rafmacalaba/data-use-mentions-extended huggingface dataset:
Löfgren’s general approach is fairly representative of a large number of trade and poverty studies carried out over the past decade. One variant is to address in greater detail how external shocks affect the _total_ income distribution of a country. For this purpose it is necessary to postulate a distribution of income for each representative household type (as in Adelman and Robinson) or to work at the level of individual workers (as in Cogneau and Robilliard, and Cockburn). If the distribution of income within a given group of households is estimated _a priori_ , it can be used in conjunction with the general equilibrium model to assess the impact of exogenous shocks on income distribution and poverty. In these studies, it is typically the case that mean and total income levels for a household group are endogenous, while higher moments of the distribution are fixed.

In an interesting and useful synthesis paper, Decaluwé, Patry, Savard, and Thorbecke (1999) consider this basic approach and provide further refinements. One innovation is to model the withingroup income distribution with a flexible Beta distribution instead of the restrictive log-normal or Pareto distributions that are commonly used. This permits them to adapt the shape of the income distribution to observed socio-economic characteristics of each household type, and it is shown that these distributions vary markedly across household groups. In turn, an endogenous poverty line is developed based on the cost of obtaining a subsistence level of consumption. Although no empirical results are presented, these innovations are ripe for use in conjunction with the new, increasingly detailed household-level data becoming available, and thus could shed additional light on the behavior of poverty following an external shock to the economy.

Cogneau and Robilliard (2000) offer a fundamentally different approach to trade, poverty, and income distribution modeling. They assess the impact of different growth strategies on poverty in Madagascar by embedding an econometrically estimated labor allocation model based on 4,508 households _within_ a general equilibrium framework.<sup>**7**</sup> The combination of micro-simulation and general
data are not available at the firm level. Differences in revenues per unit sold may reflect differences in productivity but also differences in quality. The problem may be more severe for tech companies, where product innovation frequently takes place. With these caveats in mind we set up our analysis.

Abstracting from entry and exit dynamics, there are two main sources of productivity growth. The first source is within-firm productivity growth, i.e., firms become more productive thanks to better technology or management practice. This more conventional understanding has been the subject of a long-standing literature and dominated much of policy discussions. However, recent views focus on the benefits of a second channel involving the reallocation of inputs from less to more productive firms as an important component of aggregate productivity growth. Policies associated with allocative efficiencies emphasize correcting or reducing market distortions. We focus on this source of productivity improvement, examining resource allocation across sectors, and across firms within a narrowly-defined industry.

This paper documents the evolution of productivity in developing EAP in the recent decades and identifies the sources of productivity growth. <mark>EAP’s productivity has been gradually catching up with the frontier (United States), with China leading the pack. Productivity growth is driven by sustained within-sector productivity growth. Reallocation of labor to sectors with higher productivity, such as industry and services, also contributed to productivity improvements. Nevertheless, resource misallocation remains. Firm-level evidence from four EAP countries (Indonesia, Malaysia, Philippines and Vietnam) suggests that resource misallocation across firms within a sector is large, albeit declining over time. Private domestic firms and firms with higher productivity face larger distortions.</mark> The rest of the paper is organized as follows. Section 2 provides an overview of the aggregate trends for EAP productivity, using country-level data. Focusing on labor, one of the factors of production driving the results in Section 2. Section 3 employs newly available sector-level information and examines how labor movement is linked to labor productivity differential across sectors over the last ten years.
Report - REVISED 30nov2013 (6).pdf. Accessed March 10, 2017.

Tan, J., Lane, J., & Lassibille, G. (1999). Student outcomes in Philippine elementary schools: An evaluation of four experiments. _World Bank Economic Review_ , _13_ , 493-508. doi:10.1093/wber/13.3.493 Weir, C. J. , & Roberts, J. (1991). Evaluating a teacher Training project in difficult circumstances. In S. Anivan (Ed.), _Issues in Language Programme Evaluation in the 1990's. Anthology serires 27_ , 91-109. Singapore: Southeast Asian Ministers of Education Organization.

Zhang, D., & Campbell, T. (2012). An exploration of the potential impact of the integrated experiential learning curriculum in Beijing, China. _International Journal of Science Education_ , _34_ (7), 1093-1123. doi:10.1080/09500693.2011.625057 Zhang, L., Lai, F., Pang, X., Yi, H., & Rozelle, S. (2013). The impact of teacher training on teacher and student outcomes: evidence from a randomised experiment in Beijing migrant schools. _Journal of development effectiveness_ , _5_ (3), 339-358. doi:10.1080/19439342.2013.807862 42
broadly, the Manufacturing Survey does not cover informal enterprises operating in other sectors of the economy such as services.

One limitation common to these data sources concerns the type of information collected. In particular, only a few questions are asked regarding the business environment, or the challenges faced by enterprises (Rothenberg et al., 2016). In this regard, the World Bank Enterprise Surveys offer a valuable alternative. Briefly, the WBES is a survey of non-agricultural enterprises encompassing small, medium, and large firms (World Bank, 2011). The Indonesian version of the survey has been conducted twice – first in 2009 and then in 2015. Like BPS data, the WBES also asks firms about employment, output, and income. What sets it apart from BPS data are specific questions about obstacles to doing business (e.g., access to finance, business licensing, and permits, inadequately educated workforce). Additionally, the two waves of the WBES can be combined to form a panel dataset, which significantly expands analytical pathways. As a limitation, the WBES does not include micro enterprises in its sample which might bias analysis as a vast majority of informal enterprises fall under this category.

With the exception of the Economic Census, no single data source captures the entire population of non-agricultural informal enterprises in Indonesia. This gap was partly ameliorated by the Informal Sector Survey, conducted in 2009 by Statistics Indonesia through technical assistance from the Asian Development Bank (ADB & BPS, 2011). The ISS was conducted in two phases: first, by including a separate module on informality in the August 2009 round of SAKERNAS; and second, by surveying household enterprises operating in Yogyakarta and Banten that were identified through the first phase of the project. Although the mixed survey was a costeffective strategy to measure the size of the informal sector, it has yet to be replicated on a national scale.

7
Figure B.9: Number of municipalities: payment <!-- Start of picture text --> CE MG<br>180<br>840<br>170<br>820<br>160<br>800<br>150<br>2009 2011 2013 2015 2017 2019 2021 2014 2015 2016 2017 2018 2019 2020 2021<br>PB PR<br>400<br>220 375<br>350<br>215<br>325<br>210 300<br>275<br>205<br>250<br>2009 2011 2013 2015 2017 2019 2014 2016 2018 2020 2022<br>RS SP<br>500<br>640<br>400<br>630<br>300<br>620<br>200<br>610<br>100<br>600<br>2010 2012 2014 2016 2018 2020 2008 2010 2012 2014 2016 2018 2020<br>Year Year<br>Municipalities from our data Municipalities from Siconfi<br>Municipalities<br>Municipalities<br>Municipalities<br><!-- End of picture text -->

**Notes:** This figure compares the number of municipalities in our dataset and the total of municipalities in SICONFI in payment data.

56
on lights to examine how strongly they are correlated in India. Columns 8 and 9 show a strong relationship both in levels and around a common trend. A 1 percent increase in light intensity is associated with a 1.2 percent increase in electricity consumption.

Electricity consumption seems to have a somewhat stronger relationship, especially when both variables are combined. We therefore rely on electricity consumption to track economic activity at the country and state level, for which electricity data is available, and rely on nighttime light intensity for districts and cities.

> 13It is also in line with other estimates in the literature (Stern 2018).

> 14With 1.5, our coefficient is much larger than the one Henderson et al. (2012) find in an annual panel regression using the DMSP-OLS data (their Table 2, column 1). One reason could be that VIIRS data allows for greater comparability over time as explained in section 3.2.

10
Figure 8: Temporary employment by gender. (Mid-1990s/Mid-2010s) <!-- 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>1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s<br>Argentina Brazil Chile Mexico El Salvador<br>Female Male<br><!-- End of picture text --> Source: Own calculations based on Household surveys

# 4.1.2 Profile of Non-Standard Employment

The next objective of this work is to investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. The analysis of the employment profile was made based on access to social security benefits, education level, labor income and the task content performed by the workers.

From the point of view of social security, we find that NSE shows a higher prevalence of informality compared with SE. For instance, the average prevalence of informality for our set of countries among NSE in the ending point of the study is 40% while the prevalence among SE is 20%. In this sense, a rise in the prevalence of NSE could be associated to a big set of workers without access to social security benefits.

From a dynamic perspective, there is no a common trend across countries regarding the prevalence of informality among NSE workers (Figure 9). Indeed, several countries (Uruguay, Brazil, Chile, and Peru) registered a small decrease in the prevalence of informality among NSE but there is another set of countries for which the opposite is observed (Mexico, El Salvador, Bolivia and Argentina).

It is important to note that the changes in the prevalence of informality among SE workers between the starting and ending point of the study show a very similar dynamic compared with the
# II. Trends In Education

# **Patterns** and Trends

This section traces the growth of educational opportunities in Peru from 1940 through 1980 and describes the economic and educational policy contexts in which they occurred. Due to minimal data prior to 1950, most of the discussion pertains to the years 1950-1980. For these decades, educational trends are traced by quinquennia in order to capture the effects of different administrations' policies on levels of educational attainment. To provide a basis for explaining variations in adult educational attainment, each quinquennium is matched with the birth cohort whose educational experience most likely corresponds to that period.

Trends in Enrollment. In the early 19408, schooling opportunities in Peru were quite limited. As a consequence, over one-half of all adults could neither read nor write (Appendix Table A.l);l' only one child in three enrolled in a primary or lower secondary school (Appendix Table A.2). Moreover, raising school attendance was particularly challenging. Two-thirds of all Peruvians were scattered in rural areas, and 35 percent of the population spoke only Quechua or Aymara, the two main Indian languages, while Spanish was the language of the schools (Censos Nacionales, 1981).

1/ By way of comparison, among Latin American countries, only El Salvador, Nicaragua, Honduras, Bolivia, Guatemala, and the Dominican Republic had higher illiteracy rates than Peru in the early 1940s (Drysdale and Meyers, 1975).

**2**
# **_3.2 Legal Discrimination and Gender Norms_**

We use indicators on legal discrimination against women and manifestations of gender norms from two key sources. First, we use data from the World Bank’s Women, Business and the Law (WBL) database that constructs a number of variables with regard to laws governing a women’s ability to earn an income, either as an employee or entrepreneur.<sup>12</sup> WBL variables are based on codified law and regulations, not the implementation or practice of that law. Therefore, these indicators do not take into account customary law, unless that customary law has been codified. Exceptions to this rule are common law or religious codes where decisions of such common or religious courts or schools of jurisprudence have legal standing equivalent to that of codified law.

We consider those legal indicators that might affect women’s demand for financial services, as compared to men’s, such as women’s access to institutions and ability to own, manage, control, and inherit property. In particular, we consider the ability of women: (i) to work, (ii) to head a household, (iii) to choose where to live, and (iv) to inherit property, as well as (v) requirement by law of wives to obey their husbands.<sup>13</sup> While it is not a gender norm per se, we also include a dummy variable that indicates whether separation is the default marital property regime (as opposed to a variant of community) as it affects the assets that a women has control over if they are married. Variables are coded such that the dummy variables take the value of one if the answer is yes; a dummy variable in general equals one if there exists (legal) equality between the genders, except for the variable capturing whether women are required by

> 12 For details on methodology of the World Bank’s WBL database see http:/
sum, our results suggest that the role of geographical targeting and other implementation issues need to be carefully considered when conducting simulations, and that results obtained based on program data are largely different from those obtained from nationally representative surveys. For future studies based on simulations, our results imply that samples should aim at mimicking areas where PMT will be applied, and to use selection thresholds similar to those employed in practice by social programs (or a wide range of thresholds).<sup>30</sup>

# _5.2 Mechanisms: Determinants of CBT and PMT selection_

To explore what drives the differences between CBT and PMT results, we explore the determinants of the selection of households with each selection method. Table 7 shows simple probit models where the dependent variable is the selection by CBT or PMT in each data set. A few independent variables that were found in most data sets and discussed in the targeting literature are included. These probit regressions are conducted for the seven data sets where we have information on both CBT and PMT selection, and PMT selection thresholds are adjusted to match CBT selection rates (see section 4).

> 30 Yet, it is not always possible to conduct simulations based on the narrow region where PMT will be applied based on nationally representative surveys due to sample size issues.

20
We test this view of business asssociations using data on the characteristics and performance of firms in five Russian cities. This data set was constructed by a team from the World Bank<sup>9</sup> , and includes statistics on the membership of finns in differen **t** ypes of business associations. The data were collected for 1994, a year witnessing active privatization and a dramatic decline in the volume of government subsidies to firms. These conditions suggest that 1994 is precisely the time period during which we would expect firms to actively search for survival strategies.

We estimate a bivariate model to jointly explain the firm's probability to experience a decline in output and to join a business association. We define the probability of decline in a firm's output as a function of changes in the demand for its products and other characteristics of the firm, including the potential for disorganization in its relationships and its membership in a business association. The decision to join an association, in turn, is a function of its potential for disorganization as well as local incentives for cooperation among firms. The empirical analysis supports our intuition: being a member of a business association improves a firm's performance significantly. Moreover, firms are more likely to join an association in regions where cooperation among firms is most likely. Hence, these results characterize the use of associations by firms as a mean to coordinate activities by sharing information and reducing transaction costs.

The novelty of this work is to suggest a simple, yet promising, framework of analysis and provide empirical evidence of the link between institutional disorganization, output decline, and the endogenous emergence of new coordinating institutions. In doing so, this paper offers new evidence on the role of spontaneous institutions on firm's performance. The approach employed

> 8 See also Kranton (1998) for a detailed discussion of the coexistence of different modes of exchange.

> 9 Led by Barry Ickes Che Pennsylvania State University) and Randi Ryterman (he World Banr), with the
|Marital status|
|---|
|Mass media access (_access to at least one media once a week out of radio, television and newspaper)_|
|Women empowerment in health index<br>(i.e.,_earning woman able to spend money as they wish, and able to participate in the decision-making_<br>_for the health care of herself and child)_|
|Residence (rural/urban)|
|Division|
|Women aware of community clinic nearby residence|
|Gender of sick child|
|Birth order|
|Exclusive breast feeding up to six months|
|Initiation of breastfeeding within 1 hour of birth|
|Child’s weight at birth|
|Under-5 received curative care at a facility for diarrhea or fever or acute respiratory infection|

**Source: DHS;** *represents mothers who delivered in the last five years preceding the survey

**Table 4: Supply-side determinants**

|**Variables**|**Definition**|
|---|---|
|Basic amenities index|Proportion of amenities available out of - national electricity grid, regular<br>electricity, improved water source, visual and auditory privacy,<br>functioning improved client latrine, computer with internet, emergency<br>transport, and functional communication equipment in a facility.|
|Basic equipment index|Proportion of equipment available out of - child scale, adult scale, infant<br>scale, thermometer, blood pressure apparatus, stethoscope and light source<br>in a facility.|
|Diagnostic laboratory capacity index|Proportion of lab tests available out of - tests for hemoglobin, blood<br>glucose, urine protein, urine glucose, stool microscopy, urine pregnancy<br>test and functional microscope in a facility.|
|Standard precautions for infection<br>control index<sup>2</sup>|Proportion of amenities available out of - necessary guidelines, equipment<br
were developed by Tanzania education professionals and are similar to the Uwezo annual learning assessment – a nationwide assessment used to measure learning in Tanzania (see Mbiti et al. 2021 for exact test creation). The Math test focused on counting, basic addition, subtraction, multiplication and division, while the Kiswahili test focused on correctly reading words, writing sentences and comprehension.<sup>6</sup> For calculating student learning gains, the same set of test questions were used at baseline and follow-up. The test was of a slightly higher level for Grade 3 compared to Grade 2 students. The tests were low stakes and designed to test a range of abilities such that scores could be equated across years using a set of linked questions in baseline and follow up. These features allow us to test children on the same knowledge scale. A student survey was also administered to collect data on student characteristics and student perceptions about teacher practices (e.g. practices that teachers did or did not engage in with students).

Our data set contains 52 explanatory variables that can be divided into the following categories: (i) studentlevel variables such as age, household asset ownership, baseline score and whether they attended private tuitions for the particular subject; (ii) school-level variables such as the pupil teacher ratio at the school, whether the school is in an urban or rural location and certain institutional/governance variables; and (iii) teacher level variables. We divide our teacher level variables into 4 categories: (i) Who teachers are (teacher characteristics); (ii) What teachers know (teacher knowledge measured through the teacher assessment); (iii) What teachers do (teacher practice measured through teacher classroom observation); and (iv) What teachers believe (teacher mindsets). These are discussed further in Section 3 and Annex 1.

The unit of observation is the teacher and the outcome of interest is average student learning gains between baseline and follow-up (approximately 8 months) for the teacher. We
math and language tests. In addition, Duflo et al. (2015) conduct an experimental evaluation of a similar intervention in Kenya found that the apparent benefits of class size reduction only for students in the classes taught by contract teachers and not for students in other classes with regular Government teachers who experienced a similar reduction in class size, concluding that improved practices and reduced absenteeism by contract teachers compared to Government teachers was the reason for gains in learning rather than class size reduction.

In recent years, new research has directly explored the question of how large class sizes must be to negatively affect learning outcomes. Datta and Kingdon (2021), examining large-scale test data from India with pupil fixed effects, find that negative effects from large class sizes begin at a size of approximately 40 for science subjects and 50 for non-science subjects, and that India could allow its current national PTR to increase to 40<sup>2</sup> without negatively affecting test scores.

However, there is a lack of rigorous analysis of the effects of class size reduction focused on countries where class sizes are substantially larger. Evidence from Malawi suggests that PTRs above 90 are associated with significantly lower test scores in Grade 4, equivalent to several weeks’ lost learning, even when controlling for a wide range of school, teacher and student characteristics (Asim and

> 1 The Education Quality Improvement Programme (EQUIP-Tanzania)

> 2 From a current 22.8

3