rafmacalaba/datause-encoder-data huggingface dataset:
Santander, Banco Bilbao Vizcaya, HSBC, Bank of Nova Scotia, Bank of Austria, Caisse Nationale de Credit Agricole, Chile’s Abinsa and O’Higgins banks, and Brazil’s Banco Itau, which joined the already established foreign banks (Citibank, Bank of Boston, ABN Amro, and Lloyds). Probit analysis of foreign banks’ acquisition behavior (Cull 1998) indicates that foreign banks looked for large acquirees as a means to gain market share. The tolerance for weak targets of acquisition, and the desire to build market share quickly made foreign entry particularly valuable.

Foreign bank entry initially was concentrated in the capital city, where foreign banks lent 88.5% of their loan portfolio in 1997, compared to only 40.8% for private domestic banks, but in provinces that privatized their public banks, foreign bank credit grew rapidly after 1995 (World Bank 1998). Foreign banks also initially supplied credit to sectors that were relatively neglected by domestic banks. In particular, foreign banks were much more focused on financing manufacturing sector growth; 29.8% of their loans went to that sector, in contrast to only 13.1% for private domestic banks. Foreign banks maintained comparable or slightly better loan portfolio quality than other private banks, indicating a comparable or slightly lower risk loan clientele. For the period 1997-1998, on average, non-performing loans as a fraction of total loans were 9.7% for foreign retail banks and 8.0% for foreign wholesale banks. Private domestic retail banks had average non-performing loan (NPL) ratios comparable to foreign banks (9%), but higher median NPL ratios. The average NPL ratio for private domestic wholesale banks was 19.4% (Calomiris and Powell 2001). Clearly, foreign bank entry was providing a large, increasingly diverse, and growing supply of credit to the Argentine economy.

The presence of active foreign bank acquirers with available capital also spurred the privatization of many distressed public banks, which otherwise would have been difficult to 46
7 The term JFF may consist of many factors. For example, technology growth, human capital accumulation, increases in the number of hcurs worked per worker, and improvements in the employment of factor inputs would increase EFF. We attempted to account for human capital accumulation in defwning EFF by including literacy rates, school enrollment rates, etc. Inclusion of these variables did not alter our conclusions.<sup>7</sup> Since EFF is constructed to measure the residual of real per capita GDP growth after accounting for the rate of physical capital accumulation, we refer to EFF as improvements in "efficiency." Benhabib and Spiegel (1992] construct physical capital stock measures for over 120 countries. After assuming that the relationship between the capital-output ratio and the capital-labor ratio are constant across time and countries, they use an iterative procedure using investment data to construct capital stock series. We use their data to compute GK.<sup>8</sup> There are numerous statistical and conceptual problems with the construction of physical capital stock data in such a broad cross-section of countries over such a long time interval. Consequently, we also study the ties between the financial indicators and the ratio of gross national investment divided by output, INV. We call GYP, GK, INV, and EFF "growth indicators." In summary, we study the empirical relationship between four financial indicators and four growth inc3⁄4cators. The four financial indicators are the ratio of the size of the formal financial intermediary sector to GDP (LLY), the importance of banks relative to the central bank (BANK), the percentage of credit allocated to private firms (PRIVATE), and the ratio of credit issued to private firms to GDP (PRIVY). Our growth indicator are real per capita GDP growth (GYP), the rate of physical capital accumulation (GK), the ratio of 7 We could not get complete, comparable data on the average number of hours worked per worker for the countries in our data set.

8 We get similar results when we use the capital stock series constructed for the World Bank's 1991 World Development Report.
We estimate the market access measure presented in equation 9 via a gravity trade regression, following Redding and Venables (2004). This methodology is rarely applied in regional studies because of data limitations: bilateral trade flows are often unavailable at the subnational level, particularly for developing countries. Brazil is a fortunate exception since it provides information on international trade flows at the sectoral level for each of its twenty-seven states.

Our trade data set covers the years 1991 to 2002 and eleven sectors.<sup>11</sup> It contains international trade flows between the twenty-seven Brazilian states and 170 partner countries and flows among the 170 foreign countries.

The empirical specification of the trade equation follows from Equation 8. After taking the logs, we obtain For the calculation of a sector-state specific market access variable that varies over time, we estimate equation 10 separately for every sector-year pair.

In the regressions, sector-specific market capacity ( _M js_ ) and supply capacity ( _Sis_ ) of every trading partner are captured by sector-importer ( _FM js_ ) and sectorexporter ( _FX is_ ) fixed effects. φ _ijs_ can be specified using different measures of trade costs. Specifically, we consider bilateral distance ( _dij_ ), whether partners share a common border ( _Bij_ ), the presence of a free trade agreement between the two trading partners ( _RTAij_ ) and whether the two are members of the WTO or its predecessor GATT ( _WTOij_ ). Since we estimate the trade equation separately for every sector-year pair, we can drop the subscript _s_ . Our empirical specification of the trade equation can then be written as where ν is a random bilateral error term. _ij_ In total, we run 132 regressions (12 years × 11 sectors). Given that all coefficients and fixed effects are allowed to vary over time and across sectors, this

> 11 For details on sectoral classification and data sources for the variables used in this section see appendix S4.1 and S4.2.

10
<!-- Start of picture text --> £ga<br>a2ga<br>aS<br>3<br>2a2342z<br>J°a<br>byfea)asa°<br>pa3<br>o<br>5°<br>Apr-2020 May-2020 Jun-2020 Jul-2020 Aug-2020 Sep-2020 Oct-2020 Nov-2020 Dec-2020 Feb-2021 Mar-2021<br>Months from March 2020<br>—®— Young <25 years old. —@— Adult >=25 years old<br>Note: Standard errors clustered on survey wave<br><!-- End of picture text --> <!-- Start of picture text --> 3z 2<br>2<br>| B 3<br>3<br>ao<br>a122<br>©8£g ra<br>fea)&:<br>paan°<br>&apo<br>Apr-2020 May-2020 Jum-2020 Jul-2020 Aug-2020 Sep-2020 Oct-2020 Nov-2020 Dec-2020 Feb-2021 Mar-2021<br>Months from March 2020<br>—®— Young <25 years old —-@— Adult >=25 years old<br>Note: Standard errors clustered on survey wave<br><!-- End of picture text -->

<!-- Start of picture text --> ”a<br>&<br>E<br>$-<br>‘oO2$fo]fa<br>2<br>3<br>zbyot So<br>fea}a<br>‘gu<br>oo<br>=<br>|a<br>7<br>‘Ape2020. May-2020 Jun-2020 Jul-2020 Aug-2020 Sep-2020 Ocr-2020 Nov-2020 Dec-2020 Feb-2021 Mar-2021<br>Months from March 2020<br>—®— Young <25 yearsold ——-®— Adult >=25 years old<br>‘Note: Standard errors clustered on survey wave<br><!-- End of picture text -->
**<u>Table 8: South Asia Trade Balance for Raw Food</u>**

|**Country**|**Ne**<br>|**t Imports**<br>|**(US$ millio**<br>|**n)**<br>|**Net Im**<br>|**ports as**<br>|**% of all im**<br>|**ports)**<br>|
|---|---|---|---|---|---|---|---|---|
||**1980/81**|**1990/91**|**2000/01**|**2004/05**|**1980/81**|**1990/91**|**2000/01**|**2004/05**|
|Sri Lanka|-73|-110|-167|-248|-5|-5|-3|-3|
|Bangladesh|-224|-196|-317|-674|-13|-7|-4|-6|
|India|31|313|1,214|1,865|0|2|3|2|
|Nepal|13|6|1|-53|7|1|0|-4|
|Pakistan|155|0|512|465|4|0|7|3|
|Afghanistan|23|12|-53|-82|9|3|-8|-3|

_Source: UN COMTRADE Statistics Note:_ Food is defined as raw food, excluding all cash crops, processed food products and seafood in South Asia, are illustrative of the importance of drawing the right balance in the policy choice. The evidence is clear that these restrictions accentuated the price volatility, especially in the thinly traded rice market, and created added uncertainty facing food importing countries. Trade restrictions also increase the cost of food security as countries tend to build larger food stock reserves than necessary to counter uncertainty in trade.

On average trade policies for agriculture are more restrictive in South Asia than in other regions (see Table 9) suggesting the scope for lower protection. The efficiency loss from these high trade restrictions needs to be carefully evaluated against the gains. Importantly, trade barriers among neighbors are not very effective over the medium to long term, given physical proximity and informal trade opportunities. Trade cooperation with neighbors would appear to be a more potent way of managing food security than trade bans because such bans simply fuel high-cost informal trade and rent seeking at the expense of both the farmers and the consumers.

**Table 9: Trade Restrictiveness in Agriculture in 2006**

||**Overall Trade**<br>**Restrictiveness Index**|**Tariff Trade**<br>**Restrictiveness Index**|
|---|---|---|
|EastAsia|26.6|8.7|
|Europe and Central Asia|25.9|10.3|
|Latin America & Caribbean|28.1|6.6|
|MiddleEast and North Africa|32.3|12.1|
|**South Asia**|46.4|31.4|
|Sub-Saharan Africa|24.9|13.8|

_Source:_ Kee, Hiau Looi, Alessandro Nicita, and Marcelo Olarreaga, “Estimating Trade Restrictiveness Indices.”

**c) Food Stock and Public Distribution System:** This is another area of controversy. Most countries maintain some kind of a stock to respond to supply shortages in a crisis situation.  Some countries also maintain stocks to support a public distribution system. The goal here is to reconcile the twin objectives of giving farmer appropriate incentives through higher prices but moderating the effects on consumers by providing subsidized supplies to low income group through the public distribution system.

30
**Table 3: Probability regressions for new activity specializations, baseline.**

||Dependent<br>comparativ|variable: whether<br>e advantage in a<br>_t+5_(xj,j,t+5=|country_c_has a<br>ctivity_j_at time<br>1)|
|---|---|---|---|
||(1)|(2)|(3)|
|ASj,j,t<br>|0.075***|0.190***|0.055**|
||(0.025)|(0.006)|(0.022)|
|ωj,j,t|0.061***|0.033***|0.063***|
||(0.006)|(0.003)|(0.005)|
|Country-Year FE|X|X|X|
|Industry-Year FE|X|X|X|
|Sample|Total|Advanced|Developing|
|N|137,723|56,919|80,804|
|AdjustedR<sup>2</sup><br>|0.069<br>|0.087<br>|0.065<br>|

_Notes_ : Results from linear probability regression using equation (4). The independent variables are activity specialization and average proximity , defined in equations (3) and (6). is normalized by subtracting the mean and dividing by the standard deviation to ease interpretation. Robust standard errors jj,jj,tt AASS ωω ωω (clustered by country) are reported in parentheses. * p<0.1, ** p<0.05 and *** p<0.01. Own calculations based on annual data for 2000-2018, see section 3.

_Extensive and intensive margins_ We next investigate the predictive power of proximity for specializations along the extensive and intensive margins. Columns (1-3) in Table 4 report on activity specializations along the intensive margin by adding country-industry fixed effects. Columns (4-6) report on specializations along the extensive margin, adding a country-occupation dummy instead. The role of average proximity is highly significant for both types of specializations. For developing countries proximity appears slightly more relevant for specialization along the extensive margin than the intensive margin, with one standard deviation impact size of 5.2 and 5.0 percentage points respectively. At later stages of development, proximity for specialization along the extensive margin becomes much less important, with impact of only 1.4 percentage points for the extensive margin, and 3.3 percentage points for the intensive margin for the group of advanced economies.

19
most frequently performed planned surgery under the MAP is for cataracts. This is understandable; the poor are more likely to take advantage of the MAP benefit to have this surgery, which had been postponed prior to the program for financial reasons because cataracts are not life-threatening. However, the continuous provision of benefits and inclusion of outpatient care would reduce a surge in demand for such surgeries as was observed at the beginning of the program. Cost-sharing may, however, need to be introduced once the program expands to cover more people, including those who are less poor. Further refinement of the program’s design will require good information and sound evidence. Thus, careful monitoring and constant evaluation of the program is needed to make wise policy decisions.

In conclusion, the results from this analysis using claim data show that the MAP has reached the poorest and achieved its initial objective of increasing utilization of surgical and inpatient services by the poor. However, due to data limitations, the full impact of the MAP cannot be assessed under this study. It is clear that the program design needs further improvement to better reach the targeted population and enhance program sustainability. Given the dynamic state of healthcare reform in Georgia, regular monitoring and evaluation are needed to ensure a good understanding of program performance and improve its success. Towards this end, new data have been requested and large surveys are planned to continue monitoring the outcomes of the program.

19
**Figure 1. The task content of jobs across countries, STEP-based indexes.**

<!-- Start of picture text --> Non-Routine Analytical, STEP Non-Routine Interpersonal, STEP Routine Cognitive, STEP<br>PHPHPHPHPHPHPHPHPHPHPHPHPHPH COCOCOCOCOCOCO LKLKLKLKLKLKLKLKLKCOCOCOCOCOCOCO VNVNVNVNVN<br>VN GHGHVNGHAMVNVN VN GHBOVNVNLKVNLKBOBOBOBOVNBOLKBOLKBOBOBOLKBOLKBOLKLKLKBOBOLKLKBOGEGELKGEAMLKGELKGELKRSLKLKRSRSRSRSRSRSRSRS VN GHGHVNGHAMVN VN GHBOVNVNLKVNLKPHPHBOBO BO BOVNPHLKBOLKPHBOPHBOPHBOLKPHBOLKPHBOPHLKLKPHBOPHBOPHPHBOGEPHGEGEAMGEGERSRSRSRSRSRSRSRSRS VN GHGHVNGHAM GHBOLKVNPHLKBOPHBOBOVNBOBOPHLKBOLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKBOPHLKPHBOLKPHLKPHBOGEPHGELKGEAMLKGELKGELKRSLKLKRSCOCORSCORSCOCORSCORSRSCORSRS<br>VN<br>750 800 850 900 950 750 800 850 900 950 750 800 850 900 950<br>GDP level, log points GDP level, log points GDP level, log points<br>Routine Manual, STEP Non-Routine Manual, STEP<br>VNVNVN LK<br>VN VNVNVNVN LKLKLKLK<br>GHGHVNGHAM GHBOLKPHLKBOPH BO BOBOPHBOLKLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKLKBOPHPHBOLKPHLKBOPHGEPHGELKGEAMLKGELKGELKRSLKLKRSRSRSRSRSRSRSRS VN GHGHVNGHAM GHBOVNVNLKVNLKPHBOPHBOBOBOVNBOPHLKBOLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKLKBOPHPHBOLKPHLKPHBOGEPHGEGEAMGEGERSLKRSCOCORSCORSCOCORSCORSRSCORSRS<br>COCOCOCOCOCOCO VN VNVN<br>750 800 850 900 950 750 800 850 900 950<br>GDP level, log points GDP level, log points<br>.1<br>0 .2<br>-.1 0 .1<br>-.2<br>Skill index -.3 Skill index -.1 Skill index 0<br>-.4<br>-.2<br>-.5 -.1<br>.2 .2<br>.1<br>.1<br>0<br>Skill index Skill index 0<br>-.1<br>-.2 -.1<br><!-- End of picture text --> Note: Each point shows the skill content of jobs using STEP-based indexes per country and year. The horizontal axis measures GDP per capita in PPP from WDI.

11
**Table A.8: Evolution of Mobility by Birth Cohorts Across Urban vs. Rural and Sons vs. Daughters**

|Mbili CEF|**CH**|**INA**|**IN**|**DIA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|---|
|oty<br>Specification|Linear|Quadratic|Linear|Quadratic|Linear|Quadratic|
|Outcome Var|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|
||||**Ur**|**ban**|||
|1980-1989|-0.845***<br>(0.128)|-0.846***<br>(0.136)|-0.841***<br>(0.048)|-0.843***<br>(0.047)|-0.561***<br>(0.072)|-0.561***<br>(0.073)|
|1970-1979|-0.550***<br>(0.095)|-0.551***<br>(0.099)|-0.658***<br>(0.053)|-0.657***<br>(0.053)|-0.476***<br>(0.091)|-0.470***<br>(0.090)|
|1960-1969|-0.465***<br>(0.056)|-0.470***<br>(0.057)|-0.634***<br>(0.072)|-0.631***<br>(0.070)|-0.957***<br>(0.194)|-0.957***<br>(0.194)|
|1950-1959|-0.521***|-0.524***|-0.690***|-0.694***|0.243|0.229|
||(0091)|(0091)|(0100)|(0096)|(0287)|(0287)|
||.|.|.<br>**Ru**|.<br>**ral**|.|.|
|1980-1989<br>1970-1979|-0.787***<br>(0.106)<br>-0.549***<br>(0.060)|-0.787***<br>(0.101)<br>-0.553***<br>(0.060)|-0.456***<br>(0.048)<br>0.051<br>(0.059)|-0.458***<br>(0.047)<br>0.057<br>(0.057)|-0.384***<br>(0.064)<br>-0.086<br>(0.091)|-0.386***<br>(0.065)<br>-0.086<br>(0.091)|
|1960-1969|-0.514***|-0.514***|0.631***|0.656***|0.182|0.184|
||(0.053)|(0.053)|(0.077)|(0.074)|(0.174)|(0.177)|
|1950-1959|-0.115|-0.150**|0.584***|0.577***|0.589**|0.594**|
||(0.079)|(0.075)|(0.112)|(0.105)|(0.287)|(0.287)|
||||**So**|**ns**|||
|1980-1989|-0.774***<br>(0.113)|-0.784***<br>(0.119)|-0.860***<br>(0.038)|-0.863***<br>(0.038)|-0.485***<br>(0.071)|-0.487***<br>(0.072)|
|1970-1979|-0.493***<br>(0.072)|-0.500***<br>(0.073)|-0.956***<br>(0.041)|-0.957***<br>(0.041)|-0.306***<br>(0.075)|-0.303***<br>(0.075)|
|1960-1969|-0.394***<br>(0.058)|-0.399***<br>(0.058)|-0.785***<br>(0.057)|-0.783***<br>(0.056)|-0.528**<br>(0.210)|-0.532**<br>(0.209)|
|1950-1959|-0.383***|-0.388***|-0.608***|-0.630***|0.040|0.059|
||(0.082)|(0.080)|(0.077)|(0.072)|(0.329)|(0.331)|
||||**Daug**|**hters**|||
|1980-1989|-0.724***<br>(0.111)|-0.728***<br>(0.113)|-0.262***<br>(0.050)|-0.262***<br>(0.049)|-0.473***<br>(0.063)|-0.469***<br>(0.064)|
|1970-1979|-0.315***<br>(0.072)|-0.322***<br>(0.073)|0.470***<br>(0.057)|0.471***<br>(0.057)|-0.263***<br>(0.086)|-0.262***<br>(0.086)|
|1960-1969|-0.388***|-0.396***|0.984***|0.986***|-0.049|-0.048|
||(0.062)|(0.061)|(0.074)|(0.072)|(0.171)|(0.169)|
|1950-1959|0.059|0.051|1.126***|1.121***|0.918***|0.919***|
||(0.110)|(0.108)|(0.119)|(0.121)|(0.256)|(0.256)|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) Each row uses a subsample of children who were born in the given birth cohort and the given category.

44
**Table A1. Costs and technical performance of technologies for the residential sector**

|||Capitalcost<br>||Fu|elconsumption<br>||
|---|---|---|---|---|---|---|
||Unit|Inefficient<br>technology|Efficient<br>technology|Unit|Inefficient<br>technology|Efficient<br>technology|
|||**Elect**<br>|**rical Appliance**<br>|**s**<br>|||
|Refrigerator|USD/unit|514.3|742.9|kWh/unit/year|800.0|500.0|
|TV|USD/unit|195.9|457.1|kWh/unit/year|363.2|116.8|
|Washingmachine|USD/unit|329.5|398.5|kWh/unit/year|227.1|153.6|
|Lightbulbs|USD/unit|0.6|46.9|kWh/unit/year|115.2|23.0|
|||**Bui**|**lding Insulation**||||
|Plastic double-<br>glazed windows|USD/building|272.0|544.0|cu.m/ building<br>/year||-18.5%|
|||||cu.m/ building|||
|Roof insulation|USD/building||400.9|/year||-14.7%|
|Wall Insulation|USD/building||1395.8|cu.m/ building<br>/year||-36.0%|

Sources: analysis of technologies sold on Georgian market for electric appliances, for building insulation measures – energy audits (Sustainable Development Centre Remissia 2014) Table A.2 presents penetration rates of different technologies in households and Table A.3 presents penetration rates of efficient appliances.

**Table A.2. Penetration rates of technology in households (%)**

|Appliance|2014|2035|
|---|---|---|
|Refrigerator|84%|93%|
|TV|100%|100%|
|Washingmachine|68%|83%|

Table A.3 shows the assumption on share of efficient technologies in new stock of technologies for baseline and mitigation scenarios, and value of early switching parameter. For example for refrigerators, in baseline case it is assumed that every ear 20% of newly bought refrigerators are efficient, in mitigation scenario share starts from 25% in 2015 and goes up to 82% by 2034. The baseline values are based on 2014 data from residential survey (Winrock international, 2014), and shares for mitigation scenarios are based on expert assessment for moderate mitigation measures.

31
Tables 3 and 4 and also control for the possible endogeneity of the tenure status with a Two-StageLeast-Squares regression.

We use several measures of financial vulnerability. Our first measure is FINBURDEN, an ordinal response to the question how burdensome the total housing cost is. Responses vary from 3 = a heavy burden, over 2 = somewhat a burden, to 1 = not a burden at all, higher values thus indicating a higher burden. While this is a subjective measure of financial burden, we also use several objective measures of financial vulnerability. First, we use three binary variables indicating whether a household has (i) arrears on mortgage or rent payments, (ii) arrears on utility payments, and (iii) arrears on hire purchase installments or other loan payments. Second, we use a binary variable indicating whether a household had any arrears on one of the above payments. Finally, we use an indirect indicator of financial vulnerability; a binary variable indicating whether a household has unmet medical needs due to lack of finance. As medical care should be high on the priority list of any household, not being able to meet medical needs due to lack of financial resources certainly indicates financial vulnerability.

There is a large variation across countries in indicators of financial burden and vulnerability, as reported in Table 5. Here we report data for 2007, while Appendix Tables A7 and A8 report the same statistics for 2005 and 2006, respectively. The average self-reported burden varies from 1.3 in Denmark to 2.6 in Cyprus. Self-reported burden are significantly higher in the new EU countries than in the old EU countries, with little variation over time. In 2007, the share of population that has (i) arrears on mortgage or rent payments, (ii) arrears on utility payments, or (iii) arrears on hire purchase installments or other loan payments varied from 2.7% in Luxembourg to 26.6% in Greece. While in 2005, the share of population with arrears was, on average, significantly higher in the new EU countries (16.0%) than in the old EU countries (9.7%), there was no significant difference in 2007, due 24
Based on these two observations, Duflo (2000; 2001) used the interaction between individuals’ age in 1974 and the number of schools built in their region of birth to evaluate the impact of the program. For instance, the difference between education of men who were aged 2 to 6 in 1974 (the exposed cohort) and that of men aged 12 to 17 in 1974 (the unexposed cohort) was 0.47 in regions that got more schools, and 0.36 in regions that got fewer schools. Duflo attributed the difference in differences (0.12) to the program, under the assumption that the increase in years of schooling would not have been systematically different between low and high intensity program regions in the absence of the program itself. She checked this assumption by estimating the same differences in differences for two cohorts who were never exposed to the program and found no effect.

Having controlled for region of birth and cohort of birth fixed effects, interactions between an individual’s age in 1974 and SIDP program intensity in the region of birth may be used as instruments to estimate the impact of schooling on use of maternity care services. Using this instrument requires data on two population cohorts: the “control” group of individuals born between 1950 and 1962 and the “exposed” group of individuals born between 1963 and 1974. Women belonging to these two cohorts would have entered their childbearing years during 1988-2000, the period for which IFLS data are available 25