rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>emissionstoairandwaterposeavarietyofhazardstohumanhealth,</sup> |<sup>ndustrian</sup> BOX 1: MAJOR AIR, WATER AND TOXIC POLLUTANTS yroonyarens: and economic activity. |

> [a| Rodansance ° TotalParticulatesSuspendedareParticulatesfine liquid or(TP)solian **d** particlesFine Particulatessuch as dust,(PM10}:smoke, mist, fumes or smog found in air emissions. In heavy concentrations, airborne particulates interfere with proper functioning of the human respiratory system. High levels of ambient TP in urban/industrial areas are therefore associatedParticulatewithcoatingsgreateron morbidityleaves inhibitand mortalityplant growth.from respiratoryHigh TP diseases. | concentrations may also force the use of high-cost filtration equipment by | manufacturers.diameter. They posFin **e** particulatesthe greatest respiratory(PM10) are lesshazard.than 10 micron in td Sulphur Dioxide (S0,): Sulphur dioxide is a heavy, pungent, colorless, gaseous air pollutant formed primarily by fossil fuel combustion. It is associated with morbidity and mortality from respiratory disease. In addition, SO, is a prime source of the acid rain which has damaged huge Acidforestraintractsand inrunoffthe OECDhave andraisedseveralthe aciditytransitionalin numeroussocialistlakeseconomies.beyond the | I. pointconcrete,wheremortar,indigenousmarble,fishmetals,speciesrubbercan survive.and plastics.Acid rain also degrades ! | Nitrogenoxides ofOxn **i** trogen,des {NO,):oftenNitrogencollectivelydioxidereferred(NO,) andtonitricas "NO,."oxideThe(NO)primaryare | source of NO is thermal combustion of fossil fuels, which emits NO. Higher combustion temperatures, sometimes recommended to reduce emissions of Volatile Organic Compounds (VOCs), are associated with higher production rates of NO,;. NO, emissions have important ecological impacts, since they are integral to the formation of acid rain and tropospheric ozone. | Inhalation of concentrated NO, damages the respiratory tract, resulting ina range of effects from mild reductions in pulmonary function to life| threatening pulmonary edema. le Carbon Monoxide (CO): Carbon Monoxide is a colorless, odorless, and | tasteless poisonous gas produced by incomplete fossil fuel combustion. CO | binds with hemoglobin in human blood 200 times faster than oxygen. Thus, | the blood's ability to carry oxygen to tissues is significantly impaired result in heart and brain damage, impaired perception and asphyxiation, and | after exposure to only small concentrations of CO. High doses of CO can | low doses may cause weakness, fatigue, headaches and nausea. je Volatile Organic Compounds (VOC): The term volatile organic compounds, | describes a class of thousands of substances used as solvents and | plasticsfragrances.industries.VOCs are particularlyHuman exposureimportantto VOCsinisthemainlypetrochemicalvia inhalation,and |

36
<!-- Start of picture text --> SS Standard deviation in 2030<br>0.390<br>50 2.000<br>4S 8.000<br>LOGC 9.900<br>Data: Sample covered<br>40 O 1980_2014<br>O 1990_2014<br>ony + 2000_2014<br>§ 35 X 2005_2014<br>a}<br>9 Country typology<br>5 BAll<br>2 30<br>SS LOGC ROC<br>3 HM NROC<br>g 25 M@ HoGc<br>& LOGC LOGC<br>& @ mocc<br>2 20<br>ROC<br>15<br>10<br>Allg OMocc<br>AIL =<br>5<br>g NROC<br>0%<br>05 10 15 20 25 30 35 40 AS 50 55<br>Median in 2015 (percentage)<br><!-- End of picture text -->
**Table 7: Correlations between Level 1 variables within each tool**

|**SDI (N=268)**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|(1) Share of time teaching|**1.00**|||||||
|(2) Share of time teaching and learnin|**0.81**|**1.00**||||||
|(3) Good Teacher Demeanor|**0.66**|**0.65**|**1.00**|||||
|(4) Good Pedagogical Practise|**0.63**|**0.58**|**0.68**|**1.00**||||
|(5) Classroom environment|0.13|0.11|0.08|-0.02|**1.00**|||
|(6) Materials and infrastructure|0.11|0.15|0.08|0.06|0.20|**1.00**||
|(7) Use of materials|**0.64**|**0.52**|**0.63**|**0.61**|0.01|0.00|**1.00**|
|**Stallings (N=277)**|(1)|(2)|(3)|(4)||||
|(1) Share of time teaching|**1.00**|||||||
|(2) Share of time teaching and learnin|**0.83**|**1.00**||||||
|(3) Materials and infrastructure|**0.34**|**0.31**|**1.00**|||||
|(4) Classroom environment|-0.03|-0.01|0.07|**1.00**||||
|**CLASS (N=149)**|(1)|(2)|(3)|(4)|(5)|||
|(1) Emotional Support|**1.00**|||||||
|(2) Classroom Organisation|**0.49**|**1.00**||||||
|(3) Instructional Support|**0.81**|**0.37**|**1.00**|||||
|(4) Overall CLASS score|**0.94**|**0.67**|**0.90**|**1.00**||||
|(5) Student Engagement|**0.80**|**0.55**|**0.79**|**0.85**|**1.00**|||
|**Teach (N=151)**|(1)|(2)|(3)|(4)|(5)|(6)||
|(1) Share of time teaching|**1.00**|||||||
|(2) Share of time teaching and learnin|**0.88**|**1.00**||||||
|(3) Classroom culture|0.06|0.08|**1.00**|||||
|(4) Instruction|0.13|0.12|0.15|**1.00**||||
|(5) Socioemotional Skills|-0.03|0.01|**0.34**|0.19|**1.00**|||
|(6) Overall Teach score|0.07|0.09|**0.60**|**0.68**|**0.75**|**1.00**||

Correlation coefficients greater than 0.3 are in bold.

45
is associated with a much lower probability of being very poor (17%) and a correspondingly much higher chance of falling into the top two wealth quintiles (31%). Consistent with these findings, an earlier World Bank report also suggested that migration is only effective in sheltering households from poverty when “migration is directed outside the country and the migrants are men.”<sup>13</sup> Whether migrants to South Africa are able to remit more money or migrants within Lesotho are moving in response to pre‐existing poverty, internal migration seems to be correlated with lower benefits to the sending household.

_Figure 3. In rural areas, RSA‐sending households are better off and domestic‐sending households as poor as non‐sending households (DHS 2014)_ <!-- Start of picture text --> RSA ONLY 17 26 26 23 8<br>poorest<br>poorer<br>LSO ONLY 32 27 20 14 7 middle<br>richer<br>richest<br>NON‐SENDING 31 27 23 13 6<br><!-- End of picture text --> Fieldwork findings are consistent with these data. First, rural communities see migration as a survival strategy, and not a newfound freedom or opportunity for the migrants: they characterize migration as a “necessary evil”. As such, most households send a family member away for work. In fact, in the rural communities we visited, it was hard to find non‐sending households. Most participants in in‐depth interviews and group discussions had either migrated for work themselves or had a member(s) of their household who did. Furthermore, many households that had not sent a member away either plan to do so in the future, or want to but cannot, mostly due to either a lack of financial or social resources to initiate the move, or the absence of an able‐bodied adult household member.

Second, even with remittances, households that send internal migrants are struggling financially and, critically, are not seen as better off. Communities see small differences around big holidays and the times the migrant comes home to visit, when they bring foodstuffs and other small goods and cash; the rest of the time, however, these households are struggling as much as others. More specifically, they see female

> 13 World Bank (2010).

20
construction of the index was estimated on the pooled sample. The DHS data reflect the increase in household living standards between 2005 and 2010: The average score on the wealth index increased from _-0.143_ in 2005 to _0.219_ in 2010 (Table 2).

The remainder of Table 2 shows the proximate determinants of fertility. All proximate determinants moved as to lower fertility. The percentage of women ever in union (which includes marriage and informal forms of cohabitation) decreased by one percentage point, the age at first union increased by half a year and the age at first birth with one-third of a year. Given the importance of marriage (de jure or de facto) for fertility in Rwanda (see for instance Bruck and Schindler, 2011) this has likely been associated with lower fertility through delayed child bearing. The proportion of women without any exposure to mass media (defined as women who have not listened to the radio, watched television or read a newspaper in the week preceding the survey) dropped from 43.6 percent in 2005 to 30.5 percent in 2010. The exposure to mass media is important as it potentially affects fertility through the exposure to education campaigns, information or family planning messages disseminated through mass media. Similarly, exposure to family planning messages increased considerably: In 2005, almost 60 percent of women were not exposed to family planning messages of any kind (media, billboards, brochures, posters,...) in the few months preceding the survey, compared to one third in 2010. Finally, use of modern contraception increased exponentially (but remains low): In 2010, 25 percent of women reported using modern contraception, up from 5.6 percent in 2005.

Table A1 in the Appendix replicates Table 2 but disaggregates by age. We consider three agegroups: Young women (15 to 24 years-old), “middle” women (25 to 34-years-old) and older women (35 to 49-years old). These groups are not chosen at random. All women in the youngest age-group entered their reproductive period after the 1994 genocide during a period of unprecedented social and economic change in Rwanda. The younger women in this group have no active recollection of the genocide and attended primary school after the events when enrolment and completion rates increased 14
# **5. Conclusions**

African SMEs are small in number and heterogeneous in performance, which poses a challenge for both experimental and structural methods of estimating the impact of firm policies designed to facilitate employment creation and firm growth. In reviewing African data on firms and on the types of policies governments are using with these firms, it seems there are three sets of firm types, for which different evaluation methods may be suited.

1. The vast majority of firms are microenterprises. Policies which focus on providing training, credit, or other assistance for these firms can often rely on standard randomization of a large number of firms to treatment and control groups, with a small number of surveys taken on each firm.

2. For SME projects and policies targeting smaller number of firms, one should attempt to reduce heterogeneity by focusing on a more homogeneous sub-sample within this target group – such as firms in particular industries or size categories – and then collect data in many survey rounds or time periods on these firms. Randomized experiments can be used to obtain policy impacts for this sub-sample, and the rich data gathered can provide a good basis for structural modeling.

3. For the largest firms there may be no other firms in the country that are comparable, making the use of experimental or non-experimental methods that require a comparison group for estimating the counterfactual not possible. In such cases, rich time series data on multiple dimensions of firm behavior and firm outcomes can enable time series analysis and structural modeling to get some sense of how the policy has performed.

Finally, for years now there has been talk of how we can use experimental methods and structural modeling together to get the best of both worlds – using the experiment to identify structural parameters and impacts of a particular policy, and then the structural model to undertake simulations of how firms would respond under alternative policies (e.g. Todd and Wolpin, forthcoming). At present this is more promise and rhetoric than reality when it comes to firm experiments. The collection of much more data and the use of randomization in the implementation of more firm policies is a necessary first step in permitting the future development of such research.

17
# **<u>Table 6: Encompassing regressions for SP responses to the pandemic</u>**

||<br>Baseline spe<br>OLS|<br>cification<br>_PLREG_|<br>With impu<br>OLS|ted fixed effects<br>_PLREG_|
|---|---|---|---|---|
|Log GDP per capita GDP|-0.024||0.116||
||_(0.458)_||_(0.598)_||
|Voice and accountability|-0.319|-0.370|-0.492|-0.419|
||_(0.313)_|_(0.363)_|_(0.346)_|_(0.385)_|
|Government effectiveness|1.544<sup>***</sup>|1.957<sup>***</sup>|1.591<sup>***</sup>|1.750<sup>***</sup>|
||_(0.480)_|_(0.599)_|_(0.532)_|_(0.637)_|
|Percentage of population that use internet|-0.017|-0.011|-0.020|-0.021|
||_(0.018)_|_(0.021)_|_(0.022)_|_(0.029)_|
|Share of population over 65 years|0.096<sup>*</sup>|0.088|0.173<sup>***</sup>|0.172<sup>**</sup>|
||_(0.052)_|_(0.064)_|_(0.064)_|_(0.081)_|
|Covid-19 deaths per million/1000|-0.121|-0.015|-0.108|-0.028|
||_(0.222)_|_(0.292)_|_(0.221)_|_(0.275)_|
|Imputed country effect|||-0.089<sup>*</sup>|-0.116<sup>*</sup>|
||||_(0.048)_|_(0.060)_|
|Constant|2.591||0.712||
||_(3.624)_||_(4.649)_||
|Test on log GDP sub-function in PLREG||0.323<sup>α</sup><br>0.375<sup>_β_</sup>||0.432<sup>α</sup><br>0.333<sup>_β_</sup>|
|R<sup>2</sup>|0.285||0.329||
|Numberofcountries<br>Note: <sup>***</sup>indicates that the coefficient is signifi|14<br>cant at 1% level,<sup>*</sup>|7<br><sup>*</sup>- at 5% level,<sup>*</sup>|- at 10% level.|120<br>Standard errors are|

<u><mark>Note:</mark></u><sup>***</sup> <mark>indicates that the coefficient is significant at 1% level,</mark><sup>**</sup> <mark>- at 5% level,</mark><sup>*</sup> <mark>- at 10% level. Standard errors are shown in parenthesis below the coefficients, clustered at the country level. Bootstrapped standard errors for the specifications using imputed fixed effects.</mark>

> α <mark>Value of the test that Log GDP effect is significant.</mark>

> _β_ Probability that test value is different from zero.

42
7 environmental "bads," sometimes called "brown" subsidies, undermines the credibility of a government advocating a green subsidy.<sup>12</sup> In summary, the policy of government subsidy can be justified in order to obtain public goods and promote outcomes that generate positive environmental externalities. Such subsidies enhance economic welfare. By contrast, the need to undo negative externalities is not a strong argument for a government subsidy because there are more direct instruments (such as taxes) available to correct market failure. But what if a government cannot adopt first-best policies because of government failure?

_The Risk of Government Failure_ Elected officials and bureaucrats can act against the public interest and such government failure is modeled in theories of public choice. When political economy pressures prevent a government from taking first-best approaches to correct market failures, then a rational government may pursue a second or third-best approach, such as a subsidy, to boost the demand for and supply of environmentally friendly goods.<sup>13</sup> For example, analysts have also argued that green subsidies can serve as a counterweight to governmental subsidies for climate-unfriendly practices that are politically too hard to remove. In such a situation, however, it would be illogical to credit a government subsidy as cost-effective if the subsidy improves the environment only by (partially) countering government subsidies that worsen the environment. Green subsidies are also advocated to correct the government failure of erratic policymaking unable to credibly commit to future policies (World Bank 2012, p. 67).

12In a brown subsidy, the environmental costs exceed the environmental benefits. For example, economists have found that the U.S. ethanol tax subsidy added to global GHG emissions. U.S. National Research Council 2013, p. 8.

> 13UNEP has observed that subsidies directed at less or non-polluting activities "are often considered a more practical solution where raising or introducing taxes is seen as politically awkward" (UN Environment Programme 2003, p. 137).
<!-- Start of picture text --> Employed post baseline (compared to workers with <5 schooling)<br>08<br>06 0<br>E O83<br>§ .04 .0B2<br>=)<br>B<br>e<br>a”<br>w 0064<br>a -.0033 0914+<br>¢ “o<br>“op @ Pre-covid cohort<br>-.02 Ps § Covid cohort<br>5~7 years” 8-9 years’ 10-11 years*** >12 years***<br><!-- End of picture text -->
equation (4y-investor-only at the fund level-only because the results we shall find for that measure are, in the end, similar to the investor-only results from equation **(3)**

# **m. Data**

Our data on mutual-fund holdings come from two sources. The first source is the **U.S.** Securities and Exchange Commission **(SEC).** Mutual funds are required to report holdings to the **SEC** twice a year. The second source is Morningstar. Morningstar conducts surveys of mutual fund holdings at a higher frequency: quarterly surveys are the norm for most funds. For our purposes, quarterly data are available from Morningstar for about _50%_ of the funds we examine. In those instances where our measure of **Mj,,** is based on portfolio holdings that are not measured three months apart, these observations of **AQj,,e** are multiplied **by** 3/x, where x is the number of months between **Qj,,** and **Qijt-1.** Our sample includes the holdings of **13** Latin America equity funds (open-end) from April **1993** to January **1999** (24 quarters). Those funds are **(1)** Fidelity Latin America, (2) Morgan Stanley Dean Witter Institutional Latin America, **(3)** Van Kampen Latin America (formerly Morgan Stanley), (4) BT Investment Latin America Equity, **_(5)_** TCW Galileo Latin America Equity, **(6)** TCW/Dean Witter Latin America Growth, **(7)** Excelsior Latin America, **(8)** Govett Latin America, **(9)** Ivy South America, **(10)** Scudder Latin America, **(11)** T. Rowe Price Latin America, **(12)** Merrill Lynch Latin America, and **(13)** Templeton Latin America. Not all of these funds existed from the beginning of our sample; on average we have about **10** quarters of data (out of a possible 24) per fund.

Our third source of data is Bloomberg and the International Finance Corporation **(IFC).** Bloomberg provides monthly price series for all equities held **by** the **13** funds, including ADRs. (The need for monthly price data arises in our analysis of lag-one momentum trading.) These price series are corrected for splits and dividends. The **IFC** provides information on stock market indexes, which we need for our contagion trading analysis. Our contagion trading analysis uses the **IFC** Latin America Stock Market index, the **IFC** Asia Stock Market index, and several **IFC** country stock market indexes. The **U.S.** equity return is the **S&P 500** return. **All** return data are expressed in percent 14
Tuckey biweight iterations.<sup>22</sup> The weights for the Huber estimator decline when jej > k, while the weights for the Tuckey biweight estimator decline as soon as e departs from 0, and are 0 for jej > k: The tuning constant k for the Tuckey biweight estimator is set in the estimations performed in this paper to k = 4:685�: I follow b Fox (2002) and estimate � as � = MAR=0:6745; where MAR is the median absolute residual from the OLS regression.

# 4 Estimation Results

## 4.1 The Main Regression

The estimation of equation (12) using the robust regression method outlined in section 3.2 produces the results reported in Table (2) below. The estimations are carried out using both the EMPI ala Eichengreen et al. (1996) and EMPI for which � = � = � = 1. I also include the OLS estimation results for comparison and as an indication of how well equation (12) ...ts the data.

When looking at the ...rst two columns under the Robust Regression heading, the estimation results suggest that the degree of synchronization<sup>23</sup> in output growth is not signi...cant in explaining the EMPI’s standard deviation. The results thus contradict the ...ndings of Devereux and Lane (2003) who ...nd a signi...cant positive e¤ect of business cycles synchronization on exchange rate variance for industrialized countries and a signi...cant negative e¤ect on exchange rate variance for developing countries. Also, Bayoumi and Eichengreen (1998) ...nd that increasing synchronization of output growth decreases the variability of EMPI using a sample of industrialized countries. The two latter studies however do not include a measure of synchronization for in‡ation in the vector of explanatory variables which I do. At least in the short run, a part of the output growth can be in‡ationary thus inducing possible colinearity between output growth and in‡ation that can be re‡ected in the respective measures

> 22For the exact functional forms see again Fox (2002).

> 23Recall at this point that higher synchronization of two economic variables implies lower standard deviation of the ratio of the two variables.

16
an almost total ban on rice imports. In the European Community, most of the agricultural products are heavily protected by the infamous Common Agricultural Policy (CAP).' As a result, the tariff equivalency of such nontariff barriers on agricultural trade is very high. The numbers for "Agriculture exc. Fish: Estimate I " in Table 2 show a combined rate of external barriers (i.e., tariff plus tariff-equivalency of nontariff barriers), of 25.0-32.4 percent. Further, as Table 3 shows, external protection rates for certain agricultural product are very high. The tariff equivalency is more than 100 percent in some cases.

Table 3: EC Agricultural Protection and Trade Patterns, 1989 (percent)

|SITC code|Tariff-equivalent of<br>protection and subsidies|
|---|---|
|01 Meat|6-270|
|02 Dairy products|0-200|
|03 Fish|0-30|
|04 Cereals|20-130|
|05 Vegetables and fruit|0-30|
|06 Sugar|180|
|07 Coffee, tea, spices|0-18|
|08 Animal foodstuffs|0-50|

Source: Pohl and Sorsa (1992), p. 22

# _(B) Some Evidence of Product Differentiation_

Contrary to the popular belief that agricultural trade is an international exchange of There are many detailed discussions of CAP in the literature, including Pohl and Sorsa (1992), on detailed discussion of CAP.

7
sharing venture rather than one of risk-transfer. However, this reason alone is not satisfying in explaining the access to finance gap among the MENA countries in this report.

Figure 2. Export Indicators, 2012 <!-- Start of picture text --> A. Logistics performance index, other B. Delay to export, other<br>IRQ<br>ARE<br>QAT<br>TUN MLT SAU<br>MAR BHR<br>EGY<br>YEM OMN<br>KWT<br>JOR LBN<br>IRN<br>DZA YEM<br>IRN<br>IRQ LBN<br>DZA KWT QAT<br>MAR TUNEGY JOR OMNBHR MLTSAUISR<br>ARE<br>6 8 10 12 6 8 10 12<br>Log GDP per capita (2012,USD) Log GDP per capita (2012,USD)<br>C. Documents to export, other D. Cost to export, other<br>IRQ<br>EGY DZA<br>IRN BHR OMN KWT IRQ<br>YEM<br>MAR JOR MLT SAU QAT<br>TUN LBN ISR<br>ARE IRN<br>DZA<br>YEM EGYMARJORTUN LBN MLTOMNBHRSAUISRKWTARE QAT<br>6 8 10 12 6 8 10 12<br>Log GDP per capita (2012,USD) Log GDP per capita (2012,USD)<br>Source: WDI<br>4 80<br>3.5<br>60<br>3 p<br>40<br>y<br>2.5<br>g<br>20<br>2<br>1.5 0<br>15 8000<br>6000<br>10<br>p<br>p<br>4000<br>5<br>2000<br>0 0<br><!-- End of picture text --> At the micro-level in MENA, there is inequity in access to finance by firm size. The financial sector is dominated by banks that primarily serve large firms and exclude small and medium firms. Firms in MENA use very little external financing for investment and working capital. In MENA, 5.9 percent of investments and 4.4 percent of working capital is externally financed by banks (Enterprise Surveys). This proportion is lower only in the Sub-Saharan Africa region. The percent of firms who have a checking or savings account, and who have a bank loan or line of credit is also low in the MENA region. Sixty percent of MENA firms have checking or savings accounts 7