rafmacalaba/data-use-mentions-extended huggingface dataset:
]|[0.148]|[0.208]|[1.241]|
|Share of aggregate loss|0.073|0.125|0.171|0.232|0.399|
|||12 % Increase in<br>|cereal, meat,<br>|and other foods<br>|<br>|
|Compensating variation (x1000)|-45.434|-77.802|-106.694|-145.849|-257.681|
||[0.167]|[0.125]|[0.170]|[0.246]|[1.574]|
|Share of aggregate loss|0.072|0.123|0.168|0.230|0.407|
|Observations|4,266|4,230|4,248|4,250|4,245|
|Standard errors in brackets, 100 boo<br>Source: 2012 1-2-3 Survey, DRC|tstrap replic|ations||||

Table 8 presents welfare losses from the introduction of the VAT by quintile of food expenditure. The magnitude of the welfare loss increases from the bottom to the top quintile. The bottom quintile only accounts for 7.3 percent of the aggregate welfare losses, while the top quintile accounts for 39.9 percent of the aggregate welfare loss. The results from the table cleary highlight that the current regime of the VAT is progressive.

Although our results suggest that the VAT is progressive, we do not know whether exemptions are well-targeted. To address this issue, we assess budget share devoted to exempted goods. Engel curves plotted in figures 3.2a-3.2e show the relationship between log-household food expenditure and budget share devoted to specific food commodities. Indeed, these graphs can help to access whether exemptions are well targeted. Because cereals, including bread and rice, are necessities one can infer that
- EMIS 2018 (collected in October-November 2017, and reflecting the dropout and repetition status at the end of the 2016/17 school year, prior to the allocation of teachers) with

- EMIS 2019 (collected in October-November 2018, and reflecting the dropout and repetition status at the end of the 2017/18 school year, following the first full year of school with the increased level of staffing and reduced PqTR).

The COVID-19 pandemic led to the closure of all schools in Malawi for seven months during 2020 and appears to have led to significant dropout.<sup>9</sup> As the EMIS data collected in October-November 2019 is the most recent available prior to the onset of the pandemic, we restrict our analysis to the 2017 and 2018 allocations of teachers for which lagged information is available prior to the pandemic.

Tables 5.3-5.6 show DiD results. In each table:

- The first column defines treated schools as those which received at least one additional teacher in 2017 and as a result whose PqTR was brought below 90 in that year.<sup>10</sup> The regression compares the repetition or dropout rate in schools at the end of the 2016/17 year to those at the end of the 2017/18 school year, reflecting a full year of the increased staffing.

- The second column defines treated schools as those which received at least one additional teacher in 2018 and as a result whose PqTR was brought below 90 in that year.<sup>11</sup> The regression compares the repetition or dropout rate in schools at the end of the 2017/18 year to those at the end of the 2018/19 school year, reflecting a full year of the increased staffing.

# **Repetition**

Tables 5.3 and 5.4 show DiD results for repetition rates. **Table 5.3** focuses on overall repetition across grades. We find that in schools treated in 2017, repetition rates were significantly reduced
countries, estimates the gains in student promotion from optimal allocation of existing teachers between schools at between 0.1 and 4.2 percentage points.

- In **Chapter 2** , we present a stylized algorithm, based on the MoE’s rules, to identify target schools in need of more teachers. Drawing on administrative data collected in 2017, 2018 and 2019, we apply this algorithm to identify all target schools in each year. We present estimated needs for teachers according to this algorithm, at national, district and school levels.

- In **Chapter 3** , we compare our targeting algorithm to the actual allocations of new teachers in Malawi in each of the four years, and assess the extent to which allocation met with need, both nationally and at the district level.

- In **Chapter 4** , we assess the impact of the allocation of new teachers on school PTRs.

- In **Chapter 5** , we assess the impact of reduced PTRs on student repetition rates and dropout rates.

- **Chapter 6** discusses the results.

# **2. Rules-based targeting of new teachers to schools**

The allocation of newly deployed teachers – typically 3,000 to 5,000 per year – is a multi-stage process across multiple levels of government (see Asim et al., 2019, for a detailed discussion). Teachers enter the workforce having completed pre-service training under the auspices of the Department for Teacher Education and Development (DTED) of MoE, primarily through a program known as the Initial Primary Teacher Education (IPTE). Through the Directorate of Basic Education (DBE) and the Local Government Service Commission (LASCOM), MoE allocates teachers to each of Malawi’s 34 education districts;<sup>6</sup> District Education Offices (DEOs) in each district then allocate teachers to specific schools.

Allocating new teachers equitably to schools may seem a simple matter of targeting the schools with the highest PTRs,
_Note: Children can carry out more than one type of work. Wage work captures children working for income in the past seven days across three sectors; household chores captures cooking, cleaning, taking care of household members, and so on in the past seven days; household production captures farm work, fetching water, collecting wood, and so on for the family; unpaid family work captures unpaid help in a business owned by a household member; any work captures children doing any of these types of work and/or chores. Source: authors’ estimates using LFS 2015_ Table 1 shows that there are gender differences across all the different types of work carried out. Although the wage work participation rates for girls and boys are similar, with slightly more boys than girls (0.6 percentage points difference), girls are more likely to be employed in the agriculture and service sectors and boys in the industry sector. Girls are disproportionately more engaged (~9 percentage points) in household chores than boys, with girls outnumbering boys in every single chore. Even though girls are more engaged than boys in agriculture, when looking at wage work, boys actually carry out more farming activities for the household (0.7 percentage point difference) than girls. However, overall, 1 percent more girls than boys are busy with household production tasks including fetching water, doing craftwork and collecting wood. Overall, more girls (42.2 percent) than boys (36.6 percent) are engaged in some form of work or chores.

The above data should be interpreted with caution, however. Importantly, there are significant time differences across activities. Wage work is more time consuming than other occupations, with children working on average 53 hours per 6-day week in this type of work compared to 9 or 10 hours in household production or chores during a typical week. Further, household chores are more likely to be done at weekends or after school and do not seem to be a barrier to school attendance. Overall,
relevant policy implications, we complement the rigorous estimation of the program’s impact on test scores with qualitative analysis. The qualitative analysis includes a qualitative survey, as well as focus group interviews with teachers, school administrators, and the Ministry of Education officials. The goal of the qualitative study was to understand teachers’ perceptions of the technology, whether any implementation challenges occurred, and to what extent the challenges would help explain the results obtained in the rigorous quantitative evaluation.

School principals and teachers have highly positive views about the program. For instance, about 80 percent of the mathematics teachers and 95 percent of the science teachers 25
having a disability makes it hard to precisely estimate patterns in the data beyond simple correlations.

Despite these limitations, but keeping them in mind, the data are nevertheless revealing. Consistent with other similar surveys, the 11 surveys analyzed here identify on the order of 1 to 2 percent of the population as having a disability. Countries with two surveys and varying definitions suggest that the percentage is not always sensitive to the exact definition (e.g. different definitions can give similar prevalence rates, and _vice versa_ ). In addition, other aspects of the surveys, such as the training of enumerators or the use that interviewees expect the survey to be put, might affect the overall estimated rates.

Youth with disabilities sometimes live in poorer households—but the extent of this concentration is typically neither large nor statistically significant. On the other hand, youth with disabilities are almost always substantially less likely to participate in schooling—and significantly so. Children with disabilities are less likely to start school, and in some countries have lower transition rates resulting in lower schooling attainment. The order of magnitude of the school participation disability deficit is often larger than those associated with other characteristics such as gender, rural residence, or economic status differentials. The data do not suggest that there are typically interactive effects— although the small number of disabled youth in these surveys makes hard to identify those effects.

This analysis suggests that, in developing countries, disability is associated with long-run poverty in the sense that children with disabilities are less likely to acquire the human capital that will allow them to earn higher incomes. However, the results should be treated as tentative at best. Establishing clear and consistent measures of disability for use in household surveys, and implementing these in the context of samples that are large enough to identify sufficient observations to allow detailed analysis (perhaps in the context of a census), will be a pre-requisite for further work on the relationship between disability and poverty. This should be a high priority for building empirically grounded policies to address
,115,156|1,307,071|
||(346,188)|(242,429)|(451,666)|(237,573)|
|No|14,205,739|14,780,783|11,799,655|15,115,205|
||(383,993)|(278,064)|(493,215)|(311,635)|
|Don't Know|1,109,367|831,381|3,629,792|1,137,212|
||(205,851)|(147,278)|(357,983)|(220,189)|
|Observations|947|880|1,080|881|

_Note_ : Each column reports the estimated total number of people living in households that respond “yes,” “no,” or “don’t know” to whether a series of statements were true. The questions about false beliefs were only asked in the first phone survey round in Uganda and only asked in the second round in Malawi. Standard errors are in parentheses.

34
22 A study of Argentina, summarized in Table 9, assesses the welfare cost of regulations and other government interventions in the 1980s (Fundacion de Investigaciones Economicas Latinoamericanas, 1991). The total costs of regulation and state intervention amount to over US$4 billion per year (1990 dollars), and this is only for the selective listed interventions. While the measure of costs for different activities differ somewhat (e.g., efficiency costs, additional cost to consumers, and subsidy cost), the overall total suggests that the cost of government intervention is significant.

It would be useful to assemble data on regulatory costs in other developing countries that is comparable to that assembled for Argentina. Yet, there is no shortage of specific cases where economic regulation has had adverse consequences. For example, Uruguayan firms and consumers are paying an implicit tax of at least 30 percent for water, phone and electricity (Estache, 1996). This implicit tax exceeds that of other countries in Mercosur, thus hindering the competitiveness of Uruguayan products vis a vis those of other Mercosur countries. In Brazil, economic regulation has also reduced efficiency. For example, although trucking costs are almost three times as high as rail, rail transport has only a 12 percent share of relatively short trips and a negligible 3 percent share in the longer haul interregional market. The absence of an inverse relationship between cost and market share is to a large extent attributable to inefficient regulation. Additional anecdotal evidence of regulation and of its impact in developing countries is quite ample, as shown in Boxes 3 and 4.

The costs of various kinds of process regulation can also be substantial in developing countries, due to inefficient bureaucracies and high levels of corruption. For example, customs administration in many countries tends to be plagued by inefficiency and corruption, imposing a high cost to traded goods."<sup>9</sup> Surveys in a number of developing countries indicates that, the proportion of time managers spend in managing process regulation, ranges between 10-30 percent
changing each fuel’s share of total generation. Lastly, the pandemic may have disrupted the logistics of resource extraction and fuel imports and exports, causing changes in prices large enough to stifle generation or encourage substitution from one type of generation to another.

This study investigates whether COVID-19 has affected total electricity generation, GHG emissions from the power sector, and electricity supply mix, particularly the penetration of solar and wind. Rather than focusing on a single system operator (Carvalho et al., 2021; Leach et al., 2020; Agdas and Barooah, 2020), this study uses daily electricity generation data from the United States, Brazil, India, and the majority of the European countries from January 2018 to September 2021. According to the latest data from the Emission Database for Global Atmospheric Research (EDGAR), these countries collectively account for 31% of global CO2 emissions in 2019.

To quantify the impact of the pandemic on electricity generation, we regress daily generation in each country on the stringency of the measures adopted to combat the pandemic, controlling for other factors thought to affect generation. We also regress the _share_ of a particular type of electricity (e.g., coal-fired or hydro) out of total generation on the same determinants and measures of the pandemic. Identification comes from the stringency of lockdowns and other public health measures that vary across countries, depending on how early the pandemic hit them and how proactive the governments were in implementing pandemic mitigation measures, as well as within a country over time.

The study finds that a 1-unit increase in the lockdown stringency measure is associated with a 0.473 GWh decrease in the total daily generation. The lockdown stringency is measured using the Oxford COVID-19 Government Response Tracker, which documents and compares worldwide government policy responses to the coronavirus using well-defined and consistent criteria across countries and over time. On average, COVID-19 response has decreased daily electricity generation by almost three percent, increased the share of solar electricity generation by 0.6% in generation mix, which
A shift from agriculture to manufacturing has been one of the hallmarks of job creation, poverty reduction and rapid growth in low-income countries around the world. Industrialization is also one of the pillars of the African Union’s Agenda 2063 – the blueprint for transforming the continent into a global powerhouse (at https://au.int/en/agenda2063).

Some of the signs for industrialization in Africa are encouraging. The most comprehensive information about manufacturing employment in Africa only covers 18 countries, but based on those data, manufacturing employment in Africa’s low- and middle-income countries increased from 6 million to more than 20 million from 2000 to 2018, raising the share of employment in manufacturing from 7.2 percent to 8.4 percent (Kruse et al. 2021). In comparison, the 1990s saw zero growth in Africa’s manufacturing employment. Manufacturing exports from African nations have also grown at an annual average of 9.5 percent per year (Signé 2018). But while <mark>employment and value-added shares of manufacturing in Africa are rising, both remain very low in comparison to the rest of the world (Diao et al, 2017; Nguimkeu and Zeufack 2019).</mark> The performance of the manufacturing sector varies considerably across African countries; this heterogeneity is to be expected given the vast differences in labor and natural resource abundance across Africa (World Bank 2021). Nevertheless, most African countries have a low share of formal employment in the manufacturing sector. According to the INDSTAT database of the United Nations Industrial Development Organization (UNIDO 2020), only a handful of countries in Africa in 2018 had formal employment that exceeded a total of 100,000 in firms with 10 or more employees: Ethiopia, Ghana, Kenya, Nigeria, South Africa and Tanzania. Among these countries, Nigeria stands out, with around three million workers engaged in formal manufacturing between 2010-2012 (Nigerian Manufacturing Sector Report 2014). However, very little systematic information exists about the manufacturing sector in
. This correction occurs if the measurement error of the instrument and that of the instrumented variable are independent.

27 Not shown here; results available upon request.

28 To obtain the covariance matrix of orthogonality conditions of full rank, which allows the calculation of clustered standard errors, year-state dummies are “partialled out,” and their coefficient is not calculated. By the Frisch-Waugh-Lovell theorem, in IV, the coefficients for the remaining regressors are the same as those that would be obtained if the variables were not partialled out (Baum et al. 2008). 29 For each of the 24 such goods for which we have data, we regress the district’s land cultivated with that crop over the urban population, all other control variables and the district and state-year fixed effects.

30 The census divides employment into four broad sectors: agricultural laborers, cultivators, household industry, and other nonagricultural workers. We use the last category to construct the variable, although the subsequent results are also very similar when using the total nonfarm employment share, obtained by adding the household industry category.

31 In fact, the coefficient becomes positive and very small but with a large standard error.

32 Results available upon request.

37
of the empirical investigation in the rest of this paper.

# **3 Data and descriptive evidence**

Our empirical analysis is based on two main World Bank data sources: (i) the Exporter Dynamics Database (Fernandes et al., 2016) providing firm-level exports for 55 developing countries in the period 1996-2020, and (ii) the Content of Deep Trade Agreements (Hofmann et al., 2017) on the content of RTAs in force and notified to the WTO for the period 2000-2020. Moreover, we complete our dataset by including gravity-related variables (such as distance, common border, language, etc.) from the CEPII gravity database, and data on the effectively applied tariffs faced by each exporter at the destination from the MacMap (CEPII) database.

We build our final dataset in several steps. First, we reorganize the original DTA database into a country-pair-year-specific dataset. Indeed, since a given pair of countries (origin and destination) may have two (or more) RTAs contemporaneously in force, we collapse trade agreement 5
Financial Regulation and Development default. However, if the government imposes a minimum level of safe asset holding, which has a deterministic return, this moderates both bank profits in the good state and losses in the bad state: it increases depositor repayment and stimulates more deposits. As a consequence, we show that bank profits can rise if deposit growth exceeds the decline in the intermediation margin and loan provision. In such a case, branch installation (financial development) also rises, as higher profitability leads to more branch opening and provision of additional loans. In our model, financial regulation is valuable because of the timing assumption: banks are unable to convince depositors of their safe asset investment because of profit maximization and hence the government needs to create a commitment in holding safe assets.<sup>2</sup> This might be oversimplistic, but could represent, in a reduced form, the inability of depositors to actively monitor the financial system, and regulation could be interpreted as a public good lowering monitoring costs.

While there exists substantial evidence on the beneficial effects of regulation on welfare (e.g., Laffont and Tirole 1993; Glaeser and Shleifer 2001), we are the first to show empirically that financial regulation can promote financial development and that enhanced bank safety is the channel through which this takes place. Such policies have been implemented in several countries over the past century.<sup>3</sup> However, this issue has been difficult to study for a variety of reasons. To begin with, data availability on the banking industry in low-income countries is a severe limit: with the exception of a few, yet incomplete, sources (i.e., the Bankscope database<sup>4</sup> ), most banks are reluctant to publish any documentation that goes beyond the mere legal obligations. However, even when sources are available, they are – unsurprisingly – of low quality, generally incomplete, and only focused on a few key financial variables, with limited details
Policy Research Working Paper 10570

# **Abstract**

This study assesses the short-term impact of fiscal policy, and its individual elements, on poverty and inequality in Cambodia as of 2019. It applies the Commitment to Equity methodology to data from the Cambodia Socio-economic Survey of 2019/20 and fiscal administrative data from various government ministries, departments, and agencies for the assessment. The study presents among the first empirical evidence on the impact of taxes and social spending on households in Cambodia. The study finds that: (i) Cambodia’s 2019 fiscal system reduces inequality by 0.95 Gini index points, with the largest reduction in inequality created by in-kind transfers from spending on primary education; (ii) while Cambodia’s fiscal system reduces inequality, the degree of inequality reduction is small in international comparison; and (iii) low-income households pay more in indirect taxes than they receive in cash benefits in the short term to offset the burden. As a result, the number of poor and vulnerable individuals who, in the short term, experience net cash subtractions from their incomes is greater than the number of poor and vulnerable individuals who experience net additions. Fiscal policy can deliver more net benefits to poor and vulnerable households through expanding social assistance spending. Cambodia has embarked on this expansion during the coronavirus pandemic, bringing it closer in line with comparators.

This paper is a product of the Poverty and Equity 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/prwp. The authors may be contacted at wkaramba@worldbank.org; mmyck@cenea.org.pl; ktrzcinski@worldbank.org; ktong@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