rafmacalaba/datause-encoder-data huggingface dataset:
_Annex 2 Table 5: Alternative estimates of relationship between food prices (ln) and program saturation in nationally representative data-- unit values derived after trimming values +/- 3 SD from mean._

||_M_|_odel with cutoff at satu_<br>Saturation*Above|_ration of 65perc_|_ent_<br>Saturation +<br>Saturation*Above|_Linear Model_|
|---|---|---|---|---|---|
||Saturation|<br>cutoff|Above cutoff|<br>cutoff|Saturation|
|Rice, raw regular||||||
|milled|0.078 ***|-0.251 ***|0.245 ***|-0.173 **|0.095***|
||0.013|0.088|0.067|0.086<br>|<br>0.011|
|Other raw rice and||||||
|grains|-0.157|0.475|-0.456|0.318|-0.185**|
||0.100|0.654|0.506|0.640<br>|0.089|
|Roots and tubers|0.241 ***|0.767 **|-0.515 *|1.008 ***|0.290***|
||0.050|0.351|0.270|0.344<br>|0.045|
|Eggs (fresh)|0.089 ***|0.154|-0.070|0.242 **|0.114***|
||0.016|0.110|0.085|0.107<br>|0.015|
|Eggs (processed)|0.093 ***|0.424|-0.284|0.517 *|0.102***|
||0.025|0.284|0.218|0.283<br>|0.024|
|Fish (fresh)|-0.009|0.332 ***|-0.249 ***|0.323 ***|0.001|
||0.014|0.095|0.072|0.093<br>|0.012|
|Fish (processed)|-0.066 *|0.638 **|-0.512 **|0.572 **|-0.062*|
||0.038|0.290|0.223|0.285<br>|<br>0.034|
|Meats (fresh)|0.019|0.029|-0.004|0.048<br>|0.028***|
||0.012|0.080|0.062|0.079<br>|0.010|
|Meats (processed)|1.652 ***|-4.552 *|2.523|-2.901<br>|1.238***|
||0.330|2.692|2.064|2.657<br>|0.305|
|Fruits (fresh)|-0.178 ***|0.091|0.017|-0.087<br>|<br>-0.136***|
||0.031|0.207|0.160|0.203<br>|0.028|
|Vegetables (fresh)|0.054 ***|0.161|-0.061|0.214 **|0.087***|
||0.017|0.109|0.085|0.107<br>|0.015|
|Rice and grains||||||
|processed|0.176 ***|-0.429|0.213|-0.253|0.111***|
||0.049|0.317|0.245|0.310<br>|<br>0.043|
|Coffee, cocoa, tea|0.114 ***|-0.262|0.219|-0.149<br>|0.114***|
||0.042|0.277|0.214|0.271<br>|<br>0.037|
|Sugar|-0.504 ***|0.590 ***|-0.360 ***|0.086<br>|-0.444***|
||0.026|0.174|0.135|0.170<br>|0.023|
|Milk Products|0.071|0.527|-0.456|0.598<br>|0.062|
||0.058|0.429|0.328|0.422<br>|0.053|

note: *** p<0.01, ** p<0.05, * p<.1. Standard errors in parenthesis. Each row corresponds to a separate regression. Observations are at the village-food item-year level and pooled across the years 2009, 2012, and 2015. All models include year and village fixed effects. Prices are calculated as unit values, see text for details. Source: 2009, 2012, and 2015 Family Income and Expenditure Surveys and Department of Social Welfare Administrative Data on _Pantawid_ Program Enrollment 76
March 2016b) to limit the extent to which banks can use internal models to drive capital requirements below the requirements that would apply under the standardized approaches to credit and market risk.<sup>7</sup>

## _Structure of the surveys_

All three surveys to regulators, local banks, and global banks were similarly structured and covered questions regarding: (i) impacts on the overall development and deepening of the domestic (host) financial sector, (ii) impacts on domestic financial markets, and products and services, particularly those that are relevant for development (e.g. infrastructure and SME finance), and (iii) impacts on international market conditions for domestic borrowers or issuers. The regulatory survey included two additional categories: (iv) supervisory capacity to mitigate any adverse impacts, and (v) the role of the international community. Most of the questions were posed as multiple choice with an option for respondents to provide a short commentary to motivate or illustrate their responses.

# **3. Potential Adverse Impacts of the G20 Financial Reforms on EMDEs: An Analytical Framework**

The G20 regulatory reforms were intended primarily to make the financial system safer, and to enable failing banks to be resolved in a more orderly manner without cost to the taxpayer. The intention was to make banks more risk-averse, strengthen their balance sheets, and constrain leverage in the financial system. Equally, however, these reforms may give rise to a range of (largely unintended) consequences that have an impact on EMDEs, including in particular on long-term infrastructure financing, borrowing by corporates and sovereigns, and foreign exchange and interest rate hedging transactions. Four potential sources of potential adverse impacts on EMDEs are considered below.

> 7 These new standards were finalized by the Basel Committee in December 2017 (Basel Committee, December 2017a).

11
the levels of performance in 2000 for the 1975 arrivals, and in 1990 for the 1985 arrivals. This means estimating 6 (2 years*3 OP definitions) regressions for the 1975 arrival, and 3 (1 years*3 OP definitions) for the 1985 one.

The explanatory indicators reflect a country’s situation around migration time, i.e. during the 1970s decade or its first half for the 1975 arrival, and during the 1980s decade, or its first half, for the 1985 arrival. GDPpc stands for log of per capita-GDP in constant 2000 prices averaged over 1970-1975, and 1980-1985, respectively, and is downloaded from the Penn World Tables (Version 6.2).<sup>39</sup> GDPpc may affect migrants’ performance through both quality and selectivity channels. The fact that people coming from high income countries are better off at arrival has been associated with the ease of skill transferability between US and equally developed countries. The coefficient of GDP should have a positive impact, unless selectivity forces trigger a different outcome. OPEN is the ratio of exports plus imports to GDP, and aims at capturing the degree of openness of the economy in the origin country. This variable is also available in the Penn World Tables. The coefficient of Open is expected to be positive provided that a high degree of an economy’s openness implies more information is available to prospective migrants. The English dummy in the model is constructed based on CIA – The World Factbook (2002) and takes value 1 if English is the main spoken language in a country. This variable should have a qualitative effect on performance; therefore the sign of the coefficient is expected to be positive. DIST is a selectivity indicator measured as the log of distance in miles to the US<sup>40</sup> . It affects migrants’ ability distribution by its influence on migration costs. A country in the proximity of the US has a higher propensity to send unskilled migrants compared to one that is further away. Therefore distance should have a positive impact on the entry level of occupational placement. The presence of military CONFLICT has a negative selection effect. In estimations for the 1975 arrival, military conflict<sup>41</sup> is evaluated for the 1970s decades, whereas estimations for the 1985 arrival use an index based on data for the 1980s. Variable COM<sup>42</sup> indicates whether a country has a communist regime in 1970, and respectively 1985.

The next step is to determine whether country attributes impact on the improvement in occupational placement over a decade. Equations (7) and (6) are equivalent with respect to explanatory variables, but they have different dependent variables.

> 39 Heston, Summers, Aten (2006)

> 40 From Andrew Rose datasets available at http://faculty.haas.berkeley.edu/arose

> 41 Constructed using  www. prio.no, “Armed Conflict Version 2.1” by Gledisch, Wallensteen, Eriksson, Sollenberg and Strand (2004)

> 42 Robert Barro’s  “Religion Adherence Data”

> http://www.economics.harvard.edu/faculty/barro/data_sets_barro

25
# Table A.1: **Effects of Inpres Schools on Intergenerational Educational Mobility: Dependent Variable: Children’s Years of Schooling (Full Table)**

|**Dependent Vari**|**able: Years**|**of Schooling**|||
|---|---|---|---|---|
||**Linea**|**r Model**|**Quadra**|**tic Model**|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Father’s Edu|0.450***|0.528***|0.368***|0.482***|
||(0.018)|(0.018)|(0.033)|(0.035)|
|Born 1968-72_×_Inpres|1.585***|0.513|1.534***|0.476|
||(0.387)|(0.466)|(0.380)|(0.492)|
|Father’s Edu_×_Born 1968-72|-0.053***|-0.083***|-0.081***|-0.089***|
||(0.013)|(0.017)|(0.021)|(0.024)|
|Father’s Edu_×_Inpres|0.257***|0.113*|0.561***|0.311**|
||(0.076)|(0.063)|(0.138)|(0.132)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.129**|0.043|-0.255***|-0.020|
||(0.054)|(0.062)|(0.086)|(0.099)|
|Father’s Edu Sq|||0.007***|0.004|
||||(0.002)|(0.003)|
|Father’s Edu Sq_×_Born 1968-72|||0.001|-0.000|
||||(0.002)|(0.002)|
|Father’s Edu Sq_×_Inpres|||-0.027***<br>(0.008)|-0.018*<br>(0.010)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.015**<br>(0.007)|0.008<br>(0.008)|
|R2|0.322|0.409|0.323|0.409|
|Observations|1199814|848350|1199814|848350|

Notes: Robust standard errors are in parentheses, clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

53
standard errors because individuals’ financial behaviors are likely to depend on unobserved countrycharacteristics.

We use data from the World Development Indicators, the Global Findex database and the EBA database (table 3).

**Table 3.** Data description

|**Variable**|**Definition, year**|**Source**|
|---|---|---|
|FINC|Respondent has an account at a financial institution, 2014|Demirguc-Kunt et al. (2015)|
|REGULATION|Regulation index obtained from EBA Finance data, 2017|World Bank (2017)|
|_Individual control_|_s_||
|FEMALE|Respondent is female, 2014||
|AGE|Respondent age, 2014||
|AGESQUARE|Squared value of AGE, 2014|Demirguc-Kunt et al. (2015)|
|EDUCATION|Respondent completed secondary school or higher, 2014||
|INCOME|Within-economy household income quintile, 2014||
|_Country controls_|||
|LOGGDPCAP|Log value of GDP per capita in current US$, 2014|World Develoment Indicators (2015)|
|LOGPOP|Log value of total population, 2014|p|

Column (1) in table 4 presents the estimations on account penetration using the continuous score of the regulatory index. The results indicate that the regulatory index score (0-100) for a country is positively associated with an individual within this country having an account at a financial institution. However, the association is not significant. We then try to divide the sample into four quartiles based on their scores of the regulation index. Column (2) in table 4 displays the results with the further segmentation. The estimations for regulatory quality have higher significance level, as shown in column (2), implying that radical rather than incremental changes in the regulatory framework reflected as significant change in the score of the regulation index are necessary to improve the financial inclusion scenario. For instance, adopting a dedicated legislation to regulate electronic money activities would engender more trust in the market and promote the uptake of various financial services by individuals. Further marginal effects results (Annex 1) indicate that if a country improves its regulatory framework so that its standing on the regulatory index jumps from the first to the fourth quartile, the probability of individuals within this country having an account at a financial institution increases by 12.4%, and the probability increases by 10.7% if it jumps from the first quartile to the third quartile. As expected, being poor, female, young, or relatively less schooled decreases the likelihood of an individual holding an account at a financial institution.

10
The standardization procedure used to place different indicators on a common scale precludes the ability to track changes meaningfully over time. The WBI index, for example, is constructed to have a mean of 0 and a standard deviation of 1 for each year the index is provided (1996, 1998, 2002 and 2004). Not only index values, but even rankings are not comparable across years as the composition of the sample changes. The addition of Luxembourg to the TI sample in 1997, and Iceland in 1998, reduced the rankings of most other nations. This limitation of the composite indexes often is not appreciated, as reflected not only in numerous media references to the TI index but also in many internal World Bank memos, and even in papers submitted for publication to academic journals.

The over-time comparability problem raised by changes in country coverage can be corrected, for the most part, by comparing rankings over time for a constant set of countries. For example, among the 102 countries included in the TI index in every year between 2002 and 2005, Slovenia’s rankings were 27<sup>th</sup> , 26<sup>th</sup> , 28<sup>th</sup> and 27<sup>th</sup> . Neglecting to adjust for a common sample, its ranking falls from 27<sup>th</sup> to 31<sup>st</sup> .

The above method corrects only for changes in coverage of other countries, and not for year-to-year changes in the underlying data sources and indicators available for the country in question. Even setting aside expanded country coverage, changes from one year to the next in a country’s ratings on TI or WBI could be driven purely by adding a new source to the index, or dropping an outdated one. No TI index value for any ECA country was based on the same set of sources in both 2004 and 2005.<sup>18</sup> The WBI indexes for 2002 and 2004 are based on the same set of sources for only 4 of the 27 countries in

> 18 The BEEPS data from 2002 were included in the TI index for 2004, but no BEEPS data were used in the 2005 index.  For most ECA countries, this was not the only change in TI sources used in 2004 and 2005.

20
_Table A3.3: Impacts on S/E Skills Index, Exclusion of Non-Reverse Items (Z-Scores)_

|||Gen<br>|der<br>||Ethnicity<br>|<br>||Acade<br>|mic Year<br>|Pre-<br>|Treatment Achiev<br>|ement<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Average|Male|Female|Macedonian|Albanian|Roma|Other|Sixth Grade|Seventh Grade|First Tercile|Second Tercile|Third Tercile|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Treatment 1|0.117***|0.107***|0.129***|0.135***|0.106***|-0.100|0.022|0.108***|0.120***|0.080**|0.120***|0.148***|
|“Student Self-|_(0.020)_|_(0.027)_|_(0.022)_|_(0.021)_|_(0.039)_|_(0.156)_|_(0.103)_|_(0.026)_|_(0.026)_|_(0.034)_|_(0.028)_|_(0.025)_|
|Learning”|||||||||||||
|Treatment 2|0.171***|0.164***|0.178***|0.189***|0.098***|0.245*|0.220*|0.188***|0.154***|0.120***|0.196***|0.180***|
|“Teacher|_(0.019)_|_(0.026)_|_(0.023)_|_(0.023)_|_(0.033)_|_(0.146)_|_(0.117)_|_(0.027)_|_(0.024)_|_(0.035)_|_(0.028)_|_(0.025)_|
|Delivery”|||||||||||||
|N|20,059|9,801|10,258|13,835|4,917|389|918|9,636|10,423|5,228|6,988|7,739|
|N Control|9,451|3,876|3,940|5,175|2,207|134|300|3,767|4,049|2,018|2,767|3,012|
|N Treatment 1|7,068|2,817|2,978|4,161|1,216|121|297|2,779|3,016|1,611|2,007|2,157|
|N Treatment 2|7,757|3,108|3,340|4,499|1,494|134|321|3,090|3,358|1,599|2,214|2,570|
|_R2_|_0.373_|_0.354_|_0.365_|_0.392_|_0.308_|_0.389_|_0.472_|_0.369_|_0.384_|_0.305_|_0.335_|_0.391_|

* p < 0.05, ** p < 0.01, *** p < 0.001

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Administrative data, school year 2016-2017, own calculations.

39
**Table 5a: GDP per capita growth with household credit as a share of GDP**

|**Dep. Var:**|OLS|OLS|FE|FE|GMM|2SLS|
|---|---|---|---|---|---|---|
|**growth_gdppc**|(1)|(2)|(3)|(4)|(5)|(6)|
|hhcredit|0.3144***|0.2358**|0.7880**|0.8010***|-0.1030**|0.4033|
||(0.006)|(0.043)|(0.016)|(0.015)|(0.038)|(0.103)|
|hhcredit<sup>2</sup>|-0.0031***|-0.0022**|-0.0050**|-0.0038*||-0.0043*|
||(0.001)|(0.016)|(0.016)|(0.063)||(0.086)|
|fdi|0.1539|0.2275**|0.4155*|0.4472*|0.102|0.2878*|
||(0.228)|(0.017)|(0.098)|(0.094)|(0.535)|(0.081)|
|enrollment|0.0426|0.0484|0.0252|0.0095|0.0183|-0.0036|
||(0.333)|(0.259)|(0.713)|(0.902)|(0.842)|(0.942)|
|initialgdppc|-7.059***|-6.397***|-29.33***|-31.01***|-5.307|-6.848***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.102)|(0.000)|
|financialcrisis||13.101***<br>(0.000)|||||
|bsupervision|||0.8435|3.4665|||
||||(0.501)|(0.129)|||
|bsupervisionXhhcredit||||-0.0584|||
|||||(0.175)|||
|Inflation||||||-0.207<br>(0.141)|
|Constant|22.421***|22.768***|69.443***|72.664***|30.411***|27.780***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|
|# of obs|199|199|155|155|199|147|
|R-squared|0.278|0.378|||||
|Prob>F|0.000|0.000|0.000|0.000|0.002|0.000|
|# of countries|||36|36|39||

*: in 2SLS, legal origin is used as instrument.

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

18
Policy ReseaRch WoRking PaPeR 4507

# **Abstract**

This paper examines the causes and dynamics of the shift in the gender composition of migration, and more particularly, in the access of women to migration opportunities and decision making. The context of the analysis is Albania, a natural laboratory for studying migration developments given that out-migration was practically eliminated from the end of World War II to the end of the 1980s. The authors use micro-level data from the Albania 2005 Living Standards Measurement Study including migration histories for family members since migration began. Based on discrete-time hazard models, the analysis shows an impressive expansion of female participation in international migration. Female migration, which is shown to be strongly associated with education, wealth, and social capital, appears responsive to economic incentives and constraints. Yet, using unique data on the dependency of female migration to the household demographic structure as well as the sensitivity of female migration to household-level shocks, the authors show that it is the households themselves that are the decision-making agents behind this economic calculus and there is little to suggest that increased female migration signals the emergence of female agency.

This paper—a product of the Poverty Team, Development Research Group. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gcarletto@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
it and obtain positive results. In this sense, the challenge is how to expand the activities from pilot projects to other communities around the country and disseminate in an effective manner the results of improved agricultural practices on agricultural productivity at the village level.

In terms of monitoring and enforcement of forest conservation, community involvement is key. This is especially true for reducing monitoring costs. At the national level, the establishment of the National Forestry Resources Monitoring and Assessment of Tanzania (NARFOMA) will allow measurement of carbon emissions and that may help to reduce monitoring costs. Training and building capacity can cut transaction and monitoring costs. Once a monitoring scheme is set up and the projects are established, the cost will potentially be less. If community involvement were high in PFM villages and land tenure is resolved, villagers would feel that they own the forest and they will patrol the forest using local resources; in this sense, the social capital at the village level can be used to reduce monitoring costs.

Nevertheless, solidifying village involvement will require education efforts through which people learn about the added value of the forest. In successful PFM and REDD+ projects, training and education in conservation practices and sustainable forest management was given to villagers. In general, communities in these projects state that forest conservation is important and villagers are well aware of the environmental services derived from forest protection. Some of the environmental services they mentioned are: watershed protection, rain, plant and animal diversity, fresh air, and protection from wind/erosion. Participants in the REDD+ pilot pointed out that before the project was established conservation was not important. The difference is that with the project villagers now have the rights to the forest. That is, they have the responsibility to manage the forest and incur the protection costs but they are also entitled to the returns of sustainable forest harvesting, licensing and carbon payments. Solving the land tenure problem and training and education have been 36
premium for education and there is therefore the potential for some widening of wage differentials between skilled and unskilled labor. While between-regional inequality, which explains up to a third of inequality in Russia, might remain persistent, it need not aggravate an increase in inequality. On the contrary, to the extent such inequality has roots going back to central planning, it can be mitigated through freer movement of goods and labor across Russia’s regions. In addition, depending on societal attitudes to inequality, intergovernmental fiscal transfers can play an equalizing role as well.

Unfortunately, comparable consumption aggregates are not available beyond the 19982002 period for all countries in Table 7. For this reason it is necessary to rely on published studies and different sources to examine the extent to which the decomposition exercises presented in this section can be used to look forward. Extending the time horizon of available data, Figure 7 presents a set of results from available studies using group decompositions for Hungary, Poland and Russia, where the graphs show the contribution of each component to overall inequality. Well in line with priors regarding the increase in education premia, there is a large and increasing contribution from differences between education groups. But alongside the increase in differences across different levels of education attainment, the importance and persistence of locational effects in Russia and, even in Hungary and Poland, is striking.

**Figure 7.  Decompositions by groups: Available evidence from existing sources**

**Relative importance of between-groups inequality over time: Russia, Poland and Hungary, % of total inequality**

<!-- Start of picture text --> Russia<br>IX 1992 X 2001<br>Region 10.2 Region 15.2<br>Employment 8.7 Employment 6.2<br>Age 6.6 Education 6.2<br>Location (U/R) 6.3 Age 6.1<br>Gender 2.6 Location (U/R) 5.8<br>Education 1.9 Dependency 1.3<br>Family Size 1.3 Size 1.1<br>Dependency 0.9 Gender 0.9<br><!-- End of picture text --> 31