rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> POVERTY<br>Incidence in%<br><40<br>© 40-50<br>GUINEA @ 50-60<br>@ 60-70<br>; r ) >70<br>i<br>=<br>a<br>Py ©<br>4 N!<br>ce ; » Oe<br>~~ id Ay » ,<br>@ LIBERIA<br>\<br><!-- End of picture text --> <!-- Start of picture text --> p ic<br>im<br>; * a .<br>GUINEA ~@ wn 7 a ><br>A - ‘al am<br>" : 4 U @ ; et<br>‘i t + /<br>festernFi Area » ¢S 7 Ear Ea<br>”;:<br>f<br>/<br>@ # ~ TOPOGRAPHY<br>¢ 7 ll @ Elevation(m)<br>=. < Southern : < 100<br>r. ; hoe ; a 100- 400<br>Ls m ( ©) 400-750<br>‘¢, 5 @ 750-1000<br>Sav - ) @ 1000- 1500<br>- : sa tad if¢ < @@ >15002000- 2000<br>) uuan River Basin<br><!-- End of picture text -->
- 7a -

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- Figure 1: The effect of an export subsidy,<sup>a</sup> production subsidy and TAR on total output, domestic output and exports.

<!-- Start of picture text --> 0<br>-<br>OQ initial total output<br>z<br>-<br>OQ total output with an export sibsidy<br>tv<br>OQ - total output with a production subsidy (reduction in<br>tariff rate from c to t'<br>TAR<br>OQ - total output with a production subsidy (reduction in<br>tariff rate from t to t' with TAR<br>0<br>-<br>OQ initial domestic output<br>d<br>I<br>-<br>OQ domestic output with an export subsidy<br>d<br>0 0<br>-<br>Q Q initial exports<br>d<br>I z<br>Q Q - exports with an export subsidy<br>d<br>o t'<br>Q Q - exports with a production subsidt<br>d<br>ITAR<br>Q Q - exports with TAR<br>d<br><!-- End of picture text -->
2021. Limiting the sample timeframe to 2000-2019, we identify fourteen instances of such events in the dataset, which we refer to as budget balance rule modification events and are reported out in the appendix, in Table A14. Following Davoodi et al. (2022b), we measure a country’s fiscal balance using the country’s specified budget balance target, which can differ across countries. For example, Chile’s budget balance rule pertains to its structural balance, whereas Israel’s budget balance rule pertains to its overall balance, and Uruguay’s rule pertains to its primary balance. Each of these variables are identifiable in the IMF’s Fiscal Rules and World Economic Outlook (WEO) datasets. A country’s baseline (pre-suspension/revision) fiscal balance is calculated as the three-year average target balance prior to the suspension or revision of a fiscal rule. Our summary statistics (reported in Table A15) show that following a budget balance rule modification event, the median duration for a country to return to its baseline fiscal balance is 3.5 years. The median deviation of a country’s fiscal balance from its baseline following a modification is 4.4% of GDP. In the sample analyzed, two countries, Argentina and Russia, which both suspended their budget balance rules in 2009 as a result of the global financial crisis, never returned to their presuspension baseline in the years following the suspensions, and ultimately abolished their rules.<sup>20</sup> While the sample of countries that have previously suspended, revised, and/or enacted an escape clause for budget balance rules is not large, we employ an event study analysis as an initial step to empirically answer the question of how long a country should be expected to take to return to compliance. After controlling for year and country fixed effects, our estimates indicate that following a budget balance rule modification, a country is expected to take approximately three years to return to its baseline fiscal balance. This implies that for a country that suspended its budget balance rule in 2020, its target balance would be expected to return to its 2017-2019 average by the year 2023. Further, our estimates find the deterioration of the fiscal balance to reach its peak one year following the initial modification, with the deficit in this year estimated to be almost 4% of GDP higher than the three-year average preceding the modification. As mentioned at the beginning of this section, we believe that the short time duration for which it has historically taken for sovereign governments to return to compliance after abandoning fiscal rules is a key driver underlying the results reported out in Section 3.3. The full results of our analysis can be seen in Figure 5.

# **Figure 5. Fiscal Balance Path Following Suspension/Revision of Budget Balance Rule or Escape Clause Usage**

> 20 In the case of Argentina, the budget balance rule was suspended between 2009 and 2017, and then abolished thereafter. For Russia, its short-lived budget balance rule (targeting the non-oil balance) became effective in 2008, was suspended in April 2009, and was formally abolished in 2012.

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|**COUNTRY**|**GDP/CAP**<br>**(USS 000)**|**OPENNESS**|**Total Core Non**|**Tariff Measures**|
|---|---|---|---|---|
||**1991-95**|1991-95|**1989-94**|**1995-98**|
|Thailand|1.60|0.89|36.5%|17.5%|
|Tunisia|1.40|0.87|_54.2%_|_<br>_<br>...|
|Turkey|1.81|0.43|5.2%|19.8%|
|Uganda|_0.51_|0.23|...|3.1%|
|Uruguay|2.70|0.58|32.3%|0.0°/O|
|Venezuela|2.73|0.49|...|17.7%|
|Zambia|0.28|0.71|...|1.0%|
|Zimbabwe|0.62|0.63|22.7%|..|
|Explanation:|||||
|GDP/CAP-<br>Openness-<br>Total Core NTMS-|Per capita GDP<br>Merchandise E<br>Frequency rati|in constant 1987 USS, US<br>xports plus Imports divided<br>o in % relative to total 2-dig|$ 000 (average, 1991-199<br>by GDP (average, 1991-<br>it HS categories.|5).<br>1995).|

_Source: GAIT, TPR; WTO, TPR; World Bank 1998._

# 3. Trade Remedies

Trade remedies are defined to include anti-dumping, countervailing and safeguard actions. In principle such actions are consistent with GATT provisions. The legal basis and procedures for the imposition of trade remedies in each instance are different as are the remedies, which usually do not involve quantitative restrictions but changes in duties and charges to address the problem as appropriate in each case. In the case of antidumping and countervailing duties, remedies are intended to correct for distortions that occur when exporters are obtaining subsidies and engaging in discriminatory pricing practices which result in injury to domestic producers. In the case of safeguards, the issue is simply injury to domestic producers - even if no unfair trade practices are involved9 The evidence regarding the frequency with which developing countries have taken trade remedy actions is presented in Table 11. For antidumping measures, the WTO data base contains information from which tariff line frequencies were calculated. As it has been argued that the mere initiation of investigations regarding antidumping action tends to have a restraining effect on imports - irrespective of

> 9Also, countervailing and anti-dumping actions are directed against the imports from only one country and not all tradealthough they may thereby create disincentives to exporters of other countries as well. For these reasons, it **is considered** appropriate to keep the total frequency ratios for each group of measures separate (unlike the OECD study which adds them up for each country; see OECD, 1997, Table 5.1 ).

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<!-- Start of picture text --> FIGURE III.5<br>PLOT OF TSGIP AND PT/PH<br>(QUARTERLY 1975.1 1987.3)<br>1 0<br>_~~~~~~~~~~~ _<br>-1 a0<br>a~~~~~~~~ aa a<br>-j~~~ -3 O a<br>-5 l<br>75 85 95 10<br><!-- End of picture text --> As mentioned in the theoretical analysis, one should expect a positive relationship between the RER and the ratio of the Trade S.urplus to GDP while the sign of the relation with the Terms of Trade could not be determined a Priori.

the results of the empirical fit of equation (15) for Argentina, Colombia and the U.S.A. are reported in Table 1. Since the Trade Surplus is also an endogenous variable, Two Stages Least Squares estimation was used in order to correct for the simultaneous determination of the Real Exchange Rate and the Trade Balance.In the case of U.S.A. also the terms of trade were considered to be simultaneously determined. Among the instrumental variables used was the ratio of the Fiscal Surplus to GDP, since this variable is expected to have an impact on the Trade Surplus.

It can be clearly appreciated from Table 1 that the PT/PN measure yields a substantially better fit than the Basket rate in two of the three countries, Colombia and U.S.A.. All regressions have been corrected for first order autocorrelation, given by the AR(l) coefficient whenever this procedure was deemed required.

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- How the firm sets its prices (e.g., based on production costs, competitors' prices, the target profit level, or willingness-to-pay by the consumers) and how it adjusts price for inflation (e.g., an index based on inflation, own costs, or dollars and exchange rates).

# **E. Transaction Environment**

According to transaction costs economics (Williamson 1989, 1993), incomplete information and the potential for opportunistic behavior prevent contracts from specifying outcomes under every uncertain contingency. Firms thus establish various types of relationships -including using contracts-with suppliers, dealers and customers. Maintaining many types of relationships entails distinct benefits and costs for firms. These myriad relationships require time and productive resources, but allow degrees of specialization and different levels of investments. Researchers therefore must study contractual arrangements that facilitate adaptation and dispute settlement such as the legal system, arbitration, long-term contract, and vertical integration.

Transaction cost economics suggest that to better understand the impact of these costs on firm's performance we should have at least the following information about the principal characteristics of firm's exchanges (Williamson 1989): (1) The frequency with which they occur, (2) The degree and type of uncertainty to which they are subject, and (3) The condition of asset specificity.

When asselt specificity is important, theory suggests that the transacting parties will arrange transaction structures-for instance, vertical integration, merger, long-term contracts-to economize transaction costs. Existing surveys pay only limited attention to such transaction characteristics. Future Bank surveys may become more valuable when this typie of information is collected.

An important determinant of transaction costs is the business and legal environment in which the firn operates. This set of considerations is especially important for transition economies and developing countries, where the legal and business environment is in flux and may seriously impact the firm's decisions. To investigate these issues, surveys should collect detailed information on the firns' exchanges. For example, what are the costs of establishing new trading relationships when old ones are broken? Is the trade between the parties bilateral?

14
placement of infrastructure, calculating remoteness’s importance at the household level or analyzing accessibility to individual service facilities. The unitless nature of index measurements also hinders communication to policy makers or adaptation to additional analyses; analysts must explain to a policy maker what a 4 vs. a 7 means on Dempsey’s scale, and such numbers cannot be meaningfully incorporated into other models that require physical measurements.

Both cost time models and weighted indices are complex, and their inner mechanics must be explained to users. But by employing a verifiable, commonly used metrics, cost time models give users confidence in their results and the ability to field test outputs for themselves. Additionally, cost time models produce raster outputs that can be scaled to any unit of analysis at or above the resolution of a raster pixel. These advantages make them more generically useful tools for specialists and non‐specialists alike that still successfully manage multiple input factors.

# Data collection and preparation

## Service facilities

We collected geospatial data for various categories of services facility types to objectively calculate travel times and distances to them. Our original intent was to compare these data with the reported distances and times from the survey; we have now moved this analysis to a separate paper. We amassed destination data on 77 DHQs, 7,840 banks, and 4,858 medical facilities. Table 2 summarizes these data sets and their sources.

**_Table 2: Facility data sets_**

|**Service**|**Number of facilities**|**Source**|
|---|---|---|
|District headquarters|77|Survey Department|
|Banks|7,840|Nepal Rastra Bank (NRB)|
|Medical facility|4,858|Ministry of Health|

The financial and medical facility data sets contained additional information about the specific type of facility (e.g. hospital, health post, etc.). We took advantage of this to repeat our analysis for important sub‐categories of services contained within.

## Digital Elevation Model (DEM)

DEM data enables accounting for slope when calculating travel speeds and path distances. We used 1 arc‐ second (roughly 30‐meter resolution) Shuttle Radar Topography Mission (SRTM) data for this purpose. Specifically, SRTM tiles N26E084 toN26E088, N27E081 to N27E088, N28E080 to N28E086, N29E080 to N29E084, and N30E080 to N30E082 were extracted, merged and clipped in the shape of Nepal.

## Road network

We employed road network and land cover data to calculate surface speeds. OSM data tagged as a “highway” was merged with Department of Roads (DOR) data collected through the World Bank project on “Measuring Rural Access Using New Technologies” to create a final roads layer. OSM roads not tagged as major roadways (smaller than highway=tertiary) were reclassified as paths. Overlapping roads were ignored as they did not substantially alter reported travel times.
_10_ Table 3—Household Demographics analyzed whether the sectors defined as male dominated actually require more starting capital and then assessed whether the wealth status and financial access of women who cross over into male-dominated sectors differ from those who remain in female-concentrated sectors.

To estimate the capital requirements of male-dominated sectors, we complemented the survey data collected with administrative data collected at the time of WEDP program registration. We used data on capital at business start and sector of operation from the registration dataset. In the full WEDP registration data, as of July 2016, we found approximately 10 percent (1,150 firms) who are crossovers out of 12,549 firms for which we have start-up capital information. We regressed crossover status on capital required to start the business and found that firms in male-dominated sectors, on average, required approximately three times the amount of capital to start a business than firms in the female-concentrated sectors. Because the majority of crossover firms in the WEDP registration database are in the small transport services and construction sectors, the higher starting capital requirement is unsurprising.

Because capital requirements are higher in crossover firms, we assessed whether there is a correlation between having better wealth or access to finance and crossing over into male-dominated sectors.
independently and identically distributed according to a cumulative distribution function, _Gi_ ( _Ri_ ), over the real numbers. Each partner observes only his or her own type. If both partners like risky sex (i.e., _Rm <_ 0 and _Rf <_ 0), the couple engages in risky sex and there is no violence.<sup>16</sup> Figure I depicts the game when there is a possibility of violence. If the male wants risky sex but the female does not (i.e. _Rm <_ 0 and _Rf >_ 0), the male proposes risky sex to the female. The female must decide whether to say yes (Y) or no (N) to his proposal. If she says yes, her payoff is _−Rf_ (which is a loss), while his payoff is _−Rm_ (which is a gain). If she says no, the male must decide whether to inflict violence (V) to extract risky sex or not (NV). Violence entails a cost _Vm_ to the male and _Vf_ to the female. If he plays NV, the game ends and they both receive a normalized payoff of zero. If he plays V, the female must decide whether to exit (E) or not exit (NE) the relationship. If she plays NE, she bears the full cost of the violence inflicted on her as well as the net cost of risky sex, _Rf − Vf_ , and the male’s payoff is _−Rm − Vm_ . If she plays E (dissolving the relationship), they each suffer loss _Xi_ , capturing their continuation value to remain in the relationship.

<!-- Start of picture text --> Female<br><!-- End of picture text --> <!-- Start of picture text --> Y<br><!-- End of picture text --> <!-- Start of picture text --> N<br><!-- End of picture text --> <!-- Start of picture text --> −R!, − R"<br><!-- End of picture text --> <!-- Start of picture text --> V<br><!-- End of picture text --> <!-- Start of picture text --> NV<br><!-- End of picture text --> <!-- Start of picture text --> −X!,−X"<br><!-- End of picture text --> <!-- Start of picture text --> E<br><!-- End of picture text --> <!-- Start of picture text --> NE<br><!-- End of picture text --> <!-- Start of picture text --> −R! −V!,−R" −V"<br><!-- End of picture text --> <!-- Start of picture text --> 0, 0<br><!-- End of picture text -->

**Figure I** Game tree under the possibility of violence (when _Rm <_ 0 _, Rf >_ 0)

# **6.2 Solving for Equilibrium**

The female has two decision points: (1) where she decides whether to play Y or N, which we will call _Rf_<sup>_∗_and(2)whereshewhethertoplayEorNE,whichwewillcall</sup><sup>_R_</sup> _f_<sup>_∗∗_.The</sup> 16Note that a negative net cost of risky sex implies a positive payoff from risky sex.

20
variable is coded as “0”. Whenever respondents deviated to a “one-share-one-share” or 50-50 split, this variable was coded as “1”. This outcome tests the hypothesis that the treatment leads subjects to favor a more gender equitable split.

The two other main outcomes directly assess respondents’ support for legal reform and the use of gifting: respondents were asked whether they were personally in favor of inheritance law reform. Next, they were asked whether they would be willing to gift their daughter in order to achieve a more gender equal bequest allocation. These two outcomes were rated on a 4-point Likert scale. I code them as “1” whenever a respondent strongly or somewhat favors the gender equal option and “0” otherwise. My pre-analysis plan specified further outcomes which are dropped in the following analysis as they yield very similar results.

# **Main Outcomes:**

1. **Ideal Bequest:** Assuming that you can freely allocate a bequest between your two children, a son and a daughter, how would you split the bequest among them?

2. **Reform Support:** “I am personally in favor of making gender equality in inheritance the legal norm in Tunisia.” _(4-point Likert-scale)_ 3. **Gifting:** “I would want my sons and daughters to inherit the same and if necessary, would use gifting to ensure this.” _(4-point Likert-scale)_ I collected a number of secondary outcomes to assess the prevalence of gifting in Tunisia: respondents were asked whether they or their spouse had received a gift and whether they had gifted their daughter or another female relative, if they did not have a daughter. Finally, respondents were asked how credible they find the information provided and whether they consent to participate in a follow-up survey.

Four to eight weeks later, respondents who had given consent were recontacted.<sup>11</sup> The follow-up did not remind respondents of the initial survey or the information provided: the introduction was changed and several unrelated questions were included to limit associations with the initial survey. All main outcomes were measured again and I also included more general measures of gender norms. The enumerators moreover inquired whether respondents

> 11Grewenig et al. (2020a), Grewenig et al. (2020b) and Settele (2022) conducted the second endline after two weeks. Two to four weeks distance to treatment are common choices in the experimental literature.

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**Table 5** Impact of Treatments on Male SRH Perceptions <u>(Male</u> Data)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|SRH perceptions index|||||
|treatment x post|0.293***<br>(0.076)|-0.022<br>(0.106)|-0.000|2,314|
|treatment x post x goal|0.073<br>(0.140)||||
|Female friend somewhat or<br>very likely to have STI|||||
|treatment x post|0.140**<br>(0.058)|0.053<br>(0.074)|0.581|2,314|
|treatment x post x goal|0.001<br>(0.099)||||
|Over 15% of males have an STI|||||
|treatment x post|0.123*<br>(0.064)|-0.019<br>(0.072)|0.308|2,314|
|treatment x post x goal|0.070<br>(0.095)||||
|Girls have a right to ask to use<br>a condom|||||
|treatment x post|0.122*<br>(0.064)|-0.066<br>(0.054)|0.797|2,314|
|treatment x post x Goal|0.030<br>(0.074)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 2. See notes from Table 3 for detail on table structure and control variables. Female friend somewhat or very likely to have STI is an indicator for believing that female friends are somewhat or very likely to have an STI (versus ‘not likely’). Over 15% of males have an STI is an indicator that a male responded that out of 100 of their male peers, more than 15 of them have an STI (the median response), and Girls have a right to ask to use a condom is a binary indicator that the male agrees that females have a right to ask to use a condom. The SRH perceptions index is the unweighted mean of the indicators after they have been standardized at baseline and endline separately to the mean and standard deviation among males whose connected female is in a ELA only community and was not assigned to the _Goal_ treatment. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *.

_Source._ Male baseline and endline data, balanced panel.

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