rafmacalaba/datause-encoder-data huggingface dataset:
3 In Cowing and Holtmann's study, as in most cost function studies in which a proxy for capital stock is included among the regressors, the derivative of cost with respect to capital is positive. Cowing and Holtmann interpret this as evidence of over-capitalization. However, this interpretation is, in fact, inconsistent with economic theory. An increase in capital ought always to reduce variable costs, because usage of variable inputs must decline if output is to remain unchanged. Hence 6Cv/6K ought always to be negative. That <!-- Start of picture text --> K<br>')\<br>3<br>F<br>2 2<br>K .\ ....... .<br>F' I c - .<br>Yo<br>v<br>Figure I<br><!-- End of picture text --> the derivative of cost with respect to capital is positive in such studies suggests that the dependent variable may not actually be variable costs, but may instead include some fixed costs. If, for example, all fixed costs are included in the cost variable, a positive 6Cs/6K makes perfect sense; indeed this is precisely the condition for over-capitalization.

This suggests an alternative testing strategy, which addresses the fact that it will typically be difficult to purge cost data of fixed costs: estimate a short-run total cost equation and test the hypothesis that 6Cs/6K=O. This is similar to the approach adopted long ago by Feldstein (1967), though he confusingly argued that he was investigating economies of scale rather than <!-- Start of picture text --> cs<br>to purge cost data of fixed<br>costs: estimate a short-run C CY, WV K)<br>total cost equation and test<br>the hypothesis that 6Cs/6K=O. ......<br>This is similar to the approach Cs<br>adopted long ago by Feldstein '<br>(1967), though he confusingly c2 ............<br>argued that he was<br>investigating economies of K' K3 K<br>scale rather than<br>over-capitalization. From fig _<br>1 it is evident that the Figure 2<br>partial relationship between<br>short-run total (and average)<br>cost and capital stock is<br><!-- End of picture text --> U-shaped (fig 2). Moving along this curve is equivalent to moving from one short-run average cost curve to another (fig 3). At point '1' 6C5/6K<O (and, by implication, 6CV/6K<-wK), while at point '3' 6Cs/6K>O (and, by implication, 6Cv/<sup>6</sup> K>-wK). Hence if one estimates a short-run total (or average) cost function and finds that a hospital is operating to the right of the minimum point of the partial relationship between
17 determinant of crime, either by increasing the supply of potential victims who do not know the criminal, or by reducing the chances of apprehension (Kelly, 2000). Population density is defined as the number of persons per square kilometer. We also employ the percentage of households headed by a female, which has been used as an indicator of instability, disorientation, and conflict in personal relations in the sociological literature (for example, Blau and Blau, 1982). Finally, it has been suggested that youth may be more prone to crime (Cohen and Land, 1987). We have created a variable that is equal to the percentage of population aged 21-40 for use in the empirical models.1<sup>2</sup> Finally, we include race as a control variable. Kelly (2000) argues that race is a predictor of crime through social isolation and feelings of hopelessness in black communities in the U.S. Race may also be associated with other factors linked to crime. For example, our analysis of the South Africa's 1998 Victims of Crime Survey shows race to be a strong correlate of private and public protection. Blacks are less likely than those in other racial groups to have private forms of protection (alarms, high walls, fences, armed security, guns) and also collective action (neighborhood watch).'<sup>3</sup>

# V. Results

_Main Results_ Figures 1-4 show log-log scatter plots of per capita crime levels versus inequality. There is a positive correlation for all four crimes shown. The same relationship is seen in 12 Analysis with alternative definitions of this variable, using different age groups or only males, produced results similar to those presented in this paper.

13 Detailed information available from the authors.
consumption is remarkably stable over time, allowing us to use the third moment estimated to observed NSS consumption from the NSS-2011. For the second moment, we consider two options, namely estimate it using (a) observed NSS consumption data from the NSS-2011, and (b) imputed NSS-type consumption in the CPHS (which we established does reasonably well in matching the second moment from the observed NSS log consumption data). The first moment (mean log consumption), which is the least stable moment over time, is obtained from the imputed NSS-type consumption data. The resulting estimates of the three parameters _a_ , _b_ , and _σ_<sup>2</sup> for the different years are shown in Figure 14.

The next step is to estimate the parameters of the unconditional distribution of NSS log consumption, which is assumed to follow a Normal Mixture distribution. Normal mixtures (NM) are very flexible. Two or three components are generally sufficient to closely fit any empirical distribution function underlying household consumption data.<sup>23</sup> In our case, it offers two practical advantages. First, it follows that the distribution of NSS log consumption conditional on CPHS log consumption too follows a NM distribution. Second, the parameters of the NMs associated with both the unconditional and conditional distribution of NSS log consumption can readily be derived from the parameters of the NM estimated to CPHS log consumption combined with the parameters governing the relationship between CPHS and NSS consumption (i.e. _a_ , _b_ , and _σ_<sup>2</sup> ).

We start by fitting a NM with three components for the unconditional NSS log consumption distribution. When the estimated variance of one or more of the components is negative, the number of components is reduced by one, until all components are estimated to have positive variance. See assumption 2 for details on the positive variance constraint (and why positive variance is not necessarily guaranteed). Negative variance estimates are only obtained for urban samples during 2018.

Once we have an estimate of the conditional distribution, we obtain 50 random draws of NSS consumption for each household in the CPHS sample (conditional on each household’s CPHS consumption value). For each of the 50 realizations of NSS consumption data, we evaluate the corresponding poverty headcount rates and selected measures of inequality. The point estimates of poverty and inequality are obtained by averaging over the 50 different realizations.

When a new NSS household consumption survey becomes available, both NSSconsumption and CPHS-consumption can be observed for the same year (albeit in different surveys with their own sample of households). Accordingly, one could estimate

> 23To illustrate, we report the empirical goodness of fit for the mixed normal distributions for the years 2015 and 2019 in Figure 8 of Appendix 3.1

37
are informed about staff performance, the same type of process intervention resulted in significant improvements in health care delivery and health outcomes in both the short and the longer run.

In this section, we investigate why the provision of information appeared to have played such a crucial role.<sup>22</sup> We start by presenting a stylized example of why information may matter in these kinds of programs. Then, we use process data from the two treatment groups to identify differences in decisions and actions taken in the two treatment groups (with informed and uninformed community members). We discuss alternative mechanisms and also review the literature on beneficiary control and argue that a key difference between successful and unsuccessful community empowerment interventions is access to information on the staff’s behavior.

# **6.1 What role does provision of information play?**

Why is access to information about the staff’s performance a constraint to the community’s ability to monitor and hold providers to account? Data from the baseline survey provide some suggestive evidence. For a set of indicators, like waiting time and absenteeism, we have data both from the in-charge of the facility and data collected either through visual inspections (by enumerators) or collected directly from the users. The comparison reveals a systematic pattern: the in-charge underreport performance problems. For example, the mean waiting time as reported by the clinician was 2 minutes. The household survey data, however, revealed that it was close to 2 hours. Data from unannounced staff surveys showed an absence rate of around 50 percent. In the in-charge survey, however, the clinic officers did not report problems with absenteeism and explained the low presence at the time of the survey as the result of a high, but unsubstantiated, level of staff training and outreach. Moreover, when the in-charge was asked to list key constraints facing the clinic, lack of funding, staff, material, and drugs were high on the agenda, while issues such as weak adherence to clinic guidelines, shirking, or mistreatment of patients were never listed.

Motivated by these examples, we provide a simple stylized model of how the provision of information on mean outcomes, by enabling users to better distinguish between health workers’ effort and factors outside the health workers’ control, can influence users’ willingness to take actions (to monitor the provider) and, in the end, health workers’ performance.

Consider a setup where the quality of health service at each visit _j_ and time _t_ , _qjt_ , depends on two factors that are unobserved by the patient: the health worker’s performance ( _pt_ ) and resources provided from the center ( _rjt_ ), where

> 22Lack of information was highlighted as a key constraint in the earlier work on community empowerment (see Samuel Paul 1987; Jenkins and Goetz 1999; and Goetz and Jenkins 2001).

17
_Figure 3: Monthly average rainfall in Colombo (2001-2014)_ <!-- Start of picture text --> Monthly average rainfall (mm) for 2001-2014<br>400.00<br>350.00<br>300.00<br>250.00<br>200.00<br>150.00<br>100.00<br>50.00<br>0.00<br>Jan Feb March April May June July Aug Sept Oct Nov Dec<br><!-- End of picture text --> _Source: Department of Meteorology, Sri Lanka, www.meteo.gov.lk_ Flooding is common in Colombo city and many other parts of Sri Lanka. The UN Office for the Coordination of Humanitarian Affairs (OCHA) has recorded the impacts of floods in Colombo and other surrounding districts from 1984 onwards. From 2008 onwards, floods have regularly been recorded in Colombo and nearby districts with disastrous consequences for the people. For instance, in June 2008, Colombo and nearby areas were affected by flash floods and over 400,000 people were rendered homeless.<sup>24</sup> Once again, in October 2008, Gampaha and Colombo city experienced flash floods.<sup>25</sup> On November 10, 2010, Colombo district experienced torrential rainfall of more than 440 mm, inundating most parts of Colombo city and the surrounding areas. Flash floods were reported in Colombo city, Colombo districts and the surrounding districts of Gampaha, Kalutara and Kegalle. The flash floods were largely due to the obstruction of the flow of rainwater in the draining ditches and canals by unauthorized construction on the sides of the canals and improper bridges and culverts.<sup>26</sup> Rapid assessment done by the Disaster Management Centre of Sri Lanka placed the total number affected people at 213,292 (50,235 families) of which 193,261 (43,799 families) were in Colombo district alone.<sup>27</sup> The floods caused significant damage in terms of impact on infrastructure including housing, unavailability of food, adverse effects on livelihoods and disruption of essential services like electricity and transportation. The primary survey undertaken in this study has focused mainly on

> 24 http://reliefweb.int/disaster/fl-2008-000079-lka.

> 25 http://reliefweb.int/disaster/ff-2008-000199-lka.

> 26 _Ibid._

> 27 OCHA (2010), Sri Lanka Floods Situation Report #1, 13 November 2010, United Nations Office for the Coordination of Humanitarian Affairs.

8
Chapter 2 13 Figure 2 Rail Share of Truck + Rail Traffic <!-- Start of picture text --> ( Area, 000 Km2<br>USSR (22,272) 1_ 89<br>CSFR (126)<br>Poland (305) 4 _ s<br>China (9,597) 7<br>Canada (2,305) 4 73<br>India (2,973) _i___ _ 66<br>USA (9,167) o ,<br>Hungary (92) i<br>Bulgaria (111) tso s<br>Sweden (412) 45<br>Austria (83) 46<br>Yugoslavia (255) 43<br>France (546) 32I<br>Belgium (30) H I 30 .30<br>W. Germany (244) H 29<br>Holland (34)<br>U. K. (2 42) 13!<br>Italy (294) t 1<br>Spain (499) 8<br>0 20 40 60 80 100<br>Percent<br><!-- End of picture text --> (Transit traffic not Included.)
<!-- Start of picture text --> mBenchmark - Demand+Exports —r=20 mBenchmark - Supplyt+imports —r=20<br>. —— = AA 0 oeeN<br>oS zs<br>fe)oO ¥ 9oS t~<br>7) oO<br>ni10 nm107) f/<br>U<br>‘ /<br>-15 -15 Wi“Wi<br>\<br>-20 -20<br>= AmMOwtr re NOM Yt TA = ANA Ort rT NMte A<br>(© ie © aun © ee © ee © aes © ee © en © en © ee @ (Cin © aun © en © ee © ee © en © a © ee © Ee)<br>PAHnaODnaSA GOSH + POO OD GOGO Y +<br>=xrr ri aaqaeaqgeaqaqaa =rtre rr awaaaqageaqgaaa<br>oe Oo GO Oo Oo GO Oo oOo GO OO Oo OG GO GO Oo oO oOo Oo oOo Oo<br>NNWNNNNANA AT AN NAAN NNANAN A A<br><!-- End of picture text --> <!-- Start of picture text --> Percent, year-on-year<br>0<br>-2<br>“4 mDemand<br>6 mSupply<br>8 —GDP<br>-10<br>-12<br>-14<br>-16<br>2019 2020 2021<br><!-- End of picture text --> <!-- Start of picture text --> Percent, year-on-year<br>0<br>-2<br>“4 mDemand<br>“6 mSupply<br>-8 —GDP<br>-10<br>-12<br>-14<br>-16<br>2019 2020 2021<br><!-- End of picture text -->
<!-- Start of picture text --> 60<br>UMIC+LMIC+LIC<br>50<br>=== Chatbot GenAl<br>40<br>5 == Image GenAl<br>oI :<br>a LMIC+LIC Video GenAl<br>30<br>== Google+Bing+Yahoo<br>. > ~ aa<br>10<br>2023m1Too2023m4 2023m7M——2023m10 2024m1<br><!-- End of picture text -->
Acquisition Trust, another multilateral initiative, made its first purchase commitment in April 2021.

Table 1: When low- and middle-income countries entered bilateral contracts for vaccines, they did so later than high-income countries

|World Bank country<br>income classification|Number of<br>advance<br>purchase<br>agreements|Average month contract<br>signed|Earliest month contract<br>signed|
|---|---|---|---|
|High income|211|December 2020|May 2020|
|Low income|8|May 2021|February 2021|
|Lower-middle income|105|March 2021|August 2020|
|Upper-middle income|135|February 2021|August 2020|

Source: IMF-WHO Vaccine Supply Tracker To quantify the role of these later orders in explaining late deliveries, Table 2 reports the results of a regression of the month of first delivery (measured in months since January 2020, the month the WHO declared COVID-19 a Public Health Emergency of International Concern) on the month the contract is signed (also measured in months since January 2020), fixed effects for the country’s income classification, fixed effects for each vaccine candidate, and a constant. In this regression, the effect of the contract signature date can have a causal interpretation, as vaccine developers reported publicly that they allocated vaccines through delivery queues.<sup>6</sup> The vaccine fixed effects control for developer-specific delays, providing reassurance that the effect of the contract signature date is not driven by countries that ordered later also selecting specific vaccines that ended up facing long delays due to production difficulties or regulatory delays. The vaccine fixed effects also control for vaccine price, to account for the fact that vaccines with faster delivery times may have been more expensive. Overall, the regression summarizes the average delivery

> 6 For instance, Pfizer CEO, Dr. Bourla, said, “I had asked heads of state by letters that I sent them to [place orders] because I didn’t want to see a distribution predominantly to the high-income countries, and they didn’t do it.” When they subsequently placed orders, he said, “it was too late because the doses had already been allocated.”  (Josh Nathan-Kazis, “Pfizer CEO Says Poor Countries Need to Order Covid Vaccines Earlier” Barrons. Nov. 2, 2021)

8
**Figure 8. Incidence of main components of tax benefit system by types of households, share of final income, percent.**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>two adults (or two adults (or two adults (or one adult and only working only pensioners mixed adults,<br>more) and 1 more) and 2 more) and 3+ child(ren) age adults no children<br>child children children<br>SSC Direct Taxes Pensions Direct Transfers<br>Indirect Taxes In-kind Health In-kind Education Total<br>Source: authors’ calculations using HBS-2017 data.<br><!-- End of picture text --> Geographically, the main beneficiaries are concentrated in rural areas and small towns, while those who live in big cities are net payers on average (Table 11, Figure 9). The rural and small town residents receive 12 and 5 percent of their final income as net benefits. It comes mainly in the form of pensions (23 percent). Other three components of benefit system (direct transfers, in-kind health and education services) have similar shares between 3 and 7 percent of the final income. While residents of big cities, including Chisinau, are net payers to the system on average, mainly because they benefit less from in-kind transfers and pensions, while pay much more through direct taxes and especially social contributions.

**Table 11. Incidence of main components of tax benefit system by types of location, share of final income,** **<u>percent.</u>**

||SSC|Direct Taxes|Pensions|Direct<br>Transfers|Indirect<br>Taxes|In-kind<br>Health|In-kind<br>Education|<sup>**Total**</sup>|
|---|---|---|---|---|---|---|---|---|
|bigcities|-21.1|-5.5|17.5|3.4|-10.7|7.4|4.7|**-4.3**|
|small towns|-15.6|-3.6|23.0|4.6|-13.3|4.3|5.7|**5.1**|
|rural|-10.1|-3.1|23.4|6.8|-15.0|3.0|7.4|**12.4**|
|north|-11.4|-3.1|25.2|6.3|-14.6|3.5|6.2|**12.2**|
|center|-12.4|-3.4|21.6|6.6|-14.7|2.8|7.2|**7.8**|
|south|-10.3|-2.9|24.1|5.8|-14.4|2.9|7.8|**13.1**|
|Chisinau|-21.4|-5.6|17.0|3.1|-10.7|7.8|4.6|**-5.2**|

Source: authors’ calculations using HBS-2017 data.

21
# Regulatory Structure and Risk and Infrastructure Firms

An International Comparison _Ian Alexander' Colin Mayer_<sup>_2_</sup> _Helen Weeds_<sup>_3_</sup> Prepared for the Private Sector Development Department, Private Participation in Infrastructure Group 1 London Economics (tel. 44-171-446-8400) 2 Oxford Economic Research Associates (tel. 44-1865-251-142) 3 Merton College, Oxford University
program envisioned to provide incentives to non‐Annex 1 countries like Nepal to reduce deforestation and forest degradation. The associated reductions in greenhouse gas emissions represent potentially important climate change contributions, because deforestation and forest degradation account for between 12% and 20% of annual GHG emissions (Saatchi et al., 2011; van der Werf, 2009). UNEP (2012) estimates a smaller, though still significant, share at 11%. In the 1990s, largely from the developing world, forests released about 5.8 Gt per year, which was more than all forms of transport combined (Saatchi et al., 2011).

Because monitoring and verification of carbon sequestration can be a real challenge anywhere, but particularly in developing countries, there is interest in input‐based compliance measures for which GHG impacts can be reliably estimated. Such a strategy can be an attractive way to credit offsets, because it may not involve measuring and monitoring actual carbon sequestration in forests. Promoting adoption of more sustainable fuels like biogas to replace harmful and unsustainable biomass fuels could be one part of an input‐based climate change mitigation policy. If it is known, for example, that a particular technology reduces fuelwood use, all that has to be monitored is whether the subsidy to biogas from carbon finance actually reaches households and if biogas plants are installed and used.

REDD+ may be one policy mechanism to incentivize such shifts if adoption of biogas indeed results in less fuelwood use that is creditable in international carbon markets. The purpose of this paper is to examine the potential for financing a switch from wood and other solid fuels to biogas using carbon offset markets. This could open up a much larger source of finance than the limited resources currently flowing into this sector from aid programs. The major contribution of this paper is to use a nationally representative survey of households in Nepal to estimate the reduction in firewood use that can be expected when a household acquires a biogas plant and draw conclusions regarding financing and climate change mitigation.

3
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