rafmacalaba/datause-encoder-data huggingface dataset:
# ACKNOWLEDGEMENTS

A number of persons provided us with important information for<sup>this work or assisted</sup> us in **preing** it in other ways. The Estimates and Projections Section of<sup>the U.N. Population</sup> Division has bw continually supportive in providing us with<sup>data, even before their publication as</sup> U.N. assossmC.L.. Partly as a result, nonessential differences<sup>between these projections and the</sup> biennial U.N. ixssessments have been minimized. We<sup>are grateful to the section chief, Larry</sup> Heligman, and to his continually accommodating staff, as well as to the<sup>head of the Division,</sup> Shunichi Inoue. We hope that the collaboration will continue.

Leo Morris, chief of the Behavioral Epidemiology and Demographic<sup>Research Branch</sup> of the Division of Reproductive Health at the Centers<sup>for Disease Control, kindly provided us with</sup> preliminary data from the most recent contraceptive prevalence<sup>surveys. World Bank country</sup> economists, demographers, and population specialists in the operating<sup>divisions have also provided</sup> US with reoent population estimates, bringing to our attention<sup>government reports we might not</sup> otherwse see.

Toe exercise was carried out using a personal computer program,<sup>Proj3L, written by</sup> KeLneth Hill of Johns Hopkins University. A documented<sup>version of this program on 5.25" diskettes</sup> was issued in late 1990 as a World Bank publication, _Proj3S: A Computer_<sup>_Programfor Population_</sup> **_Projections:_** _Diskenes and_ **_Reference_** _Guide._ Both the Population, Health, and Nutrition Division and the Population<sup>and Human</sup> Resources Department have been supportive of this work, despite the<sup>time required. We are grateful</sup> to the division chief, Dr. Anthony R. Measham, and the department<sup>diiector, Ann 0. Hamilton.</sup> We also wish to acknowledge the in\erest of the Bank's International<sup>Economics</sup> Socioeconomic Data Division (IECSE), which utilizes these<sup>figures--for such purtposes as estimating</sup> per capita GNP--and incorporates them into the databases it maintains<sup>and publications it distributes,</sup> and also helps coordinate communications with country economists.

In producing tht manuscript, Coni Benedicto was responsible for coordinating<sup>the paper</sup> flow.

Eduard Bos Patience W. Stephens MyT. Vu Rodolfo A. Bu!atao vii
Table 3: Rainfall Shocks, Agricultural Productivity and Non-farm Employment

|||All<br>|Lo<br>Firms<br>|g (Employmen<br>|t)<br> <br>|Large Firms<br>|<br>|
|---|---|---|---|---|---|---|---|
||Manufac-<br>turing<br>(1)|Services<br>(2)|Total<br>Non-Farm<br>(3)|Log(Agri.<br>Employm.)<br>(4)|Manufac-<br>turing<br>(5)|Services<br>(6)|Total<br>Non-farm<br>(7)|
|**Small Towns**||||||||
|**Sample**||||||||
|Rainfall Deviation|0.656**|0.310***|0.385***|-0.126|2.680***|2.875***|3.113***|
||(2.298)|(3.168)|(4.979)|(-1.064)|(2.812)|(3.080)|(3.098)|
|Observations|464|464|464|464|696|696|696|
|Number of Upazilas|232|232|232|232|232|232|232|
|**Rural Sample**||||||||
|Rainfall Deviation|1.373***|0.246**|0.337***|-0.0444|0.309|1.501**|1.064|
||(4.087)|(2.138)|(3.135)|(-0.868)|(0.386)|(2.016)|(1.449)|
|Observations|464|464|464|464|696|696|696|
|Number of Upazilas|232|232|232|232|232|232|232|
|Year & Upazila||||||||
|Fixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Area Characteristics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Note: Rainfall Deviation is defined as the difference between log(current rainfall) and log(30 year average rainfall). The regressions include a number of controls (log of travel time to Capital and/or Port cities interacted with time trend, flood prone dummy interacted with time trend, proportion of household with electricity, log of 1991 population interacted with trend, log of total active labor force, and proportion of labor force with above secondary education, share of urban households. All regressions include year and upazila fixed effects. Standard errors are clustered at upazila level. Robust t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1
In later specifications, this variable is replaced with a dummy variable indicating if the household suffered an unemployment shock, defined as the main earner becoming unemployed.

is a vector of individual and household characteristics that includes individual _i_ ’s gender, employment status.<sup>14</sup> In order to control for differences in potential access to credit, _X_ also includes controls for the income quintile of the household in _q-4_ (that is, before the shock), and age, gender, and years of education of the household’s main earner. The coefficients of the model are derived by maximum partial likelihood estimation. Duration models are estimated separately for the three countries, and also by whether the student was enrolled in secondary school or post-secondary school in time _q-4_ .

The specifications also include secondary and tertiary school completion indicator variables ( to distinguish between dropouts and exits from school associated with completion of the program. This indicator takes the value of 1 if the individual’s educational attainment changed from secondary incomplete (in _q_ -4) to secondary complete (in _q_ ), or from some tertiary (in _q_ -4) to tertiary complete ( _q_ -4). However, this information is unavailable in the Mexican data, hence results are reported without the completion indicators for Brazil and Argentina as well (Table A2 in the Appendix).

_Figure 6. Distribution of household shocks by income decile_ _Source:_ Authors’ tabulations using LABLAC (CEDLAS and the World Bank).

_Note:_ Figure A presents the percentage of households in which the main earner experienced a negative income shock between period _q-4_ and _q,_ defined as a reduction in his/her labor income of 25% or more, by income decile defined using the full urban household sample. Figure B presents the percentage of households in which the main earner experiences an unemployment shock between period _q-4_ and _q,_ defined as transitioning from employment into unemployment status, by income decile defined using the full urban household sample. Estimations were calculated pooling all the data over the period 2005-2015.

14 Specifications including the number of siblings 25 and under in the household, number of household members employed, and the main earner’s share of household labor income were also run, but these variables were not found to be significant. Similarly, _R_ , a vector of local returns to education measured as the secondary and tertiary education wage premium (for individual _i_ ’s gender and region r) was included in alternative specifications but found to not have explanatory power.

16
**Figure 3: Real Per Capita GDP Growth**

<!-- Start of picture text --> 6.0% - E Rich Countries<br>O Globalizers<br>s 50% / - Non-Globalizers<br>0<br>(L 4.0%-<br>.X3.0%-<br>Cu<br>2.0%-<br>(L<br>1.0%<br>0.0%<br>1960s 1970s 1980s 1990s<br><!-- End of picture text --> Note: Rich countries refers to the 24 OECD economies before recent expansions, plus Chile, Hong Kong, Korea, Taiwan, and Singapore. Globalizers refers to the top one-third in terms of their gowth in trade relative to GDP between 1975-79 and 1995-97 of a group of 72 developing countries for which we have data on trade as a share of GDP in constant local currency units since the mid- 1970s. Non-globalizers refers to the remaining developing countries in this group. Averages decadal and are population-weighted. Unweighted averages and alternative definitions of globalizers are reported in Table 3. Variable definitions and data sources are reported in Table 8.

30
represented 25.6 percent of the EAP, but only 12.9 percent of our sample; at the other extreme, workers who had at least completed high school were 36.0 percent of the EAP, but 47.3 percent of our sample.

Descriptive statistics for the sample of workers are presented. Average age increases by about four years over the 20-year span. Formal employees are older than informal ones, but the differences are minor, and they narrow over time; they also work slightly more hours than informal ones (but the differences are small). The share of women in the sample increases, reflecting higher female participation rates; over time, the share of women in informal employment increases. Finally, formal employees have more years of schooling than informal ones (Table 6).

**Table 6. Characteristics of the Sample of Workers, 1996–2015**

||**_All_**|**_1996_**<br>**_Formal_**|**_Informal_**|**_All_**|**_2006_**<br>**_Formal_**|**_Informal_**|**_All_**|**_2015_**<br>**_Formal_**|**_Informal_**|
|---|---|---|---|---|---|---|---|---|---|
|**Mean age, years**|32.17|32.69|30.96|34.58|35.13|33.29|36.46|36.62|36.04|
|**Median age**|30|31|28|33|34|31|35|36|34|
|**Hours worked**|42.24|42.41|41.85|42.48|42.83|41.66|42.79|43.16|41.84|
|**Years of schooling**|9.85|10.30|8.82|10.76|11.39|9.31|11.51|12.14|9.93|
|**Share of women**|39.68|39.79|39.39|44.82|44.14|46.41|45.11|43.50|49.03|
|**Formality rate**||70.1|||70.0|||71.6||
|**_Education shares_**||||||||||
|**Incomplete primary**|8.02|5.66|13.55|5.22|3.35|9.59|2.70|1.33|6.18|
|**Complete primary**|19.67|18.10|23.35|13.62|11.00|19.77|10.22|7.07|18.18|
|**Incomplete junior high**|5.23|4.53|6.85|3.65|2.73|5.79|2.30|1.55|4.21|
|**Complete junior high**|20.06|19.80|20.67|24.85|23.34|28.39|24.90|23.41|28.67|
|**Incomplete senior high**|19.33|21.44|14.38|15.57|17.04|12.13|12.44|13.06|10.88|
|**Complete senior high**|9.45|9.86|8.50|13.93|15.48|10.30|18.23|19.32|15.48|
|**University**|18.23|20.60|12.69|23.16|27.06|14.02|29.20|34.26|16.40|

_Source:_ Author’s computations based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

# **Earnings and Labor Informality Are Basically Constant**

18
# **Table A.4.3 Colombia: Innovation Inputs**

||**Innovation**|**R&D**|
|---|---|---|
|Constant|-0.75|-2.18|
|**Firms characteristics**|||
|Firm size (ln #employees)|**0.47**|**0.20**|
|Capital location<br>|**-0.15**|-0.04|
|**Human capital**|||
|Share of employee with tertiary|**0.30**|**0.36**|
|Share of employee with secondary|**0.15**|-0.09|
|**Sector**|||
|Food|Ref|Ref|
|Tobacco|N/A|**1.34**|
|Textile|**-0.32**|**-0.50**|
|Fur|**-0.30**|**-0.72**|
|Leather|0.11|-0.10|
|Wood|**-0.45**|-0.01|
|Paper production|0.08|-0.14|
|Printing|-0.09|**-0.57**|
|Fuel|0.22|**_0.53_**|
|Chemicals|0.04|**0.38**|
|Rubber|**-0.16**|**-0.25**|
|Other minerals|-0.14|0.12|
|Basic metal|-0.05|**_-0.41_**|
|Fabricated metal|-0.10|**-0.43**|
|Machinery|0.05|-0.02|
|Office machinery|N/A|N/A|
|Electronic machinery|0.07|-0.15|
|Radio TV|-0.10|0.33|
|Medical|0.14|0.28|
|Motor|0.04|0.08|
|Other transport|-0.30|-0.11|
|Furniture|-0.12|**-0.37**|
|Recycling|N/A|N/A|
|Observations<br><br>|6208|6212|
|Log likelihood<br>(<br>)<br>2<br>χ|-2756.78|-1394.62|
|2<br>_R_ (Pseudo)|0.13|0.10|
|**Test**|||
|Hosmer-Lemeshow chi2(8)|26.29 [0.0009]|3.13[0.9258]|
|area under ROC curve|0.75|0.74|

31
Policy Research Working Paper 6477

# **Abstract**

Using firm-level surveys for up to 73 countries, this paper explores the impact of introducing collateral registries for movable assets on firms’ access to bank finance. It compares firms’ access to bank finance in seven countries that introduced collateral registries for movable assets against three control groups: firms in all countries that did not introduce a registry, firms in a sample of countries matched by location and income per capita to the countries that introduced registries for movable assets, and firms in countries that undertook other types of collateral reforms but did not set up registries for movable assets. Overall, the analysis finds that introducing collateral registries for movable assets increases firms’ access to bank finance. There is also some evidence that this effect is larger among smaller firms.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at mmartinezperia@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
-5-

the income distribution and thus fits poorly to the actual data.

In the present paper we used a general interpolation device proposed by Kakwani (1980). This method utilizes, within<sup>each expenditure range, a</sup> separate, continuous differentiable function which exactly fits to the data points. The inequality and poverty measures<sup>are then coniputed</sup> by linking this function. We used a polynomial of the third<sup>degree to represent the Lorenz</sup> curve within each income class, except the<sup>first and the last open-ended</sup> classes. For the first and last expenditure<sup>ranges, a Pareto curve is used as</sup> a further refinement.4/ In this paper we used per capita household expenditure<sup>as a measure</sup> of household economic welfare.5/ Expenditure<sup>comorises all expenditures</sup> incurred by the household exclucively on domestic<sup>account including</sup> consumption out of home-grown produce or out of<sup>transfer receipts like gifts,</sup> loans, etc. The expenditure on household enterprises<sup>is excluded from</sup> consumer expenditure. Consumption out of home-grown<sup>produce is evaluated at</sup>

- a,/ It is not clear from the NSS whether the food given to<sup>poorer households</sup> (often permanent laborers) by richer households (large<sup>rural landowners</sup> and employers) gets "counted into" the consumption<sup>of the richer</sup> households. If this is so, the estimates of poverty<sup>and inequality will</sup> be overestimated. The fitting of the Pareto curve<sup>for the first and last</sup> expenditure ranges does not remedy this situation.<sup>The curve is used</sup> only as an interpolation device because information on individual households is not available.

- A better measure of household welfare will of course<sup>be the per</sup> equivalent adult consumption which corrects<sup>for the differing needs of</sup> adults and children. But this measure could<sup>not be employed because the</sup> NSS data were available only in grouped form<sup>(the groups formed on the</sup> basis of per capita household expenditure).<sup>We could have remedied this</sup> only by asuming that the ranking of households<sup>by per capita consumption</sup> is the same as that by consumption per equivalent<sup>adult. This assumption</sup> which is unlikely to hold will result in more<sup>serious estimation errors.</sup>
# **Table 5: A reversal of South-South migration could reduce the size of LMI economies by 0.14 percent by 2030**

Percent difference from baseline; difference in global GDP (last row) in US$ billion (2007 prices); <u>percent difference in low and middle-income country GDP per capita (last row) (US$ 2007 prices)</u>

|||**Real GDP**||**Real G**|**DP per cap**|**ita**|**P**|**opulation**||
|---|---|---|---|---|---|---|---|---|---|
||**2020**|**2025**|**2030**|**2020**|**2025**|**2030**|**2020**|**2025**|**2030**|
|Bangladesh|0.2|0.5|0.5|-0.8|-1.5|-1.5|1|2|2|
|Brazil|0|0|0|0|0|0|0|0|0|
|Central Asia|-0.6|-1.5|-1.9|-0.5|-1.3|-1.6|-0.1|-0.2|-0.3|
|China|0|0|0|0|0|0|0|0.1|0.1|
|India|0|-0.1|-0.1|0|0|0|-0.1|-0.1|-0.1|
|Indonesia|0.1|0.1|0.1|-0.1|-0.2|-0.2|0.2|0.3|0.3|
|Low-Income||||||||||
|Sub-Saharan<br>|-0.1|-0.4|-0.5|-0.4|-0.9|-1|0.2|0.5|0.5|
|Africa||||||||||
|Mexico|0|0|0|0|0|0|0|0|0|
|Middle-Income<br>Middle East and<br>North Africa<br>Other Former|0|-0.1|-0.1|0.1|0.1|0.1|-0.1|-0.2|-0.2|
|Soviet Union and<br>Other Eastern<br>Europe|0|0|0|-0.2|-0.5|-0.6|0.2|0.5|0.6|
|Other Latin<br>America and<br>Caribbean<br>Other Low-|-0.1|-0.2|-0.3|-0.1|-0.3|-0.3|0|0|0|
|Income South<br>and East Asia|-0.3|-0.8|-0.9|-0.5|-1.2|-1.4|0.2|0.5|0.5|
|Other Middle-<br>Income Asia|-0.3|-0.7|-0.8|0|-0.2|-0.3|-0.3|-0.5|-0.5|
|Other Middle-<br>Income Sub-|-0.3|-0.7|-0.8|0.1|0|-0.1|-0.3|-0.7|-0.7|
|Saharan Africa||||||||||
|Pakistan|-0.3|-0.7|-0.8|0.1|0|0|-0.3|-0.7|-0.7|
|South Africa|-0.2|-0.5|-0.5|0.2|0.3|0.2|-0.4|-0.7|-0.7|
|Turkey|0|-0.1|-0.1|0|0.1|0.1|-0.1|-0.1|-0.1|
|**World**|-0.01|-0.04|-0.06|-0.01|-0.04|-0.06|0.0|0.0|0.0|
|**World GDP**|-10.3|-39.7|-57.9|-0.001|-0.005|-0.006||||
|**LMI**|-0.04|-0.12|-0.14|||||||

Source: Authors’ simulations Table 6 shows the PPP-adjusted difference from the baseline in 2030 in real income for return migrants, native non-migrant workers in the home region and remaining LMI-LMI migrants by region of origin. Native non-migrants in the home region are generally worse off, and this is primarily due to the loss of remittance income. The equilibrium impact on real wages for this group is a priori ambiguous, as the inflow of return migrants exerts downward pressure on the real wage while in regions with a net outflow of migrants the decrease in the total labor supply entails an 22
# 1 Introduction

In recent years Brazil has achieved much success in reducing poverty and income inequality. According to World Bank data, relative poverty (based on a PPP US$2 per day metric) has fallen markedly, from 21 percent of the population in 2003 to 11 percent in 2009. Extreme poverty (based on a PPP US$1.25 per day metric) also dropped significantly, from 9.8 percent in 2004 to 6.1 percent in 2009. At the same time, income inequality fell significantly. Between 2001 and 2009, the income growth rate of the poorest decile of the population was 7 percent per year, while that of the richest decile was 1.7 percent; as a result, income inequality (as measured by the Gini index) fell markedly, from 0.594 in 2001 to 0.521 in 2011–a 50-year low. Key drivers of these achievements have been low inflation, sustained economic growth (at a yearly average of 4.0 percent during 2002-08 and 5.1 percent during 2010-11), well-focused social programs, and real increases in the statutory minimum wage.

Despite these achievements, however, inequality remains at relatively high levels, and there is still a large gap in access to education. Although universal coverage in primary education has been achieved, quality at both the basic and secondary levels remains a concern. Growth and private sector activity continue to be hampered by various barriers and regulations, as well as inadequate infrastructure and a weak business climate.<sup>1</sup> Gender inequality remains high, despite some significant improvements. In 2003, under his first presidency, Luiz Inácio Lula da Silva created a federal governmental body with the objective of dealing with gender equality issues. This also led to the creation of a National Plan for Women’s Policies (NPWP), which was adopted in 2004. The Plan reaffirmed the commitment by the Brazilian Federal Government and by the other governmental bodies to incorporate a gender perspective in public policies. The Plan foresaw several concrete actions to be implemented by different government sectors in cooperation with the private sector, and addressed some specific needs of mothers, specifically with reference to health care before and during pregnancy and at

> 1According to the World Economic Forum’s Global Competitiveness Report for 2012, Brazil ranks 64 out of 142 countries for the quality of its infrastructure (118 for roads, 122 for air transport, and 130 for ports). See Ter-Minassian (2012) for a perspective on Brazil’s current challenges in the area of structural reform.

3
Policy Research Working Paper 10224

# **Abstract**

Atmospheric methane is a potent greenhouse gas that has accounted for 23 percent of radiative forcing in the lower atmosphere since 1750. Since methane has a much shorter atmospheric duration than carbon dioxide and other greenhouse gases, it provides a critical opportunity for near-term atmospheric greenhouse gas reduction. Thus, 122 countries have joined the recently launched Global Methane Pledge to reduce methane emissions at least 30 percent from 2020 levels by 2030. Unfortunately, the Pledge confronts a serious information problem at the outset: the near-total absence of directly measured data for problem diagnosis, program design, and performance assessment. At present, priority areas for emissions reduction are identified with spatially formatted “bottom-up” emissions inventories, such as the Emissions Database for Global Atmospheric Research, which combines sectoral activity data with broadly calibrated emissions factors from engineering studies. This paper addresses the information problem by introducing a new World Bank database of monthly atmospheric methane concentrations, calculated for a high-resolution spatial grid from data provided by the European Space Agency’s Sentinel-5P satellite platform. It illustrates the potential utility of the database with a global study of methane emissions from irrigated rice production, which accounts for about 10 percent of agricultural methane emissions. A comparative analysis suggests that the Sentinel-5P data supplement the Emissions Database for Global Atmospheric Research data with more fine-grained spatial information, which may support local programs to track, verify, and reward adoption of methane-reducing rice production techniques. If this approach proves valuable for irrigated rice production, it seems likely to work for other methane sources as well.

This paper is a product of the Development Research Group, Development Economics. 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 sdasgupta@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