rafmacalaba/data-use-mentions-extended huggingface dataset:
groups of firms using the 2011 STEP survey. If a firm was operating in a sector that tends to have high levels of informality (e.g., agriculture, construction, wholesale and retail, restaurants, and others) or low levels of informality (e.g., financial services, real state, electricity, gas, water, manufacturing, among others). If our hypothesis is correct, we should observe that formal firms operating in highly informal sectors are more affected by the returned migrant inflows relative to the others firms.

The results are presented in Tables 6 and 7. Estimated effects confirm our hypothesis that the impacts of a positive shock of returned migrants are driven by firms that operate in sectors with 17
countries. Having a larger number of countries with tax and survey data and undertaking comparisons would allow for more general conclusions about the extent of the differences between survey and tax data for Africa, rather than just two as in Chancel et al. (2023).

# Using New Sources of Data

The most obvious research that could be undertaken using new data sources is obtaining and analyzing administrative tax data for a larger number of LMICs than is currently available. Ideally this would be tax microdata but even tax tabulations would be very useful. The first step would be to compare estimates of top incomes from surveys and administrative data, and then examine the extent to which under-reporting, item non-response and unit non-response and other challenges create any differences between household survey and administrative data.

> 8 <u>https://www.wider.unu.edu/project/building-efficient-and-fair-tax-systems-lessons-based-administrativetax-data</u>

28
</sup>

> 14 In addition to assisting with the transition to postpaid plans, this method can be used to extend credit for handset purcases, or to maintain consistent airtime balances. Many developing country operators offer small airtime loans like this; a scoring model could improve their provision.

15 See for example, Jumo.

16 See for example, Cignifi.

17 See for example, Tala and Branch.

> 18 One formulation of this concern would be if heavy phone users are willing to do more to avoid phone account closure, and the method simply picked up the level of phone usage. In that case, it may perform better predicting when repayment of a phone bill than a general loan. However, as mentioned in the text and in Figure S2, the method achieves similar performance in each quartile of usage, and in our sample that has a sufficient amount of usage, the correlation between airtime usage and repayment is very small (-0.03).

> 19 Alternately, it could be that bureau information is less predictive for this form of credit than a general loan, which would deflate the benchmark. Bureau information would represent the status quo for the extension of postpaid credit. There is not much evidence on the performance of bureau information in low income populations; the performance observed here is in the lower end of the range of published estimates for general loans from more developed settings (Table S4). However, bureau information

19
requires merging multiple data sources, proper methods for statistical matching must also be assessed, given the data availability and the country context; data fusion techniques receive increasing interest from researchers and statistical agencies (Donatiello et al., 2016a; Lamarche et al., 2020).** When common identifiers are available, research can apply the record linkage. For example, Lopez-Luzuriaga (2023) recently applied record linkage to match data on consumption from the electronic billing system with income tax data in Ecuador and investigate the frequency of envelope payments in the country. When common identifiers are unavailable, researchers rely on statistical matching (Yam & King, 2020; Lewa et al., 2021). Statistical matching allows for the synthetic data source to be derived based on data from various sources that do not contain the same unit or common identifier. The matching relies on common characteristics available in both data

# 5 Gross labor income.

6 According to the EU-SILC the share of self-employed in the economically active population is approximately 16%. The vast majority of self-employed are farmers. The vast majority of self-employed do not employ workers.

> 7 Compensation of employees: htps://ec.europa.eu/eurostat/databrowser/view/tec00013/default/table?lang=en

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|+++ / ***|
|Control of Corruption|+ / **|0 / *|+ / '|+ / **|++++ / ***|++ / **|+ / 0|++++ / ***|++++ / ***|
|Income per capita|+ / ***|+ / **|++ / ***|++ / ***|++++ / ***|+++ / ***|+++ / ***|++++ / ***|++++ / ***|
|**# of cities**|**63**|**61**|**59**|**42**|**194**|**194**|**194**|**194**|**194**|
|**Adjusted R-squared**|**0.28**|**0.11**|**0.29**|**0.37**|**0.39**|**0.28**|**0.28**|**0.45**|**0.50**|

Source: KLM city database, drawing from EOS-WEF 2003 as well as other sources as per below. Sample size: 412 cities in 134 countries. Values for magnitude (+,0,-) and significance (*,0,') in each cell were obtained by averaging results of 21 different OLS specifications. In particular, for each independent variable, for calculating the magnitude of the coefficient rating (left hand side), we computed the product of the average coefficient magnitude across all specifications times twice the standard
conform well with the empirical relationship between national poverty lines and survey mean consumption or income. Using the aforementioned constrained threshold estimator with surveys means instead of national accounts gave _Z_ ˆ * =$1.17 (s.e.=0.17) and ˆ _k_  0.472 (s.e.=0.07); n=70. Our chosen parameter values are not significantly different from these estimated coefficients.

# **3. Household survey data and poverty measures**

We have estimated all the poverty measures ourselves from the primary (unit record or tabulated) sample survey data rather than relying on pre-existing poverty measures. And all our previous estimates have been updated to ensure internal consistency. Households are ranked by either consumption or income per person. The distributions are weighted by household size and sample expansion factors. Thus our poverty counts give the number of people living in

> 17 Again, the intercept was suppressed to assure that the calculation was consistent with a strongly relative poverty line. A practical disadvantage of this method is that we lose 5 data points.

> 18 For further discussion of the discrepancies between these sources see Ravallion (2003) and Deaton (2005).

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In extensions, we also use four other independent variables at the same “ �� ” level as our main city size variable. First, we obtain the total urban population of each metro area / district from the 2014 Indonesian household survey ( _Survei Sosial Ekonomi_ , SUSENAS). Our main city size variable is essentially a weighted density measure. Using total urban population instead we can assess how sensitive our results are to the use of a simple overall population size measure. Second, we constructed two other agglomeration variables from the information in SAKERNAS that measure: (i) the sectoral specialization of the metro area/district, i.e. the share of a metro’s total employment in the same sector as the worker him/herself; and (ii) the share of skilled workers in a metro/district’s total employment, where we define skilled workers as those that have completed general senior high school or higher. Note that, similar to our main size variable, these two variables, as well as the earlier discussed “ _total urban population measure_ ” are defined at the district level for all districts that are not part of a metro area. Finally, we have calculated each district’s market access that captures a district’s accessibility to the markets of all other districts within Indonesia taking estimated travel times into account.<sup>31</sup> Taking a metro/district’s sectoral specialization, share of skilled workers and access to other districts’ markets into account, we can assess to what extent our findings are sensitive to taking explicit account of alternative explanations for agglomeration economies.

In all our regressions, _β_ is our main coefficient of interest. It measures the size of Indonesia’s agglomeration wage premium. This coefficient is of interest in and of itself, but for the purposes of this paper, we are particularly interested in seeing how the estimated size (and significance) of _β_ depends on the approach used to define metro areas. In establishing its significance, it
# **Acknowledgments**

We would like to thank seminar participants at the Philippine Department of Education, the University of the Philippines, and JICA for their useful comments. We are most grateful to Yolanda Quijano for generous support and guidance from the onset of this project, and the Bureau of Elementary Education and various divisions within the department for collaborations throughout this project, including providing us with various databases for this study. Special thanks are offered to Juliet Abunyawan and Felisberta Sanchez, who visited former Third Elementary Education Project (TEEP) division offices to collect TEEP investment data in addition to reorganizing the Division Education Development Plan database, and Ishidra Afunggol at the Research and Statistics Division, who provided technical guidance to the first author. We thank Surajit Baruah for his excellent research assistance in managing the Basic Education Information System database. The TEEP student tracking survey conducted in eight provinces and the cities Manila, Cebu, and Baguio also offered enormous opportunities for the authors to extensively visit TEEP and non-TEEP schools and communities, which helped us correctly do our analysis in this paper. Any remaining errors are ours.

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firm. The survey and test instruments were designed by the World Bank’s research team in partnership with the government and country level psychometricians and research consultants. Our endline data collection took place two years after the completion of the first (main) phase of the intervention, enabling us to observe medium-term impacts on test scores.

Our study makes two distinct contributions. This study is the only the second of which we are aware to generate rigorous experimental evidence in a low-income setting on the impact of interventions to improve school leadership on learning and progression. If the interventions can positively influence student progression and test scores in Malawi, it will have important policy implications for governments in low-income countries. Second, we evaluate a government program at national scale, with leaders from 1,198 schools (more than one-fifth of all public primary schools) from all districts in Malawi participating, in contrast to pilot interventions generally evaluated at a small scale and in particular districts or regions<sup>3</sup> . This implies the results of the study are relevant to all public primary schools in Malawi and the evaluated approach, if successful, is likely to have similar impact on student achievement if scaled up.

# 2 Design

## 2.1 Research Question and General Hypothesis

Our research question is: Can student progression and students’ test scores improve as a result of a custom-designed multi-phase school leadership training program?

Our general hypothesis is: The intervention is expected to improve utilization of resources and the behavior of teachers, and create school cultures which meet the needs of over-age students, students with special needs, low-performing students, and girls; and as a result improve student progression and test scores (compared to control).

## 2.2 Theoretical Framework

Poor outcomes in education in low-income countries reflect systemic resource limitations, poor utilization of resources, and weaknesses in education production (Hanushek, 1987; Delprato and Antequera, 2021). We employ the term _education production function_ (Bowles, 1970;

> 3 For example,
between young and adult women (Figure 4). Although the magnitude of the loss is small in terms of GDP growth rates, it is large in terms of foregone earnings. The PPP adjusted annual income loss is equivalent to US$646 million in Ethiopia (Table 2).

If young women had inactivity rates similar to those of young men, annual GDP growth rates would be up to 4.4% higher (Figure 7). Annual GDP growth rates in India, Nigeria, and Paraguay would be 4.4, 3.5, and 3.3 percent higher if girls were as economically active as boys (Figure 7). This is equivalent to almost US$165 billion (PPP adjusted) in India, for example (Table 2). In South Africa, where the gender differential in inactivity rates is small, the gains to greater economic activity of girls is much smaller, though it still exceeds US$3.7 billion.

The gender gap does not always incur higher opportunity cost than the age gap. In Bangladesh, Brazil, India, and Paraguay, the gains are much larger if girls’ inactivity rates were more similar to those of boys than of adult women since the gap in inactivity rates is larger between the sexes (within age) than the within-sex between-age gaps. While in the African countries in the sample, if girls had the same (lower) inactivity rates as adult women, the economies would enjoy larger gains than if the goal were to reduce girls’ inactivity rates to those of boys.

Accounting for the value of housework does not significantly change these estimates for those countries in which data are available. Data from Brazil and India allow us to adjust the opportunity cost of inactivity estimates to account for the productive value that home-based work generates. Such adjustments to the cost of inactivity include a proxy for the value of home-based production activities for young women. However, this decrease in the cost of inactivity is not considerable. For example, accounting for home-based
(47%) students also attend National primary schools. Thus, prior to 2003, ethnic Malay students were instructed in BM, ethnic Chinese students were instructed in Mandarin, and ethnic Indian students were instructed in Tamil in their primary education. Because of the way that PPSMI was applied to different cohorts of students, levels of schools, and types of schools, there are variations in the languages of instruction in mathematics and science across student cohorts and ethnic groups. Table 3 summarizes the languages in which eighth graders who participated in various waves of TIMSS learned mathematics and science by school level and ethnic group.

# **3. Data**

We draw data from the Trends in International Mathematics and Science Studies (TIMSS) to perform the analysis. TIMSS is an international assessment of the mathematics and science achievement of fourth and eighth grade students. It has been conducted every four years since 1995 with over 50 nations participating in each wave. The data drawn from TIMSS provide participating students’ test scores along with the background characteristics of these students, their teachers, and their schools. Similar questionnaires and formal assessments were given to each participating country to preserve consistency.

Malaysia has been an active participant of TIMSS since 1999 for the eighth graders’ (Form 2 students in Malaysia) assessments. Therefore, we have data available in 1999, 2003, 2007 and 2011. Eighth graders in 1999 and 2003 were not affected by the language policy change, PPSMI. On the other hand, eighth graders in 2007 and 2011 were affected by PPSMI. Eighth graders in 2007 are part of the older cohort of students who had to switch their learning language for mathematics and science subjects from BM, Mandarin or Tamil to English between their primary and secondary schooling years. On the other hand, eighth graders in 2011 are part of the younger 9
relationships but rather associations between these important variables. Nevertheless, it offers preliminary evidence and builds motivation for further research.

This paper contributes to the existing literature on capital market development by shedding light on how issuer composition changes as stock markets grow. Our findings have important implications for policy makers seeking to improve access to financing for firms. A broad and diverse set of issuers reflects a well-functioning stock market, one that is able to meet various financing needs in the underlying economy. As our results show, however, this may not naturally follow capital market growth. Our findings therefore highlight the need to go beyond headline growth indicators to ensure stock markets are more inclusive on the issuer side. On this front, it may be worth focusing on market liquidity, especially in low- and middle-income countries. For sectoral diversity, however, our results suggest the need to have more targeted measures that go beyond market size and liquidity.

The remainder of the paper is structured as follows. Section 2 provides a literature review. Section 3 and section 4 describe the data and methodology, respectively. Section 5 presents empirical results and section 6 provides concluding remarks.

# **2. Literature review**

Empirical research examining the relationship between issuer composition and stock market growth is limited. There is some related research, however, that studies aspects of issuer composition. For example, Bae et al. (2021) use three decades of stock market capitalization data of firms listed on domestic stock exchanges in 47 countries to examine the relationship between stock market concentration and a number of economic outcomes, such as competition, innovation, and economic growth. Their results indicate that highly concentrated stock markets, especially those dominated by a small number of large and successful firms, are associated with less efficient capital allocation, sluggish initial public offerings and innovation activity, as well as slower economic development.

Literature on how issuer composition evolves as capital markets develop in low- and middleincome countries is even scarcer. There is some evidence that issuer composition is related to
transaction costs associated with administering credit to small producers in rural areas. In environments where these costs are high, the improved credit-worthiness brought about by possession of land title may therefore not be enough to facilitate access to formal credit by small farmers. Unless complementary measures to reduce transaction costs and ensure access to credit by this group are undertaken alongside with individualized property rights through titling, the benefits from titling programs may accrue only to medium and large landowners.

The importance of the credit supply effect associated with provision of land title is supported by evidence from Feder et al.'s (1988) study in Thailand, where farmers' opinions and econometric evidence point towards improved credit supply as the main benefit from titling. Land ownership titles induced higher investment in farming capital (attached investments and other capital),<sup>14</sup> titled land had signiificantly higher market values and higher productivity per unit. In three of the four provinces covered, households' credit supply had been significantly enhanced by the availability of title. By contrast, and in line with the above, title was found to have little impact on either investment or farm income where formal credit markets were not available (Atwood 1990, Carter and Wiebe, 1994; Migot Adholla et al. 1994).15 Additional evidence from a study based on panel date from Paraguay indicate that titling had a positive income or productivity enhancing effect through credit market benefits for at least some groups of farmers. Due to a strong impact of formal title on both credit supply and investment demand, the benefits from title are relatively large (about 10% of farm income), and significantly higher than the cost of titling,. However, the impact of awarding titles was strongly size-differentiated. Estimates indicate that producers below 20 hectares remained rationed out of the credit market and therefore did not benefit at all from the credit-supply effect of title (Carter and Olinto, 1996).

This differential impact suggests that, in environments where other markets (such as credit markets) entail
_Gender of household head._ One of the most persistent gaps between the quantitative and qualitative measures of poverty is that the quantitative measures very rarely find that female headed households are less well off (or have higher poverty rates), while in qualitative and participatory poverty assessments female headed households are often identified as the poorest of the poor. There are three possible reasons for this discrepancy. First, inadequate accounting for economies of scale in household consumption expenditures in the quantitative estimates versus people's experience and perceptions (Dreze and Srinivasan, 1997). Second, the category of female headed households is heterogeneous as it includes households "headed" by females at the time of the survey because the husband was absent, but was providing remittances and households in which women are truly supporting themselves (and others) alone such as widows and divorced and single mothers. Third, is that while female headed households have the same level of expenditures, they are more vulnerable to shocks **and** hence more at risk of poverty.

Our calculations on the Mini-S-usenas sample indicate that female-headed households have slightly higher mean per capita expenditures, but their poverty rate is slightly higher than male headed households."<sup>2</sup> Furthermore, the data suggest that female-headed households have greater expenditure changes variability and, hence, higher proportion of vulnerable households (2.4 for FHH versus 2.30). The 100 villages sample also indicate that female-headed households have a slightly greater

> **12** This indicates that female-headed households have higher expenditures variability _across households_ than male headed households.

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orientation and digital orientation defines digital-tech companies ( _“digital-tech orientation”_ ).

We also consider various factors that could impact the relationship between the adoption of ethical practices and digital-tech-oriented firms, such as the firm size (“ _large_ ”), the presence of a line of credit or loan from a financial institution (“ _fin_ins_ ”), the real annual sales growth in percent (“ _sal_gro_ ”), the logarithm of GDP per capita (“ _log (GDPpercapita)_ ”)<sup>7</sup> and the “age” of the firm, given by the difference between the year of the survey and the year in which a firm began operations (“ _age_ ”).

We obtained data for cultural dimensions from Geert Hofstede’s website, which includes six dimensions: long-term orientation, individualism, power distance, uncertainty avoidance, masculinity, and indulgence. We focus on five dimensions to explore their influence on the relationship between technology and ethical practices. Specifically, we examine whether: (i) long-term orientation and individualism affect the relationship between digital-tech-oriented firms and emissions monitoring; (ii) power distance and uncertainty avoidance modify the relationship between digitaltech-oriented firms and training; and (iii) masculinity and long-term orientation influence the relationship between digital-tech-oriented firms and the presence of female top managers.

While indulgence may be related to environmental issues (Gallego-Álvarez and Ortas, 2017), we believe that long-term orientation (“ _ltowvs_ ”) and individualism (“ _idv_ ”) are more appropriate to influence the relationship between digitally oriented

> 7 For GDP per capita, we used the following data adjustments: for Bhutan, we input the data for 2022 as a proxy for 2023; for Djibouti, we complemented the data for 2012 with data for 2023; and for Kosovo, we replaced the unavailable data for 2007 with the value from 2008. These adjustments concerned a total of 667 observations.

8