rafmacalaba/datause-encoder-data huggingface dataset:
- McKenzie, D. and Sansone, D. (2017). Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria. _World Bank Policy Research Working Paper 8271_ .

- Menzel, A. and Woodruff, C. (2021). Gender wage gaps and worker mobility: Evidence from the garment sector in Bangladesh. _Labour Economics_ , 71(May):102000.

- Mikolajczak, M., Brasseur, S., and Fantini-Hauwel, C. (2014). Measuring Intrapersonal and Enterpersonal EQ: The Short Profile of Emotional Competence (S-PEC). _Personality and Individual Differences_ , 65:42–46.

- Nanda, R. (2016). Financing High-potential Entrepreneurship. _IZA World of Labor_ , 252:1–10.

- O’Brien, P. (1984). Procedures for Comparing Samples with Multiple Endpoints. _Biometrics_ , 40(4):1079–1087.

- Rahman, L. and Rao, V. (2004). The Determinants of Gender Equity in India: Examining Dyson and Moore’s Thesis with New Data. _Population and Development Review_ , 30(2):239–268.

- Rotter, J. B. (1966). Generalized Expectancies for Internal versus External Control of Reinforcement. _Psychological Monographs: General and Applied_ , 80(1):1–27.

- Sabatier, M. (2015). A Women’s Boom in the Boardroom: Effects on Performance? _Applied Economics_ , 47(26):2717–2727.

- Schochet, P. Z. (2008). Technical Methods Report: Statistical Power for Regression Discontinuity Designs in Educational Evaluations. _NCEE Working Paper 2008-4026_ .

- Schwarzer, R. and Jerusalem, M. (1995). Generalized Self-efficacy Scale. In Weinman, J., Wright, S., and Johnston, M., editors, _Measures in Health Psychology: A User’s Portfolio. Causal and Control Belief_ , pages 35–37. NFER-NELSON, Windsor, England.

- The World Bank (2008). Whispers to Voices. Gender and Social Transformation in Bangladesh. _Bangladesh Development Series Paper No. 22_ .

- Uckat, H. and Woodruff, C. (2020). Learning How to Choose or Learning How to Lead? Experiments on Selecting and Training Female Managers in Bangladesh’s Garment Industry.

37
|P053892|2000|2002|5/16/2002|7 Closed|West Bank and Gaza|
|---|---|---|---|---|---|
|P053892|2000|2003|4/15/2003|9 Closed|West Bank and Gaza|
|P053892|2000|2004|5/4/2004|11 Closed|West Bank and Gaza|
|P053892|2000|2005|4/29/2005|13 Closed|West Bank and Gaza|
|P053985|1999|2000|6/29/2000|4 Closed|West Bank and Gaza|
|P053985|1999|2001|6/27/2001|6 Closed|West Bank and Gaza|
|P053985|1999|2002|5/9/2002|8 Closed|West Bank and Gaza|
|P053985|1999|2003|4/17/2003|10 Closed|West Bank and Gaza|
|P053985|1999|2004|11/11/2003|11 Closed|West Bank and Gaza|
|P054051|2001|2001|4/6/2001|2 Closed|West Bank and Gaza|
|P054051|2001|2002|4/22/2002|4 Closed|West Bank and Gaza|
|P054051|2001|2003|3/19/2003|6 Closed|West Bank and Gaza|
|P054051|2001|2004|3/12/2004|8 Closed|West Bank and Gaza|
|P054051|2001|2005|4/2/2005|10 Closed|West Bank and Gaza|
|P054051|2001|2006|12/19/2005|11 Closed|West Bank and Gaza|
|P054051|2001|2006|3/6/2006|12 Closed|West Bank and Gaza|
|P054051|2001|2007|12/28/2006|13 Closed|West Bank and Gaza|
|P054051|2001|2007|6/22/2007|14 Closed|West Bank and Gaza|
|P054051|2001|2008|12/17/2007|15 Closed|West Bank and Gaza|
|P054051|2001|2008|6/24/2008|16 Closed|West Bank and Gaza|
|P054051|2001|2009|6/4/2009|17 Closed|West Bank and Gaza|
|P054051|2001|2020|1/2/2020|18 Closed|West Bank and Gaza|
|P054119|2003|2003||0 Closed|Brazil|
|P054119|2003|2004|6/9/2004|2 Closed|Brazil|
|P054119|2003|2005|4/26/2005|4 Closed|Brazil|
|P054119|2003|2005|6/24/2005|5 Closed|Brazil|
|P054119|2003|2006|12/29/2005|6 Closed|Brazil|
|P054119|2003|2006|2/8/2006|7 Closed|Brazil|
|P054119|2003|2007|12/27/2006|8 Closed|Brazil|
|P054119|2003|2007|6/13/2007|9 Closed|Brazil|
|P054119|2003|2008|12/14/2007|10 Closed|Brazil|
|P054119|2003|2008|6/26/2008|11 Closed|Brazil|
|P054119|2003|2009|12/29/2008|12 Closed|Brazil|
|P054119|2003|2009|6/23/2009|13 Closed|Brazil|
|P054119|2003|2010|12/29/2009|14 Closed|Brazil|
|P054120|1999|1999||2 Closed|Brazil|
|P054120|1999|2000|6/1/2000|4 Closed|Brazil|
|P054120|1999|2001|6/12/2001|6 Closed|Brazil|
|P054120|1999|2002|6/4/2002|8 Closed|Brazil|
|P054120|1999|2003|6/4/2003|10 Closed|Brazil|
|P054120|1999|2020|4/5/2020|11 Closed|Brazil|
|P054125|1999|2000||0 Closed|Colombia|
|P054125|1999|2001|6/27/2001|2 Closed|Colombia|
|P054125|1999|2002|6/14/2002|4 Closed|Colombia|
|P054125|1999|2003|6/16/2003|6 Closed|Colombia|
|P054125|1999|2004|6/18/2004|8 Closed|Colombia|
|P054125|1999|2005|5/14/2005|10 Closed|Colombia|
or by other means. An efficient registry is characterized by having a single electronic database with all information about existing security interests in movable property, accessible to the general public for a reasonable fee in real time.

- **Enforcement:** the process for enforcing a claim against movable property when the debtor defaults on the secured obligation. Enforcement mechanisms should include the possibility of enforcing out of court.

# **None of the MENA countries have introduced secured transactions legal reforms, and none**

**of them have developed modern movable collateral registries** . Most of the countries have obsolete and fragmented secured transactions provisions governed by different laws that have not been reformed in many years. The MENA region ranks last in the cross-region comparison for Legal Rights Index, and the MENA country that scores best is in position 106 overall. All of these factors have a considerable impact on access to credit for firms, especially SMEs, that are deprived of using the majority of their valuable movable assets to secure credit.

# **It is recommended that reforms in the MENA region be undertaken to improve the secured**

**transactions system** focusing on: (i) improving the legal and regulatory frameworks for secured transactions based on standard international accepted practices; (ii) developing modern electronic collateral registries, and; (iii) creating awareness about the concept of modern secured transactions among the main public and private sector stakeholders. Given the complexity of providing an overview of the secured transactions system in each country limiting the paper to a manageable size, the report focuses on general principles and how they are applied in the region. Clearly, a future reform of this area in a specific MENA jurisdiction would require a more substantive analysis of the current state of things with regard to each of these principles.

**When revising secured transactions frameworks, countries in MENA should take into account the main principles for an effective system.** The region has a unique opportunity to reform some of the obsolete concepts on secured financing that originate in unreformed civil and commercial codes that have been modeled from one country to the other and therefore share a lot of similarities. Based on this, it would also be highly recommended to undertake reforms in this area using a standardized approach when it comes to the main principles, so that cross financing secured with movables in the region can properly develop. Finally, the more appropriate way to address the reform would be by **creating a specific law on secured transactions that regulates every aspect of a secured transactions system, moving away from the current system of fragmented provisions in different bodies of the law (Civil Codes, Commercial Codes, Mortgage Laws, Debt Recovery, etc).** A number of countries with both civil (Romania, Albania, Cambodia, China, Bosnia) and common law (Ghana, India, Australia, New Zealand) systems have introduced very successful reforms in this area following the modern secured

34
# **Table 3: Household statistics**

|Explanatory variables<br>(standard deviation in parentheses)|**Burkina Faso**<br>2010<br>Nmax=553|**Rwanda**<br>2011<br>Nmax=1,008|**Senegal**<br>2011<br>Nmax=361|
|---|---|---|---|
|_Household_||||
|Number of household members|8.3 (4.1)|5.0 (2.0)|12.2 (4.5)|
|_Household head_||||
|Female|<1%|15%|5%|
|Age|44.0 (13.2)|41.3 (13.5)|52.3 (13.9)|
|Years of education|1.7 (2.7)|5.1 (3.8)|6.1 (6.5)|
|_Financial situation_||||
|Household has a bank account|9%|54%|12%|
|Received a loan in last years|38%|20%|51%|
|—Formal loan|1%|15%|14%|
|—Informal loan|36%|5%|41%|
|_Expenditures_||||
|in constant 2011 USD|152 (248)|109 (184)|195 (238)|
|per capita, in constant 2011 USD|18 (19)|24 (41)|17 (22)|
|in constant 2011 international $ (PPP)|359 (587)|236 (398)|413 (504)|
|per capita, in constant 2011 international $||||
|(PPP)|43 (46)|51 (88)|36 (48)|

_Notes_ :<sup>1</sup> improved building material refers to brick, stones, concrete, or tiles.<sup>2</sup> Improved roofing material refers mainly to zinc (Senegal) and iron sheets (Burkina Faso).

_Sources:_ Senegal (2011), Burkina Faso (2010), Rwanda (2011).

Table 3 presents descriptive household statistics. Senegalese households are the largest with more than 12 members on average, followed by Burkina Faso (eight members), and Rwanda (five members). Most households are headed by men, with the share of female head of households higher in Rwanda (15 percent) than in the other two countries. Senegalese and Rwandan households tend to have slightly higher educational attainment (6 and 5 years, on average), compared to households in Burkina Faso (2 years).

Bank accounts are held by less than 20 percent of Senegalese and Burkinabe 16
The second half of Table 4 examines differences in remitting patterns by gender for the group of migrants who migrate without their spouse. This is a more common occurrence for male migrants than females. Thus male migrants are 64 percent of total migrants in these six surveys, but are 87 percent of the migrants with a spouse outside the country. Among those in this category, men remit a lot more than women. Men with a spouse outside the country remit an average of $3,879, while women with a spouse outside the country remit $771, which amounts to a statistically significant difference of $3,108.

It is important to emphasize that these are merely descriptive results – we can refute the traditional claim that women remit more than men. However, we are not claiming that gender alone is the reason men remit more than women. There are likely to be differences in who self-selects into migration between men and women, and the differences in observed and unobserved characteristics of men and women will then lead to different remitting patterns.

# **_4.2 Semi-parametric remittance patterns_**

The other three key relationships of interest which we wish to examine in more detail are the association between remittance behavior and time abroad, education, and income. Each of these is a continuous variable, and it is of interest to flexibly examine the association between remittance behavior and this characteristic, whilst still controlling for other important factors such as the destination country survey and other basic migrant characteristics. This gives a much richer view of the patterns in the data, and shows where the data is more informative.

To do this, we use semi-parametric regression to estimate the partial linear model: where _y_ is the remittance measure, _x_ is the migrant characteristic of interest, _f(.)_ is an unknown function and _z_ is a vector of controls which enter linearly. We always include dataset fixed effects in _z_ , and then examine how the relationship _f(.)_ changes when we include further demographic controls. To obtain consistent estimates of _f(.)_ and _β_ , we use the following procedure (Robinson 1988): 17
year up to 2050 in line with the historical trends observed since 1960.<sup>4</sup> The more optimistic scenarios of Nelson et al. (2010) in terms of overall productivity growth and yields’ growth specific to maize, wheat, and cassava (exceeding 2 percent increases per year) in developing countries further confirm a favorable ending to the challenge.

The increase in returns, along with (more modest) improvements in land use and manpower<sup>5</sup> have made it possible to address the global aggregate demand to feed a population that has been growing at an accelerated pace over the last four decades (Southgate 2009). With regard to land use, it is interesting to note that worldwide expansion of area devoted to grain production was actually rather modest, from 648 million hectares in 1961 to 676 million in 2001, whereas the area used for the cultivation of fruit, vegetables, and oils nearly doubled during the same period. Tilman et al. (2011) estimate that if yields in developing countries’ croplands increased to levels close to those of high-yielding nations, the future global demand for land expansion would be still be relatively modest (less than 200 million hectares by 2050) compared to the scenario with current productivity gaps between developed and developing countries (with a required additional billion hectares of land to be cleared).<sup>6</sup> Available data on current and future caloric intake support this claim. At the start of the new century, agricultural production was guaranteeing 17 percent more calories per person than 30 years earlier, despite a 70 percent increase in population.<sup>7</sup> FAO calculations indicate that this increase is sufficient to ensure a daily intake of 2,720 kcal per person (FAO 2012a). In a similar line, Tilman et al. (2011) forecast that global demand for food calories would increase by 100 percent through 2050. Nelson et al. (2010) report a range of caloric availability for the developing world in excess of 3,000 and below 2,400 kcal per person depending on assumptions 7
Policy Research Working Paper 9279

# **Abstract**

The Korean manufacturing sector has undergone active structural transformation in the past few decades. In particular, the composition of core manufacturing products has changed over time. In the 1970s, textiles, which are used to produce fabric, clothes, apparel, and shoes, were the main product. Over time, the value added shares have shifted toward electronics, ships, and cars. By analyzing plant-level microdata, this paper documents the patterns of entry, exit, job creation and destruction, and the growth of young plants during the industrial shift. This industrial shift involved active job reallocations, as well as the entry and exit of plants. The paper quantifies the extent to which such plant-level dynamics explain aggregate productivity growth. The findings show that within-plant productivity growth, which includes the effects of fast growth of young plants as well as robust growth of large continuing plants, played an important role in the productivity growth of the Korean manufacturing sector. The contribution of reallocations between continuing plants was relatively small. Moreover, productivity growth of an industry accompanied an increase of productivity dispersion, a measure commonly interpreted as the degree of misallocation.

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 author may be contacted at ylee@sogang.ac.kr.

_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
**Table 5:** Percentage of Job Flows in a Certain Size Class, Manufacturing, 1990s

||**Gros**|**s Job **|**Realloc**|**ation**||**Job Cr**|**eation**|||**Job D**|**estructio**|**n**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|_<_20|20-49|50-99|100+|_<_20|20-49|50-99|100+|_<_20|20-49|50-99|100+|
|Germany|0.344|0.136|0.098|0.422|0.307|0.141|0.110|0.442|0.399|0.135|0.088|0.378|
|Finland|0.199|0.093|0.073|0.635|0.205|0.088|0.065|0.642|0.201|0.099|0.083|0.618|
|France|0.258|0.156|0.109|0.477|0.227|0.139|0.105|0.530|0.286|0.175|0.113|0.426|
|United Kingdom|0.198|0.116|0.102|0.583|0.209|0.116|0.103|0.572|0.183|0.113|0.101|0.604|
|Italy|0.427|0.142|0.078|0.353|0.421|0.154|0.082|0.343|0.445|0.133|0.074|0.348|
|Portugal|0.306|0.193|0.137|0.364|0.335|0.197|0.132|0.337|0.289|0.186|0.138|0.386|
|United States|0.161|0.116|0.096|0.626|0.146|0.119|0.099|0.635|0.180|0.114|0.094|0.612|
|Argentina|0.331|0.164|0.115|0.389|0.318|0.174|0.123|0.385|0.346|0.155|0.108|0.392|
|Brazil|0.288|0.145|0.100|0.466|0.290|0.162|0.105|0.443|0.297|0.127|0.092|0.484|
|Chile|0.069|0.163|0.158|0.610|0.051|0.154|0.154|0.640|0.091|0.174|0.163|0.572|
|Colombia|0.126|0.172|0.163|0.538|0.095|0.160|0.161|0.585|0.162|0.186|0.165|0.487|
|Mexico|0.258|0.124|0.103|0.515|0.201|0.115|0.100|0.584|0.343|0.137|0.106|0.414|
|Estonia (1990s)|0.227|0.172|0.142|0.459|0.246|0.180|0.146|0.429|0.206|0.164|0.137|0.493|
|Hungary (1990s)|0.159|0.121|0.111|0.609|0.165|0.135|0.117|0.583|0.154|0.107|0.106|0.633|
|Latvia (1990s)|0.431|0.155|0.110|0.305|0.451|0.157|0.120|0.272|0.400|0.154|0.092|0.354|
|Slovenia (1990s)|0.100|0.072|0.100|0.728|0.146|0.091|0.102|0.661|0.069|0.058|0.102|0.771|
|Estonia (late 1990s)|0.227|0.172|0.142|0.459|0.246|0.180|0.146|0.429|0.206|0.164|0.137|0.493|
|Hungary (late 1990s)|0.172|0.128|0.109|0.591|0.177|0.136|0.111|0.576|0.169|0.119|0.108|0.604|
|Latvia (late 1990s)|0.453|0.146|0.107|0.293|0.467|0.147|0.120|0.265|0.434|0.146|0.085|0.336|
|Slovenia (late 1990s)|0.128|0.082|0.108|0.682|0.173|0.112|0.106|0.609|0.099|0.062|0.11|0.729|
|We do not observe frm<br>are excluded from the|s with<br> sample|less tha<br> in Fran|n 10 wor<br>ce.|kers in|Chile an|d Colo|mbia, an|d frms|with sa|les belo|w a given|threshold|

Source: Own calculations based on harmonized firm-level database.
Figure 8: Brazil: 2002–2005–2008 Within-Types Growth Incidence Curve <!-- Start of picture text --> 0 20 40 60 80 100 0 20 40 60 80 100<br>% type population % type population<br>within poorest types GIC ’02−’05 within poorest type gic ’05−’08<br>within richest types GIC ’02−’05 within richest type gic ’05−’08<br>avg. growth poorest types ’02−’05 avg. growth poorest type ’05−’08<br>avg. growth richest types ’02−’05 avg. growth richest type ’05−’08<br>0 8<br>−1<br>7<br>−2<br>% yearly growth % yearly growth6<br>−3<br>−4 5<br><!-- End of picture text --> _Authors’ calculation from PNAD (Instituto Brasileiro de Geografia e Estat ́ıstica)_ Table 5: Brazil: 2002–2005–2008 dominance conditions

|quantiles/types rank|GIC||type OGIC|c|um. type OGIC|i|ndividual OGI|C<br>cu|m. individual OG|IC|
|---|---|---|---|---|---|---|---|---|---|---|
|1|5.9040|***|29.4150|***|29.4150|***|9.7517|***|9.7517|***|
|2|6.3070|***|0.9296||13.9522|***|5.3240|***|7.3602|***|
|3|6.5042|***|10.7992|***|12.6996|***|5.4259|***|6.6773|***|
|4|6.6490|***|8.7660|**|11.5471|***|6.5167|***|6.6375|***|
|5|6.7888|***|18.3645|***|12.9442|***|8.3194|***|7.0596|***|
|6|6.9937|***|-1.1505||10.0393|***|6.6153|***|6.9932|***|
|7|6.6517|***|23.7151|***|12.5330|***|6.6142|***|6.9419|***|
|8|6.6463|***|9.3222|***|12.0397|***|6.6130|***|6.9018|***|
|9|6.6600|***|16.2690|***|12.5658|***|6.6119|***|6.8700|***|
|10|6.3123|***|15.8542|***|12.9423|***|6.8518|***|6.8707|***|
|11|6.2099|***|8.8956|***|12.4650|***|6.6791|***|6.8519|***|
|12|6.0596|***|5.1427||11.5463|***|5.3881|***|6.7002|***|
|13|6.0661|***|5.6651|***|10.8799|***|5.3695|***|6.5675|***|
|14|6.2796|***|6.6171|***|10.4224|***|5.2087|***|6.4396|***|
|15|5.7342|***|5.4429|***|9.8781|***|5.2070|***|6.3336|***|

_Source: Authors’ calculations on PNAD (Instituto Brasileiro de Geografia e Estat ́ıstica)._ *=90%, **=95%, ***=99% are significance levels for the difference between curves obtained by 1,000 bootstrap replications of the statistics.

33
# **Appendix B: Data Definition and Sources**

INFLATION ≡ CPI inflation rate obtained from source b (line 64).

GAP ≡ Deviation of the real GDP growth from its trend, which is computed using Hodrick-Prescott filter with a smoothness parameter of 100. Annual real GDP growth figures are taken from source a.

FP ≡ Percentage change in the index of food production. Data on food production index is taken from source a.

OIL ≡ Percentage change in dollar price of oil obtained from source d.

DEMOC ≡ The democracy indicator. It is an additive eleven-point scale (0-10). The operational indicator of democracy is a weighted average of the scores of the competitiveness of political participation, the openness and competitiveness of executive recruitment and constraints on the chief executive, taken from source c.

DURABLE ≡ Regime durability. The number of years since the most recent regime change. The first year during which a new regime is established is set as baseline year and DURABLE is assigned the value of zero for that year. Each subsequent year adds one to the value of the variable, taken from source c.

BS ≡ Overall government balance expressed as percentage of gross domestic product, taken from source a.

CF ≡ Ratio of net private capital flows to gross domestic product, taken from source a.<sup>26</sup> The data set covering the period 1980-2001 was compiled from the following<sup>27</sup> sources:

- a. World Development Indicators, World Bank; b. International Financial Statistics CD-ROM of the IMF, Version 1.1.54;

- c. Polity IV Database; d. Federal Reserve Economic Data (FRED);

- e. IMF Country Staff Reports.

> 26 For Israel and South Africa the series are constructed by taking the ratio of the financial account—obtained from source b (line 78bjd)—to nominal GDP measured in USD, taken from source a. For South Africa, the financial account is proxied by the Net Errors and Omissions taken from source b (line 78cad) for the years 1980-1984.

> 27 Without undermining the integrity of our data set,  we had to resort to source e for the missing data points for the BS variable in few cases.

23
# **ACRONYMS AND ABBREVIATIONS**

|ADAPT|ADAPTation to changing environments in river basins|
|---|---|
|ADDS|Africa Data Dissemination Service|
|CRU|Climate Research Unit|
|EDC|European Data Centre|
|FAO|Food and Agriculture Organization|
|FRIEND|Flow Regimes from International Experimental and Network Data|
|GEF|Global Environment Facility|
|GRDC|Global Runoff Data Centre|
|GRID|Global Resources Information Database|
|IIASA|International Institute for Applied Systems Analysis|
|IGBP|International Geosphere-Biosphere Program|
|IPCC|Intergovernmental Panel on Climate Change|
|IWMI|International Water Management Institute|
|NASA|National Aeronautics and Space Administration|
|NOAA|National Oceanic and Atmospheric Administration|
|STN-30p|Simulated Topological Network (30-minute spatial resolution)|
|TRMM|Tropical Rainfall Measuring Mission|
|UNESCO|United Nations Educational Scientific and Cultural Organization|
|UNH|University of New Hampshire|
|USAID|United States Agency for International Development|
|USGS|United States Geological Survey|
|WMO|World Meteorological Organization|
|WBM|Water Balance Model|

4