rafmacalaba/datause-encoder-data huggingface dataset:
Okrent, Abigail and Julian Alston. 2012. The Demand for Disaggregated Food-AwayFrom-Home and Food-at-Home Products in the United States, ERR-139, U.S. Department of Agriculture, Economic Research Service.

Philippine Statistics Authority. 2015. _Price Indices_ . The Government of The Philippines. Web. 10 November 2015.

<mark>Puentes, Esteban, Fan Wang, Jere R. Behrman, Flavio Cunha, John Hoddinott, John A. Maluccio, Linda S. Adair, Reynaldo Martorell and Aryeh D. Stein. 2016. “Early Life Height and Weight Production Functions with Endogenous Energy and Protein Inputs.”</mark> _<mark>Economics & Human Biology</mark>_ <mark>22: 65-81.</mark> <mark>Ravallion, Martin. 2009. “How Relevant is Targeting to the Success of an Antipoverty Program?”</mark> _<mark>The World Bank Research Observer</mark>_ <mark>, 24(2), pp.205-231.</mark> <mark>Saavedra, Juan Esteban and Sandra Garcia. 2012. “Impacts of Conditional Cash Transfer Programs on Educational Outcomes in Developing Countries: A Meta-analysis.”</mark> _<mark>RAND Labor and Population Working Paper Series, WR-921-1</mark>_ <mark>.</mark> <mark>Singh, Inderjit, Lyn Squire, and John Strauss. 1986. “A Survey of Agricultural Household Models: Recent Findings and Policy Implications.”</mark> _<mark>The World Bank Economic Review</mark>_ <mark>1.1: 149-179.</mark> <mark>Sonaiya, Emmanuel and S. E. J. Swan. 2004. “Small-Scale Poultry Production: Technical Guide.”</mark> _<mark>Food and Agriculture Organization of the United Nations,</mark>_ <mark>Rome.</mark> <mark>Triyana, Margaret. 2016. “Do Health Care Providers Respond to Demand-Side Incentives? Evidence from Indonesia.”</mark> _<mark>American Economic Journal: Economic Policy</mark>_ <mark>8(4): 25588.</mark> UNICEF 2019. The State of the World’s Children 2019. Children, Food and Nutrition: Growing Well in a Changing World. UNICEF, New York.

<mark>Vogl, Tom S. 2014. “Height, Skills, and Labor Market Outcomes in Mexico.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>107: 84‐96</mark> <mark>World Bank. 2014.</mark> _<mark>A Model from Mexico for the World.</mark>_ <mark>http://www.worldbank.org/en/news/feature/2014/11/19/un-modelo-de-mexico-para-el-mundo. November 15, 2015.</mark> <mark>WHO. 2003.</mark> _<mark>Selected Vaccine Introduction Status into Routine Immunization</mark>_ <mark>. Geneva: World Health Organization. Available</mark> from: <u>www.who.int/immunization_monitoring/routine/schedule_analysis_2003.pdf.</u> <mark>WHO Multicentre Growth Reference Study Group. 2006.</mark> _<mark>WHO Child Growth Standards: Length/Height-for-age, Weight-for-age, Weight-for-length, Weight-for-height and Body Mass Index-for-age: Methods and Development</mark>_ <mark>. Geneva: World Health Organization. Available from:</mark> <u>https://www.who.int/childgrowth/standards/technical_report/en/.</u> 38
Figure **5:** MALAYSIA <!-- Start of picture text --> (a)<br>flit<br>4 .0.S<br>2 _<br>, ° ....Nb).+ ..+ e eq4 .<br>-4 / .<br>-6<br>- f fir t principalcomponefit |_<br>- - - weighte eist average of prnciapl e principal component component_<br>(c)<br>r t<br>10 -<br>a -<br>6<br>4<br>2<br>0 AA<br>-6<br>-8<br><!-- End of picture text -->

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schooling. Secondary education is less common in rural areas, but it becomes more important when the enterprise is located in a rural market town. Nonfarm enterprises in rural Tanzania buy and sell locally with little access to outside markets. Their capitalization is low with a median total value for fixed assets of only US$ 120 per enterprise. The median net earnings per enterprise are only US$ 113; the median value added is equally low at US$ 83 per worker.

# **Informality and labor productivity**

One of the most interesting finding is the difference between the relative productivity of enterprises based on registration, size, sector, and region. Survey data show that about 20% of the sampled enterprises claim to be unprofitable because total annual costs exceed sales revenues.<sup>8</sup> Standard measures such as total value added per worker are therefore unreliable. Total annual sales per average working day are used as an approximate indicator for labor productivity, a measure that also considers seasonal employment patterns and can serve as an approximate welfare indicator.

Tanzania’s rural economy is dominated by informal sector activities.<sup>9</sup> About 27% of rural enterprises in towns are registered compared to 14% in rural areas. There is little variation by enterprise size, economic sector, or region. Asked about reasons for not registering a business, 54% of rural enterprises claim that there is no need to register. However, about 30% of businesses perceive registration and license fees as too high. Informal enterprises have lower sales than their formal counterparts but informal enterprises in the manufacturing and mining sectors, however, report higher sales levels than their formal counterparts. Median sales per labor day for formal enterprises in rural towns are more than double than for their counterparts located in rural areas

> 8 An economic interpretation of this number is difficult given the fact that most entrepreneurs simply estimate their operating costs, which is more difficult than estimating sales revenues.

> 9 Formality of rural nonfarm enterprises is defined as not being registered with any government agency _and_ not complying with any legal obligation concerning taxes, safety, or labor laws. This definition somewhat oversimplifies the Tanzanian reality. Many small enterprises operate under various degrees of semi-formal legal status. For example, they do not register but pay taxes to local authorities.

10
included, that we can reject the null hypothesis that the explanatory variables have no impact on the quantity of fuelwood saved from using the Mirt stove at less than 1% level (F=12.04).<sup>10</sup> We therefore focus our discussion on Model 5. Our interaction terms between round and treatment are our variables of primary interest. Marginal effects of treatments and round together with results of hypothesis tests are presented in Appendix B.

Table 10. Pooled OLS regression results of the determinants of fuel savings

|**`Model_1 Model_2 Model_3 Model_4 Model_5`**|
|---|
|`treat1_round1 29.079** 28.624** 28.165** 35.089** 46.609***`|
|`(13.142) (12.983) (12.933) (14.952) (15.320)`|
|`treat2_round1 36.596*** 37.275*** 37.159*** 45.580*** 69.348***`|
|`(13.134) (13.070) (13.073) (14.874) (16.789)`|
|`treat3_round1 32.098** 31.294** 31.194** 37.109*** 45.501***`|
|`(13.133) (12.903) (12.824) (13.199) (14.771)`|
|`treat4_round1 39.992*** 38.680*** 38.446*** 42.597*** 43.153***`|
|`(14.191) (13.918) (13.834) (14.716) (12.615)`|
|`treat6_round1 17.609 18.734 18.713 28.467* 36.166**`|
|`(13.876) (13.906) (14.008) (16.630) (16.559)`|
|`treat1_round2 37.499*** 37.808*** 37.522*** 45.345*** 55.741***`|
|`(13.364) (13.196) (13.124) (15.118) (15.481)`|
|`treat2_round2 40.183*** 41.398*** 41.280*** 49.911*** 74.065***`|
|`(13.157) (13.145) (13.141) (14.944) (16.874)`|
|`treat3_round2 37.183*** 36.748*** 36.765*** 41.450*** 47.709***`|
|`(13.257) (13.219) (13.190) (13.906) (15.850)`|
|`treat4_round2 31.741** 30.325** 30.092** 33.262** 33.120***`|
|`(13.090) (12.726) (12.703) (13.451) (11.833)`|
|`treat5_round2 42.028*** 41.786*** 41.828*** 44.554*** 44.429***`|
|`(13.793) (13.846) (13.868) (14.687) (14.807)`|
|`treat6_round2 28.046** 28.336** 29.821** 40.695** 51.787***`|
|`(14.166) (14.272) (14.273) (16.657) (16.972)`|
|`treat1_round3 42.365*** 42.882*** 42.440*** 50.410*** 61.407***`|
|`(13.185) (12.910) (12.831) (14.889) (15.476)`|
|`treat2_round3 43.720*** 44.219*** 44.152*** 52.862*** 75.275***`|
|`(13.395) (13.445) (13.467) (15.080) (16.591)`|
|`treat3_round3 39.693*** 38.370*** 38.379*** 42.746*** 48.494***`|
|`(13.387) (13.286) (13.277) (13.725) (15.782)`|
|`treat4_round3 32.293** 31.120** 30.983** 34.958** 36.006***`|
|`(13.085) (12.711) (12.694) (13.809) (12.317)`|
|`treat5_round3 48.275*** 48.920*** 48.781*** 50.522*** 49.918***`|
|`(13.958) (14.221) (14.151) (14.480) (14.353)`|
|`treat6_round3 49.972*** 51.378*** 50.489*** 62.415*** 76.473***`|
|`(13.738) (13.921) (13.985) (16.464) (17.470)`|
|`treat1_round4 40.991*** 41.455*** 41.257*** 48.425*** 59.559***`|
|`(13.153) (12.903) (12.825) (14.782) (15.272)`|
|`treat2_round4 40.621*** 40.736*** 40.732*** 49.306*** 71.626***`|

> 10 �� �<sup>�</sup> <u>/�����</u> ~~w~~ here _k_ is the number of parameters and _n_ is the number of observations. ����<sup>�</sup> ������⁄

**28 |** P a g e
Hence, the concern is increase in inundated area and inundation depths due to climate change will further increase the inundation risks of roads.

This paper has estimated the length of roads (km) exposed to additional inundation risk due to climate change for different types of roads from the GIS overlays of the maps of additional inundation risk zones with the existing road maps for each category. Bangladesh already has one of the densest road networks in the world, leaving little room for extensive growth in the road network in future. In absence of long term planning for additional new roads, this exercise has assumed that total lengths of roads in Bangladesh will increase by another 25% over the existing stock by 2050. The assumed 25% expansion of the road network is applied uniformly across the existing network.<sup>24</sup> Table 5 summarizes the total kilometers of roads likely to be exposed to various degrees of additional inundation due to climate change. Computation indicates less than 2% of roads in 2050 will be subject to more than 1 meter of additional inundation due to climate change, while almost all (87%) will be inundated by up to 0.5 meters due to climate change. (See Table 5.) The total cost of raising roads to avoid further inundation due to climate change has been calculated using DBS=Wearing Course 50 mm thick on average (Taka 8,350 per m<sup>3</sup> ), DBS-Base Course 50 mm thick on average (Taka 7,899 per m<sup>3</sup> ), Aggregate Base Type I- 100 mm (Taka 2,819 per m<sup>3</sup> ), Aggregate Base Type II- 100mm (Taka 2,232 per m<sup>3</sup> ), roadway excavation (Taka 184 per m<sup>3</sup> ) and earth fill from borrow pit (Taka 124 per m<sup>3</sup> ).<sup>25</sup> Table 6 documents the total adaptation cost of the entire road network to offset the additional inundation risk due to climate change for different types of roads. (See Table 6.) The estimated cost for elevating the entire road network to offset additional risk due to climate change is Tk 148.56 billion or $2.12 billion. National and regional roads account for about 12% of the total costs, while the remaining 88% is required for rural and feeder roads.

> 24 Currently quality of road is poor in Bangladesh; most new investments in the road infrastructure are likely to be for upgrading roads to higher standards for instance by paving unpaved roads rather than for expansion. 25 US $1 = BGD Taka 70

13
||**Table 6 – Im**<br>|**pact of BRI**<br>|**CS Standar**<br>|**ds**<br>|||
|---|---|---|---|---|---|---|
|SAMPLE|All Imp|orters|BRICS|Only|Non-BRIC|Simporters|
|VARIABLES|(1)<br>selection|(2)<br>intensity|(3)<br>selection|(4)<br>intensity|(5)<br>selection|(6)<br>intensity|
|Restrictiveness Index|-0.875||-2.179||-0.739||
||[0.187]***||[0.798]***||[0.185]***||
|low income X restr. Index|0.040<br>[0.097]|-2.980<br>[0.812]***|0.198<br>[0.981]|4.457<br>[6.857]|0.181<br>[0.097]*|-2.381<br>[0.849]***|
|lower middle X restr. Index|0.016|-1.405|2.166|-0.694|-0.008|-0.976|
||[0.089]|[0.645]**|[0.904]**|[6.751]|[0.089]|[0.658]|
|upper middle X restr. Index|-0.113|-1.429|-0.287|-5.683|-0.172|-0.946|
||[0.078]|[0.576]**|[0.951]|[4.901]|[0.079]**|[0.651]|
|Low income<sup>d</sup>|-0.614|-1.390|-0.580|-1.270|-0.625|-1.348|
||[0.008]***|[1.126]|[0.040]***|[1.801]|[0.008]***|[1.407]|
|Lower middle<sup>d</sup>|-0.393|-0.475|-0.519|-0.162|-0.386|-0.442|
||[0.010]***|[0.609]|[0.038]***|[1.531]|[0.010]***|[0.718]|
|Upper middle<sup>d</sup>|-0.042|0.435|-0.143|1.056|-0.027|0.341|
||[0.012]***|[0.095]***|[0.045]***|[0.416]**|[0.013]**|[0.092]***|
|ln_tariff|0.005|-0.403|0.010|-0.098|0.004|-0.418|
||[0.006]|[0.062]***|[0.019]|[0.167]|[0.006]|[0.067]***|
|zeta||6.328||7.406||5.792|
|||[1.204]***||[4.575]||[1.302]***|
|zeta_sq||-1.502||-2.254||-1.211|
|||[0.419]***||[1.794]||[0.445]***|
|zeta_cb||0.112||0.217||0.076|
|||[0.049]**||[0.234]||[0.053]|
|mills||1.946||2.586||1.633|
|||[0.678]***||[1.557]*||[0.782]**|
|Observations|73,752|36,414|6,988|3,946|66,764|32,468|
|R-squared||0.430||0.391||0.443|
|Robust standard errors in bracket<br>equals one if there is positive tra<br>equation is the natural logarithm<br>regressions. Error terms clustere|s *** p<0.01, *<br>de between coun<br>of the value of t<br>d byimporter-pr|* p<0.05, * p<br>tries or zero o<br>rade. Importe<br>oduct-yeargr|<0.1. Depend<br>therwise. Th<br>r-time, and pr<br>oups. Supersc|ent variable<br>e dependent<br>oduct-time<br>ript<sup>d</sup>is for d|in the selectio<br>variable in th<br>effects also in<br>ummyvariab|n equation<br>e intensity<br>cluded in all<br>les.|

27
13

- 1995. In contrast to their findings, the sample of countries that have experienced at least ten continuous years of democracy is evenly split between presidential and parliamentary systems (22 countries in each), rather than weighted in favor of parliamentary systems. Table 1 does not necessarily refute the argument in Stepan and Skach (1993). It does suggest that with more extensive institutional databases, research can begin to accommodate new levels of hypothesisbuilding in institutional analysis, especially hypotheses that can explain such different findings as those in Table 1.

Table 1: Presidentialism, parliamentarism, and democratic consolidation among nonOECD countries

|||Regimetyp<br>(Legislative <br>|eduringthedemoc<br>IndexofElectoralCo<br>eaterthan5<br>|raticperiod<br>mpetitiveness<br>)<br>|
|---|---|---|---|---|
|||Presidential|Parliamentary|_Indirectly-<br>elected<br>president<br>(DPI) or semi-<br>presidential<br>Stepan-Skach|
|Non-OECD countries<br>thatweredemocratic<br>at leastoneyear<br>during1975- 1995|DPI Indicators<br>(1975 - 95, 122<br>countries)<br>Stepan-Skach<br>(1973 - 89, 53<br>countries)|68<br>28|40<br>25|14<br>0|
|Number ofnon-<br>OECD countriesthat<br>werecontinuously<br>democratic for at least<br>ten consecutiveyears,|DPI Indicators<br>(1975 - 95, 47<br>countries)<br>Stepan-Skach<br>(1973-89, 22.<br>countries)<br>.|22<br>5|22<br>17|5<br>.<br>0|

Note: This replicates Table 3, Stepan and Skach (1993), using variables from the Database on Political Institutions (DPI). Countries are “democratic” if they score more than 5 on the Legislative Index of Electoral Competitiveness. Regime type scores are from the SYSTEM variable, where 0 = Presidential, 1 ~indirectly elected and 2 = Parliamentary.
we consider behavioral SES measures (Figures 1 and 2). Gender differences persist in skills mostly related to self-management SES (Figure A2). Specifically, we see a significant per– – sistent female disadvantage in a dimension of listening active listening in addition to emotional regulation and perseverance. There are marginally significant (at the 10% level) – female disadvantages in PSDM and a dimension of interpersonal relatedness networking. Overall, these results are more in line with Hyde (2005) and several other meta-analyses which have found that gender differences in SES are small or insignificant.

It is noteworthy that the gender gaps observed in SES measures elsewhere in the literature often do not conform to theory nor existing literature. While women are often expected to develop more _communal_ skills while men are expected to develop _agentic_ skills (Eagly and Wood, 2012), self-reports are generally not found to be higher for women for skills such as listening, empathy, interpersonal relatedness, and collaboration. Among behavioral measures, the slight male advantage we observe in active listening is also theoretically unexpected, but we observe no male advantage in _agentic_ skills such as personal initiative and expressiveness. The slight male advantage in networking is more expected, and the male advantage in emotional regulation matches a common finding in the literature (Ajayi et al., 2022).

# **3.3 Heterogeneity of the gender gap in SES**

We next assess whether measures of cognition, education levels, social desirability and beliefs on gendered abilities are associated with self-reported SES — and whether these associations differ by gender, potentially explaining the purported gender gap in self-reported SES. For example, if self-reported measures are more strongly correlated with social desirability bias relative to behavioral measures, and if men have higher levels of social desirability or the correlation is stronger among men, then this may explain the pattern of gender gaps outlined above. Therefore, in addition to the controls used in estimating the conditional gender gap in Section 3.2, our regressions in this section include measures of cognition, education levels, social desirability and gender norms as well as their interaction with the gender dummy; see 16
**The World Bank Short-Cycle Programs Survey (WBSCPS).** This novel and unique survey collects rich information at the program and institution levels about practices, inputs, and other characteristics typically not reported in administrative datasets. The WBSCPS was administered to SCP directors between September 2019 and October 2020 in five LAC countries: Brazil (states of Ceará and São Paulo), Colombia, the Dominican Republic, Ecuador, and Peru (licensed programs). These countries were chosen based on their availability of SCP sampling frames (see below), the interest of their educational authorities in participating in the study, and their research and policy relevance.

For the survey, the SCP universe (or sampling frame) is the set of all programs offered in a country as reported by official sources. Since the universe size varies greatly across countries, we surveyed all programs in the universe in the Dominican Republic, Ecuador, and Peru (including, for the latter, all licensed programs as of October 2019). For Colombia and Brazil, we applied a stratified random sampling procedure to obtain representative SCP samples at the national level in Colombia and the state level in Brazil. The total sample size is 3,656 programs relative to a universe of 5,657 programs over all five countries. Table A2 shows the sampling methodology by country as well as the assumptions used for power calculations.

We developed a survey instrument to gather comparable information across countries (available at <u>instrument ). The instrument was shared and validated with local authorities, piloted</u> in subsample of programs, and adjusted based on pilots. The instrument is organized around the following topics: student demographics and readiness for the program; admission and graduation requirements; faculty characteristics, hiring, and evaluation; curriculum and practical training; infrastructure; online teaching; costs and financing; oversight and regulation; institutional governance; interaction with industry; job search assistance; competition; and academic and labor 11
the determinants of the success of World Bank projects. They find that the success rate of projects is negatively affected by external factors such as vulnerability to external shocks. They also show that aid dampens the negative impact of shocks on the probability of success of World Bank projects. With the exception of Guillaumont and Laajaj (2006) who find a negative impact of being at war during the project, none of the papers using the IEG database has taken into account the history of conflict as a factor for the success of a project.

In Sections 2 and 3, we present the data on World Bank projects and some descriptive statistics on aid projects in post-conflict situations as well as the econometric model. In Section 4, we explore whether projects in post-conflict countries are likely to be more or less successful than in other developing countries. We particularly investigate the timing of projects with respect to the end of wars, and try to identify when during post-conflict should projects be launched in order to maximize the chances that they succeed. We also explore whether post-conflict situations call for a special sectoral targeting of aid projects. Finally, our main results are summarized in Section 5.

# **2 Data**

The World Bank undertakes development projects in most developing countries. It generally does not implement projects, relying on partners on the ground. The cycle of each project consists of several phases. First, the implementation phase is dedicated to reaching an agreement with the recipient government on both the content and the design of the project. Once approved by the Board, the project enters its implementation phase which is undertaken by the government. The implementation is itself divided into different funding phases, whose frequency and scale is determined during the preparation phase. Each tranche is released, aborted or scaled down by the World Bank depending on the conclusions of the supervision report. The effort put into supervision is decided by the World Bank management. Country Directors allocate funds, including for supervision, from an overall country budget dedicated to different activities. Once completed or aborted, the project is evaluated by the Independent Evaluation Group (IEG) after two years.

IEG was established in 1973 as an independent unit within the World Bank Group. Former President McNamara stated on the thirtieth birthday of IEG: ‘We set up OED [ now IEG ] with professional people, well qualified to examine the activities of their peers and their colleagues at the World Bank, but with no line of authority from me or anyone under me to direct them in whatever they say’ (OED, 2003). It was originally designed to assess the effectiveness of projects, how projects are run by bor3