rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 35 25<br>30<br>2.0<br>25<br>= 20 5<br>a ES<br>2153c:<==<br>=a 1.0<br>10<br>Sa o os<br>| U<br>a : Li nthe = . = U = 40<br>You, 2008 You,2011 You,2011 Xie,2014 Rosegrant Rosegrant Molden, FAO,2008 NEPAD, Koohafkan GLOBIOM GLOBIOM GLOBIOM<br>(>086 IRR) (>12% IRR) etal, 2017 et al., 2017 2007 2003 etal,2010 = Invest Maxinvest Zeroinvest<br>(High<br>Investment<br>Scenario)<br>M Expansion @ Rehabilitation / Modernization<br>TM System Upgrade @ Efficiency Improvement<br>@ Soil Water Management & Maintenance and Operation<br>TM Depreciation @ Total<br>@ Average irrigated area increase per year [right axis] 2000-2050 [purple]<br>2010-2050 [red] 2010-2030 [yellow] 1998-2030 [black]<br><!-- End of picture text -->
**Figure 3.** Factor contributions to Short Run GDP Growth, period averages, percentage points (Excluding indirect effects of non-capital factors on capital deepening)

<!-- Start of picture text --> 8.0<br>6.0<br>2.1<br>4.0 0.6 0.1<br>0.3<br>1.3 0.9 0.2<br>2.1 1.0<br>2.0<br>2.7 2.6<br>1.6 2.0<br>0.0<br>-0.5 -0.7<br>-1.4<br>-2.0<br>1992-2001 2002-2013 2014-2019 2020-2022<br>Growth = 3.8 Growth = 6.1 Growth = 3.1 Growth = 1.7<br>Physical Capital Labor Human Capital TFP growth GDP Growth<br><!-- End of picture text --> Source: MTI Growth Accounting Tool, WB Staff calculations Amid the challenges, there were also some notable achievements. The 2002-2019 period saw positive developments in poverty reduction, a decline in inequality, and improvements in human capital (Figure 6). Peru has also made significant progress in improving access to education and quality of education in the last 20 years. The percentage of young adults (ages 25-34) with a secondary education increased from 43% in 2000 to 60% in 2017, and the percentage of young adults with a tertiary education increased from 12% to 20% over the same period. Human Capital's contribution to growth increased during 2014-2019 but deteriorated during the COVID-19 pandemic (see Figure 3).

Total population growth was close to 1.2% in the last decade after experiencing a downward trend since 1990 (Figure 7). Population growth has increased in the last 5 years due to Venezuelan immigration to Peru going from around 1.1% in 2014 to 1.9% by 2019. After the COVID crisis, the population growth decreased to 1% in 2022.

Between 2020 and 2021, during the COVID-19 crisis, accumulated GDP growth was less than 1 percent and the country is now recovering from this significant shock. Over this period the structural challenges were highlighted, and the potential growth was affected due the lower productivity and labor contributions.

5
less so for agriculture.

Figure 5 shows an additional dimension of heterogeneity, considering the self-employed vs employees. This variable is mostly recorded in the LAC and ECA regions only. It appears that stopping work is somewhat more common among the self-employed (46%, taking a simple average across countries) than among employees (39%).

# **4.3 Measures of Income Loss**

In addition to stopping wage work and payments for wage work, the HFPS data allow us to examine broader measures of household income. Because these indicators are measured at the household level, they are less influenced by the non-representative nature of the sample of respondents in some countries. We examine four income categories: total household income, farm income, non-farm income, and wage income. Results are reported in Figure 6.

Panel (a) suggests that total income loss was most prevalent in some SSA countries (Ghana, Nigeria, and Malawi), as well as some countries in the LAC region (Peru and Ecuador were the most affected). It appears that labor market disruptions have translated into widespread income loss in all countries, including high-income countries with stronger social security systems and public assistance programs. It is notable that in many countries, particularly in the EAP region, many more respondents report income loss than stopping work.

Panel (b) suggests significant drops in farm income across a broad set of LAC and SSA countries, as well as some countries in the EAP region. Drops in income from family farming suggest that the economic impacts of COVID-19 go beyond formal labor markets and the formal economy, including in many countries where these less formalized sectors are a significant share of economic activity. Based on Panel (c), other types of family businesses were hit even harder, especially in the LAC and SSA regions. This is consistent with the sectoral heterogeneity in stopping work observed in Section 4.2 where we found that agriculture, industry, and services were all hit significantly, though the latter two sectors fared even worse 20
|Enterprise is credit constrained**X**|-|-|-0.623|-0.030|
|---|---|---|---|---|
|Activity is trade|||(-0.72)|(-0.33)|
|Enterprise is credit constrained**X**|-|-|0.048|0.081|
|Activity is service|||(0.12)|(1.99)|
|Enterprise is non-credit constrained**X**|-|-|0.716|0.040|
|Activity is manufacturing and processing|||(1.64)|(0.90)|
|Enterprise is non-credit constrained**X**|-|-|-0.071|0.071|
|Activity is transport|||(-0.16)|(1.55)|
|Enterprise is non-credit constrained**X**|-|-|0.264|0.072|
|Activity is trade|||(0.32)|(0.86)|
|Enterprise is non-credit constrained**X**|-|-|0.154|0.052|
|Activity is service|||(0.42)|(1.40)|
|R<sup>2</sup>|0.316|0.206|0.317|0.211|
|Model F statistics|F(47,|F(47,|F(55,|F(55,|
||2327)=22.82,|2327)=12.85,|2319)=19.61,|2319)=11.24,|
||p>F=0.000|p>F=0.000|p>F=0.000|p>F=0.000|

> **†** Profit margin is defined by the ratio of profit to revenue.

Note: Figures in parentheses are t-statistics. Regressions additionally include all variables reported in Table 11 and community prices of consumer goods, daily wage, etc., and agroclimate characteristics (land elevation, average number of sunny months, and excess rain amount per month).

Source: HIES 2000, 2005 and 2010

**Table 17: Annual interest rates (%) in selected Asian countries by lender type (2003)**

|**Country**|**Commercial Banks**|**MFIs**|**Informal money lenders**|
|---|---|---|---|
|**Bangladesh**|10-13|20-35|180-240|
|**Cambodia**|18|45|120-180|
|**Indonesia**|18|28-63|120-720|
|**India**|12-15|20-40|24-120|
|**Nepal**|15-18|18-24|60-120|
|**Philippines**|24-29|60-80|120+|

Source: Faruqee and Khalily (2011) 48
3 This paper assesses the possible contribution of various factors to the decline of infant mortality in Kenya using a Oaxaca-Blinder decomposition analysis of changes in infant mortality as measured in Demographic and Health Surveys (DHS) conducted in 2003 and 2008. Previous studies such as Rajaratnam et al (2010) have documented the recent declines in mortality of infants and older children, many studies have documented the effectiveness of insecticide-bednet nets (ITN) in preventing malaria (see Lengeler 2004 for a review), and various studies have tracked changes in child survival in response to particular interventions, generally in narrowly defined populations (e.g. Kleinschmidt et al. 2009 and Ndirangu et al. 2010). This paper makes several contributions. First, the Oaxaca-Blinder analysis allows for an analysis which simultaneously considers several possible drivers of the change observed over time. Second, the use of DHS data makes it possible to examine changes for Kenya as a whole. Third, the use of local malaria prevalence data makes it possible to allow for a varying impact of ITNs by malaria prevalence.

The paper is organized as follows. Section 2 of the paper describes patterns in infant mortality in Sub-Saharan Africa. Section 3 presents the changes in infant mortality and the possible set of drivers in Kenya along with an analysis of correlates of infant mortality. Section 4 focuses on the Oaxaca-Blinder analysis. Section 5 considers what these results imply about the costeffectiveness of bednet interventions, and Section 6 concludes.

# **2 The Infant Mortality Decline in Sub-Saharan Africa**

A broad decline in infant mortality has occurred across Sub-Saharan Africa. Table 1 shows both infant (under age 1) and under-5 mortality for countries with recent Demographic and Health Surveys.<sup>1</sup> The most recent DHS-based estimates indicate infant mortality to be below 100 per 1000 live births in all 20 countries for which post-2005 DHS data is available and to be falling in 18 of those 20 countries. This paper focuses on infant mortality, but similar trends are found for under-5 mortality. Among the 20 countries with recent DHS data, the surveys show average annual rates of decline of 3.6 percent in infant mortality (under age 1) and 4.2 percent in under-5

> 1 Data is shown for all countries in Sub-Saharan Africa which have had at least two DHS surveys, including at least one since 2005. Other estimates are available from other surveys as well as composite estimates from the United Nations Inter-agency Group on Child Mortality. We focus on the DHS estimates because they are more clearly comparable over time and across countries than estimates assembled from a mix of sources.
19

# **Supporting Information**

# **S1 Table. Randomization strata**

|Province|Stratum|Villages-<br>per-<br>cluster|Total<br>villages<br>in<br>stratum|Intervention<br>villages in<br>stratum|Control<br>villages<br>in<br>stratum|Total<br>clusters<br>in<br>stratum|Intervention<br>clusters in<br>stratum|Control<br>clusters in<br>stratum|
|---|---|---|---|---|---|---|---|---|
|Kongo|||||||||
|Central|1|1-2|15|8|7|12|6|6|
||2|3-4|13|6|7|4|2|2|
||3|5,7|12|7|5|2|1|1|
|Kasai|1|1-2|37|19|18|26|13|13|
||2|3-5|39|21|18|11|6|5|
||3|10,12|22|12|10|2|1|1|
|Kasai|||||||||
|Central|1|1-2|34|8|26|25|6|19|
||2|3-4|34|8|26|10|2|8|
||3|6-7|13|0|13|2|0|2|
|South Kivu|1|1-2|20|9|11|13|6|7|
||2|4-8,10|74|39|35|12|6|6|
||3|12,14|26|12|14|2|1|1|
|Total|||339|149|190|121|50|71|
Women’s ability to perform activities deemed unsuitable pre-displacement often depends on the contestation of gender norms at the community level. In the above mentioned study in Darfur, De La Puente (2011) shows that IDP women were involved in health-related activities at the community level, but they did not participate in decisions related to the infrastructure or management of the camp, which were perceived as male fields. In contrast, Ramnarain (2016) finds that in the aftermath of the conflict in Nepal, widows engaged in employment outside the home and some of them even crossed over into male-dominated fields, such as construction labor or transport.

# **3. Colombian Context**

The number of IDPs in Colombia constitutes 12 percent of the 45.7 million people who had been forced to flee worldwide by the end of 2019, resulting in the second largest displacement in the world, behind only the Syrian Arab Republic (IDMC, 2020). Displacement in Colombia is directly linked to violence, but the underlying causes are as complex as the protracted conflict itself. Similar to other situations of conflict in the world, the attacks on civilians are the main triggers for displacement in Colombia (Bohra-Mishra & Massey, 2011; Czaika & Kis-Katos, 2009; Engel & Ibáñez, 2007; Lischer, 2007; Shultz et al., 2014). The violence is directly linked to land disputes and drug trafficking, but it is also used as a strategy of armed groups to exert greater power and destroy social networks (Ibáñez, 2008; Kay, 2001; Perez, 2002). Official figures indicate that the main groups responsible for the displacement are guerilla and paramilitary groups (Ibáñez, 2009).

The nature of the conflict implies that displacement is not confined to specific areas of the country. Between 1997 and 2018, more than 6 million people were forced to flee their homes from 90 percent of the country’s 1,123 municipalities (Figure 1) and its incidence has been more intense in rural areas characterized by weak institutional presence (Angrist & Kugler, 2008; Ibáñez, 2009). Nonetheless, cities hosting large populations of IDPs, such as Medellín, Cali and Bogotá, have also experienced violence by parties of the armed conflict and criminal groups, which has led to the phenomenon of intra-urban displacement (Atehortúa et al., 2013; Jacobsen, 2011).

5
<!-- Start of picture text --> Employment density as a function of distance to city center<br><!-- End of picture text --> <!-- Start of picture text --> Le umm Average of the data<br>250 000~ |<br><!-- End of picture text -->

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of services, if clubs agree to a set of good regulatory practices that will help to facilitate trade in services, this can be implemented on an MFN basis by club members scheduling whatever has been agreed as additional commitments under Art. XVIII GATS, a provision that reflected the recognition by the drafters of the GATS that clubs were likely going to be a feature of multilateral cooperation on services trade (Hoekman and Mavroidis, 2017). If club members agree certain procedures make sense to implement – that is, constitute good regulatory practices – then there is no rationale to discriminate in the application of TFS measures. While this may be the case, it is also true that the participating countries will determine the content of any agreements that emerge from discussions. Matters of interest to nonparticipants may not be addressed.

Although elements of the TFS agenda are being purposed in the plurilateral initiatives, what is on the table is less than what would have been addressed in the TFS proposal put forward by India in 2016. To some extent such gaps could be addressed by including additional TFS elements on the agendas of the different plurilateral groups. However, pursuit of a broader TFS agenda that addresses gaps that are an inherent consequence of the piecemeal approach implied by the extant plurilateral negotiations will require complementary initiatives. A first step to prepare the ground for this would be for services industry associations, sectoral regulators and standards-setting organizations to identify areas where international cooperation could further facilitate trade in services.

# **References**

Basedow, R. and C. Kauffmann, 2016. “International Trade and Good Regulatory Practices: Assessing The Trade Impacts of Regulation,” OECD Regulatory Policy Working Paper 4.

- Beverelli, C., M. Fiorini and B. Hoekman. 2017. “Services Trade Restrictiveness and Manufacturing Productivity: The Role of Institutions,” _Journal of International Economics_ 104(1): 166-82.

- Borchert, I., B. Gootiiz and A. Mattoo, 2014. “Policy Barriers to International Trade in Services: Evidence from a New Database,” _World Bank Economic Review_ , 28(1): 162-88.

- Borchert, Ingo et al. 2019. Applied services trade policy: A guide to the Services Trade Policy Database and the Services Trade Restrictions Index, WTO Staff Working Paper ERSD-2019-14.

- Borchert, I., J. Magdeleine, J. Marchetti and A. Mattoo. 2020. The Evolution of Services Trade Policy since the Great Recession. WTO Staff Working Paper ERSD-2020-02.

Chanda, R. 2009, “Mobility of Less Skilled Workers under Bilateral Agreements: Lessons for the GATS Mode 4 Negotiations”, _Journal of World Trade_ , 43(3): 479-506.

Chanda, R., 2016. “Demographics and Labour Markets: Implications for Mode 4 Trade” in M. Roy and P. Sauvé (eds.), _Research Handbook on Trade in Services._ Edward Elgar.

Drake-Brockman, J., A. Greenidge, J. Lan and Q. Zhao. 2015. “Making the Most of the LDC Services 23
**-7-**

# II. BANGLADESH

# **Backfround**

This section highlights the role of<sup>sbifting assumptions in</sup> production and the operation of factor<sup>markets in inf'uencing the choice of</sup> sectors that should be additionally<sup>taxed to raise a given amount of</sup> government revenve. The illustrations<sup>focus on excise taxation, a brief</sup> account of the characteristics of which 'ollows.i Attempts to reduce the economy's<sup>dependence on trade taxes which</sup> contribute over 50% of tax revenue<sup>have promoted increasing reliance on the</sup> excise tax which accounts for 97 percent<sup>of taxes on domestic production in</sup> Bangladesh. This is a turnover tax that<sup>is levied ex factory on domestic</sup> production and also on some services,<sup>and whose effects cascade throughout</sup> the whole economy. In recent years<sup>excise taxes have accounted for 23</sup> percent of tax revenue. While the average<sup>rate of excise tixation, defined</sup> as the ratio of collections to the gross<sup>value of excisable production is</sup> around 8 percent, two-thirds of excise<sup>tax revenue is accounted for by</sup> three categories of goods, viz., tobacco,<sup>gas and petroleum-cum-oil-cum-</sup> lubricants.

# Raising One Percent of Indirect Tax Revense

As with any tax reform, a proposed<sup>change in excise taxation has</sup> two effects that are of interest<sup>to analysts and policy makers: (i)</sup> revenue and (ii) incidence across<sup>different socioeconomic groups. To</sup> isolate those effects, the Bangladesh<sup>tax policy model, which distinguishes</sup> 35 production sectors and 10 socioeconomic<sup>groups, is asked the question:</sup>

> 1/ A similar analysis with respect<sup>to other taxes in Bangladesh is</sup> contained in Dahl and Mitra (1989)<sup>to which the reader is referred for</sup> a fuller description.
23 contractual arrangements that are not conducive to monitoring performance. Though there is some evidence that performance-related pay leads to improving achievement (Atkinson, 2004; Vegas and Umansky, 2005, Duflo, et.al. 2005), it is not fully understood what constitutes high-quality teaching plus the difficulty in monitoring behavior might lead to improper behavior, such as the cheating documented by Jacob and Levitt (2003) in the Chicago school system. Other behavior frequently observed in developing nations is that of teacher absenteeism. Countries where absenteeism is more prevalent (Table 3 ), such as India and Uganda, ranked lower in terms of efficiency scores estimated in educational attainment, while those with lower absenteeism, like Peru, ranked higher (Herrera and Pang, 2006).

||Tea|Table 3<br>cher Absenteeism||
|---|---|---|---|
||Percentage of te|achers that were absent this many tim<br>(3in India)|es in 2 visits|
||0|1<br>2|3|
|Bangladesh|73.4|23.5<br>3.2||
|Ecuador|82.8|6.9<br>10.4||
|India|49.1|32.7<br>13.5|4.8|
|Indonesia|67.7|27.5<br>4.8||
|Peru|81.0|17.3<br>1.7||
|Uganda|63.0|29.6<br>7.4||
|Note: Interpretation<br>absent once, and 3.2<br>Source: Chaudhury|of the figures. 73.4 % of t<br>percent were absent twic<br>,et.al.|he teachers in Bangladesh were never absen<br>e.|t; 23.5 % were|

Without an adequate institutional setting, educational spending will lead to negligible changes in human capital and hence on growth. A package of institutions or policies that would provide this institutional setting would: 1) promote more competition so that parental demand generates incentives to individual schools; 2) allow autonomy in local decision makers so that school mangers promote student achievement; and 3) develop an accountability system so that rewards are based on school performance (Hanushek and WoBmann, 2007).

# 2. Physical capital

In the case of physical capital (infrastructure), studies show that its growth impact depends on its quality and is heterogeneous owing to multiple factors: different types of physical capital, differences in the stage of network development, differences through time (Calderon and Serven, 2004; Hurlin, 2006; Colletaz and Hurlin, 2006). Different types of capital will have distinct effects on output. For instance, roads and railways will not have necessarily the same impact on output. Similarly, a single road with no connections will not have the same impact as one interconnected with the main highway system. These differences in the determinants of capital productivity are seldom
<mark>in urban areas and 20,000 in rural areas. A team of HCP enumerators conducts each survey round through direct household interviews, using a computer-assisted personal interviewing (CAPI) technique. The survey also contains a rotating panel component</mark> that can be used to examine the persistence and dynamics of labor market status. This rotating panel component, however, is available only for about half of the sample for two adjacent years—specifically, 2010/2011 through to 2017/2018. In this paper, <mark>we focus our analysis on the period ranging from 2010 to 2018.</mark><sup>3</sup> Table 3 presents descriptive statistics of control variables for 2010 and 2018. We observe that between 2000 and 2018, the NEET rate remained high, hovering around 30 percent, with little sign of decline. In 2018, 28 percent of young Moroccans (about 2 million people) could be classified as NEET. The percentage of youth who have secondary education or are pursuing any type of education beyond high school has increased over time. In particular, secondary education moved from 23 to 30 percent between 2010 and 2018, with tertiary education rising from 6.7 percent in 2010 to 13.8 percent in 2018.

**Table 3: Descriptive Statistics of Selected Control Variables (48,024 observations in 2010 and 55,280 in 2018)**

|**Variable**||**2010**<br>|||**2018**<br>||
|---|---|---|---|---|---|---|
||**Mean**|**Min**|**Max**|**Mean**|**Min**|**Max**|
|**NEET (1 = yes)**|32.4|0|1|28.4|0|1|
|**HH member is female (1 = yes)**|49.7|0|1|49.5|0|1|
|**HH member is 20-24 years old (1 = yes)**|55.7|0|1|54.2|0|1|
|**HH member is single**|87.8|0|1|88.3|0|1|
|**HH member is married**|11.8|0|1|11.2|0|1|
|**HH member is widower\divorced**|0.4|0|1|0.4|0|1|
|**HH living in rural area**|46.2|0|1|40.3|0|1|
|**HH living in most developed regions**|68.7|0|1|50.5|0|1|
|**No education**|12.5|0|1|5.1|0|1|
|**Koranic school**|1.3|0|1|0.7|0|1|
|**Primary school**|56.2|0|1|50.6|0|1|
|**Secondary school**|23.2|0|1|29.7|0|1|
|**Tertiary education**|6.7|0|1|13.8|0|1|
|**Asset index (normalized)**|37.4|0|1|43.6|0|1|
|**HH living in rural accommodation (1 = yes)**|35.8|0|1|26.9|0|1|
|**HH living in villa (1 = yes)**|1.3|0|1|1.3|0|1|
|**HH living in apartment (1 = yes)**|7.3|0|1|10.3|0|1|
|**HH living in traditional house (1 = yes)**|3.3|0|1|2.6|0|1|
|**HH living in modern house (1 = yes)**|47.3|0|1|55.5|0|1|
|**HH living in shanty (1 = yes)**<br>_Source:_authors’own elaboration based on the_Enquête n_|5.0<br>_ationale sur l_’_emplo_|0<br>_i_(ENE).|1|3.4|0|1|

> 3 We excluded 2016 from the sample because, in the available data set, we could not find a set of variables regarding family background that we could subsequently use in the econometric analysis. In any case, in 2016 the NEET rate was 29 percent, very similar to both previous and subsequent years.

10