend and participate.<sup>13</sup> Baseline data were then used to define program eligibility based on

> 12 The budget for the pilot allowed targeting 3000 households for a one-year period, which was much smaller than the population of the 6 municipalities. The program was therefore allocated randomly. Households were notified that funding of the project implied that the program would last 1 year, and would only cover the treatment communities. Households in the control communities did not receive any program benefits. They were notified that if there was a decision to scale up the program after the initial year, the control communities would be incorporated. People in the treatment communities understood the program was only to last for a year, and people in the control communities knew that there was a possibility they may receive the program the next year, but they also knew it was likely to depend on the result of the national elections that were to be organized at the end of that year. In that election, the government changed and the project was not scaled up.

> 13 Before the lottery, all communities in the 6 municipalities were grouped in pairs based on similarity in road access, infrastructure, (micro-) climate, crop mix, and proximity. Through the lottery, one community of each pair was selected as a treatment community, the other as control. In case of uneven number of communities, a “pair” consisted of the largest community and the combination of the two other communities. The identification of communities and community pairs was based on maps and discussions with municipality technical personnel.

10
end of the distribution, FCS has a stronger association than HFIAS. However, at the lower tail of the distribution (at the 10<sup>th</sup> and 25<sup>th</sup> quantiles) the effects of FCS and HFIAS on height-for-age are similar.

**Table 1: Height-for-age Z-score (Bangladesh)**

|||**Model**<br>|**1 - Without C**<br>|**ontrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|U<br>|nconditionalq <br>|uantile regressi<br>|on<br>|
|||10th|25th|50th|75th|
|HFIAS|-0.179***|-0.177***|-0.189***|-0.178***|-0.181***|
||(0.010)|(0.018)|(0.013)|(0.012)|(0.013)|
|CDDS|-0.088***|-0.046***|-0.083***|-0.114***|-0.112***|
||(0.010)|(0.014)|(0.012)|(0.012)|(0.015)|
|FCS|0.192***|0.180***|0.186***|0.190***|0.203***|
||(0.011)|(0.017)|(0.013)|(0.013)|(0.014)|
|MDDS|0.128***|0.145***|0.126***|0.120***|0.137***|
||(0.011)|(0.018)|(0.014)|(0.013)|(0.015)|

|||**Mode**|**l 2 - With Co**|**ntrols**||
|---|---|---|---|---|---|
|VARIABLES|OLS|U<br>|nconditionalq <br>|uantile regressi<br>|on<br>|
|||10th|25th|50th|75th|
|HFIAS|-0.032**|-0.036|-0.049***|-0.033**|-0.032*|
||(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|
|CDDS|0.036***|0.062***|0.044**|0.014|0.004|
||(0.02)|(0.03)|(0.02)|(0.02)|(0.02)|
|FCS|0.043***|0.031|0.032*|0.048***|0.045***|
||(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|
|MDDS|0.038***|0.060***|0.038***|0.035***|0.046***|
||(0.01)|(0.02)|(0.02)|(0.01)|(0.02)|

Controlling for potentially confounding factors in Model 2, all four food security measures remain statistically significant in the OLS estimation. Similar to the parsimonious model, even after controlling for confounding factors, on average food consumption score has the strongest correlation with the height-for-age. However, net of the confounding factors, neither food consumption score nor HFIAS are statistically significant at the lower end of the distribution (at the 10<sup>th</sup> quantile). At the lower end of the distribution (at the 10<sup>th</sup> and 25<sup>th</sup> quantiles), the coefficient on CDDS is the highest followed by MDDS.

In the case of weight-for-age, for the parsimonious model, results are qualitatively similar to the ones from height-for-age. All the coefficients on the food security measures are highly significant, with 17
29

# <u>Figure 3: Budget shares and progressivity indices for out-of-pocket health expenditures</u>

Household Budget Share on Out−of−Pocket Health Expenditures <!-- Start of picture text --> Kakwani Progressivity Index of Out−of−Pocket Health Expenditures<br><!-- End of picture text --> <!-- Start of picture text --> 5.1–8.3 0.20–0.38<br>3.9–5.1 0.10–0.20<br>2.9–3.9 0.00–0.10<br>2.3–2.9 -0.10–0.00<br>1.5–2.3 -0.20–-0.10<br>0.0–1.5 -0.57–-0.20<br>no data no data<br><!-- End of picture text --> Kakwani Index for Out−of−Pocket Health Expenditures, Consumption−Based <!-- Start of picture text --> Kakwani Index for Out−of−Pocket Health Expenditures, Income−Based<br><!-- End of picture text --> <!-- Start of picture text --> 0.20–0.20 0.20–0.20<br>0.10–0.20 0.10–0.20<br>0.00–0.10 0.00–0.10<br>-0.10–0.00 -0.10–0.00<br>-0.20–-0.10 -0.20–-0.10<br>-0.20–-0.20 -0.41–-0.20<br>no data no data<br>IBRD 44308 | MARCH 2019<br>This map was produced by the Cartography Unit of the World Bank<br>Group. The boundaries, colors, denominations and any other<br>information shown on this map do not imply, on the part of the World<br>Bank Group, any judgment on the legal status of any territory, or any<br>IBRD 44308 | APRIL 2019 endorsement or acceptance of such boundaries.<br><!-- End of picture text -->

Note: Surveys are the same in the left and right maps in each row, but not necessarily the same in the top and bottom maps in each column.
2020). This dynamic may in part explain the prevalence of a marriage penalty in South Asia. If so, we should expect to see larger marriage penalties among marriage cohorts with more restrictive gender attitudes on average.

To test the role of gender attitudes, we would ideally use survey measures eliciting the husband’s approval of women working outside of the home. However, the DHS does not contain survey questions on men’s attitudes specifically around the labor market participation of their wives. Instead, the DHS contains information on other gender attitudes, including with respect to domestic violence, as well as measures of household decisionmaking roles. Using the DHS women’s module, we compute two measures of gender attitudes: (i) the average agreement in the cohort with five survey questions featuring scenarios under which domestic violence toward a might could be “justified,” and (ii) the average agreement in the cohort with five questions around decisionmaking on household income, expenditures, and child-rearing. For the “attitudes toward violence” index, larger numbers indicate a larger share of female respondents in the cohort agreed with justifications for wife-beating, while for the “decisionmaking” index, larger numbers indicate that the wife participates in more household decisions. These are highly imperfect proxies for the husband’s gender attitudes toward work, since they are taken from wives’ responses and do not directly concern work. Still, they may capture meaningful variation across marriage cohorts in the permissiveness of gender attitudes. For each of these variables, we collapse the average responses at the marriage cohort-event time level and then take the mean within marriage cohorts to get a marriage cohort-specific, time-invariant measure of gender attitudes.

Table 6 and Table 7 contain the results for decisionmaking and domestic violence, respectively. In the all South Asia sample, the marriage penalty when the woman has no decisionmaking agency is 13.9 p.p. However, as agency rises, the penalty falls. A one standard deviation increase in decisionmaking power reduces the by 5.1 p.p. The positive effect of decisionmaking power obtains for all countries in Panel A of Table 6, though the estimate is not always significant and often noisier in the country-level specifications. Similar, though slightly weaker, results obtain in Panel B for the combined family formation penalty. This is likely because child penalties are driven by other factors, for 17
# _Table A.5.5. Aggregate Statistics of Sample by type of ownership (direct vs. indirect)_

|**Ownership type**|**Number**<br>**of firms**|**Operating**<br>**revenues**<br>**(thousand USD)**|**Employment**|**Firms with**<br>**minority**<br>**participation**<br>**[10-24%)**|**Firms with**<br>**blocking**<br>**minority**<br>**participation**<br>**[25-50%)**|**Firms with**<br>**majority**<br>**participation**<br>**[50%-100%]**|**Subnational**<br>**firms**|
|---|---|---|---|---|---|---|---|
|Indirectly owned|13,224|746,100,000|4,025,562|2,841|4,030|6,349|3,009|
|Directly owned|27,372|449,200,000|4,987,658|771|1,296|22,696|17,561|

# _Table A.5.6. Descriptive Statistics of Sample by type of ownership (direct vs. indirect)_

|||**Emp**|**loyment**||**O**|**perating reve**|**nues (thousa**|**nd USD)**||**State par**|**ticipation**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector type**|**Min**|**Median**|**Std. Dev**|**Max**|**Min**|**Median**|**Std. Dev**|**Max**|**Min**|**Median**|**Std. Dev**|**Max**|
|Indirectly owned|1|32|2,690|238,997|0|1,688|489,219|25,176,507|10|49|31|100|
|Directly owned|1|30|5,062|729,281|0|477|345,079|29,853,835|10|100|23|100|

# _Table A.5.7. Aggregate Statistics of Sample by Age_

|**Age bins**|**Number**<br>**of firms**|**Operating**<br>**revenues**<br>**(thousand USD)**|**Employment**|**Firms with**<br>**minority**<br>**participation**<br>**[10-24%)**|**Firms with**<br>**blocking**<br>**minority**<br>**participation**<br>**[25-50%)**|**Firms with**<br>**majority**<br>**participation**<br>**[50%-100%]**|**Subnational**<br>**firms**|
|---|---|---|---|---|---|---|---|
|0-5 years|6,080|40,052,657|393,619|613|933|4,331|3,622|
|6-15 years|13,283|287,600,000|1,495,136|1,249|1,847|8,809|7,127|
|16-25 years|10,174|351,600,000|2,842,481|920|1,268|7,457|5,186|
|26-50 years|6,638|196,200,000|1,930,631|445|624|5,279|3,253|
|50+ years|2,682|248,500,000|1,536,169|233|417|1,962|1,056|

# _Table A.5.8. Descriptive Statistics of Sample by Age_

|||**Em**|**ployment**||**O**|**perating rev**|**enues (thous**|**and USD)**||**State part**|**icipation**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector te**|||||||||||||
|**yp**|**Min**|**Median**|**Std. Dev**|**Max**|**Min**|**Median**|**Std. Dev**|**Max**|**Min**|**Median**|**Std. Dev**|**Max**|
|0-5 years|1|12|706|41,422|0|210|57,037|2,235,392|10|100|32|100|
|6-15 years|1|25|852|58,319|0|394|278,548|20,486,548|10|100|32|100|
|16-25 years|1|37|8,260|729,281|0|934|561,361|29,853,835|10|100|31|100|
|26-50 years|1|61|1,751|73,829|0|1,548|258,722|7,377,555|10|100|29|100|
|50+ years|1|110|2,757|78,484|0|3,857|764,532|23,652,460|10|100|32|100|

44
<!-- Start of picture text --> ~<br>Ps<br>— 10<br>G<br>c<br>2S<br>2@ 5<br>=<br>£<br>1980 1990 2000 2010<br>= CPI=GDP deflator<br><!-- End of picture text --> <!-- Start of picture text --> CPI GDP deflator<br>_~ 30<br>x<br>—§<br>© 20<br>&=<br>2<br>= 10<br>rend<br>£<br>0 ee<br>1980 1990 2000 2010 1980 1990 2000 2010<br>—High income—Low income—Middle income<br><!-- End of picture text --> <!-- Start of picture text --> Correlation of CPI inflation Correlation of GDP defaltor inflation<br>0.7 0.5<br>0.6<br>0.4<br>0.5<br>0.4 0.3<br>0.2<br>0.1<br>O.1<br>0 0<br>SPPP DDSh oP oP oP Ss+ SFFP FPFos FPPP PPSh PPPP APPhGP oS SgPo~<br>=_—<«Developed countries Developing countries =—=Developed countries == Developing countries<br><!-- End of picture text -->
# Off Road Speeds Road Speeds

<!-- Start of picture text --> Off Road Speeds Road Speeds On Road Speeds<br>BM verge Oo _<br>co Coe. bel<br><!-- End of picture text --> <!-- Start of picture text --> Mesh Speed<br>Fastest of each<br><!-- End of picture text --> Cost-Distance Linear distance + vertical distance <!-- Start of picture text --> FastestMeshesh ofSpeed each<br>Ze on<br>oS<br><!-- End of picture text --> Generic friction surface bel Friction surface (time to cross) per 30 meters <!-- Start of picture text --> 01<br>hours -005 S<br>hours Sz<br><!-- End of picture text --> Friction surface Large scale | im a Times per 30m add up from service to each cell

<!-- Start of picture text --> Access time<br>Total at pop. center<br>~~<br>| | | thr<br>[<br>:<br>ee | °<br>as: Se<br>Nearest service<br>(healthcare / bank/ muni. HQ)<br><!-- End of picture text -->
# **References**

- Aguiar, Mark and Mark Bils. (2015). "Has Consumption Inequality Mirrored Income Inequality?" _American Economic Review,_ 105(9): 2725-2756.

- Aristei, David and Cristiano Perugini. (2015). "The drivers of income mobility in Europe." _Economic Systems,_ 39(2): 197-224.

- Calvo, Paula Andrea, Luis-Felipe Lopez-Calva, and Josefina Posadas. (2015). “A Decade of Declining Earnings Inequality in the Russian Federation”. _World Bank Policy Research Working Paper No._ 7392. World Bank, Washington, DC.

- Commander, Simon, Andrei Tolstopiatenko, and Ruslan Yemtsov. (1999) “Channels of Redistribution: Inequality and Poverty in the Russian Transition”. _Economics of Transition,_ 7:411-447.

- Cruces, Guillermo, Peter Lanjouw, Leonardo Lucchetti, Elizaveta Perova, Renos Vakis, and Mariana Viollaz. (2015). “Estimating Poverty Transitions Repeated Cross-Sections: A Threecountry Validation Exercise”. _Journal of Economic Inequality_ , 13:161–179.

- Dang, Hai-Anh and Peter Lanjouw. (2013). “Measuring Poverty Dynamics with Synthetic Panels Based on Cross-Sections”. _World Bank Policy Research Working Paper No. 6504_ , World Bank, Washington, DC.

- ---. (2017). “Welfare Dynamics Measurement: Two Definitions of a Vulnerability Line and Their Empirical Application”. _Review of Income and Wealth,_ 63(4): 633-660.

- Dang, Hai-Anh, Peter Lanjouw, Jill Luoto, and David McKenzie. (2014). “Using Repeated CrossSections to Explore Movements in and out of Poverty”. _Journal of Development Economics_ , 107: 112-128.

- Denisova, Iina. (2012). “Income Distribution and Poverty in Russia”. _OECD Social, Employment and Migration Working Paper No. 132_ . Paris: OECD

- Federal State Statistics Service (Rosstat). (2016). _Social and economic indicators of the Russian Federation 1991-2015_ . www.gks.ru/free_doc/doc_2016/year/pril-year_2016_eng.xls

- ---. (2018). _Расходы на выплату пособий и социальную помощь_ (Social assistance expenditures). <u>http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/population/level/#</u> Accessed on February 16, 2018.

- Ferrer-i-Carbonell, Ada, Van Praag, Bernard M.S. (2001) “Poverty in Russia”. _Journal of Happiness Studies_ , 2(2): 147-172

- Fields, Gary S. (2010). "Does income mobility equalize longer-term incomes? New measures of an old concept." _The Journal of Economic Inequality,_ 8(4): 409-427.

30
|Study and Location|Definition of<br>Empowerment|Measurement<br>Concept|Data Sources|Focus|Conclusions|Implication|Scope|
|---|---|---|---|---|---|---|---|
||person herself|||||||
|Malhotra (2002)<br>_Review of 45 studies_|Enhancing assets<br>and capabilities<br>of diverse<br>individuals and<br>groups to<br>engage,<br>influence, and<br>hold accountable<br>the institutions<br>that affect them||Review of 45<br>studies using<br>quantitative<br>and/or<br>qualitative data|Women’s<br>empowerment<br>and gender<br>relations in<br>international<br>development|Empowerment<br>can occur in one<br>or more areas of<br>life, at various<br>levels, and be<br>individual or<br>collective. Most<br>studies focus on<br>the household<br>level and are<br>usually weak with<br>respect to<br>intervening<br>processes; only<br>two studies<br>measure data over<br>time|Overview of<br>indicators used in<br>past studies|List of indicators<br>covering different<br>dimensions (economic,<br>socio-cultural, familial,<br>legal, political and<br>psychological), levels<br>of aggregation<br>(household,<br>community, broader<br>arenas), in which<br>empowerment was<br>considered as an<br>outcome or an<br>intermediary process|
|Brown (2003)<br>_Hypothetical_<br>_example_|Providing<br>empowerment<br>opportunities as<br>necessary<br>prerequisites to<br>altering a<br>person’s<br>potential reality<br>and giving<br>people the<br>means to better<br>themselves|Q-sample<br>(purposive selection<br>of statements on<br>what it means to be<br>empowered), Q-<br>sorting (having<br>participants rank-<br>order them for -4 or<br>disagree to +4 or<br>agree), and Q-factor<br>analysis to show<br>how opinions are<br>patterned into<br>overall perspectives|Participatory<br>method; must<br>enter into reality<br>and become a<br>functional part of<br>the person’s<br>perspective|Poverty<br>reduction|Q-methodology<br>to provide a<br>measure of how<br>strategies that<br>emphasize the<br>material world<br>outside the<br>individual relate<br>to poor people’s<br>realities|Measures must be<br>grounded in the<br>realities of the<br>poor, ad hoc<br>categorizations<br>may not be<br>adequate|Sorting individuals into<br>groups; perceptions|

42
# Figure VIII: Years of Schooling by Sector

<!-- Start of picture text --> 15<br>10<br>5<br>High-Skill Services<br>Low-Skill Services<br>Manufacturing<br>Agriculture<br>0<br>7 8 9 10 11<br>Log GDP p.w.<br>Average Years of Schooling<br><!-- End of picture text --> _Notes_ : The figure plots average years of schooling by sector for all country ˆ year pairs in IPUMS International, against GDP per worker from version 9.0 of the Penn World Tables (Feenstra et al., 2015).

cluded for the computation of agricultural wages. However, the estimated returns to schooling are not dramatically affected when computed using self-employed income as well (Herrendorf and Schoellman, 2017; Rossi, 2022).

**New cohorts are disproportionately more likely to work outside agriculture, contributing significantly to structural transformation out of agriculture** (Schoellman and Hobijn, 2017; Porzio et al., 2022). Porzio et al. (2022) show that a key reason for this is that more recent cohorts are typically more educated, which gives them a comparative advantage in non-agriculture. Human capital growth across cohorts is both a cause and a consequence of structural transformation; on one hand, the entry of more educated cohorts allows the nonagriculture sector to expand (Figure VII.a), while on the other the increase in the demand for agricultural labor driven by technological change and income effects incentivizes the accumulation of human capital (Figure VII.b). These conclusions are consistent with a broader literature arguing that the expansion of formal education is a key ingredient of the transition from stagnation to growth (Galor, 2005).

**Overall, these findings describe a complex relationship between human capital, structural transformation, and economic development** . While human capital growth is a potential tool to accelerate the economy wide transition out of agriculture, a crucial factor is given by the extent to which the labor market is able to absorb an increase in the supply of highly educated workers. Barriers to the growth of advanced skill-intensive sectors are likely to limit the productivity effects of educational expansions, leading to an inefficient utilization of human

16
**Table 6. Destination income and firm average input prices**

|||dep.|var.: frm|-average log|real input|price||
|---|---|---|---|---|---|---|---|
|||O|LS|||IV||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|log avg. destination gdp/cap|0.07***|0.08***|0.08***|0.07**|0.71***|0.69***|0.68***|
||(0.02)|(0.03)|(0.03)|(0.03)|(0.25)|(0.26)|(0.26)|
|export share of sales||-0.02|-0.00|-0.00|-0.33**|-0.22|-0.22|
|||(0.03)|(0.03)|(0.03)|(0.13)|(0.31)|(0.32)|
|log avg. destination distance|||-0.00|-0.00|-0.00|-0.01|0.06**|
||||(0.00)|(0.00)|(0.00)|(0.01)|(0.03)|
|log sales||||0.02***|0.02***|0.02***|0.01|
|||||(0.01)|(0.01)|(0.01)|(0.01)|
|initial source interactions|Y|Y|Y|Y|Y|Y|Y|
|frm efects|Y|Y|Y|Y|Y|Y|Y|
|year efects|Y|Y|Y|Y|Y|Y|Y|
|N|45659|45659|45659|45659|45659|45659|45659|
|Kleibergen-Paap LM statistic (under-identifcation)|||||248.92|192.30|232.20|
|Kleibergen-Paap LM p-value|||||0.00|0.00|0.00|
|Kleibergen-Paap Wald rk F-stat (weak insts.)|||||2.65|2.09|2.32|
|Anderson-Rubin Wald test F-stat|||||2.17|2.18|2.18|
|Anderson-Rubin Wald test p-value|||||0.00|0.00|0.00|

Notes: Instruments are interactions of indicators for positive exports to destination in 1997 and log relative price level (reciprocal of real-exchange rate) for Portugal’s top 100 non-Euro-zone export destinations; see first-stage results in Table A4. Initial source interactions, included directly as covariates, are defined analogously to the instruments, using indicators for initial imports. Columns 5 treats only log avg. destination GDP/cap as endogenous; Column 6 adds export share of sales, and Column 7 adds log avg. destination distance to endogenous set. Petroleum exports and imports excluded. Robust standard errors in parentheses. *10% level, **5% level, ***1% level.
<!-- Start of picture text --> Eastern and Central Europe<br>co 60.0<br>3°a25 530 546 43.9 46.2 541 54.4 473<br>Be 37.4 358 374 384 40.1<br>s 299 28.0 274<br>nol 242<br>Bo<br>EN 142<br>Tv<br>3<br>Zz<br>Oo<br>& F £ &F & F & F & & F FS<br>Bulgaria Czech Rep. Hungary Poland Russia Slovak Rep. Slovenia<br>Western and Southern Europe<br>68<br>3 67.9<br>#2<br>3. 432 493<br>SewTs 259 27-4 272 ae ee<br>$a 1.9 se 13.4 119 13.0 mee 149<br>3 5.3<br>o er Fs EF FS EF FS EFF FHP EF EF FS EFF FF<br>Austria Cyprus France Germany Norway Portugal Spain Sweden<br><!-- End of picture text --> <!-- Start of picture text --> 5 Asia-Pacific<br>=} 43.2<br>a o 41.6<br>a7 vr<br>g<br>2S<br>So 16.9<br>2N 13.7 14.0<br>% o 10.1 83 96 91 10.5<br>a 49 28<br>2 18<br>~O© V S,) S) s,) Q 2 S) xv S)) 2 V s,) s,)<br>2 XS S FS FS SS S SF<br>Australia Israel Japan New Zealand Philippines<br>& Americas<br>a3 256<br>ZO 18.4<br>> N<br>go 6.4<br>Lé 23<br>Lo<br>ze 36<br>fg 11.9<br>a<br>To<br>=2 @~~.)v RS2)RY RS)S)RY sS© RS~~)2 aSS2<br>Canada Chile USA<br><!-- End of picture text -->