rafmacalaba/data-use-mentions-extended huggingface dataset:
Total 72 66 79 76 83 79 _Source_ : Global Findex database.

_Note_ : Figures show percent of male or female population aged 15 and older with an account at a financial institution or through a mobile money provider.

**Figure 3. The gender gap in account ownership varies widely within Europe and Central Asia**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Central Central Eastern Northern Russian South Southern Turkey Western Western<br>Asia Europe & Europe Europe Federation Caucasus Europe Balkans Europe<br>the Baltics<br>2011 2017<br>Percent of adults with account<br>Men Women Men Women Men Women Men Women Men Women Men Women Men Women Men Women Men Women Men Women<br><!-- End of picture text --> _Source_ : Global Findex database.

_Note_ : Figures show percent of population 15 and older with an account at a financial institution or through a mobile money provider.

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RUSMOD are close to those based on national accounts; hence the model can be seen as a reliable tool for evaluating the current performance of the Russian tax-benefit system and the distributive impact of potential tax-benefit reforms.

# **Input data**

In order to be used within the EUROMOD framework, the input data set has to meet certain requirements (Figari, et al., 2007). A number of national household surveys have been considered for this purpose, but only one fulfilled all the essential criteria: the Russian Longitudinal Monitoring Survey of the Higher School of Economics (RLMS-HSE), an annual, nationally-representative survey that began in 1994 (see Table A-2 in the Annex for an overview of the input data). RLMS-HSE is a standard multi-topic survey with an extended set of information on economic well-being and health status. It collects individual level information on demographic characteristics, within-family relationships, labor market status, primary income by source, social benefits, expenditure, as well as other characteristics relevant for assessing tax liabilities or benefit entitlements. The survey satisfies international standards in terms of sampling and quality of data collection. The sample includes both cross-sectional and panel components. In geographical terms it covers 32 (out of 80+) regions, however it is not representative at the regional level, with the exception for the two biggest cities – Moscow and St. Petersburg.

To account for time inconsistency between the input data (2016) and the policy year (2017), updating (or uprating) factors are used. Each monetary variable is updated so as to account for changes that have taken place between the year of the data and the year of the simulated tax-benefit system. Updating factors are generally based on changes in the average value of an income component 7
level of FDI in‡ows from UNCTAD. Geography is captured by a country’s total distance (in kilometers) to the main global GVC hubs, China, Germany, and the US from CEPII. For domestic industrial capacity we use manufacturing value added from WDI. To measure a country’s institutional quality, we use the political stability index from the World Governance Indicators database. We also include a measure of exchange rate appreciation based on changes in the average nominal exchange rate from WDI.

To assess the role of PTAs, we include indicator variables for a country’s participation in PTAs: the North American Free Trade Agreement (NAFTA), the EU, ASEAN, and the Southern Common Market (MERCOSUR), the number of PTA partners and a measure of the depth of those PTAs, both taken from the Content of Deep Trade Agreements database of Hofmann et al. (2017).

To capture aspects related to connectivity, we add alternatively, the time to clear imports from the Doing Business Database, the Logistics Performance Index’overall score, the share of the population that uses the internet from WDI and the share of people that speak English as a second language from Melitz and Toubal (2014). We also include a measure of domestic credit as percent of GDP from WDI to capture a country’s ...nancial development and the share of females in the total labor force from WDI. As an alternative to exchange rate depreciation we use a measure of exchange rate misalignment relative to an equilibrium exchange rate following Couharde et al. (2017).

1
but we do our best to capture the notion of the willingness of the individual to work during the previous 7 days to the interview.

For Bangladesh we have data for 64 zilas for years 2005, 2010 and 2013; for India we have data of 31 states in years 1999, 2004, 2007, 2009 and 2011; for Sri Lanka we have 17 or 25 districts depending on the year between 1992 and 2015 (data for 2005, 2009 and 2010 are not available).

We merge the GTD to the LFS for each country using year and administrative division. Thus, we have individuals' characteristics for any given year along with the total number of attacks committed, and people killed and wounded for each district, zila or state. While individual-level data vary substantially, violence-related data do not since these are measured at the administrative division level. Therefore, we collapse the original data set at the administrative unit level to build a panel-like data set with variables appropriately transformed to average age, share of males, and share of workers in the manufacturing sector of each administrative unit every year.

Our resulting data set has two shortcomings: (i) there are many observations in earnings variables missing, which is an important weakness since market wages are a determinant of labor force participation<sup>5</sup> and (ii) we lack data on the number of hours worked in India, so we drop this country from our estimations involving hours worked.

# 3.3 Summary Statistics

Table 2 contains the summary statistics. We obtain the gures presented here for individuals' (older than 15) characteristics by averaging the multiple geographical areas over time and by country separately. Violence variables also represent the average of the total number of attacks and deaths and people wounded in a given year and area (note that when we do not observe attacks, we impute a "zero" to all three variables). We observe a wide gender gap in labor force participation in all four countries, but the gap between men
was an update of the sampling frame following the 2007 population census.

The National Household Survey is a continuous survey that collects detailed information on demographics, education, health, labor market status, farm and nonfarm income, participation in social programs, and perceptions of governance, democracy, and transparency.<sup>4</sup> The survey is the official source of information on monthly indicators on trends in poverty, inequality, and living conditions among households. It is representative across the 25 regions, plus Lima Province, which is separate from the Lima Region. Within each region, the survey is representative across urban and rural areas. The survey sampled between 21,919 households, in 2003, and 36,996 households, in 2017.

The first section of the questionnaire collects information on the dwelling characteristics of households, including type of dwelling, building materials, number of rooms, ownership status, renovations recently undertaken and the associated means of financing, and utilities and other facilities serving the dwelling as well as the cost of these. The ownership status distinguishes the following six categories: (1) tenants, (2) outright owners, (3) owners by squatting, (4) owners paying mortgage, (5) individuals living for free in an accommodation provided by an employer, and (6) accommodation provided by any other individual or entity.<sup>5</sup> Tenants report the monthly rent paid, while all other individuals are asked how much they could make if they were to rent the dwelling they live in.

> 4 The National Institute of Statistics and Informatics, the national statistics office, conducted a first survey of living standards in 1985 with the support of the World Bank. Subsequently, it undertook a permanent annual national survey on living conditions and poverty in 1995. In 1997, the methodology was improved with the support of the Inter-American Development Bank, the United Nations Economic Commission for Latin America and the Caribbean, and the World Bank. Starting in 2004,
.1|6.5|*|6.9|2.5||
|<br>Value of hired labor per acre (US $)|19.2|16.1||19.2|21.1|***|
|<br>Banana on parcel (%)|32.3|64.6|***|23.9|49.2|***|
|Root crops on parcel (%)|49.5|75.6|***|38.5|77.3|***|
|Fruit crops on parcel (%)|9.2|32.3|***|4.0|18.9|***|
|Vegetables onparcel(%)|2.4|3.1||2.2|2.8||
|Number of observations|2185|148||1546|489||

<u>1 Land sales values are only for owners but not for renters.</u>

* significant at 10%; ** significant at 5%; *** significant at 1% for the t-statistic to test the equality of group means using customary tenure as a reference group.  Note that 26.4 % and 93.5 % of the parcels under freehold and _mailo_ tenure systems are respectively occupied by tenants. _Source:_ Own computation from 2004 WB/Makerere/FASID land tenure survey.

22
sumption caused by the program (Ib ́a ̃nez et al., 2024) might have reduced crimes by improving economic opportunities. Together, these findings highlight the critical role of regularization programs like PEP in enhancing legal protections, reducing social tensions, and improving safety for both migrants and host communities. Our results are in line with declines in the crime rates of newly documented migrants in Italy, as shown by Pinotti (2017a).

Our results suggest that facilitating forced migrant’s economic integration has powerful impacts that go beyond labor markets. For one, they affect migrant’s willingness to cooperate with governments and make their basic human rights be respected. Such policies seem to be particularly relevant for vulnerable groups such as women who may be facing disproportionate domestic and sexual violence by staying undocumented. Moreover, ensuring proper documentations facilitates enforcement and increases income resulting in lower crime rates.

This study contributes to work examining the relationship between immigration and crime (Butcher and Piehl, 1998b; Borjas et al., 2010; Bianchi et al., 2012; Alonso-Borrego et al., 2012; Bell et al., 2013; Spenkuch, 2014b; Baker, 2017; Pinotti, 2017b; Freedman et al., 2018; Fasani, 2018; Fasani et al., 2019; Ajzenman et al., 2020; Knight and Tribin, 2020). Specifically, for Colombia, Knight and Tribin, 2020 investigate the connection between violent crime and immigration, using data from Colombian municipalities during the recent wave of immigration from Venezuela. The authors find that after the ColombianVenezuelan border was closed and later re-opened in 2016—triggering a large influx of migrants—homicides in Colombia increased in areas near the Venezuelan border. This rise, however, was primarily driven by homicides involving Venezuelan victims, with no statistically significant increase in homicides targeting Colombians. While our study examines a related outcome, it departs from this body of work by focusing on the effects of a policy that improved the rights of undocumented migrants already residing in Colombia
conflicts; and for Definition 4, conflicts are preceded by 10 years free of confrontations that since their onset had reached more than 1,000 battle‐related deaths.

The control group of conflict‐free economies consists of a set of: 1) all the non‐conflict developing countries, 2) the developed countries with available macroeconomic data for each of the three variables of interest, and 3) the years in conflict countries that have been preceded by at least 10 years of peace following the specific criteria for each of the four definitions of armed conflict. A complete list of the armed conflicts considered in this study can be found in Appendix 1.

# **_III. B. Macroeconomic variables_**

As mentioned, this study focuses on the impact of the onset of conflict on public debt as a percentage of GDP, government consumption as percentage of GDP, and annual inflation rates. Other aspects of fiscal health, like primary and overall fiscal balances, were initially considered for the analysis but unfortunately are not available for the full sample of episodes of conflicts dating back to the 1950s. To obtain a constant sample for every macroeconomic variable for the 11 years of analysis centered on the dates of the onset of conflicts, only conflict countries with continuous observations for the entire episode‐period of analysis (11 years) are included in the conflict‐countries group.

In total, and based on the requirements previously described, 88 conflicts in 74 countries have complete information for the 11‐year episode analyses for at least one of the three macroeconomic variables. The starting years of the conflicts in the overall sample range from 1956 to 2012.<sup>6</sup> Table 1 shows the number of conflicts and conflict‐afflicted countries with complete information for each of the four definitions and the three macroeconomic variables. The variable definitions and their sources are detailed in Appendix 2.

> 6 Although the UCDP/PRIO Armed Conflict Dataset provides conflict information from 1939, the lack of consistent macroeconomic data available before
uncontrolled countries, followed by a narrowing as more and more countries decontrolled. This alerts us to the possibility that iinterest rate controls may not have been fully effective in the early years, at least for these countries. Bear in mind, however, that the sample of countries may suffer from selection bias to the extent that reporting of statistics to _IFS_ may be correlated with degree of regime liberalization.

# _Bank spreads increase_

Bank quoted gross intermediation spreads (as measured by subtracting quoted deposit from quoted lending rates) increase sharply with the general increase in rates during the 1980s. In industrial countries the increase is from 2.8 per cent in the mid-1970s to 5.2 per cent in the early 1990s; in the developing countries the spreads are wider: increasing from 4.1 per cent to 6.7 per cent. During the 1 990s, these spreads remain high in developing countries, whereas they decline in the industrial countries.<sup>9</sup> Except for one year, the median quoted rates for the developing countries are higher than for the industrial countries. The gap becomes quite wide by the late 1990s. Once again, this result cannot be extrapolated to an increase in bank profitability for a variety of reasons. For one thing, the single rates used do not purport to be average rates, but quoted rates for instruments of standard quality. Furthermore, the risk profile of borrowers and the extent to which quoted rates bundle the cost of other banking services to customers may have changed systematically over time.

# **2. Short-run dynamics of wholesale rates - overview**

We have nearly complete monthly data since 1980 on wholesalel° interest rates and inflation for some 28 significant" developing countries. In a later section we will have something to say about nominal rates, but here the focus is on rates adjusted for exchange rate

> 9 To what extent this recent reversal in industrial countries reflects disintermediation from banking is not clear: it is widely believed that industrial country banks have lost some
# **Table 5: Effect of the Interventions on Child Health**

||**Province le**|**vel intervention**||**District leve**|**l intervention**||
|---|---|---|---|---|---|---|
||**Mass me**|**dia treatment**|**Community t**|**reatment sample**|**School co**|**mponent sample**|
||Control mean|Effect of treatment|Control mean|Effect of treatment|Control mean|Effect of treatment|
|**Child health**|||||||
|Diarrhea prevalence 48 hours|0.04|0.01<br>(0.013)|0.04|-0.002<br>(0.011)|0.033|0.0012<br>(0.009)|
|Diarrhea prevalence 7 days|0.06|0.011<br>(0.018)|0.06|0.001<br>(0.015)|0.069|-0.005<br>(0.014)|
|ALRI prevalence 48 hours|0.041|-0.039***<br>(0.014)<br>[-96.5%]|0.041|-0.011<br>(0.018)|0.049|-0.0183<br>(0.020)|
|ALRI prevalence 7 days|0.051|-0.047***<br>(0.018)<br>[-89%]|0.051|-0.016<br>(0
to summarize the information contained in a large number of variables to a smaller number by creating a set of mutually uncorrelated components of the data. The first factor is the linear index of all the observed variables that captures the most common variation among them. In the case of our index of socioeconomic status, the first factor captures 52% of the common variation among the 13 asset variables.

> 27 Retrospective studies of the relationship between education inputs and outcomes do not usually account for community characteristics among the possible determinants of school performance. However, Bjorkman and Svensson (2010), in their study – based on RCTs – of the impact of participatory programs on health outcomes in Uganda, have shown that division within the community, and particularly ethnic fractionalization, may prevent collective action for improved service provision, resulting in lower outcomes than in more homogeneous societies.  Galiani et al. (2008) in their study of decentralization efforts in Argentinean secondary schools (based on a “political natural experiment”), have shown that areas that are poorly administrated and characterized by populations that do not usually voice their preferences may be left behind and end up worse off rather than better off following decentralization.
In Colombia, as in the vast majority of countries in the region, indirect taxes plus subsidies tend to be equalizers. In other words, the Gini coefficient associated with consumable income, the income that considers subsidies and indirect taxes, is relatively lower than that observed for disposable income. However, these two coefficients are not statistically different (Figure IV-6). In the case of Colombia, the effect of indirect taxes plus subsidies is similar to the average for Latin American countries. Finally, after Argentina, Uruguay, and Brazil, Colombia is the country whose transfers in education and health services reduce inequality to a greater extent (Figure IV-6). In the case of Argentina, this last impact results in a 0.111 point drop in the Gini coefficient, while for Colombia, it is 0.06.

<!-- Start of picture text --> FIGURE IV-6 : GINI COEFFICIENTS FOR LATIN AMERICAN COUNTRIES<br>(PENSIONS CONSIDERED DIRECT TRANSFERS)<br>0.6<br>0.55 0.551<br>0.517 0.515<br>0.5<br>0.45 0.455<br>0.4<br>0.35<br>0.3<br>0.25<br>Market Income plus Disposable Income Consumable Income Final Income<br>pensions<br>Argentina Uruguay Ecuador Perú Brazil<br>Venezuela Chile Colombia LAC<br>Source: The Impact of Taxes and Social Spending on Income Distribution and Poverty in Latin America. An Application of the<br>Commitment to Equity Methodology (CEQ), 2017. World Bank Group estimates for Colombia using the ENPH 2017<br><!-- End of picture text -->

# iii. The impact on poverty

In addition to the impact on inequality, which measures the relative position of households in the income distribution, it is important to measure the impacts on poverty, the results of which depend on the
effect of seasonal variations, the data were collected over a 12-month span (Hamle 1 to Sene 30, i.e. July 8 to July 7), by randomly allocating sampled households to different

> 12 Idiosyncratic shocks are, by definition, not correlated and more likely to be mutually insured within communities where information asymmetries and enforcement limitations may be smaller than across communities. On the other hand, covariate shocks that affect all households in a community more or less in the same manner are less likely to be insured by mutual insurance mechanisms _within_ communities or even by mutual insurance mechanisms _across_ communities.

> 13 While the HCES/WMS sample covered only two administrative zones in Afar and three in Somali in 2011, it included five zones and eight zones respectively in Afar and Somali in 2016.

11
and technologies but that lack the financing to scale up tend to be harmed the most by misallocation of credit. In countries where credit is expensive and hard to access, firms typically are small, leading to a misallocation of credit. Large firms that are relatively less productive end up producing a significant share of the output, while smaller more productive firms are unable to grow and to contribute as much as they could. Credit frictions tend to discourage firm entry and creative destruction, leading to poor economic performance.<sup>40</sup> A policy implication is that just developing financial markets might not be enough to generate growth – ensuring that credit flows to the firms that have growth potential is as important.<sup>41</sup> Beyond financial markets, public policy should take into account how different economic agents respond differently to policies. The regulation of entry, for instance, should consider the incentives for low‐scale

> 38 Hsieh, C.T., Klenow, P., 2009, “Misallocation and Manufacturing TFP in China and India.” Quarterly Journal of Economics, 4: 1403‐1448.

> 39 Buera, F., Kaboski, J., Shin, Y., 2011, “Finance and Development: A Tale of Two Sectors.” American Economic Review, 101(5): 1964‐2002.

> 40 Midrigan V., Xu, D., 2014, “Finance and Misallocation: Evidence from Plant‐Level Data,” American Economic Review, 104(2): 422‐58

> 41 Bruhn, M., McKenzie, D., 2014, “Entry Regulation and Formalization of Microenterprises in Developing Countries”, World Bank Research Observer, 29 (2): 186‐201, 2014.

> Didier, T., Levine, R., Llovet, R., Schmukler, S., 2014, “Capital Market Financing and Firm Growth,” Mimeo, World Bank.

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Third, Table 5 shows the effects of the intervention on the “willingness to interact with outgroups index” and its components. Results indicate that exposure to urban design elements only leads to an increase of 0.10 standard deviation on the willingness to interact with outgroups index at the 10 percent level. Surprisingly, exposure to diversity has a positive and statistically significant effect of 0.05 to 0.06 percentage points on the likelihood of forming a committee with a member of the opposite sex of any ethnicity (Columns 7-8). This finding somewhat contradicts the negative effects identified earlier on indicators for trust and perceptions toward outgroups. However, it may suggest that exposure to diversity can still promote cooperation among social groups, even when people continue to hold unfavorable perceptions about other groups.

The impact of the intervention on the “willingness to interact with outgroups index” varied by income, but not by any other characteristic. As shown in Table 5.A, for respondents with a monthly household income above the median, exposure to urban design elements, T1, is associated with an increase of 0.23 standard deviation on willingness to interact with outgroups. The effect is similar in size and sign for T2 and these differences are statistically different at the 10 percent level. This suggests that urban design and diversity can promote social interaction, but only for those with higher incomes.

Table 5. Treatment Effects on Willingness to Interact

|VARIABLES|(1)<br>Index I<br>(z-sc|(2)<br>nteract<br>ore)<br>|(3)<br>Frien<br>Eth<br>(|(4)<br>ds Diff.<br>nicity<br>=1)|(5)<br>Frien<br>Eth<br>but c|(6)<br>ds Diff.<br>nicity,<br>an't(=1)|(7)