rafmacalaba/data-use-mentions-extended huggingface dataset:
second semester of 2010.<sup>5</sup> Trends for the four CLS-WILL regions show a wide variation between and within regions. Three of the four regions faced at least one massacre between 1993 and 2009. While the Southern and Coffee Regions suffered massacres at the end of the 1990s, the Atlantic region faced massacres in 2003.

# [Graph 2 goes about here]

Escalating aggressions against the civilian population produced massive outflows of forcefully displaced persons. During the period ranging from 1999 to 2009, 3’303,979<sup>6</sup> persons were forcefully displaced after being the victims of an attack or in an effort to avoid being victimized. This figure, which is equivalent to 7.9 percent of the Colombian population, is the second highest magnitude worldwide after Sudan.<sup>7</sup> Incidences of violence in 2009 for the four CLS-WILL regions and Colombia are presented in Table 1. The figures reveal a wide variation across regions with respect to incidences of different types of violent events in Colombia. The homicide rates are highest in the Coffee region; the figure is well above the national rate. On the other hand, the forced displacement rate is indeed large in the Southern Region (almost double the national rate), which indicates that direct aggression against the civilian population is high in this region. The Central region reports the lowest incidence of violence.

# [Table 1 goes about here]

The official figures for violent events are confirmed by the responses to the rural district questionnaire of the CLS-WILL. Table 2 reports the results for the questions on the rural district questionnaire related to conflict and aggressions against the civilian population. The results show that the presence of armed groups during the last 10 years is frequent for the four regions, and ranges from 15.8 percent of rural districts in the Atlantic Region to 44.1 percent of rural districts in the Southern Region. Direct violent attacks against the population are widespread, while aggressions that cause immediate economic
|Real exchange rate growth(t-1)(%)|11,677|5.17|10.73|-20.65|31.42|
|---|---|---|---|---|---|
|Trade openness(t-1)(% GDP)|13,025|43.62|13.19|15.32|79.46|
|**_Covariates_**||||||
|Small-sized firms (labor<20)|13,025|0.54|0.50|0|1|
|Medium-sized firms (20<labor<99)|13,025|0.31|0.46|0|1|
|Large-sized firms (labor>99)|13,025|0.15|0.35|0|1|
|Generator use (dummy, 0-1)|12,818|0.50|0.50|0.00|1.00|
|Electricity losses(t-1)(% total power<br>production)|8,574|17.07|9.61|5.37|71.03|
|GDP per capita(t-1)(US$)|11,617|1693.08|1478.72|319.40|6938.85|
|GDP growth(t-1)(%)|11,617|5.58|4.11|-20.60|15.17|
|Rule of lawgrowth(t-1) (%)|8,204|-3.59|26.51|-82.63|91.82|

Source: WBES, WDI, KOF data, PWT.

# 4. Empirical strategy

Using a firm-level treatment variable, we carry out an impact assessment method to understand how access to electricity leads manufacturing firms to become more export-oriented. We consider
# **1 Introduction**

The property tax is increasingly recognized as a key component of equitable domestic revenue mobilization, with significant untapped potential for generating revenue (World Bank, 2021; Benitez, Mansour, Pecho, and Vellutini, 2023; Dom, Custers, Davenport, and Prichard, 2022; Maloney, Zambrano, Vuletin, Beylis, and Garriga, 2024). In many countries, real estate emerges as a primary repository of wealth, positioning property taxation as a potential tool for redistribution. However, as this tax grows in importance, taking into consideration its gender dimension is a central concern (Scot, Flores Ibarra, Moura, Feinmann, and Rocha, 2023; Komatsu, Ambel, Koolwal, and Yonis, 2021). Despite its relevance, the relationship between property taxation and gender outcomes remains largely understudied.

This paper provides new gender-focused empirical evidence on property ownership and taxation using administrative tax micro-data from Tres de Febrero, a large urban municipality in Argentina with over 360,000 inhabitants and about 120,000 households. Residents from Tres de Febrero are required to pay a monthly tax on their real estate, locally known as _Tasa por Servicios Generales_ (TSG), which accounts for most of the local revenues in Argentine municipalities. We leverage rich tax administrative data and exogenous variation to show how gender interacts with several fundamental issues: the distribution of property ownership, tax evasion, and tax enforcement.

The analysis unfolds in three stages. First, we examine the distribution of residential ownership, presenting novel descriptive evidence on property ownership across the municipality, with a particular focus on gender disparities and the assessed values of properties. A significant challenge for such analyses is that property ownership records often lack explicit information on owners’ gender. However, a unique feature of our setting is the structure embedded within national personal tax identifiers, which can be used to infer gender. Following methodological guidance from our team, the municipality’s staff utilized this structural feature to infer the gender assigned at birth and the owners
|YES|YES|YES|NO|
|Time lag b/w interven-<br>tion and follow-up 1|12 months|12 months|18 months|24 months|12 months|12 months|
|Time lag b/w interven-<br>tion and follow-up 2|24 months|36 months||72 months|24 months||

Notes: This table describes the average treatment effect of each intervention on the women’s number of children. Standard errors are clustered at the randomization level. The outcome of column (3) corresponds to the number of children under 5 in the household. The controls include: number of children at baseline, marital status, highest level of school completed, age, age square, a dummy equal to one if the woman is household head, and household size. If there is no baseline (as in Benin and Ghana), the number of children at baseline corresponds to the number of children born before the beginning of the treatment built from the midline household roster. We include stratification fixed effects when required. We control for time when there are two periods. *, **, *** denote significance at the 10, 5 and 1 percent levels respectively.

tervention (statistically significant at the 1% level).<sup>19</sup> Compared to Ethiopia DOT, in Togo the outcome was measured six months later (18 months after the start of the intervention, instead of 12 months) and take-up rates were substantially higher at 84%. Women in the WALN sample—who were selected because they run well-established businesses<sup>20</sup> —do not increase their fertility. This is consistent with women business owners within WALN being located on the demand curve beyond the revenue threshold after which demand for children is income inelastic; women in the WALN sample’s control group have on average 1.3 more children than women in the DOT sample’s control group.

Turning
kernel and local linear matching estimators, which use multiple nonparticipants to estimate the outcomes of the control group as opposed to pair-wise estimation. This method, used by Heckman et al. (1997) and Heckman et al. (1998), was implemented with longitudinal and crosssectional data from the National Supported Work Demonstration, another labor market program.<sup>7</sup> The results suggest that such estimators are able to replicate experimental results only when the data examined are very similar – from the same source(s), with treatment and control groups from the same geographic labor markets, and incorporating a range of variables that influence both participation and labor market outcomes.<sup>8</sup> McKenzie et al (2006) build on the finding of previous authors: the best non-experimental estimates are obtained when the treatment and control groups are drawn from the same labor markets, data sources used are the same, and groups’ characteristics are similar in that the “likelihood of receiving the treatment is similar in both groups.” They compare experimental and non-experimental methods in analyzing the income gains from migration, using a survey on the quota of Tongans allowed to immigrate to New Zealand each year on the basis of a lottery from an excess number of applicants. They generate experimental estimates of income gain from migration by comparing gains of applicants whose names were not drawn in the lottery with those who immigrated through the lottery, after taking into account those who did not immigrate even after they “won” the lottery. They also survey a non-applicant group and generate non-experimental estimates for comparison with the experimental estimates. Because migrants are more likely to have certain observable _and_ unobservable characteristics (such as ability and drive), the non-experimental methods are shown to overstate income gains from migration by between 9 and 82 percent.

The _ex ante_ labor market models described here suggest the difficulties in predicting the impacts of a program using a model representing standard economic incentives incorporated through wages. In our paper, however, the effects of
accession-related export gains with those calculated from the historical data. Section 5 offers concluding remarks.

# **2. Policy changes prior to WTO accession**

Prior to 1978, when China started liberalizing, its trade was centrally managed and opportunities for direct interaction between Chinese firms and firms importing Chinese exports were minimal as all foreign trade was channeled through centralized Foreign Trade Corporations (Martin, 1993).

By the late 1980s, according to a comprehensive World Bank (1988) study, the shares of exports and imports under the centrally planned system had substantially declined but were still high at an estimated 50 and 40 percent of exports and imports, respectively. Most research conducted during this period focused on issues pertinent to further trade decentralization. Naughton (1985) and Wong (1985) studied the process of shifting economic decision making from central authorities toward enterprises and provincial governments, others focused on reforming the two-tier (or multitier) pricing (Sicular, 1988; Byrd, 1987, 1989; Wu and Zhao, 1987) and foreign exchange systems (Desai and Bhagwati, 1979).

# _2.1 Reforming the exchange rate system_

As China started liberalizing, there was a need to reform the foreign exchange system. In addition to the official rate, which was devalued multiple times between the early 1980s and the early 1990s, authorities introduced a second-tier rate applied to trade-related transactions and legal secondary markets for foreign exchange retained by enterprises (Martin, 1993). Even with these modifications, prior to 1994, China’s foreign exchange system imposed large and unpredictable taxes on trade (see, for example, Huang _et al._ 2009). By the early 1990s, devaluations of the official 5
amenable to home-based-work by a factor of 2.9, compared to a factor of 1.1 in high income countries. Failing to correct for this bias would result in an overestimation of the resilience of developing countries, lagging regions, and poor people during the pandemic.

Jobs amenable to working from home are also less prevalent in lagging regions within countries. They are less likely to be held by young, poorly educated, and poorly paid workers as well as those with temporary contracts. Workers’ skills, as proxied by their education levels, are the strongest predictor of their ability to work from home. The labor market burden of the COVID-19 pandemic is thus more likely to be shouldered by the poor, who are more vulnerable to start with. Absent remedial action, COVID-19 is likely to exacerbate income inequality and pre-existing socioeconomic disparities, especially in high-income countries where more jobs amenable to telework are available but are disproportionately held by high-income workers. The overall labor market burden of COVID-19, by contrast, is likely to be larger in developing countries, where fewer workers will be able to continue their employment as usual, and where social protection systems are typically less generous, or lacking altogether.

This paper contributes to the rapidly growing body of literature on the amenability of jobs to working from home<sup>1</sup> by considering infrastructure constraints and by offering global estimates of the prevalence of jobs amenable to working from home. We conduct a cross-country comparative analysis using occupation-level data for 107 countries from the ILO and complement it with an indepth analysis of a range of countries (EU countries, Brazil, India, Mexico and Turkey) using individual-level data from labor force surveys. This allows us to validate the use of occupation (as opposed to individual) level data.

The already influential work of Dingel and Neiman (2020)—DN2020 from now on—uses the Occupational Information Network (O*NET) surveys containing information about whether the occupation requires working outdoors, using specialized equipment, contact with the public
|Mid 2000s|
|Access to flush toilets|% pop|4.9|16.5|13.4|25.4|23.8|40.8|
|Access to improved latrines|% pop|9.9|13.3|21.7|15.2|17.8|1.4|
|Access to traditional latrines|% pop|50.1|24.0|19.5|26.3|26.4|30.4|
|Open defecation|% pop|40.3|46.2|45.3|31.4|32.3|14.3|

Source: Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from <u>http://www.infrastructureafrica.org/aicd/tools/data</u> **.** 20
# **INTRODUCTION**

Education and migration are two strategies widely believed to improve living standards for populations living in rural areas. A vast literature demonstrates that educated rural households are better able to avail of new technologies, that consumption growth is higher among households who migrate, and that secondary schooling and migration (whether international or within-country) increase earnings.<sup>2</sup> However, two data-related issues have made it harder to assess the returns to specific skills for migrants versus non-migrants and the ability of children to acquire these skills through schooling in the first place. First, despite an extensive literature in the United States that demonstrates the importance of cognitive and socioemotional skills for labor market outcomes (Heckman, 2007), these skills have proven notoriously difficult to measure in low-income countries (Laajaj and Macours, 2021; Valerio et al., 2016). Second, long-term panels with information on skills, schooling, and earnings in low-income countries are extremely rare, although we expect more will become available in the next decade.

We address both of these gaps in this paper. We focus on the measurement of cognitive and socioemotional (SEM) skills and assess their reliability and validity relative to comparable research from the existing literature in low- and middle-income countries. To allow for potential links between migration and skills, we study children who grew up in rural Pakistani villages and were first surveyed in 2003 when they were between the ages of 5 and 15 and then re-surveyed between 2017 and 2018, regardless of where they were living at that time. At this point, 38% had migrated from their native homes, so migration appears as a potentially endogenous response to the skills that respondents have acquired, which in turn may lead to differential returns to these skills. We further corroborate our findings using data from a similar sample from Cambodia, which shares key features with the data from Pakistan.

Our results show that more schooling is associated with higher cognitive skills and, to a smaller extent, greater SEM skills.
time expense but an ongoing cost of doing business as a public company.<sup>41</sup> Sometimes there is simply a cultural reluctance to disclose information that may appear to be personal in nature.<sup>42</sup> In some markets, it may in fact be dangerous – personally dangerous – to disclose information about yourself and your company. Of course, none of these reasons is necessarily exclusive and there may be considerable overlap.<sup>43</sup> Many impediments to listing can be mitigated or removed altogether through education and training initiatives and by legal and regulatory reform. But alleviation of perceived obstacles is not of itself capable of bringing public or private sector companies to list on the exchange because the obstacles, while serious, are in fact incidental to the key incentives motivating companies to list. Recent experience in Korea provides evidence of this.

In 2005, the Korean government conducted a survey on costs incurred by public companies, with a view to investigating causes of sluggish activity in the stock market.<sup>44</sup> The survey found that 40 percent of listed companies do not see great advantages in being public. For example, 19.1 percent of listed companies consider that the costs are higher than the benefits of remaining public, and 20.9 percent see no net benefits.

Most listed companies in Korea also raised serious concerns over everincreasing compliance costs. More specifically, they indicated that the most burdensome regulations are: (i) disclosure rules (60 percent), (ii) litigation risks (50 percent), and (iii) requirements for listing maintenance (42 percent). The total compliance costs in Korea are estimated to be far lower than those in the US; however, the relative amount to the market capitalization and GDP is comparable. As expected, since the compliance costs are a fixed cost, smaller companies tend to be affected more negatively as the regulatory burdens increase.<sup>45</sup>
2025. Transition losses are driven primarily by utilities (EUR 54 million), wholesale and retail trade (EUR 29 million), and transport (EUR 19 million). The physical risk scenario is expected to lead to gradually increasing losses culminating in an increase in expected annual loss of 6.3 percent by 2050. Physical risk losses are driven primarily by wholesale and retail trade (EUR 28 million), agriculture (EUR 23 billion), and accommodation and food (EUR 14 million). Results are presented in Table 3.

# 5. Conclusions and recommendations

**This paper presents an assessment of the vulnerability to climate-relate financial risks for a sample of European CGSs.** Based on unique sectoral and spatial data collected through a survey of AECM members, to the best of our knowledge this paper represents the first attempt to measure climate-related financial risks for CGSs, both transition risks and physical risks. In particular, this paper links CGSs’ guarantee portfolio data to a set of sectoral and spatially disaggregated transition and physical risk vulnerability indicators, which allows us to identify the portfolio shares at risk per indicator. We also identify sectors that are most relevant for CGSs to focus on by creating a transition risk score which combines exposure with GHG intensity. Finally, we conduct a stylized stress test to assess potential financial losses that CGSs could incur under adverse climate-related scenarios.

**CGSs in Europe are exposed to transition risks in a broad range of sectors as well as to physical risks in vulnerable sectors and regions.** Our results show that about one-third of CGSs’ guarantee portfolios is towards sectors that have high exposure to a disorderly energy transition. The most relevant sectors for CGSs from a transition perspective include crop and animal production, land transport, and wholesale trade. European CGSs are also exposed to a broad range of climaterelated physical risks, especially wildfire, coastal flood, and river flood, with 24-31 percent of outstanding guarantees towards sectors that have elevated exposure to climate change and weather variability. The annual expected losses
agents

> 39 It is conceivable that respondents could confuse the term 'effective' with other terms we avoided, such as importance, usefulness, or frequency of use or occurrence. However, as noted below, various stages of survey implementation included a 'cognitive audit' of our survey question, which is described in more detail in Appendix A.3. Interviewees did interpret the term 'effective' as referring to a combination of both the extent of reliance and productiveness of the enforcement agent: the lowest ranking on the scale would be chosen if either the enforcement agent was not relied on, including due to lack of knowledge about that possibility for enforcement, or if it had been relied upon and it was ineffective.

> 40 Low rates of "don't know" responses may be suggestive of good comprehension of questions by respondents (Krosnick 1991). The questions used in this paper had frequencies of don't knows that were comparable to those questions that had proven satisfactory by their staying power through many cycles of the WBES surveys. Krosnick and Presser (2010) note that frequent selection of a middle point on a scale (our 'moderately') is evidence of satisficing behavior on the part of respondents. There is no evidence in our data that that the mid-point is used frequently. Given the negligible rates of "don't know" responses, we omit from our analysis any observation that has at least one "don't know" as a response to any of the set of six sub-questions.

20
∂Lit v v ∂Qit ∂Xitv = zitPit −λit ∂Xitv (2) v θitv zitμit = αitv (3) where μit is the firm’s mark-up and αitv = QPititv XPititv , or the ratio of the expenditure on input v to sales.3 The expression θitv denotes the elasticity of output with respect to input v, which would need to be estimated from a production function. Equation (3) is similar to De Loecker and Warzynski (2012), but with a multiplicative adjustment cost.4 Suppose we have firm-level input and output data, including data on input expenditures and revenue. We v could then use equation (3) to infer the product of the price mark-up and the adjustment cost, zitμit, but it would require estimating a production function to measure the elasticity of output with respect to input v.

However, it is possible to measure the change in input adjustment costs over time without estimating a production function. We make two assumptions: A1. The production function is Cobb-Douglas. This implies time invariant output elasticity.

A2. The production function of firm i stays fixed over the study period.

These assumptions are common in studies using firm-level panel data to estimate production functions. If we were to estimate the production function, we would have to make additional assumptions to address the endogeneity of input use.

Taking ratios of equation (3) for two inputs, we get The ratio of input expenditures is inversely proportional to the ratio of the input adjustment costs.

1 To simplify, set zit = 1. In other words, we are redefining the input 2 adjustment cost relative to that on 2 input 1. If zit > 1, this would mean that, in sum, factor market distortions are taxing input 2 relative to input 1. Note that the ‘implicit input tax’ τ in the two-input model in Hsieh and Klenow (2009) is defined in this relative sense: an input market distortion that favours one factor of production over the other.

Taking logs of