rafmacalaba/datause-encoder-data huggingface dataset:
across types of firms. The survey instrument also includes a great deal of detail not only on firm characteristics, but on the quality of the business environment in which the firm operates. It controls for multiple measures simultaneously, avoiding potential omitted variable bias present in studies that examine only a single dimension. This is the first paper to use firm-level data on exits to study how investment climate conditions affect exit rates and differentially impact different types of firms.

# **Impact of the investment climate**

The paper tests for the importance of several dimensions of the investment climate. To facilitate their description, they are grouped into those whose effect is likely to raise the transaction costs of doing business (raising costs, delays) and those that affect market mechanisms more fundamentally. More detailed information on specific variables is discussed in the data section below.

# _1.  Factors raising transaction costs:  red tape, crime, bribes_

Delays and inefficient delivery of services raise the costs of getting business done. Crime can be costly either in terms of goods lost or in terms of necessitating additional security measures. Bribes too raise the cost of transactions. These higher costs are hypothesized to raise the likelihood that firms will exit. These additional expenses make it harder for firms to remain profitable and thus to remain in business.

The one variable in this category that has some potentially ambiguous role is that of bribes or corruption more broadly. While bribes are an expense, broader corruption can potentially tilt market conditions in favor of certain firms. Increasing attention is being focused on the role of corruption in development and trying to measure its cost, not simply in 8
The analysis focuses, as already mentioned, on two alternative paradigms for controlling carbon emissions, namely fuel or carbon taxes versus a cap-and-trade solution, which also represents the two main competing paradigms today for addressing mitigation of climate gases. The paper extends a previous paper, Strand (2009a), where I considered a similar model with two world regions, but with only one fuel (oil) instead of two fuels as here. I then showed that the NE aggregate fuel (and carbon) tax exceeds the Pigou level. I also showed that selecting an optimal c-a-t solution is less advantageous for the importer than setting a tax. Intuitively, when the importer sets a cap, the exporter is free to extract more of the rent from the fuel market, compared to when the importer sets a tax. In the NE solution to that game, the exporter strategically adjusts its tax so as to extract maximum rent from the importer, at the given (exogenous) cap, leading to a zero equilibrium value for tradable emissions quotas in the importer region.

I here consider three different models, dealt with in Sections 3-5. In all cases region B (the fuel exporter) sets an optimal export tax (which can equally be interpreted as a domestic tax on fuel production within region B). The models differ in terms of the behavior of region A. Under model 1, I assume _NE in tax setting, with region A optimally differentiating between_ _fuels 1 and 2,_ thus setting one consumption tax for fuel 1, and another tax for fuel 2.

Under model 2, I assume that region A sets _a common carbon tax for both fuels_ . This is to recognize that when taxes are differentiated between two fuels, the tax on fuel 1 could, conceivably, be interpreted not as an emissions tax, but instead as a strategic import tax, which could be difficult to defend in view of WTO regulations. A common tax per unit of carbon emissions, which also applies to a domestically-produced and -consumed fuel, is likely to be much harder to oppose on such grounds.

Model 3 implies that region A, instead of setting a tax, selects _a c-a-t solution_ , fixing the level of carbon emissions from the region’s consumption of the two fossil fuels combined. 4
# **1. Introduction**

Many countries in Latin America provide poor families with conditional cash transfers. The first country that adopted such a program was Brazil in 1995. Other countries include Mexico (1997), Honduras (1998), Nicaragua (2000), Costa Rica, Colombia (2001), Argentina, Uruguay, Chile, and Jamaica.<sup>1</sup> Conditional cash transfer programs provide poor families with cash conditional on their children attending school and/or visiting health care centers. The attractiveness of such programs is the potential to combine short-term and long-term poverty reduction. The cash transfers reduce short-term poverty, while long-term poverty will be reduced if children of poor families acquire human capital.

The effectiveness of some of these programs has been assessed through rigorous impact evaluation studies. These studies show substantial positive effects of conditional cash transfers on school enrollment. The programs in Mexico and Nicaragua have been evaluated using randomized field experiments. In Mexico enrollment rates at the secondary school level increased from 67% to around 75% for girls and from 73% to around 78% for boys (Schultz 2004). In Nicaragua the program was targeted to pupils up to fourth grade in primary school. The program increased the enrollment rate for this group by 18 percentage points (Maluccio and Flores 2004).<sup>2</sup> Other programs have been evaluated using non-experimental research designs. Duryea and Morrison (2004) used propensity score matching to evaluate the program in Costa Rica, and find a 5 to 9 percentage points increase in the probability of attending school. Attanasio et al. (2006) have evaluated the program in Colombia using propensity score matching in a differencein-differences framework. They find an increase in school enrollment of 5 to 7 percentage points for 14 to 17 year-olds.

In this paper we evaluate the impact of a cash transfer to the poorest 40 percent families on school enrollment in Ecuador. While the program aims at increasing school attendance and visits to health care centers, the program does not impose any explicit requirement for children of treated families to attend school or visit health centers. An important consideration for the Ecuadorian government not to impose such requirements is that the administrative burden of monitoring attendance is high. Moreover, this requires careful consideration of incentive problems in the education system: interviews with teachers indicated that if they would be responsible for administering attendance, they might be inclined to report children to be present while they were actually not in school. A similar concern motivated Duflo and Hanna (2006) to

> 1 Rawlings and Rubio (2003), Caldés et al. (2004), and World Bank (2008) provide overviews of the various programs.

> 2 The program in Honduras will also be evaluated through a randomized field experiment. Results are not yet available.

1
better initial conditions allowing them to receive more advanced technological innovations than low income countries. It is also consistent with low income countries exploiting their comparative advantage in wages, which has, to date, been substantial enough to prevent displacement of labor in manufacturing. With cheaper automation and digitization, these conditions and patterns may change as discussed in Box 1.

Berman and Machin (2000) examine technology transfer in the manufacturing sectors of 37 high, middle and low‐income countries during the 1970s and 1980s. They find rapid skill upgrading in many developing countries with technology diffusion across international borders altering the skill structure of employment of similar industries in different countries. Developing countries were subject to the same transformation in later decades as the U.S. economy was in the 1960s and 1970s (with output and employment growing). There was a migration of technologies from developed to middle income countries, but not to LICs. These past experiences provide support to the presumption that the rate of adoption of technological innovation will vary across countries as will their impact.

Meschi et al (2015), examining the relationship between trade openness, technology adoption and relative demand for skilled labor in the Turkish manufacturing sector during 1980‐2001 (using firm‐ level data in a dynamic panel setting) find that R&D expenditures are positively related to skill upgrading. Moreover, foreign ownership and exporting status raised the demand for skills.<sup>30</sup> Fajnzylber and Fernandes (2009) find that Brazilian firms engaging in imports, exports or FDI exhibit a higher demand for skilled labor, but that Chinese firms engaging in these activities have a lower skilled labor demand than firms that do not. They conclude that international economic activities act as a channel for skill‐biased technology diffusion in Brazil, which has a much higher per capita income, but have an effect

> 30 This is contrary to the Hecksher‐Ohlin‐Stolper‐Samuelson theorem that would have predicted that Turkey export goods more intensive in unskilled labor. Note that if the assumption of homogenous PFs is relaxed, then international openness‐imports, exports, FDI‐ may facilitate technology diffusion.

23
Monetary Policy Review (twice a year beginning in 2001), which will represent the Reserve Bank's inflation report. At this stage, inflation forecasts and the underlying forecasting models are not released to the public.

Following the collapse of the Thai baht in July 1997 (after almost five decades of pegged exchange rate regimes), Thailand adopted a floating rage regime under which monetary targeting was followed at first. Quarterly monetary base targets (agreed with the IMF) were implemented and achieved through daily liquidity management. However, growing evidence of instability in the relationship between money, output and inflation led Thailand to announce the adoption of inflation targeting as a monetary policy framework in May 2000. Under this regime, monetary policy is decided by the Monetary Policy Board (MPB), which was appointed in April 2000 and comprises both high-level central bank officials and external experts. An amendment to the Bank of Thailand has recently been passed to give the MPB an official status and operational independence in conducting monetary policy. At the same time, price stability was reaffirmed as the overriding objective of monetary policy. The price index targeted by the central bank is an index of core inflation, which excludes raw food and energy prices (both components deemed to be highly volatile) from headline inflation. The target core inflation rate is set in the form of a band of between 0 and 3.5 percent for 2000-01 and is explicitly aimed at allowing the Bank of Thailand to cushion temporary shocks and reduce interest rate volatility. At the time of the adoption of inflation targeting, headline and core inflation were running at, respectively, 2 percent and 1.2 percent on a yearly basis. Monitoring of deviations from the target band is done on the basis of quarterly averages of core inflation, to avoid the high degree of volatility that may characterize monthly observations. If core inflation breaches the target band, the MPB must explain why the breach occurred, identify the measures that it intends to take in response, and the amount of time required to bring inflation back within the desired range. Finally, the first inflation report was issued in July 2000 and is to be issued on a quarterly basis; risks associated with inflation forecasts are shown in fan charts, as is now done by several other inflation-targeting central banks.

47
> <sup>- 23 -</sup>

Other products have been introduced without any guarantee or with a low guarantee, normally a zero rate guarantee, which most often is a guarantee that workers will at least not lose the principal amount of their investments during the whole saving period, although they may still suffer losses from inflation. Another type of zero rate guarantees is a so called "water-level" guarantee, where interest once earned can never be lost again. These products typically offer customers a choice between a few investment categories with different risk profiles, while the actual investment management is left to the company. This gives customers a choice to adjust their investment risk to their age or other aspects of their economic situation.

The demand for unit-linked products has grown considerably in recent years, although it has not reached the levels found in other European countries. Unit-link products offer customers a choice of investment funds with varying policies as well as a choice of fund managers. However, most of the unit-linked products used in Denmark carry some sort of protection against investment risk, usually a zero-rate guarantee. In addition, most customers have opted for products where the investment policy is determined by the fund managers. Thus a fair interpretation of the Danish unit-link market would be that customers have not chosen unit-link products because of the investment choice but rather because of the higher degree of transparency that these products offer. This represents a partial move away from extensive risk sharing, a partial correction that may be fully justified in the light of the large fall in guaranteed returns.

# **H.   Replacement Ratios**

Money's Worth Ratios are difficult to calculate in the Danish system because of the extensive reliance on bonus payments. An ex ante calculation would need to be based on assumed rates of future performance and bonus declaration, while an ex post calculation would require a considerable amount of data on actual bonus payments. However, while MWRs are not calculated, considerable effort is devoted in Denmark in calculating current and future replacement ratios.

These are also based on assumptions about future performance and bonus payments but they take into account all types of pension benefits and even allow for tax payments. Two reports, one from the Ministry of Economic and Business Affairs<sup>7</sup> and the other from the Danish Pension Council<sup>8</sup> , provide details of current and expected replacement ratios. Although the two reports adopt slightly different methodologies, their basic results are broadly similar.

> 7 “Increased freedom of choice in pension saving”, May 2003.

> 8 “Pension contributions, replacement ratios and mortality”, June 2005.
# **_Table A3.1 First-step regression used for calculating expected IEG Outcomes, by which value-added measures of TTL and PM qualities are generated_**

|**Variables**|**(1)**|
|---|---|
|CPIA at Approval Calendar Year|0.279**<br>(0.132)|
||0358***|
|CPIA Change Over Project Lifetime|.<br>(-0.108)<br>|
||-0341***|
|(Log) GDP per capita PPP $ (at approval Fiscal Year)|.<br>(-0.086)|
|(Log) Population (at approval Fiscal Year)|-0.423<br>(-0.258)|
|(Log) Original Commitment Amount (in millions [constant 2015 $])|0.000<br>(-0.023)|
|Education Practice|0.175*<br>(-0.105)|
||0064|
|Energy & Extractives Practice|.<br>(-0.111)|
|Environment & Natural Resources Practice|-0.259*<br>(-0.132)|
||-0.044|
|Finance & Markets Practice|<br>(-0.134)|
||-0.099|
|Governance Practice|(-0.129)|
|Health, Nutrition & Population Practice|-0.018<br>(-0.102)|
||0054|
|Macro Economics & Fiscal Management Practice|.<br>(-0.198)|
||0824***|
|Poverty & Equity Practice|.<br>(-0.106)|
|Sil Ptti & Lb|0.327***|
|oca roecon aor|(-0.107)|
|Social, Urban, Rural and Resilience Practice|0.188*<br>(-0.105)|
|Trade & Competitiveness Practice|0.168<br>(-0.206)|
||0.448***|
|Transport & ICT Practice|(-0.103)|
||0.018|
|Water Practice|(-0.097)|
|Constant|12.89***|
|Observations|3,498|
|Adjusted R-Squared|0.102|

Note: Robust standard errors are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). Regression estimated using OLS on full 4,348 data set. All specifications include country fixed effects.

46
return in the United States, but a very poor return in Dominican Republic. To follow the same example, a 5 percent return in US dollars in September 2007, when 20 year government bond rates were close to 5 percent, does not seem like a great investment compared with a 5 percent return at the end of September of 2013, when 20 year government bond rates are only 3.4 percent. In addition, comparisons of pension returns across countries, as among other factors, the reference rates are different, as shown in Table 2.

**Table 2** . Long-term nominal interest rates, December 2012<sup>a</sup> (annual returns)

|Bulgaria|3.4|Chile|5.5|
|---|---|---|---|
|Croatia|4.5|Colombia|5.5|
|Latvia|3.2|Costa Rica<sup>b</sup>|14.0|
|Lithuania|4.0|Dominican R.<sup>c</sup>|9.8|
|Poland|3.9|Mexico|5.4|
|Slovakia|3.9|Peru|4.1|
|Romania|6.7|||
|United Kingdom|1.7|United States|1.86|

a 10 year government bond rates, and convergence rates in the case of EU countries. b interest rate of 2-year bond c average interest rate of bonds with maturity of 4-7 years Source. European Central Bank and country sources Short-term Treasury rates are not the proper benchmark for measuring performance of pension funds, as they do not take into consideration the reinvestment risk. Reinvestment risk is an important risk for pension funds because of the long-term nature of their liabilities. However, reinvestment risk is not measured when short rate returns are considered. For example, in the case of Brazil, open pension funds are heavily invested in short term rates (overnight). Between September 2010 and September 2012, interest rates have decreased by approximately 500 basis points (see Figure 1). During this period the returns of pension funds have decreased by the same amount. By contrast, having the pension funds invested in bonds with longer maturities,<sup>24</sup> contributors would have not suffered lower returns as short interest rates moved down, because of the absence of reinvestment risk. As shown in Figure 2, the yield curve in October 2010 would have allowed pension funds to lock in interest rates at levels above 11.8 percent.

> 24 In the past, yield curves have been relatively flat in Brazil.

16
# **Stemming Example**

|word|stem|word|stem|
|---|---|---|---|
|chetumal|chetumal|toreado|tor|
|chetumale ̃nos|chetumale ̃n|tore ́andolo|tor|
|chiapas|chiap|tore ́o|tore|
|chicharrones|chicharron|torrenciales|torrencial|

Table 3: Example of Spanish stemmer. Source: Snowball project.

each mention of a currency value to a placeholder, DOLLAR ~~A~~ MOUNT, under the assumption that there is some valuable information in the mentioning of a value if not the value itself. All positive and negative emoticons are transformed in to POS ~~E~~ MOTICON and NEG EMOTICON, respectively. We transform all @ mentions to AT MENTION. Finally, we transform all links to a URL ~~L~~ INK placeholder.

Next, we stem the words using the Spanish stemmer from the Snowball project<sup>6</sup> . Stemming is the process of reducing words to their root form and is used primarily to reduce the number of features and document sparseness as discussed below. Table 3 contains some examples from the Snowball project documentation for the Spanish stemmer.

After removing the punctuation and stemming, we split each tweet on spaces, creating tokens or _n−_ grams where _n_ can be any value. If _n_ = 1, the tokens are unigrams. For _n_ = 2, bigrams. For _n_ = 3, trigrams, and so on. The use of tokens longer than unigrams can help preserve semantic meaning for phrases where the bag-of-words assumption might be unduly restrictive. For example, we might want to preserve the semantic meaning of phrases such as “not good” by using a bigram. Whether or not higher-order _n−_ grams improve classification is an empirical question and is discussed further below.

The last important piece for the creation of the feature vector representation is selecting a _vocabulary_ , _V_ . The _vocabulary_ can be thought of as the words we believe will allow a learning algorithm to discern the class of a document. It is not atypical in text classification problems for the size of the vocabulary, _P_ = _|V |_ , to exceed the number of observations, or samples, _N_ , resulting in an underdetermined problem. Not all classification algorithms are able to handle this situation, so vocabulary selection can become quite important for these estimators. For this study, we remove Spanish _stop words_ such as _la_ , _en_ , _y_ , and _los_ , any term that occurs in fewer than 3 tweets and any term that occurs in occurs in more than 70% of the tweets. This leaves us with a vocabulary size

> 6 The main project site is `http://snowball.tartarus.org/index.php` . We used the Python bindings from PyStemmer `https://github.com/snowballstem/pystemmer` .

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|**6**|GOAL 2: Zero Hunger (5 year moving<br>average of availability)|Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 2|UN Global SDG Indicators<br>Database|
|---|---|---|---|---|
|**7**|GOAL 3: Good Health and Well-being<br>(5 year moving average of availability)|<br>Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 3|UN Global SDG Indicators<br>Database|
|**8**|GOAL 4: Quality Education (5 year<br>moving average of availability)|Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 4|UN Global SDG Indicators<br>Database|
|**9**|GOAL 5: Gender Equality (5 year<br>moving average of availability)|<br>Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 5|UN Global SDG Indicators<br>Database|
|**10**|GOAL 6: Clean Water and Sanitation<br>(5 year moving average of availability)|<br>Dimension<br>3.1: Social<br>(SDG 1-6)|SDG 6|UN Global SDG Indicators<br>Database|
|**11**|GOAL 7: Affordable and Clean Energy<br>(5 year moving average of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 7|UN Global SDG Indicators<br>Database|
|**12**|GOAL 8: Decent Work and Economic<br>Growth (5 year moving average of<br>availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 8|UN Global SDG Indicators<br>Database|
|**13**|<br>GOAL 9: Industry, Innovation and<br>Infrastructure (5 year moving average<br>of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 9|UN Global SDG Indicators<br>Database|
|**14**|<br>GOAL 10: Reduced Inequality (5 year<br>moving average of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 10|UN Global SDG Indicators<br>Database|
|**15**|GOAL 11: Sustainable Cities and<br>Communities (5 year moving average<br>of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 11|UN Global SDG Indicators<br>Database|
|**16**|<br>GOAL 12: Responsible Consumption<br>and Production (5 year moving<br>average of availability)|<br>Dimension<br>3.2: Economic<br>(SDG 7-12)|SDG 12|UN Global SDG Indicators<br>Database|
|**17**|<br>GOAL 13: Climate Action (5 year<br>moving average of availability)|<br>Dimension<br>3.3:<br>Environmental<br>(SDG 1315)|SDG 13|UN Global SDG Indicators<br>Database|
|**18**|GOAL 15: Life on Land (5 year moving<br>average of availability)|,<br>Dimension<br>3.3:<br>Environmental<br>(SDG 1315)|SDG 15|UN Global SDG Indicators<br>Database|
|**19**|GOAL 16: Peace and Justice Strong<br>Institutions (5 year moving average of<br>availability)|,<br>Dimension<br>3.4:<br>Instituational<br>(SDG 16-17)|SDG 16|UN Global SDG Indicators<br>Database|
|**20**|GOAL 17: Partnerships to achieve the<br>Goal (5 year moving average of<br>availability)|<br>Dimension<br>3.4:<br>Instituational<br>(SDG 16-17)|SDG 17|UN Global SDG Indicators<br>Database|
|**21**|Legislation and governance|<br>Dimension<br>5.1:<br>Legislation<br>and<br>governance|SDG<br>17.18.2|UN Global SDG Indicators<br>Database|

42