rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4. Estimated Fiscal Costs, Baseline vs Policy Scenario 1, Million BGN.**

||**_Social_**<br>**_assistance_**|**_Heating_**<br>**_allowance_**|**_Total_**|
|---|---|---|---|
|_Fiscal Cost, Baseline (Million BGN)_|_32.3_|_140.2_|_172.5_|
|_Simulated Fiscal Cost, Policy Scenario 1_<br>_(Million BGN)_|_230.2_|_174.5_|_404.6_|
|_Change in Fiscal Cost, Million BGN_|_197.9_|_34.3_|_232.1_|

_Source: Own estimates using administrative accounts for the baseline and CEQ and Bulgaria SILC 2021 for the simulated fiscal costs._ _Policy scenario 2: MSA and Heating Allowance increased take-up_

**In this specific policy scenario, the simulation involves raising the Guaranteed Minimum Income (GMI) base from 75 Bulgarian leva (BGN) to 30% of the at-risk-of-poverty (AROP) poverty line.** Importantly, this scenario also assumes 100% take-up, implying that the program successfully fully reaches its intended target population. As a result of this adjustment, the coverage of the MSA extends to a larger share of the population, particularly benefiting households in the first and second deciles, albeit to a greater extent for the former. This broader coverage increases MSA's generosity, rising from 4% to 50% of market income plus pensions. In this scenario, the take-up of the heating allowance increases only marginally (from 99% in the baseline to 100% in the policy scenario).

**There is a substantial increase in government expenditure, nearly increasing tenfold from 32 million to 382.6 million BGN compared to the baseline scenario; the impact on inequality remains relatively modest due to the MSA take-up increase, while the poverty impact is moderate.** The poverty gap experiences a further reduction in this scenario; the overall effect on inequality appears limited, and the effect on poverty is moderate, emphasizing the complex interplay between program coverage, generosity, and take-up rates in achieving meaningful impacts on societal well-being.

**Figure 12.  Simulated performance and poverty and inequality impacts of the MSA Scheme under policy scenario 2**

**a. Coverage (% of decile in each decile) b. Benefit incidence (% benefits allocated to each decile)**

<!-- Start of picture text --> 57.4 1<br>17.1 2<br>3<br>10.3<br>81.1 4<br>0.5 0.1 0.2<br>5<br> 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> 31
||||(2010)|
|---|---|---|---|
||Energy efficiency|Costs related to all types of barriers (e.g.,<br>information, financial, technical, institutional)<br>to the deployment of energy efficiency activities|<br>Sarkar and Singh<br>(2010)|
||Greenhouse gas<br>policies in the<br>Australian transport<br>energy sector|Research and information costs (inquiring and<br>seeking clarification about the policy measure,<br>conducting public education, preparing<br>application and guidelines, searching for<br>information about buyers and sellers);<br>enactment costs associated with legislation,<br>implementation costs including the costs of<br>designing permit allocation system, defining<br>trading rules, and regulatory delay;<br>administration costs (communication and<br>assistance, giving information about a policy,<br>assessing applications, performing auditing<br>tasks, providing permit price forecasts, keeping<br>records, consultation processes, developing<br>required resources such as training agency staff<br>for new tasks, purchasing and installing relevant<br>equipment); contracting/trading costs<br>(interviews and supplementary information and<br>negotiating over prices to enter into a contract,<br>organising purchase or sale of permits,<br>identifying and matching potential trading<br>partners and fulfilling brokerage needs);<br>monitoring/detection costs and enforcement<br>costs involving time and money in enforcing<br>compliance through the legal system|Ofei-Mensah and<br>Bennett (2013)|
|All Sector|Kyoto market<br>mechanisms (Clean<br>Development<br>Mechanism and Joint<br>Implementation)|Relevant TCs identifies in sectors above plus<br>costs of project validation and registration,<br>monitoring, emission reduction verification and<br>certification|Michaelowa et al.<br>(2003); Krey<br>(2005)|

# **2.3. Chosen concept and taxonomy of transaction costs for low-carbon technologies**

In this paper, TCs are understood to represent the costs for (technological) entities to conduct transactions: ex-ante costs of negotiating and writing and an ex-post costs of executing, policing and enforcing (Williamson, 1981). This paper categorically excludes costs of some barriers to clean energy technologies, as well as costs associated with behavioral attributes of consumers. These include higher interest rates perceived by commercial banks due to higher risks associated with clean energy technologies; consumers’ reluctance to adopt energy efficient technologies as they value the current capital costs much high than the benefits from energy savings in future; and higher-income consumers’ indifference to monitoring their energy expenditures because they do not care much about energy expenditure as it is small compared to other expenditure items. Most existing literature on transaction costs does not include

- 9 -
||_countries, which together represent over 40 percent of the region’s total_<br>_population.”_|
|---|---|
|Setting|5<br>Describe the setting, locations, and relevant dates, including periods of<br>recruitment, exposure, follow-up, and data collection<br>_See the relevant text: “We draw upon data from the Service Delivery_<br>_Indicators (SDI) program—an ongoing program with the aim of collecting_<br>_informative and standardized measures of what providers know, what they_<br>_do, and what they have to work with...To date, the SDI program has_<br>_collected data from ten countries: Kenya (2018), Madagascar (2016),_<br>_Mozambique (2014), Nigeria (2013), Niger (2017), Senegal (2010), Sierra_<br>_Leone (2018), Tanzania (2016), Togo (2014), and Uganda (2013), and has_<br>_since expanded to countries outside the Africa region. While every context_<br>_is different, these ten countries serve as a reasonable proxy for much of_<br>_Sub-Saharan Africa, with an average income per capita, life expectancy,_<br>_and infant mortality similar to those of the continent as a whole (Appendix_<br>_Table S2).”_|
|Participants|6<br>(a) Give the eligibility criteria, and the sources and methods of selection of<br>participants<br>_See the relevant text: “All country datasets were representative at the_<br>_national level except for Nigeria which, mainly owing to security concerns,_<br>_covered 12 out of 37 states and was representative at the state level. The_<br>_surveys used a multistage cluster-sampling design to ensure data were_<br>_representative of rural and urban areas, private and public facilities, and of_<br>_each facility type (e.g., front-line clinics versus hospitals).”_|
|Variables|7<br>Clearly define all outcomes, exposures, predictors, potential confounders,<br>and effect modifiers. Give diagnostic criteria, if applicable<br>_See the relevant text: “There are three essential conditions for quality care._<br>_First, providers have sufficient knowledge to diagnose and treat patients_<br>_suffering from conditions that are amenable to care. Second, they are_<br>_available to provide care when patients seek service. Third, providers have_<br>_the means (e.g., equipment and drugs) to diagnose and treat patients.”_<br>_“Combining data from the clinical vignettes (to measure knowledge) with_<br>_data from the unannounced visit (to measure the extent to which providers_<br>_are present in the facility at regular opening hours), and visual inspections_<br>_(to quantify the availability of drugs and equipment), we estimate the_<br>_probability that a facility is ready to provide care that meets minimum_<br>_quality standards. A facility is defined as being ready to provide care that_<br>_meets minimum quality standards for a given condition if there is at least_<br>_one provider present in the clinic at a given day that can correctly diagnose_<br>_and treat that condition, and the facility has a minimum set of drugs to_<br>_treat it.”_|

37
specifications (3) and (4) show that changes in the share of male ninis are not correlated with homicide rates in Mexico. As shown by specification (4), changes in the share of male ninis aged 19 to 24 are marginally correlated with homicides rates (at the 93.3 percent level of confidence). Although statistically weak, a positive relationship between violence and homicide rates is not surprising given the profile of the crime perpetuators described above (young male adults with relatively low education levels).

Table 1: Panel data estimations for the relationship between ninis and homicide rates

||**Ho**|**micide rate INE**|**GI (1995-2013)**||
|---|---|---|---|---|
|Independent variables|(1)|(2)|(3)|(4)|
|Share of ninis|||||
|All ninis||-0.0231|-0.0385|0.0503|
|||(0.23)|(0.27)|(0.27)|
|Male ninis|||0.0334|-0.663|
||||(0.30)|(0.48)|
|Males ninis aged 19 to 24||||0.625 *<br>(0.34)|
|Controls|||||
|Share of the population aged 15 to 24|-0.0038|-0.0059|-0.014|0.079|
||(0.56)|(0.56)|(0.57)|(0.57)|
|Log of hourly labor income|-9.802***|-9.901***|-9.934***|-10.13***|
||(3.47)|(3.61)|(3.63)|(3.62)|
|Unemployment rate, aged 35 to 44|55.44|55.84|54.89|58.25|
||(39.35)|(39.58)|(40.52)|(40.47)|
|Gini (per capita labor income)|11.02|11.04|10.92|12.22|
||(15.96)|(15.98)|(16.04)|(16.02)|
|Share of informal workers|-12.71|-12.57|-12.97|-13.42|
||(17.72)|(17.79)|(18.18)|(18.14)|
|Share of employment in manufacture|<br>-40.03 *|<br>-40.29 *|<br>-40.27 *|<br>-33.32|
||(22.38)|(22.55)|(22.57)|(22.83)|
|High school dropout rate|0.406|0.406|0.404|0.412|
||(0.27)|(0.27)|(0.271)|(0.27)|
|Constant|<br>29.02 *|<br>29.86 *|<br>30.39 *|<br>25.59|
||(15.36)|(17.45)|(18.10)|(18.25)|
|Observations|607|607|607|607|
|R-squared<br>Notes: Standard errors in parentheses, *** p<|0.268<br>0.01, ** p<0.05, *|0.268<br>p<0.1; all estimatio|0.268<br>ns include state-lev|0.272<br>el fixed effects|

<u>Notes: Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; all estimations include state-level fixed effects</u> and yearly dummies. Informal workers are defined as those who work for a firm that does not have accounting records and does not contribute to social security.

18
_Table A 3_ : Correlates of discrepancy between farmers' self-assessed crop damage and enumerators' assessments.

# **Correlates of discrepancy**

||(1)|(2)|
|---|---|---|
|VARIABLES|ln(SR/CC)||SR - CC||
|Farmer reports drought damage|0.515***|11.52***|
||(0.186)|(2.914)|
|Farmer reports termite damage|-0.467***|0.228|
||(0.178)|(2.779)|
|Farmer reports damage from crop disease|-0.115|-10.29***|
||(0.232)|(3.629)|
|GPS-Based Plot Area in Hectares|-0.338|3.532|
||(0.529)|(8.273)|
|HH head is female|-0.155|-3.623|
||(0.245)|(3.831)|
|Plot manager is respondent|-0.312|3.712|
||(0.277)|(4.336)|
|Plot manager is female|-0.0530|2.384|
||(0.205)|(3.213)|
|Age of plot manager (years)|0.00861|-0.181*|
||(0.00639)|(0.0999)|
|Plot manager education (years)|0.00194|-0.0282|
||(0.0201)|(0.315)|
|Constant|0.862**|22.64***|
||(0.414)|(6.472)|
|Observations|202|202|
|Adjusted R-squared|0.075|0.119|

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 52
# **5. Application to cross-section data**

We employ the basic LASSO-PMM specification used throughout the paper to estimate welfare dynamics using cross-section data in circa 2010 and 2015, considering one nearest neighbor and 20 PMM repetitions. We take all households in the first round cross-section survey and predict their income in the second round to obtain our main results. The welfare variable used in each of the 43 countries is shown in Appendix Table A.2. Given the number of countries and our harmonized approach to welfare prediction, we only include common variables in the model, which are described in Appendix Table A.3. We begin by predicting aggregate poverty rates, then study predicted mobility transitions, and finish by plotting predicted income changes across the distribution in growth incidence curves.

# _5.1 Predicted poverty rates across the world_

We plot the poverty rate (using a $5.5 a day poverty line) that results from the cross-section data in the second round and the poverty rate that arises using the predicted income in the second round for those households surveyed the first round (Figure 5). First, as expected from the exercise with panel data, predicted and actual poverty rates are similar, given their proximity to the 45-degree line. Second, there is large heterogeneity in poverty across the world. While some countries have low poverty rates, such as Poland, there are nations where most of the population is below the poverty line, as in Rwanda. Despite the large differences across countries, LASSO-PMM approximates aggregate poverty rates well.<sup>32</sup>

> 32 We originally had cross-sectional data for 49 countries. However, countries Bangladesh, India, Pakistan from SAR and Indonesia, Mongolia, and Phillipines from EAP were removed from the analysis because they presented substantial differences between actual and predicted poverty rates.

30
will increase with the fall in protection. The dualism of the market structure will be further reduced with the abolition of the complex trading rights system,<sup>50</sup> local content requirements, and foreign balancing rules.

China’s WTO accession commitments do not require the duty drawback removal,<sup>51</sup> yet given the fall in statutory tariffs and the negative aspects associated with the drawback system, it becomes important to assess ways of unifying the country’s tariff structure. One way to accomplish this will be to remove duty drawbacks after 2007. An alternative way will be to remove tariffs on manufactured imports thus making duty drawbacks irrelevant. Yet another alternative is to abolish duty drawbacks and halve tariffs on manufactures.

We use the empirical multi-region, multi-sector general equilibrium model in section 3 to provide answers to some of these questions. The results however should be interpreted with caution. They are not predictions but are estimates reflecting the model’s assumptions and the key forces driving the outcomes.

# _4.2.1 Data and simulation design_

The starting point for the numerical simulations is the modified GTAP version 5 database in Ianchovichina and Martin (2004). This database is aggregated to 20 regions, 25 sectors and 5 factor inputs and is changed to reflect the presence of duty drawbacks.<sup>52</sup> The database also reflects growth in the world economy to 2007<sup>53</sup> and the implementation of

> 50 China has agreed to terminate limitations on trading rights upon WTO accession, and in the case of designated trading over a 5 year period.

> 51 WTO explicitly allows duty drawbacks/exemptions for use in the production of export up to the value of the liable duty.

> 52 Ianchovichina (2004) discusses in detail the implementation of the empirical model in GEMPACK (Harrison and Pearson, 1996) and the changes to the GTAP data necessary to incorporate duty drawbacks. 53 The growth projections are based on World Bank projections for aggregate real GDP and factor input growth in each region in the model and a residually determined total factor productivity growth rate to ensure consistency between the two. These projection rates are shown in Table 4 of Ianchovichina and Martin

33
O.A.M.D.G.

This violence can largely be assumed exogenous to the individual household. Most analyses into the origin of violence in Colombia find that the strongest predictors are hysteresis and spatial dependence, weaknesses of the justice system and the development of the drug trade.<sup>5</sup> Armed activities are further facilitated by a difficult geography, such as high altitudes or dense forests, to the extent that this restricts the access of the state.<sup>6</sup> There seems to be a weak role at best for poverty, inequality and social exclusion, at least in the current situation. Sanchez and Nunez (2001), with data from the 1980s and 1990s, find all three of these factors explain only a small fraction of the homicide rate, Medina and Martinez (2001), using data from the 1990s, find a weak role for poverty but none for inequality, Fajnzylber et al. (1999), using data since the 1970s, find one for inequality but none for poverty. Bourguignon et al. (2003), with data from the 1980s and 1990s, show that only a certain fraction of the income distribution, namely the percentage of people below 80% of mean income, is related to property crime. Rubio (2000a), analyzing a cross-section of Colombian municipalities for different yearly intervals since 1987, shows that traditionally important determinants of guerrilla presence at the community level, such as inequality, wealth and education, lose their explanatory power during the 1990s.

There is however some evidence of victimization patterns. Moser (1999) states that younger, lower income and less educated men are more at risk of being both victims and perpetrators of homicides, while women and their dependent children are most affected by forced displacement.

The analysis in this paper takes account of the different types of violence present in Colombia as far as available data allows. We record the incidence of a so-called public

> 5 Levitt and Rubio (2000), Sanchez et al (2003), Sanchez and Nunez (2001).

> 6 Vargas, Restrepo and Spagat (2004)

5
Firm characteristics other than size and age have been shown to matter for firm growth as well. Ramachandran and Shah (1999) find that firms controlled by minority entrepreneurs, Asian or European, did better than those controlled by indigenous entrepreneurs.<sup>11</sup> Secondary and university education are highly significant and positive for African entrepreneur while none of the entrepreneurial variables were significant in the non-African model. Their interpretation of these results is that nonAfricans benefit from various advantages of being a minority such as access to informal networks, credit, informal contractual mechanisms etc. Networks that provide contractual mechanisms for access to credit, information and other inputs are beneficial for growth. For the African entrepreneurs education may serve as a substitute for access to these networks. Mengistae (1999) uses data from Ethiopian manufacturing firms to analyze whether there also are differences between indigenous ethnic groups. Controlling for the date of start-up, sector, time of observation, the education and prior experience of the entrepreneur, and unobserved establishment effects he finds that there is one group in Ethiopia that stands out, namely the Gurage. They started up larger and grew faster. Surprisingly enough the Gurage were the least educated ethnic group and also had least vocational training.

Related to the issue of firm growth is firm survival. Exit rates in African manufacturing are significant, and highest among the smallest firms (Söderbom, Teal and Harding, 2004; Van Biesebroeck, 2005b). The study by Söderbom et al. focuses on the role of total factor productivity in determining whether or not firms survive. The authors use a pooled panel data set of firms in Ghana, Kenya and Tanzania that spans a period of five years. They find that productivity impacts on firm survival among large, but not small, firms. Figure 2, taken from their paper, illustrates this finding. Standardizing productivity by the sample mean, the graph shows that exit rates among firms with 10 employees are relatively high and almost wholly invariant to productivity. In other words, being relatively productive does not prevent firms from going out of business if they are small. Among larger firms, however, exit rates do depend on productivity, which is consistent with a “survival-of-the-fittest” process,

> 11 An early study into the relation between minorities and entrepreneurial success is that by Kilby (1983). The author argues that minority entrepreneurs often have superior initial endowments of capital, knowledge of markets and technology and have acquired traditions that help them raise productivity. The environment has also forced them to cooperate with fellow minority entrepreneurs, with whom they have built networks of trust that provides information, risk-spreading arrangements, favorable terms of credit and a larger pool of individuals to whom managerial responsibilities can be delegated.

23
(GG) consists of 3 parts: Central Government (CG), State Government (SG) - though many countries do not have that part, and Local Government (LG). Each part consists of governmental units (in case of CG - only 1 unit), which are united into 1 or more tiers (in case of CG - 1 tier). So far as data permits, Social Security Funds are consolidated with an appropriate part of GG.

Definition Governmental unit - public institutional entity, which has the following properties:

1. It is separate corporate entity, which may own assets and incur liabilities, sue and be sued. To say simpler, it has its own budget.

2. It has planning, legislative and executive powers.

Note that this definition is stronger (imposes more requirements for an institution to be a unit of government) than the one of D. Treisman used in Fan et al. (2009) and for his dataset.<sup>1</sup> At the same time, Definition 3 is weaker (imposes less restrictions) that the one used in (UCLG, 2008). There, the public entity is a governmental unit only if it has elected council,<sup>2</sup> while we allow for governmental bodies, which are fully appointed by an upper-tier government.<sup>3</sup> Definition Tier - a system of governmental units, which covers the whole territory of a country.

Tiers are needed to calculate the average population of LG (or SG or SNG - subnational) administrative unit. For instance, if LG in a country with the population P has 3 tiers (X1 units on 1st tier, X2 units on 2nd, X3 units on 3rd) then the average population of LG unit is 3P/(X1 + X2 + X3) - countries are penalized for having too many and too big governmental units at LG level. In general, the formula looks the following way: where LG ~~p~~ op is the average population of an LG unit, T is the number of tiers in the country, P is its population, and Xi is the number of LG units at the i’th tier.

Definition Local government - the lowest level governmental units (may be grouped into several tiers). Higher tiers of LG should not have substantial powers, and are used for coordination between lowest level units.

Definition State government - the second highest (after CG) level of governmental units. It should have substantial fiscal and administrative powers.

> 1The definition is the following: A is a governmental unit if (a) it has government or at least a governor, and (b) it represents level of general administration, not a particular service. Further, set of A’s of the same type represent a ”tier” if a higher ”tier” is at least in some parts is subdivided into A-type units

> 2They themselves relax this definition for some countries

> 3In fact, election of council is one of our decentralization variables

6