rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 20<br>LN16<br>oe<br>ee<br>8 NN<br>8<br>6<br>A<br>2<br>Q<br><!-- End of picture text --> <!-- Start of picture text --> — Phone Faults Per 100<br>Stations Per Month<br><!-- End of picture text --> oN Mt MOR OOorAaA MYT HY DDAAAAAAHAWOWBDWBWOEVS CrAnnmtnwonRranowrannst a amnooo oo 7 77 TD CGCeFeS FC GB anaraaraanaraanaaranrnnececceec s att et ae ae aA SP SP ANN NN ON <!-- Start of picture text --> 6<br>5<br>A<br>3 —— Internet Subscribers.<br>(million)<br>2<br>1<br>Q<br>1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text -->
<!-- Start of picture text --> — IBRD 35979<br>| Sahara De : ye Le \ 2 2 |<br>AS| ower \h KadugliJ lz, Sal<br>ee<br>NORTH-SOUTHPROPOSED eeSeEQUATO —DUATORI ) rt| ea<br> BOUNDARY a Laie’<br>@ NATIONAL CAPITAL | | x<br>——— ABC (Abysi Boundaries Commission) BOUNDARY<br>="<br>= _ INTERNATIONAL BOUNDARIES ON Se ee es ee re ae<br><!-- End of picture text -->
**Figure 10:  Effect of Changing Weight on Health in the Human Capital Index**

<!-- Start of picture text --> Human Capital Index: Robustness to Weights<br>Corr=.998 Corr=.997<br>.2 .4 .6 .8 1 .2 .4 .6 .8 1<br>Baseline Weight on Health Baseline Weight on Health<br>Corr=.993<br>.2 .4 .6 .8 1<br>Baseline Weight on Health<br>Note: Dataset version 21 Sept 2018<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Low Weight on Health High Weight on Health<br>.2 .2<br>1<br>.8<br>.6<br>.4<br>.2<br>Equal Weight on Health and Education<br><!-- End of picture text --> Notes: This graph shows the effect of changing the weights on the health and education components of the HCI. In each panel the horizontal axis corresponds to the HCI with baseline weights. In the top‐left (top‐right) panel the vertical axis corresponds to the HCI assuming a low‐end (high‐end) estimate for the return to health from the empirical literature, as discussed in Appendix A3. The bottom‐left panel assumes a much larger value for the return to health that generates the same gap in productivity between best and worst performers as is observed between the best and worst performers in learning‐adjusted years of school. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for all countries.

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Between 1970 and 1985 there was clearly a process of convergence, where Mexico City experienced a sharp decline of per capita GDP compared to the Mexican average and the South could catch up. In a less extreme way the Border States’ growth lagged the national average, and the northern and southern central regions surpassed the average. After the trade liberalization reforms in the mid-eighties the convergence pattern radically changed. The Border States have gradually increased their share, whereas Mexico City increased its share after the signing of the GATT, but started to lose its share after the introduction of NAFTA. There has been convergence between Mexico City and the Border States and in 2006 the Border States for the first time had a larger GDP per capita than the metropolitan area of Mexico City. The poorest region, the south, experienced a gradually decreasing share, which supports the stylized fact that Mexico experienced divergence after 1985.

**Figure 4: Per capita GDP per region (national average=100)**

<!-- Start of picture text --> 180.00<br>160.00<br>140.00<br>Mexico City<br>120.00<br>Border<br>100.00 Northern central<br>Southern central<br>80.00<br>South<br>60.00<br>40.00<br>1970 1985 1993 2001 2006<br>Per capita GDP (national average=100)<br><!-- End of picture text --> Source: Calculations based on information from INEGI, excluding Campeche and Tabasco.

The southern central region experienced in 2006 the same weight in GDP per capita compared to the national average as in 1970. The northern central region didn’t undergo a change of its welfare from 1985 till 2001, but could increase its per capita GDP compared to the national average since then.

Another option to divide the Mexican states, other than the method used by Chiquiar, is by clusters as identified by Aroca, Bosch and Maloney (2005), who used kernel regressions to trace empirical patterns of regional growth in the form of several income clusters that only partially correspond to traditional geographic regions. A first cluster constitutes especially rich states, which are spatially very independent and located -in three different regions of the country, with Mexico City in the center, Nuevo Leon in the north and Quintana Roo (driven by Cancun related tourism) in the south. The second cluster mainly includes northern states and some successful central states. The third cluster finally includes all other states.

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Figure 5. Host Community Poverty Trends, by Geographic Region and Group All Host Community Households <!-- Start of picture text --> Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40% 44% 43% 41% 41% 40%<br>30% 30%<br>20% 21% 21% 19% 18% 18% 20%<br>10% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Low Educated Households (Head has no or primary level education)<br>Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50% 51.4% 48.3% 47.0% 48.3% 48.4%<br>40% 40%<br>30% 29.6% 28.7% 26.2% 25.1% 25.1% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Female Headed Households<br>Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40% 40% 37% 37% 36% 36%<br>30% 30%<br>20% 20%<br>10% 16% 17% 14% 13% 12% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Poverty Rate (%) Poverty Rate (%)<br>Poverty Rate (%) Poverty Rate (%)<br>Poverty Rate (%) Poverty Rate (%)<br><!-- End of picture text --> 24
group of 15-64) size, and _Lt_ is the labor force size such that and in growth terms

# **3 Analysis of Korea’s Economic Growth**<sup>5</sup>

## **3.1 Data**

Equations (9) and (12) are our framework accounting for Korea’s economic growth (which can be used for analyzing any country’s growth). To quantify these equations, we need the following data series for our sample period 1960-2014, and their sources are in brackets as follows: (1) total population size [World Development Indicators (WDI)] for _Nt_ , (2) working-age population share [WDI] for _SW,t_ , (3) labor force participation rate [WDI] for _SE,,t_ , (4) real GDP at constant 2011 national prices (in 2011 million US$) [”rgdpna” in Penn World Table version 9.0 (PWT 9.0)] for _Yt_ , (5) capital stock at constant 2011 national prices (in 2011 million US$) [”rkna” in PWT 9.0] for _Kt_ , (6) human capital per worker [”hc” in PWT 9.0] for _ht_ , (7) labor force size [WDI] for _Lt_ , (8) labor share [”labsh” in PWT 9.0] for _β_ , (9) capital depreciation rate [”delta” in PWT 9.0] for _δ_ , (10) labor-augmenting technology level [calculated from equation (4)], and (11) investment [calculated using investment rate data ”csh ~~i~~ ” from PWT 9.0].<sup>6</sup> The value of the average labor share which we calibrate for the parameter _β_ is 0 _._ 602. The value of the average depreciation rate which we calibrate for the parameter _δ_ is 0 _._ 053.

> 4We use labor force data from WDI for _Lt_ to maintain the consistency with the data use protocol of the LTGM project so that there are possible differences in labor force participation rate between the national sources and the WDI. Furthermore, using labor force instead of employment data may generate the different growth rate of _S_<sup>�</sup> _W,t_ . However, using the national source data, we find that labor force participation rate and employment rate tightly co-move with each other and the growth rates of _S_<sup>�</sup> _W,t_ between the two measures differ only by 0.1% for the sample period.

> 5Part of the analysis of this section is based on Jeong (2016).

> 6Original data source of the WDI labor variables such as working-age population, labor force participation rate is the International Labor Organization (ILO) Statistics. The labor share and the capital depreciation rate variables are time-varying in PWT 9.0 and we take the time-series averages during our sample period 1960-2014.

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Figure 2. The position curves: inequality in the world—by countries and by income class <!-- Start of picture text --> Germany<br>India<br>Brazil Sri Lanka<br>China<br>0 5 10 15 20<br>country ventile<br>100<br>80<br>60<br>40<br>20<br>percentile of world income distribution<br>0<br><!-- End of picture text --> Source: World Income Distribution (WYD); benchmark year 2002.

World income distribution can be conventionally broken down into that part of inequality which is due to the differences between mean country incomes, and that part of inequality due to inequality within countries. Using 2002 data, Table 2 shows that, depending on the inequality measure, between 66 and 87 percent of global inequality is due to differences in countries’ mean incomes. Taking the Gini coefficient, which is the most frequently used measure in global inequality studies, income differences between world citizens amount to 65.5 Gini points out of which 55.7 points are due to the between-country component.

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only variable trade costs, the types of costs captured by Doing Business include both fixed and variable components: for instance, export documentation needs to be agreed and drafted prior to any export activity taking place (fixed cost), but then needs to be copied and slightly adapted for each shipment (variable cost). We use the distance between the exporting country and Europe (dist.) as an indicator of the extent of symmetric trade costs (rT? = +#*®) such as international transport charges, while sectoral expenditures (3,E”) and technology <u>(¢</u> and k) are proxied by (respectively) GDP multiplied by a sectoral (2-digit) import share, and GDP per capita.'® We can therefore summarize the core contentions of the paper as derived in the previous section in terms of restrictions on the parameter space of the conditional mean function: 6; > 0; 61, 39, 83,84 <0 and |34| > |82|,|G3| > |85| >> [11

The model can be estimated using standard maximum likelihood techniques.'!? The Poisson estimator has at least three desirable properties that make it particularly useful in this context. First, it has identical first order conditions to those obtained by running weighted non-linear least squares on (21) with lines_cn2 in place of ,, (Gourieroux et al., 1984). Thus, the various ( coefficients can be interpreted as elasticities, but the model does not suffer from the usual limitation of log-log models in relation to zero observations: these can simply be included in the estimation sample as usual. Santos Silva and Tenreyro (2006) exploit this property as a means of dealing with zero trade flows in the gravity model context. Second, the Poisson model has been shown to produce consistent estimates under relatively weak assumptions. It is not even necessary that the dependent variable in fact follow a Poisson distribution, so long as the conditional mean function is correctly specified (Gourieroux et al., 1984; Wooldridge, 1997). Other count data models, such as the negative binomial (Klinger and Lederman, 2004 and 2006), do not have this property and require the data to follow a particular distribution in order to ensure consistency. Third, Poisson is one of relatively few non-linear maximum likelihood '6To construct our tariff measures and sectoral expenditure proxies, we use the reference group approach of Laborde et al. (Forthcoming), which relies on observed imports for a group of similar countries so as to avoid the endogeneity inherent in using a simple import weighted average. Little turns on this choice, however, since our results do not change substantially if aggregate GDP or simple average tariffs are used instead. (Results available on request.) '7See generally: Cameron and Trivedi (2001), Wooldridge (1997), and Winkelmann (2000).

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_Differences in Labor Statistics across Survey Assignment_ Table 4 presents the findings, disaggregated by gender, for labor force participation (LFP), weekly hours, and daily earnings. In each case we test for a difference in means across survey assignments using a t-test. Row 1 of Table 4, for instance, reports the LFP rate of men from the short module (82 percent) and from the detailed module (83 percent), and finds that the difference (about 1 percentage point) is not statistically different from zero. LFP is defined following ILO guidelines – that is, having engaged in any work for pay (as wage/salaried worker), profit (as employer, self-employed, own-account worker), or family gain (as paid or unpaid worker in a family farm or family business) in the last 7 days. Domestic duties (unpaid domestic activities) are not counted as labor force participation because they are not included in the SNAs.<sup>16</sup> We find that there is no difference in LFP rates between the short and detailed module for men, but there is a significant difference for women. LFP is about 7 percentage points lower when reported by the short module. The difference between the proxy and self-reported statistic is significant for both men and women: LFP is lower by 13 and 8 percentage points respectively for the proxy surveys. The short-proxy questionnaire combination yields significantly lower LFP rates than the other designs for both sexes, around 10 percentage points in both cases.

> 16 While unpaid domestic activities such as housework, child care giving, and house repair work are outside the boundaries of SNA, other activities such as fetching water and fuel are technically considered SNA activities but are routinely excluded from employment statistics because of a lack of data due to difficulties in collecting data on and valuing these activities.

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Policy Research Working Paper 9199

# **Abstract**

A strong regulatory framework can provide essential tools for remote transactions and improve trust in digital trade. Yet, regulations can also introduce restrictions that hamper the conditions for digital markets. Based on a database of 20 Middle East and North Africa countries and 20 comparator countries around the world, this paper shows that the Middle East and North Africa region is falling behind in establishing a modern governance framework for the digital economy. The analysis focuses on a set of regulatory areas, including electronic documentation and signature, online consumer protection, data governance, cybersecurity, and intermediary liability regulations. It assesses each country’s domestic regulatory framework in light of recent international trends and regulatory models. The study shows that regulation of digital markets in countries in the region is still in its infancy, being mostly governed by general laws that were not originally intended for the digital era. Some countries have tried to support an export-oriented information technology sector by keeping an updated regulatory framework. However, regulation in most countries in the region, regardless of their level of development, still features some major loopholes that can limit consumer trust in digital markets or reduce certainty—and increase costs—for digital businesses.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region and the Macroeconomics, Trade and Investment Global Practice. . It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mmolinuevo@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 1: Japan’s Manufacturing and Employment Ratios**

<!-- Start of picture text --> 30.00% 30.00%<br>Output<br>Employment<br>25.00% 25.00%<br>20.00% 20.00%<br>15.00% 15.00%<br>10.00% 10.00%<br>5.00% 5.00%<br>0.00% 0.00%<br>1990 1995 2000 2001<br>Percentage of Output over GDP<br>Percentage of Manufacturing Employment<br><!-- End of picture text --> _Source_ : Data for Employment from Statistical Survey Dept. Statistics Bureau, Ministry of Public Management, Home Affairs, Post and Telecommunications and data for Output is from Dept of National Accounts, Economic and Social Research Institute, Cabinet Office, Japan.

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12 Table 1 provides on overview of the definition and data source of all conflict exposure proxies discussed in the following.

# **a) Child and sibling mortality**

A first proxy that captures replacement effects is whether or not a woman lost a child during the genocide (CHILDDEATH). The RDHS questionnaires record child mortality in great detail and even ask for the month of death. This allows us to precisely code CHILDDEATH to take the value one if a woman lost one or more children between April and July 1994. Moreover, we differentiate child death by gender in two further conflict proxies: SONDEATH indicates the death of at least one son during the genocide; DAUGHTERDEATH the death of at least one daughter. Fig. 2 displays the occurrence of child deaths over time as calculated from the 2000 and 2005 RDHS. Child deaths peak during the 1994 genocide, although child mortality remains relatively high in the immediate post-war period. Given that many mothers would have been killed in the genocide at the same time as their (young) children, this proxy somewhat underestimates the effects of genocide on child mortality. We interpret this proxy as capturing also the negative effects of conflict on health, sanitation and nutrition, leading to (even) higher child mortality.

A second proxy indicating replacement effects uses sibling mortality during the genocide (SIBLINGDEATH). Sibling mortality was recorded in the 2000 and 2005 surveys, but not in the 1992 RDHS. Every prime age woman was asked about all of her siblings born to the same mother. Of every sibling, information is available on the sex, date of birth, whether the sibling is still living, year of death, and whether the death was related to pregnancy or childbirth. This information is recorded irrespective of whether or not the sibling lives in the same household as the respondent and thus provides a good geographical coverage of deaths occurring across the whole country. However, no information is available on the sibling’s place of living or the place of death. The occurrence of sibling deaths over time calculated from the 2000 and 2005 RDHS is displayed in Fig. 3. The graphs from both years exhibit one single and outstanding peak which coincides with the timing of the 1994 genocide. The peak of sibling deaths is somewhat less pronounced and spread over a slightly longer time period in the 2005 survey. We suggest that this is due to the imprecise way the time of death was captured
ownership data on savings accounts and consumer durables is not difficult to collect and coverage could be improved greatly with relatively minimal effort. More challenging will be the collection of individual data on livestock and non-agricultural enterprises, which are often activities in which multiple household members participate and where it is more difficult to disentangle individual ownership rights and practices. While difficult, the collection of information on pension wealth in addition to pension income should be prioritized.

Of the other multi-topic household surveys, the KIDS questionnaire and the IFPRI household survey for the Philippines are by far the most complete, collecting individual, and in most cases, joint ownership data for five of the six broad categories of assets (Table A3). The questionnaire for the Philippines stands out for its individual data on livestock; the South Africa KIDS survey, for its data on non-agricultural business assets. It should be noted that even the best practices reviewed herein are generally still insufficient for a detailed gender analysis of asset ownership.

# **5.  Incorporating Individual Level Asset Questions into Existing LSMS Questionnaires**

A number of the questions that are needed to both identify the gender asset gap and do gender analysis are already integrated in LSMS questionnaires. Yet, no questionnaire asks consistently about all assets, and most questions on assets do not disaggregate by individual. Instead of adding a separate individual level asset module to a survey, which might result in redundancies, it is possible to incorporate selected questions into existing questionnaires and vastly increase the amount of gender-disaggregated data available for analysis.

We demonstrate how this could be done within three different LSMS questionnaires: the 2000 Guatemala questionnaire, the 1997-98 Vietnam questionnaire and the 1998-99 Ghana questionnaire.<sup>28</sup> It may be useful for readers to reference the actual questionnaires when they read this section.

These examples demonstrate that it is very easy to add some simple questions to identify individual level ownership of many of the assets. Sometimes, just the coding would need to be changed to identify the individual level owner. Other times, an additional question or two is needed. Table 5 shows the minimum information that is needed to add to each questionnaire for determining the gender asset gap. Additional questions would be needed for the more nuanced analyses of acquisition and rights over the assets. None of these three surveys, for example, include questions on extraordinary losses, conflict over assets, marital and inheritance regimes, and knowledge of property rights.

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