rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Northern<br>Mountain « Fi<br>Red<br>River Delta<br>Central Coast<br>-<br>Central<br>Highlands<br>South East<br>7<br>Mekong °<br>RiverDelta é *<br><!-- End of picture text --> <!-- Start of picture text --> Northern A><br>Mountain *® e sag:<br>ate Red<br>- River Delta<br>CentralKS :<br>ep<br>Percentage of people e 5<br>in the rich quintile SP aR ANT<br>Central<br>— hes Highlands? "p<br>Hl 20 - 40 is 4<br>C4 40 - 100 South East tah<br>Mekong ©<br>RiverDelta r”> *<br><!-- End of picture text -->
fees. Having said that, providers could opt to use the vouchers to prescribe simple treatment instead of severe because this strategy leads to an almost guaranteed purchase. In this case, providers might be revenue driven and motivated to oversell treatment, but they may not be “driving demand” (prescribing more treatment than patients would like) per our definition.

Fortunately we can test whether providers are revenue motivated independent of whether this leads doctors to drive demand for severe malaria treatment: prediction (6) in Appendix A shows that vouchers will lead to an increase in clinic revenue when doctors are purely revenue motivated, and prediction (7) says that revenue-maximizing doctors will use the informational advantage in the _DV_ treatment to increase clinic revenues relative to _PV_ .<sup>32</sup> In practice, we find no significant differences in (per-patient) clinic revenues across treatment arms (Appendix Table B15). Moreover, the average patient in both _PV_ and _DV_ pays roughly CFA 500 _less_ than in the Control group (significant at the 5 percent level for _DV_ only). Lastly, from the results we already saw, ACT prescriptions and purchases for low-risk patients, voucher use, and overall treatment rates in DV do not increase at the same rate as in PV. This is not consistent with providers who are simply trying to sell as much treatment as possible. All told, our results do not paint a picture of providers primarily motivated by revenue, at least on the margin.

# **4.4 Quantifying Contributions to Overtreatment**

What do doctors’ gatekeeping failures mean for overuse of antimalarials? Ideally, we would use the home-based RDTs to directly assess the match between prescriptions/purchases and the patient’s underlying malaria status. Unfortunately, patients who consented to the home-based test were significantly sicker and more likely to have malaria, based on our predicted risk metric, than patients who refused the test (Appendix Table B6). Since _PV_ - _DV_ differences in malaria prescriptions and purchases are concentrated among those with the lowest predicted malaria risk, relying on the home-tested subsample could understate _PV_ - _DV_ differences in appropriate treatment.

As an alternative, we focus on “expected match quality” based on predicted malaria risk (denoted _π_ ̃), which we decompose into two parts. First,

> 32The greater number of patients on voucher days versus control days complicates our test of prediction (6), so we place more weight on prediction (7).

31
Docquier, F. and A. Marfouk (2006): International migration by educational attainment (1990-2000). In C. Ozden and M. Schiff (eds): International Migration, Remittances and Development. Palgrave Macmillan: New York.

Docquier, F., B.L. Lowell and A. Marfouk (2009): A gendered assessment of highly skilled emigration. Population and Development Review, 35 (2), pages 297-321.

Docquier, F. and H. Rapoport (2012): Globalization, brain drain and development. Journal of Economic Literature, 50 (3), pages 681-730.

Dollar, D. and R. Gatti (1999): Gender inequality, income, and growth: are good times good for women? World Bank Policy Research Report on Gender and Development, Working Paper Series No. 1.

Dom ́enech, R. and A. de la Fuente (2006): Human Capital in Growth Regressions: How much difference does data quality make? Journal of the European Economic Association, 4 (1), pages 1–36.

Dumont, J.C. and G. Lemaˆıtre (2004): Counting immigrants and expatriates in OECD countries: a new perspective. Mimeo: OECD.

Dumont, J.C., J.P. Martin and G. Spielvogel (2007): Women on the move: the neglected gender dimension of the brain drain. IZA Discussion Paper 2920.

Grogger, J. and G.H. Hanson (2011): Income maximisation and the selection and sorting of international migrants. Journal of Development Economics, 95 (1), pages 42-57.

Head, K., Th. Mayer and J. Ries (2010): The erosion of colonial trade linkages after independence. Journal of International Economics, 81 (1), pages 1-14.

44
# **Figure B10: Gold**

<!-- Start of picture text --> Log scale<br>8<br>Log price Permanent component<br>7<br>6<br>5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.0<br>Medium-term cycle Transitory component<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 51 —_
|Cut-Off<br>C1<br>C2||9.25***<br>[4.29]<br>11.68***<br>[5.41]|Marginal|Effects|[3.16]<br>9.28***<br>[3.02]<br>11.85***<br>[3.85]||
|---|---|---|---|---|---|---|
||Allowed|Conditioned|Blocked|Allowed|Conditioned|Blocked|
|ln(HHI)|-0.142|0.125|0.017|-0.155|0.139|0.016|
|HIST|-0.087|0.075|0.012|-0.141|0.124|0.017|
|EQ|-0.545|0.390|0.154|-0.470|0.375|0.095|
|EF|-0.085|0.073|0.011|-0.125|0.110|0.015|
|IMP|0.122|-0.108|-0.014|0.117|-0.106|-0.012|
|EB|-0.223|0.190|0.033|-0.192|0.169|0.023|
|MKT||||0.014|-0.013|-0.001|
|FRG||||0.256|-0.222|-0.034|
|Observations||239|||239||
|Pseudo R2||0.20|||0.24||

Notes: (1) Robust z-statistics in brackets; *, ** and *** represent statistical significance at 90, 95 and 99 percent level of significance, respectively. (2) Marginal effects of binary variables measure the effect of a change from 0 to 1.

In the upper right part of Table II we present the results of the model with all three elements of equation (1), i.e. _H_ , **_Z_** and **_X_** . The two variables included in **_X_** ( _MKT_ and _FRG_ ) affect the FCC’s decision in a significant way; moreover their high explanatory power is shown by the increase in 4 percentage points in the pseudo-R<sup>2</sup> . Once we control for elements in **_X_** , variable _EQ_ loses significance, indicating a possible correlation between this variable and elements included in matrix **_X_** .<sup>23</sup> The results show that when the firms involved in the merger concentrate a low market share (less than 25 per cent), their case is more likely to be allowed than if they concentrate a high share of the market. More importantly, the marginal effects show that, contrary to what is explicitly stated in the mergers provisions, one of the strongest determinant affecting the Commission’s decision is the presence of a foreign firm in the merger. Everything else being equal, foreign firms participating in a Mexican merger have a probability 25.6 percentage points lower than mergers involving only Mexican firms of being issued (either conditioned or blocked).

> 23 Although some regressors show some degree of correlation, the Variance Inflation Factors (VIF) test for multicollinearity didn’t show a serious problem.

18
**-24 -**

Figure 7. Real producer prices for major crops in Sub-Saharan Africa <!-- Start of picture text --> Index 1970-75 = 100<br>120<br>110<br>100 X<br>90<br>80 \<br>70<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>18 country average<br>- -<br>Food crops Export crops<br><!-- End of picture text --> Figure 8. Ratio of export/food producer prices in Sub-Saharan Africa <!-- Start of picture text --> Index 1970-75 = 100<br>110<br>105<br>100<br>95-<br>90 \<br>85-<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>18 country sample<br><!-- End of picture text -->
obstacles significantly impeding them from serving the segment. In other words, the potential benefits of serving SMEs have generated incentives for banks to develop ways to overcome the institutional limitations they perceive as obstacles. Fourth, in the case of international banks present in both countries, the strategies to engage SMEs across countries are remarkably similar. Finally, the institutional environments appear to be more relevant for certain types of financing, like long-term loans in fixed rates in domestic currency. Thus, policy action might focus on complementing what banks need to expand their participation and provide the type of financing that might still be lacking.

The paper is organized as follows. Section 2 presents the data on Argentina and Chile. Section 3 discusses the extent and type of SME financing. Section 4 analyzes the drivers of bank involvement with SMEs. Section 5 describes the obstacles to SME lending. Section 6 discusses how competition and government programs affect bank involvement with SMEs. Section 7 concludes.

# **2. Data**

Our analysis is based on information gathered by means of on-site interviews with banks’ top management, the use of a tabulated questionnaire, and a detailed data request designed to obtain unique information on bank lending to SMEs, which are not available to the Central Banks of each country. The interviews and data processing are confidential, so banks felt practically no constraint in sharing their information, with the understanding that the data would be reported in an aggregate way, without disclosing each bank’s strategy or positions. The questionnaire was designed to address three broad areas: (1) measuring the extent of bank involvement with SMEs, (2) learning about the determinants of the degree of bank financing to 5
**Figures 8(a)-(d):  Portfolio Flows and Exchange Rates—Switzerland and Brazil**

<!-- Start of picture text --> (a) Swiss Net Portfolio Investment, (b) Swiss Franc, Nominal Exchange Rates<br>Quarterly, 2.5<br>Swiss Francs, millions<br>2<br>30000<br>20000 1.5<br>Pound<br>10000<br>1 Euro<br>0<br>Dollar<br>-10000 0.5<br>-20000<br>0<br>-30000<br>-40000<br>(c) Brazilian Net Portfolio Investment from (d) Real per Dollar Nominal Exchange<br>Foreigners, Quarterly, US$ millions Rate, Monthly<br>30000 2.6<br>25000 2.4<br>20000 2.2<br>15000 2<br>10000 1.8<br>5000 1.6<br>0 1.4<br>-5000 1.2<br>1<br>-10000<br>-15000<br>-20000<br>1/1/08 5/1/08 9/1/08 1/1/09 5/1/09 9/1/09 1/1/10 5/1/10 9/1/10 1/1/11 5/1/11 9/1/11<br>2008 01 2008 06 2008 11 2009 04 2009 09 2010 02 2010 07 2010 12 2011 05 2011 10 2012 03<br>2008-I 2008-II 2008-III 2008-IV 2009-I 2009-II 2009-III 2009-IV 2010-I 2010-II 2010-III 2010-IV 2011-I 2011-II 2011-III 2011-IV 2012-I<br>2008M01 2008M04 2008M07 2008M10 2009M01 2009M04 2009M07 2009M10 2010M01 2010M04 2010M07 2010M10 2011M01 2011M04 2011M07 2011M10 2012M01 2012M04<br><!-- End of picture text --> Sources: Swiss National Bank, Monthly Statistical Bulletin, Tables 3.1 and G1. _Banco Central do Brasil,_ Balance of Payments, Table 1, Preliminary data for 2007-2012.

In the post crisis period, capital flows to emerging economies rebounded strongly. The largest recipients of inflows of capital from the advanced economies have been in emerging economies in Asia, Latin America, as well as some emerging economies in other regions (e.g. South Africa and Turkey). According to a recent IMF study, in several emerging economies, _net_ inflows have risen to close to alltime highs, although on a _gross_ basis total inflows to these countries have yet to reach their pre-crisis peak.<sup>73</sup> However, the recent episode is characterized by a predominance of the highly volatile portfolio 73 IMF, “Recent Experiences in Managing Capital Inflows—Cross-Cutting Themes and Possible Policy Framework, February, 2011.

33
Figure 1: FDI Concentration has been rising across the income spectrum (20032022 <!-- Start of picture text --> 55<br>50<br>45<br>40<br>35<br>2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br>High Income Developing<br>Concentration Ratio<br>5 largest investing MNEs 3-year Moving Average<br><!-- End of picture text --> _Notes_ : The analysis relies on reported capital expenditure of FDI greenfield projects. The concentration ratio of country groups is calculated as the three-year moving average of each country’s concentration ratio, weighted by the country’s total FDI investments. Countries with on average less than 10 investing MNEs per year are excluded from the sample. Countries are considered ‘developing’ if classified as low income, lower-middle income or upper-middle income in 2003 based on the World Bank’s income classification.

Table 1: FDI concentration dynamics

|VARIABLES|(1)<br>CR5||(2)<br>HHI|(3)<br>CR5|(4)<br>HHI|(5)<br>CR5|(6)<br>HHI|
|---|---|---|---|---|---|---|---|
|Trend _×_ Developing|0.368**<br>(0.168)||0.018**<br>(0.008)|0.341**<br>(0.162)|0.018**<br>(0.008)|0.347*<br>(0.183)|0.016*<br>(0.009)|
|Observations|2,349||2,349|2,691|2,691|2,047|2,047|
|R-squared|0.831||0.749|0.856|0.791|0.799|0.718|
|Minimum # of||||||||
|Foreign Investors||5||1||1|0|

_Note_ : Each observation corresponds to a destination-year, represented as _it_ . “CR5” refers to the concentration ratio of the top 5 investing firms. Analysis in Columns 1 and 2 excludes destination countries averaging less than 5 foreign investors; Columns 3 and 4 include all destinations without restrictions on the number of foreign investors; Columns 5 and 6 exclude destinations averaging less than 10 foreign investors. “HHI” denotes the HerfindahlHirschman concentration index, computed using the corresponding sample for “CR5”. For ease of interpretation, HHI values are expressed in natural logarithm form. The indicator variable, _Developing_ , is assigned the value 1 for countries classified as Low, Lower-Middle, or Upper-Middle income, as per the 2003 World Bank classification. The analysis includes country fixed effects and (world) region by year fixed effects. Standard errors are clustered at the destination country level. Statistical significance denoted by; *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

6
Figure 5.3Air Pollution Intensity for Selectec —>tors

## VOC Pollution Intensity

15000 rs SS 10000 3 E 5000 J [=] oa =-PSso oeo o oarBBS“oo an4 oaSBRoO oaDyed oTMBS— —©BRte]omoaREE~N“ woERBECor]— oOo —_BESoO Nno al“ ISIC Codes <!-- Start of picture text -->
|<br><!-- End of picture text -->

### Particulate Intensity

60000 w=g 4000050000+ | 8 330000 z 3 20000 oa 10000 - 0; i S&F885883855EFSBESSSFERB8&8EBS885PSE8aSR BERSs 8 8B ISIC Codes

|

# PM 10 Pollution Intensity

<!-- Start of picture text --> 100000 |<br>€ 80000 ;<br>®@<br>n 60000 |<br>2 6 i<br>Cs) ;<br>2 40000 :<br>3<br>=<br>20000 [<br>o | m.<br>n wm o Sa f=1] a - a o wo - So - N ise] = So So oOo i=]<br>a$558— - a a88ond -3885N - =85nN $58N N o& N8 Nt8 Ss-— rd8<br>ISIC Codes |<br><!-- End of picture text -->
**Table 3.4.1 : Additional Canadian Data Included in AD-CAN-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-C**|**AN-Master :**||
|**U.**|**CASE_CBSA**|Case code associated with the dumping investigation administered<br>by the Canadian Border Services Agency (CBSA)|
|**V.**|**CASE_CITT**|Case code associated with the injury investigation administered by|
|||the Canadian International Trade Tribunal (CITT). Only assigned<br>in the final stage.|
|**W.**|**RELATED_CVD**|Case code of a related countervailing duty investigation against<br>the same product and country, see CVD-CAN workbook|
|**X.**|**INIT_DATE_CBSA**|Date of initiation of the case at the CBSA|
|**Y.**|**INIT_DATE_CITT**|Date of initiation of the case at the CITT|
|**Z.**|**P_AVG_DUTY**|The weighted average preliminary duty|
|**AA.**|**F_AVG_DUTY**|The weighted average final duty|
|**AB.**|**TERM_DATE**|The date a case was terminated for reasons such as the case was<br>withdrawn by the petitioner or there was insignificant evidence of<br>dumping|
|**AC.**|**NOTES**|Comments|
|**In AD-C**<br>**D.**|**AN-Products :**<br>**NOTES**|Comments|
|**In AD-C**|**AN-Domestic-Firms :**||

|**C.**|**NOTES**|Comments|
|---|---|---|
|**In AD-C**|**AN-Foreign-Firms :**||
|**D.**|**F_AD_MARGIN_FIRM**|Final dumping margin in percentage terms of normal value as<br>decided for a specific firm by the CBSA|
|**E.**|**F_AD_NDUTY_FIRM**|Final firm-specific dumping duty assessed in percentage terms of<br>normal value|
|**F.**|**F_AD_DUTYPAYABLE_FIRM**|Final dumping margin in percentage terms of export price as<br>decided for a specific firm by the CBSA|
|**G.**|**F_AD_EDUTY_FIRM**|Final firm-specific dumping duty assessed in percentage terms of<br>export price|
|**H.**|**NOTES**|Comments|

There are a few important caveats to the Canadian data with respect to the levels of duties imposed. Prior to 2001, most duties were reported in percentage terms of normal value, as opposed to percentage terms of export price. Thus, on the Master Spreadsheet, duties are in terms of normal value 20
as di¤erences in methodology or estimation errors, this decomposition exercise focuses solely on the role of ...rm heterogeneity in explaining the wedge. Firm heterogeneity matters because ...rms of di¤erent sizes have di¤erent import intensities. By restricting the census sample to large ...rms according to the IO cuto¤ criteria, we are able to account for the di¤erence between our aggregate DVAR and that of the IO table-based estimate of KWW12.

# 3.3 Extension to Non-Processing and Aggregate Exports

The methodology we have developed above is suitable for pure exporters who export all their products, and that the products are produced by using up all the materials they have imported. It requires the condition that no ...nal products or imported materials may leak to the domestic economy. A lot of exporters that engage in global value chains should satisfy this condition, in the form of processing trade, such as garment producers in Bangladesh, Guatemala, and other emerging economies.

However, many exporters are not processing exporters. Unlike processing exporters, nonprocessing exporters do not export all their outputs. In addition, they often use some of their imported materials to produce goods for domestic sales. Thus, the condition that no ...nal output or imported materials leak to the domestic economy is not met. How ...rms split their imported inputs between production for domestic sales and exports is generally unknown.

To extend our methodology to measure the DVAR of the non-processing exporters, we need to make one proportionality assumption at the ...rm level: the allocation of the ...rm’s inputs to the production for exports is proportional to the share of exports in total sales, which we may infer from our industrial survey data. This assumption is equivalent to assuming that the DVAR is the same between exports and domestic sales of the ...rms. Our proportionality assumption will likely be non-binding if ...rms produce the same products for 17
sent to parents and focused on the youngest child below 6 years old in the household. The text messages were personalized (making a reference to the name of the child) and age appropriate (with different messages sent to parents of children 0-6 months old, 7-12 months old, 13-24 months old, and older than two years old). Messages were designed to actionable advice based on a childhood cur- provide government early development riculum, and built on prior experiences with early childhood development programs in the country. The text message intervention was designed by a Nicaraguan early childhood specialist, in consultation with the ministry of the family and with support from the World Bank.

Prior to program implementation, a baseline survey was conducted in 97 rural villages in the four all households with children 0 to 6 of or municipalities, including years age pregnant women. Earlier work in nearby municipalities (Macours and Vakis, 2014) had demonstrated the role of local leaders in influencing behavioral changes for investment in nutrition and health of young children. The baseline survey therefore also identified, among households with children 0-6 (or pregnant women), all households with local opinion leaders regarding early childhood practices. This included community health workers, preschool teachers, primary school teachers, and a village leader.<sup>4</sup> In the 97 villages, there were a total 2,990 households with at least one child younger than 6 years old, including 406 opinion leaders. Five villages had no opinion leaders among households with young children.

For households without leaders, a household-level randomization determined which households would receive text messages: 75% were randomly selected to receive text messages, with the remaining 25% serving as control.

In addition to treatment assignment, orthogonal treatment variations were introduced among households selected to participate in the program. First, we randomly selected whether text messages were sent to (i) the father figure (25 percent of the sample), (ii) mother figure (25 percent of the sample) or (iii) both (25 percent of the sample). Second, the thematic content of the text messages varied. Treated households were randomly 4Not included were religious leaders, and those responsible for programs unrelated to health, education or ECD.

7
subsistence crops suggests that growing subsistence crops traps households in a cycle of poverty. Poverty imposes constraints that prevent households from diversifying into income-generating cash crops. Additionally, growing low-income-generating subsistence crops prevents households from making the investments required to grow cash crops. These constraints might be regional, as some regions in Sudan have greater access to the infrastructure, such as irrigation, that sustains commercial crop cultivation.

Figure 21: Crop Production and Poverty, 2014/15 (a) Crop Cultivation by Households as a Proportion of (b) Crop Cultivation and Poverty Total Agrarian Households <!-- Start of picture text --> 50%<br>60%<br>40%<br>40%<br>30%<br>20%<br>20%<br>0% 10%<br>Subsistence Only Only Millet Crops other<br>Crops Only* Sorghum Grown than 0%<br>Grown sorghum or Maize only Millet only Groundnut All other<br>millet or Sesame crops<br>Poverty Rate<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15. _Note_ : * The household grows only sorghum or millet.

Households that sell a higher proportion of their produce in the market have lower rates of poverty (Figure 22). Crops such as millet, sorghum, and lentils are grown predominantly to be consumed within the household. Average rates of poverty in households that produce these crops are higher than the average rates of poverty in households that produce crops that are predominantly sold in the market, such as wheat, dates, and Egyptian beans. This again suggests the nexus between subsistence crop production, lower household incomes, and poverty. However, a causal relationship is hard to establish as regions with better market infrastructure are likely to be better-off in general. Such regions will tend to sell more of their crop in the market and have a reduced likelihood of poverty, irrespective of market engagement by agrarian households.

24