rafmacalaba/datause-encoder-data huggingface dataset:
# Box 1. Integrated Centers for Special Attention to Women

According to Brazil’s National Secretariat of Women’s Policies, these centers –known as CRAMs or CIAMs--should provide the necessary care and attention to help women overcome their situation, contribute to the empowerment of women and to restoring their sense of citizenship (National Policy to Combat Violence Against Women 2011). The activities performed by the centers (according to the Technical Guidelines) should include: 1) Counseling in times of crisis, 2) Psychosocial support, 3) Legal counseling and advice, 4) Preventive actions, 5) Professional Qualification, 6) Articulation of local public and private services integrating the GBV network, and 7) Survey of local data on women in situations of violence. The CIAMs receive victims either by spontaneous demand or referrals. Most cases get forwarded by Women's Police Stations, although some women come on their own by word of mouth, either through friends, neighbors or other public services.

Data were collected from four different service units in Rio de Janeiro state, selected for practical reasons, as those were the ones maintained by the state government, which facilitated the team’s engagement. In addition, five key informants were also interviewed (one government representative, responsible for anti-VAW policies in the state and 4 CIAM service coordinators). Potential interviewees were identified by the staff of the CIAMs, and first approached by them to evaluate their interest to participate in this research. Interviews lasted about 90 minutes and were conducted by a researcher trained on VAW research. The research instruments were developed by the research team and with inputs of the Subsecretariat for Women Policies in Rio de Janeiro, under the State Secretariat for Social Assistance and Human Development (Subsecretaria de Políticas para as Mulheres do Rio de Janeiro sob a Secretaria Estadual de Assistência Social e Desenvolvimento Humano, SEASDH-RJ). All data collection followed the recommendations on the ethical conduct of research on domestic violence issued by the World Health Organization (2001). Although the profile of the interviewees was heterogeneous, most of the women were white, with low levels of education and aged between 40-59 years. Most were not working at the time of their interview. Partners and ex-partners were the most common perpetrators of violence, but some women also reported violence at the hand of siblings, children and grandchildren, overwhelmingly male. Almost all cases contained elements of psychological violence, such as controlling behavior, insults and threats and most cases involved physical and sexual violence. Most women interviewed had suffered at the hands of more than one aggressor over the course of their lives (see Table 1). Table 2 summarizes the themes explored during interviews.

**<u>Table 1: Sample characteristics</u>**

|Under 40|4|31%|
|---|---|---|
|40-59|6|38%|
|**Age**<br>Over 60|5|31%|
|Missing data|1|6%|
|**Total**|**16**|**100%**|
|**Number of**<br>One perpetrator|6|38%|
|**perpetrators over the**<br>**course of lifetime**<br>More than one<br>perpetrator|10|63%|

_8_
<!-- Start of picture text --> Crops World SSP4 Ruminant meat World SSP4 Milk World SSP4 Pig and poultry World SSP4<br>55<br><!-- End of picture text --> <!-- Start of picture text --> °<br>° ° i Hl a °<br><!-- End of picture text --> <!-- Start of picture text --> Fd & Fd &<br>& $s & $s<br>gs * s§ * gs * s§ *<br>iiii<br>=280 = 2000 = eo = 2080<br>RCP 2.6 RCP8.5 RCP 8.5* RCP 2.6 RCP8.5 RCP 8.5* RCP 2.6 RCP8.5 RCP 8.5* RCP 2.6 RCP8.5 RCP 8.5*<br><!-- End of picture text -->
|Matching|Age|Difference between theyear that the firm started operations and currentyear.|
|---|---|---|
||Dummy<br>Large firm|Dummy variable that takes value 1 if the firm has >99 permanent workers|
||Dummy<br>Medim<br>firm|Dummy variable that takes value 1 if the firm has 20-99 permanent workers|
||Dummy<br>small firm|Dummy variable that takes value 1 if the firm has <20 permanent workers|
||Dummy<br>high/<br>upper-<br>middle<br>income<br>country|Dummy variable that takes value 1 if the country has high/ upper-middle<br>income according with World Bank (2005)|
||Dummy<br>domestic<br>owners|Dummy variable that takes value 1 if the firm has private domestic owners<br>and 0 if the firma has Foreign owners|

<u>1Total annual expenditure for purchases of: machinery and equipment (including vehicles), and land and</u> buildings.

2Including wages, salaries, bonuses and social payments.

Source: Adapted from based in OECD (2001) Economic Studies No. 33, 2001/II, and from World Bank (2007).

Most of the variables listed above are related to the firm‘s risk and growth potentials. The size (sales and employment) of a firm is often taken to be a good proxy for the firm‘s risk (Cowling and Westhead 1996). The amount of fixed capital can be related to the availability of collateral, which can act as a sorting device to decide which firms are to receive loans (Bester 1985). R&D and technical efficiency of firms are related to the future growth potentials as well as current profitability. The country income level (high/ upper-middle), and the existence of domestic owners are also used to control the firm‘s risk, value, and growth potential in general.

The production workers trained will represent improvement in skill or the quality of employees. Variables indicating R&D and exporter status are introduced to observe if overdraft facility is used to enhance future productivity and expanded production facility. The level of technical efficiency might be an ultimate goal of such a government support policy.

# **5 Estimation Results**

# 5.1 Estimation of Technical Efficiency

To evaluate the efficiency of manufacturing companies, five inputs<sup>20</sup> (labor cost, materials and energy cost) and one output (sales) have been incorporated into the model. A synthesis of production function variables and the descriptive statistics for the indicators considered for the sample is presented in table 4.

> 20 Coelli, et al. (2005), a commonly-used classification of inputs involve five categories: capital (K), labor (L), energy (E), material inputs (M), and purchased services (S). The use of data according to these categories in productivity measurement is sometimes referred to as KLEMS approach. In this study, Capital (K) and Purchased Services (S) are not available.

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|Table B.7: Fiscal (pro)cyclicality of VAT rates, whol|e sample (2000-2016): I|mpacts of developm|ent level, tax ba|se, credit constrai|nts, corruption, rule|of law and ord|er||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Dependent variable: Individual income tax|||||||||||
|Cyclicality of real GDP|-0.057***<br>(0.019)|-0.168<br>(0.176)|-1.107*<br>(0.569)|-0.263***<br>(0.036)|-0.125***<br>(0.031)|-0.473*<br>(0.273)|0.023<br>(0.070)|0.118<br>(0.123)|-0.329*<br>(0.172)|-0.121**<br>(0.057)|
|**Development level**|||||||||||
|Crgdp*dvpt.level (polytomic)||0.036<br>(0.057)|||||||||
|**Tax base effect**|||||||||||
|Crgdp*tax|||0.125*<br>(0.068)||||||||
|Crgdp*tax.base.variability||||0.328***|||||||
|||||(0.017)|||||||
|**Credit constraints**|||||||||||
|Crgdp*credit.ratio|||||0.001<br>(0.000)||||||
|Crgdp*kaopen||||||0.948|||||
|||||||(0.684)|||||
|**Perceived corruption**|||||||||||
|Crgdp*pol.corruption|||||||-0.209*<br>(0.123)||||
|Crgdp*pub.service.corruption||||||||-0.452*|||
|||||||||(0.266)|||
|**Rule of law and order**|||||||||||
|Crgdp*law.order|||||||||0.055<br>(0.044)||
|Crgdp*rule.of.law||||||||||0.056<br>(0.132)|
|Constant|-0.262|33.047***|-0.139|-0.259|0.575|-0.647|19.695***|19.651***|-1.505*|19.376***|
||(0.249)|(0.566)|(0.637)|(0.388)|(0.446)|(0.975)|(0.564)|(0.429)|(0.818)|(1.391)|
|Adjusted R-squared|1.000|1.000|0.971|0.971|1.000|1.000|1.000|1.000|1.000|1.000|
|<br>Rmse|1.008|1.009|1.148|1.144|0.978|0.963|1.002|0.995|0.992|1.006|
|Joint significance (p-value)|-|0.012|0.041|0.000|0.000|0.000|0.000|0.001|0.000|0.000|
|<br>CD test (p-value)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Order of integration|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|
|Cluster|country|country|country|country|country|country|country|country|country|country|
|Covariates|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|Observations|823|823|291|291|758|722|687|687|743|687|
|# countries|50|50|19|19|50|50|47|47|48|47|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. Coefficients attached to cyclicality of GDP or its interactions are multiplied by 10000. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.3 for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

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denote marginal utility during the crisis period, c, and marginal utility in normal times, n. The third term we will return to in a moment. Equation 2b rewrites this as marginal social benefit measured in terms of "units of crisis alleviation"- the marginal benefit of a dollar during the crisis period.

What is now clear is that in calculating the value of the program to the target population, we cannot simply add the income flows from the investment project discounted at the market rate to the cash transfer. This would imply that the marginal utility of a dollar in crisis and non-crisis periods is equal which the intertemporal analysis above suggests is implausible.

In fact, the investment components represent the opposite of smoothing: every dollar spent on materials for a project that will yield future benefits takes a dollar away from present consumption. The effect is very large. In the case of workfare programs and some SIFs, often the share of the budget that goes to wages, and hence current consumption, is below **50%.8** At a very basic level the government is saying: "I know you're hungry today, so here's $100 dollars, half of which you can consume today, but the other half (augmented by the gains of investing) you will consume tomorrow when you're less hungry." For the poor to have decided to make this allocation on their own, equation (1) tells exactly what the rate of return would have to be. The project must give a return equal to the value of an additional dollar of consumption when the worker is hungry relative to the benefit of an additional dollar in normal times. The ratio of utilities preceding the returns to investment in the second term of equation 2b is arguably the "consumption" discount factor that should be used in evaluating these projects and it is likely to be extremely high. Middle class consumers in good times in the US are

> **8** IDB (1997) evaluations put the labor share in SIFs at 30%. Cited in Tendler (2000).

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# **1. Introduction**

In December 2023, representatives of 199 countries confirmed their commitments to addressing the global climate emergency and accelerating climate actions (UN 2024). The success of these global efforts rests on the willingness of individual countries to act upon their voluntarily-set mitigation targets, known as nationally determined contributions (NDCs). The ambitions to implement such policies vary widely across countries (Albalate et al. 2023).

Among factors that influence cross−country variations in climate change actions, the “climate change age gap” − the notion that younger people care more about climate change than older people − causes concerns about the implementation of the climate change agenda.<sup>2</sup> Young climate activists capturing the media and public attention reinforce the perception of a widening climate change generational gap.

The age structure of populations plays a significant role in climate policies. Population aging results in a growing share of older people who establish themselves as a powerful political force in decisions about the provision of public goods. The influence of older generations on public policy, including climate change, is reinforced by their greater participation in the electoral process compared with younger generations (Goerres 2007).

Differences in attitudes toward climate change among generations are often attributed to the fact that younger people will live longer under deteriorating climate conditions and, thus, be more affected by its consequences. Inversely, the shorter time horizons of the aging population and the fact that the main climate change costs accrue in the future suggest that older people should be less supportive of climate change policies. In fact, the empirical evidence of the presence and the size of the climate change age gap is mixed and context-dependent (e.g., Gray et al. 2019).

In this paper, we develop a theoretical model that explains why and how people of different ages perceive the increasing threat of global warming. The model guides our empirical analysis, which relies on data from 38 countries of Europe, Central Asia, the Middle East, and North Africa collected in 2022−23, during the fourth round of the Life in Transition Survey (LITS).

> 2 Factors affecting climate change action include:  political trust and beliefs (Fairbrother et al. 2019); public opposition and political polarization (Lachapelle et al., 2012, <u>Hornsey et al. 2016); gender, race, education, and income</u> distribution (Ballew et al. 2020); and social norms and social capital (Bergquist et al. 2019)

2
40 More rocently a number of authors have extended Mankiw's work both theoretically and empirically. Vittorio Grilli (1989) has pointed out that Mankiw's tests fail to exploit a number of important implications of the theory, including the fact that seignorage and income taxes should have a unit root, and that there should exist cointegration between different measures of government revenue and between revenues and expectations. His empirical results for a group of 10 European nations are mixed, suggesting that while in some countries seignorage has behaved as piedicted by optimal taxation theory, in others it has not done so. Grilli also extends Mankiw's work by allowing the possibility of a variable velocity and by explicitly incorporating the fact that fixed exchange rate agreements constraint the ability to use seignorage.

Poterba and Rotemberg (1990) make a distinction between governments that can commit to a course of action and those that cannot do so. In their model, in the commitment case inflation and taxes will be positively correlated, while in the absence of commitment inflation will 1.. a positive function of both taxes and total government liabilities as a percentage of GNP. They estimate both versions of the model, using OLS and instrumental variables, on time series data for five countries -- France, Germany, Japan, the U.K., and the U.S. -- and conclude that the evidence does not provide a generalized support to the optimal taxation view of inflation. As in previous cases this theory only seems to hold for the case of the United States. This type of work has recently been criticized on two different counts. On the one hand, Dornbusch (1989) has pointed out that while the theory is based on Max&inal tax rates, most (if not all) empirical tests have used computed avage rrtes. On the other hand, Judd (1990) points out that the welfare costs of the inflation tax should be related to expected inflation,
# **2.3 SUMMARY STATISTICS**

Table 2 shows the summary statistics by firm “size.”<sup>10</sup> The data set contains 2,020 companies, 59% of which are large companies that have at least 250 employees, a turnover of at least 50 million euros or a balance sheet exceeding 43 million euros. Of these, 30% are multinational enterprises which have the same characteristics plus engage in multinational activity. Only 11% of the data set are small and medium‐ sized enterprises (SMEs) with fewer than 250 employees and a turnover of 50 million euros or less or a balance sheet of 43 million euros or less.<sup>11</sup>

**Table 2: Distribution of companies by company size**

|**Size**|**Obs**|**%**|
|---|---|---|
|Large|1,200|59%|
|MNE|601|30%|
|SME|219|11%|
|**Total**|**2,020**|**100%**|

Table 3 shows the distribution of companies by organization type for all companies, and also by firm “size”. While three‐quarters of the companies (76.3%) are private, i.e. they are owned either by a non‐ governmental organization or by a number of stakeholders, this share reaches almost 90% for MNEs. Another 7% of MNEs are subsidiaries, i.e. they are controlled by another company through the ownership of 50% or more of the voting stock, while only 3% are state‐owned. The share of state‐owned enterprises is considerably higher for large companies and SMEs.

**Table 3: Distribution of companies by organization type**

||**_Al_**|**_l_**|**_MN_**|**_Es_**|**_Lar_**|**_ge_**|**_SM_**|**_Es_**|
|---|---|---|---|---|---|---|---|---|
|**Organization type **|**Obs**|**%**|**Obs**|**%**|**Obs**|**%**|**Obs**|**%**|
|Cooperative|33|1.6%<br>|6|1.0%<br>|20|1.7%|7|3.2%|
|Non‐profit organization|51|2.5%<br>|1|0.2%<br>|18|1.5%|32|14.6%|
|Partnership|14|0.7%<br>|2|0.3%<br>|9|0.8%|3|1.4%|
|Private company|1,541|76.3%<br>|530|88.2%<br>|882|73.5%|129|58.9%|
|Public institution|51|2.5%<br>|2|0.3%<br>|39|3.3%|10|4.6%|
|State‐owned company|180|8.9%<br>|18|3.0%<br>|135|11.3%|27|12.3%|
|Subsidiary|150|7.4%<br>|42|7.0%<br>|97|8.1%|11|5.0%|
|**Total**|**2,020**|**100.0%**|**601**|**100.0%**|**1,200**|**100.0%**|**219**|**100.0%**|

Table 4 shows the distribution of firms by sector. Of the large firms, 17% are specialized in financial services, while this sector plays a smaller role for MNEs (11.5%) and SMEs (9.1%). And while the energy sector represents the second largest sector for large firms (8.7%), this is not the case for MNEs (4.7%). By contrast, 9.3% of all MNEs are in technology hardware, while this share drops to only 1% for large firms and SMEs. Food and beverages plays a major role across all firms, in particular SMEs (8.2%) and MNEs

> 10 The variable “size” by GRI is somewhat misleading, as it combines information on both firm size and multinational activity. While large companies by definition do not engage in multinational activity, this cannot be guaranteed for SMEs. However, since the data set covers 81 countries, including many with lower‐ and middle‐income status, the likelihood of SMEs engaging in multinational activity is low.

> 11 GRI follows the standard definition of SMEs by the OECD: https://stats.oecd.org/glossary/detail.asp?ID=3123

8
Tax administration records provide data on whether the property owner visited the tax office in response to the notice, whether the owner paid the tax, and how much tax was paid. Visiting the tax office was the first step of the compliance process. For most property owners, this was their first interaction with the tax system, so they first had to obtain a personal tax identification number (TIN). To track responses, each notice included a unique number, and property owners were asked to bring the notice along when they came to the tax office. Data on tax payments come from the integrated tax administration systems that record all tax payments electronically and are available for this sample from 2017 to 2020.

Although tax revenue leakage may occur in several ways, this paper focuses on the extensive margin of compliance, where it is possible to determine with certainty the compliance status of a property owner— that is, whether or not the property owner made a tax payment. In addition, the paper also reports whether the property owner responded to the tax notice at all and the total amount paid.

The unit of observation for outcomes is at the owner level because payments are recorded by the property owner’s TIN; however, randomization was done at the property level. To address this discrepancy, we use the property-level randomization to infer owner-level treatment assignments using the cumulative exposure that each property owner had to different elements of the treatments, as follows: (i) property owners who have only one property are assigned to the treatment group for that property; (ii) property owners who have multiple properties that were all assigned to the same treatment group (or to a single treatment group and the control group) are assigned to that treatment group; (iii) property owners who have multiple properties and who have any property assigned to the identification and penalty treatment group are assigned to the identification and penalty treatment group because they are considered to have been exposed to both the identification and penalty messages; and (iv) property owners who have multiple properties and for whom some properties were assigned to the identification treatment group and others to the penalty group are assigned to the identification and penalty treatment group because they were exposed to both the identification and penalty messages. This process affects the 10 percent of property owners in the 14
search costs for downstream traders and consumers (Stiglitz (1986), Chau et al. (2009)), the focus of the models below is on credit provision role of the DOTs in the oil market. This is based on empirical evidence and in-depth knowledge of the market from surveys and monitoring by the authors over the years.<sup>11</sup> As discussed before, quality guarantee and search costs are not important in our context, and thus are not considered as alternative explanations.<sup>12</sup> In the first model, the DOTs provide low cost credit to the wholesale traders, and the traders are not credit rationed when they have to borrow from the credit market such as banks. The standard double marginalization model with pure marketing intermediaries is a special case of this model (called the ‘policy model’ here after) which represents a possible basis for the understanding and intent of the policy makers. The DOTs in the second model not only provide low cost credit, they also relax binding credit constraints faced by the wholesale traders when borrowing from banks. As we shall show later, this model is new and qualitatively distinct from the standard model.

# **(4.1) DOTs as Providers of Low Cost Credit**

We build a model of vertical chain with three layers in the market: refiners, DOTs and traders (wholesalers). Although the edible oil supply chain also includes retailers, we ignore them as the focus is on the effects of the elimination of the DOTs on wholesale prices. This simplifies the algebra. We shall assume that there are sufficiently many wholesalers that they effectively have no market power. All the conclusions would remain intact in a model that includes a competitive retail sector and the wholesalers exercise some market power. Besides, we shall see in the next section that most of the effects of the reform were on the margin between the crude import price and the wholesale price.

> 11One of the authors (Helal Uddin) has been involved in research on the edible oils market in Bangladesh for more than 5 years now. All four authors were involved in monitoring the Palm oil marketing chain in Bangladesh for six months in 2013.

> 12A small but growing literature has underscored the effects of cell phones on spatial price distribution in agricultural markets. For example, Fafchamps and Acker (2015) find that better access to phones reduce price dispersion, but has no effect on the average price of perishable goods. They do not find any effects on price dispersion or average price of non-perishable goods (our focus is on oils which is non-perishable). As we show below, our results are different, average price of Palm oil was affected significantly by the reform.

11