rafmacalaba/data-use-mentions-extended huggingface dataset:
in remote schools, resulting in continued disparities. Even where teachers are successfully placed in schools in remote areas, they may not remain for a significant period of time, exploiting these same formal and informal channels to obtain transfers to more desirable postings. A ‘rural allowance’ scheme is in place to motivate teachers to remain in remote postings, but it too is inadequately targeted with minimal incentive effect (see Asim et al., 2019, for a detailed exploration of these dynamics).

In recent years, a number of steps have been taken by the Ministry of Education (MoE) of Malawi, with support from the World Bank, to introduce more well-defined rules for allocation of teachers. Beginning in 2017, districts were advised to prioritize schools with the highest PTRs, not only those with PTR above 60. At the same time, districts were instructed to prioritize schools with fewer teachers than grades, in an attempt to address the common practice of multi-grade teaching in understaffed schools.<sup>4</sup> In this paper, we assess the level of adherence to rules-based allocation of teachers in Malawi’s schools over the period 2017-2019; and the impact of improvements in allocations on school PTRs and on outcomes. We draw data from multiple rounds of administrative data from the Government of Malawi’s Educational Management Information System (EMIS) database. EMIS data is collected via an Annual School Census (ASC) and includes a wide range of data on school size, conditions, staffing, finances, infrastructure and equipment. We employ data on grades offered, staffing and enrollment to establish the schools which meet each of the criteria established by the government. In addition, we employ administrative data on the allocation of new teachers to schools provided by district-level officials via the MoE.

Our ultimate outcomes of interest are student dropout and promotion rates. These outcomes are informed by the international evidence on the impacts of improvements in school teacher staffing and reductions in PTRs. We anticipate reductions in class sizes at the most overcrowded
This high demand for housing has left much of the population living in substandard conditions. In 2012 roughly half of Kigali’s housing stock was considered by the government as “Low quality, overcrowded units which need to be replaced”. Rwanda’s Environment Management Authority echoed this concern, recently reporting that 83% of Kigali’s population was living in slum housing quality, with many neighborhoods built on steep or sandy slopes, and lack basic access to utilities and transport networks

> 40 Uwimbabazi, Penine and Ralph Lawrence. Compelling Factors of Urbanization and Rural‐Urban Migration in Rwanda. Rwanda Journal Volume 22, Series B, 2011: <u>Social Sciences.</u>

> 41 National Institute of Statistics of Rwanda. 2012. “2012 Population and Housing Census: Report on the provisional Results.” Published November 2012.

> 42 2015 "City Mayors: World's fastest growing urban areas". <u>Retrieved</u> August 2015.

> 43 Institute of Statistics, Rwanda 2012 (http://www.kigalicity.gov.rw/).

> 44 2012, City of Kigali. The Kigali Housing Market Brochure.

20
vocational postsecondary education.

> 6 Data on aspirations for these countries are from the Young Lives Surveys, which measure educational aspirations with a similar survey question in: “Imagine you had no constraints and could study for as long as you liked, or go back to school if you have already left. What level of formal education would you like to complete?” (Favara 2017, Ross 2019).

> 7 The proportion of students aspiring to a doctorate may seem high, considering that many students of this age might not actually know that the doctorate level exists nor what it entails. Given that students were presented this question on a paper they were filling themselves with an ascending list of educational levels, they might have checked the highest one, possibly with limited knowledge of it. In this case, this could be interpreted as a strong signal that students see value in education and want the most of it if they had no constraints.

11
grouped into homogeneous groups, and simple random samples are selected from each group. Individual observations are then weighed using sampling weights that consider the selection probabilities in strata (firm size, business sector, and geographic region), enabling companies to be representative within countries.

The paper uses the most recent standardized data set for the period 2006-2022, which includes 67 surveys. After processing the database, 13,025 manufacturing firms in 39 SSA countries remained. The database provides information on companies' perception regarding the business environment and on their characteristics as well as their performances. The distribution of surveyed firms is unbalanced among industries. The food industry accounts for 26.46% of the surveyed firms (Table A.1), followed by the wearing apparel industry (13.79%), the furniture industry (11.14%) and the metal industry (9.00%). These four industries represent more than half of the surveyed manufacturing firms (60.39%). In addition, some countries have many more companies surveyed than others (Table B.1 & Table B.2). Nigeria, Kenya, South Africa, and Zambia are countries whose firms are the most represented in the database. The survey waves in Nigeria (2017 and 2014) alone represent 16.82% of interviewed firms. Surveys conducted in Kenya (2007, 2013 and 2018) represent 9.39% of the total sample. In South Africa (2007) and Zambia (2007 and 2013), firms interviewed represent 5.03% and 4.98% of the total sample, respectively.

# 3.2. Variables

Six groups of variables are used in this paper: ISSC’s measures, electricity access measure, firm-level, and country-level control variables, as well as covariates (at country- and firm-level).

## **1.** **<u>Intra-Sectoral Structural Change Measures</u>**

- _Intra-sectoral allocation of firms:_ The ISSC
.3|

_Source: World Development Indicators, and Global Economic Prospects (for 2015 estimates)._ A similar growth divergence in the Sub‐Saharan African experience is captured well by recent data. Contrary to the more‐uniform stagnation experienced before, there are now countries in that region that are growing at a pace earlier associated with East Asian nations and among the highest‐growing nations in the world currently. Table 3 provides a cross‐country view of Sub‐Saharan Africa.

**Table 3. GDP Growth in Sub‐Saharan Africa**

||Average 2001<br>‐2008|2009|Average 2010<br>‐2013|2014|2015<br>(est.)|
|---|---|---|---|---|---|
|_Top 5 higher expected_|_growth in 2015_|||||
|Ethiopia|8.3|8.8|10.7|10.3|10.2|
|Côte d'Ivoire|0.7|3.3|4.4|8.5|8.4|
|Congo,Dem. Rep.|4.6|2.9|7.4|9.0|8|
|Rwanda|8.2|6.3|7.2|7.0|7.4|
|Tanzania|6.8|5.4|6.7|7.0|7.2|

_Top 5 lower expected growth in 2015_ 10
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Road standards match traffic levels in Burkina Faso**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Underengineered Correctly engineered Overengineered<br>Nigeria Benin South Africa Rwanda Uganda Chad Ethiopia Senegal Tanzania Ghana Malawi Cote d'Ivoire Cameroon Burkina Faso Kenya Niger Lesotho Mozambique Madagascar Namibia Zambia Average<br><!-- End of picture text --> _Source:_ Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data) using RONET.

# **Challenges**

Burkina Faso established a system for funding road maintenance when it adopted a secondgeneration road fund in 2007. This was an important and significant step toward effectively provisioning for road maintenance. But the level of spending is 50 percent short of the estimated requirements, if we take into consideration the size,

**Figure 7. Maintenance and rehabilitation needs of the main road network are underfunded**

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>-<br>(10) Senegal Burkina FasoCote d'Ivoire Liberia Niger Mali Ghana<br>(20)<br>(30)<br>(40)<br>(50)<br>(60)<br>(70)<br>requirements<br>Spending as percentage of<br><!-- End of picture text --> _Source:_ Gwillliam and others 2008.

conditions, and standards of the existing networks (figure 7). Not surprisingly, the country faces a huge rehabilitation backlog that must be addressed before the trunk
with an emissions trading system (Yunis and Aliakbari 2020). According to the World Bank’s Carbon Pricing Dashboard (World Bank 2021), 27 countries have implemented a carbon tax nationally, and nine countries have implemented an emissions trading system; these figures exclude sub-national entities including provinces and states that have also implemented carbon pricing.

The economic impact of carbon taxes has been of considerable concern to policy makers, and a large body of literature has emerged on carbon taxes in particular since the early 1990s. The bulk 2
->

# b. By step (% of total time)

_Source:_ Based on data collected from World Bank 2009; AICD ports and railway databases; Nathan Associates 2010; Teravaninthorn and Raballand 2009; and World Bank 2010.

> 3 The baseline for transit times at the border of the Democratic Republic of Congo and Zambia was reported to be 16 days for mining freight.

15
**Table 1: OLS estimations of labor productivity and exports**

||Between estimation<br>|FD estimation<br>|
|---|---|---|
||(1)|(2)|
||<br>Panel A: Weighting bilateral route<br>importer trade in industry (sum|<br>s by the interaction of exporter and<br>med across all bilateral routes)|
||DV: Log bilateral exports|DV: Δ Log bilateral exports|
|Log country-industry labor|0.640***||
|productivity|(0.242)||
|Δ Log country-industry labor<br>productivity||0.573***<br>(0.185)|
|Observations|149,547|103,839|
|Importer-Industry-Yr FE|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|
||Panel B: Excludi|ng sample weights|
||DV: Log bilateral exports|DV: Δ Log bilateral exports|
|Log country-industry labor|0.361***||
|productivity|(0.091)||
|Δ Log country-industry labor<br>productivity||0.210***<br>(0.041)|
|Observations|149,547|103,839|
|Importer-Industry-Yr FE|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: Panel estimations consider manufacturing exports taken from the WTF database. Data are organized by exporter-importer-industry-year. Industries are defined at the three-digit level of the ISIC Revision 2 system. Annual data are collapsed into five-year groupings beginning with 1980-1984 and extending to 1995-1999. The dependent variable in Column 1 is the log mean nominal value (US$) of bilateral exports for the five years; the dependent variable in Column 2 is the change in log exports from the
>|0.37|0.35|0.28|0.28|101|1766|18335|
|Nagaland|0.54 0.84|<sup>56</sup>|0.29|0.32|0.28|0.24|94|2690|18301|
|Sikkim|- 0.27|-|-|0.32|-|0.21|125|1001|18371|
|Tripura|0.76 0.83|9|0.42|0.41|0.46|0.43|89|377|15252|

29 Source: NFHS I and II 30 Under the budget heading “ICDS (General). Source: Yi-Kyoung Lee and Selvaraju, from Lok Sabha Unstarred Question No 89 dated 18 February 2003, and Rajya Sabha Unstarred Question No. 4417, dated 2 May 2003.

31 Under the budget heading “ICDS (General). Source: Yi-Kyoung Lee and Selvaraju, from Lok Sabha Unstarred Question No. 1241, dated 31 July 2003, and calculated using the number of children aged 0-6 years from the 2001 census * prevalence of more than 2SD underweight from the NHFS2.

> 32 - Source: Government of India, Ministry of Finance _Economic Survey_ , 2003-04, http://indiabudget.nic.in/es2002

> <u>03/chapt2003/tab18.pdf.  The data are at current prices at the time of the Economic Survey.</u>

20
_Figure 3: Lack of implementation capacity is the largest barrier to improving student learning according to officials in EAP and globally._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Indonesia Philippines Vietnam Mongolia Lao PDR Non-EAP<br>Implementation capacity Money<br>Lack of interest from govt Political resistance from others<br>Political resistance from teachers' unions Other<br>No barrier No answer<br><!-- End of picture text --> Source: CDG–World Bank survey, 2022.

- 4 The Effects of Poverty and Malnutrition on Student Learning

- 4.1 EAP policy makers underestimate the contribution of poverty to low levels of learning.

In Indonesia, Vietnam and Mongolia, surveyed policy makers attributed low levels of student learning to school-based factors, such as poor instruction, lack of books and learning materials, inadequate school facilities and lack of internet connectivity (Figure 4). These factors can be influenced by education policy and financing. The impacts of poverty and malnutrition are acknowledged in some EAP countries, especially in Lao PDR and the Philippines. But in other countries, officials tend to undercount the influence of socioeconomic factors in favor of school-based factors. This may indicate optimism about the power of education and the potential for disadvantaged students to benefit from learning. There is also a clear and statistically significant correlation between per capita GDP and the share of officials attributing low learning to poverty and malnutrition across all countries in the dataset. The higher incomes in EAP are associated with officials being less likely to explain low learning by economic hardship.

In contrast to the average perceptions of EAP officials, the weight of socio-economic conditions and individual characteristics remain large in many surveyed EAP countries (Afkar et al.
of the distribution and hence poverty is lower in 2017/18 than in previous years.

# **2.2. Non-official data sources**

The subsample of the IHDS survey that was collected in 2017 for three states (Bihar, Rajasthan and Uttarakhand) also shows an increase in mean consumption between 2011/12 and 2017. Real income, consumption and food expenditures grew at an annualized rate of 3.5 percent, 2.7 percent and 1.9 percent, respectively. This is indicative because, historically, growth of consumption expenditure reported in the CES has been faster than in IHDS, although average consumption is higher in IHDS than in CES. For instance, between 2004/05 and 2011/12, the mean real consumption per capita in rural India had average annual growth of 3.3 percent in CES and 2.1 percent in IHDS, as well as 3.8 and 2.9 percent, respectively, in urban areas (see Figure 1 and Figure 2).

The CP survey also shows an upward trend in average real consumption and incomes between 2014 and 2018, although it matters whether the comparison is carried out relative to 2014 or 2015. Comparing with respect to 2014, the growth incidence curves show positive consumption growth throughout the distribution with few exceptions (top panel of Figure 4). In contrast, if comparing with the respect to 2015, households below the 15<sup>th</sup> percentile experience a decline in real consumption in years 2016 and 2017, which then turns positive for all percentiles in 2018 (middle panel of Figure 4). This is because the bottom of the distribution grows very fast between 2014 and 2015 (bottom panel of Figure 4). The survey data collected by CMIE seems to indicate a 5
|Yes|7343|87.5|7479|85.5|7537|85.6|
|---|---|---|---|---|---|---|
|No|1030|12.5|1240|14.5|1095|14.4|
|Ethnic group|||||||
|Brahmin|1159|13.6|1122|12.8|1187|12.1|
|Chhetri|1682|17.5|1829|17.8|1899|18.4|
|Occupational|1248|14.6|1720|21.1|1173|12.6|
|All other|4284|54.4|4048|48.3|4373|56.8|

Notes: Weighted to national level with weights provided by the Nepal DHS in each year. Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

35
deviate from the 10 percent ownership threshold, ii) not use uniform industrial classification, or iii) not report all types of investment properly (short-term intra-company loans, re-invested earnings are often missing).

11 The data does not cover merge and acquisition or other equity and non-equity investments. Typically, projects are captured at the announcement stage for capital-intensive projects and at the opening stage of services operations with limited capital investment required. Capital-intensive projects take on average 2 years to become operational.

12 Recent academic research using fDi Markets data includes: (Crescenzi, Pietrobelli and Rabellotti 2013), Paniagua and Sapena 2014), (Amoroso, Dosso and Moncada-Paternò-Castello 2015), and (Antonietti, Bronzini and Cainelli 2015).

10
u> each purchase recorded in the UNHS by estimating the share of the good or service’s price that is a VAT charge.  This imputation assumes an effective (rather than statutory) rate of VAT applies to each purchase.  For goods and services that are exempt in the Ugandan VAT schedule, there may still be “embedded” VAT in the final purchase price when exempt producers pay non-refundable VAT on inputs that is then passed through to the final price of that producer’s item.  To estimate the potential embedded VAT in the price of VAT-exempt goods and services, we make use of an Input-Output (IO) model of the production side of the Uganda economy. For more details, please consult Harris et. al

18
experience), we estimate the following equation: where ∆ln ( _FPijc_ ) stands for changes in the use of factors of production, labor and capital.

# **5 Data and Estimation Validation**

To validate the estimations of the effect of digital technology adoption on TFPR when applied to the WBES database, we estimate the same specification as the baseline specification reported in De Loecker (2013), which relies on data from Slovenia. This involves the estimation of a value-added Cobb-Douglas production function on labor, capital, and productivity, where the latter is assumed to be an endogenous process of learning by exporting. Table 1 presents the results from the production function elasticities, while Table 2 displays the median learning-by-exporting effects on TFPR.

19
the 20 states of our sample. Figure 1 provides a visual presentation. These 20 states are a subset of the initial 35 states/union territories available. The 15 exclusions were due to three potential factors: 1) the state was not sampled across all of our surveys, 2) the small sample size for the state raised data quality concerns, or 3) persistent conflict and political turmoil existed in the region. Our explicit criteria with respect to size are that the district has a population of at least one million in the 2001 census and has 50 or more establishments sampled. The exclusions are minor in terms of economic activity, and the resulting panel accounts for over 90% of employment in both sectors throughout the period of study.

In Table 2a, relative female business-ownership rates in unorganized manufacturing are particularly high in Karnataka, Kerala, and Tamil Nandu. These states have an average female establishment ownership rate exceeding 45%. In contrast, low female ownership shares are evident in Delhi, Bihar, Haryana, and Gujarat. The average female business-ownership share

> 3Comparable entrepreneurship tables are available upon request. We document female business owner ratios in Tables 2a-3b so that the longitudinal pattern in services can be discussed.

6
container.

Very often, higher efficiency and thus lower shipping costs can offset the handling charges. Another indicator of efficiency is the turnaround time of ships at ports which means the average container movements per crane or ship hour within a port. Such costs are incurred by ship owners, who may pass them on to exports and importers.

_<u>Fink, Mattoo, Neagu (2001) Working Paper on Trade in International Maritime Services</u>_ The authors compile a Cargo Handling Restrictions Index, which is a 0-1 index that captures restrictions and special requirements imposed on foreign suppliers of cargo handling services. The index assumes a value of 0, if no restriction exists, 0.25 for minor restrictions, 0.5 if a join venture condition is imposed, 0.75 if high national participation is needed, and 1 if foreign companies are not permitted to provide cargo handling services. The Mandatory Port Restrictions Index captures the extent to which ports services are mandatory for incoming ships. The variable is constructed adding 0.125 for each of the following services if they are mandatory: pilotage, towing, tug assistance, navigation aids, berthing, waste disposal, anchorage.

# _Global Competitiveness Report (CCR)_

The _Port Efficiency_ indicator in the GCR is one-to-seven index ranking port efficiency, based on surveys performed to representative firms of each country. The specific question is “Port facilities and inland waterways are extensive and efficient” (1 being strongly disagree to 7 being strongly agree).

31 ____________________________________________________________________________________________________________
</sup>|X|

**Notes:** The “Baseline” survey was conducted with respondents before treatment assignment was revealed. The “Midline” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “Weekly” surveys were conducted after each week of work (if any). The “Endline” survey was conducted after the end of the eight week engagement and all work had ceased.

> _∗_ Physical Health, Savings, and Cognitive Ability are measured differently during the weekly surveys than at baseline or endline.

66
# **6 Main Results**

## **6.1 Levels and composition of income at the top**

We start this section characterizing the levels and composition of total income, adjusted to national accounts aggregates, at the top of the distribution.

In Figure 2, we present the average income for individuals within the top 10% highest earners in 2019, where we separate them in 37 bins – 9 bins for those between the top 10% but below the top 1% ( _≈_ 50,000 individuals per bin); 9 bins for those in the top 1% but below the top 0.1% ( _≈_ 5,000/bin); 9 bins for those in the top 0.1% but below the top 0.01% ( _≈_ 500/bin); and 10 bins for those at the top 0.01% ( _≈_ 50/bin). We present income levels both in local currency (Lempiras) as well as in USD PPP.

In 2019, the average income of individuals just in the top 10% of the distribution was L213,000 or USD 20,000 per year. This level is rather close to the exemption threshold for Personal Income Tax (PIT) filing in 2019, suggesting that 90% of the adults in Honduras receive incomes below the exemption level.<sup>19</sup> Average incomes increase fast once we move towards those at the very top – those just above the top 1% receive L590,000 or USD 56,000 per year, those at the top 0.1% receive almost five times as much at L3 million or USD 285,000; and the 500 highest earners at the top 0.01% receive more than L30 million or USD 3.1 million in 2019.<sup>20</sup> Consistent with findings from other countries in Latin America, the highest earners in Honduras receive incomes that are