rafmacalaba/datause-encoder-data huggingface dataset:
# Table A.1: Individual Characteristics, NFHS

||N|Mean|S.D.|
|---|---|---|---|
|**Panel A: NFHS ever-married sample, women**||||
|Physical violence index|65,292|0.00|1.00|
|Psychological violence index|65,292|0.00|1.00|
|Sexual violence index|65,292|0.00|1.00|
|Attitudes index|65,221|-0.02|1.01|
|Worked last week|65,292|0.25|0.43|
|Worked last 12 months|65,292|0.33|0.47|
|Worked in agriculture last week|64,727|0.12|0.33|
|Worked in non-agriculture last week|64,727|0.12|0.32|
|Using any contraception|65,292|0.53|0.50|
|Using modern contraception|65,292|0.46|0.50|
|Using traditional contraception|65,292|0.07|0.25|
|Husband’s ideal number of sons greater than actual number|46,355|0.30|0.46|
|Husband’s ideal number of sons greater than wife’s ideal|46,116|0.23|0.42|
|Age|65,292|33.02|8.11|
|Rural|65,292|0.71|0.45|
|Hindu|65,292|0.75|0.43|
|Muslim|65,292|0.13|0.34|
|Christian|65,292|0.07|0.25|
|Sikh|65,292|0.02|0.14|
|SC/ST|62,122|0.38|0.49|
|OBC|62,122|0.41|0.49|
|Years of schooling|65,292|5.93|5.20|
|**Panel B: NFHS retrospective panel, woman-year**||||
|Years since the last birth|235,036|6.96|6.57|
|Gave birth|235,036|0.09|0.29|
|Number of children|235,036|2.39|1.61|
|**Panel C: NFHS ever-married sample, men**||||
|Attitudes index|70,829|0.03|0.97|
|Worked last week|70,922|0.91|0.29|
|Age|70,923|37.58|8.81|
|Rural|70,923|0.70|0.46|
|Hindu|70,923|0.76|0.43|
|Muslim|70,923|0.13|0.34|
|Christian|70,923|0.07|0.25|
|Sikh|70,923|0.02|0.14|
|SC/ST|67,152|0.38|0.49|
|OBC|67,152|0.41|0.49|
|Years of schooling|70,923|7.56|4.94|

_Notes_ : The table presents the number of observations, the means, and standard deviations (S.D.) for demographics, intimate partner violence outcomes, attitudes outcomes, labor market outcomes, contraceptive use, fertility preferences and behavior, and husband’s characteristics. The samples in Panels A and B include ever-married women in the domestic violence module from the 2015-16 National Family Health Survey (NFHS), with Panel A presenting the summary statistics for the cross-sectional data and Panel B presenting them for the retrospective woman-year panel. The sample in Panel C includes ever-married men from the 2015-16 NFHS.

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very much a work in progress<sup>5</sup> ). On the other hand, of the seven cases with some measurable policy impact, three followed losses in court: in these cases, the political authorities acceded to the petitioners‘ requests (at least in part) despite losing in court.

By far the most consequential litigation stream was a series of three cases involving judicial review of government funding for K-12 education, which contributed to a significant increase in funding for education in Indonesia. In the _Judicial Review of the 2005 State Budget Law_ , and in two subsequent challenges on the same grounds, the Constitutional Court ordered the government to comply with a constitutional requirement that specified that the government devote 20% of its expenditures to education. These rulings contributed to an increase in education‘s share of the budget from 7% to nearly 12% in the next few years (and eventually 20%, once the definition of the numerator changed). Gauri and Brinks estimated, conservatively, that at least 750,000 students received significantly better schooling as a result of more financing (out of some 50 million students enrolled in primary and secondary education).

In Indonesia, the poorest are underrepresented in public primary education because middle class families commonly use public schools while many of the poorest families are not enrolled at all. Still, these middle class families are not rich by global standards: World Bank figures show that approximately _half_ of Indonesians consumed less than US$2/day in 2007.<sup>6</sup> Adding money to the public school budget might draw more unenrolled, lower income families into the system. Still, we estimate the beneficiary mix as if the additional money did not affect the income distribution among public school students. Using data from the government and the World Bank, we estimate that 36.3% of these students were from the lowest two income

> 5 See for instance http://www.ilo.org/public/english/region/asro/bangkok/events/sis/download/paper24.pdf

> 6 <u>http://go.worldbank.org/BEQZ2K3MR0</u>

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and science, and a lower sense of school belonging, plus they experience more frequent bullying. Additionally, boys’ schools tend to be less safe and orderly, and teachers in boys’ schools tend to also report lower levels of job satisfaction. At grade 8, both mathematics and science teachers are younger, on average, in boys’ schools compared to teachers in girls’ schools, while, in line with findings at grade 4, more grade 8 teachers in boys’ schools have education and mathematics or science as their major area of study, compared to teachers in girls’ schools, who tend to primarily have mathematics or science but not education as their major area of study. Grade 8 teachers in boys’ schools also report having attended fewer hours of professional development on mathematics and science compared to teachers in girls’ schools. Additionally, grade 8 boys’ schools tend to be less safe and orderly, with lower levels of discipline and more frequent bullying among students compared to girls’ schools, while teachers in boys’ schools tend to also report lower levels of job satisfaction.<sup>7</sup>

**Table 3. Contextual categorical variables by student gender, TIMSS 2019**

|||**Grade**|**4**||**Grade**|**8**|
|---|---|---|---|---|---|---|
||**Male**|**Female**|<br>**_/_**|**Male**|**Female**|<br>**_/_**|
|**Student-level variables**|**%**|**%**|**_φφc_**|**%**|**%**|**_φφc_**|
|_Student immigration status_|||0.029|||0.051**|
|native|64.8|66.9||76.7|81.4||
|second-generation immigrant students|28.8|27.6||15.4|11.7||
|first-generation immigrant students|6.4|5.5||7.9|6.8||
|_Student owns mobile phone_|||0.036**|||0.000|
|yes|62.7|58.3||81.4|82.3||
|no|37.3|41.7||18.6|17.7||
|_Preschool attendance and duration_|||0.018||||
|did not attend|30.8|30.0|||||
|1 year or less|39.7|39.0|||||
|2 years|18.3|18.3|||||
|3 years or more|11.2|12.7|||||
|_Student absenteeism_|||0.131***|||0.182***|
|never or almost never|36.6|47.8||34.3|20.0||
|once every two months|10.3|9.9||15.8|11.7||
|once a month|12.3|10.5||17.7|21.7||
|once every two weeks|10.0|7.3||13.7|21.5||
|once a week|30.8|24.5||18.6|25.1||
|_Time spent on mathematics homework_||||||0.051**|
|15 minutes or less||||75.8|73.3||
|16 minutes or more||||24.2|26.7||
|_Time spent on science homework_||||||0.045**|
|15 minutes or less||||71.0|77.4||
|16 minutes or more||||29.0|22.6||

7 The TIMSS variable describing teachers’ qualifications for grade 4 shows that a majority of teachers in Saudi Arabia have secondary education. This is not consistent with official data from the Ministry of Education or other available data sources, which indicate that most teachers in the country have at least a bachelor’s degree. Due to this inconsistency, the authors of the study decided to omit this variable from the analysis.

11
away from a food-based budget.<sup>13</sup> For example, just 78% of comprehensive consumption in the 2010 VHLSS would be counted under the 1993 definition, and the average food share would be 54% rather than 46% (World Bank 2012). Correspondingly, the poverty line was also changed in 2010, raising it to VND 653,000 per person per month (US$2.26 per day in 2005 PPP terms). Under this line, 21% of Vietnam’s population in 2010 was counted as poor, with headcount poverty rates of 27% in rural areas and 6% in urban areas. The much lower poverty line used previously had seen headcount poverty rates fall to 15% by 2008 (from 58% in 1993).

With these new baseline measures of consumption and poverty in place, the challenge for statistical authorities in Vietnam is to make consistent time-space comparisons of real living standards, inequality, and poverty in the future. While the SCOLI program may continue, the earlier VLSS experience and the current situation in most developing countries is that spatial price surveys are not fielded, even as part of household living standards surveys. Moreover, the SCOLI in 2010 was donor funded, and absent this support, the GSO may revert to using the CPI to calculate spatial deflators, so there is interest in how “no-price” methods of deriving spatial deflators perform. The experience of Vietnam in 2010 and 2012 where there is a benchmark from a comprehensive, spatially disaggregated price survey therefore gives a rare opportunity to assess how well a “no-price” method, such as the food Engel curve, works in practice.

When researchers deflate for welfare analysis they typically want an empirical approximation to the true cost-of-living index (COLI): the ratio of minimum expenditure at alternative prices to

> 13. Annex 2.1 of World Bank (2012) summarizes the differences between the comprehensive consumption aggregate and the one which held fast to the 1993 definition of consumption.

12
superior productivity performance but do not expand employment much in countries such as Tanzania and Ethiopia.

• **Service sectors (Trade, Transport and Finance and others):** There is evidence of unconditional convergence across many service sectors (IMF (2018); Kinfemichael and Morshed (2019)). The transport and financial services sectors show convergence across three different balanced panel datasets. In contrast, there has been no evidence of convergence in trade services (wholesale, retail trade, accommodation, and food services) over 1995-2018 despite having shown unconditional convergence over an earlier timeperiod. Lagakos (2016) argued that in the retail trade sector, developing countries rationally choose “traditional technologies” with low measured labor productivity instead of “modern technologies” with high productivity across two dimensions. First, low car ownership rates among households in poor countries cause modern stores to locate further than traditional stores from residential centers less attractive. This situation is related with “appropriate technology” suggested by Basu and Weil (1998) and Acemoglu and Zilibotti (2001). Second, traditional retail technologies offer an opportunity for entrepreneurs to operate informally, thus earning a price advantage over modern retail technologies, which are larger in scale and cannot evade taxes as easily as smaller, traditional stores.

# **3.3 Robustness analysis**

## **3.3.1 Robustness 1: Time-varying regression**

The baseline results were based on three sample periods (1970-2018, 1970-1995, and 1995-2018). For robustness and to provide further insights, a rolling window methodology is employed in which regressions (2) and (3) are estimated with OLS over the 10-year rolling window. This results in 34 regressions. Figure 6 shows the time-varying contributions of within and between sector effects on aggregate convergence. The result is line with the baseline regressions. The between sector effects have contributed to aggregate convergence largely and continuously since 1990s. In addition, the within sector growth has played an important role in aggregate convergence since 2000. Furthermore, agricultural productivity growth has been a significant contributor to aggregate convergence since the late 1980s (Figure 6). Finally, due to the commodity price boom during the 2000s, the productivity in the mining sector had also contributed although its contribution subsequently 9
expand an existing one. In this way, countries are able to benefit from inward investment to achieve a higher growth rate.

Aside from financial market regulations, the impact of broader government regulations on the interaction between FDI and growth has not been analyzed so far. Our paper intends to fill this gap, as we explore the linkages between government regulations, FDI and economic growth in a comprehensive manner. It can be argued that countries may only benefit from foreign investment inflows if they have appropriate local government regulations and institutions in place. Excessive regulations are likely to restrict growth through FDI if human and capital resources are prevented from reallocation. For example, if starting and closing down a business are hindered by extensive and costly government regulations, involving many bureaucratic procedures demanding entrepreneurs’ time and resources, capital flows are prevented from being reallocated to the most productive sectors.

Likewise, if restrictive employment laws for hiring and firing of employees cause a lower labor market turnover, technology spillovers to domestic firms are less likely to occur. A similar argument can be made for other forms of government regulations, such as protecting (foreign and domestic) investors by ensuring creditor rights and enforcement of contracts. Both are difficult tasks involving high uncertainty, considerable time and very large expenses. Hence, multinationals would reduce forward and backward linkages with the local economy, thereby affecting the likelihood of horizontal or vertical spillovers taking place. In summary, restrictive government regulations may prevent productivity increases related to the exploitation of technology spillovers from foreign direct investment inflows.

Against this backdrop, we will use a broad range of government regulations, employing the extensive Doing Business database on government regulations provided by the World Bank (2004), and test our hypothesis that countries with restrictive regulations cannot exploit FDI inflows efficiently. The World Bank database has the main advantage of focusing on relatively consistent and objective data in measuring regulations across countries. More explicitly, the effects of starting and closing down a business, labor market regulations, enforcing contracts, creditor rights and obtaining credit are examined. These forms of regulations are likely to affect the reallocation of resources and, consequently, the positive effects of FDI inflows in an economy.

5
Further comparison between the SPI and the SCI is provided in Dang _et al._ (2023) and in Dang _et al._ (forthcoming).

# _Open Data Inventory (ODIN) index_

The ODIN’s objective is “to provide an objective and reproducible measure of the public availability of national statistics, and their adherence to open data standards” (Open Data Watch, 2023). The ODIN currently provides annual data starting from 2015 but offers comparable data starting only from 2016. In contrast with the SPI’s data sources, the ODIN does not assess data published for countries on international organizations’ websites. Consequently, the ODIN focuses on data that are available from the official website of the NSO and any official government website that is accessible from the NSO site. It assesses the coverage and openness of statistics, where coverage refers to the availability of important statistical indicators classified into three main groups of social statistics, economic and financial statistics, and environmental statistics (which are further broken down into 22 topical sub-groups or data categories).

Each data category is assessed on five elements of coverage (i.e., availability of indicators and disaggregations, availability of data in the last five and 10 years, and availability of data at the first and the second administrative geographic levels), and five elements of openness (i.e., availability of data in machine-readable format and non-proprietary format, availability of reference metadata, availability of download options that make the data more accessible, and availability of an open data license or open data terms of use). Aggregate scores are computed across categories and elements, resulting in the overall ODIN score being an index of how complete and open an NSO’s data offerings are.

# _Open Data Barometer (ODB) and Global Data Barometer (GDB)_

The ODB provides “a global measure of how governments are publishing and using open data for accountability, innovation and social impact”. While first starting in 2013, the ODB has been further expanded to include other data aspects with its successor, the GDB, since 2017. Following a different approach from those of the SPI and the ODIN, the latest GDB collects primary data from an expert survey and supplements these primary data with other secondary data sources to generate its metrics (Global Data Barometer, 2022).

6
12 accounting for more than a third of manufacturing employment in 2004 (Staritz and Frederick 2011b). As the industry started consolidating<sup>13</sup> firms after the end of the MFA, employment declined by approximately 18 percent to 280,000 in 2009 (see figure 1).

Figure 1 Total Employment in the Apparel Sector in Cambodia and Sri Lanka <!-- Start of picture text --> 1995 2001 2002 2004 2005 2007 2008 2009<br>Year<br>Cambodia Sri Lanka<br>400000<br>300000<br>200000<br>Number of workers<br>100000<br>0<br><!-- End of picture text --> _Source:_ Kelegama 2005a, 2005b, 2006, 2009; Tait 2008; Barrie 2009; Saheed 2010; Garment Manufacturers’ Association in Cambodia (GMAC). _Note:_ Operating employment numbers are presented for Cambodia.

In addition to employment, working conditions directly impact a worker’s well-being through the number of hours that are worked overtime, a hazardous work environment, social benefits, or workplace discrimination. But working conditions also have an indirect impact on employment and wage opportunities through the demand side: buyers might pay lower prices or refuse to buy at all if they know that producers exploit child labor or mistreat employees.
<!-- Start of picture text --> i<br>4<br>3 +<br>/ ! /<br>Telecom / { ;<br>:f /. f Transport<br>1 OO: Banking - $e7 —4H Insurance<br>/<br>/<br>/<br>/<br>/<br>0<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text -->
than what is given during eld visits from the EAs. Peer learning will also likely be more pronounced during a centralized training, as CFs with similar characteristics get to share information and jointly interact with the material.

Third, a direct training could increase CFs' adoption through other channels than knowledge. For instance, the training may improve EA-to-CF accountability. Directly trained CFs may demand more information from EAs (Björkman and Svensson, 2009; Banerjee et al., 2010). Being formally trained could also build empowerment, reinforcing the identity of the CF as community messenger and their propensity to lead village-level demonstration activities. Similarly, attending a training in the district town for a few days may make CFs feel special relative to control CFs. Alternatively, a centralized training could create a momentum among peers to adopt the new practices, akin to herd behavior (Banerjee, 1992; Karlan et al., 2014).

We nd a statistically signicant increase in CF adoption of SLM when CFs have access to a direct training in addition to the status quo extension modality. Private returns in the form of labor savings and yield benets in dry years accompany increases in adoption. However, CFs' knowledge scores on the SLM curriculum are unaected by the direct training.

Increasing demonstration of SLM practices could reinforce the perceived benets of SLM among peers by increasing knowledge and reducing the uncertainty of SLM benets (Foster and Rosenzweig, 1995). Yet, boosting CF demonstration and adoption through a direct training does not aect other farmers' practices within the community in our context. Patterns of CF-farmer interactions suggest that the direct training did not additionally stimulate CFs to fulll their role as village messengers. Interestingly, variations in treatment eects by CF characteristics and similarity in cropping patterns indicate that relevance of expected cost-benet margins aect the diusion process. For example, pit planting adoption rates increase when a CF's crop portfolio matches the farmers'. Hence, our results corroborate the idea that the proximity of the source of information may be the primary constraint on changes in farmer behavior (Munshi, 2004; Feder and Savastano, 2006; Bandiera and Rasul, 2006; Conley and Udry, 2010) and, therefore, that the process of CF selection may aect the pace of diusion (Beaman et al., 2014; BenYishay and Mobarak, 2014).

Overall, our ndings suggest augmenting decentralized extension programs with a direct training modality can improve their eectiveness in getting CFs to demonstrate new technologies. Our costbenet analysis shows net private returns of up to USD 76 per CF. Yet, a direct CF training leads 4
_Gender Equality and Economic Development: The Role for ICTs_ 4 tend to improve gender equality in education and employment. Further, this paper also shows that reducing gender inequality in education also reduces gender inequality in employment, which implies that improving ICT infrastructure or ICT availability improves gender equality in the labor market via both direct and indirect means.

This paper is laid out as follows. Section 2 reviews the status of gender inequality around the world, and shows that while the situation is improving, gender inequality still persists in all regions of the world. Section 3 reviews the literature documenting empirical evidence of the effects of gender inequality on economic development. This section also presents the various channels via which gender inequality in education and employment tends to reduce economic growth. The various characteristics of ICTs, and how a higher level of ICT infrastructure facilitates economic development and improves gender equality are discussed in Section 4. Section 5 briefly mentions the other possible determinants of gender equality in education and employment. Section 6 presents the definitions of variables used in the regressions, and a description of the econometric methodology. The main estimation results are highlighted in Section 7 and Section 8 provides some concluding remarks.

# **2. Gender Inequality Around the World**

World Bank (2001) reports that gender inequality in various forms is still prevalent around the world. Using an index of gender inequality, Figure 3 illustrates the persistence of gender inequality in basic rights in every region of the world, including the developed OECD countries<sup>1</sup> . It should be noted that gender equality in basic rights tends to be weaker in the poorer regions of the world. This indicates that gender equality tends to improve with economic development, a point that will be revisited below.

Figure 4 focuses on gender inequality in education and presents the female to male ratio of the youth literacy rates of the various regions. It can be seen that from 1990 to 2002, the ratio has been consistently increasing in all regions of the world, indicating an improvement in gender equality in education. Note that gender equality in education in the South Asia, Sub-Saharan Africa and the Middle East and North Africa regions has been consistently below the global average. In the 2002, the ratios were 79 percent, 89

> 1 The rights indicator is an average of three indexes of gender equality in rights collected for more than 100 countries by Humana (1992).  The individual rights indexes focus on gender equality of political and legal rights, social and economic rights, and rights in marriage and in divorce proceedings.  The indexes are constructed using a consistent methodology across countries in which the extent of rights is evaluated (on a scale of 1 to 4) against rights specified in several human rights instruments of the United Nations.