rafmacalaba/data-use-mentions-extended huggingface dataset:
degree of accommodative stand of the government during the containment and initial restructuring process of a crisis.

We also collect data on a number of accommodative policies. Data on these policy responses come from Honohan and Klingebiel (2003), and are updated for recent crises by Kane and Klingebiel (2002), Caprio and Klingebiel (2003), and IMF (2003). As noted, we focus on three policies: substantial LIQUIDITY SUPPORT, EXPLICIT GUARANTEE on deposits, and regulatory FORBEARANCE. We use a dummy variable for each of the three policies. Data on policy responses are collected for 35 banking crisis episodes from 1977 to the present.<sup>9</sup> In addition to explicit guarantees, deposits can also have an implicit government guarantee on deposits. For example, deposits in government-owned financial institutions are often (assumed to be) implicitly guaranteed by the government. Like explicit guarantees, implicit guarantees may affect the fiscal outlays during a crisis, and we will therefore want to control for the existence of implicit guarantees in our empirical analysis. We consider an implicit guarantee to be in place if the banking system is largely state-owned (75 percent or more) and the government has not issued an explicit guarantee. We refer to this dummy variable as IMPLICIT GUARANTEE.

Many financial crises were proceeded by asset bubbles and credit booms. These may also affect the severity of the crises and the fiscal outlays, because when the boom is larger, the bust may also be larger and the crisis may be more costly to resolve. Since we do not have data on asset prices for most of the pre-crisis periods in our sample, we can only use (excessive) credit growth as a control measure for the size of the pre-crisis boom. We measure credit growth as the difference between real growth in bank credit to

> 9 We do not have data on policy responses for Jordan.

16
).

In the second step, that is, in column 4 of Table 1, we regress the number of days against distance (without maritime preferences) and then use the equivalent tariff estimates from column 1 to infer the elasticity of trade costs (which we do not observe) to distance as in Hummels (2007), using _exp_ [( _β_<sup>_days_</sup> _/ − σ_ ) _−_ 1]) _∗ βdist_ . The results in column 6 replicate the analysis on data with maritime preferences, using the equivalent tariffs derived from column 2. The estimated equation reads: Column 3 and 5 show the estimation using the log number of days as a dependent variable by sake of comparison. In both columns 3 and 5, the estimated elasticity is around one: the number of days is proportional to the distance, as expected.

The regression results on the number of days indicate an elasticity of trade costs to distance of 0.180 when transportation time is calculated without the maritime preference (column 4) and 0.178 when more weight is given to the maritime route (column 6). The implied elasticity for African exporters is therefore slightly higher than 0.151, the one estimated for the United States in Hummels (2007). The evidence presented here suggests that transport time data can be used with some confidence to quantify the economic impact of trade reforms and infrastructure investments in Africa.<sup>17</sup>

> 15Note that the trade data used for the transport time validation also include domestic sales. The construction of the trade data is detailed in Section 1.2.

> 16The average of elasticities of substitution significant at the 1 percent level is 6.3 in Fontagn ́e, Guimbard & Orefice (2022).

> 17Note that the estimates presented to date are for 2018, and repeating the analysis for 2014 produces virtually unchanged results. The corresponding tables are available on request.

8
not-hold clusters (column (1)) is small. In some countries, we find that a high share of landowners need permission to exercise rights. In Ethiopia, for example, men and women with mostly joint rights reported always needing permission, as well as 75 percent of men and 84 percent of women in the exclusive cluster. The comparable levels are lower in Malawi and Tanzania. Permission is not necessarily needed to exercise all rights (this applied to about 60 percent of mostly exclusive landowners in Ethiopia, and only around 20 percent in Malawi and Tanzania). Again, the numbers are much higher for the joint clusters. We also find that the increase in the share of respondents not needing permission to exercise any right over land (when moving from those who have mostly joint rights to mostly exclusive rights) is much greater for women as compared to men.

> 22 Data on tenure security for Malawi was not available.  In Ethiopia and Tanzania, respondents were asked “on a scale from 1 to 5, where 1 is not at all likely and 5 is extremely likely, how likely are you to involuntarily lose ownership or use rights to this parcel in the next 5 years?” Our secure tenure variable takes the value 1 for the response “not at all likely”, and the value 0 for other responses (i.e. some likelihood of losing tenure).

24
(1)|-|10 (16)|11 (17)|
|Total|26 (44)|37 (64)|32 (53)|13 (17)|18 (38)|63 (108)|

7 Question D.13 in the GFICPS 2017 was: “Are financial institutions subject to explicit caps on interest rates or other methods that limit loan pricing, e.g. maximum profit margins or maximum spread?” 8 Regional and income level classifications are consistent with the World Bank classification while legal origins are based on the JuriGLOBE database of the University of Ottawa.

9 We conducted a two-sample t test for equality of means by income group and found no statistically significant difference between high income and low and middle income economies.

10 China’s influence is even greater if the sample is weighted by deposit money banks' assets: these stand at 175 percent of the Chinese GDP and 29 percent of our full country sample (source: Global Financial Development Database).

11 Seventy-six percent of the countries under common law in our sample have IRCs. That is significantly higher (at 5%, one-tail) than countries with civil codes or mixed legal systems.

7
Our sample covered the years from 1997 to 2000 because export quantities were only available for this period. We also removed all observations for which the export value was zero, and there was no information on the quantity of shipments.

The Brazilian firms’ employment data were obtained from the RAIS _(Relacao Anual de Informacoes Sociais_ ), a collection of annual reports with individual information on workers and employees. Similarly, we aggregated the monthly worker-plant information to years and firms.<sup>6</sup> The data on AD activity were obtained from the Global Antidumping Database of the World Bank.<sup>7</sup> This database provides detailed case-level information on AD filings and outcomes, such as the dates of the filing and the decision, the target country, the final decision of the AD authority, and the HS codes of the products subject to filings. To match the six-digit HS product codes with three-digit ISIC codes, we used the concordances in the World Bank’s Trade Production and Protection Database. Finally, for the product-level import data, we used the UN COMTRADE database.

# **_<<A>>_ EMPIRICAL ANALYSIS**

As discussed above, we are primarily interested in the variations in Brazilian firms’ unit export values within products across the export destinations. To quantify the effect of AD duties and the retaliation threats on the prices of exports being shipped, we begin by estimating the following baseline equation: where _f_ denotes the firm, _p_ denotes the six-digit HS product code, and _d_ denotes the export destination. As noted earlier, the variable ufpdt denotes the unit values of the exported products; (ADduty)pdt is a binary indicator and is unity if there is an AD duty in force in year _t_ 7
>0% 0.0<br>Share of population satisfied with life, left axis<br>Average satisfaction score (1-10), right axis<br>Romania HungaryArmenia GeorgiaSerbia Ukraine Moldova BiH FYROM BulgariaLithuania Latvia Albania AzerbaijanMontenegroRussia Croatia KyrgyzstanItalyTurkeyBelarus Slovakia Kazakhstan Kosovo Overall Estonia Czech MongoliaPoland Slovenia Uzbekistan France UK TajikistanGermanySweden<br><!-- End of picture text --> Verbal responses to Likert scale questions (in the LiTS case “strongly disagree”, “disagree” etc.) may be preferred because these categories may be more understandable to the respondents than the 0-10 scale. On the other hand, such verbal categories may not carry the same meaning to everyone. There is some evidence that the numeric scale is less ambiguous, and potentially less problematic for interpersonal comparability (van Praag 2004).

We investigate the consistency between the two life satisfaction measures. Table 3 presents, for each of the countries in the LiTS II sample, the median life satisfaction score on the 1-10 scale for each of the categories of the life satisfaction measures based on the strongly disagree – strongly agree scale.

> 19 The implied correlation coefficient between the two variables is 0.85.

13
Financial literacy is higher among the younger and the more highly educated populations and lower in rural areas and among those living outside of major cities. Importantly, we find that financial literacy is significantly positively related to retirement planning and the use of private pension funds and products, with financially literate individuals being 25%–30% more likely to plan for retirement using private pension funds.

The paper proceeds as follows: Section 2 describes our dataset, the main variables, and presents summary statistics; Sections 3 and 4 presents the empirical strategy and reports the results; and Section 5 concludes.

# **2. The Dataset**

We use information from the second wave of a dataset collected via face-to-face interviews<sup>3</sup> of 1,400 individuals in June 2009. The sample was designed to be nationally representative at the individual and the household level and was weighted by gender, age, education, and region (excluding the North-Caucasian [Chechnya] federal district).

As shown in the first column of Table A1, our sample consists of 42.2% male respondents, consistent with national census averages (Russia Census, 2002), with the average age of the sample at around 46 years.<sup>4</sup> Most individuals (62.3%) reported living in households with three or more individuals, with 13.5% living alone. A majority of our sample (56.3%) is in the workforce, and 31.9% of the sample live in urban regions, defined as settlements with a population greater than 500,000 (14.2% in Moscow, St. Petersburg, and nearby areas). The education level of individuals in our sample is higher than the level in comparative emerging markets: only 8.4% of the sample has less than a secondary education, and 22.7% have initiated or completed a higher education degree program.

> 3 It is interesting to note that most comparable financial
Safety||||||||
|(% Households Responding)|0.4648|0.4733|0.1698|0.0563|0.2278|1||
|Bad Security Condition||||||||
|(% Households Responding)|0.6797|0.6877|0.3327|0.1346|0.3034|0.6027|1|

_Sources:_ The first three indicators are from the provincial database; the subsequent two indicators are from the NRVA survey; and the two last indicators are from the Asia Foundation Survey of the Afghan People. Correlation coefficients are computed for 2011.

10
>jobseekers (LFS based unemployment rate, age 15-74)<br>registered unemployment rate<br>long-term registered unemployment rate<br>gross wages (Riga region = 100)<br><!-- End of picture text --> _Source_ : Central Statistical Bureau of Latvia and Calculations. Registered unemployment rates are based on number of economically active population in the age brackets from 15 years to the retirement age.

**Evolution of regional labor markets.** A large increase in the unemployment gap between Latgale and the rest of the country emerged in 1993-1995 (see Appendix Figure 8).  Since this time, the general trend was of an improving the relative standing of this region in terms of unemployment. However, a further worsening in wages disparities between Latgale and other regions was experienced. The relative wage advantage of Kurzeme declined between 1993 and 2000. Recently, unemployment rates have become more similar to those in other regions.  The position of the Zemgale labor market (as seen from the perspective of administrative data) was more or less stable throughout the whole period. During the last three years wage growth was very similar across all regions except Kurzeme, where it was slower. Vidzeme was the only region in which registered unemployment increased.  A deeper analysis in the next chapter shows, however, that the gap between Riga and other regions both in terms of relative wages and unemployment risk in 2002 was wider than five years ago.

**NUTS 4 regions: persistent disparities.** Labor market conditions also differ within the five NUTS 3 regions. Appendix Figure 7 suggests a negative relationship between unemployment rates and wage levels across districts. The 33 NUTS 4 level regions include Riga, with three-quarters of a million residents, 6

18
Table 9. Who Suffers the Casualties?

||Post-Election Period|Pre-Elect|ion Period|
|---|---|---|---|
||Casualties sufered by:|Casualties|sufered by:|
||AFP<br>Rebel|AFP|Rebel|
||(1)<br>(2)|(3)|(4)|
|Incumbent victory|0.61**<br>0.10|–0.012|0.14|
||(0.30)<br>(0.071)|(0.33)|(0.11)|
|Margin|–9.37*<br>–1.89|5.53|–2.25|
||(4.98)<br>(1.33)|(9.46)|(3.24)|
|Margin _×_ incumbent victory|–2.98<br>0.33|–24.5|–1.92|
||(12.9)<br>(1.36)|(15.6)|(4.39)|
|Mean of dep. var.|0.38<br>0.08|0.50|0.10|
|No. of municipalities|153<br>153|153|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC). _Note:_ The running variable of the RD design is the incumbent margin of victory. All regressions are weighted by a triangular kernel with a bandwidth of 0.05 and include the same control variables as in Table 3. Reported values are marginal effects, calculated at the sample means. The dependent variable in columns marked “AFP” is
**C. Robustness Checks.** In Table A.14, we test the robustness of our findings to alternate modes of inference for four of our main outcomes. Our results are robust to a) clustering the standard errors by village, b) to wild-clustered bootstrapped errors at the village level, and c) to multiple hypotheses testing correction.

# **6 Conclusions**

Restrictive norms, resource constraints, and social isolation can limit women’s access to and utilization of various public services and programs, especially in developing countries. These constraints are more pronounced in contexts like rural India, where women lack freedom of movement and are socioeconomically dependent on family members, and in domains such as fertility and FP. In this study, we evaluate an innovative approach to improve married women’s access to FP that not only overcomes financial barriers but also social barriers to FP use, such as opposition from mothers-in-law and lack of peer support.

Although our study is based in one Indian district, its findings are relevant not only to other parts of India and South Asia, but also to a number of settings where women face significant socioeconomic and mobility barriers and where extended households are prevalent. Moreover, the relevance of our findings and interventions extends to areas beyond FP-RH (e.g., female education and labor force participation) where women’s inability to access public spaces and restrictions imposed by family members are key barriers to their access (Bernhardt et al., 2018; Bursztyn and Jensen, 2017) and where peer support can make a substantive difference (Field et al., 2016; Kandpal and Baylis, 2019).

We draw a number of lessons from this study that are relevant for policy making and future research. First, our results highlight a need to move beyond dyadic models of household bargaining and decision-making that are based on nuclear family structures. In most household surveys and collective models of the household, a woman’s decision-making ability within the household is bench-marked against that of
. The marginal increase in the loan portfolio will particularly benefit those companies or individuals that have a sound repayment history but lack other obvious indicators of creditworthiness such as wealth or that lack guarantees.

The use of PCR data may then enable lenders to predict credit risk more accurately and more efficiently. Credit registry data facilitates the evaluation of prospective borrowers, reducing the need for more costly and intrusive background and reference checks. If an adequate supply of data is available for statistical analysis, then automated or semi-automated credit decision tools 12
|Table 1. Descriptive statistics of the skills and industrial emissions dataset|||||
|---|---|---|---|---|
|Variable|mean|SD|obs.|countries|
|Literacy skills (standardized)|0.16|0.95|81,265|22|
|Numeracy skills (standardized)|0.17|0.95|81,265|22|
|Problem solving skills (standardized)|0.02|0.97|55,146|18|
|Log CO2 per real value-added (industry of employment), log tonnes per million euros|4.05|1.67|81,278|22|
|Annual percent growth in CO2 per real value-added (industry of employment)|-3.25|5.57|81,278|22|
|Female|0.48|0.5|81,277|22|
|Age|40.51|12.31|73,635|20|
|Highest level of education:|||||
|None|0.01|0.1|75,107|21|
|Primary|0.03|0.18|75,107|21|
|Lower secondary|0.1|0.31|75,107|21|
|Upper secondary (vocational)|0.16|0.37|75,107|21|
|Upper secondary (general)|0.32|0.47|75,107|21|
|Post-secondary (technical)|0.11|0.31|75,107|21|
|Post-secondary (academic)|0.26|0.44|75,107|21|
|Means and standard deviations are weighted by sampling weights that have been adjusted to give<br>numeracy, and problem-solving skills, mean and SD
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 10

# **Table 1 Urban definitions and rural proportions according to national statistical offices**

Sources: Rural population from World Development Indicators, based on United Nations (2002); Urban definitions from United Nations (2002)

|||**_Urban_**|**_definition_**|**_based on:_**||
|---|---|---|---|---|---|
|**Country**|**Rural**|**Size t**|**hreshold**<br>|**Administrative**|<br>**Other**|
||**popula**<br>**percen**<br>**2002**|**tion**<br>**tage,**<br>**for loc**|**alities**<br> <br> <br>|**centers**<br>**of**<br>**municipios,**<br>**cantons,**||
|||||**provinces**||
|Argentina||8.0|2000|||
|Belize||51.8|||Not available|
|Bolivia||36.6|2000|||
|Brazil||17.8||X||
|Chile||13.7|||Populated centers with urban|
||||||characteristics|
|Colombia||24.0|1500|||
|Costa Rica||40.0||X||
|Cuba||24.3|2000||**or**<br>with<br>specified|
||||||infrastructure services
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 6

## **Agricultural suitability and forest cover**

These data (see figure 4) were taken from the global agroecological zoning (GAEZ) data assembled by IIASA and FAO, (http://www.iiasa.ac.at/Research/LUC/GAEZ/index.htm, version 1.0). That exercise used agroclimatic data to create a suitability index (SI) for rainfed cropping under mixed input levels. Here we use categories from Plate 56, distinguishing marginal and unsuitable lands (SI <25) from more favorable lands, and distinguishing forested and nonforested areas.

## **Data assembly**

We used the GPW grid cells as our reference. We defined population density as GPWreported population (UN adjusted) divided by reported non-water, non-ice area of the cell. Centroids of the GPW grid cells were used to extract data from the accessibility maps, the GAEZ data, and a dataset of country boundaries.

# **Results**

Figure 4 maps the population density data from GPW 3. Keep in mind that these represent, for the most part, municipio level averages. Table 2 tabulates population proportions<sup>5</sup> , for LAC as a whole and for selected countries, by population density threshold, breaking out those areas more than one hour travel time from cities with more than 100,000 people (here designated as ‘remote’). The proportions in all cases refer to the total national population, so for instance, we read that 46% of all Argentinians live at population densities below 150, and 44% of all Argentinians live in cells that both have densities below 150 and are more than one hour travel time from a city of 100,000. The appendix graphs this data in the the form of cumulative densities, showing for each density d, the proportion of national population (or area) with density less than or equal to d. Because we are
electricity subsidies. Overall, the negative attitude of respondents toward the electricity service is a result of frustration with unreliable supply and perceptions of poor sector governance. Despite struggling to afford electricity, respondents appear willing to pay higher prices provided service quality improves and governance problems are addressed. However, most respondents are skeptical that such improvements will take place.

When governments undertake subsidy reforms, they have two main levers to help households adapt to higher prices: (i) compensating households for the price increase; and (ii) encouraging households to adjust their consumption patterns (Yemtsov et al., forthcoming). In this analysis we have focused on the former, and illustrated how Pakistan can better cushion the effect of energy price fluctuations on poor and economically vulnerable families by targeting compensation using the existing NSER database. However, a pilot would be needed to examine the cost‐effectiveness of the approach given the costs of implementation.

22
poverty lines.

The characteristics of children living in monetary poor households and disaggregates by geographic region, income group, and residence in a fragile or conflict-affected country are described. The profile of extreme poor children is also presented by age group, gender, location and household characteristics, providing useful information for governments and all partners working to improve the lives of the poorest children and their families.

# Extreme child poverty trends

The number of children living in extreme poverty decreased by an estimated 63.3 million between 2013 and 2019, from 383 million to 319 million.<sup>4</sup> A slow decrease between 2013 and 2016 in the absolute numbers of children in poverty is largely explained by rapid population growth in Sub-Saharan Africa.

Poverty estimates for 2020, 2021 and 2022 are “nowcasted”—that is, gross domestic product (GDP) growth rates have been used to forecast household incomes, assuming all households experience equal growth in percentage terms in per capita consumption or income.<sup>5</sup> Nowcast estimates show an increase in extreme child poverty in 2020 at the height of the pandemic, whereas poverty reduction resumed in 3 Although the analysis covers child poverty based on all three international poverty lines, that is extreme poverty ($2.15, 2017 PPP) and the higher poverty lines of $3.65 and $6.85, often referred to as lower middle income and upper middle income international poverty lines - the key focus of the trend analysis and the profiling is on extreme child poverty ($2.15).

4 Estimated trends from 2013 to 2018 are based on back casted projections. These are obtained by scaling the distribution of per capita income or consumption by the amount of per capita GDP growth, applied to the latest available survey for each country.

5 Household per capita consumption or income is multiplied by the growth rate of per capita GDP to shift the distribution forwards and backwards, under the assumption that the growth rate in per capita GDP is fully