rafmacalaba/datause-encoder-data huggingface dataset:
Table 1 illustrates the extent of heterogeneity in foreign bank participation within each region by reporting the maximum and minimum share of assets held by foreign banks in each region along with the coefficient of variation. The table also reports the median for each region. Across all regions, there is at least one country with practically no foreign bank participation: Vietnam in East Asia, Uzbekistan in Eastern Europe, Cuba in Latin America, Iran, Libya and Yemen in the Middle East and Northern Africa region, Bangladesh and Sri Lanka in South Asia, and Ethiopia in Sub-Saharan Africa. However, the median and maximum share of assets held by foreign banks is quite different across regions. The median (at 5 percent) and maximum values (for Pakistan at 23 percent) are lowest for South Asia and highest in Eastern Europe and Central Asia, where the median is 60 percent and the maximum corresponding to Estonia is almost 100 percent. In East Asia, the median share of assets held by foreign banks is 16 percent and the maximum corresponding to Korea is 44 percent. In the case of Latin America, the median share is 31 percent and the maximum corresponds to Peru, where foreign bank participation is at 95 percent. The median share of assets held by foreign banks is 11 percent in the Middle East and North Africa region. Lebanon with 34 percent of assets held by foreign banks is the country with the highest share of foreign bank participation in this region. Finally, the median share of assets held by foreign banks in Sub-Saharan Africa is 51 percent and the share is highest in countries such as Madagascar, Mozambique, and Swaziland, which have close to 100 percent foreign bank participation.

Not surprisingly, the degree of variability in the share of foreign bank participation within region, as captured by the coefficient of variation, is lowest for Eastern Europe and Sub-Saharan Africa, at 0.6, and highest for South Asia at 1.3. This statistic is 0.8 for Latin America, 1.1 for Middle East and North Africa, and 1 for East Asia. In other words, while most countries in Eastern Europe, Latin America, and Sub-Saharan Africa have embraced foreign bank entry, we observe greater variation in the degree of foreign bank participation in the remaining regions of the developing world.

One drawback with using the figures discussed so far on the share of assets held by foreign banks is that these statistics do not consider the importance of cross-border loans (i.e., those booked outside the host country). Hence, to address this potential limitation, we also look at data on total foreign claims to GDP (see Figure 3). These data, which come from the 5
**Annex table 3 (continued): Number of cattle, chickens, pigs and sheep in ECA countries, 1995, 2000, 2005 and 2009 (continued)**

|||Pig|s ( ̳000)|||She|ep ( ̳000)||
|---|---|---|---|---|---|---|---|---|
|Country|1995|2000|2005|2009|1995|2000|2005|2009|
|Albania|100|103|147|160|2,480|1,939|1,760|1,768|
|Armenia|82|71|89|85|623|506|557|527|
|Azerbaijan|33|20|23|10|4,373|5,280|6,887|7,685|
|<br>Belarus|4,005|3,566|3,407|3,704|230|92|59|53|
|Bosnia & Herzegovina|400|450|653|529|490|584|903|1,055|
|Bulgaria|1,986|1,512|931|784|3,398|2,549|1,693|1,475|
|Croatia|1,175|1,233|1,205|1,250|453|529|796|619|
|Czech Republic|3,867|3,688|2,877|1,909|134|90|148|197|
|Estonia|460|286|340|365|62|28|38|78|
|Georgia|367|411|484|86|754|553|689|690|
|<br>Hungary|4,356|5,335|4,059|3,383|947|934|1,397|1,236|
|<br>Kazakhstan|1,983|984|1,292|1,347|24,273|8,725|11,519|14,126|
|Kyrgyzstan|118|105|83|63|4,924|3,264|2,965|3,606|
|Latvia|501|394|436|384|86|29|39|67|
|Lithuania|1,260|936|1,073|897|40|14|22|48|
|Macedonia, FYR|172|226|156|194|2,466|1,289|1,244|455|
|Moldova|1,061|683|398|284|1,411|930|823|762|
|Poland|20,418|17,122|18,112|14,279|713|362|316|286|
|Romania|7,758|5,848|6,495|6,174|10,897|8,121|7,425|8,882|
|Russian Federation|24,859|18,271|13,413|16,162|31,818|12,603|15,494|19,602|
|Serbia & Montenegro|4,192|4,087|3,177|3,604|2,671|1,917|1,837|1,814|
|Slovakia|2,037|1,562|1,149|741|397|337|321|362|
|Slovenia|571|558|534|432|29|73|119|139|
|Tajikistan|33|1|1|0|1,958|1,472|1,782|2,579|
|<br>Turkey|8|3|4|2|35,646|30,256|25,201|23,975|
|<br>Turkmenistan|128|35|30|30|6,100|7,500|13,089|13,513|
|Ukraine|13,946|10,073|6,466|6,526|4,793|1,060|875|1,096|
|Uzbekistan|350|80|87|92|9,053|8,000|9,555|11,405|

Source: FAO (2011) 57
17 Table 3: Share of human capital in wage bill

||Wage premia by<br>|Share of work<br>|force by educa<br>|tional attainment,<br>|1985<br>||
|---|---|---|---|---|---|---|
||educational attainment<br>under assumption:<br>A<br>|B<br>|Developing<br>Countries<br>I___|Sub-Saharan<br>Africa<br>Acn_|Latin American<br>and Caribbean|South Asia|OECD|
|No Schooling|1.00<br>1.00|<br>49.7%|48.1%|<br>22.4%|<br>69.0%|<br>3.3%|
|Some Primary|1.40<br>1.56|<br>21.3%|<br>33.2%|<br>43.4%|<br>8.9%|<br>19.4%|
|Primary Complete|1.97<br>2.44|<br>10.1%|<br>8.5%|<br>13.2%|<br>4.8%|<br>18.3%|
|Some Secondary|2.77<br>3.42|<br>8.7%|<br>7.7%|<br>8.4%|<br>8.8%|<br>20.7%|
|Secondary|3.90<br>4.81|<br>5.9%|<br>1.6%|<br>5.5%|<br>5.3%|<br>20.1%|
|Some Tertiary|5.47<br>6.06|<br>1.4%|<br>0.2%|<br>2.5%|<br>0.9%|<br>7.7%|
|Tertiary|7.69<br>7.63|<br>3.0%|<br>0.8%|<br>4.6%|2.3%|<br>10.5%|
|<br>Average yea|rs of schooling<br>||||||
|||17<br>3.56|1<br>2.67|1<br>4.47|1<br>2.81|<br>8.88|
|Calculated s|hare of return to human cap|ital in total wag|e bill under ass|umptions:|||
|A) Assuming that the|Mincerian return is 10%|36%|<br>26%|<br>43%|1<br>30%|<br>62%|
|B) Assuming the Mi<br>16%, secondary 12|ncerian return is; primary<br>% and tertiary 8 %.|49%|<br>38%|<br>56%|<br>42%|<br>73%|

Sources: Data on educational attainment by region from Barro and Lee (1993).

These are obviously very rough calculations and do not create tight bounds but do suggest the outer limits for reasonable estimates". Both methods suggest that the human capital share of the wage bill should be between .35 and .7 and hence, hence if the wage bill is .6 the share of human capital in GDP should be between .21 and .42. One way to interpret these shares is that the share of educational capital, and hence the coefficient in the above growth accounting regression really ought to be around .3.

Another way to use non-regression growth accounting is to use the shares of human and physical capital from national accounts rather than estimated from regressions. I define the growth rate of TFP to be: 29 One noticeable feature is that the human capital share does appear to be higher in the wealthier countries which is inconsisteiu with the imposition on the regressions. Three points. First, this increase in the human capital/ output ratio is also found by Judson (1993). Second, in this case the result is an artifact of assuming equal wage increments across coumries. Third. die constancy of factor shares is a condition that has been imposed in nearly all Solow type regressions.
# _Preliminary Tests on Variables_

Before going further into our econometric inquiry, we need to run some preliminary tests on the time series variables in order to ensure their stationarity and avoid running spurious regressions. For this, we have to detect the degree of integration of the expected determinants. A variable is integrated of order one, noted I(1), if that variables is not stationary in level while its first difference is stationary. Here, the first difference of a given variable X means the variable d.Xt= Xt– Xt-1. Time periods are all quarterly.

We use the Dickey-Fuller and the Phillips-Perron tests for this purpose. The null hypothesis of those tests is the presence of unit roots in the variable considered, i.e. the variable is not stationary. We reject the test if the statistical value is greater than the critical value for a given significance level (1 percent generally) in absolute value. Thus, rejecting the null hypothesis means that the variable of interest is stationary, i.e. I(0). If we do not reject the null hypothesis on a variable in levels but reject that null hypothesis on the variable in difference, that means that our variable is I(1). The results of the different tests are presented in tables 2 and 3.

**Table 2: Dickey-Fuller and Phillips-Perron Test on variables in levels**

||**Dickey**|**-Fuller Test**|**Philips- **|**Perron Test**|
|---|---|---|---|---|
||Test statistics|Critical value at 1%|Z(t) statistics|Critical value at 1%|
|**Variables**|||||
|Savings rate|-4.1|-4.3|-6.0*|-3.7|
|GDPper cap|-4.3|-4.3|-3.6|-3.7|
|lnREER|-1.9|-4.3|-0.8|-3.7|
|TD rate|-3.1|-4.3|-2.3|-3.7|
|Deficit|-2.9|-4.3|-3.6|-3.7|
|Openness|-2.4|-4.3|-4.4|-3.7|

* Rejection at 1%

**Table 3: Dickey-Fuller and Phillips-Perron Test on variables in difference**

||**Dickey-**|**Fuller Test**|**Philips- **|**Perron Test**|
|---|---|---|---|---|
||Test statistics|Critical value at 1%|Z(t) statistics|Critical value at 1%|
|**Variables**|||||
|d.Savings rate|-25.9*|-4.3|-19.8*|-3.7|
|d.GDPpc|-31.4*|-4.3|-16.9*|-3.7|
|d.lnREER|-6.7*|-4.3|-6.6*|-3.7|
|d.TD rate|-3.7*|-4.3|-3.9*|-3.7|
|d.Deficit|-7.2*|-4.3|-10.5*|-3.7|
|d.Openness|-12.2*|-4.3|-12.2*|-3.7|

* Rejection at 1%

We notice that almost all the variables are integrated of order 1 with both tests. We then need to find a co-integration relationship between all the variables I(1). This relation will be interpreted as the long-run relationship between the variable and will be useful for a probable Error Correction Model (ECM).

7
# **The Value of Mortality Risk Reductions in Delhi, India**

# **I. Introduction**

Each year over 1 million people die in road crashes. Over 75% of these deaths occur in developing countries, where vulnerable road users (pedestrians, motorcyclists and cyclists) constitute the majority of fatalities. According to recent estimates, the situation is likely to get worse: Kopits and Cropper (2003) predict that road traffic fatalities will increase by over 80% in developing countries between 2000 and 2020. This estimate, however, assumes that historic trends in road safety will continue. Actions could be taken to reduce fatalities, but such actions are often difficult to justify unless the benefits of road safety improvements can be quantified.

For governments in developing countries to make informed decisions about investments in traffic safety, it is imperative that the benefits of road traffic improvements be monetized and compared with the costs. This, however, requires estimates of the value of reductions in risk of death. Ideally, a reduction in the risk of dying in a traffic accident should be valued by what a person would pay for it. This value should reflect not only the loss in income to the person’s family, but the loss of enjoyment from living the remainder of his life. Since estimates of willingness to pay to reduce risk of death do not exist for most developing countries, foregone earnings—the human capital approach—is used instead to value lives lost. The concern is that this may understate the value of improvements in road safety.

This paper reports the results of a contingent valuation survey in Delhi, India that was designed to provide estimates of the value of mortality risk reductions in a traffic safety context. These estimates can be used both to calculate the benefits of specific traffic safety improvements and to compute the social cost of traffic crashes.

- 1 -
In an effort to tightly manage the exchange rate, the government rationed foreign exchange and created multiple windows for foreign exchange transactions, which created a parallel market for foreign exchange. Exporters that had to surrender foreign exchange at official rates effectively faced a high tax, further discouraging non-oil exports.<sup>9</sup> This was not enough to resolve the imbalance: an inflationary process further fuelled by substantial deficit financing through money issue by the public sector, brought about further real exchange rate appreciation.

The periods of the highest over-valuation, as indicated by rapid increases in parallel market premia, obtained in the periods after high oil prices came to an end. As a result, while the nominal exchange rate remained relatively stable, the black market premium reached 330 percent when oil prices collapsed (Figure 10b). Failure to adjust the exchange rate to the decline in expenditure necessitated by declining oil prices and more difficult access to external finance, resulted in unemployment rising instead. In the end, the oil price collapse, high interest rates and public debt problems as of early-1980s, made the high nominal exchange rate unsustainable and massive nominal and real exchange rate depreciation followed (Figure10a). Thus exchange rate policy has also been responsible for the ‘boom and bust’ cycles experienced by Nigeria over the past 30 years.

# **Figure 10 a. The REER 1980-2002 b. Premium between Official and Market Exchange Rate**

<!-- Start of picture text --> 600 500<br>550 450<br>500 400<br>450<br>350<br>400<br>350 300<br>300 250<br>250 200<br>200 150<br>150 100<br>100 50<br>50<br>0<br>0<br>-50<br>Real effective exchange rate index (1995 = 100) Spread between Official and Parallel Market rates (%)<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> Source: World Bank (2004), Nigeria: Petroleum Revenue Management Some real appreciation was inevitable, given the high oil prices during the 1970s and the inevitability of at least some increase in expenditure following the windfall. But a reasonable question is whether this appreciation should have been moderated keeping in mind that oil prices could subsequently

> 9 At the same time, granting import licenses at preferential exchang rates has led to a substantial redistribution of oil wealth, effectively subsidizing well-connected importers. On this, see Pinto (1987, 1990, 1991)).

17
|**Table A3: The determinants of educa**<br>**attainment-OLS**|**tional**|
|---|---|
|Dependent variable: years ofschooling||
|Age|0.571***<br>(0.09)|
|AgeSquared|-0.745***<br>(0.12)|
|Sex|0.173|
||(0.35)|
|Distance to school when 6|-0.640***<br>(0.20)|
|Father farmer|-2.513***<br>(0.38)|
|Father civil servant|1.193***<br>(0.32)|
|Mother farmer|-1.250***<br>(0.56)|
|Mother housewife|-0.605*<br>(0.34)|
|Constant|3.134*|
||(1.61)|
|N|592|
|R2|0.308|
|Adjusted R2|0.298|

Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-25-
21 The literature on primary and secondary schooling in a hybrid/online setting gives mixed results. While some studies find that remote learning modalities can improve student performance, most studies are pessimistic (see Web Appx. Section C).<sup>39</sup> UNESCO (2020b) also asked ministries about how effective they think each modality has been over the past two years. Using their responses, we obtain a measure of the effectiveness ( _e_ ) of each modality in each income group. Globally, online platforms were seen as the most effective modality (78%), followed by take-home packages and the television (75%), and finally the radio (67%) (see Web Appx. Table A.3).

It is not obvious how to aggregate this information in order to obtain a reach ( _c_ ) x effectiveness ( _e_ ) rate for each group. We could thus make the following choices:

1. In the HICs, the television and the radio are seen as much less effective, hence their reliance on online platforms and take-home packages. Reach _×_ effectiveness is 77% for the former while it is 82% for the latter. Taking the lower bound of that range (take-home packages were used less often), _c × e_ could be 75%.

2. In the LICs, given the low reach of online platforms, the radio and the television are used the most. Reach _×_ effectiveness is 32% for the former while it is 22% for the latter. Privileging the lower bound of the estimated range, _c × e_ could be 25%.<sup>40</sup> 3. In the MICs, the radio was the least effective modality. Focusing on the other ones, reach _×_ effectiveness is 27% for online platforms, 53% for the television, and 79% for take-home packages. We could roughly assume _c × e_ = 50% on average.

However, the estimates are provided by ministries of education that may have imperfect information, or incentives to over-estimate the effectiveness of their preferred modalities. For a few countries in late 2020, (pre-college) teacher surveys show much lower reach _×_ effectiveness rates. For seven HICs with a large population, McKinsey (2020) finds an average rate of 4.2 out of 10, hence 42%. Given an effective closure rate of 44% in year 1 in the HICs, _ν_ = 0.44 (1-0.42) = 0.26, or a loss of 26%. Likewise, McKinsey explains that pre-college students were two months behind by November 2020, suggesting a 29% loss. Were we to use the ministries’ estimate, we would have 0.44 (1-0.75) = 0.11, hence a 11% loss only. Thus, we assume that reach _×_ effectiveness = 40% in year 1 and 45% in year 2, thus allowing for improvement over time.

> 39Azevedo et al. (2020) writes: “While mitigation strategies in the time of COVID-19 are often referred to as remote learning [...] in reality what many school systems rolled out was emergency response teaching.” 40Take-home packages are less than 50% efficient in LICs given their lack of resources.
# 4 The E¤ect of Payment Systems E¢ciency on Credit Creation

In this section we develop and apply three di¤erent ways of identifying the e¤ect of payment systems reforms on credit that exploit both the time series and cross-sectional variations in payment systems development in our dataset.

## 4.1 Reduced-Form Evidence: Trend-Break Model

We ...rst propose the following reduced-form speci...cation to identify trend changes in the supply of credit around the transition to modern payment systems: where  (...nancial intermediation) stands for the credit over GDP ratio in country  in quarter  The independent variable of interest is the post-reform trend  starting at one for the ...rst post-reform quarter which measures the augmentation in credit growth from the country-speci...c trend . As  trends upwards over the sample period (for some countries) we model the e¤ect of payment systems reforms as an acceleration in credit growth above country-speci...c sample trends.

The regression includes country dummies 1 to control for all time-invariant omitted variables. Also included are seasonal dummies and year-...xed e¤ects to control for shocks common to all countries, such as credit accelerations – whatever their cause – that a¤ect the whole region. Standard errors are corrected for clustering of the error terms at the country level, to allow for serial correlation and heteroskedasticity in the error term over time.

¡2 is a vector of observable control variables. The set of controls follows closely the extant literature on the determinants of ...nancial deepening (measured by private credit over GDP). Speci...cally, it includes those factors that Cottarelli et al (2003) (among others) emphasize as the main causes of the credit boom in Eastern Europe; namely ...scal consolidations and structural reforms in the banking sector.

The ratio of total government expenditures over GDP is used to capture the potential for reductions in public spending to crowd-in credit to the private sector. We therefore expect a negative sign on this variable. We also include the level of activity, namely the log real per capita GDP and expect, based on prior evidence, e.g. Detragiache, Tressel and Gupta (2006), this variable to have a positive e¤ect on ...nancial depth (private credit over GDP).

In addition, we control for the e¤ect of in‡ation on credit. Following Boyd, Levine and Smith (2001) and Cottarelli et al (2003) we allow for this e¤ect to be non-linear, through inclusion of a high-in‡ation dummy, which takes the value of 1 when in‡ation is above a threshold (equal to the 75th percentile), and an interaction term of in‡ation with the 8
area. Respondents are randomly drawn from a 360 household randomized treatment trial (see Section 3 below for more discussion). The satisfaction survey covered all 360 randomly sampled treatment households and was conducted in October/November 2013 (5-6 months after households received _Mirt_ stoves).

The paper is organized as follows: the next section discusses key improved cookstove literature. Section 3 presents the experimental methodology and Section 4 presents results, including evidence from the field that on per-kilogram food cooked and per-meal bases the _Mirt_ stove saves fuelwood. The last section concludes and draws implications for REDD+.

# **2. Key ICS Literature**

Policy momentum behind improved biomass cookstoves was very strong through the 1980s as concerns about deforestation and impending shortages of fuelwood built under the heading of the firewood crisis (e.g. Foley and van Buren, 1980). The optimism for improved stoves is reflected in a veritable flood of grey literature reports and guides that promoted improved cookstoves (e.g. Shaye et al, 1984; Borthwick and Howard, 1988). _Ex post_ program evaluations were often lacking by modern standards (e.g. Bluffstone, 1989; Wood, 1983; 1987), however, and were often done by organizations actively promoting such technologies.

By the 1990s the urgency of the fuelwood crisis had died down as it became clear that market adjustments could reduce potential crises (Bluffstone, 1995) and criticisms were widespread (Manibog, 1984; Gil, 1987; Jones, 1988). Barnes et al (1993), for example, argue that a key reason for disappointing results was that fuelwood savings had typically been overestimated. More recently, Mobarak et al (2012) argue that improved cookstoves are adopted and used at puzzlingly low rates because users - particularly women - do not perceive indoor air pollution as a significant health hazard. They therefore prioritize other needs over ICS adoption and are not willing to pay much of ICS. They emphasize designing and disseminating ICS with features that are highly valued by users, such as reduced operating costs.

The importance of the almost 3 billion people who cook with wood on a regular basis remains, however, and in recent years improved biomass cookstoves have regained 6 | P a g e
and content of PTAs and a measure of vertical FDI derived from detailed sector-level information for more than one million firms worldwide.

Consistent with the theory, we establish two main findings. First, deep trade agreements are associated to an increase in FDI. Second, PTA provisions that improve the contractibility of components relative to headquarter services are associated to more FDI. However, once we look at the content of PTAs, deep integration is not associated to more vertical FDI anymore. In other words, it is the content more than the depth of PTAs that affects the way goods are traded internationally -i.e. within-firms or at arm's length. As this result is consistent with the “property rights” approach to the boundaries of multinational firms, but not with the “transaction cost” theory, it provides evidence in support of the first approach.

27