rafmacalaba/datause-encoder-data huggingface dataset:
- Dces time use inside the household vary by age, ethnicity, demographic variables etc. as much as it does by gender?

There are two competing views about why time use patterns differ by gender in developing countries. On the one hand, it is argued that social roles and norms dictate a segregation of activities by gender. Women are predominantly found doing household chores and men in income-generating activities, because those are largely the roles that society prescribes for them. On the other hand, economists and behavioral scientists have argued that men and women in developing countries respond to economic incentives and constraints to alter their time use. An extreme position in this regard is that work activities are divided along the lines of comparative advantage-men are better at market work and women at housework. However, a more tempered neoclassical view argues that male-female time use responds to economic changes as much as other behavioral factors such as consumption.

While it is relatively straightforward to include economic opportunities and constraints, demographic and regional characteristics, into an empirical analysis of time use, it is quite difficult to pinpoint the exact variables that capture social norms or values that also influence intra-household time use.<sup>2</sup> However, as far as social norms and values stay unchanged over a relatively short period of time, an analysis of time use using panel data can control for some of these differences in norms by controlling for unobserved differences in time use. This paper takes advantage of the fact that unlike in previous studies in the literature, the availability of panel data from Peru allows us to determine the adequacy of the economic model in describing intrahousehold time use. If economic variables are found to not influence time use significantly, then it can be argued that the variations could be from differences in other factors such as social norms and roles. This paper estimates the determinants of time allocation of adult men and women.

> 2 Dummies for ethnicity or the status of an individual within the household hierarchy may be incomplete in capturing the effects of social norms.

2
impact of textbooks and workbooks on student learning. However, when considering well identied studies only, they noted weak evidence. Older studies suggest that books improve achievement (Heyneman et al., 1984; Jamison et al., 1981), while more recent studies in Kenya (Glewwe et al., 1998 and Glewwe et al., 2009) and in Sierra Leone (Sabarwal et al., 2014) contradict these ndings.

Most of these previous studies, however, have had some methodological limitations. The most serious methodological issue with observational studies is the non-random assignment of teachers to in-service training programs or students to book provision. A few quasi-experimental studies have attempted to address these issues (Rothstein, 2010; Jacob and Lefgren, 2004; Angrist and Lavy, 2001). A number of issues arising from non-random assignment need to be addressed. For instance, factors like self-initiation, relationships with supervisors, personal connections and political participation confound with a teacher's decision to attend in-service training as well as her general motivation and capacity to teach (see Jacob and Lefgren (2004)). Similarly, a student's access to books confound with a number of other covariates such as parental education, wealth, and school resources, which directly aect student outcomes.

This study uses data from the randomized assignments of teachers into a training program or the provision of books to randomly selected primary schools in Mongolia under the Rural Education and Development (READ) project to examine the impacts of these interventions on student achievement. The randomization is nationally representativeit covers the entire rural population of the whole country, as opposed to a typical small-scale randomization study from which generalization to national population is not feasible. This enables us to address limitations arising from nonrandom assignment and provides a basis to generalize about the impact of the interventions.

In addition, this study investigates the dierential impact of in-service teacher training or book provision as a stand-alone intervention vis-à-vis in-service training accompanied by provision of age-appropriate books. Some previous evidence on the topic suggests that provision of education inputs as a bundle is more eective in improving outcomes (see McEwan (2014); Evans and Popova (2014); and Conn (2014) for detailed review). The evaluation of these interrelated investments sheds light on the potential complementarity of educational inputs, and non-linearity of returns to 2
comparator countries on both measures. The low revenue to wage bill ratio reflects both comparatively low revenue per worker and high labor costs (World Bank, 2017).<sup>6</sup>

**Figure 2.2: Labor productivity and revenue to labor costs ratio in services (2009)**

<!-- Start of picture text --> Revenues per worker Revenues to labor costs<br>100000 10<br>98009 9.7<br>80,000 8<br>8.0<br>7.7<br>71906<br>60,000 6 6.6 6.0<br>52051<br>40,000 4 4.5<br>37837 37165 3.8<br>31531<br>20,000 2<br>14056<br>0 0<br>Poland China RussiaTurkey_micro ECA Turkey Malaysia China Poland Russia ECA MalaysiaTurkey_microTurkey<br>Revenues per workers (2009 USD) Ratio revenues to labor costs (2009)<br><!-- End of picture text --> Source: World Bank Enterprise Surveys; World Bank (2017). Turkey_micro consists of firms with five or fewer full‐ time employees. All other figures include firms with five employees or more.

**Turkey’s services sector has seen little growth over the past 15 years, contrary to global trends.** Services value‐added has hovered at around 60 percent of GDP since 2000. The top panel of Figure 2.3 shows the positive association worldwide between the share of services value‐added in GDP and the real GDP per capita over time, where each dot represents a country in a given year. In other words, as countries get richer, they tend to increase the share of services in the economy. However, Turkey has largely shifted horizontally to the right—instead of upwards and to the right—indicating that the share of services in GDP has not been growing much since 2000. Manufacturing, which represented about 19 percent of GDP in 2015 (which is higher than the world average) has been declining slightly (shown by a shift to the bottom right in the bottom panel of Figure 2.2).

**Figure 2.3: World value‐added shares for Turkey (2000‐2015)**

<!-- Start of picture text --> Services value-added in GDP<br>6 8 10 12<br>GDP per capita PPP (log)<br>100<br>80<br>60<br>40<br>Services value added in GDP 20<br>0<br><!-- End of picture text --> 6 In Turkey, the manufacturing sector also faces high labor costs, but it is able to use labor more productively.

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<!-- Start of picture text --> Figure 5: Global distribution of income (using top-heavy adjustment)<br>compared with standard income from surveys alone, logarithmic scale, population-weighted<br>s ‘<br>or<br>i=<br>8<br>Sy<br>= \ 2008<br>3 \ N<br>=2\\~S<br>Qa® = Ss —!,<br>1988 ,<br>oO —— J Se<br>[o) ooo ooo OX x<<S< ¥¥SY YY ¥¥Y x x x<br>ve) oMNO C000 HN N OF ODO NO ODO S °o o<br>- K-N MOTO NM - —-N MOTO S S S<br>annual PPP-adjusted 2005 USD<br>—— standard, 1988 ——-— top-heavy, 1988 ——— standard, 2008 —-—-— top-heavy, 2008<br>The top-heavy adjustment with Pareto tail is shown by the dashed lines. Bandwidth is the same by year, but may differ across years.<br>Top-heavy adjustment sample (i.e. whole-country for CHN, IND and IDN, and excl. countries w/o national accounts data) used throughout.<br><!-- End of picture text -->
# **(1) Introduction**

Gender bias against girls in developing countries has been the focus of a large and growing economic and sociological literature. Son preference, selective abortion, genital mutilation, mobility, and social restrictions such as Purdah are widely noted. There is a broad consensus that education is a key policy instrument for tackling gender disparity and socioeconomic inequality (see, for example, Stiglitz (2012), Duflo (2012), Jayachandran (2015)). This paper provides an analysis of the gender gap in education in China and India from the perspective of intergenerational mobility.<sup>2</sup> Most of the studies of intergenerational mobility, both in developed and developing countries, focus on the father-son linkage, and research on women in general and on gender bias in particular remains scant.<sup>3</sup> Many existing studies on intergenerational mobility, especially in developing countries, lack a well-articulated theoretical foundation. As emphasized recently by Mogstad (2017), it is thus difficult to interpret the estimates, or understand the underlying economic mechanisms. We develop a model of intergenerational educational persistence in the tradition of Becker and Tomes (1986) that captures different sources of gender bias against girls in the family, the school, and the labor market. The sources of gender bias in the family are: (i) biased estimate of academic ability, (ii) lower weight to the welfare of a daughter compared to that of a son (we call it “pure son preference”), and (iii) lower expected returns from a daughter’s education. The expected returns may in part reflect biases in the labor market. These factors affect the financial investment in education of the daughters, but the daughters may also face bias in non-financial aspects such as home tutoring.<sup>4</sup> Moreover, unfavorable school environment, for example, the absence of bathrooms for girls, can result in dropouts when a girl reaches puberty.

> 2In most developing countries, girls have historically lagged behind boys in educational attainment. However, there is evidence that the gender gap in schooling attainment may be narrowing over time. Reverse gender gap has emerged in Latin America in educational attainment (Grant and Behrman (2010)).

> 3Among the few available contributions on intergenerational mobility of daughters, see Chadwick and Solon (2002) on United States, Azam (2016) on India, and Torche (2015b) on Mexico. But they do not study the effects of gender bias. For a discussion on gender and intergenerational mobility with a focus on persistence in gender attitude and labor market participation, see Luke (2019). For evidence on intergenerational transmission of gender attitudes in India, see Dhar et al. (2019).

> 4A better educated parent may act as a more effective home tutor, but son preference may mean that priority (and more attention) is given to sons.

1
To better understand the differences in the distribution of technology sophistication across countries, we examine the first-order stochastic dominance of the EXT distribution across countries. We conduct the KS-based multiple test, introduced in Goldman and Kaplan (2018), which takes its null as the equivalence of all cumulative density function (CDF) values between two distributions.<sup>22</sup> The KS test calculates the maximum absolute difference between the two cumulative distributions with the null hypothesis that the samples are drawn from the same distribution. Instead of calculating one maximum difference, the KS-based multiple test computes all the differences between the two cumulative distributions. Appendix Figure C.1 shows the pairwise comparisons of CDFs and the results of the KS-based multiple test for each value in the variable of interest. Our tests confirm that the cross-firm distribution of EXT in Brazil first order stochastic dominates in a restricted sense<sup>23</sup> the distribution of Vietnam for most of the domain of the technology indices, which in turn also first-order stochastic dominates in a restricted sense the distribution of EXT in Senegal.<sup>24</sup>

# **4.3 A within-between decomposition**

Next, we explore the cross-firm dispersion in technology sophistication. Figure 6 establishes that there is significant dispersion in technology across firms, within each country. Furthermore, the cross-firm dispersion in technology sophistication seems to differ across countries.<sup>25</sup> We explore the magnitude of the dispersion of firm-level technology sophistication within countries by conducting a variance-covariance decomposition. Let _Tj,c_ denote the average technology sophistication of firm _j_ in country _c_ , _Tc_ the average technology sophistication in country _c_ , and _T_ the average technology sophistication across all firms. Then,

> 22We use the STATA discomp package developed in Kaplan (2019). Because of ”multiple testing problem” that increases the Type I error ( _α_ ), the KS-based multiple test uses ”familywise error rate” (FWER) that provides the probability of rejecting at least one true null hypothesis. For the details of this test, please see Goldman and Kaplan (2018) and Kaplan (2019).

> 23See Atkinson (1987)

> 24Specifically for the comparison Brazil-Vietnam, we find stochastic dominance for the domain of the distribution of EXT between [1.74, 4.70], which includes 97% of Cear ́a firms and 96% of Senegal firms. For the pair of distributions Brazil-Senegal we reject the test of equality for the domain of EXT [1.10, 4.70], which includes 98% of the firms in both countries’ samples; and for Vietnam-Senegal the domain of EXT where we reject equality of the distributions is [1.08, 3.97] that include 95% and 97% of firms in the sample respectively. The test is also rejected for some values above that range, but not for others.

> 25Bloom and Van Reenen (2007) find a similarly large cross-firm dispersion in management practices.

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- 4 -

Servén, 2004).<sup>5</sup> For the same reasons, more agriculture-related infrastructures are expected to reduce farmers’ costs and accelerate growth in agriculture (e.g., Antle, 1983; Mundlak _et al_ ., 2004; Gardner, 2005). Investments in rural infrastructure are often deemed as most effective to promote agricultural growth and reduce poverty, along with agricultural research and education (Fan _et al._ , 2002; World Bank, 2005).

However, the empirical linkage between agricultural growth and infrastructure is not easily demonstrated. Figure 2 depicts the relationship between a proxy of infrastructure stocks— i.e., road density—and unexplained (compound annual) growth after controlling for the initial level of economic development measured by per capita GDP in logarithm. As expected, there is a significant positive relationship between these two variables.<sup>6</sup> By contrast, the same specification yields a negative association when accounting for only agricultural growth (Figure 3). Thus, accumulated transport infrastructure seems to play a significant role in encouraging overall economic development but not agricultural growth. Notably, as shown in Table 1, the above contrast is not characteristics of only the road density but almost common despite the choice of infrastructure variables (i.e., electric power consumption and teledensity).<sup>7</sup> 5 Calderón and Servén (2004) show that growth is generated by the quantity of infrastructure stocks but not by the quality of infrastructure. However, there might be a practical sense that the quantity would likely matter at the earlier stage of development, and then the quality would become more important later on.

6 On a level basis, obviously, there is a strong correlation between GDP and infrastructure stocks (e.g., World Bank, 1994). On one hand accumulated public infrastructure would improve economic efficiency and increase GDP, and on the other hand higher national income would afford more investment in infrastructure.

7 When the share of population with access to improved water sources is used for infrastructure, both GDP and agricultural growth have been found insignificantly associated with the infrastructure proxy.
To explore these results further, Figure 6 presents the comparison between country-specific extreme and moderate poverty rankings based on the $2.5 and $5.3 per person per day poverty lines and the 2011 PPPs with the corresponding estimates based on the $2.5 and $4 per person per day poverty lines and the 2005 PPPs. Any deviation from the 45 degree line denotes differences between the poverty rankings. The figure shows that, with few exceptions, most notably the Dominican Republican, country-specific extreme and moderate poverty rankings are generally stable.

- _4.4 A decomposition approach to understand changes introduced by new poverty lines and PPPs_

Poverty headcounts calculated using 2011 PPP values differ from those calculated using 2005 PPP values, not only because the value of the poverty lines are different, but also because the base year (i.e, 2005 or 2011) and the relative costs of living between countries (i.e., 2011 PPP round or 2005 PPP round) are different. For instance, an extreme poverty line of $2.5 per person per day in 2005 PPP values is drastically different from an extreme poverty line of $2.5 per person per day in 2011 PPP values. As explained above, the $2.5 per person per day poverty line at 2011 PPP values is lower, in real terms, than the $2.5 per person per day poverty line at 2005 PPP values. The difference between the headcounts calculated with both poverty lines is due not only to the general inflation in prices that all the countries experienced, but also to the fact that (i) the relative prices between countries – reflected in the PPP values – changed from 2005 and 2011, and (ii) the set of underlying official poverty lines used to calculated the regional lines also changed.

Thus, the difference between the poverty headcounts using both the poverty lines in 2005 PPP values and the ones in 2011 PPP values can be decomposed into three components. The first component is the change of the level of the nominal value of the poverty line. In the case of the extreme poverty line, the nominal value of the line did not change (i.e., it remained in $2.5 per person per day), but it did change from $4 per person per day to $5.3 per person per day for the case of the moderate poverty line. The second component is the effect of changing the base period from 2005 PPP values to 2011 PPP values. That is, the welfare aggregate of each country is deflated to either 2005 or 2011 PPP values using its corresponding CPI. As the CPI of each country evolved differently over time, the welfare distribution of the LAC region as a whole would differ depending on whether the base year is 2005 or 2011. Finally, the third component is the effect of changing the spatial deflator (i.e., relative prices) from 2005 PPP to 2011 PPP. Most of the 17
compiled by the US Treasury. Second, we include a number of broader measures of capital account openness. In particular, we include the Chinn and Ito Index of Financial Openness (Chinn and Ito, 2008) which measures _de jure_ openness to capital flows.<sup>12</sup> We also include measures of inflows, namely, foreign direct investment (FDI) inflows, portfolio (equity and debt) inflows and other inflows (mostly bank lending flows). All these variables are scaled by GDP. These data come from the IMF Balance of Payment Statistics. Third, we include measures of stock market size and liquidity. As a measure of stock market size, we include the ratio of stock market capitalization to GDP. Liquidity is measured by the stock market turnover ratio, defined as the value of total shares traded to the stock market capitalization. Data on these variables come from the World Bank Financial Structure Database.<sup>13</sup> To establish the importance of a wake-up call channel at work in explaining the degree of comovement between the US market and other markets, we compute different measures of banking, corporate, macro, and sectoral vulnerabilities. When it comes to banking, we examine the significance of variables such as the capital-adequacy ratio (measured by the share of equity to assets), the share of liquid assets to total assets, and the ratio of credit to the private sector to GDP. The first two variables are computed with data from Bankscope and the last one comes from the World Bank Financial Structure Database. Finally, we also examine the significance of an index of banking activities restrictions, which incorporates information as to whether banks are allowed to underwrite securities and insurance products, commercialize real estate or own non-financial firms. The index, which comes from the Bank Regulation and Supervision Database collected by the World Bank, takes higher values the more significant the restrictions in place.14 A number of variables are included to capture corporate sector vulnerability. First, we compute the ratio of total debt, and separately, short-term debt to total assets. Second, we also examine the significance of return on assets, a measure of firm profitability. Third, we look at a

> 12 The data itself can be found at http://web.pdx.edu/~ito/.

> 13 This database can be found at

> http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20696167~pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html. 14 This data can be found at ~ <u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037 pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html. An explanation of how the index was constructed can be</u> found at Barth, Caprio and Levine (2001).

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# Data and descriptive statistics

## Data

This paper uses data primarily from Better Work Vietnam’s Compliance Synthesis Report. The compliance data are collected during unannounced visits where monitoring teams of usually two people carry out an assessment of working conditions in participating factories. In Vietnam, the monitoring instrument contains several hundred questions designed to evaluate working conditions relative to national law and international standards. For each question, Better Work Vietnam then decides whether the factory is or is not compliant. Like other studies, this paper relies on Better Work Vietnam’s assessment of compliance and does not re‐evaluate their compliance decision (see, for example, Robertson 2017).

An initial compliance assessment is undertaken when a firm first joins Better Work Vietnam. As part of membership in the program, Better Work Vietnam supports the factory through advisory services during the cycle and monitors firm compliance in the cycle. Factory visits and assessments are subsequently undertaken once a year for every year (or cycle) the firm is in the Better Work Vietnam program. The data set thus contains compliance data of a firm over time. The number of assessments per firm varies, depending on when the firm jointed (or exited) the Better Work Vietnam’s program. Table 1 shows the factory counts by the maximum number of compliance assessments. A total of 1,508 factory assessments are available for 461 firms.

The data are available starting in 2010 through 2018. The number of factories for which compliance data is available for each year is given in Table 2 (including both new and revisited factories). Data for 2018 run only through June, which explains why the number of factories is smaller in 2018 than other years. Table 3 presents the factory count by evaluation cycle and shows that there are both firms entering and exiting the sample each year.

**Table 1: Number of factory assessments per factory**

|**Number of factory assessments**|**Number of firms**|
|---|---|
|1|124|
|2|84|
|3|76|
|4|63|
|5|29|
|6|28|
|7|33|
|8|24|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

**Table 2: Number of assessed factories per year**

|**Year**|**Number of firms**|
|---|---|
|2010|65|
|2011|116|
|2012|136|
|2013|139|
|2014|177|
|2015|219|
|2016|253|
|2017|286|

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