rafmacalaba/datause-encoder-data huggingface dataset:
sizes by country, region, and gender are shown in Table A1.<sup>13</sup> The paper’s dataset comprises 157,483 observations, encompassing all respondents who provided complete information on individual characteristics. However, the sample size varies when analyzing different outcomes, as detailed in A.7. Questions posed later in the survey experienced higher rates of nonresponse.

The sample for the Gender Equality at Home survey is comprised of individuals who have access to the internet and personal Facebook accounts. As such, the results should not be viewed as representative of a general population in each country. Nevertheless, the survey’s extensive geographic reach and substantial sample size provide a unique opportunity to explore the research questions on a global scale. Given the significant increase in internet bandwidth and Facebook’s expanding user base, which stands at approximately 2.9 billion monthly active users at the time of this writing, this survey offers valuable insights despite its limitations.<sup>14</sup> Table 1 outlines the demographic characteristics of our sample, split by gender of the respondent. The total sample comprises of 152,555 individuals, with 52% of the sample being women. The demographic characteristics are broadly similar across female and male respondents. On average, 58% of the individuals in our sample are in a long-term partnership or married. The majority, 73%, are over 25 years old. Education levels are high, with 60% possessing an education beyond secondary level. Additionally, 60% of respondents reside in urban areas. Notable gender-based differences in the sample include a 19% lower likelihood of women identifying as the head of the household compared to men, and a 7% lower likelihood of women owning land.

# **2.3 Description of Key Variables**

## **2.3.1 Economic Behaviors**

The dependent variables in our study are categorized as follows: labor supply (employment status in the previous year and weekly hours worked), intrahousehold decision-making power (categorized as either solely female, solely male, jointly made, or none), and household production (identification as the primary caregiver for children, and the allocation of time to caregiving and domestic responsibilities). Details about these variables, including their con-

> 13The sample size calculation achieved a 95% confidence level for estimating regional as well as country level proportions with an average 3% error rate and an 80% power to detect differences across regions and by gender for the online population. Finite population correction was used for countries with smaller online populations. The targeted sample sizes per country ranged from 600 respondents to 5,000 respondents.

> 14Total number of individuals on Facebook are estimated to be equal to the number of monthly active users worldwide as of 4th quarter 2018 that were using Facebook Source: https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/.

7
- 26 _

# I. ConcluiLons

The aLm of this paper has been to provide eMpirical evidence on **wage** and non-wage costs data for a representativ sample of developing countries. The saalysis has been based on cross country comparisons of dollar labor cost levels for production workers in manufacturing. The crux of the study involves the critLcal effect of labor costs on non-traditLonal exports, and the likely distortion ezerted by NC regulations in many LDCo.

ividence included in **this paper reveals a** major decline of real labor cost levels in LDCs during the l9SOs, both with regard to domestic WPI and to the WPI and labor cost levels in the U.S., whlch is taken as representative of the industrial economies. This decline, however, has not been generally accomplished through deregulation of **the** labor market, as **Indicated** by persistently significant ratios of non-wage costs to labor earnings, but mainly through macroeconomic trends, particularly inflation and nominal devaluations. The degree of cistortion introduced by NWC vith respect to the opportunity cost of labor is more marked in the poorest LDCs, where the small **size** of the formal sector contrasts with the relatively high degree of protection provided through NWC regulations. In general, it is possible to Infer that the countries achLeving more success in the manufacturing export industry exhibit both relatively few labor market regulations and increasing labor cost levels in the long run. This evidence supports proposals for carrying out adjustment prograwn favoring export promotion and higher labor mobility within LDCs, which also favor reduction in labor costs through lses government intervention. However, in analyzing the degree of distortion associated with labor cost differentials between wanufacturing and agriculture in any glven country, we found positive statistical evidence in only a mall
Flexibility-of-firing 0 to 100, higher values index indicating more rigid (db_flex_firing) regulation Workers' legal protections against dismissal, including grounds for dismissal, procedures for dismissal (individual and collective), notice period, and severance payment. The constitutional principles dealing with protection against dismissal are also coded.

and Industrial Relations, the areas subject to statutory regulation in all countries were identified. Those include hiring of workers, conditions of employment, and firing of workers

|**BANKRUPTCY**|||||
|---|---|---|---|---|
|**Variable name in**<br>**corresponding**<br>**database**<br>**(name in our**<br>**database)**|**Scale**|**Description**|**Years**|**Source**|
|Goals-of-insolvency<br>index<br>(db_close_insolv)|0 to 100|The measure documents the success in reaching the three<br>goals of insolvency, as stated in Hart (1999). It is calculated<br>as the simple average of the cost of insolvency (rescaled<br>from 0 to 100, where higher scores indicate less cost), time<br>of insolvency (rescaled from 0 to 100, where higher scores<br>indicate less time), the observance of absolute priority of<br>claims, and the efficient outcome achieved. A score 100 on<br>the index means perfect efficiency.|2003|**Doing Business, The World Bank**<br>See Djankov, Simeon, Oliver Hart, Tatiana Nenova, and<br>Andrei Shleifer, "Efficiency in Bankruptcy", working paper,<br>Department of Economics, Harvard University, July 2003.|
|Cost measure<br>(db_close_cost)|%|Cost is defined as the cost of the entire bankruptcy process,<br>including court costs, insolvency practitioners' costs, the<br>cost of independent assessors, lawyers, accountants, etc.<br>In all cases, the cost estimate excludes bribes. The cost<br>figures are averages of the estimates in a multiple-choice<br>question, where the respondents choose among the<br>following options: 0-2 percent, 3-5 percent, 6-10 percent,<br>11-25 percent, 26-50 percent, and more than 50 percent of<br>the insolvency estate value.|2003||
|Court-powers index<br>(db_close_court)|0 to 100|The measure documents the degree to which the court<br>drives insolvency proceedings. It is an average of three<br>indicators: whether the court appoints and replaces the<br>insolvency administrator with no restrictions imposed by<br>law, whether the reports of the administrator are accessible<br>only to the court and not creditors, and whether the court<br>decides on the adoption of the rehabilitation plan. The<br>index is scaled from 0 to 100, where higher values indicate<br>more court involvement in the insolvency process.|2003||
|**GOVERNANCE**|||||
|**Variable name in**<br>**corresponding**<br>**database**|**Scale**|**Description**|**Years**|**Source**|

43
**<u>Appendix 1:</u>**

|**Ta**|**ble A1: Descript**|**ive Statistic**|**s for Internet**<br>**Standard**|**Users (i**|**n levels)**|
|---|---|---|---|---|---|
|**Year**|**Observations**|**Mean**|**Dev.**|**Min**|**Max**|
|1990|214|12337|137204|0|2000000|
|1991|214|20185|206316|0|3000000|
|1992|214|32483|310060|0|4500000|
|1993|214|47021|414435|0|6000000|
|1994|214|96232|894788|0|13000000|
|1995|214|184903|1720460|0|25000000|
|1996|214|345366|3107373|0|45000000|
|1997|214|547334|4209711|0|60000000|
|1998|214|853528|5968722|0|84600000|
|1999|214|1287533|7453445|0|102000000|
|2000|213|1832099|9376954|0|124000000|
|2001|204|2405333|11300000|0|143000000|
|2002|201|3082273|13200000|1000|159000000|
|2003|200|3602316|14100000|1400|162000000|
|2004|201|4249475|16200000|1600|185000000|
|2005|161|4859031|13400000|2000|111000000|

|<br>|**Table A2: Descri**<br>|**ptive Stat**<br>**percen**<br>|**istics for Internet**<br>**tages)**<br>|**Users (**<br>|**in**<br>|
|---|---|---|---|---|---|
|**Year**|**Observations**|**Mean**|**Standard Dev.**|**Min**|**Max**|
|1990|210|0.024|0.11|0|0.804|
|1991|211|0.052|0.217|0|1.404|
|1992|213|0.095|0.345|0|2.21|
|1993|213|0.144|0.463|0|2.775|
|1994|213|0.288|0.893|0|6.747|
|1995|213|0.624|1.75|0|13.714|
|1996|213|1.203|2.923|0|18.211|
|1997|213|2.118|4.546|0|27.487|
|1998|213|3.451|6.597|0|36.329|
|1999|213|5.451|9.591|0|53.818|
|2000|213|8.078|12.164|0|59.786|
|2001|204|10.546|14.424|0|59.93|
|2002|200|13.107|16.355|0.004|64.792|
|2003|200|15.275|17.612|0.053|67.474|
|2004|200|17.667|19.598|0.079|77|
|2005|152|20.668|21.356|0.1|87.755|

27
the early 1980’s to over 34% in 2003, which ranks 41<sup>st</sup> among 89 countries (Table 3-A).

However, the situation has changed considerably in recent years: Since the middle of 2003 through the first quarter of 2006, Indian stock prices have appreciated rapidly, with the popular Sensex index rising from about 3000 to over 10,000 in a period of less than three years. In fact, as shown in Figure 1, the rise of the Indian equity market in this period allowed investors to earn a higher return (“buy and hold return”) from investing in the Mumbai Stock Exchange (formerly the Bombay Stock Exchange, or BSE) Index than from investing in the S&P 500 Index and other indices in the U.K., China, and Japan during the period 1992-2005. Many credit the continuing reforms and more or less steady growth as well as increasing foreign direct and portfolio investment in the country for this explosion in share values.<sup>14</sup> Table 3-C shows the comparative position of the two major Indian exchanges (the NYSEtype “floor exchange”, the BSE, and the NASDAQ-type electronic exchange, National Stock Exchange, (NSE)) vis-à-vis other major exchanges in the world. At the end of 2004, BSE was the sixteenth largest stock market in the world in terms of market capitalization, while NSE ranked next at seventeenth. Table 3-C also shows that trading in the BSE is the most concentrated among the largest exchanges in the world, with the top 5% of companies (in terms of market capitalization) accounting for close to 90% of all trades, but the (share) turnover velocity of BSE (43.1% for the year) is much lower than that of exchanges with similar concentration ratios. These results are consistent with our prior conclusion that the majority of shares in the BSE are not frequently traded. By contrast, the trading patterns at the NSE are similar to those of NASDAQ with more stocks actively and frequently traded (lower concentration ratio and higher turnover velocity).<sup>15</sup>

> 14 According to the Reserve Bank of India’s _Handbook of Indian Statistics_ , both foreign direct investment and portfolio investment (in stocks and bonds) have been growing fast during the past 15 years, with the latter twice the size of the former. The cumulative foreign investment inflows equals 11.58% of GDP in 2005, as compared to 0.03% in 1990. 15 Morck et al. (2000) find that stock prices are more synchronous in emerging countries than in developed countries. They contribute this phenomenon to poor minority investor protection and imperfect regulation of markets in emerging

14
# **Data Appendix**

Detailed data on antidumping, countervailing duties, global safeguards and China‐specific safeguards are available from the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2010a). For antidumping and countervailing duty policies, the data in Bown (2010a) are derived from original government source documents. Each government reports tariff‐line product codes that are subject to the investigations, the dates and countries from whom imports are being investigated, and the decisions of whether to impose preliminary and final trade barriers, as well as when they are removed. The data on use of global safeguards and China‐specific safeguards are derived from both original government source documents and what they report to the WTO’s Committee on Safeguards. Bown (2010a) provides a complete discussion of the data sources, as well as the other information contained in the database that is not utilized in the analysis here.

The tariff line product codes from Bown (2010a) are then matched to bilateral import data at the 6‐digit Harmonized System (HS) product‐level is taken from Comtrade via WITS. The following countries had missing years of import data at the HS‐06 level: Argentina (1990‐1992), China (1990‐1991), Japan (2009), South Korea (2009), USA (1990) and South Africa (1990‐1991). The “value” share measures derived throughout the paper are based on non‐oil import data only.

Because the composition of the European Union changes over the 1990‐2009 period and I am especially interested in recent changes to EU change policy against non‐members, I define the European Union as being made up of the EU‐27 member countries throughout the entire sample. Given that definition, I focus on extra‐EU imports and I drop all EU trade policy actions against other (even eventual) EU member states during the time period. For example, an EU antidumping case against Romania in 2002 would be dropped from the sample, since Romania eventually became part of the EU27 in 2007.

34
suggest that each additional level of schooling has significantly positive impacts on earnings in all regions. For the individuals who are employed, others being equal, those with secondary education have higher earnings than those with basic education or less; and those with tertiary education (in particular university and above) have even higher earnings than those with secondary education.

# **Table 11 -- Determination of employment and earnings at the national level in Croatia (2002-2004) -- using level of schooling as indicator of human capital**

|_Regre_<br>_Estimation of income equations (dependant variable: monthl_<br>|_ssion 3_<br>_y earnings (in thous_<br>|_Regre_<br>_and kuna))_<br>|_ssion 4_<br>|
|---|---|---|---|
|Coef.|t-statistics|Coef.|t-statistics|
|age<br>0.113<br> <br>|15.71<br>|0.109<br>|15.48<br>|
|age*age<br>-0.001<br>|-13.24<br>|-0.001|-13.08|
|Level of schooling ("No school or uncompleted basic educat<br>|ion" as reference)|||
|Basic education<br>0.649|12.96|0.571|11.57|
|Lower secondary vocational education<br>1.645|34.07|1.441|30.03|
|Higher secondary education<br>1.986|29.43|1.726|25.83|
|Two-year post secondary education<br>2.815<br>|49.13|2.577|45.24|
|University education and above<br>3.781|69.98|3.494|64.72|
|sex (male=0, female=1)<br>-0.660|-36.11|-0.688|-38.25|
|Regional dummies (Central Region as reference)||||
|Eastern Region<br>||0.065<br>|2.56<br>|
|Zagreb Region||0.729|29.46|
|Adriatic North Region||0.634|21.62|
|Adriatic South Region||0.538|19.74|
|constant<br>-0.661|-4.25|-0.743|-4.89|
|_Estimation of selection equations_<br>||||
|Coef.|t-statistics|Coef.|t-statistics|
|age<br>0.220|81.64|0.220|81.28|
|age*age<br>-0.003<br>|-87.7<br>|-0.003|-87.31|
|Level of schooling ("No school or Uncompleted basic educa|tion" as reference)|||
|Basic education<br>-0.041|-1.8|-0.034|-1.48|
|Lower secondary vocational education<br>0.278|12.69|0.307|13.86|
|Higher secondary education<br>-0.183|-6.02|-0.141|-4.59|
|Two-year post secondary education<br>0.564|19.11|0.606|20.35|
|University education and above<br>0.615|22.74|0.662|24.08|
|sex (male=0, female=1)<br>-0.213|-19.15|-0.212|-18.96|
|Regional dummies (Central Region as reference)||||
|Eastern Region||-0.213|-14.92|
|Zagreb Region||-0.205|-14.5|
|Adriatic North Region||-0.109|-6.38|
|Adriatic South Region||-0.316|-21.11|
|married<br>0.201|16.44|0.201|16.39|
|household head<br>0.197|15.46|0.197|15.35|
|constant<br>-4.062|-78.35|-3.927|-74.79|
|Log likelihood<br>-120705||-119837||
|Number of observations<br>78148||78148||

Note: All coefficients are significant in 1%, except the dummy "basic education", which is not significant at 5% in the selection equations.

20
21 Table 1: Data for the four empirical illustrations

||California school<br>districts|OECD Program for<br>International Student<br>Assessment (PISA)|OECD health systems|Vietnam district<br>hospitals|
|---|---|---|---|---|
|Sample coverage|313 unified school<br>districts in<br>California.|49 countries.|29 OECD countries.|795 district<br>hospitals in Vietnam<br>with 50-500 beds.|
|Time period|2003/04-2008/09|2000, 2003, 2006|1960-2005|1998, 1999, 2000|
|Sample size|1878|118|945|1039|
|Expenditure<br>measures|Current<br>expenditures in June<br>2000 prices, per<br>pupil|Secondary education<br>expenditure in<br>international dollars<br>at 2005 prices, per<br>secondary pupil|Total health spending<br>in international dollars<br>at 2000 prices, per<br>capita|Recurrent costs of<br>hospital, per case|
|Mean and standard<br>deviations of<br>expenditures|9742.44 (2662.77)|5492.25 (3637.27)|1294.55 (891.70)|262.83 (158.36)|
|Outcome measures|California Standards<br>Test (CST) scores<br>of grade 5 and grade<br>7 pupils in math and<br>science. Averaged<br>for one-outcome<br>exercise.|Average PISA test<br>scores in math,<br>reading, and science<br>of 15-year-old<br>students in grade 7<br>or higher. Averaged<br>for one-outcome<br>exercise.|Potential years of life<br>lost (PYLL) per<br>100,000 population<br>through deaths<br>occurring before the<br>age of 70 from six<br>causes of death<br>considered to be<br>amenable to medical<br>care. Aggregated for<br>one-outcome exercise|Outpatient and<br>inpatient cases.<br>Aggregated for one-<br>outcome exercise|
|Outcome<br>[mean; std. dev.]|Grade 5‟s math<br>[356.25; 33.96]<br>Grade 5‟s reading<br>[346.58; 22.20]<br>Grade 7‟s math<br>[339.05; 24.81]<br>Grade 7‟s reading<br>[345.54; 22.74]<br>Average outcome<br>[346.86; 24.50]|Math<br>[475.85; 56.61]<br>Reading<br>[473.34; 47.45]<br>Science<br>[479.68; 48.30]<br>Average outcome<br>[476.33; 49.86]|PYLL-agg. cancer<br>[378.20; 106.50]<br>PYLL-MCH<br>[584.18; 444.59]<br>PYLL-diabetes<br>[52.67; 36.84]<br>PYLL-heart disease<br>[530.55; 306.48]<br>PYLL-cerebrovascular<br>[228.62; 134.89]<br>PYLL-Flu/pneumonia<br>[149.29; 178.54]<br>Aggregate outcome<br>[1923.51; 924.54]|Inpatient cases<br>[4914.95; 3021.12]<br>Outpatient cases<br>[2546.67; 4665.88]<br>Surgery cases<br>[423.23; 501.74]<br>Aggregate outcome<br>[7884.85; 6065.08]|
|Data sources|California<br>Department of<br>Education Data|PISA and WDI<br>Databases|OECD Health Data|Vietnam annual<br>hospital inventory<br>exercise|
relevance.<sup>12</sup> According to the decentralized system, sub-national governments are responsible for providing secondary education. While spending on junior education by districts is significantly lower than that of primary; higher central spending in junior education, partially, compensates for this.<sup>13</sup> The largest share of central government routine expenditure was allocated to tertiary education, as salary expenditures for basic and secondary education –the largest component of district routine expenditures– are financed through the DAU transfer and accounted for as subnational expenditures.

**Table** **<u>6</u>** <u>Social Returns to Education Per Level of Education 2004</u>

|**Level of Education**<br>**Rate of Return**|
|---|
|Primary education<br>.04|
|<br>Junior secondary<br>.25|
|Senior secondary<br>.28|
|_Source_: Author’s calculations.|

Identifying the optimal allocation of resources across education programs is crucial if the government is to increase education spending, as is suggested by the Constitutional Court. The low levels of enrollment rates of junior secondary education are a clear sign that larger efforts are required to improve access to this level of education. In addition, social rates of return in secondary education are higher than in primary education. Cost-benefit analysis yields useful insights by comparing education programs based on their returns to society. Estimates of the returns to education investments are defined as the discount rates that equate a stream of education benefits to a stream of costs for providing education, at different levels, at a given point in time. The senior secondary level achieves the highest rates of return with 28 percent, slightly above the junior secondary school level, with 25 percent. By contrast, primary education is low with an estimated 4 percent (Table 6).<sup>14</sup>

> 12 See Renstra, MoNE, 2005.

> 13 While spending per student is actually higher for junior education, this does not indicate an adequate level of sp ending at that level. It reflects the fact that costs of secondary education provision are typically higher and that the number of enrolled students in junior secondary education is low.

> 14 Education benefits were computed based in wage differentials (additional average earnings from those of the same age group at a previous level of education) from the Indonesian Labor Force Survey (Sakernas) 2006, and education costs from unit cost estimations reported by MoNE (2005). See Annex 2 for further information on the methodology employed for this computation. These calculations are currently being revised and modifications might be included in the next draft of this paper. See Annex 2 for a more thorough discussion on the methodology employed for the computation of social rates of returns to education.

8
**Appendix 3: Determinants of Heteroskedasticity (from heteroskedasticity Probit)**

|||**Daug **|**hters**|||**So**|**ns**||
|---|---|---|---|---|---|---|---|---|
|**variables**|**Klein**|**and Vella‐1**|**Klein a**|**nd Vella‐2**|**Klein**|**and Vella‐1**|**Klein**|**and Vella‐2**|
||**Coef.**|**Z stat.**|**Coef.**|**Z stat.**|**Coef.**|**Z stat.**|**Coef.**|**Z stat.**|
|_1988_|||||||||
|Mother age|‐0.03|‐1.37|‐0.05|(‐2.12)**|‐0.02|‐0.88|‐0.03|(‐1.69)*|
|Father age|0.00|0.03|‐0.02|‐0.92|‐0.00|‐0.03|0.01|0.65|
|Coast dummy|‐0.02|‐0.08|‐0.25|‐1.07|‐0.34|‐1.58|‐0.19|0.91|
|Topography (terrain)|0.04|0.11|9.88|1.34|‐0.27|‐1.41|‐0.08|‐0.31|
|Topography (mountain)|‐0.59|(‐2.16)**|0.64|1.08|‐0.41|(‐1.61)*|0.13|0.35|
|Parents' ethnic|||||||||
|minority|0.18|0.41|||‐1.67|(‐1.79)*|||
|Old revolutionary base|‐0.02|‐0.06|||‐0.09|‐0.35|||
|Ethnic minority region|‐0.00|‐0.02|||1.42|1.47|||
|Impoverished region|7.81|0.89|||0.26|0.73|||
|Suburb of big city|‐0.39|‐0.65|||‐1.67|(‐1.84)*|||
|County index|‐0.02|‐0.13|||‐0.61|(‐4.44)***|||
|_2002_|||||||||
|Mother age|‐0.04|‐1.61|‐0.01|‐0.24|‐0.05|(‐2.69)***|‐0.04|(‐2.14)***|
|Father age|0.17|6.31***|0.03|1.86*|0.06|3.78***|0.04|2.95***|
|Coast dummy|0.74|2.76***|0.28|0.79|0.63|3.31***|0.32|1.88*|
|Topography (terrain)|0.29|2.17**|0.11|0.59|‐0.22|(‐1.94)*|‐0.27|(‐2.51)**|
|Topography (mountain)<br>Parents' ethnic|2.09|5.39***|0.37|1.28|0.44|2.76***|0.23|1.57|
|minority|‐0.51|‐1.39|||‐0.43|(‐1.63)*|||
|Old revolutionary base|‐2.08|(‐4.31)***|||0.44|2.76***|||
|Ethnic minority region|‐1.29|(‐2.41)**|||0.13|0.47|||
|Impoverished region|‐0.11|‐0.68|||0.01|0.11|||
|Suburb of big city|0.41|1.04|||‐0.12|‐0.69|||
|Countyindex|‐0.27|(‐2.33)**|||‐0.14|‐1.21|||

Note:

1. Children>=1967;

2. "Klein and Vella‐1 Estimate" uses parents characteristics, location dummies and index of county fixed effect for heteroscadesicity; "Klein and Vella‐2 Estimate" uses father age, mother's age, coastal dummy, and topography for heteroscadesicity; 3. Parents' education level is a binary measure, it is equal to 1 if at least one of parents has more than primary schooling; children's education is measured by years of schooling; 4. "***", "**", "*" stands for significance of 1%, 5%, and 10%, respectively.

48