rafmacalaba/datause-encoder-data huggingface dataset:
- i. Type of information on population: Non-numerical to numerical.

- ii. Type of population coverage: Specific to general.

- iii. Type of population involvement: Active to passive.

- iv. Type of inference methodology: Inductive to deductive.

- v. Type of disciplinary framework: Broad social sciences to neoclassical economics.

An excellent example of qualitative data analysis is the World Bank’s Voices of <u>the Poor</u> study. Sixty thousand poor women and men told their stories, which were then compiled. For purposes of the present paper, what stands out is that the number one problem expressed by the poor is the lack of income-earning opportunities. The poor themselves see clearly that the labor market plays a critical role in improving their livelihoods.

Another nice example is Pierre and Scarpetta’s (2004) study of employment regulations as viewed by employers. The authors found that not all firms are affected in the same way by onerous labor regulations and they respond differently to them. In particular, when labor regulations made hiring and firing very costly, small firms reported that they relied mainly on temporary employment, while medium and large firms relied more on on-the-job training.

And then there is the famous work conducted in the Indian village of Palanpur over the last quarter-century, which demonstrated among other things the crucial role played by non-farm employment in reducing rural poverty. See, for example, Bliss and Stern (1982), Drèze, Lanjouw, and Stern (1992), and Lanjouw and Stern (2003).

In my judgment, qualitative data analysis can supplement but in no way should supplant quantitative data analysis. The best labor market work will involve a judicious mix of qualitative and quantitative research.

# **<u>Lessons Learned: Finding the Right Model</u>**

Where empirical labor market policy research has proven most useful is in telling us what the right model of labor markets is in a particular country context. Here are some examples where 1) important lessons have been learned, but 2) more remains to be learned.

41
**Table 9. ECD Outcomes RDD Results by Outcome and Round**

||**Prenatal**|**Delivery**|**Height for age**|**Weight for age**|**Weight for height**|**Preschool**|
|---|---|---|---|---|---|---|
|**2008**|||||||
|50% bandwidth|0.145|˗0.158|˗0.695|˗0.257|1.287***|˗0.090|
|100% bandwidth|0.087|˗0.095|˗0.688|˗0.307|0.874*|˗0.031|
|200% bandwidth|˗0.003|0.003|˗0.370|˗0.327|0.234|˗0.056|
|**2011**|||||||
|50% bandwidth|0.301**|0.304***|0.578|0.307|˗0.126|˗0.021|
|100% bandwidth|0.155|0.198**|0.318|˗0.265|˗0.652|˗0.099|
|200% bandwidth|0.131|0.140*|˗0.347|˗0.614|0.062|˗0.100|
|**2013**|||||||
|50% bandwidth|˗0.262|˗0.224|0.654|˗2.153|˗2.022|0.038|
|100% bandwidth|˗0.206|˗0.221*|0.708|˗0.372|˗0.502|˗0.013|
|200% bandwidth|˗0.238**|˗0.183*|0.708|˗0.479|˗0.854|˗0.029|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level.

26
_Brazilian spatial labor dynamics Page 32_

# Employment Growth.  Brazil. 1991/2000.

<!-- Start of picture text --> Variação no emprego - 91/00<br>-8508.163 - 0<br>0 - 7000<br>7000 - 21000<br>21000 - 215865.8<br>N<br>W E<br>0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Kilometers<br>S<br><!-- End of picture text -->

**Map 1**
of the GDP per capita (valued at a daily value). Sensitivity analyses uses the value-added in industry for working adults, which is significantly higher than for agriculture.

# 9. Proportion of HAI patients participating in labor force

Participation in the labor force is based on three variables: whether the patient is of working age, whether a patient of working age is active in the labor force, and the gender of the inpatient. This analysis includes not only patients but also their caregivers, who will lose a similar amount of time from their work. The proportion of HAIs suffered by children is assumed to be 40% which is based on data from Kenya where under five children account for 38% of inpatient admissions. The proportion of women is assumed to be 65% compared to men (35%) due to the contribution of maternal conditions to hospital admissions, also based on data from Kenya. For adults, not all will be participating in the labor force. Female and male workforce participation is taken from the World Bank statistics for each country.

# 10. HAI case fatality rate

The mortality of patients that suffer an HAI is higher than those that do not suffer an HAI, even in developed countries with the best medical systems. From a long-term follow-up study in Norway, for example, patients with HAIs were 40% more likely to die within 1 year relative to those without HAIs (Koch et al, 2015). Evidence gathered by the American Thoracic Society suggests the mortality related to the healthcare-associated pneumonia or “attributable mortality” has been estimated to be between 33% and 50% (American Thoracic Society, 2005). There are only a few studies from Africa. An exploratory literature review with a focus on the nine countries of the present study found seven studies, shown in Table 9. Two studies presented case fatality of only HAI patients, while five compared HAI and non-HAI patients. Differences ranged from 4.2% to 14.6%, with two studies from Ethiopia with a difference of 6.9%. Therefore, in the baseline analysis, the rate of 6.9% is used for all the countries. In sensitivity analysis, a low rate of 4% and a high rate of 15% are used.

**Table 9. African studies with estimates of mortality rates from HAI**

|**Country**|**Context**|**Case fatal**|**ity rate**||**Implied e**<br>**(over bas**|**xcess CFR**<br>**eline)**|**Reference**|
|---|---|---|---|---|---|---|---|
|||**HAI (all)**|**AMR**|**Non-HAI,**<br>**non-AMR**|**HAI (all)**|**AMR**||
|Benin|39 hospitals|22%||7.4%|14.6%||Ahoyo et al,2012|
|Congo,<br>Dem.<br>Rep.|1<br>teaching<br>hospital|10%||-|-||Ngolet et al (2021)|
|Ethiopia|1 hospital|10.8%||3.9%|6.9%||Taye,2005|
|Ethiopia|1 specialized<br>hospital|14.7%||7.8%|6.9%||Gidey et al, 2023|
|Ethiopia|1<br>teaching<br>hospital|7.5%||3.3%|4.2%||Ali et al, 2018|
|Kenya|1<br>hospital<br>(children)|14.6%||-|-||Patil et al, 2022|
|Sudan|1 emergency<br>hospital|10.3%||2.1%|8.2%||Spagnolello et al,<br>2022|

Patients with an infection with antimicrobial resistance have been shown to have a higher fatality rate than those with a non-AMR healthcare-associated infection. A systematic review 37
<!-- Start of picture text --> Figure 19: How do you envision the future of Mali?<br>93 94<br>86<br>75<br>20<br>2 5 6 3 2<br>Independence of the North Autonomy of the North Establish full government control<br>over the North<br>IDPs Refugees Niger Refguees Mauritania Returnees<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

95% of the refugees in Mauritania envision an independent or autonomous North and 26% of the refugees in Mauritania even state the independence of Azawad (=the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80% of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96% of the IDPs, 88% of the refugees in Niger and 95% of the returnees), the majority of IDPs (61%) and returnees (70%) as well as 38% of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13% of the refugees in Niger also mention war and 27% the integration of the North. Nonetheless, 49% of IDPs, 86% of returnees and 89% of refugees believe a stable and sustainable peace accord can be achieved.

> by 4% of the population living Timbuktu, 2% in Gao and nobody in Kidal. Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali’s northern territories is decisively rejected.  See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account.

26
**Figure 1: Trade measures for medical products (weekly, January-July 2020)**

<!-- Start of picture text --> Export restrictions Import liberalization<br>Weekly evolution over 1 Jan-17 July 2020 by implementation date Weekly evolution over 1 Jan-17 July 2020 by implementation date<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>Number of restrictive measures for medical products 0 Number of liberalising measures for medical products 0<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28<br><!-- End of picture text -->

**_Source_** _:_ COVID-19 Trade Policy database, own calculations. **_Note:_** The data at the end of each week do not consider the measures that were removed (with a removal date in that week).

**Figure 2. Duration of trade measures for medical products by implementation date**

<!-- Start of picture text --> 2/1/2020 3/1/2020 4/1/2020 5/1/2020 6/1/2020 7/1/2020<br>Implementation date<br>Liberalising measures Restrictive measures<br>1000<br>800<br>600<br>400<br>Duration in days<br>200<br>0<br><!-- End of picture text --> _Source:_ COVID-19 Trade Policy database, own calculations _._ High and upper middle-income countries, according to the World Bank income classification, enacted more trade policy measures targeting the medical sector than other countries (Evenett et al. 2020). As already mentioned, many of the trade restrictive and liberalizing measures that were adopted to increase the availability of personal protective equipment and medical supplies at the beginning of the COVID-19 pandemic outbreak, were subsequently removed. We consider this dynamic in the empirical analysis.

6
# 4. **Conclusion**

This paper attempted to examine whether a fiscal stimulus can be an effective countercyclical tool in Tunisia. Indeed, Tunisia is now affected by the global crisis, through the EU. About 80% of Tunisia’s exports are shipped to Europe, which is now in recession. Growth has declined by 6 percentage points in the EU –compared to 2008 and this is leading to a significant drop in the EU’s external demand, thereby hitting Tunisia. The latter’s exports dropped from 12% growth in 2007 to only 1% in 2008 and have entered the negative territory in the first quarter of 2009. Therefore, it is urgent to identify policies that can counterbalance somewhat the expected decline in GDP growth.

We estimate a Structural Vector Error Correction model for Tunisia in order to identify the impact of fiscal policy shocks on real output. The results suggest that public investment is an important growth-enhancing medium-term countercyclical instrument that can effectively counterbalance the shortfall in growth driven by the sharp decline in external demand. The multipliers endogenously estimated from the SVEC model show that raising public investment by 1 dinar yields 0.12 dinar the first year, 0.30 dinar the second year, half a dinar the third year and 1.08 dinars the 6th years. An increase in recurrent expenditure has a small but positive and persistent impact on real output. The feedback effect from real output to fiscal variables indicate a countercyclical pattern of real public investment and a relative rigidity/inelasticity of recurrent expenditures to output fluctuations.

This result provides some confidence regarding the effectiveness of a fiscal spending stimulus in the case of Tunisia. Indeed, our results are based on historical data and on an average level of efficiency of public spending. If Tunisia speeds up procurement procedures and targets highgrowth-enhancing projects, our spending multipliers can be considered as a lower band, i.e., one dinar spent today could yield a higher additional output than in the past. In addition to a rapid execution, to maximize impact, the public spending should be well-targeted. We also find that reallocation of public spending is not growth neutral. Indeed, reducing fuel subsidies to increase public investment or even maintenance or other non-subsidies items can help boost growth. A finding that deserves further investigation however is the contra-cyclical nature of public investment. Does it reflect an explicit or implicit fiscal rule or does it indicate the existence of strong stabilizers?

12
# **Table 6: Estimations for the likelihood of using deposit and credit services replacing remittances with migration**

This table shows marginal effects from the estimations of probit models. In columns (1)-(4), the dependent variable is 1 if the household has a deposit account. In columns (5)-(8), the dependent variable is 1 if the household received a loan and in columns (9)-(12) it is one if the household requested a loan and zero otherwise. Migrant refers to a dummy that equals one if the household has at least one member living overseas. All other independent variables are defined in Table 1. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

||Depen<br>ho|dent variable:<br>usehold has a|Dummy for<br>deposit acco|whether<br>unt|Depen|dent variable:<br>household re|Dummy for<br>ceived a loa|whether<br>n|Depen|dent variable:<br>household re|Dummy fo<br>quested a lo|r whether<br>an|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Migrant|0.089|0.097|0.108|0.204|-0.012|-0.006|-0.022|-0.036|-0.015|-0.007|-0.024|0.000|
||[5.44]***|[5.86]***|[5.54]***|[4.13]***|[-1.10]|[-0.60]|[-1.35]|[-0.47]|[-1.40]|[-0.74]|[-1.43]|[-0.00]|
|Real per capita income|0.013|0.013|0.015|0.024|0.001|0.001|0.002|-0.020|0.002|0.002|0.002|-0.019|
||[5.02]***|[4.87]***|[4.76]***|[2.83]***|[0.61]|[0.72]|[0.79]|[-1.64]|[0.85]|[0.93]|[0.68]|[-1.70]*|
|House and land ownership|0.043|0.045|0.048|0.093|0.020|0.021|0.031|0.007|0.018|0.019|0.027|-0.040|
||[2.64]***|[2.76]***|[2.48]**|[1.16]|[1.85]*|[2.23]**|[1.84]*|[0.07]|[1.58]|[1.87]*|[1.52]|[-0.44]|
|Adults average education|0.024|0.024|0.029|0.032|0.006|0.006|0.012|0.003|0.006|0.005|0.011|0.016|
||[8.38]***|[8.05]***|[7.95]***|[2.31]**|[3.28]***|[3.13]***|[3.68]***|<br>[0.14]|[3.11]***|[2.94]***|[3.28]***|<br>[0.83]|
|Agriculture|-0.026|-0.019|-0.011|0.038|0.009|0.011|-0.010|-0.120|0.010|0.014|-0.002|-0.070|
||[-1.15]|[-0.86]|[-0.42]|[0.65]|[0.64]|[0.91]|[-0.45]|[-1.05]|[0.72]|[1.06]|[-0.09]|[-0.68]|
|Electricity|0.109|0.111|0.133|0.321|0.017|0.019|0.033|-0.062|0.022|0.023|0.040|-0.058|
||[6.39]***|[6.52]***|[6.48]***|[4.26]***|[1.50]|[1.91]*|[1.82]*|[-0.61]|[1.89]*|[2.24]**|[2.12]**|[-0.62]|
|Returned migrant|0.088|0.092|0.109|0.182|0.011|0.025|0.025|0.703|0.016|0.030|0.032|0.240|
||[2.36]**|[2.45]**|[2.30]**|[1.30]|[0.47]|[1.04]|[0.59]|[4.31]***|[0.62]|[1.18]|[0.73]|[0.87]|
|Observations|2380|2380|2046|796|2353|2353|1366|373|2359|2359|1424|416|
|Pseudo R<sup>2</sup>|0.146|0.157|0.211|0.139|0.026|0.057|0.111|0.088|0.024|0.058|0.102|0.081|
|Log likelihood|-967.0|-954.4|-830.6|-463.4|-546.4|-529.0|-422.2|-226.1|-588.5|-567.9|-462.2|-252.1|
|Department fixed effects|No|Yes|No|No|No|Yes|No|No|No|Yes|No|No|
|Municipalities fixed effects|No|No|Yes|No|No|No|Yes|No|No|No|Yes|No|
|Household fixed effects|No|No|No|Yes|No|No|No|Yes|No|No|No|Yes|

25
41

**Table 4: Regression results for postneonatal mortality in 2003 and 2008 in Kenya**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Intermediate malaria risk zone (IMRZ)|0.0038|0.0032|0.0025|0.0008|0.0012|0.0037|-0.0000|
|High malaria risk zone (HMRZ)|0.0662***|0.0614***|0.0605***|0.0605***|0.0579***|0.0616***|0.0381**|
|Interaction LMRZ and ITN ownership|0.0080|0.0085|0.0102|0.0108|0.0128|0.0115|0.0122|
|Interaction IMRZ and ITN ownership|0.0045|0.0043|0.0054|0.0092|0.0099|0.0084|0.0041|
|Interaction HMRZ and ITN ownership|-0.0386**|-0.0383**|-0.0373**|-0.0349**|-0.0342**|-0.0379**|-0.0424***|
|2008 survey|-0.0195**|-0.0130|-0.0134|-0.0143|-0.0141|-0.0119|-0.0183*|
|Full immunization (district level)||-0.0417|-0.0415|-0.0420|-0.0509*|-0.0495|-0.0017|
|Birth in clinic|||-0.0078|-0.0057|-0.0033|-0.0059|-0.0089|
|Antenatal tetanus protection|||0.0070|0.0072|0.0073|0.0069|0.0083|
|Antenatal antimalarial drugs|||-0.0007|-0.0007|-0.0000|-0.0002|0.0012|
|Antenatal iron supplement|||-0.0006|-0.0006|-0.0010|-0.0016|-0.0044|
|Unimproved water source (not surface)||||-0.0257**|-0.0251**|-0.0276***|-0.0310***|
|Improved water source||||-0.0090|-0.0065|-0.0136|-0.0135|
|Unimproved sanitation||||-0.0034|-0.0078|-0.0176|-0.0186|
|Improved sanitation||||-0.0145|-0.0139|-0.0257*|-0.0263*|
|Mother has primary education|||||0.0213|0.0193|0.0077|
|Mother has secondary education|||||0.0018|-0.0019|-0.0147|
|Mother has tertiary education|||||-0.0048|-0.0082|-0.0220|
|2nd wealth quintile||||||0.0145|0.0124|
|3rd wealth quintile||||||0.0316**|0.0311**|
|4th wealth quintile||||||0.0181*|0.0185*|
|5th wealth quintile (richest)||||||0.0301**|0.0316**|
|Rural area|||||||0.0015|
|Central province|||||||0.0010|
|Coast province|||||||-0.0061|
|Eastern province|||||||-0.0128|
|Nyanza province|||||||0.0553**|
|Rift Valley province|||||||-0.0075|
|Western province|||||||0.0149|
|North Eastern province|||||||0.0165|
|Constant|0.0237***|0.0473***|0.0475***|0.0612***|0.0538***|0.0483***|0.0381*|
|Number of observations|4668|4668|4668|4668|4668|4668|4668|
|R-squared|0.0234|0.0249|0.0255|0.0288|0.0316|0.0344|0.0433|
|F-statistics|4.4604|4.0084|3.0946|2.4864|2.8731|2.6283|2.2600|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata. Notes: Linear probability model, pooled sample. Dependent variable is a dummy variable on child's survival status. It takes value 1 if the child died in postneonatal or infant period. For categorical variables with more than two categories, the left-out categories are: Low malaria risk zone (LMRZ), Surface water, No sanitation, Mother has no education, 1st wealth quintile (poorest), Nairobi province. Full immunization variable measures fraction of children with all vaccinations at district level. Vaccination records are not available for deceased children. For definition of "all vaccinations" see Appendix. Significance levels are marked as follows: * 10%, ** 5%, *** 1%.
This procedure is useful for our purposes and is common in the literature, for example Guasch, Laffont and Straub (2006) successfully adopt it.

# **3 Data and Literature Review**

In the previous section we had to introduce Leibniz’s fictitious “the best of all possible worlds” scenario to imagine that projects are evaluated independently and variables are available. Actually this procedure is rather standard in several MDBs and at the same World Bank. This institution presents a whole department dedicated to evaluating projects and other Bank products: the Independent Evaluation Group (IEG, formerly known as OED, Operations Evaluation Department). Its evaluation goals intend to provide an objective assessment of the results of the World Bank’s work, through independent and rigorous analysis<sup>2</sup> . Though these data may be criticized and discussed, we feel that they embody important pieces of information, which may lead to useful policy recommendations.

In this part, we would also like to stress that a few papers have already used IEG data to research on policy-relevant issues, though with different research questions and methodologies<sup>3</sup> . For example Isham, Kaufmann and Pritchett (1997) and Isham and Kaufmann (1999) link a specific indicator of project performance<sup>4</sup> with country-level variables, respectively civil liberties and sound macroeconomic policies. Other papers connect project performance to country-wide variables, including institutional quality (Dollar and Levine 2005), conflict (Chauvet et al 2010) and volatility (Guillamont and Laajaj 2006). Another line of research, attempts to explain project-level data with project-level observations: Deininger, Squire, and Basu (1998) analyze the synergies between project success and knowledge/advisory products produced by the World Bank<sup>5</sup> ; Pohl and Mihaljek (1998) analyze the degree of project uncertainty between calculated ex-ante and realized ex-post project performances (ERR); while both Dollar and Svensson (2000), Kilby (2000) and Chauvet et al (2006) disentangle the impact of project preparation and

> 2 In Annex B we explain the reasons why we consider these ratings to be realistic, serious, reliable and credible. 3 We would like to thank Aart Kraay for the exhausting references provided.

> 4 They use the Economic Rate of Return (ERR), which is a measure of the estimated (ex-ante ERR) and realized (expost ERR) of economic impact of the project. It is generally defined as the discount rate at which a stream of costs and benefits has a net present value of zero.

> 5 The expression knowledge products or advisory products refers to the Economic and Sector Works (ESW) and Non Lending Technical Assistance (NLTA): these are “background papers” which provide the World Bank project staff with analytical and advisory documentation, which may be useful in drafting the project, its aims and its implementation.

6
# **Effects of a Multi-Faceted Education Program on Enrollment, Equity, Learning, and School Management: Evidence from India**

Clara Delavallade, The World Bank Alan Griffith, University of Washington Rebecca Thornton, University of Illinois<sup>1</sup> JEL classification: O15, I25, J16, O19 Keywords: education, randomized experiment, school effectiveness, learning, gender equality 1 Delavallade: Africa Gender Innovation Lab, World Bank, 1818 H Street, NW Washington, DC 20433, US (email: cdelavallade@worldbank.org), Griffith: Department of Economics, University of Washington, Savery Hall, 410 Spokane Ln, Seattle, WA 98105, US (email: alangrif@uw.edu), Thornton: Department of Economics, University of Illinois at Urbana-Champaign, 214 David Kinley Hall, 1407 W. Gregory, Urbana, IL 61801, US (email: rebeccat@illinois.edu). The authors thank the members of the NGO field team for their cooperation and contributions in all stages of the project and would especially like to acknowledge the useful comments provided by many seminar participants. Oxana Azgaldova, Kwong-Yu Wong, and Flor Paz provided valuable research assistance. All errors are our own.