rafmacalaba/data-use-mentions-extended huggingface dataset:
hered to the very strong patterns of occupational segregation in Ghana. Data from a nationally representative survey show that female apprenticeships were limited almost exclusively to garment making and cosmetology. In contrast, just over a third of male apprenticeships were in construction trades (Ghana Statistical Service, 2014).

Participants were matched with a training provider, often referred to as a master craftsperson (MCP). Participants would work in the MCP’s firm and obtain skills through learning by doing in an unstructured environment similar to a traditional apprenticeship The NAP was to last one but in train- program. training period supposed year, practice ers generally kept their apprentices for 18 months to almost 4 years, depending on the district and trade. The length of training was ultimately decided by each trainer. Because most trainers considered one year to be too short, they pushed back on COTVET’s suggested duration. Since the program was decentralized, COTVET could not enforce the one-year training term. The program was meant to pay trainers 150 GHS to train an apprentice, an amount equivalent to the traditional apprenticeship entrance fee. As a result of the government’s fiscal crisis, however, COTVET was unable to pay this fee. But the research team was able to secure donor funds to 100 GHS to each pay participating trainer. The program was also supposed to provide participants with a tool kit relevant to their trade (for example, a sewing machine for garment makers). But most tool kits were never delivered. While the program provided no subsidy to apprentices, firm owners typically paid apprentices small wages or “chop money” (about 20 GHS a month in our midline surveys of firm owners), which increased with seniority and varied with firm productivity or revenues. Thus the program essentially functioned as a subsidized version of a traditional apprenticeship with training timelines of around three years and limited government monitoring or additional support.

# **3 Research Design**

## **3.1 Participant Recruitment and Randomization Procedure**

We use a
|First stage<br>Table 8: Export participation and share of workers trained by occupational group,|(1)<br>IV estimates|(2)<br>for switchers|
|---|---|---|
|Dependent variable: Export (=1 if yes)|||
|Industry-specific trade-weighted log real exchange rate|0.004**|0.004**|
||(0.002)|(0.002)|
|F-statistic|10.673|10.771|
|Second stage|||
|A: Dependent variable: share of workers in skill level 4 receiving technical upgrading|||
|Export (=1 if yes)|0.052|0.049|
||(0.067)|(0.065)|
|B: Dependent variable: share of workers in skill level 3 receiving technical upgrading|||
|Export (=1 if yes)|0.086|0.097|
||(0.229)|(0.241)|
|C: Dependent variable: share of workers in skill level 2 receiving technical upgrading|||
|Export (=1 if yes)|0.061*|0.063*|
||(0.034)|(0.035)|
|D: Dependent variable: share of workers in skill level 1 receiving technical upgrading|||
|Export (=1 if yes)|0.104**|0.106**|
||(0.042)|(0.042)|
|Firm effects|Yes|Yes|
|Firm controls|No|Yes|
|Observations|18170|18170|
|Notes: Robust standard errors clustered by firm in parentheses. *** p<0.01, ** p<0.05, * <br>year effects. Firms controls
- b. Husband Quality.

- i. Husband’s highest grade completed, highest certificate attained. S25 Q2,4

- ii. Husband’s wage rate  S26 Q5

- iii. Currently employed S26 Q6..

- iv. Husband’s score on cognitive test

- v. Husband HIV status.

- vi. Husband marital fidelity.  Partners ever:  S32 Q2, Partners 12 mo.  S32 Q3.  Concurrence:  S32 Q15 answer for spouse (column 1)

- vii. Husband’s mental health (constructed in same manner as CR) and then standardized.

# **Super-index of husband quality:  i-vii.**

- c. Husband Gender Empowerment.

- i. Husband Index of GE:  S30 Q1-9

- ii. Husband Index of wife’s autonomy:  S30 Q13-21.

- iii. Husband Index of justification for abuse S30 Q26-28.

- iv. Husband Index of divorce prospects:  S31 Q10-11, 15,16

- v. Husband desired fertility.  S30 Q49

# **Super-index of husband gender empowerment:  i-v.**

3. <u>Unmarried Core Respondent Outcomes: to be analyzed within the sample of unmarried</u> core respondents.

- a. Empowerment unmarried:

- i. Index of autonomy:  S17 Q2a-d, Q4, Q6

- ii. Index of abuse:  S17 Q14,16,17a-e.

# **Super-index of unmarried empowerment:  i-ii.**

4. <u>Child-Level Outcomes: Can be analyzed two different ways. First, unconditionally</u> examining simple comparison between treatment and control. Secondly, conditionally including dummies for the age of child and the age of the mother so as to drop out effects coming from delayed fertility.

- a. Health Outcomes:

- i. Birthweight:  S21 Q20

- ii. Vaccinations:  S22 Q5, 7, 8, 10, 11 (index)

- **iii. Neonatal/Infant/Child mortality:  S18 Q16-21; S21 Q10 (use Round 3 data for
started working, interest in the job and youth likelihood to reach the dream job (self-efficacy); (2) household characteristics Z2i(t−1), i.e., household size, adult head of the household engaged in STEM-related jobs, and income quintile; and finally (3) community characteristics T2i(t−1), i.e., place of residence (urban and rural), highly regarded job by friends and parents and role models in the community.

Lastly, we explore how personal characteristics, household characteristics and exposure to migration correlates with the desire to migrate (Y3i), in a set of multivariate logit regressions (m=3). In addition, we explore the correlation of youth’s characteristics X3i(t−1), household characteristics Z3i(t−1), and exposure to migration with the desired location selected by the youth that expressed their desire to migrate T3i(t).

For all set of logit regressions, household and community data are obtained from previous LSMSISA pre-COVID-19 surveys in Ethiopia, Malawi, and Nigeria. Standard errors are clustered at the enumeration-areas (EA) level.

# **4. Descriptive Results**

# **4.1. Educational levels and aspirations**

> 6 The STEM-related occupations are defined following the PISA25 definition that includes science and engineering professionals, health professionals, science technicians and associate professionals, and formation and communication technology (ICT) professionals (OECD, 2021).

8
<mark>“Upholding the Malay Language and Strengthening the Command of English.” A</mark> lthough English would now only be learned as a second language, more emphasis would be placed on building students’ English language skills by hiring retired teachers, employing additional assistant teachers for larger classes, and having extra class hours (Chapman, 2009). Table 1 summarizes the multiple shifts in the language of instruction policies in Malaysia over the past few decades.

In this paper, we focus on the shift in the language of instruction policy in 2003 to gauge its impact on the mathematics and science test scores of Malaysian students. Prior to the implementation of PPSMI, students learned mathematics and science in BM, Mandarin or Tamil – depending on the type of primary schools they attend – and when they progressed to secondary schools, they learned the two subjects in BM. With the implementation of PPSMI, students entering primary schools for the first time since 2003 had to learn the two subjects in English regardless of the types of primary schools they attend. Similarly, students progressing to secondary schools from 2003 onwards also had to learn the two subjects in English. On the other hand, students who had already started their primary or secondary schools prior to 2003 were not affected by the policy change. How PPSMI was applied to different types of schools and levels of education is summarized in Table 2.

Since students sort into different types of primary schools based on their ethnicity, the prePPSMI language of instruction in a primary school and a student’s ethnicity are highly related. National primary schools enroll mostly ethnic Malay students, Chinese National-type primary schools enroll mostly ethnic Chinese students, while Indian National-type primary schools enroll mostly ethnic Indian students.<sup>1</sup> Nonetheless, a sizable number of ethnic Chinese (8%) and Indian

> 1 According to the Ministry of Education of Malaysia’s (2012) statistics for 2000, 98% of ethnic Malay primary school students enrolled in a National school, 92
create an implausibly large change in the combined HLO score. The end result of this step is to add 54 country‐year observations based on EGRA assessments.<sup>11</sup> Finally, the HLO dataset includes one observation for Sri Lanka in 2013 based on the national assessment, which was linked to TIMSS. These steps result in a combined HLO score with 668 country‐ year observations covering 162 countries. The median country has three time series observations between 2000 and 2017. The HCI is based on a cross‐section of test scores in 2017, taking the most recent test score available in the period 2006‐2017. For 133 of these countries, the most recently‐ available test score falls in the five‐year period 2013‐2017. A cross‐section of most recently‐available test scores are displayed in the bottom panel of **Figure 2** . Test scores range from just below 600 in the best‐performing countries to just above 300 in the worst‐performing countries.

# **A2.3  Adjusting Expected Years of School for Quality**

The next step is to transform test scores into a quality‐adjustment factor for years of school. There are (at least) five options for doing so:<sup>12</sup>

- The first is to exploit the fact that in some settings the same test is administered to children in different grades.  If children in different grades are on average similar in their abilities, the difference in average performance between children in the two grades can be interpreted a measure of the “productivity” of the additional year in terms of higher test scores.  This gradient can then be compared across countries to infer cross‐country differences in average school quality.  One application of this is done directly by PISA, and exploits the fact that PISA tests are administered to children at age 15, irrespective of what grade they are in.  While most PISA test takers are in 9<sup>th</
first stage and we set for all leaders. 44 Using WDI growth data produces a similar fraction of significant leaders (6.6%) and democrats (31%). PWT 7.1 γγ= 0 produces smaller estimates of A , which reduces the fraction of significant leaders to 3.7% and increases the TT fraction of democrats. The set of significant leaders varies across data sets: see Online Appendix 3 for a discussion. μμ σσ

32
expression in the context of foreign exchange markets) – the faster the acceleration of the financial sector the greater its predictive power of a subsequent bust.

Section 2 overviews the data, section 3 outline the methodology, section 4 discusses the results. Section 5 concludes.

# **2. Data**

We obtain annual data on real value added and employment in 10 broad economic sectors covering a panel of 28 countries constructed by Timmer and de Vries (2009) through Groningen Growth and Development Centre (GGDC), 10-Industry Database (http://www.ggdc.net). The data cover the years 1947 through 2005; however, up to 1949 data on only 4 countries are available with the coverage jumping sharply to 26 in 1950 and to 28 in 1960. The 10 sectors are agriculture, mining, manufacturing, public utilities (electricity, gas, and water), construction, wholesale and retail (including hotels, restaurants); transport, storage, and communication; community, social, and personal services; government services, and finance, insurance, and real estate. Previous studies using the GGDC data include McMillan and Rodrik (2011). Following these authors, we increase the level of aggregation to 9 sectors by combing the data on community, social, and personal services with government services, because a number of countries, especially in Latin America, do not distinguish between the two when reporting employment or value added. We refer to the consolidated sector collectively as government.<sup>1</sup> The additional controls, including real GDP per capita, inflation rates, real interest rates, and the agricultural and industrial shares of the economy, were obtained from World Bank’s World Development Indicators (WDI) database. Political stability, rule of law, and regulatory quality indicators were obtained from the World Bank Governance Indicators database <u>(http://www.govindicators.org) from indexes constructed by Kaufmann et al (2009).</u>
that all dual-exchange-rate restrictions were eliminated. What would be the predicted impact on remittances, using the estimated panel coefficients from Table 4, columns 2 and 4?

The results of these related thought experiments are presented in Table 5. Regional estimates are constructed as a weighted average of the country-specific estimates of the percentage impact on remittances, with the weights proportional to recorded remittance flows. For developing countries as a whole, the thought experiments imply an increase in remittances on the order of about 35 to 75 percent (using the mean of, respectively, the 2-percent transaction cost and the 5-percent transaction-cost experiments). At a regional level, the increases would be especially large in Sub-Saharan Africa, the region where costs are currently highest and many dual exchange systems prevailed.

One important issue concerns how to interpret these results, and to what extent they relate to the presence of “informal” remittances. We are assuming that costs are the main determinant of informality. In some countries, there may be other reasons why migrants prefer to send money informally. For instance, illegal migrants may prefer to avoid formal channels or may not have access to formal channels. Because of potential taxation in the home country, migrants may prefer to keep remittances out of government’s reach. There may be a history of informality and a well-developed informal sector that is very competitive. We attempted to control for some of these features by including data on the size of the informal economy and income tax rates, insensitive to the specific values chosen for these variables, since much of the explanatory power of the panel regressions stems from the dual exchange rate indicator.

22
**Table** **<u>5: Inequality by CEQ Income Concepts and Gender</u>**

||**Market**<br>**Income**|**Gross**<br>**Income**|**Net**<br>**Market**<br>**Income**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|**Final**<br>**Income**|
|---|---|---|---|---|---|---|
|Men|0.386|0.381|0.376|0.370|0.372|0.358|
|Women|0.344|0.337|0.338|0.331|0.332|0.321|
|Within-group component|0.366|0.360|0.358|0.352|0.354|0.341|
|Between-group component|0.004|0.004|0.004|0.004|0.004|0.004|
|Total|0.371|0.364|0.362|0.356|0.357|0.344|

_Source_ : Authors’ calculations; ESS 2018/19 data.

Indirect taxes and subsidies have little effect on inequality for both men and women; the Theil index remains the same for both disposable and consumable incomes (Table 5). However, direct taxes and transfers reduce within group inequality for both groups; when we move from market to disposable income, the Theil index declines from 0.385 to 0.370 for men, and from 0.344 to 0.331 for women (Table 4).

However
cases in which it is not significant occur for the metal products industry. Including the current exports dummy does not alter the positive sign or the significance of the coefficient on the cumulative exports index. This coefficient is less robust, though, to the inclusion of the exporter dummy: in the food products and plastics industries, it remains positive but not statistically significant. These industry-specific results suggest that the relationship between export experience and productivity might vary across industries. Our last question is whether we can explain this variation.

We explore two hypotheses to explain differences in learning-by-exporting across industries. The first hypothesis is that plants have more scope for learning-by-exporting when they export to high-income countries. This hypothesis is motivated by the presumption that consumers in high-income countries are more discriminating about the quality of the goods they import. Therefore, their markets are likely to be more competitive than the markets of low-income countries. As a result, Colombian manufacturers exporting to those countries will face higher demands on such aspects as product quality, delivery time, and post-sale services, which in turn give managers and workers more opportunities for learning and productivity enhancement.

To investigate this possibility, we construct an additional variable using data from the _World Trade Flows, 1980-1997_ database (WTDB) compiled by R. Feenstra: the share of industry exports going to high-income countries.<sup>24</sup> The list of high-income countries is obtained from the World Bank.<sup>25</sup> To match these additional data to our main dataset, we convert the industry classification codes of the WTDB files from the U.S. Department of Commerce Bureau of

> 24 The data can be downloaded from http://data.econ.ucdavis.edu/international/.

30
well as one commodity without domestic production (refined petroleum). Among the sectors, government production is represented by 8 services, covering human development, infrastructure and other areas. The factors of production are split into land (for the agricultural activities) and different types of labor and capital. Tables 2.3-2.5 summarize the macroeconomic and sectoral structure of the archetype LIC net exporter and importer economies in 2009: a macro SAM (common to both archetypes) and tables showing sector structure (in terms of value-added, production, employment, and trade) for each of the two archetypes.<sup>14</sup> In addition, median LIC observations were collected for a set of MAMS-relevant HD indicators.

> 14 A SAM is a square matrix with identical row and column accounts, providing a comprehensive representation of payments flows in the economy of a geographical unit (typically a country) during a period of time (typically one year). Cell entries represent payments from its column account to its row account. In a SAM without errors, row and column totals are equal. SAMs appear with widely varying degrees of disaggregation. The payments flows are expressed in current local currency or some transformation thereof – in Table 2.3, the value of each cell has been transformed into percent of GDP at market prices in the same year. For more on SAMs, see for example Reinert and Roland-Holst (1997) and Round (2003). The detailed LIC MAMS SAMs for this application are available on request.

7
sectoral average number of days to clear direct exports through customs in a country (in natural logarithms) from the Enterprise Surveys Indicator Database as an inverse instrument. One could argue that longer waiting times at customs motivate foreign firms to rely more strongly on the host market for sales. The coefficient signs in the IV 2SLS regressions are also positive, but insignificant (columns 6 and 7). However, the Hansen J-test of over-identifying restrictions implies that the instruments are not accepted in these specifications.

Analogously, a sector’s average percentage of domestic input purchases of FDI firms in a country ( _inp_ ) might be endogenous, as the sectoral share of inputs purchased locally might be

> 18 Görg and Strobl (2001) and Görg and Greenaway (2004), however, argue that studies using cross-sectional data tend to find positive FDI spillovers, which they relate to potential time-invariant effects across units (firms or sectors) that are correlated with the FDI presence variable without being caused by it. Interestingly, we find negative FDI spillover effects regardless of whether we control for this potential endogeneity (IV 2SLS) or not (OLS).

20
treatment communities. That is, toilets that would not have been constructed in the absence of those loans. To interpret this statistic, it is therefore helpful to consider trends in toilet uptake in control areas. Using toilet ownership status and date of toilet construction reported during endline survey in August-Sept 2017, in Figure 3 we plot toilet coverage over time in our study area from 2010 up until 2018. We do this by treatment status (red lines for control and blue lines for treatment villages) and by officially classified VG status (solid lines for VG households and dotted lines for non-VGs). We use household’s VG status here rather than SBM’s eligibility status because we are interested in the time trend by subsidy eligibility irrespective of whether or not the household was classified as having a toilet at SBM baseline (prior to SBM baseline subsidy eligibility depended on VG status and not on the household’s SBM baseline record on toilet ownership). The vertical red line depicts the launch of the micro-credit intervention at the beginning of 2015, which happened shortly after the start of SBM (October 2014). VG households without a toilet were in principle eligible to receive a sanitation subsidy, particularly under SBM.

Figure 3 clearly shows that sanitation coverage among our sample population had started increasing even before the introduction of the sanitation loan product. In 2010 virtually none of the MF clients owned a toilet, and by the time the intervention started in 2015, on average more than 20% did. Prior to the launch of the credit and SBM interventions, we do not observe any statistically significant differences in the level and the evolution of toilet coverage, neither between control and treatment GPs, nor between VG and non-VG households. This is confirmed through sample balance checks (available upon request). This pattern changes remarkably from around 2015 onwards, with two notable trends in particular: (i) In control sites - where SBM was active but where no micro-credit for sanitation was available - toilet construction among VG households accelerated. Toilet coverage
–2009, when the world oil price fell similarly?

- What characterizes policies and implementation that seem to sustain reform efforts?

This paper updates a previous publication that traced the evolution of pricing policies for petroleum products from 2009 to 2013 (Kojima 2013a), and expands the scope to include natural gas. Because several countries have taken advantage of low world oil prices to raise taxes and levies on fuels, fuel tax increases are also covered. Subsidies in the power sector are common and fossil fuels may be sources of power generation; as such subsidies in the power sector are also discussed to the extent applicable.<sup>3</sup> The paper uses publicly available information—including government price surveys and price announcements, laws, regulations, decrees, decisions, press releases, data on government websites, annual reports of ministries and central banks, and reports issued by oil companies—to answer the first four questions, with preference given to government sources wherever they are available. As such, the degree of detail available is an indication of the transparency of pricing and subsidy policies. At the same

> 3 One difficulty is attribution. Subsidies in the power sector arise for a number of reasons, of which not fully covering fuel costs is only one. If subsidies are needed because aggregate losses (technical, commercial, collection) are far in excess of any reasonable benchmark, labeling the subsidies as fossil fuel subsidies may not be appropriate. If, on the other hand, subsidies have arisen primarily because of much higher fuel purchase costs—for example, having to rely far more on fuel oil and diesel because of a drought‐induced drop in hydropower generation or because of gas supply interruptions—it would be reasonable to classify such subsidies as fossil fuel subsidies.