rafmacalaba/data-use-mentions-extended huggingface dataset:
0.004)|(0.002)|(0.002)|
|Eligible child||-0.042***|-0.025**||-0.046***|-0.032***||-0.047***|-0.034***|
|||(0.012)|(0.010)||(0.011)|(0.008)||(0.011)|(0.008)|
|Method|DD|DDD|DDD-FE|DD|DDD|DDD-FE|DD|DDD|DDD-FE|
|Observations|22,737|76,951|76,951|67,431|226,400|226,400|67,788|227,559|227,559|
|Mean|0.538|0.534|0.534|0.529|0.520|0.520|0.530|0.521|0.521|

Note: Column 1-3 are restricted to PODES years only. Column 4-6 infer preschool data in-between PODES years using the closest upper year available, e.g. year 1992 sandwiched between PODES 1990 and 1993 will use 1993 round. Column 7-9 predict preschool density using linear projection with the closest two data points available, e.g. year 1992 fits a linear projection using preschool density data in PODES 1990 and 1993. Column 1, 4, 7 regress work participation on preschool density in DD method. Column 2, 5, 8 regress work participation on the interaction of preschool density and preschool eligibility dummy in DDD method. Column 3, 6, 9 add individual fixed effect to the DDD method. All regressions include district, year, mother’s age fixed effects and an urban residence dummy; and do not include district-specific trends. Panel A and B look at the effect of public and private preschool densities
23 The main effect of rising dollar costs is tu discourage fixed capital holding and encourage the hoLding of inventories and trade credit. So when Uruguay slowed the devaluation rate in the latter 1970s, capital accumulation was induced by both net worth expansion (due to higher net earnings) and by shifts in portfolio composition. 1/ No doubt part of the explanation for the latter Iies in the high import contenc of manufacturing s'ctor capital.

Recalling that the net worth effects of a devaluation were also found to be strong, it is worth asking how they combined with the substitution effects identified above. Referring back to section D.1(c), one may confirm that net worth ef A.cts compound the negative substitution effect of a devaluation on cash and trade credit. So devaluation strips firms of their most liquid assets because strong net worth and substitution effects compourd one another.

# Sales Growth

Rapid sales growth appears to induce short run substitution toward cash, securities, and inventories. This shift is financed by reductions in fixed capital (per unit net worth) and by increased dollar borrowing. Except for the latter effect, therefore, firms which improve their sales performance tend to become more liquid; at least initially. Some of the substitution effects induced by sales growth might seem, at first gLance, counterintuitive. But one must keep in mind that the total portfolio response to a change in sales includes net worth effects as well. Hence, for example, the

- 1/ de Melo and Tybout (1986) found a negative association between the real exchange rate and the leveL of investment during the post 1973 period using Uruguayan macro data. Since low real exchange rates are associated with low rates of devaluation, this finding seems to conform to the micro results reported here.
trade in a country case study by using more recent and more detailed trade data in a comprehensive analysis of trade for Indonesia, separating impacts of domestic and external lockdowns on trade. This is unlike other studies on Indonesia such as Eschachasthi (2022), Ing and Vadila (2022), and Olivia et al. (2020). Also existing studies on Indonesia, we assess lockdowns in the context of trade by sea and air. Lockdowns disrupted both maritime shipping, which is the main mode 4
for each country. In addition to providing quality control on the initial assessments, the purpose of this final stage is to improve the cross-country comparability of the ratings by ensuring that countries are benchmarked appropriately.

Indicators based on surveys present averages by country of the responses of a large number of respondents to a variety of questions relating to governance. Typically, survey respondents are asked to rate aspects of governance on a categorical scale. The sampling frame of the surveys we use varies. Some sources focus on the opinions of 4
4 handful of studies (Kateregga, 2009; Abdullah and Mariel, 2010; Karki et al., 2010; Twerefou, 2014; Oseni, 2017).

Our analysis is based on a nationally representative survey of Nepali households, collected as part of the World Bank Multi-Tier Framework for Assessing Energy Access Program. The survey was done less than a year since the residential load shedding had been eliminated, and the respondents were asked to indicate their willingness to pay to avoid the number of days with outages they had experienced before the termination of the load shedding schedule of October 2016. The uniqueness of our empirical setting yields several important advantages for using this contingent valuation approach. First, stated preferences allow us to get around the issue that it is not possible to estimate, and thus infer the value of lost electricity service from, a residential electricity demand function, as tariffs in Nepal are the same throughout the national territory, do not vary over the day, and have stayed the same for the last 7-8 years. Second, we take advantage of the respondents' _actual experience_ with improved reliability of power supply, and by construction avoids a problem frequently faced in stated preference studies—namely unfamiliarity with the good to be valued (Mitchell and Carson, 1989). Finally, thanks to collected data on _actual outages_ at the transformer substation-level, we can validate the quality of respondents' recall – an issue in contingent valuation studies, for which typically there is no easy solution (Hanemann, 1994).

Our analysis starts with calculating the WTP per kWh lost (i.e., the Value of Lost Load, VoLL) given assumptions about the load or exact information about the kWh used in a typical day. We then calculate the WTP per outage-day avoided, and analyze its key drivers, accounting for potential endogeneity between reported WTP for reduced outages and the choice of backup equipment. Finally, we assess the internal validity of our estimates by regressing the WTP on the
consumption falls below the poverty line. We defined village-based poverty from the PODES, based on the Indonesian family planning board’s (BKKBN) census of poor households. The indicators are whether a household has an earth floor, whether household members have separate clothing for work and leisure, whether they are able to perform their religious duties, and if _rel_ households are able to seek medical care when needed. Relative poverty is defined as _pij_ = _pij_ / _~~p~~ j_ where _pij_

> <sup>_~~p~~_</sup> _j_ the fraction of poor households in village<sup>_i_</sup> in region j. is the mean poverty rate in all villages in district j.  Our relative measure was based on a question which asked whether there had been a recent increase in these “poverty letters.”

23
be biased by adjustment of the dependent variable for the unemployment rate. This might happen for instance if people were working informally and declaring themselves unemployed in order to receive benefits as is indicated by above-mentioned regression coefficients for passive labor market policies. However, analysis of unadjusted data yields very similar results to those presented in Table 4.<sup>38</sup> Due to limited data available, we were not able to examine differences between old European countries and NMS group.

**_Table 4. Results of Model 2 – dependent variable CONTRIB_**

||S2 – Model 2.2|S3 – Mode|l 2.3|
|---|---|---|---|
||Total|Total||
|EPL|222.1883|9.4872|**|
|MW|-0.7356|0.0106||
|TU||0.2190|*|
|TAX|-0.4715 ***|-0.3733|**|
|LMPA|-127.6588 ***|-138.9402|***|
|LMPP|36.2993 ***|63.5549|***|
|lnGDPPC|19.5971 ***|16.0823|***|
|FISF|-0.3434 ***|-0.1456|***|
|BUSF|0.3849 ***|0.0196||
|CORR|-18.2655 ***|-21.1944|***|
|REGQUAL|13.9020|25.5393|***|
|Constant|-41.0798 **|-54.3431|**|

38 Regression estimation results for the share of labor force not contributing to the pension system unadjusted for the unemployment rate used as dependent variable are available from the authors upon request.

22
mental disability, age and gender of the person, education, occupational and marital status of the household head, as well as household income.

Research by Abayasekara and Gunasekara (2019) using 2016 Labour Force Survey data reveals some of the risk factors that predispose young people to become NEET in Sri Lanka. Using binomial and multinomial logistic regression models, the results indicate that the risk factors center on being female, belonging to an ethnic and religious minority, being in the 20–24 age group, having very low or very high levels of education, being English-illiterate, belonging to a low‐income household or to a male-headed household, having young children, and living in a more remote area. The authors also offer important policy recommendations for how to reduce Sri Lanka’s NEET rate and engage more youth in education and in the labor force.

Looking at developed countries, Quintano, Mazzocchi and Rocca (2018) analyze the determinants of the NEET condition in Italy through a step-by-step procedure. They first determine the main characteristics of being NEET, then focus on specific homogeneous clusters of NEETs. The decomposition of the clusters into different probabilities of being NEET enables the effect of various personal characteristics to be verified. Using a bivariate selection probit model based on the propensity to look for a job against the condition of being inactive, the authors assess the influence of unobserved factors on the professional condition of young people. The results confirm the crucial role of education, as well as the importance of economic and social disparities between men and women in the Italian territorial districts.

# 4.  Data and Methodology

## 1. Data and descriptive statistics

<mark>In this paper, we make use of the “</mark> _<mark>Enquête nationale sur l’emploi</mark>_ <mark>”, a nationally representative labor force survey conducted by the Moroccan</mark> _<mark>Haut-Commissariat au Plan</mark>_ <mark>(HCP). Its main objective
# **6. References**

- Adewuya A., B. Ola, T. Adewumi. 2007. The 12-month prevalence of DSM-IV anxiety disorders among Nigerian secondary school adolescents aged 13-18 years. Journal of Adolescence 30(6):1071-1076

- Akresh, Richard and Leonardo Lucchetti and Harsha Thirumurthy. 2011. “Wars and Child Health: Evidence from the Eritrean-Ethiopian Conflict.” _Journal of Development Economics_ . Volume 99, Issue 2, November 2012, Pages 330–340

- Aizer, A., Stroud, L. and Buka, S., 2016. Maternal stress and child outcomes: Evidence from siblings. _Journal of Human Resources_ , 51(3), pp.523-555.

- Alderman, H., John Hoddinott, and Bill Kinsey. 2006. “Long Term Consequences of Early Childhood Malnutrition.” _Oxford Economic Papers_ , 58(3): 450-474.

- Almond, Douglas and Janet Currie. 2011. “Human Capital Development before Age Five.” In O. Ashenfelter and D. Card (Eds), Handbook of Labor Economics 4:1315-1486.

- Almond, D., Edlund, L. and Palme, M., 2009. Chernobyl's subclinical legacy: prenatal exposure to radioactive fallout and school outcomes in Sweden. _The Quarterly journal of economics_ , _124_ (4), pp.1729-1772.

- Baltagi, Badi. 2003. “Economic Analysis of Panel Data.” 3<sup>rd</sup> edition. John Wiley & Sons Ltd, Chichester.

- Baker-Henningham, H. and López Bóo, F. 2010. Early childhood stimulation interventions in developing countries: A comprehensive literature review

- Bedoya, M., Espinoza, K. and Sanchez, A., 2020. Alcohol-induced physical intimate partner violence and child development in Peru. _Oxford Development Studies_ , pp.1-16.

- Berger, E. & C. Spiess. (2011). "Maternal Life Satisfaction and Child Outcomes: Are They Related?" _Journal of
It has long been known that birth rates tend to be higher for the poor in the developing world, which is the main reason why household size tends to be negatively correlated with household consumption or income per person.<sup>14</sup> The same analyses of the DHS data summarized above provide estimates of the differentials in fertility rates.<sup>15</sup> Averaged over 45 developing economies, the total fertility rate is estimated to be 6.2 for the poorest quintile as compared to 3.3 for the richest quintile, with a population average of 4.7. However, to estimate _BRD_ it is simpler to use instead the overall age-specific fertility rate calculated as the number of births per 1,000 women aged 15-49.<sup>16</sup> The overall birth rate per capita of women 15-49 is 154.0 for the poorest quintile versus 113.4 for the population as a whole. The implied _BRD_ is 1.4, under the assumption that the population share of women 15-49 is the same for the poor as the population as a whole. (That assumption may not hold in reality, but it is probably not far wrong.) There are regional differences, with implied _BRD_ values of 1.8 for East Asia, 1.3 for EECA, 1.9 for Latin America, 1.3 for the Middle East and North Africa, and 1.2 for both South Asia and Sub-Saharan Africa.

Returning to the Bidani-Ravallion method, analogously to Figure 2, Figure 3 plots the crude birth rate (CBR) against the poverty rate. (Notice that the correlation with poverty is stronger for the birth rate; the simple correlation coefficient is 0.77, versus 0.51 for the death rates in Figure 2.) For the sample as a whole, the implied birth rate for the poor is 61.3 (standard

> 14 Economies of size in household consumption will attenuate, though probably not eliminate, these differences
high susceptibility to landslides changes depending on the adopted tolerance level. The higher the level of tolerance, the lower the length of roads and highways under high or extremely high susceptibility.

Although this study demonstrated the methodology to identify road susceptibility, accuracy of the result is limited due to the limitation of available data. Lack of comprehensive landslide data resulted in the reduced relative importance of slope towards landslide occurrence. It is reflected as the lowest weight given to slope in the figure 6. To improve this model and support other similar studies, improvement in data collection is necessary. Data on landslides that occur in remote steep areas are needed to improve the accuracy in estimating the weights of the factors that contribute to the occurrence of landslide events. In addition to data on the locations of past landslides, other data related to the impacts of past landslides (e.g., extent, number of damaged houses, and number of affected people) can be used to train machine learning models to predict vulnerability to landslides.

# **5. Conclusions**

Weight uncertainty in mapping susceptibility to landslides can have substantial investment implications. Quantifying and understanding this uncertainty is a key in selecting an acceptable uncertainty tolerance level for robust decision-making. In this study, we propose a new approach that combines machine learning with large-scale sensitivity analysis in an attempt to better quantify factor weights in landslide susceptibility mapping and their associated uncertainties. The proposed approach is demonstrated for a case study of road susceptibility to rainfall-triggered landslides in the CHT and Sylhet divisions of Bangladesh.

The case study results reveal that distance from existing roads and highways is the most influential factor in the occurrence of landslide events, highlighting the vital role that 25
0.01)<br>0.04_∗∗∗_<br>0.04_∗∗∗_<br>(0.01)<br>(0.01)<br>0.09_∗∗∗_<br>(0.03)<br>0.02<br>(0.02)<br>0.00<br>(0.03)<br>_−_0.06_∗∗_<br>(0.03)<br>0.69<br>0.69<br>1,970<br>1,970<br>4,201<br>4,201<br>d projects from AidData<br> and Health Survey; an<br> at the country level. C<br>s whose dwellings inclu|
|rences: Aid Efective<br>Piped water<br>(3)<br>(4)<br>0.13_∗∗∗_<br>0.12_∗∗∗_<br>(0.03)<br>(0.03)<br>0.07_∗∗∗_<br>0.07_∗∗_<br>(0.02)<br>(0.02)<br>0.06_∗∗∗_<br>0.05_∗∗_<br>(0.02)<br>(0.02)<br>0.11_∗_<br>(0.06)<br>_−_0.03<br>(0.05)<br>0.02<br>(0.06)<br>0.04<br>(0.06)<br>0.30<br>0.30<br>1,971<br>1,
|[0.018]|[0.005]|[0.008]|
||-1.596***|-1.563***|-1.314***|-1.779***|-2.593***|
|Other Foods|[0.008]|[0.018]|[0.008]|[0.008]|[0.023]|
||-7.662***|-0.896***|-5.526***|-7.521***|-6.992***|
||[0.084]|[0.003]|[0.117]|[0.193]|[0.253]|
|Observations|21,239|6,766|4,785|5,743|3,945|

Standard errors in brackets, 100 bootstrap replications *** p<0.01, ** p<0.05, * p<0.1 Source: 2012 1-2-3 Survey, DRC Table 6 shows compensated own price elasticities, which has been computed with the twostep approach presented above for expenditure elasticities. All the entries in the table are strictly negative, suggesting that the Slutsky matrix is likely to be negative definite. Own price elasticities vary by area of residence and poverty status. These compensated elasticities provide a means to assess the welfare implications of the change in relative prices induced by the VAT.

Price elasticity of Marshallian demand  _ij_ is related to the compensated elasticities  _ij_ with the Slutsky equation: where  _i_ is the expenditure elasticity for commodity i, and<sup>_s_</sup> _i_<sup>its budget share. Because income</sup> and substitution effects do not counteract, a first order or second order approximation of the welfare loss would respectively overestimate and
SDC Platinum is one of the most widely used databases on research exploring transaction-level issuance data. Some prominent studies such as Henderson et al. (2006), Kim and Weisbach (2008), and Bruno and Shin (2017) use the same database.

> 4 We divide non-financial industries across eight main categories by using the first digit of the Standard Industrial Classification (SIC) codes: agriculture, forestry, and finishing; mining; construction; manufacturing; transportation and utilities; wholesale trade; retail trade; and services.

> 5 We use the list of offshore financial centers as defined by the IMF.

9
>0.0 0.00<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Note: AEs = advanced economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, EMDE = emerging market and developing economy, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa, TFP = total factor productivity. A. GDP-weighted arithmetic average of total factor productivity growth. Includes 53 EMDEs and 29 advanced economies.

B. Number of economies among 29 advanced economies and 53 EMDEs in which potential total factor productivity growth is lower than the previous decade average.

C. GDP-weighted average of GDP per capita differential to advanced economies between 2009 and 2021. In percent of advanced-economy per capita incomes.

D. Simple average of number of climate disasters (1980-2018) and financial crises (1980-2018) per year in each region.
Contract Labor (Abolition and Regulation) Act of 1970. The Industrial Employment Act provides for the definition of job content, employee status and area of work by state law or by collective agreement, after which changes would not be made without getting the consent of all workers.<sup>15</sup> Zagha (1999) points out that this has always made it difficult for businesses ‘to shift workers not only between plants and locations, but also between different jobs in the same plant.’ As a way out of such restrictions businesses may resort to contract workers, as per the provision of yet another law, namely, the Contract Labor Act. This law gives state governments the right to abolish contract labor in any industry in any part of the state. In states where recourse to contract labor has been more restricted as a result, keeping employment below the threshold level of 100 employees or contracting out jobs has been the only way of maintaining flexibility in the allocation of manpower.

Although labor law in India is primarily federal in origin, ample room has been provided for variation across states in effective restriction on labor market transactions by the fact that states have always been responsible for the enforcement of the same laws. They also have the power to amend some of them substantially. Based on the extent to which states have actually used this power for subsidiary legislation since the late 1950’s, Besley and Burgess (2004) have classified states into categories reflecting restrictiveness of labor regulation. Their analysis of shows that states that amended laws in a “prolabor” direction experienced slower growth on the average while those that changed laws in favor of employers grew significantly faster. In this paper we assess the link between industrial productivity and a plant-level indicator of the restrictiveness labor regulation which enables us to control for a potential gap between legislation and enforcement. The indicator we use is the plant manager’s response to the World Bank Investment Climate Survey question on whether or not managers considered labor regulation to be a major or
# 4. Data

This study is based on data of four waves of the Nigerian General Household Panel<sup>14</sup> Survey (GHS 1 – 2010/2011; GHS2 – 2012/2013; GHS3 – 2015/2016; GHS4 – 2018/2019), and one wave of the Nigerian Multiple Indicator Cluster Survey (MICS 5 – 2016/2017). The GHS surveys have been conducted by the Nigerian National Bureau of Statistic in collaboration with the World Bank’s Living Standard Measurement Study (LSMS) team. Each wave covers close to 5,000 households and consists of two visits covering different survey modules; one post-planting and another post-harvest season to account for seasonal variation. The monetary poverty analysis relies on the household roster of the post-harvest visit and the per capita consumption aggregate values.<sup>15</sup> For the multi-dimensional poverty analysis this study complements the post-harvest modules with those from the post-planting survey (see footnote).<sup>16</sup> The MICS covers close to 34,000 households and was conducted by the Nigerian National Bureau of Statistics together with UNICEF<sup>17</sup> . Estimates based on both surveys are presented in weighted form.

While both surveys are nationally representative, the GHS is only representative up to the regional level whereas the MICS is representative at the state level. Even though Nigeria is a very heterogeneous, country that may suggest advantages of using surveys representative at the state level, there are important benefits of also using the GHS survey data for this analysis. Though only representative at the regional level, the GHS surveys allow a comparison of the overlap of monetary poverty and multidimensional poverty in terms of deprivations and provide evidence on the time trend. The MICS, though representative at the state level, allows only the calculation of deprivation levels, as it does not include information to infer about monetary poverty.

> 14 Though a panel survey, the data can be used as an individual level panel across visits