rafmacalaba/datause-encoder-data huggingface dataset:
We classified roads into four categories based on expected speed, as seen in Table 3. The classified road network was converted to a raster layer with a 30‐meter tile size to use as an input for the Cost Time layer.

**_Table 3: Road Network Classification_**

|**Road Type **|**Abbreviation**|**Class**|
|---|---|---|
|Strategic Road Network|SRN|**1**|
|District Road Core Network|DRCN|**2**|
|Strategic Urban Road|SUR||
|Urban Road|UR|**3**|
|District Road Core Network<br>_(unpaved)_|DRCN<br>_(unpaved)_||
|Village Road|VR||
|Others/non-recognized|NR|**4**|
|Paths|Paths||

Visual comparison of early model results, road network data and WorldPop population data revealed significant pockets of population uncovered by existing roads geospatial data. Further review using Bing, Google and Digital Globe satellite imagery indicated that in most cases minor roads or paths did in fact reach these population clusters. We contracted Kathmandu Living Labs, a Nepali non‐profit organization specialized in open geospatial data, to “trace” major missing roads and major paths from freely available satellite imagery into OpenStreetMap. Tracing work focused on pre‐determined priority areas covering roughly 7000 km<sup>2</sup> . Tracers were instructed to connect their traced roads or paths to the nearest road or path where possible, to ensure the connectivity of the transport network data set.

Priority areas fit two criteria:

**1.** Population density greater than 1 person / 100m<sup>2</sup> in the WorldPop data set

**2.** Travel times to any medical facility greater than 24 hours in the preliminary monsoon model

We employed the medical facility layer as it has the greatest nationwide coverage of all the data sets. The 24‐ hour cutoff was chosen after a desk review of freely available satellite imagery in these areas; we determined it struck a good balance between highlighting extreme cases of error and appropriately narrowing the focus for a potentially unwieldy assignment.

# Land cover and river network

Land cover determines travel times for the off‐road travel surface. Our analysis employs the International Centre for Integrated Mountain Development (ICIMOD)’s “Land cover of Nepal 2010” layer as it offered the strongest combination of recentness, completeness and resolution. Additional river network data was extracted from OSM in a vector polyline format, converted to a raster and merged with the ICIMOD data set. Cells were reclassified from eight to seven categories based on expected speeds across each land surface when flat (see Table 6).
and Narayanganj City Corporation only. A baseline survey was fielded prior to the initiation of the project in May/June 2011 based on a two-stage process. The first stage included a complete census of all households in the five project unions of Jaldhaka Upazila (Kaimari, Saulmari, Khutamara, Kathali, and Mirganj Unions) and in the 36 government-recognized slums of Narayanganj. The census questionnaire collected information on all the variables needed to compute household level PMT scores. The census survey provided information on 36,610 households in the Jaldhaka unions, and 7,980 households in the slums of Narayanganj. Information on household demographics, household head characteristics, household assets, type of housing, access to facilities and overseas remittances allowed the construction of a PMT score for each household (see Table B2 in Annex II for a complete list of variables collected and weights associated). The universe of households to draw from was further restricted using households’ likelihood of being a _Shombhob_ beneficiary: households with PMT scores above the 50<sup>th</sup> percentile were discarded and the rest was divided between households with PMT scores below the 25<sup>th</sup> percentile (potential beneficiaries) and households with PMT scores between 25<sup>th</sup> and 50<sup>th</sup> percentile (potential controls).<sup>13</sup> In the second stage, a random sample of 3,000 households was drawn from the census list and interviewed. This stratified sample resulted in having 2,400 households from rural Jaldhaka Upazila, and 600 households from the slums of Narayanganj. Data was stratified in each location to ensure representativeness of the four different types of families (no qualifying children, households with young children (0-36 months old) only, households with children going to primary school (6-15 years old) only, and households with both). Households were randomly selected within each of the four demographic groups and within each of the two PMT score groups (below the 25<sup>th</sup> percentile and between the 25<sup>th</sup> and 50<sup>th</sup> percentiles).

> 13 The choice of the eligibility threshold was 25th percentile since the incidence of extreme poverty at the time of the project design was 25 percent. However, during implementation the cut off line was lowered due to changes in the poverty rates as well as to limit the number of beneficiaries to a manageable size.

12
likelihood of having a top woman manager at a firm. This is a large increase given that only 19.5 percent of the firms in our sample have a top woman manager.

To summarize, this study contributes to the literature in the following ways: (i) it investigates the relationship between gender disparity in access to education and the likelihood of female top managers in developing economies, a hitherto unexplored area; (ii) it uses a sample of unique cross-country comparable firm level data sets that follow the same sampling methodology and design; (iii) it combines both the literature on gender and development – which has been limited with regards to the determinants of women managers, and the literature on women’s careers on management that has typically focused on developed economies or suffers from nonrepresentative data (at the economy level), which hinders the ability to make economy-wide inferences; and (iv) it explores the importance of firm-size and other firm-level and country-level attributes for the relationship between women’s access to education and the likelihood of female top managers.

# **2. Conceptual Model**

Career choices can be seen as a series of decisions based on a comparison of costs and benefits (see for example, Michael Keane and Kenneth Wolpin 1997). These costs and benefits may differ by gender, potentially providing a different set of incentives for men and women. The gender specific comparisons of costs and benefits serve as the theoretical underpinning of most research on the determinants of women managers.<sup>2</sup> Assuming that an individual has high career ambitions, there are combinations of factors that influence the chances of a woman as opposed to a man attaining a top managerial position in a firm. At the theoretical level, these factors can be grouped into two types. On the one hand, firm characteristics would define how woman-friendly the firm

> 2 See for example, Jacob Mincer (1962), Gary Becker (1965), Florence Jaumotte (2003) and Andrew Morrison, Dhushyanth Raju and Nishtha Sinha (2007) for an overview of the more recent literature.

5
# Appendix : Table 1

## Table 1 : Proposed criteria for assessing housing subsidies programs

|_Criterion_|_Questions to be answered_|_Measurement indicators_|
|---|---|---|
|Administrative<br>simplicity|- Is the subsidy easy and relatively inexpensive to manage ?<br>- Are users aware of the existence and modalities of the program ?<br>- does an “average” household/ firm need help (social worker,<br>administration) to register in the program?|- Direct and indirect administrative cost / subsidy amount<br>- Qualitative or Survey assessment|
|Transparency|- Are there well-defined eligibility criteria to the subsidy ?<br>- Are responsibilities in subsidy attribution clearly defined ?<br>- Is subsidy attribution managed by an independent body ?|- existence of eligibility and participation criteria, and<br>nature (law, decree, regulation, rule internal to the<br>implementation agency, etc.)<br>- proportion of participants chosen on open and known<br>rules (vs. quotas of administration, local authorities, etc.)<br>- knowledge of those in the population<br>-_ad hoc_assessment of transparency|
|Incentives /<br>Political<br>accountability-<br>responsibility /<br>Popularity|What are the incentives of stakeholders (State, local governments,<br>households, firms) to use but not abuse the subsidy ?<br>- Are individuals / institutions responsible for choosing participants<br>in the program accountable ?<br>- Are individuals or institutions in charge of the implementation of<br>the program given adequate financial and technical means ?<br>- Do institutions in charge of the program have incentives to keep<br>the program under control (financial mass, cost recovery,...)?<br>- Does the program receive a large support ? Who supports it and<br>who ‘s against it ?|_Ad hoc_qualitative analysis of incentives.|

55
Table 5: Montreal Protocol Spillovers in Consumption of Environmentally Harmful Emissions

|||_Dependent_|_variable:_||
|---|---|---|---|---|
||HCFCs|(ihs)|HFCs|(ihs)|
||(1)|(2)|(3)|(4)|
|Post RTA|_−_0.322<sup>_∗∗∗_</sup>|_−_0.577<sup>_∗∗∗_</sup>|0.074<sup>_∗∗∗_</sup>|0.334<sup>_∗∗∗_</sup>|
||(0.088)|(0.116)|(0.025)|(0.040)|
|Post_×_ODS|_−_0.387<sup>_∗∗_</sup>|_−_0.409<sup>_∗∗_</sup>|_−_0.113<sup>_∗∗∗_</sup>|_−_0.128|
||(0.178)|(0.206)|(0.037)|(0.144)|
|CFC target (ihs)|_−_0.083<sup>_∗∗∗_</sup><br>(0.016)|_−_0.105<sup>_∗∗∗_</sup><br>(0.018)|||
|Post_×_CFC targ||0.116<sup>_∗∗∗_</sup><br>(0.017)|||
|Post_×_ODS_×_CFC targ||0.064<sup>_∗_</sup><br>(0.036)|||
|HCFC target (ihs)|||_−_0.257<sup>_∗∗∗_</sup><br>(0.039)|_−_0.189<sup>_∗∗∗_</sup><br>(0.034)|
|Post_×_HCFC targ||||_−_0.042<sup>_∗∗∗_</sup><br>(0.006)|
|Post_×_ODS_×_HCFC targ||||0.012<br>(0.019)|
|Observations|4,616|4,616|1,022|1,022|
|R<sup>2</sup>|0.910|0.914|0.998|0.998|
|Mean|658|658|28.8m|28.8m|

Regressions on a stacked country-level panel with a _±_ 3 year event window around RTA signature. The HCFC outcome is the inverse hyperbolic sine of aggregated HCFC consumption in ODP units. The HFC outcome is the inverse hyperbolic sine of the sum of HFC 134a, HFC 125, HFC 32, HFC 152a, and HFC 23 in 100-year GWP units. All models include country-event fixed effects. Robust standard errors are (two-way) clustered at the country-event and country-year levels. We report mean consumption in tons of ODP (for HCFCs) and tons of 100-year GWP (for HFCs). Statistical significance from two-sided _t_ tests are denoted by<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01.

27
# **_3.3. Descriptive Statistics_**

The process of adaptation to cultural change is not independent of the individual characteristics that follow migrant repatriation. Therefore, a series of respondent and household characteristics were used to estimate the differences between returned migrants and non-migrants to account for changes in gender attitudes: demographic and socioeconomic variables (sex, age, religion, education, employment status, access to loan), and living conditions variables (number of children under 5 years old living in the household, household size, owning land or house, place of residence, and household wealth).

Table 2 reports the summary statistics of the covariate variables by types of respondents: returned migrants from Gulf countries and Saudi Arabia, as well as non-migrants. The average international returning migrant had lived abroad for about 11 years. Over 65 percent of the returning migrants were over the age of 45, over 90 percent were married, and a majority had completed secondary school. The average household size was between 4 and 5. Most migrants owned their own home, while less than 20 percent owned land. Nearly 30 percent of all migrants were from urban areas. Based on the PCA, most migrants belonged to the upper three quintiles of the Wealth Index, and nearly 50 percent of them came from North Kerala, i.e., from the districts of Kasaragod, Kannur, Wayanad, Kozhikode, Malappuram.<sup>8</sup> Summary statistics on gender attitudes by type of migrants, presented in Appendix A, indicate some differences across migrant groups, with those returning from Saudi Arabia exhibiting more extreme views about women’s economic participation, mobility, and role in preserving the honor of the household. For example, 20.3 percent of returned migrants from Saudi Arabia shared the view that women should not work outside the home if economic conditions are better, as compared to 17.3 percent of returned migrants from GCC countries. The corresponding figures for those thinking women took jobs away from men when they worked outside the home were 12.9 and 10.5 percent, respectively. On the other hand, less than 25 percent of returning migrants from the Gulf thought it is shameful if women earned more than the husband, while

> 8 On the basis of geographical, historical, and cultural similarities, the districts are grouped into North Kerala (Kasaragod, Kannur, Wayanad, Kozhikode, Malappuram), Central Kerala (Palakkad, Thrissur, Eranak ulam, Idukki), and South Kerala (Thiruvananthapuram, Kollam, Alappuzha, Pathanamthitta, Kottayam).

15
The estimates here of the influence of various factors, such as weighting and quality change, on measures of manufactured goods prices are, therefore, derived from the NBER indexes.

The influence of each factor is measured in two ways. One is by its effect on the change from the initial year of the indexes, 1953, to the final year, expressed also as an annual rate of change. Since this method carries the risk that it may be excessively influenced by the circumstances of the first and last years, a linear logarithmic trend is also fitted to each of the series and the estimated coefficient of time used as the average rate of change.

Excorts to the World vs. Excorts to DeveloRing Countries The composition of exports by developed countries (DCs) to developing coun.tries (LDCs) is somewhat different from that of their exports to developed countries and to the world as a whole. An indication of the effect of that difference on price measures is given by comparisons of changes in the price indexes for the two destinations between 1953 and 1991 (Table 1): Table 1 Comparison of Prices of Developed-Country Exports of Manufactures to the World and to Developing Countries

|||1988/1953|
|---|---|---|
|Weights of|DC expo<br>World|LDC<br>rts to<br>Relative to<br>LDCs<br>World|
|1963|514|484<br>94.2|
|1975|485|454<br>93.5|
|1986|450|428<br>95.1|

Source: Appendix Tables 1 and 2.

The prices of developed country exports of manufactures to developing countries
- Shavelson, R. J., Domingue, B. W., Mariño, J. P., Mantilla, A. M., Forero, A. M. & Wiley, E E. (2016), ‘On the practices and challenges of measuring higher education value added: the case of Colombia’, _Assessment & Evaluation in Higher Education_ , 41(5), p695-720.

- Smith, J. & Stange, K. (2016), ‘A New Measure of College Quality to Study the Effects of College Sector and Peers on Degree Attainment’, _Education Finance and Policy_ , 11(4), p369-403.

- Stevens, A. H., Kurlaender, M., and Grosz, M. (2019), ‘Career technical education and labor market outcomes evidence from California community colleges’, _Journal of Human Resources_ , 54(4), p986– 1036.

- Rodriguez, J., Reyes, L. & Urzua, S. (2016), “Heterogenous Economic Returns to Post-secondary Degrees: Evidence from Chile,” _The Journal of Human Resources_ , Spring 2016, Vol. 51, n. 2, p416-460.

- Teixeira, P., Rocha, V., Biscaia, R. & Cardoso, M. F. (2013), ‘Competition and diversification in public and private higher education’, _Applied Economics_ , 45(35), p4949-4958.

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- Wiederspan, M. (2016), ‘Denying loan access: The student-level consequences when community colleges opt out of the Stafford loan program’, _Economics of Education Review,_ 51, p79-96.

- World Bank (2019). World Development Report 2019: The Changing Nature of Work. Washington, DC: World Bank. doi:10.1596/978-1-4648-1328-3.

- Xu, D. & Jaggars, S. S. (2011), ‘The effectiveness of distance education across Virginia's community colleges: Evidence from introductory college-level Math and English courses’, _Educational Evaluation and Policy Analysis_ , 33(3), p360-377.

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41
significantly over the period and these conclusions together lead to a conclusion of a positive effect of PAFPA in the agricultural sector, when the outcome variable is revenue. This corresponds to the conclusion of the women, and since all sampled individuals of the agricultural sector are in fact women, the conclusion is no surprise.

_Table 7. Estimation results for the agricultural sector_

||||ln(C|A)|||||ln(rev|enue)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||OLS||Rand|om effe|cts||OLS||Rand|om eff|ects|
||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||
|can read|-0.34|0.50|0.51|-0.36|0.67|0.60|-0.60|0.68|0.38|-0.63|0.84|0.46|
|age 30-39|-0.07|0.19|0.72|-0.04|0.25|0.89|0.09|0.25|0.71|0.14|0.32|0.67|
|age 40-49|-0.22|0.17|0.19|-0.24|0.23|0.30|-0.09|0.23|0.68|-0.10|0.29|0.74|
|age>49|-0.22|0.17|0.19|-0.24|0.22|0.28|-0.09|0.23|0.70|-0.11|0.28|0.69|
|PB|-0.20|0.12|0.08|-0.21|0.12|0.08|0.32|0.16|0.05|0.30|0.16|0.07|
|PA|-0.21|0.12|0.07|-0.25|0.12|0.04|0.25|0.16|0.11|0.22|0.16|0.19|
|NPB|0.01|0.11|0.91|0.01|0.06|0.84|0.50|0.15|0.00|0.50|0.11|0.00|
|constant|10.05|0.18|0.00|10.07|0.23|0.00|9.34|0.24|0.00|9.37|0.29|0.00|
|PB-NPB=0|3.38||0.07|3.43||0.06|1.27||0.26|1.49||0.22|
|PA-PB=0|0.01||0.94|0.27||0.61|0.16||0.69|0.48||0.49|
|PA-PB=-NPB|0.00||0.98|0.05||0.82|3.85||0.05|6.79||0.01|
|B&P LM-test||||59.61||0.00||||28.65||0.00|
|Hausman-test||||0.00||1.00||||0.00||1.00|
|N|274|||274|||274|||274|||

In table 8 the similar estimation results from the sector of tailoring can be seen. In the random effects model of CA no explanatory variables seem to have impact on the CA. Furthermore, there are no differences between the participants and the nonparticipants and neither between the development of their CA in the period. From the estimations of the revenue of the tailors it can be concluded that both groups have experienced a rise in the revenue during the period, but since these changes are concluded not to be significantly different from each other, no impact of PAFPA is found.

19
TABLE A-2. FIRST-STAGE REGRESSION RESULTS USING AS INSTRUMENTS PRE-COMMUNIST LITERACY RATE AND 1989-90 URBANIZATION RATE (DEPENDENT VARIABLE: POLITICAL ALTERNATION)

||Coefficient<br>(_p_levels)|
|---|---|
|GDP per<br>capita in 1990<sup>a</sup>|-1.966***<br>(0.000)|
|War<br>(cumulative)|-0.061<br>(0.238)|
|Fuel/GDP in 1996-97|30.50***<br>(0.000)|
|Democracy<br>(_Polity2_)|0.172***<br>(0.000)|
|Communist years|-0.351**<br>(0.017)|
|Literacy rate|0.039***<br>(0.001)|
|Urbanization rate|-0.015<br>(0.37)|
|Constant|18.66***<br>(0.000)|
|F test of excluded<br>instruments|11.50***<br>(0.0004)|
|Anderson canonical<br>correlation test|62.36***<br>(0.000)|
|R<sup>2</sup>|0.75|
|_N_|183|

_Note:_ The results are the first-stage estimates for the second-stage regressions reported in Table 6, columns (3) and (6), respectively. Coefficients with three, two and one asterisk are statistically significant at respectively 10, 5 and 1 percent. _p_ values shown between brackets.

a. In natural logs.

39
nouncement date, which usually happens around mid-month, and the date when the rebalancing is made effective, which is the last trading day of the month, and thus occurs 11 to 17 days after the announcement. Also, the MSCI quarterly reports provide information on the benchmark weight (the relative weight) of each stock in each index.

To compute elasticity estimates with the conventional methods used in the literature, we collect from the MSCI website information about the AUM tracking the MSCI Emerging Markets Index (MSCI EM) and the MSCI All Country World Index (MSCI ACWI). Since these data are provided only for June 2015 and November 2016, we complement them with data from the EPFR Global Fund Flows database. This database covers a subsample of the population of mutual funds and ETFs and provides information on the benchmark indexes they track. For both indexes, we compute the ratio of the AUM reported by MSCI relative to those resulting from EPFR in each of the two periods for which we have data from both sources. Then, we compute the median coverage ratio for each index and multiply it by the AUM in EPFR to infer the total AUM benchmarked against the indexes.<sup>8</sup> Finally, to measure the AUM of passive funds and ETFs benchmarked against the indexes, we rely on EPFR data only, which has an extremely good coverage of the population of such funds. At the end of 2016, the Investment Company Institute (ICI) reported total assets for ETFs worldwide of 3.5 trillion dollars, which is also the total assets under management for ETFs in EPFR database.

# **2.2 The Colombian Stock Exchange**

The BVC runs like a standard electronic central limit order book. Trades are submitted via authorized broker-dealers registered at the exchange and buy and sell orders meet via an automated trading system. In contrast to fragmented markets in the United States and Europe, the BVC is the sole authorized trading venue for Colombian stocks, which means that we can observe the universe of transaction of Colombian stocks and avoid biases from incomplete reporting (e.g., from multiple trading venues such as multilateral trading facilities, dark pools, or from rival exchanges). Also, at the time of each addition/deletion, none of the stocks in our sample had dual listings, nor were they traded through global depositary receipts. Thus, all the transactions around rebalancing events, and hence, any change in ownership for stocks in our sample are reported in the BVC trading records.

symbols and announcement dates for each episode are given in Appendix Table OA1.

8We apply the ratio for the MSCI Emerging Markets Index also to the MSCI EM Latin America Index and the MSCI EM Colombia Index.

5
**Table 7. Discrete time hazard analysis for migration both to Italy and Greece and beyond Italy and Greece using logistic regression for sex‐specific and both sexes combined to test effects of networks, Albania LSMS 2005 (control variables not shown)**

|||**Male**<br>|||**Female**<br>||**Testing Difference**<br>**in Joint Model**<br>|
|---|---|---|---|---|---|---|---|
|**First‐Migration**|**OR**|**SE(b)**|**P>|z|**|**OR**|**SE(b)**|**P>|z|**|**P>|z|**|
|||**Migrati**|**on to Ital**|**y and Gre**|**ece**|||
|**Family/Friends in 1990**|0.810|0.177|0.335|1.178|0.404|0.634|‐|
|**Relatives in 1990**|1.063|0.127|0.609|1.001|0.189|0.994|‐|
|**Family Network**|1.278|0.049|0.000|1.346|0.051|0.000|0.179|
|**Community Network**|1.578|0.545|0.187|2.768|1.291|0.029|0.050|
|**Number of Cases**||**25373**|||**34983**||**60356**|
|**Log Pseudo likelihood**||**‐5301.5**|||**‐2819.2**||**‐8203.0**|
|||**Migration**<br>|**beyond I**<br>|**taly and G**<br>|**reece**<br>|||
|**Family/Friends in 1990**|1.035|0.423|0.932|0.212|0.227|0.148|‐|
|**Relatives in 1990**|1.956|0.416|0.002|1.352|0.409|0.320|‐|
|**Family Network**|1.390|0.090|0.000|0.976|0.091|0.793|0.004|
|**Community Network**|0.557|0.666|0.624|2.138|2.218|0.464|0.018|
|**Number of Cases**||**18350**|||**32023**||**50373**|
|**Log Pseudo likelihood**||**‐1331.8**|||**‐907.6**||**‐2283.9**|

29