rafmacalaba/datause-encoder-data huggingface dataset:
Figure 2 shows that loan applications and approvals develop differently for the three subgroups. Agro loan applications drop considerably between April and June 2008, but interestingly, repeat borrowers are much less discouraged.<sup>16</sup> After this short period of retrenchment loan applications again steadily increase for both new and repeat borrowers (Figure 2a). This is a first indication that the agricultural sector remained mostly unaffected by the economic crisis. Nevertheless, the bank’s tight refinancing clearly affects agro lending because clients were obviously deterred from applying for loans. _Loan approval rates_ (approved loans as a share of applied loans) decrease somewhat, for the new borrowers in particular, during the period of the tight refinancing but remain at high levels of more than 94% afterwards (Figure 3a).

For micro loans, we similarly observe a considerable decrease in loan applications in the second quarter of 2008 for new and repeat loans (Figure 2b). The _approval rate_ analysis (Figure 3b) shows that new micro loans are on average about 4 percentage points less likely to be approved than repeat micro loans and that approval rates are more volatile for new than for repeat micro borrowers. However, while the discouragement effect seems to be smaller for repeat borrowers, their approval rates decrease slightly after the economic crisis hit. These findings suggest that both the bank’s liquidity squeeze as well as the economic downturn affected micro borrowers’ credit availability Figure 2c reveals that the demand for new and repeat SME loans is more volatile; however there is also a clear decline in the number of loan applications after March 2008. Considering the _approval rates_ of SME loans (Figure 3c), we observe a negative trend after the start of the economic crisis. Approval rates for SME loans decrease much more than for agro and micro loans from around 90% to 50% for new SME loans and from above 95% to 75% for repeat SME loans. This indicates that the comparatively bigger firms in our sample are more seriously hit by the crisis. There are several potential reasons for these findings: First, during the tight refinancing period it may have been easier and cheaper for the bank to “save” a certain amount of liquidity by discouraging as well as denying some SME loans in contrast to an even larger number of micro loans. Second, the larger firms may have had to cope with a larger and more persistent decrease in demand for their products once the economic crisis hit implying that lending to the more heterogeneous SMEs became more

> 16 Partly the drop may be due to seasonal effects as agro clients’ income streams usually peak in spring and summer. However, if the decrease was only driven by cyclical patterns in agriculture, effects should have been similar for new and repeat agro borrowers (as is the case exactly one year later during the second quarter of 2009). Apart from that, micro and SME loans show a sharp decrease in loan applications starting at exactly the same point in time.

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# **Figure 1: Stock of Health Care Workers before and after Ebola**

<!-- Start of picture text --> 12000<br>987 964<br>10000<br>8000<br>4408 4353<br>6000<br>4000<br>5480 5457 136 129<br>2000 51 46 1017 945<br>978 900<br>1305 1246<br>815 766<br>0<br>Pre-Ebola May 2015 Pre-Ebola May 2015 Pre-Ebola May 2015<br>Guinea Liberia Sierra Leone<br>Other health workers Nurses and midwives Doctors<br><!-- End of picture text --> Source: Author calculations using data from the WHO.<sup><u>8,10</u></sup> Note: Data on pre-Ebola stock of health care workers is for the most recent years available for each country: 2004 (nurses and midwives) and 2005 (doctors) for Guinea, 2008 for Liberia, and 2010 for Sierra Leone.

While there have been reports of disproportionate infections and deaths among health care workers, we are unaware of any detailed analysis of the likely consequences of these deaths on non-Ebola mortality. This paper addresses this gap in the literature by modeling how the loss of different types of health care workers to Ebola will affect non-Ebola mortality. We estimate that after Ebola has been eliminated, maternal mortality will increase by 38% in Guinea, 74% in Sierra Leone, and as much as 111% in Liberia because of the reduction in health personnel caused by the epidemic. This means that across the three most affected countries, an additional 4,022 women will die per year due to the loss in health care worker resources owing to Ebola. Estimates of the additional deaths among infants and children under five years old have more variation but are likely to be positive and large. In order to avoid these deaths, 240 doctors, nurses, and midwives would need to be hired immediately across Guinea, Liberia, and Sierra Leone. Moreover, in order meet the estimated number of health care workers needed to reach adequate levels of health coverage according to the Millennium Development Goals, a total of 43,565 doctors, nurses, and midwives would need to be hired. These estimates provide broad support to the importance of 3
46 The expenditure share of LPG for all households increased in both urban and rural quintile, while the share for urban user households remained almost constant. The share for rural user households actually increased with quintile level (Table A2.14).

Table A2.14 : Shares of total household expenditure on LPG for all households and for purchasing households in Pakistan (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|<br>**Rural**|**User households**<br>**Urban**|**Total**|
|---|---|---|---|---|---|---|
|**1**|0.1|0.1|0.1|2.5|4.4|2.9|
|**2**|0.1|0.2|0.1|2.6|4.4|3.0|
|**3**|0.2|0.4|0.2|2.8|4.9|3.4|
|**4**|0.3|0.3|0.3|3.3|4.4|3.6|
|**5**|0.6|0.4|0.5|3.3|4.6|3.7|
|**All**|0.3|0.3|0.3|3.1|4.6|3.5|

_Source:_ Authors’ calculations.

# **Sri Lanka (HIES 2006–2007)**

The data are taken from the 2006–2007 Household Income and Expenditure Survey (HIES) administered by the Department of Census and Statistics in the Ministry of Finance and Planning of the Government of Sri Lanka. The fieldwork for the survey was carried out between July 2006 and June 2007.

The value of all non-purchased items was about 18 percent of total purchased expenditure, while non-purchased food accounted for about 25 percent of all non-purchased items. These ratios were nearly constant across quintile groups. All expenditures on food items were deflated using a Laspeyres index provided in the survey data set.

The questionnaire asked about the average expenditure on and quantity of LPG purchased in a month as well as the household’s primary cooking fuel. Twenty-six percent of households reported LPG purchase in the previous month while 17 percent named LPG as their primary cooking fuel. Firewood was the most commonly cited primary cooking fuel (Figure A2.4), even in the top quintiles. According to a separate question, 83 percent of households collected some firewood and a quarter of households purchased it.

Figure A2.4: Main source of energy for cooking across all households in Sri Lanka <!-- Start of picture text --> Firewood 80<br>LPG 17<br>Kerosene 2.4<br>Sawdust/paddy husk 0.21<br>Electricity 0.14<br>Others 0.55<br>0 20 40 60 80 100<br>Percentage of households<br><!-- End of picture text --> _Source:_ Authors’ calculations.
- 20 -

With Background Held Constant. What I. tbe Private School Effect?

The estimated differential in the achievement of public and private school students can be computed from the parameters presented in Table 3, to hold constant the effect of background. Because private and public school achievement differs in terms of intercept and slope, the comparison is affected by the values of other explanatory variables, as well as the coefficients in these equations. Thus, we compute the unconditional private school effect as follows: from the entire sample of private and public school students, consider a randomly chosen pupil with the average characteristics of a public school student (i.e., standardized according to the public school means). The unconditional effect measures the change in the student's test scores had that student gone to a private school. The same calculation can be performed standardizing at the F-type and 0-type means to test the robustness of the results, which are presented in Table 5a.

Table 5a: Private School Effects on Grade 8 Mathematics Achievement, Dominican Republic, 1982-83, Holding Background Characteristics Constant and Controlling for Selection Bias (Two-Stage Correlation.)

|Characteristics<br>of the Randomly<br>|<br>Pre<br>|dicted Sc<br>|ore<br>|Differ<br>|gntial<br>|
|---|---|---|---|---|---|
|Chosen Student Set at Mean of:|Public|0-Type|F-Type|0-Pub.|F-Pub.|
|Public school subsample|9.79|12.86|17.26|3.08|7.47|
|Private 0-type subsample|10.31|12.67|17.41|2.36|7.10|
|Private F-type subsample|11.29|12.54|18.36|1.26|7.07|
Morocco’s irrigated agriculture is organized in 9 water districts (ORMVAs), 2 of which are isolated inform the rest of the economy and we consider the remaining 7 ORMVAs. Among the 82 agricultural and agriculture-related production activities, 66 are in crop production, five in livestock, and 11 in processing agriculture, both up and down stream from the farm firm. To capture the spatial nature of irrigated agriculture, 66 crop production activities are further distinguished according to whether they are within or outside the seven ORMVAs. Among the 33 activities within the water authority perimeters, 21 are irrigated crop production and 11 are rain-fed. Because water is either costly or presently impossible to transport between perimeters, the seven ORMVAs are further sub-divided into 20 perimeters.

The data are organized into a social accounting matrix (SAM). The data include perimeter level information on water charge fees, cropping mix, water and land allocation by crop and area, employment of labor and capital and intermediate input use by crop. National level data on employment, trade, non-farm production and resource flows are also entered into the SAM. These data are used to calculate the parameters of the model.

Unlike a standard SAM that often includes only national level data, the Morocco SAM in this study is multi dimensional, taking into account crop production activities. A schematic presentation of the major features of the macro framework is presented in Figure 2.

Data for the farm model (‘micro’ model) are from the same data source as those for perimeters in the ‘macro’ model, i.e., each perimeter is aggregated from farm level data. For this reason, the production activities in the farm model are compatible with the ‘macro’ CGE model. For the analysis preformed here, the representative farm is chosen from the irrigated area, and hence, only irrigated crops are included in the farm model.

The farm model accounts for monthly water allocation by crop. Typically, the representative farm only grows some of the crops produced in the perimeter. Only a number of crops (10) are included in the farm model. Just as the case with the CGE model, the farm model is calibrated to the data in such a way that the solution of the farm model for the base period reproduces the observed farm data exactly.

# **Figure 2: Depiction of the major features of the general equilibrium model including sectoral and spatial disaggregation and embedded farm model**

11
16 The second period began in 1981 with a policy announcement that foreign workers were to be phased out completely by 1986 (except in construction, ship-building and domestic services from nontraditional source countries). This was followed by measures to impose levies on all unskilled foreign workers and to restrict access to the social security system (Central Provident Fund) to skilled foreign labor only. Rising labor demand was met by extending permits to migrants from other (mostly Asian) countries of origin. As economic development objectives shifted toward high-tech industries, the booming construction sector contributed to continued foreign labor growth. When economic fortunes turned, however, immigration policy effectively reversed the inflow of foreign labor; during the 1985-6 recession, 102,000 jobs were eliminated and 60,000 foreign workers were forcibly repatriated.

A comprehensive levy system was implemented in 1987 and dependency ceilings were introduced (foreign workers were limited to 50 percent of a firm's total employment). Levies were viewed as flexible pricing mechanisms to equalize the cost of foreign labor relative to domestic labor; this change in official policy reflected an admission that foreign labor was in fact an integral part of Singapore's work force (in contrast to the earlier objective of minimizing imported labor). Administrative measures were added thereafter to improve enforcement (such as punishment by caning for overstayers). Levies were extended to Malaysians in 1989, and the dependency ceiling was lowered to 40 percent. The criteria for issuing employment passes and permanent residence status were liberalized in 1989, especially to attract Hong Kong residents in the wake of the Tiananmen Square crackdown in China. This is evident from the considerable jump in the number of permanent residents from 1988 to 1989 to 1990 (see Figure 2). Under the 1990 Employment of Foreign Workers Act, employers wishing to hire foreigners are required to apply for permits, and violators are subject to fines and/or imprisonment.

<!-- Start of picture text --> Nmbr Nubrof Figure Persons 2 Aquiring<br>Singapore Permanent Residency<br>25,000<br>20,000<br>15,000<br>10,000 L- -o -<br>5,000<br>0<br>sp<br>Source: Chew and Chew (1995)<br><!-- End of picture text --> The third period of foreign labor management policy coincides with the strong economic growth experienced during the 1990s which did not diminish the demand for labor; in response to employers'
manual. The methodological annex further equips practitioners with a toolkit for effective implementation.

Policy makers focusing on skills policy are urged to adopt a narrow definition of green jobs, emphasizing specific tasks aimed at reducing environmental impact. The paper recommends relying on the task approach and the proposed methodology to avoid assumptions inherent in classifications from other countries. Moreover, the utilization of online vacancy data with rich skills information is encouraged for profiling green occupations as well as understanding which skills may become in-demand in non-green occupations as the greening of the economy advances, offering a more dynamic and real-time understanding.

The overarching recommendation for policy makers involves the development of a robust labor market information system. This includes investments in detailed occupational classifications or job title taxonomies, updated more frequently than traditional classifications, and informed by ISCO-08, online job vacancy data, ONET-type data, and qualitative insights from labor market stakeholders. Additionally, the collection of skills and tasks data for narrowly defined occupations is emphasized, drawing lessons from established frameworks like ONET, ESCO, PIIAC, and STEP. Regular and comprehensive surveys are proposed to monitor changes in the labor market, inform skills policies, and enhance the understanding of green jobs and the skills they require.

It is crucial to recognize that green skills policy is just one facet of broader skills policies promoting the transition to a green economy. The paper underscores the importance of comprehensive policy instruments, such as advanced labor market information systems, to keep the population informed about in-demand jobs across various sectors. The integration of identified green jobs into competency standards, qualification frameworks, curricula, and certification programs is highlighted as an essential step for countries with well-established systems and a critical focus for those in the process of modernizing their educational, TVET, and employment systems. Accelerating this modernization is deemed vital to capitalize on the opportunities presented by the green transition.

# **6. References**

- Alatas, H.; Granata, J.; and Posadas, J. (2020) Indonesia’s Occupational Tasks and Skills: from occupational employment demand to tasks and skills requirements. World Bank.

- Apella, I.; Rovner (2021) Cambios ocupacionales y formación a lo largo de toda la vida laboral. Aportes del sistema de información ocupacional de Uruguay (O*NET UY). Working Draft.

- Acemoglu, D.; Autor, D. H. (2011): Skills, tasks and technologies: Implications for employment and earnings (Chapter 12). In: Ashenfelter, A.; Card D. (Eds.) Handbook of Labor Economics, 4B, 10431171.

- ASEAN and ILO (2021). Regional Study on Green Jobs Policy Readiness in ASEAN. Final Report. ILO Thailand. ISBN 978-623-6945-15-5

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Arm 1 in which the self-identified most knowledgeable household member is the sole respondent, Arm 5 increases the incidence of joint reported dwelling ownership, on average, by 11.4 percentage points. This marginal effect corresponds to 81 percent of the comparable average for Arm 1. The comparable marginal effect associated with Arm 5 estimated within the sample of female respondents with regard to joint reported financial account ownership is 3.9 percentage points, corresponding to 76 percent of the comparable average for Arm 1. Similar treatment effects are derived also for the male respondents in Arm 5 households in the analysis of (overall and joint) documented and (joint) economic ownership of dwelling and agricultural land as well as (joint) reported ownership of livestock and financial accounts.

When multiple people are interviewed from the same household, another challenge is to determine when they are reporting on the same asset. For example, if a man says that he owns a parcel of land individually, and a woman in the same household says she owns one jointly with him, it is useful to know if they are talking about the same parcel. One way to do this is to use the approach of the Gender Asset Gap Project and begin with an asset inventory and then use it to ask respondents further questions about the specific assets identified. Respondents may be asked if they own additional assets that were not listed.<sup>19</sup> The second approach to questionnaire design uses the individual as the unit of analysis, determining whether the individual is an owner or a right holder. The DHS and the WEAI surveys use a version of this approach, which makes it easy to create measures of the incidence of ownership by sex and individual characteristics. The questionnaire design is closely related to respondent selection, which we now turn to.

# **4.4 Respondent Selection**

Research demonstrating that the choice of respondent can influence the conclusions of a study highlights the importance of understanding three key issues. First, it is critical to know who can provide the most accurate and complete information on specific topics. Second, in some circumstances, it may be useful to know when household members would provide different information in response to the same question. For example, knowing if the husband and wife disagree about who owns a particular asset may provide useful insights. This disagreement may be correlated with other differences in perceptions or

> 19 The Gender Asset Gap Project found relatively few assets added in the individual interviews.  Additional work could be done to determine whether this is because the inventory approach does result in most household assets being listed or whether respondents become fatigued and uninterested in listing additional assets. After experimenting with respondent-specific asset rosters while interviewing multiple adults in each household in the context of MEXA Arm 4 and Arm 5, Kilic and Moylan (2016) recommend the approach the followed by the Gender Asset Gap project as well.

20
on care-required days. More than 70% of the doctor visits that the subsidy generates are recommended by WHO guidelines. By contrast, providing health information to parents through CHW visits has no aggregate effects on demand in our sample, consistent with parents already being able to distinguish need for care. That said, for children up to one year old who receive the subsidy, differences in care seeking when care is required vs. not are less pronounced, and CHW visits appear to lead to an additional reduction in underuse of care. This suggests that the subsidy and information interventions may complement each other in improving care for the age group with the highest mortality risk. In an abridged 2014 follow-up survey, we find a corresponding increase in formal care for diarrhea among children under two years of age in the full program group only, although we are unable to classify whether this care is required by WHO standards (Dean and Sautmann (2023)). In the same follow up we also show that CHW improved prevention, and that the full program leads to significant reductions in diarrhea incidence two years later.

Overall, our results are consistent with cost constituting a primary barrier to appropriate care in our population, along with potentially higher information barriers for the youngest age group, in line with qualitative evidence on the greater difficulty parents have in recognizing illness in young children (Charlet et al. (2017)). The results are encouraging for the planned roll-out of subsidized care in Mali, supported potentially by CHW activities focused on better curative care for infants. Our findings complement a study in Bamako over the same period by the NGO Muso and the Malian Ministry of Health in 2008-2015 (Johnson et al. (2013, 2018)), which found reduced incidence of febrile illness and mortality after the introduction of CHW visits, user fee removal, and health system strengthening measures (although the program effects cannot be easily distinguished from other time trends). Our experiment directly shows changes in curative care seeking in response to the subsidy, pointing to better formal care seeking as an important pathway for positive health outcome effects. The CHW effects for acute care utilization call for further study, given the important role of CHW interventions in health policy in general and in Mali in particular.

6
Table 16. Host Community Near the Syrian Border, 2011 & 2013 Means Comparison Test

||mean(2013)-<br>mean(2011)|two-sided<br>means test<br>p-value|2013-<br>mean|2011-<br>mean|
|---|---|---|---|---|
|N children|-0.097|0.0000|1.28|1.38|
|N adults|-0.084|0.0000|2.24|2.33|
|N old|0.021|0.0048|0.58|0.56|
|**Characteristics of the head of household**|||||
|0-14|0.000|.|-|-|
|15-24|-0.002|0.0434|0.01|0.02|
|25-39|-0.014|0.0009|0.28|0.29|
|40-54|0.001|0.7810|0.35|0.35|
|55+|0.016|0.0007|0.36|0.34|
|1-Male, 0-Female|-0.009|0.0123|0.83|0.83|
|None|-0.002|0.6283|0.24|0.24|
|Primary|-1.2%|0.0124|41.6%|42.7%|
|Secondary|0.4%|0.3095|24.5%|24.1%|
|Post-Secondary|1.0%|0.0007|10.3%|9.4%|
|**Al workers in the household**|||||
|Share of Employed Adults|1.0%|0.0000|25.5%|24.6%|
|Share of Unemployed Adults|0.1%|0.3775|2.4%|2.3%|
|Total Hours Worked in the Whole Family|-0.526|0.0085|51.31|51.83|
|Mean Hours Worked out of all working individuals|-0.542|0.0014|50.45|50.99|
|Share Workers White Collar|3.5%|0.0000|34.4%|30.9%|
|Share Workers Blue Collar|-3.2%|0.0000|45.9%|49.1%|
|Share with Permanent Contract Type|-0.1%|0.7832|36.2%|36.4%|
|1-9 employees|-2.6%|0.0000|44.3%|46.9%|
|10-49 employees|1.3%|0.0048|17.5%|16.3%|
|50+ employees|1.6%|0.0004|18.5%|16.9%|
|Share Agriculture, forestry, fishing-Mining and quarrying|-1.4%|0.0002|12.2%|13.6%|
|Share Manufacturing Industry|1.3%|0.0009|13.2%|11.9%|
|Share Electricity, Gas , Steam, Water and Sewerage|-0.1%|0.3614|1.0%|1.1%|
|Share Construction|1.2%|0.0005|9.7%|8.4%|
|Share Commerce and services|-1.4%|0.0055|23.6%|25.0%|
|Share Others|0.7%|0.1739|20.8%|20.1%|
|Registered with Any SS institution related to your main job|8.0%|0.0000|63.5%|55.5%|
|Share of workers registered with any social security<br>|6.3%|0.0000|52.4%|46.1%|
|institution related to your main job|||||

_Notes_ : 2011 N = 21,460, 2013 N = 21, 467 _Source_ : LFS 2011 & 2013. World Bank author’s calculations.

31