rafmacalaba/datause-encoder-data huggingface dataset:
VI. ADDITIONAL EVIDENCE This analysis aims to shed light on why PYP did not reduce CO by evaluating the effect of PYP on other outcome variables associated with car use: gasoline consumption, vehicle registrations, and vehicle sales.

# _The Effect of PYP on Gasoline Consumption_

When analyzing gasoline consumption, it is important to distinguish the PYP effect from the impact of gasoline prices, as this provides insights about the relative effectiveness of market and nonmarket policies. This analysis utilizes data (January 1996–September 2010) on monthly gasoline consumption (regular and premium) obtained from the Finance Agency of Bogotá, gasoline prices published by Ecopetrol,<sup>25</sup> and GDP and total population figures reported by the National Department of Statistics (DANE). The consumption equation is as follows: �� ��������1� ������2� � ���� ����� ����� � D ��� � �� , �2� where _C_ t is the total gasoline consumption per capita in logs at time _t_ , PYP1 is an indicator variable equal to one from August 1998 to January 2009 and zero otherwise, and PYP2 is an indicator variable equal to one after February 2009 and zero otherwise. _P_ t is the gasoline price and _M_ t is the GDP per capita, both in logs at time _t_ . Furthermore, ���� is a polynomial time trend, �� is a group of month-of-year dummies, and �� is the error term. �� and �� measure the effect of PYP on consumption. This specification is closely related to the work by Eskeland and Feyzioglu (1997a) for HNC in Mexico City, although the estimation here includes a polynomial time trend to 25. Monthly prices from 1999 to 2010 were obtained from http://www.ecopetrol.com.co/precios.htm. Information from the digital news archive _El Tiempo_ (www.eltiempo.com) was used to fill some gaps in the series. The prices taken from the news corresponded to official prices and matched perfectly the prices for which information from Ecopetrol was also available.

16
# **4 Empirical Strategy**

I identify the causal effect of debt relief using a regression discontinuity design based on the program eligibility criteria and data from a household survey of 2,897 debt relief beneficiaries. The identification strategy exploits the fact that, unlike previous debt relief initiatives, eligibility for debt relief under the program depended on the amount of land pledged as collateral at the time a loan was disbursed. This creates a discontinuity in the amount of debt relief around the eligibility threshold of two hectares and induces quasi-random variation in debt relief status: households below the two hectare threshold received 100% unconditional debt relief, while those above the cutoff qualified for 25% of relief conditional on settling the remainder of their outstanding balance.

Presuming that banks followed the rules of the debt relief program faithfully, the causal effect of debt relief can be estimated using a _sharp_ regression discontinuity design (Imbens and Lemieux, 2008a; Hahn, Todd and Van der Klaauw, 2001) that compares households in the immediate vicinity of the program cutoff. Identification using the sharp regression discontinuity design rests on the assumption that treatment status is determined by a cutoff score _<u>x</u>_ along a forcing variable _xi_ and therefore quasirandomly assigned. In the setting studied here, the forcing variable is the amount of land pledged as collateral at the time the loan was disbursed. Without loss of generality, I rescale this variable so that the program eligibility cutoff is centered at zero and use “hectares from cutoff” as the assignment variable throughout the analysis.

Because assignment to treatment ( _Ti_ = 1) or control ( _Ti_ = 0) follows the known rule _Ti_ = **1** _{xi >_ _<u>x</u>_ _~~}~~ ∀ i_ , treatment effect of debt relief, _τRD_ on an outcome _yi_ can be estimated as the difference between the regression functions at the discontinuity _<u>x</u>_ .

Intuitively, if households in the immediate vicinity of the cutoff _<u>x</u>_ do not differ in their observable preprogram characteristics and cannot affect their treatment status once the program has been announced, any differences in observed ex-post outcomes can be attributed to the quasi-random variation in debt relief arising from a household’s documented pre-program landholding _xi_ .

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For volcanic activities one normally uses a CMA of either 8km or 18km (OMI team, 2012), where the former is a middle tropospheric column (TRM) and is for use in medium eruptions, while the latter is an upper tropospheric and stratospheric column (STL) and is for explosive eruptions. Despite this difference, the data are interchangeable in the sense that one can interpolate from one CMA to the others.

OMI is more sensitive above clouds, which both measures mentioned will normally be. The standard deviation for both measures is as low as 0.1DU over Indonesia. The data for both STL and TRM are very similar and this paper uses the STL-data as that are most useful for the biggest events.

# **6.2 Creation of Damage Index and Results**

## **6.2.1 Damage Index**

When constructing a damage index based on SO2 values from ash clouds, one has to set thresholds for distance from the event and from the centroid of the nightlight cell and also a lower sulphur dioxide-value. There are no papers or literature that have estimated any parameter values and there are no usable local data, so the thresholds have been set somewhat arbitrarily.

First off, one wants to set a distance threshold estimating how far away the eruption could cause damage. Note that one wants ground results and not for the aviation industry, since planes can be affected very far away as evidenced by the total stand still of planes across Europe during the 2010 Eyjafjallaj ̈okull eruption. We decided to set a very relaxed condition with any point closer than 10 degrees of latitude and longitude included. Figure 9 portrays the plume approximately 7 hours after one of the biggest Merapi eruptions on 4 November 2010, where the plume moved relatively slowly and after 1000km it dissipated at the lower altitudes, which shows that our 10 degree threshold works well.

Secondly, to match the nightlight data with the OMI/Aura data, a maximum distance between a nightlight point and the nearest SO2 point is set at 50km. The SO2 points are fairly scattered due to cloud covers, hence to get a more consistent grid of nightlight and SO2 values we have chosen a distance that is approximately two times the longest side of an OMI cell.

Finally, a minimum SO2 value in Dobson Units is chosen. According to the Belgian Institute for Space Aeronomy, a typical normal level in air is 0.1DU and a strong eruption is above 10, which is the threshold value chosen.

Once the thresholds have been set, the same nightlight weighting method as for our other indices is applied and then the weights are multiplied with the SO2 value to get an intensity value. The equation is: where _i_ is the nightlight cell on date t, and _wi,T −_ 1 is the previously used weight where _i_ is the nightlight cell, _T −_ 1 is the nightlight strength from the prior year and it is divided by the sum of total nightlights in the province or district.

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approach in this case.

For simplicity, we assume that non-agricultural employment is rationed at a fixed wage, as in Harris and Todaro (1970) (see Fafchamps and Shilpi, 2005, for some evidence supporting such model of urban employment in Nepal). Without this assumption, we would have to explain why households with access to both agricultural and non-agricultural off-farm employment choose to do both, which is quite a common situation in the data. At any rate, letting non-agricultural earnings, e(τ ), depend (negatively) on transport costs captures the higher probability of finding or being able to partake of such work near the main town. Our revised formula thus becomes

# 3.2 A hedonic approach: Long-run equilibrium

An alternative measure of road benefits uses the hedonic approach based on land values (see also Jacoby, 2000). For the land value or rent gradient to reflect spatial welfare differentials requires that households be able to freely migrate across locations.<sup>15</sup> In this case, from the ex ante perspective of households deciding where to locate, the land endowment A is zero. At the same time, however, in-migration entails an ex post fall in A, or, equivalently, a rise in population density. Since the marginal household must be indifferent to where it settles and buys or rents land, we have Here we assume, for simplicity, that migrants have the average labor endowment T = T , and we ignore non-farm employment for the same reason.

Equation 10 implicitly defines A(τ ), average landholdings in long-run equilibrium. Let <u>dr</u><sup><u>dr</u></sup> <u>dA</u> dτ<sup>= rτ+</sup> dA dτ<sup>be the overall rent gradient, the sum of the short-run gradient introduced</sup> earlier and the long-run effect on rents of changes in population density. Differentiating the equilibrium condition with respect to τ and using Roy’s identity once again gives ι<sup>∗</sup> τ<sup>+ σ=0,whereforthemarginalhouseholdι∗</sup> τ<sup>=−</sup> X yi − q − a dτ<sup><u>dr</u>.</sup> Rearranging, we i get Replacing a by A(τ ) (they must be the same in equilibrium) and integrating then delivers

> 15 By contrast, much of the urban economics literature treats cities as closed to migration so that land values do not necessarily reflect the differential benefits from amenties. On this point see Arnott and Stiglitz (1981), Polinsky and Shavell (1976), as well as the survey of Bartik and Smith (1987).

13
The results show marked differences in the dynamics of wages across education levels. The left panel of Figure 9 shows the career path of three individuals who work in the private sector. One has no education, the other has only secondary education and the third one has higher education. Not surprisingly, for the individual with no education the wage curve starts at a lower level and is also flatter. The various career paths also show that individuals working in the public sector, regardless of the level of education, enjoy a better situation both in terms of wages and pensions (see right panel in Figure 9). On the contrary, individuals working in the agricultural sector and/or the urban informal sector receive lower wages than in the private sector – and they do not have pensions.

**Figure 9:  Selected Career Paths by Level of Education and Economic Sector**

<!-- Start of picture text --> 2.5 2.5<br>Public<br>sector<br>2 2<br>Public sector with<br>private sector<br>1.5 University 1.5 pension<br>Secondary<br>education<br>1 1<br>Private sector<br>0.5 0.5<br>Informal<br>No education sector<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Age Age<br>Source: Authors’ calculations.<br>Net revenues (proportion average earnings) Net revenues (proportion average earnings)<br><!-- End of picture text --> There are also important variations across sectors in terms of the internal rates of return on investments on different levels of education. Higher education for instance, is better valued in the public sector (Figure 10). Secondary education with a technical diploma from a vocational training institution is highly valued in the private sector. In the informal sector, on the other hand, the highest rates of return are for investments in 9 years of basic education plus a technical diploma from a vocational training institution. In all sectors, investments in only primary education or primary education plus a technical diploma are not worth much. These investments are important, only as that they allow individuals to access higher educational levels.

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Japanese public ESCO project.<sup>32</sup> The expected CO2 reduction rate is about 18 percent on average.

There are several important implications of these figures. First of all, energy saving potential is highly building-specific. Not surprisingly, large-scale, energy-intensive buildings, such as hospitals, can expect more savings in energy spending (Figure 12). For small facilities consuming less energy, such as administrative offices, the energy saving potential varies considerably.

The payback period of the public ESCO projects in Japan is much longer than a commercial norm. In the private sector, the maximum payback period that is acceptable is about eight to nine years, which is 60 percent of the statutory useful life of normal equipment (i.e., 15 years). However, the public-sector ESCO projects sometimes have a payback period of more than 10 years. In several cases, it exceeds 30 years (Figure 13).<sup>33</sup> This long time horizon is consistent with a common view in the public sector that renovation of old equipment is necessary anyway; efficiency in investment is less important.

The long payback period also implied that a sustainable contractual and legal framework is vital between the public and private sectors. The average contract period in our sample projects is 9.5 years with a maximum of 15 years, during which many unanticipated events can happen. Thus, it is essential to establish a solid institutional framework to deal with those events. In the developing world, this may be an important challenge to develop the ESCO market. It is still difficult to make a contract of more than five years in many developing countries.

> 32 This is equivalent to the total amount of CO2 emissions from 10,000 passenger cars that drive about 500km from Tokyo to Osaka under the assumption that a passenger car emits 150gCO2 per km. It can be considerable, given the fact that the daily traffic between the two cities was about 70,000 vehicles in 2011.

> 33 In such a case, municipalities make 10-year contracts with ESCOs and will recover their investment beyond the contract period.
# **4. Poverty Maps of Vietnam**

As discussed in Section 2.3, the second stage in constructing a poverty map is to combine the regression coefficients estimated from the VLSS in the first stage and the Census data on the same household characteristics. This gives us predicted expenditures for each household in the Census which are then used to estimate the incidence of poverty (the poverty headcount) for individual regions and provinces, as well as the standard errors associated with these estimates. We present the estimates of the incidence of poverty first at the regional level and then at the provincial level.

# **4.1 Regional poverty estimates**

Regional poverty headcounts and their standard errors, as estimated directly from the 1998 Vietnam Living Standards Survey, are shown in the first two columns of Table 4. For the country as a whole, the incidence of poverty is 37.4 percent with a 95 percent confidence interval of ± 3.2 percentage points. The regional poverty headcounts range from 0.9 percent in urban Hanoi and Ho Chi Minh City to 65.2 percent in the rural Northern Uplands. The standard errors suggest that the degree of precision in the estimates of regional poverty using the VLSS is relatively low: four of the nine regions have confidence limits of **±** 10 percentage points or more.

By combining the urban-rural regression models and the Census data (as described in Section 2), we get an alternative set of estimates of regional headcount poverty rates and standard errors, shown in the second pair of columns in Table 4. Seven of the nine regional estimates are within 3 percentage points of the corresponding estimate from the VLSS. However, the Census-based poverty estimates tend to be less extreme: they are higher than 19
# **Introduction**

New firm entry is a critical part of the process of creative destruction that Joseph Schumpeter (1911) argued is so important for the continued dynamism of the modern economy. That it affects economic growth has been documented in previous work.<sup>1</sup> However, much less is known about the business environments that promote new firm creation. This is an important concern for policymakers, who in country after country are trying to implement policies that will foster entry – witness, for example, the debate in Continental Europe on the lack of home-grown venture capital in promoting new firm creation in high tech industries.<sup>2</sup> A first step is to understand what the cross-country picture really looks like. We use a comprehensive, recently available database of firms across a number of developed and transition countries in Europe to address this question. Some facts are striking. For instance, one might believe that Italy, with so many small firms, should have tremendous entry. Actually, entry in Italy (the number of firms less than two years of age to the total number of firms) is only 3.8 percent compared to 13.5 percent on average for other European countries in the G-7.

What is especially little understood is the role of regulations, especially bureaucratic regulations on setting up limited liability companies, in explaining variations in patterns of entry. The early debate on such corporations emphasized the possibility that crooks might register with little capital and dupe unsuspecting investors or consumers. For instance, the Times of London thundered against the principle of free incorporation through limited liability thus in 1824: “Nothing can be so unjust as for a few persons abounding in wealth to offer a portion of their excess for the information of a company, to play with that excess for the information of a company – to lend the importance of their whole name and credit to the society, and then should the funds prove insufficient to answer all demands, to retire into the security of their unhazarded fortune, and leave the bait to devoured by the poor deceived fish.”<sup>_3_</sup> According to this view, entry regulations serves the public interest by preventing fraud.

> 1 For example, Hause and Du Rietz (1984), Asplund and Nocke (2003), Black and Strahan (2002).

> 2 “Europeans Now Seek to Revive Start-Up Spirit”, Wall Street Journal, February 6, 2002.

> 3 As quoted in Paul Halpern, Michael Trebilcock and Stuart Turnbull, 1980, “An Economic Analysis of Limited Liability in Corporate Law”, _University of Toronto Law Review_ 117: 30.

1
_Note_ : The figure is a partial scatter plot, visually representing the regression of changes in the mortgage access (2006–2010 average) on the mortgage rate (2006–2010 average). The abbreviations next to the observations are the three-letter country codes as defined by the International Organization for Standardization.

_Note_ : The figure is a partial scatter plot, visually representing the regression of changes in the mortgage penetration (2006–2010 average) on the mortgage rate (2006–2010 average). The abbreviations next to the observations are the three-letter country codes as defined by the International Organization for Standardization.

This general point of view is complemented by a specific case study of the housing financial system of three specific African countries (Tanzania, Zambia and Cameroon), presented in the appendix. The description mainly highlights the fact that the formal housing finance system is not well developed and is bank based— it cannot therefore respond to the demand from economic agents. We are therefore not surprised to see that housing supply is not sufficiently abundant or affordable to meet the population’s housing needs **.**

# **3.3. SSA housing market and finance policy typology and comparison**

In order to take into account the heterogeneity of SSA countries, the 48 SSA countries are broken down into various groups depending on characteristics such as wealth, legal origins, oil exporting, ecoclimate (Sahel or forested), and regional proximity; see figure 3.6.

**Figure 3.6:** Poverty Level across Different Types of SSA Countries

<!-- Start of picture text --> 3.0 60<br>2.5 58<br>2.0<br>56<br>1.5<br>1.0 54<br>0.5 52<br>0.0 50<br>-0.5<br>48<br>-1.0<br>-1.5 46<br>-2.0 44<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21<br>Oil exporters (LHS) Frontier markets (LHS)<br>Fragile states (LHS) Others (LHS)<br>Actual poverty rate in SSA (percent of population, RHS)<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> _Source_ : Authors’ calculation based on WDI database We checked to see if there are also differences in mortgage market policy and development across SSA countries. Additionally, we compared the effective performance of housing finance policy, depth, penetration, housing need, cheapest price, and input price with the benchmark performance.

# Wealth-based breakdown

From a wealth point of view, expected income levels may affect housing finance indicators. In fact, table A.1 in the appendix shows that upper-middle-income countries perform better on housing finance indicators.

# Breakdown based on legal origins

The origin of a country’s legal system may affect its housing market and finance policies. Intuitively, colonized countries seem likely to have inherited their legal systems, and thus the way their housing markets 9
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**Figure 3. Meter installation rate and electricity consumption in Albania**

<!-- Start of picture text --> Estimated power<br>consumption (kWh)<br>by district<br>(LSMS 2005)<br>!( !( !(!( !( Power consumption (kWh) !(!( 112 - 127128 - 170<br>!( !( !(!( 171 - 194195 - 213<br>!( !( !( !( !( 214 - 241<br>!( !( !( !( 242 - 270<br>!( !( 271 - 303<br>!( !( !(<br>!(<br>!( !( !( !(<br>!( !( !( !( !( No-metered !(<br>!( !( !( customers (%)<br>!( !( - 0.08 %<br>!( !( 0.09 - 1.11.2 - 4.5<br>4.6 - 20.3<br>!( 20.4 - 50.1<br>50.2 - 91.1<br>4<br><!-- End of picture text --> Source: Author’s drawing based on LSMS 2005 and ERE data.

**Table 1. Household** **<u>characteristics by electricity meter installation</u>**

||W|ith met|er|Wi|thout m|eter|Wilcoxon|
|---|---|---|---|---|---|---|---|
|Variable|||Std|||Std|k tt|
||Obs.|Mean|.<br>Dev.|<sup>Obs. </sup>|<sup>Mean</sup>|.<br>Dev.|ran-sum es<br>statistics(z)<br>|
|Monthly income|4184|39094|55092|<br>578|31758|32911|<br>–5.64***|
|Number of household members|4184|4.516|1.593|578|4.810|1.637|4.60***|
|Urban resident|4184|0.679|0.467|578|0.484|0.500|–9.20***|
|Number of home appliances:||||||||
|Color TV|4184|1.154|0.512|578|1.028|0.466|–6.00***|
|Video|4184|0.373|0.504|578|0.280|0.461|–4.21***|
|Refrigerator|4184|0.969|0.269|578|0.919|0.341|–4.26***|
|Freezer|4184|0.012|0.121|578|0.019|0.149|1.38|
|Washer|4184|0.739|0.449|578|0.576|0.505|–8.06***|
|Dishwasher|4184|0.019|0.136|578|0.019|0.149|–0.26|
|Electric or gas stove|4184|0.809|0.435|578|0.734|0.501|–4.15***|
|Air conditioner|4184|0.087|0.340|578|0.050|0.287|–3.29***|
|Water Boiler|4184|0.645|0.505|578|0.545|0.512|–4.43***|
|Computer|4184|0.088|0.291|<br>578|0.040|0.204|<br>–3.94***|

Note: *** indicates the 1% significance level.

In developing countries, low levels of metering tend to complicate other governance problems in the electricity sector. First, without reliable metering systems, electricity operators have little incentive to improve the system efficiency, because of their low