rafmacalaba/datause-encoder-data huggingface dataset:
over 100 countries in six different regions. Aterido et al. (2007) have outlined key features of the database in a recent paper:<sup>27</sup> ‘The median sample size is 350 firms, with several large countries having substantially larger samples...The sample of firms in each country is stratified by size, sector and location...The unit of analysis is the “Establishment” in the manufacture and service sectors. Most firms are registered with local authorities, although they may be only in partial compliance with labor and tax authorities.’ The core questions are generally answered by the manager or owner of the establishment in face-to-face interviews. Accounting data may be provided by the establishment’s accountant and/or human resource manager. Some countries have attached nationally specific modules answered by workers (for instance the Thailand 2007 PICS survey). Among the earlier surveys, there is still some variation of the core questions, so that comparative analyses of multiple business climate variables may require a focus on a subset of the total database. Aterido et al. suggest a highly comparable subset of around 50,000 firms in 80 countries.<sup>28</sup> _Structure and Content of the Core Business Climate Survey Instrument_ The standardized core survey instrument is organized into two distinct parts.<sup>29</sup> The first part provides general information about the firm and the business climate it faces. The second part collects accounting information such as production costs, investment flows, balance sheet information and workforce statistics. The questions about the firm and the business climate in the first part include:

- _General information about the firm:_ age, ownership, activities, location.

- _Sales and supplies:_ imports and exports, supply and demand conditions, competition.

- _Business climate constraints:_ evaluation of general obstacles

- _Infrastructure and services:_ power, water, transport, computers, business services

- _Finance:_ sources of finance, terms of finance, financial services, auditing, land ownership

- _Labor relations:_ worker skills, status and training; skill availability; over-employment; unionization and strikes

- _Business-government relations:_ quality of public services, consistency of policy and administration, customs processing, regulatory compliance costs (management time, delays, bribes), informality, capture.

- _Conflict resolution/legal environment:_ confidence in legal system, resolution of credit disputes

- _Crime:_ security costs, cost of crimes, use and performance of police services

- _Capacity, innovation, learning:_ utilization, new products, planning horizon, sources of technology, worker and management education and experience.

> 27 Hallward-Driemeier, M.; and R. Aterido. 2007: 20. See also: Aterido, R., M. Hallward-Driemeier and C. Pagés. 2007: 10-11.

> 28 : Aterido, R., M. Hallward-Driemeier and C. Pagés. 2007: 10.

> 29 PICS Implementation Manual (Nov. 2003) <u>http://iresearch.worldbank.org/InvestmentClimate/Help/pics_manual.pdf</u> [07/21/2008]

9
**Table 11. Tobit Regressions - "Contribution Density" of Self Employed, Without and With Preference Variables**

(Men of Working Age who Responded to the PRIESO)

||w/oPref.V<br>Sa|ariables,Full<br>mple|w/oPref.V<br>S|ariables,Exp.<br>ample|WithPrefVa<br>Sam|riables,Exp.<br>ple|
|---|---|---|---|---|---|---|
|CONDEN|Coefficient|Std.Error|Coefficient|Std.Error|Coefficient|Std.Error|
|AGE99|**0.261**|**(0.113)****|**0.460**|**(0.183)****|**0.583**|**(0.152)*****|
|AGE<sup>2</sup><br>|**-0.005**|**(0.003)***|**-0.010**|**(0.005)****|**-0.014**|**(0.004)*****|
|AGE<sup>3</sup>|3.40E-05|-2.20E-05|7.9E-05|(3.7E-05)**|1.IE-04|(3.2E-05)***|
|HEAD|-0.022|-0.134|||||
|MARRIED|0.135|-0.095|||||
|YEDU|**0.019**|**(0.009)****|**0.031**|**(0.019)***|**0.052**|**(0.017)*****|
|WORKED|0.088|-0.2|||||
|SPCONT|-0.031|-0.078|||||
|PAYGREG|-0.292|(0.141)**|-0.161|(0.267)|-0.247|(0.223)|
|CREDCON|-0.151|(0.077)**|-0.088|(0.164)|-0.060|(0.140)|
|EXKIDS|0.014|-0.022|||||
|LEDKIDS|0.002|-0.007|||||
|FAMEX|0.105|-0.066|||||
|GIVES|0.071|-0.067|||||
|GETS|0.058|-0.099|||||
|LRENT|**-0.014**|**(0.008)***|-0.005|(0.016)|-0.011|(0.013)|
|COLATT|-0.006|-0.081|-0.136|(0.160)|-0.204|(0.136)|
|FINASST|**0.311**|**(0.173)***||(droppedduet|ocollinearity)||
|WRKASST|-0.09|-0.07|-0.207|(0.135)|-0.288|(0.116)**|
|SLIFEX|**0.006**|**(0.003)****|0.008|(0.005)|**0.016**|**(0.005)*****|
|LYJUB|0.023|(0.010)**|-0.018|(0.022)|-0.001|(0.019)|
|LYPASIS|0.03|-0.026|||||
|TIMEPREF|||||-0.710|(0.177)***|
|RISKPREF|||||-2.9E-04|(7.7E-05)***|
|Intercept|-4.218|(1.460)***|-6.442|(2.417)***|-7.015|(1.938)***|
|Numberofobs|186||62||60||
|leftcensored|46||18||17||
|uncensored|140||44||43||
|Loglikelihood|-110.764||-43.512||-28.516||
|LRZ'(22)|62.35|LR<br>2(11)|14.73|LRX<sup>2 </sup>(13)|3997||
|Prob>x2|0.000||0.000||0.000||
|PseudoR<sup>2</sup>|||||||
||0||0.145||0.412||

"***" statistically significant at 1%; "l'" at 5%; and "*" at 10% 50
- 19 -

# Table 7: kmprovements In Housing

|County, Year|Gongzhullng<br>(N-200)|Juro<br>(N-19|ng<br>9)|Tal<br>(N-200|)|Xiajl<br>(N-2|ang<br>00)|
|---|---|---|---|---|---|---|---|
|:em|1983<br>1987|1983|1988|1983|1988|1983|1988|
|with non-straw roof|22<br>51|94|98|51|85|99|99|
|with non-earth<br>floor|11<br>23|12|27|15|44|14|36|
|with non-earth<br>wall|22<br>48|85|95|51|85|69|80|
|who Invested In housing<br>rovement since 1983|60|47||68|||46|
|g. size of housing<br>nvestment (Yuan) a/|**4,**35|3,792||5,233||4,5|06|
|Ratio of housing Inprovement<br>nvestment to productive<br>nvestment b/|2.71|3.57||10.88||1.|82|

I Calculated for the subsample of Individuals with housing<sup>klprovement.</sup> Calculated for whole sample.
standardized difference of the means of the linear index of the propensity score in the treated and (matched) non-treated group (Rubin's' B). The second way is calculating the ratio of treated to (match) non-treated variances of the propensity score index (Rubin's R). Rubin's B should be less than 25 and Rubin's R should be between 0.5 and 2 for the samples to be considered sufficiently balanced. Another method recommended by Sianesi (2004) is to use the pseudo-R2s before and after matching. The pseudo-R2 after matching should be very low and have no systematic differences. The results of these balance checks are found in Table 2 in the Appendix.

The ATT of equation 1 (DiD) estimates the hosting arrangement's impact on refugees' "life satisfaction" between 2010 and 2015 for households within the common-support region.<sup>4</sup> The ATT of equation 3 (propensity score matching) estimates the impact on "material living conditions" because the baseline level of the material living condition could not be retrieved from the survey.

# 4.3. Summary statistics

The analyses are based on a total sample of 2,399 refugee households displaced by the Syrian crisis, including registered and unregistered refugees. Most refugees started arriving in Jordan in 2010, and the mean year of stay is 3.5 years. About 50 percent of the surveyed households lived outside of camps. Among the camp residents, 832 households lived in the Zaatari refugee camp, and 359 lived in the Azraq refugee camp.

The main dependent variables (the outcomes of interest) are "life satisfaction" and the refugees' "material living conditions".

<mark>The life satisfaction question is:</mark> _"Think about your overall satisfaction with your life over the last few years: your happiness, wealth, security, hope for the future, etc. For each year (from 2005 to 2020), how do you rate your life on a scale of 1-10, with 1 meaning, 'my life could not be worse,' and 10 meaning 'my life could not be better'?"_ <mark>The question, therefore, is a combination of retrospective, present feelings, and projection for the future. The descriptive result (2005 to 2020) is described in the Figures 1 - 3; however, I</mark>

> 4 An alternative specification could be to use a fixed-effect model. For consistency, I discuss only the results of the DiD in this main paper but placed the results of the fixed-effect model in the Appendix.

13
# **Table 1: Mortality as a function of per capita income and period**

||Sub<br>|-Saharan African cou<br>|ntries<br>||Countries in other regi<br> <br>|ons<br>|
|---|---|---|---|---|---|---|
||Child Mortality<br>(ln)<sup>#</sup>|Child Mortality<br>(ln)<sup>##</sup>|Adult Mortality<br>(ln)|Child Mortality<br>(ln)<sup>#</sup>|<br>Child Mortality<br>(ln)<sup>##</sup>|Adult Mortality<br>(ln)|
|GDP pc (ln)|-0.395|-0.288|-0.053|-0.449|-0.317|-0.263|
||(0.021)**|(0.021)**|(0.036)|(0.022)**|(0.049)**|(0.065)**|
|1980-84|-0.192|-0.087|-0.067|-0.410|-0.114|0.027|
||(0.068)**|(0.063)|(0.108)|(0.076)**|(0.128)|(0.170)|
|1985-89|-0.289|-0.189|0.051|-0.627|-0.319|-0.047|
||(0.064)**|(0.062)**|(0.107)|(0.071)**|(0.127)*|(0.170)|
|1990-94|-0.334|-0.237|0.251|-0.800|-0.512|0.006|
||(0.064)**|(0.062)**|(0.107)*|(0.066)**|(0.125)**|(0.166)|
|1995-99|-0.407|-0.278|0.502|-1.014|-0.614|-0.099|
||(0.063)**|(0.063)**|(0.109)**|(0.064)**|(0.135)**|(0.180)|
|2000-04|-0.448|-0.325|0.742|-1.162|-0.778|0.058|
||(0.063)**|(0.070)**|(0.120)**|(0.064)**|(0.146)**|(0.194)|
|Obs.|329|166|166|589|61|61|
|R-squared|0.58|0.56|0.34|0.63|0.58|0.25|

Note: Standard errors in parentheses. * significant at 5%; ** significant at 1%. # indicates full sample; ## indicates sample of countries/years countries that are also in the adult mortality database (“overlapping” countries).

45
34

# **Table 1**

_Data available on evaluated programs from studies vs. interviews_

||Percentage d<br>From impact evaluation<br>reports only|ata collected<br>After interviews with<br>implementers|Total number<br>of indicators|
|---|---|---|---|
|Overarching Aspects|64%|78%|27|
|Content|47%|66%|10|
|Delivery|42%|69%|14|
|TOTAL|51%|75%|51|
|For interviewed program|s only:|98%|51|

Percentage data collected refers to the percentage of indicators for which data were collected across the 33 programs in our evaluated sample. This is calculated by the number of programs for which each indicator has data, summed for every indicator in a given section (or total) and divided by the number of indicators in that section (or total), and finally divided by the 33 programs.
Time to obtain antenatal care and DTP immunization <!-- Start of picture text --> 8<br>7<br>7<br>6<br>6<br>5<br>5<br>4<br>3<br>3<br>2 2 2 2 2 2 2<br>2<br>1<br>1<br>0<br>Obtain combined oral Obtain an antenatal care Obtain DTP immunization<br>contraception visit<br>Chile Ghana India Vietnam<br>Source: Living Life data<br>Calendar days<br><!-- End of picture text --> While the time frame to obtain HIV/AIDS testing reported by contributors in all four economies ranged from 2 to 5 days, the total time it takes to start HIV/AIDS treatment can vary from 2 days in Ghana up to 59 days in Vietnam, where patients undergo pre-therapy adherence counseling sessions before receiving their ART medications (Trinh et al. 2011). Figure 3.6 shows the time it takes to obtain both HIV/AIDS treatment in a public health facility in all four economies.

**Figure 3.6: Time to Obtain HIV/AIDS Treatment in a Public Health Facility (calendar days)**

<!-- Start of picture text --> Time to obtain HIV/AIDS testing and treatment in a<br>public health facility<br>70<br>59<br>60<br>50<br>40<br>30<br>20<br>10 5 3 2 3 7 2 6<br>0<br>HIV/AIDS testing HIV/AIDS treatment (ART)<br>Chile Ghana India Vietnam<br>Source: Living Life data<br>Calendar days<br><!-- End of picture text --> The timeframe to obtain tuberculosis testing and treatment reported by contributors is the same in Chile, Ghana, and India. In Vietnam, it takes 1 day longer to access to tuberculosis testing than in other countries. On the other hand, Vietnamese citizens can start tuberculosis 1 day sooner than citizens in the other three economies (Figure 3.7).

23 | P a g e
<!-- Start of picture text --> Figure S1.2f: Residuals with Time Trend, CO at ITO<br>Blue Line (second extension)<br>Source: Based on data described in the text<br>Figure S1.2g: Residuals with Time Trend, CO at Siri Fort<br>Blue Line (second extension)<br>Source: Based on data described in the text<br>1<br>.5<br>0<br>-.5<br>-1<br>-1.5<br>8Oct2006 12Oct2006 16Oct2006 20Oct2006 24Oct2006 28Oct2006 1Nov2006 5Nov2006 9Nov2006 13Nov2006 17Nov2006 21Nov2006 25Nov2006 29Nov2006 3Dec2006 7Dec2006 11Dec2006 15Dec2006<br>1<br>.5<br>0<br>-.5<br>-1<br>-1.5<br>8Oct2006 12Oct2006 16Oct2006 20Oct2006 24Oct2006 28Oct2006 1Nov2006 5Nov2006 9Nov2006 13Nov2006 17Nov2006 21Nov2006 25Nov2006 29Nov2006 3Dec2006 7Dec2006 11Dec2006 15Dec2006<br><!-- End of picture text --> 54
33 Table A1.3: Median vulnerability and changes in vulnerability by region and oil trade status, excluding high-income countries

|||**Num**|**ber of cou**|**ntries**||**Median in**|**percentag**|**e of GDP**||
|---|---|---|---|---|---|---|---|---|---|
||**Oil**||||**V**|**V**|**V**|∆|∆|
|||||||||||
|**Region**|<br>**trade**|**1999**|**2008**|**2009**|<br>**1999**|<br>**2008**|<br>**2009**|<br>**99–08**|<br>**99–09**|
|East Asia &<br>|Import|12|14|14|2.6|7.0|4.8|4.2|3.2|
|Pacific|Export|4|2|2|-3.1|-2.9|-1.3|6.1|7.7|
|Europe and|Import|16|16|16|3.3|5.1|3.7|2.1|0.7|
|Central Asia|Export|5|5|5|-18.8|-21.5|-12.8|-2.3|-0.9|
|Latin America<br>|Import|23|22|21|2.1|7.2|5.2|5.1|2.7|
|& the Caribbean|Export|6|7|7|-3.2|-3.5|-2.5|-1.4|-0.5|
|Middle East &<br>|Import|4|4|5|5.7|13.3|8.3|7.6|3.4|
|North Africa|Export|8|8|7|-17.0|-24.9|-17.8|0.6|0.1|
|South Asia|Import|7|7|7|2.2|6.5|4.1|3.9|1.5|
||Export|0|0|0|—|—|—|—|—|
|Sub-Saharan<br>|Import|38|36|36|1.9|6.8|4.4|4.3|2.0|
|Africa|Export|7|9|9|-38.3|-35.7|-30.6|-2.8|-0.3|

_Sources:_ Table A1.1 and author’s calculations.

_Notes:_ The oil trade status refers to being a net oil importer or exporter in the year specified for V and in the terminal year for ∆ V. Reporting to the first decimal point is for formatting purposes and does not imply that numbers exceeding 10 percent have three significant figures. — = not applicable.

Table A1.4: Decomposition analysis for 1999–2008 (percentage of GDP)

|**Country**|**V**<br>**1999**|**V**<br>**2008**|∆**V**|∆**P**|∆**C**|**Data**<br>**source**|
|---|---|---|---|---|---|---|
|Albania|2.7|4.1|1.4|-1.9|3.3|IEA|
|Algeria|-17.1|-32.3|-15.2|-18.4|3.2|IEA|
|Angola|-76.5|-77.5|-1.0|-0.7|-0.4|IEA|
|Antigua and Barbuda|3.4|12.1|8.6|0.0|8.6|EIA|
|Argentina|-0.8|-1.6|-0.9|-6.3|5.5|EIA|
|Armenia|1.4|3.0|1.6|0.0|1.6|IEA|
|Australia|0.4|1.4|0.9|-0.8|1.7|EIA|
|Austria|0.7|2.2|1.5|-0.1|1.6|EIA|
|Azerbaijan|-24.3|-59.3|-34.9|-25.7|-9.2|IEA|
|Bahamas, The|2.4|14.4|12.0|0.0|12.0|EIA|
|Bahrain|-16.6|-24.2|-7.6|-12.0|4.4|IEA|
|Bangladesh|0.9|3.9|3.0|-0.2|3.2|EIA|
|Barbados|2.1|6.4|4.3|-0.5|4.8|EIA|
|Belarus|6.1|7.1|1.0|0.0|1.0|EIA|
|Belgium|1.5|5.0|3.5|0.0|3.5|EIA|
|Belize|2.9|10.1|7.2|-9.1|16.3|EIA|
|Benin|2.8|14.8|12.1|0.0|12.1|IEA|
|Bhutan|1.6|4.5|2.9|0.0|2.9|EIA|
|Bosnia and Herzegovina|3.0|5.2|2.2|0.0|2.2|EIA|
|Botswana|1.2|5.0|3.8|0.0|3.8|IEA|
|Brazil|0.8|0.0|-0.8|-3.5|2.7|EIA|
- 30 -

# Table (3.2) Sectoral Distribution of GDP, Exports and Labor in the Sudan

|Indicator|Year|Agriculture|Manufacturing|Services|
|---|---|---|---|---|
||1970|43|15|42|
|Percent share in GDP|1980|34|14|52|
||1988|36|15|49|
||1970/71|89|11|..|
|Contribution to value of export(/)|1977/78|95|53||
||1982/83|90|10||
||1970/71|70|3|27|
|Distribution of labor force(%)|1977/78|69|4|27|
||1982/83|70|5|31|

Source: An updated version of Table **3,** Elbadawi (1988)
# E. Methodolobgy

xvi. Approaches tv cducation cost and financing issues vary greatly, as does sector coverage, even when country circumstances are similar. The sector specialist must bring experience and judgment to bear, but there is still too much room for subjective conclusions and recommendations. These compromise the credibility of policy recommendations.

xvii. A big problem for the analyst is not knowing what part of total education financing is represented by the public budget allocated to the relevant ministry. For costeffective and equitable financing, a multi-source sector budget is required that includes allocations from ministries other than the Ministry of Education as well as from local and nongovernment sources. Such a comprehensive budget also provides a basis for more meaningful crosscountry comparisons.

xviii. A particularly acute problem in the education sector is the imbalance between capital and recurrent spending. PERs in recent years have usually included in sector expenditure analysis both types of spending.

xix. The success or failure of public expenditure recommendations often rests upon the Bank's understanding of the mechanics and politics of the budget process--the relationship of the national accounts to ministerial budgets, the relationship of the expenditure plan and budget to actual spending, and questions of intra- and intersectoral expenditure controls. Several PERs analyze budgeting issues thoroughly and in detail, but rarely discuss their effect on the education sector.

# F. Conclusions and Recommendations

xx. Public expenditure reviews are an excel 'vehicle for analyzing important sector issues in the context of the overall economic and iiscal situation. PERs should give as accurate a picture as possible of how funds to the sector are allocated and disbursed. Providing this picture entails an evaluation of the expenditure data and the expenditure process. In particular: a) If the budget process and the relationship of the budget to actual spending are not transparent, then a budget process review may be a prerequisite for an expenditure review.

b) The PER cannot be substituted for CESW. If the necessary data are not available, ex ante sector work is a prerequisite for any PER that attempts to make and evaluate macro and sector links.

c) For in-depth analysis of sector cost and financing issues, single-sector PERs, or possibly social sector PERs, are more appropriate than multisector reviews.

xoi. Extensive detail is not a substitute for a well-focused analytic discussion of the principal sector issues, priorities, and policies, and their relationship--economic, political, and institutional--to the country's development program. Foremost, there should be a clear iv
Indeed, the literature suggests non‐linearity in temperature impacts per degree of added heat stress, and that the most noticeable labor productivity impacts occur beyond some heat threshold (between 25°C and 32°C). Most studies using daily weather variation suggest that the majority of damages occur on extreme heat days (days with temperatures above 25°C). This is true in the context of mortality responses (Deschenes and Greenstone, 2013), labor supply responses (Graff‐Zivin and Neidell, 2013), and individual‐ and regional output responses (Cachone et al, 2012; Sudarshan et al, 2013; Park, 2015).

Given the likelihood that any variation in<sup>Δ��</sup> will depend on the initial climate – that is, �ΔT<sup>�</sup> whether a household is situated in hot or cold average climates – it may thus be useful to understand the covariance structure between household wealth and average climates. Thus, we seek to understand the covariance structure between household wealth and average (current) exposure to temperature stress ��<sup>�</sup> �� : that is, �����<sup>�</sup> �, ��� , where we use household wealth as a proxy measure for income.

# _2) Occupational Exposure Bias_

Poorer households may be more likely to engage in work that is more exposed to the elements, ��� |, �� , in which case we would expect even uniform warming to result in outsized ����| �� ��0 welfare losses for poorer households due to the higher likelihood of heat‐related health and productivity impacts. Conversely, it may be the case in some countries that relatively affluent ��� households are employed in sectors more susceptible to heat exposure: |, �� . ����| �� ��0

Exposed sectors may include outdoor work intensive sectors such as agriculture or construction, as well as transportation (e.g. rickshaw drivers) and manual labor intensive industries where air conditioning is missing or inadequate. And workers in these sectors are not only economically vulnerable but often experience the highest mortality rates after a heatwave. For instance, after the May 2015 heatwave in India, in the state of Andhra Pradesh, which experienced the most severe impacts, a majority of 900 reported victims were elderly or low‐income workers (Al Jazeera, 2015; Vice News, 2015).

Understanding the covariance structure between household wealth and occupational exposure to temperature stress is thus an additional factor that may determine the distributional impacts of heat stress arising from climate change.<sup>18</sup>

> 18 Note that it is also possible that manual labor and outdoor work intensive occupations pay lower wages on average. According to the US Bureau of Labor Statistics, the average construction laborer makes 25 percent less than the median US worker, and laborers in Farming, Fishing, and Forestry occupations make 48 percent less.

11
to identify whether reports tend to be more downloaded in subject countries or, for example, in the United States, enabling one to track the flow of World Bank knowledge around the world. Second, it would useful to assess which type of policy reports were most effective in contributing to changes in government regulation. Third, some World Bank reports may be relevant for other World Bank reports. World Bank (2004) used citation counts tabulated by Thomas Scientific (later Thomas Reuters) to demonstrate that those ESWs that more frequently cited Bank research were also more likely to receive higher QAG quality ratings. This metric is different from what we propose because it looks at what was cited within a policy report, not how often a policy report was cited. Analyzing how World Bank reports influence each other could improve our understanding of how internal knowledge spreads within the institution.

23
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**Step 3** : Next to each action, write down who will manage it, the resources required for this action, and source of funds if any.

|**Time**<br>**before/after**<br>**sowing**|**Actions**|**Person**<br>**Responsible**<br>**(Name)**|**Resources**<br>**Required**<br>**(Input,**<br>**Tool etc.)**|**Source of Funds**|
|---|---|---|---|---|
|June2016|||||
|July-August 2016 etc.|||||

5