rafmacalaba/datause-encoder-data huggingface dataset:
municipalities that report the presence of a DEAM in the MUNIC survey do not appear in SPM records. For municipalities registered with SPM, the exact date of establishment is used. For municipalities that only appear in MUNIC data, the year when their presence in a municipality is reported for the first time is considered the year of establishment. We note that this uncertainty regarding the dates of establishment makes for a noisy treatment variable. This implies that our estimates will be lower bounds, as in some years (the years between MUNIC surveys) DEAMs may have been established, but the municipalities are not counted as treated if their establishment has not been identified by the SPM.

<!-- Start of picture text --> Growth of Women's Police Stations Over Time<br>2004 2005 2006 2007 2008 2009<br>year<br>Source: See text. Only municipalities with high quality mortality data<br>210<br>200<br>190<br>180<br>Women Police Stations<br>170<br>160<br><!-- End of picture text --> Since homicides due to domestic violence are a subset of our outcome variable, we include controls that are likely to impact domestic violence as well as violence more generally, such as street crime. We focus on demographic variables, especially the ones that may affect intra-household bargaining, and municipal characteristics. The primary source of our demographic variables is IBGE (Instituto Brasileiro Geografia e Economia, a national statistical agency). We include log of female population in our age group of interest and population density, as the number of homicides clearly depends on the size of the population and the proximity of people to one another. We also calculate the percent of population that is female, should scarcity of women influence how they are treated; indeed a significant negative correlation between this variable and the female homicide rate supports this hypothesis. Gender gap in education and access to own resources may affect intra-household bargaining. Thus we include the ratio of percent of female voters who have 11
**<u>Table 2: Verification of randomization</u>**

|**Balance at baseline**||**Treatme**|**ntgroups**<br>||**Differences**|
|---|---|---|---|---|---|
|**across treatment status**|(1) BRC|(2) BRC + TPIN|(3)<br>BRC+IS+BBA|Control|F test|
|N|745|293|1,207|757|3,002|
|**_Strata variables_**||||||
|Female|39.1|39.9|40.3|39.8|0.1|
|Lilongwe|51|49.5|50.2|48|0.5|
|Large firm|49.4|53.6|47.7|51.1|1.4|
|Age of firm|8|7.7|7.7|8.3|1.2|
|High capture|37.6|35.2|35.6|36.3|0.3|
|Manufacturing|6.3|6.8|6.3|7.3|0.3|
|Retail|71.1|72|71.3|70.4|0.1|
|Services|22.6|21.2|22.4|22.3|0.1|
|**_Firm Characteristics_**||||||
|Number of people working in business|2|2|2.1|2|0.2|
|Number of owners|1.1|1.1|1.1|1.1|1.9|
|Owner started business|89.7|88.7|91.9|91.2|1.4|
|Owns space where operates business|36.2|31.1|32.6|35.3|1.5|
|# new products introduced past 12 months|0.6|0.8|0.7|0.5|1.4|
|Business with access to electricity|26.9|25.3|28.1|25.1|0.8|
|# of competitors|14.1|13.8|15.2|14.6|0.2|
|<br>Time to nearest bank (minutes)|21.3|18.9|20.5|21.3|3.3**|
|**_Individual Characteristics_**||||||
|Owner age|33.6|32.8|33.3|34|1.8|
|Married / Living with someone|78.9|79.9|80|81.5|0.5|
|HH decision making index (0-100)|83.5|83.7|84.3|84.9|0.6|
|Main provider of income to household|77.6|76|78|74.7|1|
|Literate|92.7|92.8|90.7|91|1.1|
|Primary school completed is max education|34.8|34.8|35.9|36.5|0.2|
|Secondary school completed is max education|23.9|26.3|24.3|24|0.2|
|Higher education completed|5.5|4.8|5.4|5.2|0.1|
|High capture|37.6|35.2|35.6|36.3|0.3|
|Financial Literacy knowledge (0-1)|0.4|0.4|0.4|0.4|1.5|
|Mother Entrepreneur|23.5|15.4|21|22.6|3.6**|
|Mother in Wage Employment|5.9|5.1|5.6|6.6|0.4|
|<br>Father Entrepreneur|20.3|16.7|20.6|24.3|2.9**|
|Father in Wage Employment|25.5|23.9|28.7|27.5|1.4|
|Spouse Entrepreneur|28.9|28.3|27.1|30.9|1.1|
|Spouse in Wage Employment|14.9|11.6|16.1|14.5|1.5|

29
<mark>UNHCR policies guiding targeting of assistance to Syrian refugees have changed since 2013/14, and therefore policy recommendations for Syrian refugees in Jordan cannot be based on the findings presented here. Rather, our study shows that it is possible to apply a gender lens to available data and produce results that quantify differences between male and female headed households’ incidence of poverty and identify some of the demographic characteristics that are linked to greater poverty risk. Moreover, our findings indicate that it is of utmost importance to distinguish between different types of female and male principal applicant households to understand poverty in the setting of the Syrian refugees in Jordan.</mark> More generally, our findings suggest that unless gender disadvantage is considered in the design of development policies to replace humanitarian assistance the poverty reduction gains it achieves may not be sustained. In the short‐term it will be important to build the capacity for women to move into the labor market so that they can have access to economic opportunities which can replace the value of assistance or the advantages that an adult male brings to a household. Including cash transfers and other types of social protection that can reach the households that are especially vulnerable – unaccompanied children, siblings and single caregivers‐ is equally important. Finally, the strong protective role that education plays underlines the importance of ensuring remedial action is taken so that all children and young adults who have missed education due to displacement can make up the schooling they have missed.

<mark>Finally, although the UNHCR data enabled us to examine some of the impacts that gender differences have on household poverty, there are some important data gaps. The JD‐HV data show the proportion of both male and female principal applicants who are employed and do daily or irregular work. However, employment details of other members of the household and more detailed gender‐disaggregated data on labor market participation (that includes the numbers of days/hours worked, daily or hourly earnings, sector of employment, and type of work) are needed to understand the difference between labor market opportunities for men and women. Other research points to the increased prevalence of GBV, especially faced by women and girls. Besides being a human rights violation, GBV is a manifestation of women’s lack of agency and can affect women and girl’s ability to access economic opportunities and services due to mobility constraints caused by actual and perceived GBV risks. GBV is thus a major cause of gender‐based vulnerability and therefore good data on GBV risk (that is, collected following UN Guidelines (UNDESA and UNWomen, 2013)) is needed to increase understanding of the impacts of exposure to GBV on refugee</mark> women and their families and allow us to explore the links between GBV and household poverty.

31
As it appears implausible that for a given combination of fund and donor characteristics, the utility peaks at different levels of _α_ , we assume that _Ui_<sup>_f_(</sup><sup>_α_)isunimodal.</sup> Finally, we assume that the donor does not benefit from funds that are financed only by other donors, as their development outcomes will be attributed only to members of those funds. This assumption is broadly in line with the donors’ strong concern for visibility that is one of the most important general drivers of multi-bi aid according to a survey carried out by the OECD/DAC Secretariat (Tortora and Steensen 2014).<sup>5</sup> Let trust fund membership be denoted by an indicator variable _Mi_<sup>_f_,with</sup><sup>_M f_</sup> _i_<sup>=1if</sup> donor _i_ is a member of trust fund _f_ , and _Mi_<sup>_f_=0otherwise.Theutilityofdonor</sup><sup>_i_from</sup> trust fund _f_ is then given by: Donor _i_ decides about membership in any given fund by considering the utility this fund will provide to it, subject to a budget constraint that limits its multi-bi contributions to _M_ ̄ _i_ (i.e., the overall aid budget net of the resources required for bilateral and multilateral commitments). For simplicity, we assume that donor _i_ contributes an equal amount (say, 1 unit) to each trust fund in which it participates. Then, _M_ ̄ _i_ also reflects the maximum number of trust funds the donor can participate in.

Let _F_ be the overall number of possible trust funds a donor might create or join.<sup>6</sup> The optimization problem then becomes: The obvious solution is that the _M_ ̄ _i_ trust funds providing the greatest utility will be funded. We denote the utility of the marginal fund the donor will become a member of (i.e., the _M_<sup> ̄</sup> _i_ -best fund) as _U_<sup> ̄</sup> _i_ . We abstract from the case that two or more funds would have exactly the same utility. Then the optimal participation decision for each individual fund is given by: For illustrative purposes, let us assume for the moment that _U_<sup> ̄</sup> _i_ is fixed. This allows us to graphically demonstrate the implications of our model. Figure 2 shows _Ui_<sup>_f_(</sup><sup>_α_)forthree</sup>

> 5 We acknowledge that this assumption is an oversimplification, as the typical donor in reality is likely to be somewhat altruistic and obtain some utility from favorable development outcomes produced without its involvement.

> 6 For purposes of this illustrative model, we take other donors’ participation decisions as given.

9
equivalent to 5.3% of GDP, on average, over the last decade (Figure 1).<sup>4</sup> The Government of Egypt (GOE) started reforming fossil fuel subsidies in 2012 by increasing the prices of liquefied petroleum gas (LPG), gasoline, natural gas, and diesel. It dropped to less than 2.6% in 2016, increasing to more than 6.7% in 2018.<sup>5</sup> Therefore, it is important to understand the environmental impacts of fossil fuel subsidy removals in addition to the economic impacts.

**Figure 1. Fuel subsidies in Egypt (2021 Million US$) and the ratio of fuel subsidies to GDP (%)**

<!-- Start of picture text --> 40,000 12.0%<br>35,000<br>10.0%<br>30,000<br>8.0%<br>25,000<br>20,000 6.0%<br>15,000<br>4.0%<br>10,000<br>2.0%<br>5,000<br>0 0.0%<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Year<br>Petroleum products Natural gas Electricity % of GDP<br>Subsidy as % of GDP<br>Energy subsidy (2021 Million US$)<br><!-- End of picture text --> Source: IEA (2022) and World Bank (2023) It is possible to introduce a carbon tax in an economy where fossil fuel subsidies still exist because the distributional implications of a carbon tax and the removal/reduction of fossil fuel subsidies fall differently on economic agents (households, producers). A carbon tax falls mostly on emission-intensive fuels (e.g., coal) and heavy petroleum products (fuel oil) which are mainly used for power generation and industrial applications. Removal of fossil fuel subsidies impacts fuels that are mainly used by households for cooking and home heating. However, introducing a carbon tax without removing fossil fuel subsidies gets criticism because both policies cause an

> 4 If the electricity subsidy is also included, the total energy subsidy accounts for, on average, 7.6% of the GDP during the same period.

> 5 The change in subsidy after 2016 is mostly influenced by the exchange rate. The US$ value of 100 EGP was 10 in 2016, it decreased by 44% to 5.6 in 2018, and increased to 6.4 in 2021.
elasticities of response<sup>19</sup> from Hertel _et al_ . (2016), this gap would translate into a price decline of around 4.1 percent.

To measure the extent to which the convergent assumption embodied in the SSP2 scenario is affecting food demand, Figure 9b and the second column of Table 4 report the results of the counterfactual uniform growth scenario, taking global income to the same level in 2050 as the SSP2 scenario. This implies annual global GDP growth of 3.1 percent in both scenarios. The move to a uniform growth scenario causes a much larger decline in food demand growth (from 102 percent to 78 percent) than that in food supply (from 112 to 104 percent). The resulting gap between supply and demand of 13.9 percent in log-difference terms would lead to a price decline of 12.0 percent. Comparing the results between SSP2 and uniform growth scenarios, the net effect on prices of moving from the uniform scenario to the SSP2 scenario is to increase prices by 7.9 percentage points. The growth convergence inherent in the SSP2 scenario is therefore partly offsetting what would otherwise have been substantial downward pressure on world food prices.

The differential GDP growth rates embodied in SSP2 scenario may reflect features of the data set other than convergence. If, for instance, it reflected a bias towards more rapid growth in the middle-income countries with the highest income elasticities, it would generate more rapid growth in global demand than one with the highest growth rate among the countries with the lowest incomes. To focus more directly on the convergence issue, we construct an alternative scenario in which country growth rates relative to the United States are determined solely by the convergence rate estimated for the SSP2 projection (‘Pure convergence’ scenario). Specifically, each country’s annual growth rate is computed as 1.2 - 0.85 [���� - �����] where ���� is the initial log GDP for country _i,_ ����� is the initial log GDP of the United States and 1.2 is the projected growth rate for the United States in SSP2. Since this scenario would lead to a lower world GDP growth of 2.7 percent, in parallel to the base comparison, we construct a uniform scenario in which the world economies grow at the uniform rate of 2.7 percent. The results are shown in Figures 9cd and in Columns 3-4 in Table 4. Similar to the SSP2 scenario, the impact of convergence is larger on the

> 19 The demand and supply elasticities implied in the global agricultural models tend to vary substantially (Table 3, Hertel _et al_ . 2016). The emulator elasticities are: for food demand (-0.29), for the response of output due to substitution between land and other inputs (0.51), and for land supply (0.36). Together they imply a flexibility of price response � to a proportional gap between supply and demand in the order of 0.86 ~~[~~ �.�� ~~.~~ ≈ 0.86]. Thus, the gap of around 4.8 percent in log-difference terms between our supply and demand projections implies a price decline of about 4.1 percent.

22
-26-

Table 7 The Relion Between Finm Specuadve Asset Holdings and Subsidized Loans under the Altenative Defiition of Speculative Assets a

||1984|1985|1986|1987|1988|
|---|---|---|---|---|---|
|Subsidized Loans()|0.327|0.204|0.066|0.250|0.168|
||(4.78)|(6.05)|(4.53)|(7.34)|(2.64)|
|Constant RegmneI(no)|0.042|0.041|0.041|0.033|_0.055_|
||(3.50)|(4.95)|(10.40)|(6.25)|(5.61)|
|Constant: Regime H_(a)_|0.060<br>(13.92)|0.033<br>(13.13)|0.022<br>(8.51)|0.036<br>(9.96)|0.045<br>(10.28)|
|Proportion of Firms|0.527|0.488|0.444|0.424|0.364|
|in Regine I (X)|(30.22)|(28.58)|(27.46)|(25.93)|(22.32)|
|Number of Finrs|241|240|238|239|241|

a: Changes in specuative assets include the decline in other capital, where other capital is defined as total assets minus subsidized loans.

Absolute values of t-statistics are in parentheses.
Table 4 reports the results for large and small states.<sup>8</sup> The seven large states together account for approximately 75 percent of national GDP, and are located mainly in the South and Southeast regions of Brazil (São Paulo, Rio de Janeiro, Minas Gerais, Rio Grande do Sul, Paraná, Bahia, and Santa Catarina). According to the results, smaller states are more pro-cyclical than larger states for all revenue (except intergovernmental transfers) and expenditure categories. As in the results for all states, personnel expenditures show a higher degree of pro-cyclicality among expenditure categories. Large states show pro-cyclical behavior for both total revenues and total expenditures (driven mainly by personnel expenditures).

Table 4: Cyclical Sensitivity of Fiscal Policy: Subnational Level – Big and Small States

|Variables|AllStates||Big States||SmallState|s|
|---|---|---|---|---|---|---|
|**_Revenues:_**|||||||
|Total Revenue|0.273|***|0.409|***|0.215|***|
||[0.036]||[0.142]||[0.049]||
|Intergovernment Transfers|0.091||0.306||0.083||
||[0.066]||[0.223]||[0.067]||
|Tax Revenue|0.309|***|0.110||0.280|***|
||[0.030]||[0.096]||[0.044]||
|V.A.T. (ICMS)|0.312|***|0.070||0.256|***|
||[0.040]||[0.112]||[0.049]||
|**_Expenditures:_**|||||||
|Total Expenditure|0.305|***|0.255|*|0.352|***|
||[0.031]||[0.165]||[0.074]||
|Primary Expenditure|0.703|***|0.104||0.319|***|
||[0.057]||[0.162]||[0.074]||
|Maintenance and capital|0.340|***|0.306||0.396|***|
||[0.088]||[0.313]||[0.112]||
|Personnel Expenditure|0.703|***|0.448|**|0.538|***|
||[0.056]||[0.224]||[0.104]||
|Interest Payments|0.516|***|1.067|*|0.479|**|
|***, **, and * denote signific|[0.126]<br>ance at the 1|%, 5%,|[0.648]<br>and 10% lev|els, re|[0.230]<br>spectively.||

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Large states include Sao Paulo, Rio de Janeiro, Minas Gerais, Rio Grande do Sul, Paraná, Bahia, and Santa Catarina. <mark>Estimation done with FGLS.</mark>

> 8 From here on, all the results are related to the FGLS estimation.

11
numerical studies of regional arrangements that assess the impact of commitments to multinational firms who will undertake foreign direct investment in services.<sup>4</sup> Given substantial demand by governments for advice on their prospective trade policies in the regional and multilateral arenas, it is unfortunate that the profession does not have a framework for assessing the ex ante welfare impact of preferential reduction of barriers against foreign direct investment in business services. We attempt to fill that gap in this paper. Crucial to the analysis, we incorporate the Dixit-Stiglitz-Ethier mechanism of endogenous productivity gains from additional varieties of imperfectly produced goods and services.

Jensen and Tarr (2010) have shown that if domestic firms capture rents from the barriers against multinational providers of services, then these rents are analogous to tariff losses in goods, and losses may occur from preferential liberalization of services. Moreover, with imperfect competition, preferential liberalization will lead to a gain of varieties from the partner countries, but a loss of varieties from excluded countries; the lost varieties can lead to a net loss of welfare. We simulate examples of cases where there is a loss of welfare due to the loss of varieties and show how key elasticities in the model influence the result.

> 4 Earlier papers that examined the impact of barriers to foreign direct investment in services include the following. Markusen, Rutherford and Tarr (2005) developed a stylized model where foreign direct investment is required for entry of new multinational competitors in services, but they did not apply this model to the data of an actual economy. Brown and Stern (2001) and Dee et al. (2003) employ multicountry numerical models with many of the same features of  Markusen, Rutherford and Tarr.  Their models contain three sectors, agriculture, manufacturing and services, and are thus also rather stylized. The Dixit-Stiglitz endogenous productivity effect from the impact of service sector liberalization on product variety is not mentioned in the results of Brown and Stern and are interpreted as of little relevance in Dee et al.<sup>4</sup> Konan and Maskus (2006) develop a small open economy constant returns to scale model with an initial monopoly in services sectors that results in monopoly rents and increased costs. They examine the impact of exogenously allowing additional firms to enter.  The papers by Jensen, Rutherford and Tarr (2007) and Rutherford and Tarr (2008) on Russian WTO accession are the closest to this model; but there was only one aggregate rest of the world in those models, so there was no possible consideration of regional impacts in those papers.

4
Income inequality is lower in rural areas than in urban and metropolitan areas in Haiti and large disparities exist in the distribution of income across locality and regions (Table 5.7). In 2001, rural Haiti had a Gini coefficient of 58.9, slightly lower than in the metropolitan area (61.4) and significantly lower than urban Haiti (66.7). Based on expenditure surveys of 1986/1987 and 1999/2000, Pedersen and Lockwood (2001) also find that income inequality has increased in the Port-au-Prince area and rural inequality has decreased.

Figure <u>5.3: Squared Poverty Gap (P2) for Haiti and its Regions, 2001 (percent)</u> <!-- Start of picture text --> Total Haiti<br>Rural<br>Urban<br>Metropolitan<br>Southeast<br>South<br>West<br>Northwest<br>Northeast<br>North<br>Grand-Anse<br>Center<br>Artibonite<br>0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0<br><!-- End of picture text --> _<u>Source:</u>_ <u>Calculations based on HLCS 2001.</u> In 2001, regions with the least unequal distribution of per-capita income were Southeast (53.8), Center (55.6) and Northwest (55.8). The Northeast experienced the highest Gini coefficient of 69.8. Of rural areas, the Southeast had the lowest income inequality (Gini coefficient of 51.2) and the Northeast the highest (Gini coefficient of 72.4).

The share of total income by decile varies little among the lowest deciles and locations. However, the share of total income obtained by the top decile in Haiti is large, 43.3, 53.7, and 43.2 percent in metropolitan, urban and rural Haiti respectively (Figure 5.5). This compares to the lowest decile where the populations in the metropolitan, urban and rural areas receive 0.7, 0.3, and 0.5 percent respectively of total income in the particular location.

27
# <u>Table 3. Linear regression model estimates</u>

|Dependent variable:|Log CO2 e<br>industry rea|missions per<br>l value-added|millions of<br>(2012 euro)|Average an<br>emissions pe<br>(20|nual growth ra<br>r industry real<br>10-2019), % p|tio in CO2<br>value-added<br>oint|
|---|---|---|---|---|---|---|
|Skill measure domain|Literacy|Numeracy|Problem<br>solving|Literacy|Numeracy|Problem<br>solving|
|Skill measure (standardized)|-0.11***<br>(0.006)|-0.088***<br>(0.005)|-0.123***<br>(0.007)|-0.434***<br>(0.025)|-0.439***<br>(0.025)|-0.255***<br>(0.03)|
|Female|-0.65***<br>(0.008)|-0.667***<br>(0.008)|-0.616***<br>(0.011)|-0.369***<br>(0.034)|-0.445***<br>(0.035)|-0.496***<br>(0.043)|
|Other controls: age, age square, ln GDP pc|yes|yes|yes|yes|yes|yes|
|Constant|15.372***|15.412***|15.077***|107.092***|107.036***|110.881***|
||(0.094)|(0.094)|(0.104)|(0.535)|(0.536)|(0.56)|
|Observations|73621|73621|48673|73686|73686|48735|
|R2|0.17|0.17|0.18|0.02|0.02|0.03|
|countries|21|21|17|21|21|17|

Estimated using ten plausible values for each skill measure following OECD (2013); jackknife standard errors reported in parentheses. Statistical significance at the 1, 5 and 10 percent levels denoted by ***, **, and * respectively.

16
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