rafmacalaba/datause-encoder-data huggingface dataset:
than for males for all three levels of educational attainment. This pattern is observed in both OECD and non-OECD countries.

# **2.2 The Gender Gap Reversal in Educational Attainment**

Several papers have reported a convergence followed by a reversal in the relative number of females attending tertiary education in the US (Charles and Luoh 2003; Goldin, Katz and Kuziemko 2006, Chiappori, Iyigun and Weiss 2009; Becker, Hubbard and Murphy 2010 _a_ ; Becker, Hubbard and Murphy 2010 _b_ ; Autor and Wasserman 2013).<sup>9</sup> In table II and Figure I, we look at the evolution of the female-to-male ratio in tertiary enrollment worldwide, by grouping country-level data into seven world regions.<sup>10</sup> As shown in Column 5 and 6 of Table II, young males in 1950 used to outnumber females among participants to tertiary education in virtually all countries in the sample (92%). In contrast, by 2010, 73% of countries in the sample had seen their gender imbalances in tertiary enrollment revert from a male majority to a female majority. Even among non-OECD countries, 66% had experienced the gender gap reversal in tertiary enrollment by 2010. In advanced economies, Europe and Central Asia and Latin America, the share of countries where females outnumber males among tertiary education students is over 90%. In contrast, most countries in South Asia and Sub-saharan Africa have yet to experience the gender gap reversal in tertiary enrollment.

When averaged over all countries in the sample, the Barro-Lee data show a reversal in the gender gap in tertiary enrollment, as reported in Panel A of Figure I. For the oldest cohorts born in the 1920s, the average female-to-male ratio in tertiary enrollment for all countries in the sample was around 0.40, meaning that for ten females enrolled in tertiary education, only four females were enrolled. This ratio was fairly similar across all regions of the world. In contrast, among the most recent cohorts, the average female-to-male ratio had reached 1.22 globally, meaning that there were 22% more females enrolled in tertiary education than males worldwide.

> 9Pekkarinen (2012) has also reported this phenomenon for Scandinavian countries. 10This regional grouping follows the World Bank classification of world regions and includes: advanced economies, Latin American and the Caribean, Europe and Central Asia, East-Asia an Pacific, the Middle-East and North Africa, South Asia and Sub-saharan Africa

7
Policy Research Working Paper 6733

# **Abstract**

Over the past two decades, international trade has become a privileged engine of growth for much of the developing world. With the global economy evolving continuously and rapidly, countries must pay close attention to their positioning on the map of global trade and production. Within this framework, countries must also become aware of how they fare relative to competitors and to their past export performance. Of particular importance is the extent to which their performance is driven by exporter own supply-side capacity as opposed to external or compositional factors, including product and geographical specialization and how these trends compare across countries. This paper describes a new initiative that uses quarterly data for 2005q1–2013q1 to compute comparable indicators of export performance for 228 countries and territories.

The database, the Export Competitiveness Database, reveals interesting patterns in trade performance. Export performance, stripped of compositional effects, was strongest for countries from the Asia and Pacific region, on average. Moreover, such performance was almost entirely driven by exporting country specific factors, with changes reflecting growth in volume rather than price developments. All emerging and developing regions have, on average, improved export performance. The indicators in the database trace the legacy of supply-side capacity and the overall export performance of the double-dip recession in the euro area. An illustrative set of results suggests that the paper’s measure of competitiveness correlates to a country’s nominal and real effective exchange rate, factors that are commonly perceived as important determinants of competitiveness.

This paper is a product of the International Trade Unit, Poverty Reduction and Economic Management Network. 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 corresponding author may be contacted at dtaglioni@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
<!-- Start of picture text --> Corn<br>N<br>me<br>8<br>F i<br>So a ss __<br>= ccna<br>2002 2003 2004 2005 2006 2007<br>Period<br>—?— Biofuel —?— income<br>——®— Exchange rate<br>——®— energy prices<br><!-- End of picture text -->
77

# PART **2.** IMPROVING THE EFFICIENCY OF CRISIS RESOLUTION

Part 1 reviewed the principal actions taken by governments in the three countries, assessed those actions, and suggested guidelines and principles that either were important to the governments' accomplishments or, if adopted, would have improved the results they achieved. Part 2 draws mainly on work undertaken by the World Bank in Korea in 1998 to develop the conceptual underpinnings for two fundamental improvements in crisis management techniques that might have substantially imnproved outcomes. One key improvement is to have the crisis management team develop an explicit, comprehensive crisis resolution strategy. The second improvement is to link financial restructuring more closely with corporate restructuring. A common theme in both recommendations is to improve the efficiency of government efforts by maximizing the impact of the public funds used in resolving a crisis.

## CHAPTER 9. DEVELOPING A COMPREHENSIVE STRATEGY

Resolving a financial crisis is similar to dealing with an urgent and complex business problem in the private sector. As it is for managers in the private sector, a key task for the crisis resolution team is to design and execute a strategy for solving the many problems posed by a cnsis.

Explicitly defining a coinprehensive strategy is essential because it allows the crisis management team to avoid working in a reactionary mode, merely responding to events. The resolution process is long and complex. Some problems can be solved in weeks, some can be solved in months, and still others may require several years to solve. The problems themselves are intertwined. For example, solutions to financial problems in banks have implications for debt workouts in the large corporate sector and vice versa. Steps taken in nationalizing banks have implications for the government's ability to privatize them. The manner in which nationalized banks are privatized has implications for government's ability to raise private capital to resolve other banks. Moreover, solutions to financial.problems have to be tempered by recognition of their political and social implications (for example, foreign entry or layoffs). Adopting a comprehensive strategy enables the crisis management team to integrate the various components
# **Annex I – Sensitivity to Model Selection Technique**

Sensitivity analysis was conducted with respect to the method of automated model selection used in the FH application. The main alternative assessed was the lasso technique implemented using the _lassopack_ suite of commands in Stata (Ahrens et al., 2018). In contrast to stepwise approaches which iteratively add variables to the model and assess the p-value of the indicator (keeping the variable in the model if it is below a certain threshold), the lasso approach minimizes the residual sum of squares subject to a constraint on the absolute size of coefficient estimates. There are thought to be two major advantages of lasso approaches over least squares. First the lasso tends to produce sparse solutions and thus facilitates model interpretation. Secondly, lasso can at times outperform least squares in terms of prediction due to lower variance. However, the stepwise models adopted in this paper were already quite sparse, and there were no great advantages observed in terms of minimizing the variance.

Each of three lasso options are contrasted with simple step-wise selection. In no case are the differences remarkable. In this context, stepwise was maintained as the preferred approach due to its greater replication simplicity and ease of explanation to non-expert audiences.

## **Figure 42: Lasso AIC vs. Stepwise**

<!-- Start of picture text --> 1<br>R2 = 0.9445<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br><!-- End of picture text -->
|**Table 3: Interacti**|**on with Insp **|**ections(log od**|**ds ratios)**||
|---|---|---|---|---|
|Dependent variable: Quality<br>Certificate Y:1 N:0|**(1)**|**(2)**|**(3)**|**(4)**|
|Informal Competition (CY avg.)|0.040|3.548**|4.044***|3.722**|
|*Inspections (CY avg.)|(1.039)|(1.534)|(1.482)|(1.559)|
|Informal Competition (CY avg.)|3.096***|3.918***|3.961***|8.649|
||(0.764)|(0.981)|(0.952)|(11.599)|
|Inspections (CY avg.)|-0.832|-4.198***|-4.532***|-4.272***|
||(0.948)|(1.336)|(1.230)|(1.311)|
|GDP per capita (logs)||2.701**|3.389***|3.972***|
|||(1.203)|(0.980)|(1.010)|
|Labor Productivity (logs)||0.481***|0.414***|0.405***|
|||(0.118)|(0.133)|(0.128)|
|Number of workers (logs)||1.194***|1.010***|0.093|
|||(0.189)|(0.186)|(0.840)|
|Age of firm (logs)||0.289**|0.347**|0.337**|
|||(0.140)|(0.142)|(0.140)|
|Manager experience (logs)||-0.298*|-0.265|-0.264|
|||(0.174)|(0.185)|(0.186)|
|Herfindahl index|||-0.126|-0.198|
||||(1.026)|(1.123)|
|Exports (proportion of sales)|||0.658*|0.701*|
||||(0.397)|(0.408)|
|Foreign ownership (%)|||0.012***|0.012***|
||||(0.004)|(0.004)|
|Women top manager Y:1 N:0|||-0.276|-0.250|
||||(0.312)|(0.310)|
|Annual employment growth (%)|||-0.007|-0.007|
||||(0.006)|(0.006)|
|New Product Y:1 N:0|||0.125|0.121|
||||(0.209)|(0.207)|
|Line of Credit Y:1 N:0|||0.001|0.001|
||||(0.002)|(0.002)|
|Number of power outages (per month)|||-0.017|-0.016|
||||(0.019)|(0.019)|
|Foreign Technology Y:1 N:0|||0.008***|0.008***|
||||(0.002)|(0.002)|
|Website Y:1 N:0|||0.006***|0.006***|
||||(0.002)|(0.002)|
|Total population (logs)|||1.096|1.292|
||||(2.647)|(2.367)|
|Informal Competition (CY||||-0.942|
|<br>avg.)*GDP per capita (logs)|||||

43
# **Table 4: Income Growth Transitions by Rural, Urban and Sector 1993-2000**

||||||Rural|||2000|Urb|an||Average|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Starting|||#||#||#|||Ending|
|||<br>income|#,Obs|Farm|<br>Obs|Nonfarm|<br>Obs|Farm|,<br>Obs|Nonfarm|# Obs|<br>income|
|Rural|Farm|48,470|1,590|40,660|1,268|78,519|274|22,305|31|107,177|17|44,868|
|||||-11.2%||31.1%||-56.4%||116.0%||-7.4%|
||Nonfarm|103,792|1,415|50,006|329|121,182|984|49,788|18|150,425|84|109,966|
|1993||||-37.4%||11.0%||-40.0%||-9.5%||5.9%|
|Urban|Farm|90,049|293|101,827|52|118,379|15|79,019|143|93,485|83|92,595|
|||||-15.4%||-22.2%||4.0%||8.8%||2.8%|
||Nonfarm|192,893|2,220|0|24|269,483|109|90,356|111|176,623|1,976|176,623|
|||||-100.0%||28.3%||-32.0%||-6.7%||-8.4%|
|All||95,904|5,518||1,673||1,382||303||2,160|107,875|

**Note** : Data are for individuals who were aged 15-55 in 1993 and were working in _both_ years. Income figures are median monthly income in Rupiah in 1996 prices.  Starting income figures are for 1993; all other income figures are for 2000.  Income values and percentage changes in each cell represent the median incomes and percentage change in income of those individuals who made that transition. For example, the real income of the median rural agricultural worker that moved into non-agricultural activities in rural areas increased by 31%, ending at Rp 78,519/month in 2000.

20
years in Zimbabwe.

Table 2: Mortality risks in Japan and Zimbabwe

||(1)<br>Japan|(2)<br>Zimbabwe|
|---|---|---|
|Population per age group (%)|||
|0-29|26.6|69.5|
|30-59|39.0|25.8|
|60+|34.4|4.69|
|Residual life-expectancy per age group|||
|0-29|69.1|52.7|
|30-59|40.1|28.8|
|60+|15.6|11.2|
|Covid-19 LYs per person|||
|No intervention and no saturation|0.102|0.0212|
|No intervention and saturation|0.204|0.0298|

Moving beyond that illustrative two-country example, Figure 4 plots years of life lost to Covid-19 under the no-intervention scenarios ( _LY_<sup>_NI_</sup> per person) - both with and without saturation - against GDP per capita for all countries in our sample. Notice that once again both axes are in logarithmic scale. The gure shows that potential mortality burdens are several times larger in high-income countries than in low-income countries. Under the saturation scenario, a large fraction of high-income countries have a per capita number of lost-years above 0.1 years per person. This is in sharp contrast with low-income countries, whose per capita number of lost-years is always below 0.05 years per person under the same scenario. In particular, the number is 0.2 years per person in Japan and 0.03 years per person in Zimbabwe, implying that the potential mortality burden in Japan is more than six times larger. Strikingly, high-income countries still have considerably larger potential mortality burdens under the no-saturation scenario than low-income countries under the saturation scenario. In particular, the number of lost-years per person is more than three times larger in Japan under the no-saturation scenario than in Zimbabwe under the saturation scenario.

Another striking aspect of Figure 4 is the heterogeneity of mortality burdens even among countries with similar levels of GDP per capita. Above $5,000 per capita, one country can have more than twice the mortality burden of another country with similar GDP per capita. This illustrates the roles that population pyramids and residual life-expectancies play in shaping Covid-19 mortality, quite separately from pure income considerations.

17
|Armenia|No|||1|||
|---|---|---|---|---|---|---|
|Benin|No||||||
|Bhutan|No||||1||
|Burundi|No||2||||
|Cameroon|No|4|2|1|1||
|Colombia|No||||||
|Gambia|No|||1|1||
|Ghana|Yes|3|4|2|1|X|
|Kenya|Yes|4|4|1|||
|Kyrgyz Republic|No|1|1||||
|Lesotho|No|2|||||
|Mauritius|No||||||
|Mongolia|No||||||
|Nicaragua|No||||||
|Pakistan|No||||||
|Sierra Leone|No|2|2||1||
|South Africa|No|1|1||||

# **3. Transport-related questions in non-LSMS surveys**

The review also examines transport-related questions in other types of household surveys, including the household surveys captured by International Household Survey Network (IHSN), Gender Data Navigator<sup>6</sup> , and the Afrobarometer surveys.

## **3.1. Non-LSMS Household Surveys**

Besides LSMS surveys, transport information is also collected by a wide variety of household surveys, according to IHSN, a tool for screening the content of 2,087 surveys and censuses in lowand middle-income countries as of 2014. Table 6 lists the numbers of surveys conducted between 2010 and 2014 that contain some detailed information on household transport characteristics and ownership of vehicles, where many surveys are Household Income and Expenditure Surveys, Household Budget Surveys, and Demographic Health Surveys. These household surveys are not as multi-topic or integrated as LSMS surveys, but some also collect abundant information on transport.

**Table 6. Number of household surveys with transport information (2010 - 2014)**

|**Information collected**|**Number of**<br>**surveys **|**Number of**<br>**countries**|
|---|---|---|
|Reasons for not attendingschool|||
|-<br>Distance/time to school|113|56|
|-<br>Lack of access to transportation|14|10|
|Gettingto school|||
|-<br>Distance to school|25|19|
|-<br>Time to school|33|21|

> 6 http://datanavigator.ihsn.org

15
change is then obtained by adding the change in the predicted value relative to a benchmark and dividing by the total number of observations. A major advantage of the simulation approach is that it allows greater flexibility, both in the specification of the type of policy experiment (e.g., differential changes in water access) as well as in the valuation. Since the valuation is computed for individual house observations, the results can be obtained for any desired level of spatial aggregation, such as by ward. In essence this boils down to a discrete approximation to the notion ˆ of marginal willingness to pay. Given a vector of coefficient estimates β , the conditional expectation _E_ [ _p_ | _Z_ ] is obtained as

> since _E_ [ μ | _Z_ ] = 0 by assumption.

For the sake of simplicity, assume that interest is focused on attribute Zk and separate the matrix of observations and corresponding coefficient vector into Zk and the other variables, as Z = [ Z-k zk] and β = [β−k βk ]. The predicted value can then be decomposed into a part that does not change with the value of zk and a part that does:

> Consider the prediction _p_ ˆ 0 using the observed values for Z-k and zk. Now, consider a new vector of attributes z<sup>1</sup> k<sup>, reflecting a policy change, such as a given percentage increase in access to</sup> water. The new vector of predicted values follows as:

with the change in valuation as _p_ ˆ 0 − _p_ ˆ , or ( _z_ 1 _k_ − _zk_ ) β _k_ . Since this is a vector of observationspecific changes, it can be averaged over all observations or over any relevant spatial subset of observations.

The presence of a spatially lagged dependent variable in the hedonic equation complicates this 1 approach slightly. Instead of the simple difference, ( _zk_ − _zk_ ) β _k_ , the total effect is obtained as ( _I_ − ρ<sup>ˆ</sup> _W_ )<sup>−1</sup> ( _z_ 1 _k_ − _zk_ ) β _k_ , where ρ ˆ is the estimated spatial autoregressive coefficient and ( _I_ − ρ<sup>ˆ</sup> _W_ )<sup>−1</sup> is the inverse of a n × n matrix, of the same dimension as the data set. In large samples, a power approximation is used to avoid numerical problems. These procedures are implemented in the PySAL library of routines for spatial analysis (Rey and Anselin 2007). A
# **Table 4: Index Values**

||γ=-1<br>W ei|γ=-2<br>ghted Resul|γ=-0.5<br>ts|γ=-1<br>Unw|γ=-2<br>eighted Res|γ=-0.5<br>ults|
|---|---|---|---|---|---|---|
|Site Id|Index 1|Index 2|Index 3|Index 4|Index 5|Index 6|
|B|561.09|462.16|523.21|67.85|19.55|93.49|
|D|187.48|63.62|272.38|11.64|1.38|24.99|
|F|44.71|113.95|23.70|51.91|46.28|40.66|
|A|39.46|151.73|17.03|459.71|618.36|293.17|
|G|19.50|31.61|12.96|22.64|12.84|22.24|
|C|17.81|10.06|20.06|205.79|40.66|342.44|
|I|12.45|6.15|14.99|1.45|0.25|2.59|
|J|10.72|6.13|11.99|0.17|0.03|0.28|
|N|7.63|4.31|8.60|0.25|0.05|0.42|
|E|7.38|54.24|2.30|87.99|226.26|40.58|
|T|5.37|3.03|6.05|2.34|0.46|3.90|
|AD|5.03|3.81|4.89|2.06|0.55|2.97|
|L|4.22|1.82|5.43|4.95|0.75|9.41|
|P|4.16|2.11|4.93|4.82|0.86|8.45|
|R|3.83|3.03|3.64|4.46|1.24|6.28|
|O|3.80|1.64|4.89|4.45|0.67|8.47|
|K|3.74|9.54|1.98|4.34|3.87|3.40|
|AH|3.72|3.47|3.25|0.08|0.02|0.10|
|AF|3.46|4.81|2.49|3.99|1.94|4.24|
|M|3.43|1.94|3.86|5.61|1.11|9.33|
|S|3.17|1.76|3.60|3.69|0.72|6.20|
|AA|2.99|2.01|3.09|3.47|0.82|5.29|
|AB|2.97|2.00|3.07|3.45|0.81|5.26|
|AK|2.76|1.27|3.46|0.21|0.03|0.39|
|AE|2.74|1.91|2.77|3.23|0.79|4.84|
|AG|2.17|3.77|1.39|0.07|0.05|0.07|
|H|2.07|16.94|0.61|2.40|6.87|1.05|
|AV|1.86|1.47|1.77|0.04|0.01|0.05|
|AC|1.82|0.48|3.01|2.12|0.19|5.19|
|AI|1.81|0.93|2.14|2.11|0.38|3.67|
|AW|1.66|1.25|1.63|0.92|0.24|1.33|
|AM|1.58|0.85|1.83|1.84|0.35|3.13|
|BA|1.23|1.42|0.97|0.36|0.15|0.42|
|AS|1.22|0.96|1.16|1.42|0.39|1.99|
|AT|1.20|0.95|1.14|1.40|0.39|1.97|
|AQ|1.17|0.57|1.41|1.35|0.23|2.41|
|AX|0.94|0.66|0.96|1.10|0.27|1.64|
|AO|0.91|0.24|1.51|1.06|0.10|2.59|
|AN|0.77|2.94|0.33|0.95|1.27|0.60|
|AZ|0.74|0.37|0.88|0.87|0.15|1.52|
|AY|0.66|0.22|0.96|0.76|0.09|1.64|
|BJ|0.63|0.29|0.78|0.01|0.00|0.01|
|BI|0.63|0.50|0.60|0.63|0.18|0.89|
|BE|0.60|1.19|0.37|0.24|0.17|0.22|
|BN|0.58|0.47|0.54|0.02|0.00|0.02|
|BM|0.55|0.59|0.45|0.02|0.01|0.03|
|BK|0.52|0.72|0.37|0.61|0.30|0.65|

21
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# **8. Acknowledgments**

The authors would like to thank GFDRR-EU which funded the South Asia Region Building Resilience to Landslide and Geohazard Risk in the South Asia Region Program and Africa Fellowship Program for funding the work.

31
Table D4: Ethnicity randomization checks 1

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Kalenjin|Judge plur. Kamba|Judge plur. Kikuyu|
|Pla. plur. Kalenjin|0.00699<br>(0.00894)|||
|Def. plur. Kalenjin|-0.00933<br>(0.0103)|||
|Pla. plur. Kamba||-0.00785<br>(0.00651)||
|Def. plur. Kamba||0.00337<br>(0.00771)||
|Pla. plur. Kikuyu|||0.00640<br>(0.00760)|
|Def. plur. Kikuyu|||0.00167<br>(0.00759)|
|DV mean|0.0895|0.125|0.275|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|14594|14594|14944|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

40