rafmacalaba/datause-encoder-data huggingface dataset:
structural primary balance as provided in the IMF WEO dataset produces results similar to those obtained for advanced economies or countries with strong political institutions, which is consistent with the limited availability of this measure for countries outside these groups. All results are available upon request.

## **6.5 Design and intensity of fiscal rules**

We have extended our analysis to the potential role of the strength and intensity of fiscal rules. A growing body of research highlights the importance of rule design in promoting fiscal discipline (Guerguil, Mandon, and Tapsoba 2017; Caselli and Reynaud 2020; Gootjes, de Haan, and Jong-A-Pin 2021). Our results indicate that while strongly designed rules typically lead to a sustained improvement in the primary balance, weakly designed rules tent to generate only short-term effects.

However, when we account for the quality of political institutions, we find no significant medium-term differences between weakly and strongly designed fiscal rules. The key distinction lies in the speed of impact: strong rule design accelerates improvements in the primary balance, a desirable outcome. Further tests indicate that neither the number nor type of fiscal rules—whether sustainability-oriented rules (i.e., deficit and debt limits) or operational rules (i.e., expenditure and revenue constraints)—drive these results: Once we control for institutional quality, all findings consistently point in the same direction. Overall, these results emphasize the pivotal role of political institutions in embedding fiscal discipline into government budgets through fiscal rules. For brevity, these additional findings are available upon request.

# **7. Conclusions**

An increasing number of countries have adopted fiscal rules to ensure fiscal sustainability and constrain suboptimal macroeconomic stabilization policies. This trend stems from two factors: rising government debt levels requiring more disciplined fiscal governance frameworks, and the fact that as more countries adopt these rules, they are also becoming the _de facto benchmark_ for fiscal policy.

Our paper fills a gap in the literature by exploring how the effects of fiscal rules develop over time and how these effects depend on the conditions under which the rules are adopted. Using a large sample of 108 countries, the results confirm that fiscal rules have positive effects on primary balances, though these effects take time to materialize.

32
|**_Countries_**|**_Governance& _**<br>**_Enterprise_**<br>**_restructuring_**|**_Competition_**<br>**_Policy_**|**_Trade_**<br>**_&_**<br>**_Foreign_**<br>**_System_**|**_Exchange_**|
|---|---|---|---|---|
|**_Czech Republic_**|**_3_**|**_3_**|**_4+_**||
|**_Estonia_**|**_3_**|**_3-_**|**_4_**<br>**_4+_**||
|**_FYR Macedonia_**|**_2_**|**_1_**|**_4_**||
|**_Georgia_**|**_2_**|**_2_**|**_4_**<br>**_4_**||
|**_Hungary_**|**_+3_**|**_3_**|**_4+_**||
|**_Kazakhstan_**|**_2_**|**_2_**|**_4_**<br>**_3_**||
|**_Kyrgyzstan_**|**_2_**|**_2_**|**_4_**<br>**_4+_**||
|**_Latvia_**|**_3-_**|**_3-_**|**_4_**<br>**_4+_**||
|**_Lithuania_**|**_3-_**|**_2+_**|**_4_**<br>**_4_**||
|**_Moldova_**|**_2_**|**_2_**|**_4_**<br>**_4_**||
|**_Poland_**|**_3_**|**_3_**|**_4+_**||
|**_Romania_**|**_2_**|**_2_**|**_4_**||
|**_Russian Federation_**|**_2_**|**_2+_**|**_2+_**<br>**_3_**||
|**_Slovak Republic_**|**_3-_**|**_3_**|**_4+_**||
|**_Slovenia_**|**_3-_**|**_2_**|**_4+_**<br>**-**||
|**_Tajikistan_**|**_2-_**|**_1_**|**_3-_**<br>**_3-_**||
|**_Turkmenistan_**|**_2-_**|**_1_**|**_1_**<br>**_1_**||
|**_Ukraine_**|**_2_**|**_2_**|**_3-_**<br>**_3-_**||
|**_Uzbekistan_**|**_2_**|**_2_**|**_2-_**<br>**_2-_**||

_Source: EBRD, Transition Report 1998_

# IV. Market Access Issues

# A. The CEE

With few exceptions (a number of former Yugoslavia republics, Albania), CEE countries signed association agreements with the EU-which involve free trade arrangements in many sectors and preferential treatment in others. The bulk of these countries' imports (over 70%) is covered by preferential European arrangements and a significant portion of the remainder involves energy and raw materials which are not protected. Indeed, these countries seem to 18
and that these light-touch interventions are capable of generating meaningful behavioral change.

# **5.2.5 Is It Necessary to Use Real Reward Payoffs?**

We now examine whether similar results are observed when the choice over the career mentoring course for women is hypothetical. Two of Qualtrics panel partners who supply Qualtrics with respondents (constituting 20% of respondents) would not allow us to randomly select respondents to receive a reward as this went against their contract with respondents. As those making the hypothetical choice were not randomly selected, Table A.5 in the appendix compares the characteristics of respondents in these panels with the other respondents. The respondents who are given the hypothetical choice are demographically very similar to the other respondents. Demographic differences are small in magnitude and not strongly statistically significant. Respondents in the hypothetical sample however report that they and their family members (spouses and mothers) are more supportive of women working and husbands sharing daily childcare. The magnitude of the differences are relatively small but we nevertheless control for these variables in the specifications below (doing so does not affect the results).

Column (1) of Table 6 reports results for the entire sample (men and women). Although participants faced with a hypothetical choice were 7.9 percentage points more likely to choose the career mentoring course than participants who were making a real choice (48.6% versus 40.7%), this result is driven by the interaction between making a hypothetical choice and treatment, rather than the choice being hypothetical, per se. Respondents in treatment arms where the choice was hypothetical were 22 percentage points less likely to choose the career mentoring course over the shopping voucher. This is observed for both men and women (columns 2 and 3). The hypothetical choice also interacts with social desirability bias. If their choice is hypothetical, participants who are most concerned about appearing to behave in a socially desirable way (social desirability index = 5) are 28 percentage points (72%) more likely to choose the career mentoring course than those who are the least concerned (social desirability index=0).

Columns (2) and (3) present the results of estimating the same models on the sample of female respondents and male respondents, respectively. The results are broadly similar for men and women. One difference is that women are more likely to choose the career mentoring course per se when the choice is hypothetical, whereas men are more susceptible to social desirability bias (possibly because male respondents may feel more social pressure to support working women than women who would be seen to be acting in their selfinterest).<sup>33</sup> Columns (4) to (6) present the results estimated over only the sub-sample of respon-

> 33The interaction between social desirability bias and treatment and the triple interaction between treatment, hypothetical choice and the social desirability index were both insignificant in all specifications.

18
# **Panel (b): Household characteristics**

||**Bihar**|**Odisha**|**Tamil Nadu**|
|---|---|---|---|
|Sample size|8969|2462|3384|
|Per capita household monthly consumption expenditure (average in rupees)|610.1|1176.9|2150.3|
|Land holding (average in acres)|0.5|0.6|1.96|
|Female household head|16.3|5.9|16.7|
|Number of members in the household (average)|5.9|5.2|4.4|
|Distance to nearest town ( average in kilometers)|22.5|54.4|18.8|
|Employed adults females|68.2|17.5|62.1|
|**_Education profile of the household head_**||||
|Never went to school|56.6|20.5|31.3|
|Primary|18.2|33.2|10.1|
|Above primary but below or equal to senior secondary|22.5|20.1|54.7|
|Above senior secondary|2.6|26.2|3.9|
|**Panel (c): Characteristics of female respondents (means)**||||
||**Bihar**|**Odisha**|**TN**|
|Age|34.1|42.3|39.8|
|Age at the time of marriage|17.9|18.9|19.3|
|Employment|80.3|24.1|76.9|
|**_Marital status of the female respondent_**||||
|Married|96.3|93.1|90|
|Unmarried|1.3|0.9|0.7|
|Widowed/Separated/Not cohabiting|2.4|5.9|9.3|
|**_Education profile of female respondents_**||||
|Never went to school|78.1|37.6|40|
|Primary|11|29.4|9.1|
|Above primary but below or equal to senior secondary|10.3|17.7|48.5|
|Above senior secondary|0.6|15.3|2.4|
|<br>**_Intra-household decision making: Does female respondent provide any input in the f_**<br>Purchase of household durables|**_ollowing decisions_**<br>91.7|**_made in th_**<br>82.9|**_e household?_**<br>86.3|
|Children's education|84.1|63|84.8|
|Own livelihood activity|79.5|64.2|77.4|
|Politics (like who to vote for)|78.6|41.6|87.3|
|**_Mobility of female respondent_**||||
|Bank|20.1|21.1|76|
|Store|75.1|45.3|-|
|Health centre|93.1|94.1|-|
|Friend/ neighbour/ relative|97.4|96|-|
|Taluk Office|-|-|30|
|Police Station|-|-|5.5|

Source: Authors' calculations based on data collected by Social Observatory, World Bank and state Government of Bihar, Odisha and Tamil Nadu, respectively.

26
14 cost shares. This may reflect either scale economies,<sup>or also the fact that programs with intensive</sup> health inputs tend to be smaller. There is little differenca in<sup>non-food cost share by operating agency.</sup> NGO-operated programs (and also WFP-funded ones)<sup>have lower mean non-food cost shares than</sup> government-operated ones (although the medians are not<sup>different.</sup> Table 7 provides some information on non-food costs<sup>for programs within one country,</sup> namely Brazil. There was some experimentation in program<sup>design which yields some interesting</sup> information. Firstly, the exclusion or inclusion of health<sup>inputs can change the share of food in total</sup> costs from 25% to 73% (data from the Nutrition through<sup>the Health System - PNS - program).</sup> Secondly, the Complementary Food Program (PCA) distributing<sup>weaning foods has a lower share of</sup> non-food costs than the PNS variant without health inputs,<sup>but only because of the much higher unit</sup> costs of food (blended foods being more expensive than traditional<sup>ones). Finally, the degree of</sup> subsidization of foods also affects the share of food in total<sup>costs, but at the same time has quite</sup> dramatic effects on participation (The Integrated Nutrition<sup>and Health Program - PINS - study</sup> experimented with different levels of subsidy). Data on the<sup>proportion of families continuing to buy</sup> subsidized foods after two years were as follows: 74% in<sup>the group with the 60% subsidy continued</sup> buying, 37% in the group with the 45% subsidy and a growth<sup>monitoring requirement,</sup><sup>_55%_in the</sup> group with the _45%_ subsidy (and no growth monitoring),<sup>and 25% in the group with the 30%</sup> subsidy. Participation data such ase these are extremely<sup>useful as an additional way to assess cost</sup> data, but are rarely available.

Table 8 provides a typology of the costs of different programs,<sup>both unit costs and non-food</sup> cost share in total costs. For comparison, older information<sup>on these topics are included from Mateus</sup> (1983). The latter document is somewhat of an advocacy<sup>piece on targeting food subsidies, and the</sup> data in his table appear to be notional, rather than being<sup>based on specific project data. Since the</sup> Mateus study has been cited (for example by Kennedy and Alderman,<sup>1987, one of the more</sup>
main source of income or are dependent on remittances as a part of it.<sup>44</sup> The CENFRI MAP study identifies migrants as a key population that would benefit from the targeting of other financial services, such as repatriation funeral insurance and cross‐border payment of dependents’ insurance premiums, retirement or other long‐term savings, or cross‐border payment of loans. As such, efforts to promote financial inclusion should leverage the fact that migration is so common and use remittances as their entry‐point. Given households’ increasing dependence on female migrants’ remittances, policy can address two key areas: 1) provide cheaper, more formal ways to send money to rural family households; and 2) promote and/or provide some educational savings product to help finance children’s education. If successful, remittances can be an important channel through which to promote broader financial inclusion among women.

With regard to the first, female migrants continue to use informal channels of remitting money home. This results in them sending money inconsistently and less efficiently, often when a trusted family or friend travels to rural areas, or when the migrant herself can make the trip home. A better way for these women to send money home is clearly needed, and the promotion of new technologies to do so, such as mPesa, could be a priority. Consistent with CENFRI MAP study recommendations, low‐cost remittance services need to be cheaper for both salaried workers and for private dependents (domestic workers and cross‐border traders). This can be achieved by increasing resources in rural areas (recruit agents and partner with more banks) and teaming with trusted retailers, such as Shoprite. At the same time, mobile coverage has grown exponentially according to the Lesotho Communication Authority’s “The State of Communications Sector and ICT Indicators in Lesotho” (2009). To take advantage, women might need nudges, or incentives, to try them out. Customer incentives have been found to lead to early adoption of mPesa.<sup>45</sup> “Money back guarantees”, which emphasize the lack of risk involved, free top‐up offers, and replicating loyalty credit card schemes have been highlighted as potential avenues to increase up‐take of mobile money.

Second, providing female migrants some kind of educational savings product might be particularly useful. Many migrants are working to finance their children’s education, and the need to finance education is a key concern among migrants. Mechanisms, such as commitment savings products, might be particularly useful to help women set aside money each month to pay for their children’s education both when finances are tight and for the longer‐term.

> 44 CENFRI MAP study (http://cenfri.org/making‐access‐possible/map‐lesotho).

> 45 http://www.gsma.com/mobilefordevelopment/programmes/mobile‐money‐for‐the‐unbanked/mmu‐examples.

48
suggests sub-optimality of no-intervention for these countries.

̃ Table 3: Lives and livelihoods - break-even _α_ as of early June 2020, under no intervention scenario and saturation, medians by World Bank (WB) income groups

||(1)<br>LIC|(2)<br>LMIC|(3)<br>UMIC|(4)<br>HIC|(5)<br>World|
|---|---|---|---|---|---|
|Median of economic and demogra<br>|phic cha|racteristi|cs|||
|GDP p.c. in 2017 (2011 PPP$)|1,697|5,481|13,822|35,938|11,676|
|Residual life expectancy|47.82|46.47|45.11|41.86|45.12|
|Median of Covid-19 mortality, no|interven|tion and|saturatio|n||
|Deaths per capita|0.00169|0.00289|0.00503|0.0113|0.00434|
|LYs per death|17.77|16.10|14.97|11.89|14.95|
|LYs per person|0.0302|0.0445|0.0762|0.128|0.0655|
|Median shock on poverty HCR||||||
|Poverty line (median)|1.9|3.2|5.5|21.7|5.5|
|Baseline scenario, distribution-neutral|0.019|0.021|0.021|0.028|0.023|
|Baseline scenario, +3.6% in Gini|0.036|0.0355|0.043|0.040|0.037|
|Median break-even ̃_α_, no interven|tion and|saturati|on|||
|Baseline scenario, distribution-neutral|0.609|0.491|0.323|0.259|0.387|
|Baseline scenario, +3.6% in Gini|1.089|0.869|0.642|0.354|0.646|

Yet, we cannot rule out no-intervention as a plausible policy in some low-income countries. Their median break-even _α_ ̃ are slightly larger than one under one of the two ̃ scenarios shown in Table 3. At least some low-income countries have break-even _α_ larger than two (see Figure 5). For values of _α_ ̃ in that range, the welfare comparison in the poorest countries remains theoretically ambiguous, although only for observers placing a low normative value on an additional year of life relative to a year in poverty.

Given an observer's xed normative choice of _α_ , the lower _α_ ̃ the greater the welfare losses associated with the no-intervention scenario, relative to the actual early June estimates. The downward-sloping relationship in Figure 5 thus indicates that the welfare losses from policy inaction relative to the responses observed until early June 2020 increase sharply with GDP per capita. This eect is emphasized when using a common poverty line for all countries, such as the international extreme poverty line (IPL) of $1.90 a day (Ferreira et al., 2016). Figure 6 provides a scatter plot of _α_ ̃ for all countries using the IPL. The slope of the tted line is very steep. When using extreme poverty in all countries, we obtain _α_ ̃ values well below 0.1 in high-income countries.<sup>25</sup> This is because even in the case of a deep recession, high-income countries would have very little extreme poverty, because incomes are far above the extreme threshold.

> 25  ̃ Even when using the $3.2 a day or $5.5 a day lines instead of the extreme line, break-even _α_ values remain below 0.1 in high-income countries. See Figure A.11 in the Appendix.

20
|Urban Cost|Urban Cost<br>Control|= 1 if estimations include a<br>local measure of urban costs,<br>e.g. land rents, house prices,<br>localprice indices|estimations do not include<br>an urban cost control|0.032|0.177|
|---|---|---|---|---|---|
|Industry|Manufacturing<br>Sector<br>Services Sector|=1 if estimates reflect the<br>manufacturingsector<br>=1 if estimates reflect the<br>services sector|estimates are not specific<br>to manufacturing or<br>services|0.382<br>0.155|0.486<br>0.362|
|Skill|Skilled workers<br>Unskilled workers|= 1 if estimates reflect skilled<br>workers (including managers,<br>scientists or non-routine<br>occupations)<br>= 1 if estimates reflect<br>unskilled workers (routine<br>occupations)|estimates are not specific<br>to skilled or unskilled<br>workers|0.021<br>0.027|0.143<br>0.161|
|Time Period|Post-1990|= 1 if mid-point of data is 1990<br>onwards|mid-point of data is before<br>1990|0.782|0.413|
||Published|= 1 if study is published in a<br>peer-reviewed journal|study is unpublished|0.712|0.453|
|Study Quality|Number of<br>Citations (as of<br>April 2020)|Log number of citations -<br>normalized by study<br>publicationyear|-|0.000|1.349|
|Spatial<br>Measure|City - Level<br>Sub City - Level|=1 if study uses city-level<br>spatial units<br>= 1 if study uses sub-city-level<br>spatial units|study uses regional level<br>spatial units|0.521<br>0.310|0.500<br>0.463|
|Agglomeration|Density Measure|= 1 if agglomeration is<br>measured by population or<br>employment density|agglomeration is<br>measured by size, such as|0.452|0.498|
|Measure|Market Potential<br>Measure|= 1 if agglomeration is<br>measured by effective density<br>or marketpotential|total employment or<br>population|0.257|0.437|
|Localization|Localization<br>Control|= 1 if a localization control is<br>included (reflecting the size of<br>own industry)|estimations do not include<br>a localization control|0.312|0.464|
||Panel Data|= 1 if study uses panel data|study uses cross-section<br>data|0.590|0.492|
|Data|Firm Data|= 1 if study uses firm-level<br>micro data|study does not use micro|0.352|0.478|
||Worker Data|= 1 if study uses worker-level<br>micro data|data|0.388|0.488|
||Panel Fixed<br>Effects|= 1 if panel fixed effects are<br>included or estimation is in<br>differences|study does not use fixed<br>effects|0.209|0.407|
|Endogeneity|IV estimation -<br>contemporaneous|= 1 if estimates reflect IV<br>estimation, using<br>contemporaneous or short-<br>lagged instruments (including<br>GMM)|study does not use IV<br>estimation|0.069|0.254|

11
Finally, other constraints from the supply side related to higher reservation wages could explain the high unemployment rate for female job seekers. It is influenced by high opportunity costs, especially because of preferences for public sector employment, the working conditions in the private (informal) sectors, and other explanations presented in section 3 of this report. Female unemployment in Tunisia is thus a complex challenge and cannot be fully explained by one factor alone. Closing the gender gap in employment requires a comprehensive, multisectoral approach aimed at tackling binding constraints from both the demand side and the supply side.

Policy implications: Addressing discrimination in the Tunisian labor market In the IT sector, where discrimination is widespread, it is important to undertake prevention measures. Enacting laws that prohibit any form of discrimination in employment is an important first step. Tunisia currently scores 75 on the World Bank’s Women, Business, and Law index,<sup>18</sup> indicating that the country’s economy gives women only three-quarters of the legal rights of men. Although this average is significantly higher than the MENA average of 47.37, there is room for improvement.

Currently, Tunisia’s laws do not mandate equal remuneration for work of equal value, and women are not allowed to work in the same industries and same night hours as men.<sup>19</sup> In addition, because this study finds that veiled women are 8.5 percentage points less likely than nonveiled women to receive a callback from employers, it is important to gradually influence the customs around inserting photos in applicants’ resumes. More also needs to be done to ensure that women are not discriminated against at the interview stage or throughout their career progression based on their appearances, religious views, and family circumstances.

In instances in which discrimination in the workplace appears to be the result of statistical discrimination (for example, employers believe that female applicants are still, on average, less qualified or less productive than male applicants), Tunisia’s National Employment Agency <mark>(Agence nationale pour l'emploi et le travail indépendent) could strengthen its labor market programs and job intermediation services between employers and job seekers, e</mark> specially in workplaces where most workers are men <mark>. Offering internships and on-the-job training c</mark> an serve

> 18 https://wbl.worldbank.org/.

> 19 World Bank, Women, Business and the Law database, 2019, https://wbl.worldbank.org/.

44