rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: Collective Investment Scheme Providers (December 2005) <!-- Start of picture text --> 60 10,000<br>9,000<br>50<br>8,000<br>7,000<br>40<br>6,000<br>30 5,000<br>4,000<br>20<br>3,000<br>2,000<br>10<br>1,000<br>0 0<br>Fiduciarias - FCO Fiduciarias - FCE Fiduciarias - FPV Comisionistas de SAFI - FI Fondos Mutuos<br>Bolsa - FV Vigilados (FMI)<br>Number of Funds Number of Fund Managers Assets under Management (Col$ billion)<br>Number<br>Col$ Billion<br><!-- End of picture text --> _Source: Superintendencia Financiera, own analysis._ _Note: FPVs managed by AFPs and insurance companies are not included. Many of the same fiduciarias offer FCOs, FCEs and FPVs, while the vast majority of fondos mutuos vigilados manage their own investments._

**The existence of specialized exclusive vehicles for the management of collective investment schemes is standard practice in other countries** . For example, all peer countries require such schemes to be managed by dedicated vehicles, primarily in order to protect the interests of small investors<sup>25</sup> . Chile has a legal vehicle for mutual funds management ( _administradora de fondos mutuos_ ) that can be owned by banks, insurance companies or brokerage companies. Both Mexico and Spain also have specialized vehicles for mutual fund management ( _sociedad de inversion_ and _sociedad de gestión de instituciones de inversión colectiva_ respectively) that can be owned by banks.

Figure 4: Size of Mutual Fund Industry in Selected Countries (2004) <!-- Start of picture text --> Spain 32%<br>Colombia 4%<br>Mexico 5%<br>Chile 13%<br>0% 5% 10% 15% 20% 25% 30% 35% 40%<br>% of GDP<br><!-- End of picture text --> _Source: Investment Company Institute, World Bank, Superintendencia Financiera, own analysis. Note: Figures for Colombia include FCO, FCE, FV and FI._

> 25 For example, IOSCO (1996) states: “ _It is advisable that the manager of a CIS [Collective Investment Scheme] should be prohibited... from conducting any other business than that of a CIS in the CIS or in any portfolio. This requirement is necessary to prevent a conflict of interest between the business of the CIS and such other business_ ”.

22
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40
# **4.3.2 Job seekers**

Why do job seekers not request reference letters from employers? We report here the results from Experiment 3, which is designed to test the relative importance of the cost and perceived benefits of obtaining letters. As explained in Section 3, a sub-group of job seekers previously encouraged to obtain a letter receives a reminder on how to return it. Participants receiving this reminder were randomized into three groups. The control group received only the reminder, while the other two groups received either information on the returns to having a reference letter or a monetary incentive. We estimate the following specification: The outcome _yij_ is a binary measure of whether individual _i_ residing in location _j_ returned the reference letter. We report estimates with and without controlling for covariate vector _Xi_ . To account for differences across space, we control for location fixed effects _λj_ .

Table 7 shows the estimated coefficients. Pooling the information and monetary incentive treatment groups, we find a statistically significant increase in the share of people who obtain a letter of 7.4 percentage points (Column 2). When we estimate the effect of each treatment arm separately, we find that the information treatment effect is 12.6 p.p. and statistically significant, while the effect of the monetary incentive is much smaller (4.5 p.p.) and statistically indistinguishable from 0 (Column 4). We can reject that treatment effects are identical at the ten percent significance level. Overall, the results from Experiment 3 show that job seekers may underestimate the potential benefits of reference letters. This, in turn, could help explain the low usage of letters in this market. One of the reasons why job seekers would come to believe that reference letters are not beneficial is that the type of letter in circulation at baseline may in fact be of lower value. Clearly, the effectiveness of the reference letter depends on the noisiness of the signal relative to the resume. Reviewing a total of 30 reference letters collected from job seekers in our sample at baseline reveals that existing letters in the market are generally of low quality. The majority of letters lack information on the workers’ position (48% include this information), responsibilities (38%), skills (28%) duration of employment (48%), and reason for termination of employment (18%). In addition, only 48% of letters are signed and only 56% provide contact information. If job seekers are using reference letters that are both less informative and credible, they may incorrectly infer that all letters are 27
We find that households in transformers with a higher intensity of treatment (i.e., a higher saturation of households within a transformer receive CFLs) have fewer days reported without electricity due to unplanned outages.<sup>10</sup> This result is robust to including a number of controls, such as the baseline number of reported outages, and is supported by calculations of expected peak load reduction. We also find evidence suggesting that households in transformers with improved reliability consume more hours of electricity services due to fewer outages, and that the household energy savings are larger after accounting for this technological externality. Lastly, using these results we perform a simple cost-benefit analysis, which illustrates the importance of accounting for these spillovers and technological externalities when estimating the welfare impacts of such a program.

Our experiment contributes to the literature on energy efficiency in several ways, and offers a novel experimental design for identifying externalities at various treatment levels.

First, it contributes to the debate regarding the impacts of energy efficient technologies and their potential to reduce energy demand. Energy efficiency depends not only on the development of relevant technologies, but also on the interaction of end-user behavior with such technologies (Allcott and Mullainathan, 2010). Prior to ours, no experimental or quasiexperimental studies existed measuring the impacts of energy efficient lighting. The one previous study of a large-scale CFL distribution program found that approximately 20% of initial electricity savings dissipated over time (Costolanski et al., 2013), thereby contributing to skepticism regarding the gains from efficient lighting technologies and from energy efficiency more generally. Our experimental finding that CFL treatment results in a significant electricity consumption reduction very close in size to the expected technologically feasible reductions contributes to balance the evidence from such existing misidentified studies. Our finding is not only relevant for developing countries, but is also consistent with results from the United States, where lighting is a relatively smaller share of energy consumption.<sup>11</sup> This new evidence suggests that energy efficient lighting, and perhaps other energy efficient technologies as well, should remain on the menu of potential policy options.

Second, to our knowledge, our experiment is the first to use a randomized saturation design

> 10We collect data on reported days without electricity due to unplanned outages during both the baseline and follow-up surveys.

> 11Burlig et al. (2016) are performing a machine-learning analysis of energy efficiency in California schools. Their preliminary results indicate substantial savings from the energy efficient lighting interventions.

4
27 Furthermore, it can be implemented with a standard household expenditure or multipurpose survey. By contrast, the alternative approach focuses (purportedly) on costs incurred in the current period, ignoring costs incurred in other periods. Because of this, and because it overlooks the fact that some costs (e.g. forgone returns on assets and loan repayments) are likely to be incurred in part in the period when the medical bills are incurred, it is likely to provide a lower bound, possibly a highly conservative one.

Subject to the caveats associated with the naïve methods of measuring financial protection, some general points emerge from the empirical literature. Financial protection in health appears to vary across countries. In part this reflects the role of per capita income: on average, higher rates of catastrophic payments are found in poorer countries, so that the world’s poor who can least afford large out-of-pocket payments for health care at greatest risk. However, differences exist across countries within income groups. These differences appear to reflect in part income inequality, but also the extent to which health care payments are pre-paid through some form of insurance.

The role of insurance or pre-payment comes through in country studies: expansion of coverage tends to reduce the incidence of catastrophic spending and impoverishment, while a reduction in the depth of coverage has tended to be associated with higher rates. Caveats emerge, however. Studies point to a variety of factors that together influence the size of the effect of insurance on financial protection: insurance tends to increase the _quantity_ of care received, putting upward pressure on out-of-pocket payments; some benefit packages are not especially generous, with high deductibles, high co-insurance rates, low reimbursement ceilings and multiple exclusions; providers may
- 41 -

Table 4

# Heterosoedasticitv Tests

||Table 2|.a Models|Table|3.a Models|
|---|---|---|---|---|
||rIK2|Sig. Level|nR <sup>2</sup>|Sig. Level|
|1.|13.00|0.250|13.65|0.250|
|2.|13.65|0.375|15.60|0.250|
|3.|11.05|0.500|13.00|0.375|
|4.|19.50|0.100|25.35|0.025|
|5.|17.55|0.175|20.80|0.100|
|6.|21.45|0.050|20.80|0.075|
|7.|18.20|0.250|18.85|0.175|
|8.|17.55|0.250|16.90|0.250|
|9.|20.15|0.175|26.65|0.038|
|10.|20.15|0.175|22.75|0.100|

Note: (a) The test is a panel data extension of White (1980), and is based on the regression of the squared residuals from the equations of tables (2) and (3) on the moments of the independent variables (see footnote 13).
Recent work by Sheahan and Barrett (2017) also documents pesticide use in Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda using the LSMS-ISA data: 16% of farming households apply at least one agri-chemical to their crop during the primary season but again find considerable heterogeneity in use both across and within countries. RuLIS data on agri-chemical use among crop farming households in 13 SSA countries is illustrated in Figure 2, for small-scale food producers and larger household farms separately.<sup>9</sup> Similar to the observed patterns of fertilizer use, chemical input use is considerably lower among small-scale food producers than among larger farms. In Burkina Faso, for example, only 25% of small-holder crop-farming households apply any chemicals, while 49% of households operating larger farms report using chemical inputs. Among these countries, Ghana exhibits the highest incidence, with 63% of households applying chemical inputs. Haggblade et al. (2021) document that pesticide use has tripled in West Africa in the last twenty years, arguing that growth in use is driven by a combination of increasing labor costs, increasing pest pressures, and falling prices of generic pesticides in markets.

_Figure 2. Share of crop farming households using chemicals. Source: RuLIS._ The most commonly used pesticide in the world is glyphosate herbicide (Benbrook 2016). Glyphosate is a non-selective herbicide, often used by farmers to kill weeds before planting. It is sold in concentrated form and diluted with water before application with a sprayer. Use of agri-chemicals remains relatively low in Sub-Saharan Africa but sales of glyphosate have increased in recent years;

> 9 Data extracted from RuLIS (https://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en). The year of data collection for each country in Figure 2 is as follows: Burkina Faso (2014), Cameroon (2014), Ethiopia (2016), Ghana (2013), Malawi (2017), Mali (2017), Niger (2014), Nigeria (2019), Rwanda (2014), Senegal (2011), Sierra Leone (2011), Tanzania (2015), Uganda (2016).

7
15 Romania’s 19 percent and EU-8 16 percent. Overall, between 1993-99 Turkish exports outpaced growth in EU-25 external demand to the tune of 5 percent, which was above the performance of Bulgaria (4 percent) and Croatia (-2 percent), but below that of Romania (10 percent) or EU-8 (12 percent).

**Table 7:** _EU-25 external imports from EU candidate states in 1996-2004 and their shares in EU25 external imports (in percent and billion of US dollars)_

||||||**E**|**U25 Im**|**ports ($**|**billion**|**)**||1996<br>Index|2004<br>index|Least<br>Growt|Sq.<br>h(%)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Partner|1993|1996|1997|1998|1999|2000|2001|2002|2003|2004|1993=<br>100|<br>1996=<br>100|<br>1993<br>-99|2000-<br>04|
|Turkey|8.4|12.9|13.8|15.5|16.6|16.8|19.1|22.3|29.9|35.4|153|<br>274|11.0|19.4|
|Bulgaria|1.3|2.3|2.5|2.7|2.6|3.0|3.3|3.5|4.6|5.4|178|<br>233|10.1|14.9|
|Croatia|2.8|3.0|2.6|2.7|2.5|2.5|2.6|2.6|3.5|4.1|107|<br>137|<br>-2.9|13.1|
|Romania|2.2|4.9|5.5|6.3|6.7|7.7|9.2|10.5|13.9|16.1|221|<br>325|16.8|18.9|
|**Memo:**|||||||||||||||
|EU-8|34.5|66.5|71.1|82.6|86.2|95.4|105.1|114.6|154.0|172.9|193|<br>260|14.3|15.7|
|||||**Shar**<br>|**e in EU**<br>|**25 Exte**<br>|**rnal Im**<br>|**ports (**<br>|**%)**<br>|||<br>|<br>||
|Turkey|1.53|1.73|1.79|1.97|2.02|1.80|2.08|2.44|2.70|2.88|113|<br>166|<br>4.6|11.9|
|Bulgaria|0.24|0.31|0.33|0.35|0.31|0.32|0.36|0.38|0.42|0.44|131|<br>141|<br>3.7|7.5|
|Croatia|0.51|0.40|0.34|0.34|0.31|0.27|0.28|0.29|0.32|0.34|79|<br>83|<br>-9.4|5.7|
|Romania|0.41|0.66|0.71|0.80|0.82|0.83|0.99|1.15|1.26|1.31|163|<br>197|10.3|11.5|
|**Memo:**|||||||||||||||
|EU-8|6.28|8.92|9.19|10.49|10.49|10.24|11.42|12.53|13.88|14.07|142|<br>158|<br>7.8|8.3|

Source: Based on EU as reporter from UN COMTRADE Statistics.

**Figure 5:** _Change in shares in EU external imports in 1992-2004 (1996=100)_

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Turkey Bulgaria EU-8 Croatia Romania<br><!-- End of picture text --> Source: As in Table 7.

Second, once extraordinary factors accountable for the trade performance of former Centrally Planned economies waned away, Turkey emerged in 2000-04 as one of the top export performers even taking as the base Turkey’s share in EU external imports in 1999 (2.03 percent) instead of a depressed one in 2000. This would lower its growth rate of the share from 14 percent to still respectable 9 percent per year as compared with 10 percent for EU-8.

Hence, the time profile of changes in Turkey’s share in EU import demand in 1996-2004 does not set it apart from other EU Eastern Enlargement countries except for two outliers—
**- -** **<u>Table 3 Number of product lines with non zero specific tariffs in 2001 2020 by industry</u>**

|**Code**|**Industry**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**<br>**Ye**|**2011**<br>**ar**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1|Electric industry|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|2|Oil extraction|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|3|Oil processing|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|4|Gas|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|5|Coalmining|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|6|Other fuel industries|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|7|Ferrous metallurgy|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|
|8|Non-ferrous metallurgy|1|8|8|8|8|4|4|4|4|8|8|4|4|4|4|4|4|4|4|4|
|9|Chemicals and<br>petrochemicals|22|30|30|31|30|33|33|33|37|38|48|14|14|13|11|11|7|7|7|7|
|10|Mechanical engineering<br>and metal working|120|125|137|138|141|130|132|137|150|153|148|86|89|86|77|76|75|63|65|65|
|11|Timber, wood, pulp and<br>paper|39|39|39|39|39|50|50|48|48|51|48|59|59|59|56|55|51|43|43|43|
|12|Construction materials|17|17|17|17|17|19|19|19|19|19|15|3|3|3|3|3|3|3|3|3|
|13|Light industry|598|596|596|596|596|579|580|580|580|547|557|468|469|468|467|467|466|466|466|466|
|14|Food industry|748|752|882|889|896|1,050|983|1,048|998|1,072|1,137|752|762|763|760|755|755|755|755|755|
|15|Other industries|9|9|9|9|9|9|9|9|9|9|9|7|7|7|7|7|6|6|6|6|
|17|Agriculture and forestry|50|50|50|50|50|40|40|40|40|45|46|41|41|41|41|41|41|41|41|41|
|22|Other goods-producing<br>sectors|6|6|6|6|6|6|6|6|6|6|4|6|6|6|6|6|6|6|6|6|
||**Total**|1,609|1,624|1,774|1,783|1,792|1,920|1,856|1,924|1,891|1,948|2,020|1,440|1,454|1,450|1,432|1,425|1,414|1,394|1,396|1,396|

Note: Table reports number of lines at 10-digit level with a specific tariff greater than zero by industries of the Russian input-output table. Mapping from 10 digit codes to industries of the Russian input-output table is based on the Rosstat classification.