rafmacalaba/datause-encoder-data huggingface dataset:
# **_The parameters_**

The elasticities of substitution and transformation of the CES and CET functions have been fixed a priori at “reasonable levels.”<sup>13</sup> For the production function elasticities we have for example a much lower elasticity between capital and highly skilled labor than between the other factors<sup>14</sup> .

The derivation of the scale and share parameters of the functions follows the usual procedure. Based on the initial values of the variables and exogenous parameters the scales/shares are computed endogenously.

The parameters of the Human Capital accumulation model were estimated on the basis of data on student flows (enrollment, dropout, repetition and advancement rates) across different levels of the education system provided by the Ministry of Education. The flows are subject to change as the demographic composition of the population changes even if the transition variables of the education system remain constant.

Finally, the parameters of the wage curve were estimated using time-series on wages provided by the “Caisse Nationale de Sécurité Sociale” (CNSS).

# **IV. Policy Simulations**

This section starts by presenting the dynamics of key macroeconomic aggregates under the status-quo or baseline scenario. It then discusses the impact of alternative policy interventions in the education and social protection system, implemented individually or as a package (see Table 2). The focus is on the following aggregates: (i) the growth rate of GDP; (ii) the level of investments; (iii) the fiscal deficit; (iv) the aggregate unemployment rate in urban areas ; (v) the size of the informal sector in terms of its contribution to total urban employment; and (vi) unemployment rates by skills level. It is important to mention that the focus of the analysis is more on the direction of changes observed among the various output indicators than about the magnitude of the changes. The tables with the results of the various simulations are presented in the Appendix.

> 13 See table A1 in the appendix 14 Fallon and Layard (1975) hypothesis.

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- 9 -

Agricultural Roads (DOLIDAR), and individual District Development Committees (DDCs). DDCs are the main implementing agencies to prepare District Transport Management Plans, prioritize the local needs, mobilize community groups, and procure and manage civil works and services. However, the DDCs planning and implementation capacity remains weak. Thus, DOLIDAR is playing an important role in coordinating DDCs and providing technical and engineering support to them. MOLD is responsible for general oversight and monitoring of the whole program.

We collected procurement and contract data from 155 rural road upgrading works in 19 Terai districts assisted by the World Bank.<sup>2</sup> Data were also collected from projects financed by the government and other aid agencies. In each district, about 8 road contracts were reviewed―half from World Bank-financed projects and half from government-owned projects (Figure 3). Because of the selection criteria of the RAIDP, these districts have the relatively high road-sector development performance and thus high road density (Figure 4).

**Figure 3. Sample coverage by district**

<!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Number of sample contracts<br>Banke Bardiya Dhading Dhanusa Kailali Kapilvastu Kaski Mahottari Makwanpur Nawalparasi Nuwakot Palpa Rautahat Rupandehi Salyan Sarlahi Siraha Syangja Udayapur<br><!-- End of picture text --> 2 One of the 20 districts assisted by the RAIDP has not yet had road work contracts that can be evaluated at the time of our data collection.
**Table 5: Mean welfare and poverty gaps**

||**All**|**Adults**<br>**(18 or**<br>**more)**|**Children**<br>**(0-17)**|**Infants**<br>**(0-4)**|
|---|---|---|---|---|
|**Poverty Gaps**|||||
|Mean poverty gap (PPP$ per capita per day)|$0.07|$0.05|$0.11|$0.12|
|Relative poverty gapindex(% ofpovertyline)|_3.7_|_2.6_|_6.0_|_6.6_|
|Population weightedrelative poverty gapindex(%)|_3.7_|_1.8_|_1.9_|_0.6_|
|_contribution of each group to relative poverty gap index (%)_|_100.0_|_47.6_|_52.4_|_16.8_|
|**Welfare of the poor (PPP$ per capita per day)**|||||
|Mean welfare ofextreme poor|$1.34|$1.35|$1.32|$1.31|
|_as percentage of PPP$1.90 poverty line_|70.4|71.3|69.5|69.1|
|Median welfare ofextreme poor|$1.42|$1.44|$1.40|$1.39|
|_as percentage of PPP$1.90 poverty line_|_74.9_|_76.0_|_73.5_|_73.0_|
|**Aggregate poverty gap (millions PPP$)**|||||
|Perday|364.0|172.1|191.9|61.8|
|Peryear|132,862|62,821|70,041|22,548|
|**Headcount poverty rate (%)**|12.5|9.2|19.5|21.0|
|**Number of poor (millions)**|655.0|326.3|328.7|104.0|
|**Total population (millions)**|5249.1|3563.0|1686.1|495.3|

Source: GMD, UNDESA, WDI, PovcalNet Finally, we examine the incidence of poverty defined according to a higher poverty line of $3.10 per day (2011 PPP). Table 6 displays the results, which corroborate Figures 1 and 2 in showing the high rates of poverty among children at the higher poverty line. Over 44 percent of all children age 0-17 live on less than $3.10 per day, as opposed to 26.6 percent of all adults. Furthermore, children make up about 44 percent of all extremely or moderately poor, even though they comprise less than a third of the global population.

**Table 6: Extreme or moderate poverty for adults and children**

||**Moderate or**<br>**extreme poor**<br>**(millions)**|**Moderate or**<br>**extreme**<br>**headcount**<br>**poverty rate (%)**|**Share of**<br>**moderate or**<br>**extreme poor**<br>**(%)**|**Share of sample**<br>**population (%)**|
|---|---|---|---|---|
|Children0-17|749.6|44.5|44.2|32.1|
|Adults 18 and above|947.2|26.6|55.8|67.9|
|Total|1696.9|32.3|100.0|100.0|

Source: GMD, UNDESA, WDI, PovcalNet Note Moderate or extreme poverty rate includes all children living in households with welfare per capita lower than $3.10 per day. The 750 million extreme or moderate poor children shown in the table include the 329 million extremely poor children.

# **3. Child poverty by country characteristics and region**

How are these high rates of child poverty distributed across countries, regions, and country income groups? As seen in Table 7, the vast majority – 94 percent – of poor children live in low income or lower middle income countries; India alone accounts for nearly 30 percent of all poor children. Not 10
<!-- Start of picture text --> Figure 7.1: Agricultural Labor Productivity 1/ in LAC Countries in 2002<br>Measured in 1995 USD per Worker<br>10000<br>9000<br>8000<br>7000<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>Source: Caballero (2005) based on CEPAL (for agricultural value added) and FAO (for agricultural labor<br>force) data assembled by Dirven (2004). 1/ Defined as agricultural value added divided by the agricultural<br>labor force and measured in US Dollars of 1995.<br>Hai Bol Hon EcuEl SalGua Per Mex Nic Pan ParR.D. Col Cub Bra Ven Chi C.R Uru Arg.LAC<br><!-- End of picture text --> According to an _agricultural orientation_ index Argentina practices expenditure discrimination against the farming sector. The index is constructed by dividing the share of agricultural spending in total spending by the share of agriculture in GDP (Table 7.2). The index measures the intensity of the fiscal effort in agriculture relative to the economic importance of the sector. Argentina has an agriculture orientation index of 0.07, or a fifth of the average of the LAC region (0.33). Argentina, hence, practices public expenditure discrimination against its agriculture.

Table 7.2: Public Expenditure in Agriculture as Percentage of Total Public Expenditure in LAC Countries, Average 1996-2000

|Brazil|0.23|
|---|---|
|Mexico|1.79|
|Bolivia|0.41|
|Colombia|0.06|
|Peru|0.19|
|Argentina|0.07|
|Chile|0.37|
|Costa Rica|0.12|
|El Salvador|0.08|
|Guatemala|0.07|
|Nicaragua|0.20|
|Dominican Republic|0.37|
|AverageLAC|0.33|
|Source: Caballero(2005)based on data from Kerrigan(2001).||

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# **6 Endogeneity**

The inclusion of the set of fixed effects discussed above strongly reduces any omitted variable concern. Namely, any firm specific productivity shock, as well as any import demand shock and country-pair specific transaction cost are captured by fixed effect. In this setting, the only endogeneity concern may come from the presence of unobserved factors affecting contemporaneously a _change_ in the RTA depth and in the exports of firm _f_ in country _i_ . To further reduce any endogeneity concern, we propose an Instrumental Variable (IV) approach based on the idea that each country ( _i_ or _j_ ) sets the depth of _new_ RTAs based on the depth of existing ones (in the vein of the domino effect of RTA formation by Baldwin and Jaimovich 2012). Our instrumental variable is therefore the following: where the two terms in brackets represent: (i) the average depth of RTAs signed by country _i_ with trade partners _k_ = _j_ within the _j_ ’s macro region, and (ii) the average depth of RTAs signed by country _j_ with trade partners _z_ = _i_ within the _i_ ’s macro region.<sup>22</sup> We use the leave-one-out means to construct the average depth in brackets to address the finite sample bias coming from using own-observation information. The exclusion restriction is based on: (i) the absence of a direct effect of firms’ exports toward destination _j_ on the average depth of RTAs signed by country _i_ and _j_ with third-countries, and (ii) on the absence of a direct effect of average RTA depth signed with third countries on firms’ exports toward _j_ . While condition (i) is likely to hold, condition (ii) deserves careful discussion. Indeed, the presence of a trade diversion effect of deep RTAs threatens the validity of our IV: if country _i_ signs a deep RTA with third country _k_ ( _k_ = _j_ ) and this diverts firm’s exports from _j_ to _k_ , the validity of the IV is challenged. Notice, however, that the average reduction in trade costs of country _i_ and country _j_ with third countries is captured here by respectively firm-year and destination-year fixed effects. We are therefore confident of the exclusion restriction validity in our empirical setting. Still, in the next section we present a plausible exogeneity test aimed at supporting the validity of our results even in presence of weak deviation for the perfect validity of exclusion restriction assumption.

Our baseline results are confirmed by the 2SLS estimations addressing any residual endogeneity concerns - see Table 9 column 1. The (instrumented) measure of RTA depth has a positive and significant effect on the exports of the average firm. Also our results on the hetero-

> 22 Macro-regions are: East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, North America, South Asia, Sub-Saharan Africa.

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May 2021 (Table A2). Our analysis is, therefore, based on the panel of households interviewed in the pre-war and post-war-onset phone surveys between April 2020 and May 2021 (Table A4). While the pre-pandemic (and pre-conflict) sample of the LSMS-ISA survey in Ethiopia is randomly selected from urban and rural households in each district, the follow-up phone surveys are subject to two levels of non-random selection issues. First, the phone sample may differ in systematic ways from the original 2019 sample, partly because of ownership of mobile phones may be correlated with wealth (see Table A4). Second, phone surveys in conflict hotspot areas are likely to suffer from pervasive non-response, partly because of inaccessibility and network problems. The likelihood of being contacted in the phone surveys is likely to be greater for those who are relatively better off economically, as well as those located in areas with better access to telecommunication services (due to better infrastructure or less destruction by the war). In line with this, Table A3 shows that sample attrition increases with the presence of armed conflicts because the response rate for all regions is higher than for Tigray. This can be explained by the disruption in telecommunication and electricity services in the region. As the war continued, telecommunication and electricity services were suspended or intermittent in areas under the control of the Tigray region government. In areas that came under the control of the federal government and provisional regional government in Tigray, telecommunication and electricity services were restored, which allowed access to some households by phone.

To account for these systematic non-responses in the phone surveys, we constructed inverse probability sampling weights which we used in our subsequent analysis. Our final sample consists of those households appearing (at least once) in the pre-war and post-war-onset phone survey rounds, implying that the weights need to be constructed considering attrition and nonresponses in both phases.<sup>14</sup> We use a rich set of household and location characteristics collected in the 2019 survey to characterize and predict the (joint) probability of response in the pre-war and post-war-onset phone surveys using a logit model (see Table A3). We then construct sampling weights as the inverse of the predicted probability of responses in both pre-war and post-war-onset phone surveys. We note that applying the weights markedly reduces the differences between the unweighted and weighted mean differences in the observable characteristics sample (Table A4). In particular, Table A4 shows that applying the sampling weights makes observable characteristics

> 14 Households appearing only either in the pre-war or post-war phone surveys were dropped from the analysis and assumed as nonresponses in constructing the sample weights.

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Policy Research Working Paper 8577

# **Abstract**

The 2018 database on Health Equity and Financial Protection indicators provides data on equity in the delivery of health service interventions and health outcomes, and on financial protection in health. This paper provides a brief history of the database, gives an overview of the contents of the 2018 version of the database, and then gets into the details of the construction of its two sides—the health equity side and the financial protection side. The paper also provides illustrative uses of the database, including the extent of and trends in inequity in maternal and child health intervention coverage, the extent of inequities in women’s cancer screening and inpatient care utilization, and trends and inequalities in the incidence of catastrophic health expenditures.

This paper is a product of the Development Research Group and the Health Nutrition and Population Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/research. The lead author may be contacted at awagstaff@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
US$ 55.2 billion in 2015 and peaked at US$ 66.4 billion in 2008 (EY 2015). The majority of this Greenfield FDI is from Western Europe and other African countries. At the country level, the United States (US) ranks first among the top 10 Greenfield investors in Africa, followed by United Kingdom (UK) and South Africa (tied at No. 2), the United Arab Emirates (UAE), France and Germany. China and India rank 7 and 8 respectively (Table 1). In terms of job creation, out of all firms that received Greenfield FDI in Africa, those that received it from UAE created the most jobs (24 percent) in 2014, followed by firms that received Greenfield FDI from the US (7.2 percent) and France (6.6 percent). Firms that received Greenfield FDI from China and India on the other hand, created 9.4 percent of jobs, with China creating 5.8 percent.

Table 1: Top Investors, Sectors and Jobs created by Greenfield FDI in Africa (2014)

|Investor's rank|Investor<br>Investor|s and jobs create<br>FDI Projects<br>(% of<br>Africa’s total)|d<br>Jobs created<br>(% of Africa’s<br>total FDI-<br>related jobs)|Top 10 sect<br>Sector|ors and jobs cr<br>Project share<br>(% of<br>Africa’s total)|eated<br>Jobs created<br>(% of<br>Africa’s total<br>FDI-related<br>jobs)|
|---|---|---|---|---|---|---|
|1|United States<br>|13.8|7.2|Technology, media,<br>telecommunication|19.6|6.4|
|2|United<br>Kingdom|7.2|3.3|Financial services|18.1|2|
|2|South Africa|7.2|4.3|Consumer products<br>and retail|14.1|31.5|
|4|United Arabs<br>Emirates|6.8|23.7|Real estate, hospitality<br>and construction|8|33.6|
|5|France|6.7|6.6|Business services|7.5|1.6|
|6|Germany|4.8|2.3|Transport and logistics|6.3|3.1|
|7|China|4.4|5.8|Diversified industrial<br>products|5.3|1.5|
|8|India|3.8|3.6|Automotive|4.1|5.7|
|9|Portugal|3.7|0.9|Coal, oil and natural<br>gas|3.5|3.7|
|10|Spain|3.5|2.2|Chemicals|3.3|2.4|

Source: fDi Markets (2015) At the sectoral level, technology, media and telecommunication topped the list of the most attractive sectors for Greenfield FDI projects in Africa, receiving 19.6 percent of Africa’s FDI 13
73 gasoline was decreased twice, by a mere 0.2 percent in December 2014 and 0.06 percent in February 2015, while the price of diesel was decreased five times but by no more than 0.085 percent at any given time.

**Figure 19: Special tax on production and service on gasoline and diesel fuel in Mexico**

<!-- Start of picture text --> 0.40<br>Regular gasoline Premium gasoline Diesel<br>0.30<br>0.20<br>0.10<br>0.00<br>‐0.10<br>‐0.20<br>‐0.30<br>‐0.40<br>‐0.50<br>US$/liter<br>Jan‐09 Jul‐09 Jan‐10 Jul‐10 Jan‐11 Jul‐11 Jan‐12 Jul‐12 Jan‐13 Jul‐13 Jan‐14 Jul‐14 Jan‐15 Jul‐15<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on data from <u>http://sie.energia.gob.mx/.</u> LPG prices were deregulated until March 2001, when the Ministry of Economy began to set ceilings on end‐user prices and the Energy Regulatory Commission began to regulate producer prices. In 2005, the energy regulator set regular monthly increases of between 0.75 and 1.75 percent. The average end‐user prices were increased by 0.33 percent a month in 2006 and 2007, and by 0.032 pesos per kg (US$3 per tonne) a month in 2008. In 2009, the government had hoped to make a one‐time price adjustment and then freeze the prices for a year, and fixed the weighted average national retail price of LPG before VAT at 8.03 pesos (US$0.59) per kg. In 2010, the pricing policy returned to increasing end‐user prices by 0.05 pesos (US$0.004) per kg a month before VAT. In July 2011, the increase was set at 0.02 pesos (less than US$0.02) per kg per month, although the actual increases were larger (SENER 2012). The practice of steadily increasing prices broadly continued until January 2015, when price adjustment stopped.

The Hydrocarbons Law of August 2014 introduced significant reform measures for both upstream and downstream sectors. In the downstream, the law opens up imports, refining, and sale of petroleum products—activities that are currently confined to the national oil company, Pemex—to other participants over time to no later than January 1, 2017. The maximum retail prices of gasoline and diesel will be established by an executive decree between 2015 and 2017. Starting on January 1, 2018, the retail prices will be set by market conditions. In the light of continuing low world prices, the finance ministry in September 2015 proposed that the pricing policy move to market‐based pricing two years earlier in 2016. The Hydrocarbons Law similarly stipulates that LPG prices will be deregulated by January 1, 2017, or earlier, subject to a support program for LPG consumers having been established.
corruption. SSA countries follow LAC countries in terms of the economic significance of tax simplification on tax corruption. In the SSA region, a 10 percent drop in TAXPAY and TAXTIME causes a 5.4 percent and 4.5 percent decline in tax corruption, successively. The combined effect of two cuts on tax corruption is close to -10 percent. The impact of tax simplification on tax corruption is more limited in the ECA and MENA regions. While a 10 percent cut in TAXPAY leads to a 2.8 percent drop in tax corruption, the same amount of cut in TAXTIME leads to only a 1.8 percent cut. When two effects are combined, the total impact on tax corruption is predicted to be -4.6 percent for these countries. The weakest economic significance is observed in the SASIA and EAP regions. Even the combined effects of 10 percent cuts in TAXPAY and TAXTIME lead to only a 2.6 percent decline in tax corruption.

# **_3.4 Importance of Development Levels of Countries in Determining Tax Corruption_**

In order to understand the importance of the development level of countries in determining tax corruption, the countries included in the study are split into two groups. In the first group, low-income and lower middle-income countries are included. While identifying the countries’ income group, the World Bank’s classifications are taken into account. 56 countries of the dataset belong to the first set. The second group consists of upper middle-income and high-income countries; there are 48 countries in this group. The descriptive statistics associated with these two groups are presented in Table 10. The lower-income group has larger tax corruption ratios on average and their tax systems are more complex, which is measured by the time to comply with tax requirements and the number of tax payments.

34
blog.<sup>17</sup> As such, while the findings of the experiment are internally valid only for the Development Impact blog, they seem likely to extend more generally to other research-oriented development blogs.

# _5.1. Randomized Encouragement_

We took the 619 graduate student and field staff respondents to our baseline survey that had provided contact email addresses as the sample for this experiment.<sup>18</sup> We stratified the data by three variables thought to potentially influence attitudes towards research methods and interest in the World Bank: whether they were a Ph.D. student, field staff, or Masters student; whether or not they said they read Chris Blattman‟s blog at baseline; and whether or not they said they were involved in a randomized experiment at baseline. Individuals were then randomly assigned to treatment and control within strata, with Appendix Table 1 showing that this succeeded in balancing baseline observable characteristics.

The encouragement then consisted of two emails. The first was sent on April 6<sup>th</sup> , 5 days after the blog launched, thanking this group for participating in the survey and alerting them to the new blog. They were told about the purpose of the blog and some of the topics that would be discussed, as well as saying “We consider you our most important audience for such a blog, and so want to make it something interactive and useful. We therefore very much would like if you check out the new blog, comment on things, and raise any issues or thoughts on things you would like the blog to discuss.”. They then received a second, shorter, email 3 weeks after the blog had been launched, which asked how the blog was doing so

> 17 Chris Blattman traffic statistics based on a blog posting in which he said he had 800,000 page views in 2010.

> 18 We did not use the World Bank or Assistant Professor samples because of the smaller size of these groups, and because the World Bank economists would be automatically notified about a new World Bank blog.

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