rafmacalaba/datause-encoder-data huggingface dataset:
vehicles more stringent, the EU council of Ministers, in March 1994, adopted EU Directives 94/12/EC. The new emission limits were applied starting 1 January 1996 for new models and 1 January 1997 for existing models. Unlike previous regulations, it set separate standards for gasoline and diesel-fueled vehicles (CONCAWE, 2006). Tables 5 and 6 below display the EU’s commitment to reducing the transport sector emissions: The EU has, over time, adopted tougher standards for all vehicular pollutants.

**Table 5. EU Emission Standards for Passenger Cars (Category M1*), g/km**

|**Tier**|**Date**|**CO**|**HC**|**HC+NOx**|**NOx**|**PM**|
|---|---|---|---|---|---|---|
|Diesel|||||||
|Euro 1†|1992.07|2.72 (3.16)|-|0.97 (1.13)|-|0.14 (0.18)|
|Euro 2, IDI|1996.01|1|-|0.7|-|0.08|
|Euro 2, DI|1996.01<sup>a</sup>|1|-|0.9|-|0.1|
|Euro 3|2000.01|0.64|-|0.56|0.5|0.05|
|Euro 4|2005.01<br>|0.5<br>|-|0.3|0.25|0.025|
|Euro 5|2009.09<sup>b</sup>|0.5|-|0.23|0.18|0.005<sup>e</sup>|
|Euro 6|2014.09|0.5|-|0.17|0.08|0.005<sup>e</sup>|
|Petrol (Gasoli|ne)||||||
|Euro 1†|1992.07|2.72 (3.16)|-|0.97 (1.13)|-|-|
|Euro 2|1996.01|2.2|-|0.5|-|-|
|Euro 3|2000.01|2.3|0.2|-|0.15|-|
|Euro 4|2005.01<br>|1<br>|0.1|-|0.08|-<br>|
|Euro 5|2009.09<sup>b</sup>|1|0.10<sup>c</sup>|-|0.06|0.005<sup>d,e</sup><br>|
|Euro 6|2014.09|1|0.10<sup>c</sup>|-|0.06|0.005<sup>d,e</sup>|

* At the Euro 1..4 stages, passenger vehicles > 2,500 kg were type approved as Category N1 vehicles † Values in brackets are conformity of production (COP) limits

a - until 1999.09.30 (after that date DI engines must meet the IDI limits) b - 2011.01 for all models c - and NMHC = 0.068 g/km d - applicable only to vehicles using DI engines e - proposed to be changed to 0.003 g/km using the PMP measurement procedure Source: Dieselnet (undated) 20
- (Li) The residentLal and commercial **sectors are** both of consLderable lmportance and **use** substantial amounts of oll, gas and slectrieLty. These sectors are likely to be relatively loss important ln developing countrie where commerce and home heatlng L typlcally much uss developed.

- (LiL) The Lnis-try sector ls the largest, and overall is the only **sector** to use substantial amounts of coal. Since coal tends to **have the** worst pollution characteristics this sector li of key lmportance for studying the potential for fuel switching.

- (Lv) BlectrLcLty, whlch **ls** a secondary fuel, uslng the other prLmary fuels for its goneratLon, ls very Lmportant when viewed in total. Nearly 500 MTOE of electrLLty is used so that the fuel mlx ln thli single intermediate se*tor is worth studylng.

These figures show that the key sectors for study are Lndustry and power goneratLon, although more evidence ls requlred to show the potentlal for change withLn these sectors. Chanass **i el Use** in **the** O.3.CD.A-

**The** lengthy run of consistent data published by the I.E.A. from the O.E.C.D. countries allows **a** comparison to be made between 1971 and 1988. The earlier year **is** before **the** first oil shock so that changes between the two dates reflect changes due both to growth and to shLfts in the price of fuel to other inputs and in the relatLve price of fuels. Table 2.2 glves data on shares of fuel use by the industrLal sector.

Table 2.2s Shares of Fuel Use by Industry in ITON for the O.E.C.D. in 1971 and 1988

||Coal|Other<br>Solid<br>Fuel|Oll|Gas|ElectricLty|Total<br>(MTOE)|
|---|---|---|---|---|---|---|
|1971|20.2|3.5|39.7|23.1|13.4|946.95|
|1988|17.8|7.4|31.4|24.7|20.6|949.38|

Sources As for Table 2.1.

This table reveals two key poLnts for the study of fuel substltutlon over the perLod:
||(6.178)|(6.282)|(3.140)|(3.172)|(2.814)|(2.807)|
|---|---|---|---|---|---|---|
|Women can work in|4.173|3.185|-27.171***|-25.654***|-0.860|-0.608|
|the same industries as|||||||
|men|||||||
||(6.741)|(6.843)|(3.744)|(3.879)|(2.009)|(2.053)|
|Ratio of Female to|-1.399|-0.822|-11.870**|-11.328*|2.352***|2.486***|
|Male Population|||||||
||(1.338)|(1.382)|(5.789)|(5.785)|(0.572)|(0.566)|
|Fertility Rate|-25.246***|-28.560***|-46.903***|-46.026***|-15.380***|-15.262***|
||(8.963)|(9.345)|(7.940)|(7.955)|(3.649)|(3.646)|
|Starting a Business|0.025|0.038|0.035|0.035|0.081**|0.081**|
|(DTF)|(0.088)|(0.089)|(0.081)|(0.081)|(0.039)|(0.039)|
|Inflation (%)|1.153**|1.158**|0.640**|0.677**|0.059|0.056|
||(0.525)|(0.531)|(0.284)|(0.282)|(0.065)|(0.065)|
|year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry<br>fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|1,418.287**|1,612.929**|2,736.879***|2,601.910***|397.642**|398.045**|
||(640.445)|(648.725)|(832.182)|(824.868)|(171.873)|(171.380)|
|Number of|27,517|27,517|25,587|25,587|36,944|36,944|
|observations|||||||
|R-squared|0.503|0.504|0.515|0.516|0.514|0.514|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

49
derived from visual interpretation of Landsat remotely sensed data. The land cover maps describe land cover status in 1990 and 1970 and provide a comparison of changes over this period. Based on field surveys, the report estimates total biomass and average biomass volume per hectare for different land cover classes at the district and regional level.<sup>7</sup> We use the data available in this report to help us model the relationship between biomass and remotely-sensed data.

# <u>Household data</u>

The household data used in this analysis comes from the second integrated household survey (IHS2). The IHS2 was a comprehensive socio-economic survey of living standards in Malawi. The National Statistics Office administered the IHS2 household questionnaires to 11,280 households from 564 rural and urban enumeration areas between March 2004 and April 2005. This paper focuses on the data from the 492 rural enumeration areas, covering 9,840 households. Together with the household data the survey also collected community level information from each of the enumeration areas.

The nationally representative data is divided into three rural regions of North, Center, and South. We further divide the regions into eight agricultural development district (ADD) zones. These are: Karonga, Mzuzu, Kasungu, Salima, Lilongwe, Machinga, Blanyre, and Ngabu.

The community level data includes, among other things, information about access to basic services such as distances to asphalt road, daily, weekly and ADMARK markets, post office, banks, urban centers, Boma, and other resources.<sup>8</sup> The data on the economic activities at the community level contain information about three important sources of income. The various sources of income include agriculture and fishing as well as use of fuelwood and charcoal. The summary statistics for the community indicators are in Table 1.

The data on the fuelwood collection, its use and source are in the household part of the survey. Availability of alternative to fuelwood collection is measured by indicators if the household used

> 7 Because one of the goals of the project was also to characterize the land cover classes in terms of woody biomass, defined as “the volume of trees including bark from ground to top including branches with a diameter above 2 centimeters over bark” the project carried out a biomass assessment in the evergreen forest, Brachystegia forest in hilly and flat areas, in intensive agriculture land and, in extensive agriculture in forested land.

> 8 A _Boma_ is a local administrative headquarter. The ADMARK market stands for agricultural input market established by Agricultural Development and Marketing Corporation.

7
The transfer scheme makes use of our knowledge of the spatial distribution of poverty in such a way that poverty is minimized at the national level. We consider the gains from spatial targeting at alternative levels of disaggregation. As poverty measure we use the squared poverty gap, which is particularly sensitive to the severity of poverty by calculating the distance between a poor person’s income level and the poverty line and giving more weight to larger distances.<sup>6</sup> We specify a poverty line that accords with a poverty rate of around 20% nationally, in each respective year, and we postulate a modest hypothetical budget that would be insufficient, in and of itself, to eliminate all poverty, even if it were perfectly targeted at the household level. Our results show that in both 1999 and 2009 there are potentially large gains in targeting performance from disaggregating to the local level. These benefits are even more clearly seen when we examine the squared poverty gap as our poverty measure of choice. The impact on the headcount rate is, unsurprisingly, more muted, given that we are not able to “optimize” our transfer scheme with respect to this poverty measure.

The benefits from spatial targeting become increasingly evident as one makes use of more and more disaggregated data on poverty. We show that a given impact on poverty can be achieved at considerably less expense with detailed spatial targeting than with a uniform transfer. Importantly, we find that the benefits from spatial targeting, at any level of disaggregation, are more evident in 2009 rather than 1999. This follows from our earlier finding that poverty in 2009 is more spatially concentrated than was the case in 1999, and supports our assertion that in developing countries that experience uneven spatial progress geographic targeting can be a successful policy to reduce growing wealth disparities.

# **_7.1. Transfer scheme_**

> 6 We focus on the squared poverty gap because of its appealing properties from both a conceptual and technical point of view.  The basic approach explored here would also work for other poverty measures, particularly FGT measures with values of parameter α greater than 1.  However, with the headcount measure (the FGT measure with α=0) welfare ‘optimization’ is not well defined and the approach taken here is thus less obviously applicable (see for example Ray, 1998, pg 254-255).

24
_(Box 6)_ turn them into something that effectively addresses public health. Work is continuing on trying to understand the dynamics of influenza viruses at a global scale and studying the similarities and differences among potential transmission pathways in different regions. A key continuing uncertainty is whether wild birds are both vectors and victims of H5N1. While poultry trading is the primary means of spreading the virus, the role that wild birds play in long-distance spread is still unclear. Recent research based on tracking bird movements over long distances suggests that H5N1 often kills its victims before they travel far.

As of mid-2008, H5N1 pathogenicity is continuing to gradually rise in wild birds in endemic areas, but the avian influenza disease situation in farmed birds is being held in check by vaccination. Eleven outbreaks of H5N1 were reported worldwide in June 2008 in five countries, compared with 65 outbreaks in June 2006 and 55 in June 2007. The global situation had improved markedly by mid-2008, although cases are still underestimated and underreported in many countries because of limitations in country disease surveillance systems.

Sources: Reid et al. (2009), Normile (2008), WHO and FAO website updates.

49
35 Table 7: Brazil - Per Capita Income of Household Groups, Three Metropolitan Areas, 1984 (multiples of minimum wage) <!-- Start of picture text --> Percentile of Recife SAo Paulo Porto Alegre<br>Income Distribution<br>Lowest 5ib 0.12 0.28 0.29<br>Lowest 10th 0.18 0.42 0.41<br>Lowest 25th 0.32 0.75 0.72<br>Lowest 50th 0.60 1.36 1.35<br><!-- End of picture text -->

# Source: author's tabulations

Note: Income refers to the sum of the income from all sources of all adult members of the household. Adult members include the household head, his or her spouse, and all other members age 18 or more.
that about 80% of the export growth is due to the intensive margin and 20% is due to the extensive margin – to the addition of new products to the export mix. Using Customs export transaction data we can have a closer look at the relative contribution of the intensive and the extensive margins to export growth and at the issue of product diversification in Georgian exports between 2006 and 2012.

Diversification happens because existing firms add new products to their export mix and because new firms export products that were not exported before. We can decompose the extensive margin into a “within-firm” component, which represents the contribution of products exported by continuing firms (firms exporting both in 2006 and 2012) and an “entry/exit” component, which represents the net effect of new products exported by entrant firms minus the products dropped by exiting exporters. Between 2006 and 2012 25% of the export growth due to product diversification happens within existing firms while 75% is due to new firms.

There is substantial experimentation with new products that happen at small scale. These products, however, struggle to contribute substantially to export growth probably because of low survival rates, which impedes their consolidation. The number of export products increased from 1,497 in 2006 to 2,024 in 2012. In 2012 the product mix is equally split between continuing products (products already exported in 2006) and new products. However, new products only account for 16% of total exports in terms of value.

The entry of new exporters into the market is an important driver of product diversification in Georgia. Thirty percent of the growth in the number of product exported is due to within firm diversification while 70% is due to the entry of new firms.

From the perspective of destinations, export growth is obtained almost entirely from increased exports to the same destinations. The extensive margin accounts for a mere 2.8% of total export growth in the period. This implies that at the firm level destination diversification has been minimal and much lower than product diversification. Contrary to what it is seen for product diversification the major part of the destination diversification happens within continuing firms (62.2%) while new destinations introduced by entrants account for 37.8% of the extensive margin.

8
# **B.7 Sensitivity analysis**

The sensitivity analysis determines which parameters are crucial for the stability of the model. We use Dynare’s global sensitivity analysis toolkit. The tool first samples from a distribution (prior range, prior/posterior distributions) and groups draws into two subsets: Violating behavior and not-violating behavior. Behavior can be defined flexibly such as stability or indeterminacy of the model. Then, it plots cumulative density functions of behavior (blue) and non-behavior (red). It conducts Kolmogorov-Smirnov test under the null hypothesis that two distributions are identical against they are different. In the diagonals of Figure B2, as the vertical distance between blue and red density functions increases, the probability of rejecting the null goes up. We simulate from the prior range to investigate stability in a wide range and plot parameters with p-values less than 0 _._ 001 in their order of significance.

Model stability is sensitive to the long-run elasticity of substitution between renewable and fossil energy _σE_ , elasticity of substitution between value-added and energy _σSZ_ , and monetary policy - wage inflation rule parameters. High _σE_ reduces the probability of finding a solution. Blue line above red line indicates that stable solutions accumulate at low values of _σE_ .

Monetary policy parameters are important for the indeterminacy of the model. The second panel of Figure B2 zooms in indeterminacy and shows two distinct monetary policy regimes in covariance plots of _φRP_ and _φWE_ . Unlike a standard DSGE model, a less than one-to-one response to inflation in Taylor Rule ( _φRP ≤_ 1) does not necessarily cause indeterminacy. If Phillips curve coefficient is negative ( _φWE ≤_ 0), no restriction is needed for _φRP_ for the rational expectations solution. On the other hand, if _φWE >_ 0, the model converges to the DSGE case: _φRP_ must be greater than 1 to rule out indeterminacy.<sup>30</sup>

> 30Given high estimated _φW E_ for T ̈urkiye, the model cannot explore the _φRP <_ 1 region. Investigating regime switches further is in our research agenda.

73
_I i_ of ozone exposure (indices _O_ 3 , i=SUM6, AOT40, W126), to compute the impact on the output of maize, rice and wheat. The percentage change in yields is given by: The final step is to calculate the monetary value of these damages. The value is given by the health _V_ impact from (A125) multiplied by the willingness to pay value of each type of health effect ( _ht_ ), and the value of crop damages is the value of the crop ( _Vcrop t_ , ) multiplied by the percentage change in crop yields: ‐74‐
<!-- Start of picture text --> Understatement Overstatement<br>LUXIRL SRB<br>More Overstated NORNLD KOR EGYTUR<br>DNK VNMCHN<br>UKRLTU<br>CHE NZLFIN ALB<br>ISR BGRJOR BLR<br>AUSJPNUSASWEGRCDEUGTMPRYBIHRUSHUNESTCYPNIC<br>SVN<br>MKDBRA ROUPOLNGA LVA<br>MOZHRVSVKCZECANBEL<br>SAU IDN<br>AUT<br>Less Understated CRISGP FRAITA<br>ZAF PRT Less Overstated<br>ISL<br>THA<br>CHL<br>LKABHR URY<br>GBR<br>COTUNMYSL<br>MEX<br>MLT ESP<br>ECU<br>MNG MAR<br>NAM INDHND ARG<br>BWA<br>More Understated<br>PHL<br>PER<br>BLZ<br>-5 -4 -3 -2 -1 0 1<br>Difference<br>1<br>0<br>-1<br>-2<br>Difference*<br>-3<br>-4<br>-5<br><!-- End of picture text --> Figure S8: Effect on Composite Estimate for 2020 when Normalized to C-Grade Weight _Notes._ Difference=Reported-Composite. Difference* is similar, but using the composite based on the assumption of a grade C data weighting _λ_ = 0 _._ 60 for all countries. See Tables S6 and S7 for ISO codes correspondence with country names and underlying values.

S19
# **1 Introduction**

State-owned enterprises (SOEs) operate in key economic sectors, such as natural resources and infrastructure, and provide basic goods and services to households and firms. As such, they play a crucial role in economic growth and development, especially in low- and middle-income economies.

SOEs are among the largest companies in both developed and developing countries. They are often dominant players in key sectors, especially infrastructure, numbering in the hundreds in various countries, including China, the Russian Federation, and Vietnam (Christiansen 2011; Szamosszegi and Kyle 2011; Wang, Guthrie, and Xiao 2011; OECD 2016, 2018). Total SOE assets in 2018 were estimated to be $45 trillion, about half of global GDP (IMF 2020).

SOEs are particularly prevalent in infrastructure, where they invest more than public entities, such as ministries and agencies, and the private sector combined in emerging and low-income economies.<sup>2</sup> Infrastructure SOEs are arguably the most important SOEs for the overall health and growth of an economy, as they provide access to essential services in areas such as water, sanitation, energy, transportation, and communications, among others. In fact, many developing countries use infrastructure as the main tool for economic development.

Access to basic services is a necessary but not a sufficient condition for industrial development and economic growth. Infrastructure needs to act as a productivity enhancer, removing barriers to investment and improving the overall business climate. In many cases, governments use large infrastructure projects for political objectives—to show progress through physical construction or to inject money into the economy—rather than as an engine of development.<sup>3</sup> Some projects end up being too costly to maintain for the economic benefits they bring, placing a burden on the budget and becoming a major source of fiscal risk (Nellis 2005).

Because of their high fixed costs, and significant economies of scale, many infrastructure SOEs are natural monopolies. For this reason, one would expect them to be smaller in number but much larger in size than the average SOEs in other sectors and to outperform similar private firms in the same sectors.

In practice, they do not. The main obstacle to improvements in the performance of infrastructure SOEs, especially those fully owned by the government, is the fact that they are used for quasifiscal operations (QFOs) and are not compensated for them. When they “are directed by their

> 2 World Bank (2017) finds that 83 percent of infrastructure projects are funded with public resources, 66 percent of total funding by SOEs.

> 3 See https://thediplomat.com/2020/01/the-trouble-with-indonesias-infrastructure-obsession/.

2
Given the data limitations we face, we focus our strategy to exploiting cross-sectional variation in the data and linking some welfare indicator (e.g. consumption per capita) or some component thereof (food versus non-food expenditure) to a climate-related shock defined on the basis of available rainfall data focusing mainly on rural households. As noted earlier, the yield of crops such as rice (staple food in Indonesia) and soybean can is very much affected by changes in precipitation patterns which are strongly influenced by ENSO.

Given the importance of rice farming in the rural economy of Indonesia, we define climate shocks with reference to this activity. Naylor et al. (2007) explain that El Niño events can cause a delay in monsoon onset of up to 60 days. The same authors define “ _onset_ ” as the number of days after August 1 when cumulative rainfall reaches 20 cm<sup>5</sup> , and “delay” as the number of days above the mean onset date over the 1979-2004 period. Since farmers will typically begin planting after monsoon onset, late onset may affect prospects for a second harvest later in the season and possibly change crop combinations (with potentially significant consequences on production and market prices).

While delayed onset is an important determinant of harvest, we also need to consider the amount of rainfall after the onset. After farmers plant the rice fields, 60-120 cm of rainfall are needed during the 3-4 month grow-out period (Naylor et al. 2002). Thus, the second dimension of our shock involves the deviation of the amount of post-onset rainfall from the 25 year mean for each weather station. We define the amount of post-onset rainfall as the total amount of rainfall during the 90 day period following the monsoon onset date.

**Figure 1: Timing** **<u>of typical climate events and the IFLS3</u>**

<!-- Start of picture text --> Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><---- ----- 1999 ----- ----| |----- ----- ----- ----- ----- 2000 ----- ----- ----- ----- ----- ----|<br>"rainy season year"<br>Onset IFLS3, late Jun-end Oct<br>(20 cm) Post-onset 90 day<br><!-- End of picture text -->

The timing of these events in relation to the IFLS3 survey is illustrated in Figure 1. Considering that the degree of rainfall variability can differ across areas and that households may

> 5 This is the amount of rainfall needed to moisten ground sufficiently for planting.  It is believed that about 100 cm of rain are needed throughout the season for cultivation.

5
**11**

|||La|Table 5<br>bor**Regulations**|||
|---|---|---|---|---|---|
|_Country_|_Payroll_<br>_Taxesa_|_Severance_<br>_Payments'_|_Employment_<br>_Growth_<br>_(1992-1995)'_|_Unemployment_<br>_Rate (1996)_|_Collective_<br>_Bargaining_|
|**Australia**|27.8|Low|1.0|9.0|Centralized|
|**Chile**|20.9|Low|2.3|6.3|Firm Level|
|**Japan**|22.9|None|0.6|2.5|Firm Level|
|**Malaysia**|24.3|Low|3.3|2.8|Firm Level|
|**New Zealand**|11.5|None|1.4|8.0|Firm Level|
|**United States**|20.1|None|1.8|5.5|Firm Level|
|**Argentina**|50<br>_<br>0d|High|-0.7|17.2|Centralized|
|**France**|54.7|High|-0.4|11.6|Centralized|
|**Italy**|52.8|High|-1.7|10.2|Centralized|
|**Spain**|38.2|High|-1.6|22.4|Centralized|

**a** Payroll taxes are firm donations plus obligatory personal contributions. The values for France, Spain, Italy and Japan correspond to 1994, those for Malaysia to 1995, and those for Argentina and Chile to 1996.

**b** Severance payments based on OECD indexes.

c Employment growth is measured as annual average percentage growth. **d** Argentina amended its labor laws in 1996, and payroll taxes now average 41.0.

Source: Guasch (1997)